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API/COMPAT: add pydatetime-style positional args to Timestamp constructor
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 114607f117756..62601821488d3 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -98,6 +98,7 @@ time. pd.Timestamp(datetime(2012, 5, 1)) pd.Timestamp('2012-05-01') + pd.Timestamp(2012, 5, 1) However, in many cases it is more natural to associate things like change variables with a time span instead. The span represented by ``Period`` can be diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index b86a7a81625e2..29a177f2bcceb 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -38,6 +38,14 @@ Other enhancements idx = pd.Index(["a1a2", "b1", "c1"]) idx.str.extractall("[ab](?P<digit>\d)") +- ``Timestamp``s can now accept positional and keyword parameters like :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`) + + .. ipython:: python + + Timestamp(2012, 1, 1) + + Timestamp(2012, 1, 1, 8, 30) + .. _whatsnew_0182.api: API changes diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 4543047a8a72a..6ae1354730987 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -180,6 +180,52 @@ def test_constructor_invalid(self): with tm.assertRaisesRegexp(ValueError, 'Cannot convert Period'): Timestamp(Period('1000-01-01')) + def test_constructor_positional(self): + # GH 10758 + with tm.assertRaises(TypeError): + Timestamp(2000, 1) + with tm.assertRaises(ValueError): + Timestamp(2000, 0, 1) + with tm.assertRaises(ValueError): + Timestamp(2000, 13, 1) + with tm.assertRaises(ValueError): + Timestamp(2000, 1, 0) + with tm.assertRaises(ValueError): + Timestamp(2000, 1, 32) + + # GH 11630 + self.assertEqual( + repr(Timestamp(2015, 11, 12)), + repr(Timestamp('20151112'))) + + self.assertEqual( + repr(Timestamp(2015, 11, 12, 1, 2, 3, 999999)), + repr(Timestamp('2015-11-12 01:02:03.999999'))) + + self.assertIs(Timestamp(None), pd.NaT) + + def test_constructor_keyword(self): + # GH 10758 + with tm.assertRaises(TypeError): + Timestamp(year=2000, month=1) + with tm.assertRaises(ValueError): + Timestamp(year=2000, month=0, day=1) + with tm.assertRaises(ValueError): + Timestamp(year=2000, month=13, day=1) + with tm.assertRaises(ValueError): + Timestamp(year=2000, month=1, day=0) + with tm.assertRaises(ValueError): + Timestamp(year=2000, month=1, day=32) + + self.assertEqual( + repr(Timestamp(year=2015, month=11, day=12)), + repr(Timestamp('20151112'))) + + self.assertEqual( + repr(Timestamp(year=2015, month=11, day=12, + hour=1, minute=2, second=3, microsecond=999999)), + repr(Timestamp('2015-11-12 01:02:03.999999'))) + def test_conversion(self): # GH 9255 ts = Timestamp('2000-01-01') diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index a240558025090..9588a6c420422 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -214,8 +214,8 @@ cdef inline bint _is_fixed_offset(object tz): return 0 return 1 - _zero_time = datetime_time(0, 0) +_no_input = object() # Python front end to C extension type _Timestamp # This serves as the box for datetime64 @@ -225,6 +225,10 @@ class Timestamp(_Timestamp): for the entries that make up a DatetimeIndex, and other timeseries oriented data structures in pandas. + There are essentially three calling conventions for the constructor. The + primary form accepts four parameters. They can be passed by position or + keyword. + Parameters ---------- ts_input : datetime-like, str, int, float @@ -235,6 +239,23 @@ class Timestamp(_Timestamp): Time zone for time which Timestamp will have. unit : string numpy unit used for conversion, if ts_input is int or float + + The other two forms mimic the parameters from ``datetime.datetime``. They + can be passed by either position or keyword, but not both mixed together. + + :func:`datetime.datetime` Parameters + ------------------------------------ + + .. versionadded:: 0.18.2 + + year : int + month : int + day : int + hour : int, optional, default is 0 + minute : int, optional, default is 0 + second : int, optional, default is 0 + microsecond : int, optional, default is 0 + tzinfo : datetime.tzinfo, optional, default is None """ @classmethod @@ -288,10 +309,46 @@ class Timestamp(_Timestamp): def combine(cls, date, time): return cls(datetime.combine(date, time)) - def __new__(cls, object ts_input, object offset=None, tz=None, unit=None): + def __new__(cls, + object ts_input=_no_input, object offset=None, tz=None, unit=None, + year=None, month=None, day=None, + hour=None, minute=None, second=None, microsecond=None, + tzinfo=None): + # The parameter list folds together legacy parameter names (the first + # four) and positional and keyword parameter names from pydatetime. + # + # There are three calling forms: + # + # - In the legacy form, the first parameter, ts_input, is required + # and may be datetime-like, str, int, or float. The second + # parameter, offset, is optional and may be str or DateOffset. + # + # - ints in the first, second, and third arguments indicate + # pydatetime positional arguments. Only the first 8 arguments + # (standing in for year, month, day, hour, minute, second, + # microsecond, tzinfo) may be non-None. As a shortcut, we just + # check that the second argument is an int. + # + # - Nones for the first four (legacy) arguments indicate pydatetime + # keyword arguments. year, month, and day are required. As a + # shortcut, we just check that the first argument was not passed. + # + # Mixing pydatetime positional and keyword arguments is forbidden! + cdef _TSObject ts cdef _Timestamp ts_base + if ts_input is _no_input: + # User passed keyword arguments. + return Timestamp(datetime(year, month, day, hour or 0, + minute or 0, second or 0, microsecond or 0, tzinfo), + tz=tzinfo) + elif is_integer_object(offset): + # User passed positional arguments: + # Timestamp(year, month, day[, hour[, minute[, second[, microsecond[, tzinfo]]]]]) + return Timestamp(datetime(ts_input, offset, tz, unit or 0, + year or 0, month or 0, day or 0, hour), tz=hour) + ts = convert_to_tsobject(ts_input, tz, unit, 0, 0) if ts.value == NPY_NAT:
- [X] closes #10758 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry If more tests are desired, please let me know where to put them. Same for the whatsnew entry. This is my first pull request for Pandas. cc @jreback
https://api.github.com/repos/pandas-dev/pandas/pulls/12482
2016-02-27T15:47:56Z
2016-05-20T14:16:17Z
null
2016-05-20T16:14:51Z
BUG: support fused types in roll_min/max #12373
diff --git a/.travis.yml b/.travis.yml index e08bd3b0413c9..0c2f1b0d0fe2d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -19,15 +19,16 @@ git: matrix: fast_finish: true include: - - python: 2.7 + - language: objective-c + os: osx + compiler: clang + osx_image: xcode6.4 env: - - JOB_NAME: "27_nslow_nnet_COMPAT" - - NOSE_ARGS="not slow and not network and not disabled" - - CLIPBOARD=xclip - - LOCALE_OVERRIDE="it_IT.UTF-8" - - BUILD_TYPE=conda - - INSTALL_TEST=true - - JOB_TAG=_COMPAT + - JOB_NAME: "35_osx" + - NOSE_ARGS="not slow and not network and not disabled" + - BUILD_TYPE=conda + - JOB_TAG=_OSX + - TRAVIS_PYTHON_VERSION=3.5 - python: 2.7 env: - JOB_NAME: "27_slow_nnet_LOCALE" @@ -75,18 +76,20 @@ matrix: - NOSE_ARGS="not slow and not disabled" - FULL_DEPS=true - BUILD_TEST=true - - python: 2.7 - env: - - JOB_NAME: "27_numpy_dev" - - JOB_TAG=_NUMPY_DEV - - NOSE_ARGS="not slow and not network and not disabled" - - PANDAS_TESTING_MODE="deprecate" - python: 3.5 env: - JOB_NAME: "35_numpy_dev" - JOB_TAG=_NUMPY_DEV - NOSE_ARGS="not slow and not network and not disabled" - PANDAS_TESTING_MODE="deprecate" + - python: 2.7 + env: + - JOB_NAME: "27_nslow_nnet_COMPAT" + - NOSE_ARGS="not slow and not network and not disabled" + - LOCALE_OVERRIDE="it_IT.UTF-8" + - BUILD_TYPE=conda + - INSTALL_TEST=true + - JOB_TAG=_COMPAT - python: 2.7 env: - JOB_NAME: "doc_build" @@ -108,12 +111,6 @@ matrix: - NOSE_ARGS="slow and not network and not disabled" - FULL_DEPS=true - CLIPBOARD=xsel - - python: 2.7 - env: - - JOB_NAME: "27_numpy_dev" - - JOB_TAG=_NUMPY_DEV - - NOSE_ARGS="not slow and not network and not disabled" - - PANDAS_TESTING_MODE="deprecate" - python: 2.7 env: - JOB_NAME: "27_build_test_conda" @@ -127,6 +124,14 @@ matrix: - JOB_TAG=_NUMPY_DEV - NOSE_ARGS="not slow and not network and not disabled" - PANDAS_TESTING_MODE="deprecate" + - python: 2.7 + env: + - JOB_NAME: "27_nslow_nnet_COMPAT" + - NOSE_ARGS="not slow and not network and not disabled" + - LOCALE_OVERRIDE="it_IT.UTF-8" + - BUILD_TYPE=conda + - INSTALL_TEST=true + - JOB_TAG=_COMPAT - python: 2.7 env: - JOB_NAME: "doc_build" @@ -139,16 +144,13 @@ before_install: - echo "before_install" - echo $VIRTUAL_ENV - export PATH="$HOME/miniconda/bin:$PATH" - - sudo apt-get install ccache - df -h - date - pwd - uname -a - python -V - ci/before_install_travis.sh - # Xvfb stuff for clipboard functionality; see the travis-ci documentation - export DISPLAY=:99.0 - - sh -e /etc/init.d/xvfb start install: - echo "install" @@ -157,8 +159,7 @@ install: - ci/submit_ccache.sh before_script: - - mysql -e 'create database pandas_nosetest;' - - psql -c 'create database pandas_nosetest;' -U postgres + - ci/install_db.sh script: - echo "script" diff --git a/README.md b/README.md index 6295e7374e1ee..54c772da4d270 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,8 @@ +<div align="center"> + <img src="http://pandas.pydata.org/_static/pandas_logo.png"><br> +</div> +----------------- + # pandas: powerful Python data analysis toolkit <table> diff --git a/appveyor.yml b/appveyor.yml index 650137b995121..7941820204916 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -8,9 +8,6 @@ matrix: fast_finish: true # immediately finish build once one of the jobs fails. -# set clone depth -clone_depth: 300 - environment: global: # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the @@ -23,7 +20,7 @@ environment: PYTHON_VERSION: "3.4" PYTHON_ARCH: "64" CONDA_PY: "34" - CONDA_NPY: "110" + CONDA_NPY: "19" - PYTHON: "C:\\Python27_64" PYTHON_VERSION: "2.7" @@ -49,7 +46,8 @@ init: - "ECHO %PYTHON_VERSION% %PYTHON%" install: - # this installs the appropriate Miniconda (Py2/Py3, 32/64 bit), + # this installs the appropriate Miniconda (Py2/Py3, 32/64 bit) + # updates conda & installs: conda-build jinja2 anaconda-client - powershell .\ci\install.ps1 - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PATH% - echo "install" @@ -57,6 +55,9 @@ install: - ls -ltr - git tag --sort v:refname + # this can conflict with git + - cmd: rmdir C:\cygwin /s /q + # install our build environment - cmd: conda config --set show_channel_urls yes --set always_yes yes --set changeps1 no - cmd: conda update -q conda @@ -64,21 +65,22 @@ install: - cmd: conda config --set ssl_verify false # this is now the downloaded conda... - - conda info -a + - cmd: conda info -a # build em using the local source checkout in the correct windows env - - conda install conda-build - - cmd: '%CMD_IN_ENV% conda build ci\appveyor.recipe -q --no-test' + - cmd: '%CMD_IN_ENV% conda build ci\appveyor.recipe -q' # create our env - - SET REQ=ci\requirements-%PYTHON_VERSION%-%PYTHON_ARCH%.run - cmd: conda create -q -n pandas python=%PYTHON_VERSION% nose - cmd: activate pandas - - cmd: conda install -q --file=%REQ% - - ps: conda install -q (conda build ci\appveyor.recipe -q --output --no-test) + - SET REQ=ci\requirements-%PYTHON_VERSION%-%PYTHON_ARCH%.run + - cmd: echo "installing requirements from %REQ%" + - cmd: conda install -n pandas -q --file=%REQ% + - ps: conda install -n pandas (conda build ci\appveyor.recipe -q --output) test_script: # tests - cd \ - - conda list pandas - - nosetests --exe -A "not slow and not network and not disabled" pandas + - cmd: activate pandas + - cmd: conda list + - cmd: nosetests --exe -A "not slow and not network and not disabled" pandas diff --git a/ci/appveyor.recipe/meta.yaml b/ci/appveyor.recipe/meta.yaml index c497acf33b6e0..ccba8a29784b5 100644 --- a/ci/appveyor.recipe/meta.yaml +++ b/ci/appveyor.recipe/meta.yaml @@ -1,10 +1,10 @@ package: name: pandas - version: {{ environ.get('GIT_DESCRIBE_TAG','') }} + version: 0.18.0 build: - number: {{ environ.get('GIT_DESCRIBE_NUMBER',0) }} - string: np{{ environ.get('CONDA_NPY') }}py{{ environ.get('CONDA_PY') }}_{{ environ.get('GIT_BUILD_STR','') }} + number: {{environ.get('APPVEYOR_BUILD_NUMBER', 0)}} # [win] + string: np{{ environ.get('CONDA_NPY') }}py{{ environ.get('CONDA_PY') }}_{{ environ.get('APPVEYOR_BUILD_NUMBER', 0) }} # [win] source: diff --git a/ci/before_install_travis.sh b/ci/before_install_travis.sh index e4376e1bf21c2..76775ecbc78f0 100755 --- a/ci/before_install_travis.sh +++ b/ci/before_install_travis.sh @@ -8,6 +8,10 @@ echo "inside $0" # overview -sudo apt-get update $APT_ARGS # run apt-get update for all versions +if [ "${TRAVIS_OS_NAME}" == "linux" ]; then + sudo apt-get update $APT_ARGS # run apt-get update for all versions + + sh -e /etc/init.d/xvfb start +fi true # never fail because bad things happened here diff --git a/ci/build_docs.sh b/ci/build_docs.sh index c0843593f85ff..8594cd4af34e5 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -1,5 +1,10 @@ #!/bin/bash +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not doing build_docs on non-linux" + exit 0 +fi + cd "$TRAVIS_BUILD_DIR" echo "inside $0" @@ -19,7 +24,7 @@ if [ x"$DOC_BUILD" != x"" ]; then source activate pandas conda install -n pandas -c r r rpy2 --yes - time sudo apt-get $APT_ARGS install dvipng + time sudo apt-get $APT_ARGS install dvipng texlive-latex-base texlive-latex-extra mv "$TRAVIS_BUILD_DIR"/doc /tmp cd /tmp/doc @@ -31,6 +36,10 @@ if [ x"$DOC_BUILD" != x"" ]; then echo ./make.py ./make.py + echo ######################## + echo # Create and send docs # + echo ######################## + cd /tmp/doc/build/html git config --global user.email "pandas-docs-bot@localhost.foo" git config --global user.name "pandas-docs-bot" diff --git a/ci/install.ps1 b/ci/install.ps1 index c964973c67693..16c92dc76d273 100644 --- a/ci/install.ps1 +++ b/ci/install.ps1 @@ -7,11 +7,7 @@ $MINICONDA_URL = "http://repo.continuum.io/miniconda/" function DownloadMiniconda ($python_version, $platform_suffix) { $webclient = New-Object System.Net.WebClient - if ($python_version -match "3.4") { - $filename = "Miniconda3-latest-Windows-" + $platform_suffix + ".exe" - } else { - $filename = "Miniconda-latest-Windows-" + $platform_suffix + ".exe" - } + $filename = "Miniconda3-latest-Windows-" + $platform_suffix + ".exe" $url = $MINICONDA_URL + $filename $basedir = $pwd.Path + "\" diff --git a/ci/install_db.sh b/ci/install_db.sh new file mode 100755 index 0000000000000..e4e6d7a5a9b85 --- /dev/null +++ b/ci/install_db.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not using dbs on non-linux" + exit 0 +fi + +echo "installing dbs" +mysql -e 'create database pandas_nosetest;' +psql -c 'create database pandas_nosetest;' -U postgres + +echo "done" +exit 0 diff --git a/ci/install_travis.sh b/ci/install_travis.sh index 335286d7d1676..1b1eae7b44e45 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -38,16 +38,16 @@ if [ -n "$LOCALE_OVERRIDE" ]; then # make sure the locale is available # probably useless, since you would need to relogin time sudo locale-gen "$LOCALE_OVERRIDE" -fi -# Need to enable for locale testing. The location of the locale file(s) is -# distro specific. For example, on Arch Linux all of the locales are in a -# commented file--/etc/locale.gen--that must be commented in to be used -# whereas Ubuntu looks in /var/lib/locales/supported.d/* and generates locales -# based on what's in the files in that folder -time echo 'it_CH.UTF-8 UTF-8' | sudo tee -a /var/lib/locales/supported.d/it -time sudo locale-gen + # Need to enable for locale testing. The location of the locale file(s) is + # distro specific. For example, on Arch Linux all of the locales are in a + # commented file--/etc/locale.gen--that must be commented in to be used + # whereas Ubuntu looks in /var/lib/locales/supported.d/* and generates locales + # based on what's in the files in that folder + time echo 'it_CH.UTF-8 UTF-8' | sudo tee -a /var/lib/locales/supported.d/it + time sudo locale-gen +fi # install gui for clipboard testing if [ -n "$CLIPBOARD_GUI" ]; then @@ -67,7 +67,12 @@ fi python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" [ "$python_major_version" == "2" ] && python_major_version="" -wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 +# install miniconda +if [ "${TRAVIS_OS_NAME}" == "osx" ]; then + wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh || exit 1 +else + wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 +fi bash miniconda.sh -b -p $HOME/miniconda || exit 1 conda config --set always_yes yes --set changeps1 no || exit 1 @@ -94,7 +99,7 @@ time conda install -n pandas --file=${REQ} || exit 1 source activate pandas # set the compiler cache to work -if [ "$IRON_TOKEN" ]; then +if [[ "$IRON_TOKEN" && "${TRAVIS_OS_NAME}" == "linux" ]]; then export PATH=/usr/lib/ccache:/usr/lib64/ccache:$PATH gcc=$(which gcc) echo "gcc: $gcc" @@ -113,24 +118,31 @@ if [ "$BUILD_TEST" ]; then else # build but don't install + echo "build em" time python setup.py build_ext --inplace || exit 1 # we may have run installations + echo "conda installs" REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.run" time conda install -n pandas --file=${REQ} || exit 1 # we may have additional pip installs + echo "pip installs" REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.pip" if [ -e ${REQ} ]; then pip install -r $REQ fi # remove any installed pandas package - conda remove pandas + # w/o removing anything else + echo "removing installed pandas" + conda remove pandas --force # install our pandas + echo "running setup.py develop" python setup.py develop || exit 1 fi -true +echo "done" +exit 0 diff --git a/ci/prep_ccache.sh b/ci/prep_ccache.sh index 34e1f2520c422..7e586cc4d3085 100755 --- a/ci/prep_ccache.sh +++ b/ci/prep_ccache.sh @@ -1,5 +1,10 @@ #!/bin/bash +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not using ccache on non-linux" + exit 0 +fi + if [ "$IRON_TOKEN" ]; then home_dir=$(pwd) diff --git a/ci/requirements-2.7-64.run b/ci/requirements-2.7-64.run index 260ae8125e040..42b5a789ae31a 100644 --- a/ci/requirements-2.7-64.run +++ b/ci/requirements-2.7-64.run @@ -1,6 +1,6 @@ dateutil pytz -numpy +numpy=1.10* xlwt numexpr pytables @@ -13,10 +13,6 @@ scipy xlsxwriter boto bottleneck -patsy html5lib beautiful-soup jinja2=2.8 - -#pymysql=0.6.3 -#psycopg2=2.5.2 diff --git a/ci/requirements-2.7.pip b/ci/requirements-2.7.pip index 9bc533110cea3..54596ad2a8169 100644 --- a/ci/requirements-2.7.pip +++ b/ci/requirements-2.7.pip @@ -2,5 +2,6 @@ blosc httplib2 google-api-python-client == 1.2 python-gflags == 2.0 +oauth2client == 1.5.0 pathlib py diff --git a/ci/requirements-3.4-64.run b/ci/requirements-3.4-64.run index 5eb8a5666fa74..106cc5b7168ba 100644 --- a/ci/requirements-3.4-64.run +++ b/ci/requirements-3.4-64.run @@ -1,17 +1,12 @@ python-dateutil pytz -numpy +numpy=1.9* openpyxl xlsxwriter xlrd xlwt -html5lib -patsy -beautiful-soup scipy numexpr pytables -lxml -sqlalchemy bottleneck jinja2=2.8 diff --git a/ci/requirements-3.4.build b/ci/requirements-3.4.build index 11901862776bc..4a4bd9d433428 100644 --- a/ci/requirements-3.4.build +++ b/ci/requirements-3.4.build @@ -1,3 +1,3 @@ numpy=1.8.1 cython -libgfortran +libgfortran=1.0 diff --git a/ci/requirements-3.4.pip b/ci/requirements-3.4.pip index 62be867437af1..55986a0220bf0 100644 --- a/ci/requirements-3.4.pip +++ b/ci/requirements-3.4.pip @@ -2,3 +2,4 @@ python-dateutil==2.2 blosc httplib2 google-api-python-client +oauth2client diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run index f0101d34204a3..f9f226e3f1465 100644 --- a/ci/requirements-3.4_SLOW.run +++ b/ci/requirements-3.4_SLOW.run @@ -9,7 +9,7 @@ html5lib patsy beautiful-soup scipy -numexpr +numexpr=2.4.4 pytables matplotlib lxml diff --git a/ci/requirements-3.5-64.run b/ci/requirements-3.5-64.run index 6beeb2fc31369..96de21e3daa5e 100644 --- a/ci/requirements-3.5-64.run +++ b/ci/requirements-3.5-64.run @@ -1,25 +1,12 @@ python-dateutil pytz -numpy +numpy=1.10* openpyxl xlsxwriter xlrd xlwt -patsy scipy numexpr pytables -html5lib -lxml matplotlib -jinja2 blosc - -# currently causing some warnings -#sqlalchemy -#pymysql -#psycopg2 - -# not available from conda -#beautiful-soup -#bottleneck diff --git a/ci/requirements-3.5.run b/ci/requirements-3.5.run index 4ba3b473b3edd..333641caf26c4 100644 --- a/ci/requirements-3.5.run +++ b/ci/requirements-3.5.run @@ -5,7 +5,6 @@ openpyxl xlsxwriter xlrd xlwt -patsy scipy numexpr pytables @@ -18,6 +17,7 @@ sqlalchemy pymysql psycopg2 xarray +boto # incompat with conda ATM # beautiful-soup diff --git a/ci/requirements-3.5_OSX.build b/ci/requirements-3.5_OSX.build new file mode 100644 index 0000000000000..9558cf00ddf5c --- /dev/null +++ b/ci/requirements-3.5_OSX.build @@ -0,0 +1,4 @@ +python-dateutil +pytz +numpy +cython diff --git a/ci/requirements-3.5_OSX.run b/ci/requirements-3.5_OSX.run new file mode 100644 index 0000000000000..80e12ac3fed34 --- /dev/null +++ b/ci/requirements-3.5_OSX.run @@ -0,0 +1,20 @@ +python-dateutil +pytz +numpy +openpyxl +xlsxwriter +xlrd +xlwt +scipy +numexpr +pytables +html5lib +lxml +matplotlib +jinja2 +bottleneck +xarray +boto + +# incompat with conda ATM +# beautiful-soup diff --git a/ci/submit_ccache.sh b/ci/submit_ccache.sh index da421489230dd..7630bb7cc2760 100755 --- a/ci/submit_ccache.sh +++ b/ci/submit_ccache.sh @@ -1,18 +1,23 @@ #!/bin/bash -home_dir=$(pwd) -ccache -s - -MISSES=$(ccache -s | grep "cache miss" | grep -Po "\d+") -echo "MISSES: $MISSES" - -if [ x"$MISSES" == x"0" ]; then - echo "No cache misses detected, skipping upload" - exit 0 +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not using ccache on non-linux" + exit 0 fi if [ "$IRON_TOKEN" ]; then + home_dir=$(pwd) + ccache -s + + MISSES=$(ccache -s | grep "cache miss" | grep -Po "\d+") + echo "MISSES: $MISSES" + + if [ x"$MISSES" == x"0" ]; then + echo "No cache misses detected, skipping upload" + exit 0 + fi + # install the compiler cache sudo apt-get $APT_ARGS install ccache p7zip-full # iron_cache, pending py3 fixes upstream @@ -29,6 +34,6 @@ if [ "$IRON_TOKEN" ]; then split -b 500000 -d $HOME/ccache.7z $HOME/ccache. python ci/ironcache/put.py -fi; +fi exit 0 diff --git a/conda.recipe/meta.yaml b/conda.recipe/meta.yaml index 2dba848704d45..2aee11772896f 100644 --- a/conda.recipe/meta.yaml +++ b/conda.recipe/meta.yaml @@ -1,6 +1,6 @@ package: name: pandas - version: {{ GIT_DESCRIBE_TAG }} + version: {{ GIT_DESCRIBE_TAG|replace("v","") }} build: number: {{ GIT_DESCRIBE_NUMBER|int }} diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index a28eb39f36917..4d1354a515b1c 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -281,7 +281,7 @@ As usual, **both sides** of the slicers are included as this is label indexing. .. warning:: You should specify all axes in the ``.loc`` specifier, meaning the indexer for the **index** and - for the **columns**. Their are some ambiguous cases where the passed indexer could be mis-interpreted + for the **columns**. There are some ambiguous cases where the passed indexer could be mis-interpreted as indexing *both* axes, rather than into say the MuliIndex for the rows. You should do this: @@ -717,6 +717,10 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index. Int64Index and RangeIndex ~~~~~~~~~~~~~~~~~~~~~~~~~ +.. warning:: + + Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`. + ``Int64Index`` is a fundamental basic index in *pandas*. This is an Immutable array implementing an ordered, sliceable set. Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``NDFrame`` objects. @@ -736,7 +740,6 @@ Float64Index operations by about 30x and boolean indexing operations on the ``Float64Index`` itself are about 2x as fast. - .. versionadded:: 0.13.0 By default a ``Float64Index`` will be automatically created when passing floating, or mixed-integer-floating values in index creation. @@ -797,12 +800,12 @@ In non-float indexes, slicing using floats will raise a ``TypeError`` .. warning:: - Using a scalar float indexer has been removed in 0.18.0, so the following will raise a ``TypeError`` + Using a scalar float indexer for ``.iloc`` has been removed in 0.18.0, so the following will raise a ``TypeError`` .. code-block:: python - In [3]: pd.Series(range(5))[3.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'> + In [3]: pd.Series(range(5)).iloc[3.0] + TypeError: cannot do positional indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'> Further the treatment of ``.ix`` with a float indexer on a non-float index, will be label based, and thus coerce the index. diff --git a/doc/source/api.rst b/doc/source/api.rst index 59f0f0a82a892..85f1984f29291 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1215,7 +1215,6 @@ Serialization / IO / Conversion Panel.to_pickle Panel.to_excel Panel.to_hdf - Panel.to_json Panel.to_sparse Panel.to_frame Panel.to_xarray @@ -1694,16 +1693,18 @@ application to columns of a specific data type. .. autosummary:: :toctree: generated/ + DataFrameGroupBy.agg + DataFrameGroupBy.all + DataFrameGroupBy.any DataFrameGroupBy.bfill + DataFrameGroupBy.corr + DataFrameGroupBy.count + DataFrameGroupBy.cov DataFrameGroupBy.cummax DataFrameGroupBy.cummin DataFrameGroupBy.cumprod DataFrameGroupBy.cumsum DataFrameGroupBy.describe - DataFrameGroupBy.all - DataFrameGroupBy.any - DataFrameGroupBy.corr - DataFrameGroupBy.cov DataFrameGroupBy.diff DataFrameGroupBy.ffill DataFrameGroupBy.fillna @@ -1717,6 +1718,7 @@ application to columns of a specific data type. DataFrameGroupBy.rank DataFrameGroupBy.resample DataFrameGroupBy.shift + DataFrameGroupBy.size DataFrameGroupBy.skew DataFrameGroupBy.take DataFrameGroupBy.tshift diff --git a/doc/source/basics.rst b/doc/source/basics.rst index d0469078aa3e9..1e30921e7248f 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -370,6 +370,7 @@ be broadcast: or it can return False if broadcasting can not be done: .. ipython:: python + :okwarning: np.array([1, 2, 3]) == np.array([1, 2]) @@ -1757,7 +1758,7 @@ but occasionally has non-dates intermixed and you want to represent as missing. 'foo', 1.0, 1, pd.Timestamp('20010104'), '20010105'], dtype='O') s - s.convert_objects(convert_dates='coerce') + pd.to_datetime(s, errors='coerce') In addition, :meth:`~DataFrame.convert_objects` will attempt the *soft* conversion of any *object* dtypes, meaning that if all the objects in a Series are of the same type, the Series will have that dtype. diff --git a/doc/source/comparison_with_sql.rst b/doc/source/comparison_with_sql.rst index 5dc083db7d147..26e76e8c5a4f6 100644 --- a/doc/source/comparison_with_sql.rst +++ b/doc/source/comparison_with_sql.rst @@ -138,7 +138,7 @@ Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series. GROUP BY -------- -In pandas, SQL's GROUP BY operations performed using the similarly named +In pandas, SQL's GROUP BY operations are performed using the similarly named :meth:`~pandas.DataFrame.groupby` method. :meth:`~pandas.DataFrame.groupby` typically refers to a process where we'd like to split a dataset into groups, apply some function (typically aggregation) , and then combine the groups together. @@ -163,23 +163,24 @@ The pandas equivalent would be: tips.groupby('sex').size() -Notice that in the pandas code we used :meth:`~pandas.DataFrameGroupBy.size` and not -:meth:`~pandas.DataFrameGroupBy.count`. This is because :meth:`~pandas.DataFrameGroupBy.count` -applies the function to each column, returning the number of ``not null`` records within each. +Notice that in the pandas code we used :meth:`~pandas.core.groupby.DataFrameGroupBy.size` and not +:meth:`~pandas.core.groupby.DataFrameGroupBy.count`. This is because +:meth:`~pandas.core.groupby.DataFrameGroupBy.count` applies the function to each column, returning +the number of ``not null`` records within each. .. ipython:: python tips.groupby('sex').count() -Alternatively, we could have applied the :meth:`~pandas.DataFrameGroupBy.count` method to an -individual column: +Alternatively, we could have applied the :meth:`~pandas.core.groupby.DataFrameGroupBy.count` method +to an individual column: .. ipython:: python tips.groupby('sex')['total_bill'].count() Multiple functions can also be applied at once. For instance, say we'd like to see how tip amount -differs by day of the week - :meth:`~pandas.DataFrameGroupBy.agg` allows you to pass a dictionary +differs by day of the week - :meth:`~pandas.core.groupby.DataFrameGroupBy.agg` allows you to pass a dictionary to your grouped DataFrame, indicating which functions to apply to specific columns. .. code-block:: sql diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 2b8cf7e41431b..81b69d77b323a 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -250,11 +250,15 @@ accept the following arguments: result is NA) - ``center``: boolean, whether to set the labels at the center (default is False) +.. note:: + + The ``min`` and ``max`` functions will by default return the result as a float. For integer inputs, integer outputs can be obtained by passing True as the ``as_float`` argument. + .. warning:: The ``freq`` and ``how`` arguments were in the API prior to 0.18.0 changes. These are deprecated in the new API. You can simply resample the input prior to creating a window function. - For example, instead of ``s.rolling(window=5,freq='D').max()`` to get the max value on a rolling 5 Day window, one could use ``s.resample('D',how='max').rolling(window=5).max()``, which first resamples the data to daily data, then provides a rolling 5 day window. + For example, instead of ``s.rolling(window=5,freq='D').max()`` to get the max value on a rolling 5 Day window, one could use ``s.resample('D').max().rolling(window=5).max()``, which first resamples the data to daily data, then provides a rolling 5 day window. We can then call methods on these ``rolling`` objects. These return like-indexed objects: @@ -477,9 +481,7 @@ Aggregation ----------- Once the ``Rolling``, ``Expanding`` or ``EWM`` objects have been created, several methods are available to -perform multiple computations on the data. This is very similar to a ``.groupby.agg`` seen :ref:`here <groupby.aggregate>`. - -An obvious one is aggregation via the ``aggregate`` or equivalently ``agg`` method: +perform multiple computations on the data. This is very similar to a ``.groupby(...).agg`` seen :ref:`here <groupby.aggregate>`. .. ipython:: python @@ -545,7 +547,7 @@ columns of a DataFrame: 'B' : lambda x: np.std(x, ddof=1)}) The function names can also be strings. In order for a string to be valid it -must be implemented on the Windowed object +must be implemented on the windowed object .. ipython:: python @@ -647,7 +649,7 @@ Exponentially Weighted Windows A related set of functions are exponentially weighted versions of several of the above statistics. A similar interface to ``.rolling`` and ``.expanding`` is accessed -thru the ``.ewm`` method to receive a :class:`~pandas.core.window.EWM` object. +thru the ``.ewm`` method to receive an :class:`~pandas.core.window.EWM` object. A number of expanding EW (exponentially weighted) methods are provided: @@ -733,24 +735,29 @@ therefore there is an assumption that :math:`x_0` is not an ordinary value but rather an exponentially weighted moment of the infinite series up to that point. -One must have :math:`0 < \alpha \leq 1`, but rather than pass :math:`\alpha` -directly, it's easier to think about either the **span**, **center of mass -(com)** or **halflife** of an EW moment: +One must have :math:`0 < \alpha \leq 1`, and while since version 0.18.0 +it has been possible to pass :math:`\alpha` directly, it's often easier +to think about either the **span**, **center of mass (com)** or **half-life** +of an EW moment: .. math:: \alpha = \begin{cases} - \frac{2}{s + 1}, & s = \text{span}\\ - \frac{1}{1 + c}, & c = \text{center of mass}\\ - 1 - \exp^{\frac{\log 0.5}{h}}, & h = \text{half life} + \frac{2}{s + 1}, & \text{for span}\ s \geq 1\\ + \frac{1}{1 + c}, & \text{for center of mass}\ c \geq 0\\ + 1 - \exp^{\frac{\log 0.5}{h}}, & \text{for half-life}\ h > 0 \end{cases} -One must specify precisely one of the three to the EW functions. **Span** -corresponds to what is commonly called a "20-day EW moving average" for -example. **Center of mass** has a more physical interpretation. For example, -**span** = 20 corresponds to **com** = 9.5. **Halflife** is the period of -time for the exponential weight to reduce to one half. +One must specify precisely one of **span**, **center of mass**, **half-life** +and **alpha** to the EW functions: + +- **Span** corresponds to what is commonly called an "N-day EW moving average". +- **Center of mass** has a more physical interpretation and can be thought of + in terms of span: :math:`c = (s - 1) / 2`. +- **Half-life** is the period of time for the exponential weight to reduce to + one half. +- **Alpha** specifies the smoothing factor directly. Here is an example for a univariate time series: @@ -801,5 +808,5 @@ are scaled by debiasing factors (For :math:`w_i = 1`, this reduces to the usual :math:`N / (N - 1)` factor, with :math:`N = t + 1`.) -See http://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_variance +See `Weighted Sample Variance <http://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_variance>`__ for further details. diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index 599724d88bf63..d674ee293e4ed 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -692,6 +692,7 @@ R package): .. ipython:: python :suppress: + :okwarning: # restore GlobalPrintConfig pd.reset_option('^display\.') diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index 9503675af8681..b4b79a87f898a 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -98,8 +98,9 @@ First we're going to need to import the cython magic function to ipython (for cython versions >=0.21 you can use ``%load_ext Cython``): .. ipython:: python + :okwarning: - %load_ext cythonmagic + %load_ext Cython Now, let's simply copy our functions over to cython as is (the suffix diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb index 1881727001546..77813a03c704a 100644 --- a/doc/source/html-styling.ipynb +++ b/doc/source/html-styling.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -93,7 +93,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\">\n", + " <table id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -114,346 +114,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - 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" \n", - " 1.392855\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -462,10 +358,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2c320>" + "<pandas.core.style.Styler at 0x10b1ba8d0>" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -497,7 +393,7 @@ " ' <style type=\"text/css\" >',\n", " ' ',\n", " ' ',\n", - " ' #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {',\n", + " ' #T_a45fea9e_c56b_11e5_893a_a45e60bd97fbrow0_col2 {',\n", " ' ',\n", " ' background-color: red;',\n", " ' ',\n", @@ -505,7 +401,7 @@ " ' ']" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -532,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -558,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -570,301 +466,301 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -872,7 +768,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\">\n", + " <table id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -893,346 +789,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - 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" \n", - " 10.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -1241,10 +1033,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c351d0>" + "<pandas.core.style.Styler at 0x111d9dc50>" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1274,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -1290,7 +1082,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -1302,31 +1094,31 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: yellow;\n", " \n", @@ -1334,7 +1126,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\">\n", + " <table id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -1355,346 +1147,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - 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" \n", - " -1.438713\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.481165\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -1703,10 +1391,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35160>" + "<pandas.core.style.Styler at 0x111dd4898>" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1724,7 +1412,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -1736,7 +1424,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", @@ -1744,7 +1432,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", @@ -1752,7 +1440,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", @@ -1760,7 +1448,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", @@ -1768,7 +1456,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", @@ -1776,7 +1464,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", @@ -1784,7 +1472,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", @@ -1792,7 +1480,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", @@ -1800,7 +1488,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", @@ -1808,7 +1496,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", @@ -1816,7 +1504,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", @@ -1824,7 +1512,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", @@ -1832,7 +1520,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", @@ -1840,7 +1528,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", @@ -1848,7 +1536,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", @@ -1856,7 +1544,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", @@ -1864,7 +1552,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", @@ -1872,7 +1560,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", @@ -1880,7 +1568,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", @@ -1888,7 +1576,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", @@ -1896,7 +1584,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", @@ -1904,7 +1592,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", @@ -1912,7 +1600,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", @@ -1920,7 +1608,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", @@ -1928,7 +1616,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", @@ -1936,7 +1624,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", @@ -1944,7 +1632,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", @@ -1952,7 +1640,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", @@ -1960,7 +1648,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -1968,7 +1656,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", @@ -1976,7 +1664,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", @@ -1984,7 +1672,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", @@ -1992,7 +1680,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", @@ -2000,7 +1688,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", @@ -2008,7 +1696,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", @@ -2016,7 +1704,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", @@ -2024,7 +1712,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", @@ -2032,7 +1720,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", @@ -2040,7 +1728,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", @@ -2048,7 +1736,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", @@ -2056,7 +1744,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", @@ -2064,7 +1752,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", @@ -2072,7 +1760,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", @@ -2080,7 +1768,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", @@ -2088,7 +1776,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", @@ -2096,7 +1784,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", @@ -2104,7 +1792,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", @@ -2112,7 +1800,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", @@ -2120,7 +1808,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", @@ -2128,7 +1816,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -2138,7 +1826,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\">\n", + " <table id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -2159,346 +1847,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -2507,10 +2091,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2ce48>" + "<pandas.core.style.Styler at 0x111dc0dd8>" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -2537,7 +2121,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -2559,7 +2143,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -2571,7 +2155,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0 {\n", + " #T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: darkorange;\n", " \n", @@ -2579,7 +2163,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\">\n", + " <table id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -2600,346 +2184,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - 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" \n", - " 2\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -2948,10 +2428,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d278>" + "<pandas.core.style.Styler at 0x111dd4eb8>" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2999,7 +2479,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -3011,19 +2491,19 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2 {\n", + " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1 {\n", + " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3 {\n", + " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", @@ -3031,7 +2511,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\">\n", + " <table id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -3052,346 +2532,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " 0.104011\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - 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" \n", - " 1.392855\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -3400,10 +2776,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d438>" + "<pandas.core.style.Styler at 0x111dd4ac8>" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3421,7 +2797,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -3433,49 +2809,49 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -3483,7 +2859,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\">\n", + " <table id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -3504,346 +2880,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.31628\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - 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" \n", - " 2\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - 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" \n", - " 0.104011\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -3852,10 +3124,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d4e0>" + "<pandas.core.style.Styler at 0x111daa668>" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -3882,19 +3154,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Builtin Styles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself." + "## Finer Control: Display Values\n", + "\n", + "We distinguish the *display* value from the *actual* value in `Styler`.\n", + "To control the display value, the text is printed in each cell, use `Styler.format`. Cells can be formatted according to a [format spec string](https://docs.python.org/3/library/string.html#format-specification-mini-language) or a callable that takes a single value and returns a string." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -3906,15 +3174,9 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: red;\n", - " \n", - " }\n", - " \n", " </style>\n", "\n", - " <table id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\">\n", + " <table id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -3935,346 +3197,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 100.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 132.92%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -31.63%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -99.08%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 200.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -107.08%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -143.87%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 56.44%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 29.57%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 300.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -162.64%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 21.96%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 67.88%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 188.93%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 400.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 96.15%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 10.40%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -48.12%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 85.02%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 500.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 145.34%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 105.77%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 16.56%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 51.50%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 600.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -133.69%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 56.29%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 139.29%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -6.33%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 700.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 12.17%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 120.76%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.20%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 162.78%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 800.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 35.45%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 103.75%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -38.57%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 51.98%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 900.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 168.66%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -132.60%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 142.90%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -208.94%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 1000.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -12.98%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 63.15%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -58.65%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 29.07%\n", " \n", " </tr>\n", " \n", @@ -4283,28 +3441,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35a90>" + "<pandas.core.style.Styler at 0x111dd45f8>" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.highlight_null(null_color='red')" + "df.style.format(\"{:.2%}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap." + "Use a dictionary to format specific columns." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -4316,669 +3474,265 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #e5ffe5;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1 {\n", - " \n", - " background-color: #188d18;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" None>\n", " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #e5ffe5;\n", + "\n", + " <thead>\n", " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3 {\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " background-color: #c7eec7;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4 {\n", + " </thead>\n", + " <tbody>\n", " \n", - " background-color: #a6dca6;\n", + " <tr>\n", + " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1000\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.32\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -100\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " +0.56\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " background-color: #ccf1cc;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1 {\n", - " \n", - " background-color: #c0eac0;\n", - " \n", - " }\n", - " \n", - 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" \n", - " background-color: #cff3cf;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4 {\n", - " \n", - " background-color: #4fac4f;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0 {\n", - " \n", - " background-color: #198e19;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1 {\n", - " \n", - " background-color: #008000;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2 {\n", - " \n", - " background-color: #dcfadc;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3 {\n", - " \n", - " background-color: #008000;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4 {\n", - " \n", - " background-color: #e5ffe5;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0 {\n", - " \n", - " background-color: #008000;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1 {\n", - " \n", - " background-color: #7ec67e;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2 {\n", - " \n", - " background-color: #319b31;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3 {\n", - " \n", - " background-color: #e5ffe5;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4 {\n", - " \n", - " background-color: #5cb35c;\n", - " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\">\n", - " \n", - "\n", - " <thead>\n", - " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", - " \n", - " </thead>\n", - " <tbody>\n", + " <tr>\n", + " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -200\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " +0.68\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 1000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.48\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " +0.17\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -100\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " +1.39\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.39\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 2000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " +1.43\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.59\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -4987,33 +3741,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35828>" + "<pandas.core.style.Styler at 0x111dd4588>" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import seaborn as sns\n", - "\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "\n", - "s = df.style.background_gradient(cmap=cm)\n", - "s" + "df.style.format({'B': \"{:0<4.0f}\", 'D': '{:+.2f}'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still." + "Or pass in a callable (or dictionary of callables) for more flexible handling." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -5025,159 +3774,9 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1 {\n", - " \n", - " background-color: #e5e419;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3 {\n", - " \n", - " background-color: #46327e;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0 {\n", - " \n", - " background-color: #3b528b;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1 {\n", - " \n", - " background-color: #433e85;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3 {\n", - " \n", - " background-color: #bddf26;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4 {\n", - " \n", - " background-color: #25838e;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0 {\n", - " \n", - " background-color: #21918c;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2 {\n", - " \n", - " background-color: #35b779;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0 {\n", - " \n", - " background-color: #5ec962;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1 {\n", - " \n", - " background-color: #95d840;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2 {\n", - " \n", - " background-color: #26ad81;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4 {\n", - " \n", - " background-color: #2cb17e;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3 {\n", - " \n", - " background-color: #1f9e89;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4 {\n", - " \n", - " background-color: #1f958b;\n", - " \n", - " }\n", - " \n", " </style>\n", "\n", - " <table id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\">\n", + " <table id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -5198,176 +3797,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " ±1.33\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " ±1.07\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " ±1.63\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " ±0.96\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " ±1.45\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " ±1.34\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " ±0.12\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " ±0.35\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " ±1.69\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " ±0.13\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -5376,22 +4041,35 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c354a8>" + "<pandas.core.style.Styler at 0x111dd4c50>" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Uses the full color range\n", - "df.loc[:4].style.background_gradient(cmap='viridis')" + "df.style.format({\"B\": lambda x: \"±{:.2f}\".format(abs(x))})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Builtin Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -5403,212 +4081,18 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #31688e;\n", + " #T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col2 {\n", " \n", - " : ;\n", + " background-color: red;\n", " \n", " }\n", " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1 {\n", - " \n", - " background-color: #efe51c;\n", - " \n", - " : ;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" None>\n", " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " background-color: red;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3 {\n", - " \n", - " background-color: #277f8e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0 {\n", - " \n", - " background-color: #21918c;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1 {\n", - " \n", - " background-color: #25858e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3 {\n", - " \n", - " background-color: #d5e21a;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4 {\n", - " \n", - " background-color: #29af7f;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0 {\n", - " \n", - " background-color: #35b779;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2 {\n", - " \n", - " background-color: #6ccd5a;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0 {\n", - " \n", - " background-color: #90d743;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1 {\n", - " \n", - " background-color: #b8de29;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2 {\n", - " \n", - " background-color: #5ac864;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4 {\n", - " \n", - " background-color: #63cb5f;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3 {\n", - " \n", - " background-color: #46c06f;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4 {\n", - " \n", - " background-color: #3bbb75;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\">\n", - " \n", - "\n", - " <thead>\n", + "\n", + " <thead>\n", " \n", " <tr>\n", " \n", @@ -5626,176 +4110,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -5804,32 +4354,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2c400>" + "<pandas.core.style.Styler at 0x111dd4d68>" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Compreess the color range\n", - "(df.loc[:4]\n", - " .style\n", - " .background_gradient(cmap='viridis', low=.5, high=0)\n", - " .highlight_null('red'))" + "df.style.highlight_null(null_color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can include \"bar charts\" in your DataFrame." + "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -5841,209 +4387,309 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col0 {\n", " \n", - " width: 10em;\n", + " background-color: #e5ffe5;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col1 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);\n", + " background-color: #188d18;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col2 {\n", " \n", - " width: 10em;\n", + " background-color: #e5ffe5;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col3 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);\n", + " background-color: #c7eec7;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col4 {\n", " \n", - " width: 10em;\n", + " background-color: #a6dca6;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col0 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);\n", + " background-color: #ccf1cc;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col1 {\n", " \n", - " width: 10em;\n", + " background-color: #c0eac0;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col2 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 16.77000113307442%, transparent 0%);\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col3 {\n", " \n", - " width: 10em;\n", + " background-color: #62b662;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col4 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 22.22222222222222%, transparent 0%);\n", + " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col0 {\n", " \n", - " width: 10em;\n", + " background-color: #b3e3b3;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col1 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col2 {\n", " \n", - " width: 10em;\n", + " background-color: #56af56;\n", " \n", - 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" <table id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\">\n", + " <table id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -6064,346 +4710,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -6412,28 +4954,33 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35dd8>" + "<pandas.core.style.Styler at 0x111dc0a58>" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.bar(subset=['A', 'B'], color='#d65f5f')" + "import seaborn as sns\n", + "\n", + "cm = sns.light_palette(\"green\", as_cmap=True)\n", + "\n", + "s = df.style.background_gradient(cmap=cm)\n", + "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "There's also `.highlight_min` and `.highlight_max`." + "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -6445,466 +4992,159 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: yellow;\n", + " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: yellow;\n", + " background-color: #e5e419;\n", " \n", " }\n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: yellow;\n", + " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col3 {\n", " \n", - 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" \n", - " -0.99081\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", - " \n", - " </tr>\n", - " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th class=\"col_heading level2 col0\">None\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <th class=\"blank\">\n", " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", - " \n", - " </tr>\n", + " </thead>\n", + " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", @@ -7273,28 +5299,22 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35d30>" + "<pandas.core.style.Styler at 0x111d9dcc0>" ] }, - "execution_count": 21, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.highlight_min(axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `Styler.set_properties` when the style doesn't actually depend on the values." + "# Uses the full color range\n", + "df.loc[:4].style.background_gradient(cmap='viridis')" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -7306,509 +5326,599 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col0 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: black;\n", + " background-color: #efe51c;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col2 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #440154;\n", " \n", - " border-color: white;\n", + " background-color: red;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: black;\n", + " background-color: #277f8e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col4 {\n", " \n", - " color: lawngreen;\n", + " background-color: #31688e;\n", " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: black;\n", + " background-color: #21918c;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col1 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #25858e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col3 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #d5e21a;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: black;\n", + " background-color: #29af7f;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col0 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #35b779;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col2 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #6ccd5a;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col4 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: black;\n", + " background-color: #90d743;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col1 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #b8de29;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: black;\n", + " background-color: #5ac864;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col3 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: black;\n", + " background-color: #63cb5f;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col0 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col2 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: black;\n", + " background-color: #46c06f;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col4 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #3bbb75;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0 {\n", + " </style>\n", + "\n", + " <table id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" None>\n", + " \n", + "\n", + " <thead>\n", " \n", - " color: lawngreen;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " background-color: black;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " border-color: white;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - " color: lawngreen;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " background-color: black;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", - " border-color: white;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", + " \n", + " </tr>\n", " \n", - " }\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", + " \n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111daaba8>" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compreess the color range\n", + "(df.loc[:4]\n", + " .style\n", + " .background_gradient(cmap='viridis', low=.5, high=0)\n", + " .highlight_null('red'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can include \"bar charts\" in your DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2 {\n", + " \n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3 {\n", - " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4 {\n", - " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0 {\n", - " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", + " height: 80%;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 16.77000113307442%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 22.22222222222222%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: black;\n", + " width: 10em;\n", " \n", - " border-color: white;\n", + " height: 80%;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 33.333333333333336%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 78.1150827834652%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 44.44444444444444%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 92.96229618327422%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 55.55555555555556%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 8.737388253449494%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 66.66666666666667%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 52.764243600289866%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 77.77777777777777%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 59.79187201238315%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 88.88888888888889%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 100.00000000000001%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 45.17326030334935%, transparent 0%);\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\">\n", + " <table id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -7829,346 +5939,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -8177,37 +6183,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2cf60>" + "<pandas.core.style.Styler at 0x111dc0d68>" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.set_properties(**{'background-color': 'black',\n", - " 'color': 'lawngreen',\n", - " 'border-color': 'white'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sharing Styles" + "df.style.bar(subset=['A', 'B'], color='#d65f5f')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with `df1.style.export`, and import it on the second DataFrame with `df1.style.set`" + "There's also `.highlight_min` and `.highlight_max`." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -8219,669 +6216,295 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col4 {\n", " \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1 {\n", - " \n", - " color: black;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col2 {\n", " \n", - " color: black;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col1 {\n", " \n", - " color: red;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col3 {\n", " \n", - " color: red;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col0 {\n", " \n", - " color: black;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1 {\n", - " \n", - " color: red;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" None>\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2 {\n", + "\n", + " <thead>\n", " \n", - " color: red;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3 {\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " color: black;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - 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" \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1 {\n", - " \n", - " color: red;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2 {\n", - " \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3 {\n", - " \n", - " color: red;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4 {\n", - " \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\">\n", - " \n", - "\n", - " <thead>\n", - " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - 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" \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", - " \n", - " </tr>\n", + " <tr>\n", + " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - 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" <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -8890,23 +6513,21 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d160>" + "<pandas.core.style.Styler at 0x111dd47f0>" ] }, - "execution_count": 23, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df2 = -df\n", - "style1 = df.style.applymap(color_negative_red)\n", - "style1" + "df.style.highlight_max(axis=0)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -8918,309 +6539,39 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col0 {\n", + " #T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow0_col0 {\n", " \n", - " color: red;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - 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" \n", - " -5.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " -1.453425\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " -1.057737\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " -0.165562\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " -0.515018\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " -6.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " 1.336936\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " -0.562861\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " -1.392855\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " 0.063328\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " -7.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " -0.121668\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " -1.207603\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " 0.00204\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " -1.627796\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " -8.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " -0.354493\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " -1.037528\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " 0.385684\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " -0.519818\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " -9.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " -1.686583\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " 1.325963\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " -1.428984\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " 2.089354\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " -10.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " 0.12982\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " -0.631523\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " 0.586538\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " -0.29072\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -9589,65 +6836,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d198>" + "<pandas.core.style.Styler at 0x111dc0780>" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "style2 = df2.style\n", - "style2.use(style1.export())\n", - "style2" + "df.style.highlight_min(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that you're able share the styles even though they're data aware. The styles are re-evaluated on the new DataFrame they've been `use`d upon." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Other options\n", - "\n", - "You've seen a few methods for data-driven styling.\n", - "`Styler` also provides a few other options for styles that don't depend on the data.\n", - "\n", - "- precision\n", - "- captions\n", - "- table-wide styles\n", - "\n", - "Each of these can be specified in two ways:\n", - "\n", - "- A keyword argument to `pandas.core.Styler`\n", - "- A call to one of the `.set_` methods, e.g. `.set_caption`\n", - "\n", - "The best method to use depends on the context. Use the `Styler` constructor when building many styled DataFrames that should all share the same properties. For interactive use, the`.set_` methods are more convenient." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Precision" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can control the precision of floats using pandas' regular `display.precision` option." + "Use `Styler.set_properties` when the style doesn't actually depend on the values." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -9659,409 +6869,509 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col1 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col2 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col3 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col4 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col1 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col2 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col3 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col1 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col2 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col3 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " background-color: yellow;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - 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" #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col3 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col1 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - 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" color: black;\n", + " background-color: black;\n", " \n", - " background-color: yellow;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col1 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col2 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col3 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\">\n", + " <table id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -10082,346 +7392,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.33\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.32\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.07\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.44\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.3\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.63\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.22\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.68\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.89\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.96\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.1\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.48\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.85\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.45\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.06\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.17\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.34\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.39\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.06\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.12\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.21\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.63\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.35\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.04\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.39\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.69\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.33\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.43\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.09\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.13\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.63\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.59\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -10430,32 +7636,37 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7dbe0>" + "<pandas.core.style.Styler at 0x111dd42b0>" ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "with pd.option_context('display.precision', 2):\n", - " html = (df.style\n", - " .applymap(color_negative_red)\n", - " .apply(highlight_max))\n", - "html" + "df.style.set_properties(**{'background-color': 'black',\n", + " 'color': 'lawngreen',\n", + " 'border-color': 'white'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or through a `set_precision` method." + "## Sharing Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with `df1.style.export`, and import it on the second DataFrame with `df1.style.set`" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -10467,409 +7678,309 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\">\n", + " <table id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -10890,346 +8001,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.33\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.32\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.07\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.44\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.3\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.63\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.22\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.68\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.89\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.96\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.1\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.48\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.85\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.45\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.06\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.17\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.34\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.39\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.06\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.12\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.21\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.63\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.35\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.04\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.39\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.69\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.33\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.43\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.09\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.13\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.63\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.59\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -11238,45 +8245,23 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7dc18>" + "<pandas.core.style.Styler at 0x111daa828>" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style\\\n", - " .applymap(color_negative_red)\\\n", - " .apply(highlight_max)\\\n", - " .set_precision(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use `df.round(2).style` if you'd prefer to round from the start." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Captions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Regular table captions can be added in a few ways." + "df2 = -df\n", + "style1 = df.style.applymap(color_negative_red)\n", + "style1" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -11288,311 +8273,309 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #188d18;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: #c7eec7;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col4 {\n", " \n", - " background-color: #a6dca6;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #ccf1cc;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col1 {\n", " \n", - " background-color: #c0eac0;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col3 {\n", " \n", - " background-color: #62b662;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #5cb35c;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col0 {\n", " \n", - " background-color: #b3e3b3;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col2 {\n", " \n", - " background-color: #56af56;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: #56af56;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col4 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: #99d599;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col1 {\n", " \n", - " background-color: #329c32;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: #5fb55f;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col3 {\n", " \n", - " background-color: #daf9da;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: #3ba13b;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col0 {\n", " \n", - " background-color: #80c780;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: #108910;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col2 {\n", " \n", - " background-color: #0d870d;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: #90d090;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col4 {\n", " \n", - " background-color: #4fac4f;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col0 {\n", " \n", - " background-color: #66b866;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col1 {\n", " \n", - " background-color: #d2f4d2;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col2 {\n", " \n", - " background-color: #389f38;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col3 {\n", " \n", - " background-color: #048204;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col4 {\n", " \n", - " background-color: #70be70;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col0 {\n", " \n", - " background-color: #4daa4d;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col1 {\n", " \n", - " background-color: #6cbc6c;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col2 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col3 {\n", " \n", - " background-color: #a3daa3;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col4 {\n", " \n", - " background-color: #0e880e;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col0 {\n", " \n", - " background-color: #329c32;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col1 {\n", " \n", - " background-color: #5cb35c;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col2 {\n", " \n", - " background-color: #0e880e;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col3 {\n", " \n", - " background-color: #cff3cf;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col4 {\n", " \n", - " background-color: #4fac4f;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col0 {\n", " \n", - " background-color: #198e19;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col1 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col2 {\n", " \n", - " background-color: #dcfadc;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col4 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #7ec67e;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col2 {\n", " \n", - " background-color: #319b31;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: #5cb35c;\n", + " color: red;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\">\n", - " \n", - " <caption>Colormaps, with a caption.</caption>\n", + " <table id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -11613,346 +8596,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " 0.31628\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " 0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " -2\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - 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" <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " -1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " -4\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " -0.961538\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - 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" \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " -1.39285\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " 0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " -7\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - 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" <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " -0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " -9\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " -1.68658\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " 1.32596\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " -1.42898\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " 2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " -10\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " 0.12982\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " -0.631523\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " 0.586538\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " -0.29072\n", " \n", " </tr>\n", " \n", @@ -11961,38 +8840,65 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d828>" + "<pandas.core.style.Styler at 0x111dc0390>" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.set_caption('Colormaps, with a caption.')\\\n", - " .background_gradient(cmap=cm)" + "style2 = df2.style\n", + "style2.use(style1.export())\n", + "style2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Table Styles" + "Notice that you're able share the styles even though they're data aware. The styles are re-evaluated on the new DataFrame they've been `use`d upon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The next option you have are \"table styles\".\n", - "These are styles that apply to the table as a whole, but don't look at the data.\n", - "Certain sytlings, including pseudo-selectors like `:hover` can only be used this way." + "## Other options\n", + "\n", + "You've seen a few methods for data-driven styling.\n", + "`Styler` also provides a few other options for styles that don't depend on the data.\n", + "\n", + "- precision\n", + "- captions\n", + "- table-wide styles\n", + "\n", + "Each of these can be specified in two ways:\n", + "\n", + "- A keyword argument to `pandas.core.Styler`\n", + "- A call to one of the `.set_` methods, e.g. `.set_caption`\n", + "\n", + "The best method to use depends on the context. Use the `Styler` constructor when building many styled DataFrames that should all share the same properties. For interactive use, the`.set_` methods are more convenient." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Precision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can control the precision of floats using pandas' regular `display.precision` option." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -12003,795 +8909,410 @@ "\n", " <style type=\"text/css\" >\n", " \n", - " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb tr:hover {\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #ffff99;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb th {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col1 {\n", " \n", - " font-size: 150%;\n", + " color: black;\n", " \n", - " text-align: center;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb caption {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col2 {\n", " \n", - " caption-side: bottom;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col3 {\n", + " \n", + " color: red;\n", + " \n", + " : ;\n", + " \n", + " }\n", " \n", - " </style>\n", - "\n", - " <table id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\">\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col4 {\n", + " \n", + " color: red;\n", + " \n", + " : ;\n", + " \n", + " }\n", " \n", - " <caption>Hover to highlight.</caption>\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col0 {\n", + " \n", + " color: black;\n", + " \n", + " : ;\n", + " \n", + " }\n", " \n", - "\n", - " <thead>\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col1 {\n", " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", + " color: red;\n", " \n", - " </thead>\n", - " <tbody>\n", + " : ;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col2 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", - " \n", - " </tr>\n", + " color: red;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", - " \n", - " </tr>\n", + " : ;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col3 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", - " \n", - " </tr>\n", + " color: black;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - 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" \n", - " 0.519818\n", - " \n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col4 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", - " \n", - " </tr>\n", + " color: black;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", - " \n", - " </tr>\n", + " : ;\n", " \n", - " </tbody>\n", - " </table>\n", - " " - ], - "text/plain": [ - "<pandas.core.style.Styler at 0x114c42710>" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import HTML\n", - "\n", - "def hover(hover_color=\"#ffff99\"):\n", - " return dict(selector=\"tr:hover\",\n", - " props=[(\"background-color\", \"%s\" % hover_color)])\n", - "\n", - "styles = [\n", - " hover(),\n", - " dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n", - " (\"text-align\", \"center\")]),\n", - " dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])\n", - "]\n", - "html = (df.style.set_table_styles(styles)\n", - " .set_caption(\"Hover to highlight.\"))\n", - "html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`table_styles` should be a list of dictionaries.\n", - "Each dictionary should have the `selector` and `props` keys.\n", - "The value for `selector` should be a valid CSS selector.\n", - "Recall that all the styles are already attached to an `id`, unique to\n", - "each `Styler`. This selector is in addition to that `id`.\n", - "The value for `props` should be a list of tuples of `('attribute', 'value')`.\n", - "\n", - "`table_styles` are extremely flexible, but not as fun to type out by hand.\n", - "We hope to collect some useful ones either in pandas, or preferable in a new package that [builds on top](#Extensibility) the tools here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Limitations\n", - "\n", - "- DataFrame only `(use Series.to_frame().style)`\n", - "- The index and columns must be unique\n", - "- No large repr, and performance isn't great; this is intended for summary DataFrames\n", - "- You can only style the *values*, not the index or columns\n", - "- You can only apply styles, you can't insert new HTML entities\n", - "\n", - "Some of these will be addressed in the future.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Terms\n", - "\n", - "- Style function: a function that's passed into `Styler.apply` or `Styler.applymap` and returns values like `'css attribute: value'`\n", - "- Builtin style functions: style functions that are methods on `Styler`\n", - "- table style: a dictionary with the two keys `selector` and `props`. `selector` is the CSS selector that `props` will apply to. `props` is a list of `(attribute, value)` tuples. A list of table styles passed into `Styler`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fun stuff\n", - "\n", - "Here are a few interesting examples.\n", - "\n", - "`Styler` interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " <style type=\"text/css\" >\n", - " \n", + " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col0 {\n", " \n", - " background-color: #557e79;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #779894;\n", + " color: red;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col2 {\n", " \n", - " background-color: #557e79;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - 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" background-color: #557e79;\n", + " color: black;\n", + " \n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #e7eeed;\n", + " color: red;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col2 {\n", " \n", - " background-color: #9bb4b0;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #557e79;\n", + " color: red;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: #d7e2e0;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\">\n", + " <table id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -12812,346 +9333,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.32\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.56\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.3\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.22\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.68\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.9\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.96\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.48\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.85\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.5\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.17\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.52\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.56\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.12\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.002\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.35\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.39\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.52\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.7\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.1\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.13\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.63\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.59\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29\n", " \n", " </tr>\n", " \n", @@ -13160,47 +9577,32 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x113487dd8>" + "<pandas.core.style.Styler at 0x111e0a160>" ] }, + "execution_count": 26, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "from IPython.html import widgets\n", - "@widgets.interact\n", - "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n", - " return df.style.background_gradient(\n", - " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n", - " as_cmap=True)\n", - " )" + "with pd.option_context('display.precision', 2):\n", + " html = (df.style\n", + " .applymap(color_negative_red)\n", + " .apply(highlight_max))\n", + "html" ] }, { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "def magnify():\n", - " return [dict(selector=\"th\",\n", - " props=[(\"font-size\", \"4pt\")]),\n", - " dict(selector=\"td\",\n", - " props=[('padding', \"0em 0em\")]),\n", - " dict(selector=\"th:hover\",\n", - " props=[(\"font-size\", \"12pt\")]),\n", - " dict(selector=\"tr:hover td:hover\",\n", - " props=[('max-width', '200px'),\n", - " ('font-size', '12pt')])\n", - "]" + "Or through a `set_precision` method." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -13211,1376 +9613,2365 @@ "\n", " <style type=\"text/css\" >\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th {\n", + " \n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col0 {\n", " \n", - " font-size: 4pt;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb td {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col1 {\n", " \n", - " padding: 0em 0em;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th:hover {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col2 {\n", " \n", - " font-size: 12pt;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb tr:hover td:hover {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col3 {\n", " \n", - " max-width: 200px;\n", + " color: red;\n", " \n", - " font-size: 12pt;\n", + " : ;\n", " \n", " }\n", " \n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #ecf2f8;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col4 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #b8cce5;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #efb1be;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col1 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #f3c2cc;\n", - " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4 {\n", - " \n", - " background-color: #eeaab7;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col3 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #f8dce2;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6 {\n", - " \n", - " background-color: #f2c1cb;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col0 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #83a6d2;\n", - " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8 {\n", - " \n", - " background-color: #de5f79;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col2 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: #c3d4e9;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10 {\n", - " \n", - " background-color: #eeacba;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: #f7dae0;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12 {\n", - " \n", - " background-color: #6f98ca;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col1 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: #e890a1;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col3 {\n", " \n", - " background-color: #f2f2f2;\n", + " color: red;\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15 {\n", - " \n", - " background-color: #ea96a7;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16 {\n", - " \n", - " background-color: #adc4e1;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - 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" \n", - " background-color: #f3c2cc;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col3 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11 {\n", - " \n", - " background-color: #f8dfe4;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12 {\n", - " \n", - " background-color: #b0c6e2;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col0 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13 {\n", - " \n", - " background-color: #ec9faf;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col1 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - 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" \n", - " background-color: #efb1be;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col0 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18 {\n", - " \n", - " background-color: #f2f2f2;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col1 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19 {\n", - " \n", - " background-color: #f8dce2;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col2 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20 {\n", - " \n", - " background-color: #a0bbdc;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col3 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21 {\n", - " \n", - " background-color: #d73c5b;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22 {\n", - " \n", - " background-color: #86a8d3;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" None>\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23 {\n", + "\n", + " <thead>\n", " \n", - " background-color: #d9e4f1;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.3\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.32\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99\n", + " \n", + " </tr>\n", " \n", - " background-color: #ecf2f8;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.56\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.3\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.22\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.68\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.9\n", + " \n", + " </tr>\n", " \n", - 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You can always use `df.round(2).style` if you'd prefer to round from the start." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Captions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Regular table captions can be added in a few ways." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", + " \n", + " \n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col0 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2 {\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #188d18;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col2 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - 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" \n", - " background-color: #e58195;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col0 {\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7 {\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #4479bb;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #7ec67e;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8 {\n", - " \n", - " background-color: #f2c1cb;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col2 {\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #319b31;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9 {\n", - " \n", - " background-color: #b1c7e2;\n", - " \n", - " max-width: 80px;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col3 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10 {\n", - " \n", - " background-color: #d4e0ef;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col4 {\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11 {\n", - " \n", - " background-color: #c1d3e8;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" None>\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12 {\n", - " \n", - " background-color: #f2f2f2;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", + " <caption>Colormaps, with a caption.</caption>\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13 {\n", - " \n", - " background-color: #cedced;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + "\n", + " <thead>\n", " \n", - 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" }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " background-color: #eeacba;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", - 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" }\n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111e0add8>" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.style.set_caption('Colormaps, with a caption.')\\\n", + " .background_gradient(cmap=cm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Table Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next option you have are \"table styles\".\n", + "These are styles that apply to the table as a whole, but don't look at the data.\n", + "Certain sytlings, including pseudo-selectors like `:hover` can only be used this way." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19 {\n", - " \n", - " background-color: #f5ccd4;\n", + " #T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb tr:hover {\n", " \n", - 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" }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", " \n", - " background-color: #f4c9d2;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", + " \n", + " </tr>\n", " \n", - " background-color: #f5ced6;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111e12f28>" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "\n", + "def hover(hover_color=\"#ffff99\"):\n", + " return dict(selector=\"tr:hover\",\n", + " props=[(\"background-color\", \"%s\" % hover_color)])\n", + "\n", + "styles = [\n", + " hover(),\n", + " dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n", + " (\"text-align\", \"center\")]),\n", + " dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])\n", + "]\n", + "html = (df.style.set_table_styles(styles)\n", + " .set_caption(\"Hover to highlight.\"))\n", + "html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`table_styles` should be a list of dictionaries.\n", + "Each dictionary should have the `selector` and `props` keys.\n", + "The value for `selector` should be a valid CSS selector.\n", + "Recall that all the styles are already attached to an `id`, unique to\n", + "each `Styler`. This selector is in addition to that `id`.\n", + "The value for `props` should be a list of tuples of `('attribute', 'value')`.\n", + "\n", + "`table_styles` are extremely flexible, but not as fun to type out by hand.\n", + "We hope to collect some useful ones either in pandas, or preferable in a new package that [builds on top](#Extensibility) the tools here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Limitations\n", + "\n", + "- DataFrame only `(use Series.to_frame().style)`\n", + "- The index and columns must be unique\n", + "- No large repr, and performance isn't great; this is intended for summary DataFrames\n", + "- You can only style the *values*, not the index or columns\n", + "- You can only apply styles, you can't insert new HTML entities\n", + "\n", + "Some of these will be addressed in the future.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Terms\n", + "\n", + "- Style function: a function that's passed into `Styler.apply` or `Styler.applymap` and returns values like `'css attribute: value'`\n", + "- Builtin style functions: style functions that are methods on `Styler`\n", + "- table style: a dictionary with the two keys `selector` and `props`. `selector` is the CSS selector that `props` will apply to. `props` is a list of `(attribute, value)` tuples. A list of table styles passed into `Styler`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fun stuff\n", + "\n", + "Here are a few interesting examples.\n", + "\n", + "`Styler` interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", + " \n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col0 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #779894;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col2 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5 {\n", + " 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#T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #fbeaed;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col4 {\n", " \n", - " background-color: #6490c6;\n", + " background-color: #557e79;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col0 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #e7eeed;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col2 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #9bb4b0;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #557e79;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col4 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #d7e2e0;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4 {\n", + " </style>\n", + "\n", + " <table id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" None>\n", + " \n", + "\n", + " <thead>\n", " \n", - " background-color: #4479bb;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - " background-color: #fae6ea;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", " \n", - " background-color: #e68598;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", + " \n", + " </tr>\n", " \n", - " }\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", + " \n", + " </tr>\n", + " \n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111dc08d0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.html import widgets\n", + "@widgets.interact\n", + "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n", + " return df.style.background_gradient(\n", + " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n", + " as_cmap=True)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def magnify():\n", + " return [dict(selector=\"th\",\n", + " props=[(\"font-size\", \"4pt\")]),\n", + " dict(selector=\"td\",\n", + " props=[('padding', \"0em 0em\")]),\n", + " dict(selector=\"th:hover\",\n", + " props=[(\"font-size\", \"12pt\")]),\n", + " dict(selector=\"tr:hover td:hover\",\n", + " props=[('max-width', '200px'),\n", + " ('font-size', '12pt')])\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb th {\n", " \n", - " background-color: #6d96ca;\n", + " font-size: 4pt;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb td {\n", " \n", - " font-size: 1pt;\n", + " padding: 0em 0em;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb th:hover {\n", " \n", - " background-color: #f9e3e7;\n", + " font-size: 12pt;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb tr:hover td:hover {\n", " \n", - " font-size: 1pt;\n", + " max-width: 200px;\n", + " \n", + " font-size: 12pt;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9 {\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #ecf2f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -14588,9 +11979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #b8cce5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14598,9 +11989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: #e0e9f4;\n", + " background-color: #efb1be;\n", " \n", " max-width: 80px;\n", " \n", @@ -14608,9 +11999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -14618,9 +12009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col4 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #eeaab7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14628,9 +12019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col5 {\n", " \n", - " background-color: #e47a90;\n", + " background-color: #f8dce2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14638,9 +12029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col6 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14648,9 +12039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col7 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #83a6d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14658,9 +12049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col8 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #de5f79;\n", " \n", " max-width: 80px;\n", " \n", @@ -14668,9 +12059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col9 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #c3d4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -14678,9 +12069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col10 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -14688,9 +12079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col11 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -14698,9 +12089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -14708,9 +12099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col13 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #e890a1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14718,9 +12109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col14 {\n", " \n", - " background-color: #a3bddd;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14728,9 +12119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col15 {\n", " \n", - " background-color: #7199cb;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14738,9 +12129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col16 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #adc4e1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14748,9 +12139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col17 {\n", " \n", - " background-color: #4e80be;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14758,9 +12149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col18 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #b7cbe5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14768,9 +12159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -14778,9 +12169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col20 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #6590c7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14788,9 +12179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col21 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -14798,9 +12189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col22 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14808,9 +12199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col23 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #cfddee;\n", " \n", " max-width: 80px;\n", " \n", @@ -14818,9 +12209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col24 {\n", " \n", - " background-color: #f2c1cb;\n", + " background-color: #e58094;\n", " \n", " max-width: 80px;\n", " \n", @@ -14828,9 +12219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e4798e;\n", " \n", " max-width: 80px;\n", " \n", @@ -14838,9 +12229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col1 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #aec5e1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14848,9 +12239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #c5d5ea;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -14858,9 +12249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col3 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #ec9faf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14868,9 +12259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -14878,9 +12269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col5 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #f9e0e5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14888,9 +12279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col6 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #db4f6b;\n", " \n", " max-width: 80px;\n", " \n", @@ -14898,9 +12289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col7 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14908,9 +12299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col8 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #e57f93;\n", " \n", " max-width: 80px;\n", " \n", @@ -14918,9 +12309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col9 {\n", " \n", - " background-color: #d8e3f1;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14928,9 +12319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col10 {\n", " \n", - " background-color: #eb9bab;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -14938,9 +12329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col11 {\n", " \n", - " background-color: #a3bddd;\n", + " background-color: #f8dfe4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14948,9 +12339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #b0c6e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14958,9 +12349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col13 {\n", " \n", - " background-color: #81a4d1;\n", + " background-color: #ec9faf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14968,9 +12359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col14 {\n", " \n", - " background-color: #95b3d8;\n", + " background-color: #e27389;\n", " \n", " max-width: 80px;\n", " \n", @@ -14978,9 +12369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col15 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #eb9dac;\n", " \n", " max-width: 80px;\n", " \n", @@ -14988,9 +12379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col16 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f1b8c3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14998,9 +12389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col17 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #efb1be;\n", " \n", " max-width: 80px;\n", " \n", @@ -15008,9 +12399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col18 {\n", " \n", - " background-color: #f2bdc8;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15018,9 +12409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f8dce2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15028,9 +12419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col20 {\n", " \n", - " background-color: #5686c1;\n", + " background-color: #a0bbdc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15038,9 +12429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col21 {\n", " \n", - " background-color: #f0b5c1;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15048,9 +12439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col22 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #86a8d3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15058,9 +12449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col23 {\n", " \n", - " background-color: #628fc6;\n", + " background-color: #d9e4f1;\n", " \n", " max-width: 80px;\n", " \n", @@ -15068,9 +12459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col24 {\n", " \n", - " background-color: #e68396;\n", + " background-color: #ecf2f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15078,9 +12469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col0 {\n", " \n", - " background-color: #eda7b5;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15088,9 +12479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #5887c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15098,9 +12489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col2 {\n", " \n", - " background-color: #e8eff7;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15108,9 +12499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: #eaf0f7;\n", + " background-color: #c7d7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15118,9 +12509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col4 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #82a5d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -15128,9 +12519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col5 {\n", " \n", - " background-color: #de5c76;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15138,9 +12529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col6 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e78a9d;\n", " \n", " max-width: 80px;\n", " \n", @@ -15148,9 +12539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col7 {\n", " \n", - " background-color: #dd5671;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15158,9 +12549,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col8 {\n", " \n", - " background-color: #e993a4;\n", + " background-color: #f1bbc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15168,9 +12559,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col9 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #779dcd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15178,9 +12569,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col10 {\n", " \n", - " background-color: #e991a3;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -15188,9 +12579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col11 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15198,9 +12589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #e0e9f4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15208,9 +12599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col13 {\n", " \n", - " background-color: #a0bbdc;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15218,9 +12609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col14 {\n", " \n", - " background-color: #96b4d9;\n", + " background-color: #e78a9d;\n", " \n", " max-width: 80px;\n", " \n", @@ -15228,9 +12619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col15 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15238,9 +12629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col16 {\n", " \n", - " background-color: #d3dfef;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15248,9 +12639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col17 {\n", " \n", - " background-color: #487cbc;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15258,9 +12649,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col18 {\n", " \n", - " background-color: #ea9aaa;\n", + " background-color: #b9cde6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15268,9 +12659,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col19 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15278,9 +12669,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #a0bbdc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15288,9 +12679,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col21 {\n", " \n", - " background-color: #eb9ead;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15298,9 +12689,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col22 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #618ec5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15308,9 +12699,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col23 {\n", " \n", - " background-color: #4d7fbe;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15318,9 +12709,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col24 {\n", " \n", - " background-color: #e994a5;\n", + " background-color: #c3d4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -15328,7 +12719,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col0 {\n", " \n", " background-color: #f6d5db;\n", " \n", @@ -15338,9 +12729,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col1 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #5384c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -15348,9 +12739,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: #d5e1f0;\n", + " background-color: #f1bbc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15358,9 +12749,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col3 {\n", " \n", - " background-color: #f0b4c0;\n", + " background-color: #c7d7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15368,9 +12759,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15378,9 +12769,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col5 {\n", " \n", - " background-color: #da4966;\n", + " background-color: #e7eef6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15388,9 +12779,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col6 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e58195;\n", " \n", " max-width: 80px;\n", " \n", @@ -15398,9 +12789,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15408,9 +12799,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col8 {\n", " \n", - " background-color: #ea96a7;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15418,9 +12809,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col9 {\n", " \n", - " background-color: #ecf2f8;\n", + " background-color: #b1c7e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15428,9 +12819,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col10 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -15438,9 +12829,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col11 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #c1d3e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15448,9 +12839,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col12 {\n", " \n", - " background-color: #dc516d;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15458,9 +12849,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col13 {\n", " \n", - " background-color: #91b0d7;\n", + " background-color: #cedced;\n", " \n", " max-width: 80px;\n", " \n", @@ -15468,9 +12859,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col14 {\n", " \n", - " background-color: #a8c0df;\n", + " background-color: #e7899c;\n", " \n", " max-width: 80px;\n", " \n", @@ -15478,9 +12869,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col15 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -15488,9 +12879,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col16 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #e2eaf4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15498,9 +12889,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col17 {\n", " \n", - " background-color: #6590c7;\n", + " background-color: #f7d6dd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15508,9 +12899,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col18 {\n", " \n", - " background-color: #ea99a9;\n", + " background-color: #a8c0df;\n", " \n", " max-width: 80px;\n", " \n", @@ -15518,9 +12909,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15528,9 +12919,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #b7cbe5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15538,9 +12929,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col21 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15548,9 +12939,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col22 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #739acc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15558,9 +12949,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col23 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #8dadd5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15568,9 +12959,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col24 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #cddbed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15578,9 +12969,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col0 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -15588,9 +12979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #5887c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15598,9 +12989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col2 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15608,9 +12999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15618,9 +13009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15628,9 +13019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col5 {\n", " \n", - " background-color: #e27389;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -15638,9 +13029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col6 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #e78c9e;\n", " \n", " max-width: 80px;\n", " \n", @@ -15648,9 +13039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #5182bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15658,9 +13049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col8 {\n", " \n", - " background-color: #ea9aaa;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15668,9 +13059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col9 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #c1d3e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15678,9 +13069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col10 {\n", " \n", - " background-color: #e993a4;\n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15688,9 +13079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col11 {\n", " \n", - " background-color: #b9cde6;\n", + " background-color: #c9d8eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15698,9 +13089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col12 {\n", " \n", - " background-color: #de5f79;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15708,9 +13099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col13 {\n", " \n", - " background-color: #b3c9e3;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15718,9 +13109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col14 {\n", " \n", - " background-color: #9fbadc;\n", + " background-color: #e47c91;\n", " \n", " max-width: 80px;\n", " \n", @@ -15728,9 +13119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col15 {\n", " \n", - " background-color: #6f98ca;\n", + " background-color: #eda8b6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15738,9 +13129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col16 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -15748,9 +13139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col17 {\n", " \n", - " background-color: #6f98ca;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15758,9 +13149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col18 {\n", " \n", - " background-color: #ea96a7;\n", + " background-color: #b3c9e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15768,9 +13159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col19 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15778,9 +13169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15788,9 +13179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col21 {\n", " \n", - " background-color: #f0b7c2;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15798,9 +13189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col22 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #7da2cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15808,9 +13199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col23 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15818,9 +13209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col24 {\n", " \n", - " background-color: #f2bdc8;\n", + " background-color: #a2bcdd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15828,9 +13219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col0 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15838,9 +13229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col1 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #6490c6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15848,9 +13239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col2 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15858,9 +13249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col3 {\n", " \n", - " background-color: #efb1be;\n", + " background-color: #f3c6cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15868,9 +13259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col4 {\n", " \n", - " background-color: #eaf0f7;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15878,9 +13269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col5 {\n", " \n", - " background-color: #e0657d;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -15888,9 +13279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col6 {\n", " \n", - " background-color: #eca1b0;\n", + " background-color: #e68598;\n", " \n", " max-width: 80px;\n", " \n", @@ -15898,9 +13289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6d96ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15908,9 +13299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col8 {\n", " \n", - " background-color: #e27087;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15918,9 +13309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col9 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15928,9 +13319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col10 {\n", " \n", - " background-color: #e68699;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15938,9 +13329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col11 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #e0e9f4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15948,9 +13339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15958,9 +13349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col13 {\n", " \n", - " background-color: #d1deee;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15968,9 +13359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col14 {\n", " \n", - " background-color: #82a5d1;\n", + " background-color: #e47a90;\n", " \n", " max-width: 80px;\n", " \n", @@ -15978,9 +13369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col15 {\n", " \n", - " background-color: #7099cb;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15988,9 +13379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col16 {\n", " \n", - " background-color: #a9c1e0;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -15998,9 +13389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col17 {\n", " \n", - " background-color: #6892c8;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -16008,9 +13399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col18 {\n", " \n", - " background-color: #f7d6dd;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -16018,9 +13409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f6d2d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16028,9 +13419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #b5cae4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16038,9 +13429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col21 {\n", " \n", - " background-color: #e4ecf5;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16048,9 +13439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col22 {\n", " \n", - " background-color: #d8e3f1;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -16058,9 +13449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col23 {\n", " \n", - " background-color: #477bbc;\n", + " background-color: #a3bddd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16068,9 +13459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col24 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #7199cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16078,9 +13469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col0 {\n", " \n", - " background-color: #e7eef6;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -16088,9 +13479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col1 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #4e80be;\n", " \n", " max-width: 80px;\n", " \n", @@ -16098,9 +13489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col2 {\n", " \n", - " background-color: #a6bfde;\n", + " background-color: #f8dee3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16108,9 +13499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col3 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16118,9 +13509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col4 {\n", " \n", - " background-color: #a9c1e0;\n", + " background-color: #6a94c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16128,9 +13519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col5 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16138,9 +13529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col6 {\n", " \n", - " background-color: #ea99a9;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -16148,9 +13539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16158,9 +13549,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col8 {\n", " \n", - " background-color: #f0b7c2;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16168,9 +13559,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col9 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16178,9 +13569,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col10 {\n", " \n", - " background-color: #eb9ead;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -16188,9 +13579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col11 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #c5d5ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -16198,9 +13589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col12 {\n", " \n", - " background-color: #d8415f;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -16208,9 +13599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col13 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #c6d6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -16218,9 +13609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col14 {\n", " \n", - " background-color: #5182bf;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16228,9 +13619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col15 {\n", " \n", - " background-color: #457abb;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16238,9 +13629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col16 {\n", " \n", - " background-color: #92b1d7;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -16248,9 +13639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col17 {\n", " \n", - " background-color: #7ba0cf;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16258,9 +13649,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col18 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #d8e3f1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16268,9 +13659,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col19 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #eb9bab;\n", " \n", " max-width: 80px;\n", " \n", @@ -16278,9 +13669,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col20 {\n", " \n", - " background-color: #5485c1;\n", + " background-color: #a3bddd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16288,9 +13679,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col21 {\n", " \n", - " background-color: #f5cfd7;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16298,9 +13689,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col22 {\n", " \n", - " background-color: #d4e0ef;\n", + " background-color: #81a4d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16308,9 +13699,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col23 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16318,9 +13709,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col24 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16328,9 +13719,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col0 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16338,9 +13729,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col1 {\n", " \n", - " background-color: #b0c6e2;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16348,9 +13739,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col2 {\n", " \n", - " background-color: #9fbadc;\n", + " background-color: #f2bdc8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16358,9 +13749,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col3 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16368,9 +13759,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col4 {\n", " \n", - " background-color: #cad9ec;\n", + " background-color: #5686c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16378,9 +13769,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col5 {\n", " \n", - " background-color: #e991a3;\n", + " background-color: #f0b5c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16388,9 +13779,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col6 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16398,9 +13789,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col7 {\n", " \n", - " background-color: #de5c76;\n", + " background-color: #628fc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -16408,9 +13799,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col8 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #e68396;\n", " \n", " max-width: 80px;\n", " \n", @@ -16418,9 +13809,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col9 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #eda7b5;\n", " \n", " max-width: 80px;\n", " \n", @@ -16428,9 +13819,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col10 {\n", " \n", - " background-color: #eb9dac;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16438,9 +13829,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col11 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16448,9 +13839,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16458,9 +13849,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col13 {\n", " \n", - " background-color: #acc3e1;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16468,9 +13859,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col14 {\n", " \n", - " background-color: #497dbd;\n", + " background-color: #de5c76;\n", " \n", " max-width: 80px;\n", " \n", @@ -16478,9 +13869,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col15 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16488,9 +13879,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col16 {\n", " \n", - " background-color: #bccfe7;\n", + " background-color: #dd5671;\n", " \n", " max-width: 80px;\n", " \n", @@ -16498,9 +13889,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col17 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #e993a4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16508,9 +13899,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col18 {\n", " \n", - " background-color: #eda6b4;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16518,9 +13909,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col19 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #e991a3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16528,9 +13919,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col20 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16538,9 +13929,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col21 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16548,9 +13939,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col22 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #a0bbdc;\n", " \n", " max-width: 80px;\n", " \n", @@ -16558,9 +13949,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col23 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #96b4d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16568,9 +13959,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col24 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16578,9 +13969,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col0 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #d3dfef;\n", " \n", " max-width: 80px;\n", " \n", @@ -16588,9 +13979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col1 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #487cbc;\n", " \n", " max-width: 80px;\n", " \n", @@ -16598,9 +13989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col2 {\n", " \n", - " background-color: #bbcee6;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -16608,9 +13999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16618,9 +14009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16628,9 +14019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col5 {\n", " \n", - " background-color: #e78a9d;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -16638,9 +14029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col6 {\n", " \n", - " background-color: #eda7b5;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -16648,9 +14039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col7 {\n", " \n", - " background-color: #dc546f;\n", + " background-color: #4d7fbe;\n", " \n", " max-width: 80px;\n", " \n", @@ -16658,9 +14049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col8 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #e994a5;\n", " \n", " max-width: 80px;\n", " \n", @@ -16668,9 +14059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col9 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -16678,9 +14069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col10 {\n", " \n", - " background-color: #eeaab7;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16688,9 +14079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col11 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #d5e1f0;\n", " \n", " max-width: 80px;\n", " \n", @@ -16698,9 +14089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #f0b4c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -16708,9 +14099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col13 {\n", " \n", - " background-color: #b8cce5;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16718,9 +14109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col14 {\n", " \n", - " background-color: #7099cb;\n", + " background-color: #da4966;\n", " \n", " max-width: 80px;\n", " \n", @@ -16728,9 +14119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col15 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16738,9 +14129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col16 {\n", " \n", - " background-color: #91b0d7;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16748,9 +14139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col17 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16758,9 +14149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col18 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #ecf2f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16768,9 +14159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col19 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -16778,9 +14169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col20 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #dde7f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16788,9 +14179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col21 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #dc516d;\n", " \n", " max-width: 80px;\n", " \n", @@ -16798,9 +14189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col22 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #91b0d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16808,9 +14199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col23 {\n", " \n", - " background-color: #6b95c9;\n", + " background-color: #a8c0df;\n", " \n", " max-width: 80px;\n", " \n", @@ -16818,9 +14209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col24 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16828,9 +14219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: #e8eff7;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16838,9 +14229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #6590c7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16848,9 +14239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col2 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #ea99a9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16858,9 +14249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #95b3d8;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16868,9 +14259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16878,9 +14269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col5 {\n", " \n", - " background-color: #eeabb8;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -16888,9 +14279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col6 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16898,9 +14289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col7 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #6a94c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16908,9 +14299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col8 {\n", " \n", - " background-color: #eca4b3;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -16918,9 +14309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col9 {\n", " \n", - " background-color: #f7d6dd;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16928,9 +14319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col10 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16938,9 +14329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col11 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #dfe8f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16948,9 +14339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -16958,9 +14349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col13 {\n", " \n", - " background-color: #9bb7da;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16968,9 +14359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col14 {\n", " \n", - " background-color: #618ec5;\n", + " background-color: #e27389;\n", " \n", " max-width: 80px;\n", " \n", @@ -16978,9 +14369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col15 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -16988,9 +14379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col16 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16998,9 +14389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col17 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -17008,9 +14399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col18 {\n", " \n", - " background-color: #f5cfd7;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17018,9 +14409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e993a4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17028,9 +14419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col20 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #b9cde6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17038,9 +14429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col21 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #de5f79;\n", " \n", " max-width: 80px;\n", " \n", @@ -17048,9 +14439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col22 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #b3c9e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17058,9 +14449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col23 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17068,9 +14459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col24 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -17078,9 +14469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col0 {\n", " \n", - " background-color: #cedced;\n", + " background-color: #c6d6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -17088,9 +14479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col1 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -17098,9 +14489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col2 {\n", " \n", - " background-color: #9cb8db;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17108,9 +14499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col3 {\n", " \n", - " background-color: #749bcc;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17118,9 +14509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col4 {\n", " \n", - " background-color: #b2c8e3;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17128,9 +14519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col5 {\n", " \n", - " background-color: #f8dfe4;\n", + " background-color: #f0b7c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17138,9 +14529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col6 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -17148,9 +14539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col7 {\n", " \n", - " background-color: #eeabb8;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -17158,9 +14549,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col8 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #f2bdc8;\n", " \n", " max-width: 80px;\n", " \n", @@ -17168,9 +14559,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col9 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17178,9 +14569,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col10 {\n", " \n", - " background-color: #e2eaf4;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17188,9 +14579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col11 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17198,9 +14589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #efb1be;\n", " \n", " max-width: 80px;\n", " \n", @@ -17208,9 +14599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col13 {\n", " \n", - " background-color: #94b2d8;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17218,9 +14609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col14 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #e0657d;\n", " \n", " max-width: 80px;\n", " \n", @@ -17228,9 +14619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col15 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #eca1b0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17238,9 +14629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col16 {\n", " \n", - " background-color: #6993c8;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17248,9 +14639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col17 {\n", " \n", - " background-color: #6590c7;\n", + " background-color: #e27087;\n", " \n", " max-width: 80px;\n", " \n", @@ -17258,9 +14649,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col18 {\n", " \n", - " background-color: #e2eaf4;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17268,9 +14659,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e68699;\n", " \n", " max-width: 80px;\n", " \n", @@ -17278,9 +14669,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col20 {\n", " \n", - " background-color: #457abb;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -17288,9 +14679,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col21 {\n", " \n", - " background-color: #e3ebf5;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17298,9 +14689,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col22 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #d1deee;\n", " \n", " max-width: 80px;\n", " \n", @@ -17308,9 +14699,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col23 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #82a5d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17318,9 +14709,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col24 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #7099cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17328,9 +14719,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col0 {\n", " \n", - " background-color: #96b4d9;\n", + " background-color: #a9c1e0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17338,9 +14729,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col1 {\n", " \n", - " background-color: #b0c6e2;\n", + " background-color: #6892c8;\n", " \n", " max-width: 80px;\n", " \n", @@ -17348,9 +14739,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col2 {\n", " \n", - " background-color: #b2c8e3;\n", + " background-color: #f7d6dd;\n", " \n", " max-width: 80px;\n", " \n", @@ -17358,9 +14749,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col3 {\n", " \n", - " background-color: #4a7ebd;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17368,9 +14759,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col4 {\n", " \n", - " background-color: #92b1d7;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17378,9 +14769,29 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col5 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e4ecf5;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col6 {\n", + " \n", + " background-color: #d8e3f1;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col7 {\n", + " \n", + " background-color: #477bbc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17388,7 +14799,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col8 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17398,9 +14809,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col9 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #e7eef6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17408,9 +14819,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col10 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17418,9 +14829,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col11 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #a6bfde;\n", " \n", " max-width: 80px;\n", " \n", @@ -17428,9 +14839,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col12 {\n", " \n", - " background-color: #c9d8eb;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17438,9 +14849,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col13 {\n", " \n", - " background-color: #bfd1e8;\n", + " background-color: #a9c1e0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17448,9 +14859,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col14 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -17458,9 +14869,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col15 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #ea99a9;\n", " \n", " max-width: 80px;\n", " \n", @@ -17468,9 +14879,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col16 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17478,9 +14889,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col17 {\n", " \n", - " background-color: #5a88c3;\n", + " background-color: #f0b7c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17488,9 +14899,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col18 {\n", " \n", - " background-color: #628fc6;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -17498,9 +14909,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col19 {\n", " \n", - " background-color: #749bcc;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -17508,9 +14919,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col20 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17518,9 +14929,19 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col21 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #d8415f;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col22 {\n", + " \n", + " background-color: #b5cae4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17528,7 +14949,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col23 {\n", " \n", " background-color: #5182bf;\n", " \n", @@ -17538,9 +14959,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col24 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #457abb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17548,9 +14969,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col0 {\n", " \n", - " background-color: #d4e0ef;\n", + " background-color: #92b1d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17558,9 +14979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col1 {\n", " \n", - " background-color: #5182bf;\n", + " background-color: #7ba0cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -17568,9 +14989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col2 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -17578,9 +14999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col3 {\n", " \n", - " background-color: #85a7d2;\n", + " background-color: #f7d7de;\n", " \n", " max-width: 80px;\n", " \n", @@ -17588,9 +15009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col4 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #5485c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17598,9 +15019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col5 {\n", " \n", - " background-color: #bccfe7;\n", + " background-color: #f5cfd7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17608,9 +15029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col6 {\n", " \n", - " background-color: #5f8cc5;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -17618,9 +15039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col7 {\n", " \n", - " background-color: #a2bcdd;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17628,9 +15049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17638,9 +15059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col9 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #dfe8f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17648,9 +15069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col10 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #b0c6e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17658,9 +15079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col11 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17668,9 +15089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col12 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17678,9 +15099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col13 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #cad9ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17688,9 +15109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col14 {\n", " \n", - " background-color: #b7cbe5;\n", + " background-color: #e991a3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17698,9 +15119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col15 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17708,9 +15129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col16 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #de5c76;\n", " \n", " max-width: 80px;\n", " \n", @@ -17718,9 +15139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col17 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17728,9 +15149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col18 {\n", " \n", - " background-color: #739acc;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17738,9 +15159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col19 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #eb9dac;\n", " \n", " max-width: 80px;\n", " \n", @@ -17748,9 +15169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col20 {\n", " \n", - " background-color: #6d96ca;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17758,9 +15179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col21 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17768,9 +15189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col22 {\n", " \n", - " background-color: #eeaebb;\n", + " background-color: #acc3e1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17778,9 +15199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col23 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #497dbd;\n", " \n", " max-width: 80px;\n", " \n", @@ -17788,9 +15209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col24 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17798,9 +15219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #bccfe7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17808,9 +15229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col1 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17818,9 +15239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col2 {\n", " \n", - " background-color: #ec9faf;\n", + " background-color: #eda6b4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17828,9 +15249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col3 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17838,9 +15259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17848,9 +15269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col5 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -17858,9 +15279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col6 {\n", " \n", - " background-color: #4c7ebd;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17868,9 +15289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col7 {\n", " \n", - " background-color: #d1deee;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17878,9 +15299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col8 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17888,9 +15309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col9 {\n", " \n", - " background-color: #f7d9df;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17898,9 +15319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col10 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #dde7f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17908,9 +15329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col11 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #bbcee6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17918,9 +15339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col12 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17928,9 +15349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col13 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17938,9 +15359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col14 {\n", " \n", - " background-color: #bccfe7;\n", + " background-color: #e78a9d;\n", " \n", " max-width: 80px;\n", " \n", @@ -17948,9 +15369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col15 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #eda7b5;\n", " \n", " max-width: 80px;\n", " \n", @@ -17958,9 +15379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col16 {\n", " \n", - " background-color: #9eb9db;\n", + " background-color: #dc546f;\n", " \n", " max-width: 80px;\n", " \n", @@ -17968,9 +15389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col17 {\n", " \n", - " background-color: #5485c1;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17978,9 +15399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col18 {\n", " \n", - " background-color: #8babd4;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17988,9 +15409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col19 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #eeaab7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17998,9 +15419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col20 {\n", " \n", - " background-color: #5b89c3;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18008,9 +15429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col21 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -18018,9 +15439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col22 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #b8cce5;\n", " \n", " max-width: 80px;\n", " \n", @@ -18028,9 +15449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col23 {\n", " \n", - " background-color: #497dbd;\n", + " background-color: #7099cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -18038,9 +15459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col24 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #5e8bc4;\n", " \n", " max-width: 80px;\n", " \n", @@ -18048,9 +15469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #91b0d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18058,9 +15479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col1 {\n", " \n", - " background-color: #5686c1;\n", + " background-color: #86a8d3;\n", " \n", " max-width: 80px;\n", " \n", @@ -18068,9 +15489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col2 {\n", " \n", - " background-color: #eda8b6;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -18078,9 +15499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col3 {\n", " \n", - " background-color: #d9e4f1;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18088,9 +15509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col4 {\n", " \n", - " background-color: #d5e1f0;\n", + " background-color: #5e8bc4;\n", " \n", " max-width: 80px;\n", " \n", @@ -18098,9 +15519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col5 {\n", " \n", - " background-color: #bfd1e8;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -18108,9 +15529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col6 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -18118,9 +15539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col7 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #6b95c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -18128,9 +15549,19 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col8 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #f3c6cf;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col9 {\n", + " \n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18138,7 +15569,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col10 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -18148,9 +15579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col11 {\n", " \n", - " background-color: #eeaab7;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18158,9 +15589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col12 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -18168,9 +15599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col13 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -18178,9 +15609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col14 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #eeabb8;\n", " \n", " max-width: 80px;\n", " \n", @@ -18188,9 +15619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col15 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -18198,9 +15629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col16 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -18208,9 +15639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col17 {\n", " \n", - " background-color: #9eb9db;\n", + " background-color: #eca4b3;\n", " \n", " max-width: 80px;\n", " \n", @@ -18218,2771 +15649,3087 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col18 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f7d6dd;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " 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80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col24 {\n", + " \n", + " background-color: #89aad4;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " </style>\n", + "\n", + " <table id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" None>\n", + " \n", + " <caption>Hover to magify</caption>\n", + " \n", + "\n", + " <thead>\n", + " \n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">0\n", + " \n", + " <th class=\"col_heading level0 col1\">1\n", + " \n", + " <th class=\"col_heading level0 col2\">2\n", + " \n", + " <th class=\"col_heading level0 col3\">3\n", + " \n", + " <th class=\"col_heading level0 col4\">4\n", + " \n", + " <th class=\"col_heading level0 col5\">5\n", + " \n", + " <th class=\"col_heading level0 col6\">6\n", + " \n", + " <th class=\"col_heading level0 col7\">7\n", + " \n", + " <th class=\"col_heading level0 col8\">8\n", + " \n", + " <th class=\"col_heading level0 col9\">9\n", + " \n", + " <th class=\"col_heading level0 col10\">10\n", + " \n", + " <th class=\"col_heading level0 col11\">11\n", + " \n", + " <th class=\"col_heading level0 col12\">12\n", + " \n", + " <th class=\"col_heading level0 col13\">13\n", + " \n", + " <th class=\"col_heading level0 col14\">14\n", + " \n", + " <th class=\"col_heading level0 col15\">15\n", + " \n", + " <th class=\"col_heading level0 col16\">16\n", + " \n", + " <th class=\"col_heading level0 col17\">17\n", + " \n", + " <th class=\"col_heading level0 col18\">18\n", + " \n", + " <th class=\"col_heading level0 col19\">19\n", + " \n", + " <th class=\"col_heading level0 col20\">20\n", + " \n", + " <th class=\"col_heading level0 col21\">21\n", + " \n", + " <th class=\"col_heading level0 col22\">22\n", + " \n", + " <th class=\"col_heading level0 col23\">23\n", + " \n", + " <th class=\"col_heading level0 col24\">24\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", + " </thead>\n", + " <tbody>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " -0.84\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.59\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.96\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n", + " -0.22\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n", + " -0.62\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n", + " 0.87\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n", + " -0.92\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n", + " -0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n", + " -1.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n", + " 0.076\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n", + " 1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n", + " 1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n", + " -0.42\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n", + " 0.68\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n", + " -1.6\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " 1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n", + " 0.0037\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n", + " 1.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n", + " -0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n", + " -0.015\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n", + " -1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n", + " -0.68\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n", + " -0.81\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n", + " 0.35\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n", + " -0.055\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n", + " 0.78\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n", + " 0.44\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " -0.65\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " 3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n", + " -0.37\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n", + " -3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n", + " 3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n", + " -1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n", + " 0.21\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n", + " -0.24\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n", + " -0.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n", + " -0.78\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n", + " -3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n", + " -0.82\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n", + " -0.21\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n", + " -0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n", + " 0.86\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n", + " -0.68\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col20\" class=\"data row2 col20\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col21\" class=\"data row2 col21\">\n", + " -4.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col22\" class=\"data row2 col22\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col23\" class=\"data row2 col23\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col24\" class=\"data row2 col24\">\n", + " 0.61\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " 0.43\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col5\" class=\"data row3 col5\">\n", + " -0.43\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col6\" class=\"data row3 col6\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col7\" class=\"data row3 col7\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n", + " 1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n", + " 0.12\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n", + " 0.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n", + " -0.89\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n", + " 0.27\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n", + " -3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n", + " -2.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n", + " -0.31\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n", + " 0.91\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n", + " -5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n", + " 0.28\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " -1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 5.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n", + " -3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n", + " 4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n", + " -0.72\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n", + " 1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n", + " -0.18\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n", + " 0.83\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n", + " -0.22\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n", + " -4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n", + " -0.97\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n", + " -6.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n", + " 3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n", + " 2.1\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row5\">\n", + " 5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " -0.84\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n", + " -0.99\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n", + " 3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n", + " -0.94\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n", + " 1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n", + " 0.087\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n", + " -0.11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n", + " -0.85\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n", + " 0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n", + " -6.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n", + " 3.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row6\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " -0.74\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 5.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n", + " 3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n", + " 0.34\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n", + " 0.57\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n", + " 0.54\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n", + " -4.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n", + " -4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col17\" class=\"data row6 col17\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col18\" class=\"data row6 col18\">\n", + " -0.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col19\" class=\"data row6 col19\">\n", + " -4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col20\" class=\"data row6 col20\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col21\" class=\"data row6 col21\">\n", + " -8.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col22\" class=\"data row6 col22\">\n", + " 3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col23\" class=\"data row6 col23\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n", + " 5.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " -0.44\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.21\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n", + " 5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n", + " 0.18\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n", + " 0.11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n", + " 0.036\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n", + " -6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n", + " -0.45\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n", + " -6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n", + " 0.71\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n", + " 0.67\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n", + " -7.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n", + " 6.7\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 0.92\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " -0.65\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n", + " -3.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n", + " -1.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n", + " 0.82\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n", + " -2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n", + " -0.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n", + " -6.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n", + " -0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n", + " -6.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n", + " -3.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n", + " -0.043\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n", + " -4.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n", + " 0.51\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n", + " -5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n", + " 3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n", + " 2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n", + " 5.1\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 0.38\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " 5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 7.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n", + " -0.44\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n", + " -0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n", + " -0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n", + " 0.26\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n", + " -3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n", + " -0.82\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n", + " -6.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n", + " -8.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n", + " 0.41\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n", + " -4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n", + " -6.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n", + " 5.2\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row10\">\n", + " 10\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n", + " -0.98\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n", + " 7.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n", + " -0.59\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n", + " -0.71\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n", + " -0.46\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n", + " 0.48\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n", + " -6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n", + " -3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n", + " -7.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n", + " -5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n", + " -0.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n", + " -4.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n", + " -0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n", + " -7.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n", + " 5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n", + " 5.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row11\">\n", + " 11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n", + " 4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n", + " -0.49\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n", + " 0.017\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n", + " -0.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n", + " 0.49\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n", + " 2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n", + " -5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n", + " -8.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n", + " -3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n", + " -4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n", + " -7.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n", + " 5.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row12\">\n", + " 12\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n", + " 3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n", + " -3.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n", + " 0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n", + " 6.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n", + " -0.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n", + " 2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n", + " 0.75\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n", + " -5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n", + " -7.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n", + " -4.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n", + " -9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n", + " 6.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n", + " 5.6\n", + " \n", + " </tr>\n", " \n", - " background-color: #89aad4;\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row13\">\n", + " 13\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n", + " 4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n", + " 6.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n", + " 8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n", + " -0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n", + " 0.71\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n", + " -0.16\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n", + " -0.46\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n", + " -5.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n", + " -3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n", + " -7.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n", + " -4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n", + " 0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n", + " -3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n", + " -8.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n", + " 6.7\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row14\">\n", + " 14\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n", + " 3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n", + " 4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n", + " 6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n", + " -0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n", + " -0.97\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n", + " 1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n", + " 3.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n", + " 0.29\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n", + " -4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n", + " -6.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n", + " -2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n", + " -9.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n", + " 6.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n", + " 7.5\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row15\">\n", + " 15\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n", + " -4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n", + " 5.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n", + " -3.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n", + " -0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n", + " -4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n", + " -5.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n", + " -4.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n", + " -11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n", + " 2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n", + " 6.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n", + " 5.9\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\">\n", - " \n", - " <caption>Hover to magify</caption>\n", - " \n", - "\n", - " <thead>\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row16\">\n", + " 16\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n", + " 4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n", + " 4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n", + " -2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n", + " -0.47\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n", + " -4.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n", + " 1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n", + " 0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n", + " 0.099\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n", + " -3.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n", + " -5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n", + " -3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n", + " -0.56\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n", + " -13\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n", + " 6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n", + " 4.9\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th class=\"blank\">\n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row17\">\n", + " 17\n", " \n", - " <th class=\"col_heading level0 col0\">0\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n", + " 5.6\n", " \n", - " <th class=\"col_heading level0 col1\">1\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n", + " 4.6\n", " \n", - " <th class=\"col_heading level0 col2\">2\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n", + " -3.5\n", " \n", - " <th class=\"col_heading level0 col3\">3\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n", + " -3.8\n", " \n", - " <th class=\"col_heading level0 col4\">4\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n", + " 6.6\n", " \n", - " <th class=\"col_heading level0 col5\">5\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n", + " -2.6\n", " \n", - " <th class=\"col_heading level0 col6\">6\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n", + " -0.75\n", " \n", - " <th class=\"col_heading level0 col7\">7\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n", + " 6.6\n", " \n", - " <th class=\"col_heading level0 col8\">8\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n", + " -4.8\n", " \n", - " <th class=\"col_heading level0 col9\">9\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n", + " 3.6\n", " \n", - " <th class=\"col_heading level0 col10\">10\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n", + " -0.29\n", " \n", - " <th class=\"col_heading level0 col11\">11\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n", + " 0.56\n", " \n", - " <th class=\"col_heading level0 col12\">12\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n", + " 5.8\n", " \n", - " <th class=\"col_heading level0 col13\">13\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n", + " 2\n", " \n", - " <th class=\"col_heading level0 col14\">14\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n", + " -2.3\n", " \n", - " <th class=\"col_heading level0 col15\">15\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n", + " -2.3\n", " \n", - " <th class=\"col_heading level0 col16\">16\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n", + " -5\n", " \n", - " <th class=\"col_heading level0 col17\">17\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n", + " -3.2\n", " \n", - " <th class=\"col_heading level0 col18\">18\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n", + " -3.1\n", " \n", - " <th class=\"col_heading level0 col19\">19\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n", + " -2.4\n", " \n", - " <th class=\"col_heading level0 col20\">20\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n", + " 0.84\n", " \n", - " <th class=\"col_heading level0 col21\">21\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n", + " -13\n", " \n", - " <th class=\"col_heading level0 col22\">22\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n", + " 3.6\n", " \n", - " <th class=\"col_heading level0 col23\">23\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n", + " 7.4\n", " \n", - " <th class=\"col_heading level0 col24\">24\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n", + " 4.7\n", " \n", " </tr>\n", " \n", - " </thead>\n", - " <tbody>\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row18\">\n", + " 18\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n", + " -4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n", + " 7.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n", + " 7.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n", + " -4.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n", + " -0.63\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n", + " 0.35\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n", + " 7.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n", + " 0.87\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n", + " -4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n", + " -9.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n", + " 7.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n", + " 4.4\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row0\">\n", - " \n", - " 0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " -0.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.96\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n", - " \n", - " -0.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n", - " \n", - " -0.62\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n", - " \n", - " 1.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n", - " \n", - " -2.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n", - " \n", - " 0.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n", - " \n", - " -0.92\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n", - " \n", - " -0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n", - " \n", - " 2.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n", - " \n", - " -1.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n", - " \n", - " 0.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n", - " \n", - " -1.25\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n", - " \n", - " 1.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n", - " \n", - " -1.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n", - " \n", - " 1.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n", - " \n", - " -0.42\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n", - " \n", - " 2.29\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n", - " \n", - " -2.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n", - " \n", - " 2.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n", - " \n", - " 0.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n", - " \n", - " -1.58\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row1\">\n", - " \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " -1.75\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " 1.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " -1.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 1.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n", - " \n", - " 0.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n", - " \n", - " -2.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n", - " \n", - " 3.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n", - " \n", - " -1.66\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n", - " \n", - " 1.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n", - " \n", - " -0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n", - " \n", - " -0.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n", - " \n", - " 1.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n", - " \n", - " -1.09\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n", - " \n", - " -1.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n", - " \n", - " -0.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n", - " \n", - " -0.81\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n", - " \n", - " 0.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n", - " \n", - " -0.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n", - " \n", - " 1.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n", - " \n", - " -2.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n", - " \n", - " 2.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n", - " \n", - " 0.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n", - " \n", - " 0.44\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row2\">\n", - " \n", - " 2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " -0.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " 3.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " -1.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 2.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n", - " \n", - " -0.37\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n", - " \n", - " -3.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n", - " \n", - " 3.73\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n", - " \n", - " -1.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n", - " \n", - " 2.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n", - " \n", - " 0.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n", - " \n", - " -0.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n", - " \n", - " -0.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n", - " \n", - " -0.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n", - " \n", - " -3.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n", - " \n", - " -0.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n", - " \n", - " -0.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n", - " \n", - " -0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n", - " \n", - " 0.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n", - " \n", - " 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n", - " \n", - " -2.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n", - " \n", - " 1.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n", - " \n", - " 0.12\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n", - " \n", - " 0.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n", - " \n", - " -0.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n", - " \n", - " 0.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n", - " \n", - " -3.67\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n", - " \n", - " -2.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n", - " \n", - " -0.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n", - " \n", - " -1.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n", - " \n", - " 1.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n", - " \n", - " 0.91\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n", - " \n", - " -5.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n", - " \n", - " 2.81\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n", - " \n", - " 2.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n", - " \n", - " 0.28\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row4\">\n", - " \n", - " 4\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " -3.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 4.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " -1.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " -1.7\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 5.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n", - " \n", - " -1.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n", - " \n", - " -3.81\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n", - " \n", - " 4.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n", - " \n", - " -0.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n", - " \n", - " 1.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n", - " \n", - " -0.18\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n", - " \n", - " 0.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n", - " \n", - " -0.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n", - " \n", - " -1.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n", - " \n", - " -4.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n", - " \n", - " -2.88\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n", - " \n", - " -0.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n", - " \n", - " -1.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n", - " \n", - " 1.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n", - " \n", - " -1.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n", - " \n", - " 2.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n", - " \n", - " -6.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n", - " \n", - " 3.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n", - " \n", - " 2.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n", - " \n", - " 2.09\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row5\">\n", - " \n", - " 5\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " -0.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " 4.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " -1.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " -2.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " 5.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n", - " \n", - " -0.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n", - " \n", - " -4.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n", - " \n", - " 3.94\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n", - " \n", - " -1.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n", - " \n", - " -0.94\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n", - " \n", - " 1.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n", - " \n", - " 0.09\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n", - " \n", - " -1.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n", - " \n", - " -0.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n", - " \n", - " -4.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n", - " \n", - " -0.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n", - " \n", - " -2.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n", - " \n", - " -1.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n", - " \n", - " 0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n", - " \n", - " -1.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n", - " \n", - " 1.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n", - " \n", - " -6.51\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n", - " \n", - " 2.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n", - " \n", - " 2.14\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n", - " \n", - " 3.77\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row6\">\n", - " \n", - " 6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " -0.74\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 5.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " -2.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 4.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n", - " \n", - " -1.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n", - " \n", - " -3.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n", - " \n", - " 3.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n", - " \n", - " -3.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n", - " \n", - " -1.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n", - " \n", - " 0.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n", - " \n", - " 0.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n", - " \n", - " -1.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n", - " \n", - " 0.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n", - " \n", - " -4.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n", - " \n", - " 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n", - " \n", - " 5.81\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row7\">\n", - " \n", - " 7\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " -0.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 4.69\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " -2.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 5.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n", - " \n", - " -2.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n", - " \n", - " 5.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n", - " \n", - " -4.5\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n", - " \n", - " -3.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n", - " \n", - " -1.73\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n", - " \n", - " 0.18\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n", - " \n", - " 0.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n", - " \n", - " 0.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n", - " \n", - " -5.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n", - " \n", - " -0.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n", - " \n", - " -6.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n", - " \n", - " -3.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n", - " \n", - " 0.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n", - " \n", - " -3.95\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n", - " \n", - " 0.67\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n", - " \n", - " -7.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n", - " \n", - " 2.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n", - " \n", - " 3.39\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n", - " \n", - " 6.66\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row8\">\n", - " \n", - " 8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 0.92\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 5.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -3.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " -0.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " 5.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n", - " \n", - " -3.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n", - " \n", - " 5.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n", - " \n", - " -3.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n", - " \n", - " -1.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n", - " \n", - " -1.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n", - " \n", - " 0.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n", - " \n", - " -2.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n", - " \n", - " -0.4\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n", - " \n", - " -6.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n", - " \n", - " -0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n", - " \n", - " -6.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n", - " \n", - " -3.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n", - " \n", - " -0.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n", - " \n", - " -4.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n", - " \n", - " 0.51\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n", - " \n", - " -5.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n", - " \n", - " 3.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n", - " \n", - " 2.4\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n", - " \n", - " 5.08\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row9\">\n", - " \n", - " 9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 0.38\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " 5.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " -4.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 7.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n", - " \n", - " -2.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n", - " \n", - " -0.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n", - " \n", - " 5.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n", - " \n", - " -1.96\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n", - " \n", - " -0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n", - " \n", - " -0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n", - " \n", - " 0.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n", - " \n", - " -3.37\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n", - " \n", - " -0.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n", - " \n", - " -6.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n", - " \n", - " -2.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n", - " \n", - " -8.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n", - " \n", - " -4.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n", - " \n", - " 0.41\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n", - " \n", - " -4.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n", - " \n", - " 1.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n", - " \n", - " -6.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n", - " \n", - " 2.14\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n", - " \n", - " 3.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n", - " \n", - " 5.16\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row10\">\n", - " \n", - " 10\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n", - " \n", - " 2.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n", - " \n", - " 5.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n", - " \n", - " -3.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n", - " \n", - " -0.98\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n", - " \n", - " 7.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n", - " \n", - " -2.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n", - " \n", - " -0.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n", - " \n", - " 5.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n", - " \n", - " -2.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n", - " \n", - " -0.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n", - " \n", - " -0.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n", - " \n", - " 1.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n", - " \n", - " -2.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n", - " \n", - " 0.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n", - " \n", - " -5.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n", - " \n", - " -3.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n", - " \n", - " -7.77\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n", - " \n", - " -5.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n", - " \n", - " -0.7\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n", - " \n", - " -4.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n", - " \n", - " -0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n", - " \n", - " -7.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n", - " \n", - " 1.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n", - " \n", - " 5.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n", - " \n", - " 5.81\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row11\">\n", - " \n", - " 11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n", - " \n", - " 1.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n", - " \n", - " 4.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n", - " \n", - " -2.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n", - " \n", - " -1.38\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n", - " \n", - " 5.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n", - " \n", - " -0.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n", - " \n", - " 0.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n", - " \n", - " 5.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n", - " \n", - " -1.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n", - " \n", - " -0.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n", - " \n", - " 0.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n", - " \n", - " 1.96\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n", - " \n", - " -1.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n", - " \n", - " 1.88\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n", - " \n", - " -5.92\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n", - " \n", - " -4.55\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n", - " \n", - " -8.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n", - " \n", - " -3.42\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n", - " \n", - " -2.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n", - " \n", - " -4.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n", - " \n", - " -1.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n", - " \n", - " -7.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n", - " \n", - " 1.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n", - " \n", - " 5.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n", - " \n", - " 5.83\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row12\">\n", - " \n", - " 12\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n", - " \n", - " 3.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n", - " \n", - " 4.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n", - " \n", - " -3.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n", - " \n", - " -2.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n", - " \n", - " 5.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n", - " \n", - " -2.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n", - " \n", - " 0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n", - " \n", - " 6.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n", - " \n", - " -2.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n", - " \n", - " -0.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n", - " \n", - " 1.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n", - " \n", - " 2.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n", - " \n", - " -1.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n", - " \n", - " 0.75\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n", - " \n", - " -5.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n", - " \n", - " -4.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n", - " \n", - " -7.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n", - " \n", - " -2.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n", - " \n", - " -2.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n", - " \n", - " -4.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n", - " \n", - " -8.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n", - " \n", - " 2.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n", - " \n", - " 6.42\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n", - " \n", - " 5.6\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row13\">\n", - " \n", - " 13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n", - " \n", - " 2.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n", - " \n", - " 4.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n", - " \n", - " -3.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n", - " \n", - " -2.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n", - " \n", - " 6.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n", - " \n", - " -3.25\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n", - " \n", - " -2.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n", - " \n", - " 7.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n", - " \n", - " -2.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n", - " \n", - " -0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n", - " \n", - " 0.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n", - " \n", - " 2.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n", - " \n", - " -0.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n", - " \n", - " -0.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n", - " \n", - " -5.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n", - " \n", - " -3.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n", - " \n", - " -7.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n", - " \n", - " -4.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n", - " \n", - " 0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n", - " \n", - " -3.67\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n", - " \n", - " -1.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n", - " \n", - " -8.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n", - " \n", - " 2.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n", - " \n", - " 5.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n", - " \n", - " 6.71\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row14\">\n", - " \n", - " 14\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n", - " \n", - " 3.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n", - " \n", - " 4.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n", - " \n", - " -3.88\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n", - " \n", - " -1.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n", - " \n", - " 6.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n", - " \n", - " -3.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n", - " \n", - " -1.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n", - " \n", - " 5.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n", - " \n", - " -2.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n", - " \n", - " -0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n", - " \n", - " -0.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n", - " \n", - " 1.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n", - " \n", - " 3.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n", - " \n", - " 0.29\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n", - " \n", - " -4.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n", - " \n", - " -4.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n", - " \n", - " -6.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n", - " \n", - " -4.5\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n", - " \n", - " -2.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n", - " \n", - " -2.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n", - " \n", - " -1.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n", - " \n", - " -9.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n", - " \n", - " 3.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n", - " \n", - " 6.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n", - " \n", - " 7.53\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row15\">\n", - " \n", - " 15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n", - " \n", - " 5.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n", - " \n", - " 5.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n", - " \n", - " -3.98\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n", - " \n", - " -2.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n", - " \n", - " 5.91\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n", - " \n", - " -3.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n", - " \n", - " -1.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n", - " \n", - " 5.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n", - " \n", - " -3.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n", - " \n", - " -0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n", - " \n", - " -1.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n", - " \n", - " 2.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n", - " \n", - " 4.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n", - " \n", - " 1.01\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n", - " \n", - " -3.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n", - " \n", - " -4.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n", - " \n", - " -5.74\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n", - " \n", - " -4.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n", - " \n", - " -2.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n", - " \n", - " -1.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n", - " \n", - " -1.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n", - " \n", - " -11.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n", - " \n", - " 2.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n", - " \n", - " 6.69\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n", - " \n", - " 5.91\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row16\">\n", - " \n", - " 16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n", - " \n", - " 4.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n", - " \n", - " 4.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n", - " \n", - " -2.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n", - " \n", - " -3.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n", - " \n", - " 6.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n", - " \n", - " -2.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n", - " \n", - " -0.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n", - " \n", - " 5.28\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n", - " \n", - " -4.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n", - " \n", - " 1.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n", - " \n", - " 0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n", - " \n", - " 0.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n", - " \n", - " 5.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n", - " \n", - " 1.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n", - " \n", - " -3.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n", - " \n", - " -3.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n", - " \n", - " -5.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n", - " \n", - " -2.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n", - " \n", - " -2.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n", - " \n", - " -1.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n", - " \n", - " -0.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n", - " \n", - " -13.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n", - " \n", - " 2.07\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n", - " \n", - " 6.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n", - " \n", - " 4.94\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row17\">\n", - " \n", - " 17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n", - " \n", - " 5.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n", - " \n", - " 4.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n", - " \n", - " -3.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n", - " \n", - " -3.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n", - " \n", - " 6.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n", - " \n", - " -2.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n", - " \n", - " -0.75\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n", - " \n", - " 6.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n", - " \n", - " -4.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n", - " \n", - " 3.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n", - " \n", - " -0.29\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n", - " \n", - " 0.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n", - " \n", - " 5.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n", - " \n", - " 2.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n", - " \n", - " -2.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n", - " \n", - " -2.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n", - " \n", - " -4.95\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n", - " \n", - " -3.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n", - " \n", - " -3.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n", - " \n", - " -2.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n", - " \n", - " 0.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n", - " \n", - " -12.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n", - " \n", - " 3.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n", - " \n", - " 7.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n", - " \n", - " 4.7\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row18\">\n", - " \n", - " 18\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n", - " \n", - " 5.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n", - " \n", - " 5.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n", - " \n", - " -2.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n", - " \n", - " -4.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n", - " \n", - " 7.12\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n", - " \n", - " -3.32\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n", - " \n", - " -1.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n", - " \n", - " 7.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n", - " \n", - " -4.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n", - " \n", - " 1.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n", - " \n", - " -0.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n", - " \n", - " 0.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n", - " \n", - " 7.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n", - " \n", - " 0.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n", - " \n", - " -1.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n", - " \n", - " -2.09\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n", - " \n", - " -4.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n", - " \n", - " -2.55\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n", - " \n", - " -2.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n", - " \n", - " -2.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n", - " \n", - " 1.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n", - " \n", - " -9.74\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n", - " \n", - " 3.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n", - " \n", - " 7.07\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n", - " \n", - " 4.39\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row19\">\n", - " \n", - " 19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n", - " \n", - " 4.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n", - " \n", - " 6.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n", - " \n", - " -4.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n", - " \n", - " -4.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n", - " \n", - " 7.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n", - " \n", - " -4.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n", - " \n", - " -1.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n", - " \n", - " 6.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n", - " \n", - " -5.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n", - " \n", - " -0.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n", - " \n", - " 0.01\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n", - " \n", - " 1.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n", - " \n", - " 6.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n", - " \n", - " -1.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n", - " \n", - " -2.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n", - " \n", - " -1.66\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n", - " \n", - " -5.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n", - " \n", - " -3.25\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n", - " \n", - " -2.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n", - " \n", - " -1.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n", - " \n", - " 1.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n", - " \n", - " -10.66\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n", - " \n", - " 2.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n", - " \n", - " 7.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n", - " \n", - " 3.94\n", - " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row19\">\n", + " 19\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n", + " 6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n", + " 7.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n", + " 6.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n", + " -5.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n", + " -0.24\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n", + " 0.0072\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n", + " 1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n", + " 6.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n", + " -5.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n", + " 1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n", + " -11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n", + " 7.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n", + " 3.9\n", " \n", " </tr>\n", " \n", @@ -20991,10 +18738,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x114b9b1d0>" + "<pandas.core.style.Styler at 0x111c7ea58>" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 6fa58bf620005..7494f8ae88307 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -53,6 +53,10 @@ advanced indexing. but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This should be a transparent change with only very limited API implications (See the :ref:`Internal Refactoring <whatsnew_0150.refactoring>`) +.. warning:: + + Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`. + See the :ref:`MultiIndex / Advanced Indexing <advanced>` for ``MultiIndex`` and more advanced indexing documentation. See the :ref:`cookbook<cookbook.selection>` for some advanced strategies @@ -1625,6 +1629,7 @@ This is the correct access method This *can* work at times, but is not guaranteed, and so should be avoided .. ipython:: python + :okwarning: dfc = dfc.copy() dfc['A'][0] = 111 diff --git a/doc/source/install.rst b/doc/source/install.rst index 3836180af520f..f0e6955f38612 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -225,7 +225,7 @@ Recommended Dependencies * `numexpr <https://github.com/pydata/numexpr>`__: for accelerating certain numerical operations. ``numexpr`` uses multiple cores as well as smart chunking and caching to achieve large speedups. - If installed, must be Version 2.1 or higher. Version 2.4.6 or higher on Windows is highly recommended. + If installed, must be Version 2.1 or higher (excluding a buggy 2.4.4). Version 2.4.6 or higher is highly recommended. * `bottleneck <http://berkeleyanalytics.com/bottleneck>`__: for accelerating certain types of ``nan`` evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups. @@ -253,7 +253,6 @@ Optional Dependencies - `SQLite <https://docs.python.org/3.5/library/sqlite3.html>`__: for SQLite, this is included in Python's standard library by default. * `matplotlib <http://matplotlib.sourceforge.net/>`__: for plotting -* `statsmodels <http://statsmodels.sourceforge.net/>`__: Needed for parts of :mod:`pandas.stats` * `openpyxl <http://packages.python.org/openpyxl/>`__, `xlrd/xlwt <http://www.python-excel.org/>`__: Needed for Excel I/O * `XlsxWriter <https://pypi.python.org/pypi/XlsxWriter>`__: Alternative Excel writer * `Jinja2 <http://jinja.pocoo.org/>`__: Template engine for conditional HTML formatting. @@ -267,9 +266,11 @@ Optional Dependencies <http://www.vergenet.net/~conrad/software/xsel/>`__, or `xclip <http://sourceforge.net/projects/xclip/>`__: necessary to use :func:`~pandas.io.clipboard.read_clipboard`. Most package managers on Linux distributions will have ``xclip`` and/or ``xsel`` immediately available for installation. -* Google's `python-gflags <http://code.google.com/p/python-gflags/>`__ - and `google-api-python-client <http://github.com/google/google-api-python-client>`__: Needed for :mod:`~pandas.io.gbq` -* `httplib2 <http://pypi.python.org/pypi/httplib2>`__: Needed for :mod:`~pandas.io.gbq` +* Google's `python-gflags <http://code.google.com/p/python-gflags/>`__ , + `oauth2client <https://github.com/google/oauth2client>`__ , + `httplib2 <http://pypi.python.org/pypi/httplib2>`__ + and `google-api-python-client <http://github.com/google/google-api-python-client>`__ + : Needed for :mod:`~pandas.io.gbq` * One of the following combinations of libraries is needed to use the top-level :func:`~pandas.io.html.read_html` function: diff --git a/doc/source/io.rst b/doc/source/io.rst index acb8bc1eceda5..06863d14f298d 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4168,6 +4168,12 @@ DataFrame with a shape and data types derived from the source table. Additionally, DataFrames can be inserted into new BigQuery tables or appended to existing tables. +You will need to install some additional dependencies: + +- Google's `python-gflags <http://code.google.com/p/python-gflags/>`__ +- `httplib2 <http://pypi.python.org/pypi/httplib2>`__ +- `google-api-python-client <http://github.com/google/google-api-python-client>`__ + .. warning:: To use this module, you will need a valid BigQuery account. Refer to the @@ -4191,6 +4197,9 @@ The key functions are: Authentication '''''''''''''' + +.. versionadded:: 0.18.0 + Authentication to the Google ``BigQuery`` service is via ``OAuth 2.0``. Is possible to authenticate with either user account credentials or service account credentials. @@ -4206,6 +4215,8 @@ is particularly useful when working on remote servers (eg. jupyter iPython noteb Additional information on service accounts can be found `here <https://developers.google.com/identity/protocols/OAuth2#serviceaccount>`__. +You will need to install an additional dependency: `oauth2client <https://github.com/google/oauth2client>`__. + .. note:: The `'private_key'` parameter can be set to either the file path of the service account key @@ -4552,7 +4563,7 @@ SAS Formats .. versionadded:: 0.17.0 The top-level function :func:`read_sas` can read (but not write) SAS -`xport` (.XPT) and `SAS7BDAT` (.sas7bdat) format files (v0.18.0). +`xport` (.XPT) and `SAS7BDAT` (.sas7bdat) format files were added in *v0.18.0*. SAS files only contain two value types: ASCII text and floating point values (usually 8 bytes but sometimes truncated). For xport files, diff --git a/doc/source/options.rst b/doc/source/options.rst index be1543f20a461..98187d7be762e 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -107,6 +107,7 @@ All options also have a default value, and you can use ``reset_option`` to do ju It's also possible to reset multiple options at once (using a regex): .. ipython:: python + :okwarning: pd.reset_option("^display") @@ -266,8 +267,10 @@ Options are 'right', and 'left'. -List of Options ---------------- +.. _options.available: + +Available Options +----------------- ========================== ============ ================================== Option Default Function @@ -437,6 +440,7 @@ For instance: .. ipython:: python :suppress: + :okwarning: pd.reset_option('^display\.') @@ -499,5 +503,3 @@ Enabling ``display.unicode.ambiguous_as_wide`` lets pandas to figure these chara pd.set_option('display.unicode.east_asian_width', False) pd.set_option('display.unicode.ambiguous_as_wide', False) - - diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index 74cdc5a526585..71d3bbed223e5 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -136,6 +136,7 @@ DataFrames into the equivalent R object (that is, **data.frame**): .. ipython:: python + import pandas.rpy.common as com df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7,8,9]}, index=["one", "two", "three"]) r_dataframe = com.convert_to_r_dataframe(df) @@ -154,6 +155,7 @@ R matrices bear no information on the data type): .. ipython:: python + import pandas.rpy.common as com r_matrix = com.convert_to_r_matrix(df) print(type(r_matrix)) diff --git a/doc/source/release.rst b/doc/source/release.rst index 04d74270ec938..859a01890d68f 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt plt.close('all') - options.display.max_rows=15 + pd.options.display.max_rows=15 import pandas.util.testing as tm ************* @@ -40,7 +40,7 @@ analysis / manipulation tool available in any language. pandas 0.18.0 ------------- -**Release date:** (January ??, 2016) +**Release date:** (March 13, 2016) This is a major release from 0.17.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all @@ -70,6 +70,107 @@ of all enhancements and bugs that have been fixed in 0.17.1. Thanks ~~~~~~ +- ARF +- Alex Alekseyev +- Andrew McPherson +- Andrew Rosenfeld +- Anthonios Partheniou +- Anton I. Sipos +- Ben +- Ben North +- Bran Yang +- Chris +- Chris Carroux +- Christopher C. Aycock +- Christopher Scanlin +- Cody +- Da Wang +- Daniel Grady +- Dorozhko Anton +- Dr-Irv +- Erik M. Bray +- Evan Wright +- Francis T. O'Donovan +- Frank Cleary +- Gianluca Rossi +- Graham Jeffries +- Guillaume Horel +- Henry Hammond +- Isaac Schwabacher +- Jean-Mathieu Deschenes +- Jeff Reback +- Joe Jevnik +- John Freeman +- John Fremlin +- Jonas Hoersch +- Joris Van den Bossche +- Joris Vankerschaver +- Justin Lecher +- Justin Lin +- Ka Wo Chen +- Keming Zhang +- Kerby Shedden +- Kyle +- Marco Farrugia +- MasonGallo +- MattRijk +- Matthew Lurie +- Maximilian Roos +- Mayank Asthana +- Mortada Mehyar +- Moussa Taifi +- Navreet Gill +- Nicolas Bonnotte +- Paul Reiners +- Philip Gura +- Pietro Battiston +- RahulHP +- Randy Carnevale +- Rinoc Johnson +- Rishipuri +- Sangmin Park +- Scott E Lasley +- Sereger13 +- Shannon Wang +- Skipper Seabold +- Thierry Moisan +- Thomas A Caswell +- Toby Dylan Hocking +- Tom Augspurger +- Travis +- Trent Hauck +- Tux1 +- Varun +- Wes McKinney +- Will Thompson +- Yoav Ram +- Yoong Kang Lim +- Yoshiki Vázquez Baeza +- Young Joong Kim +- Younggun Kim +- Yuval Langer +- alex argunov +- behzad nouri +- boombard +- brian-pantano +- chromy +- daniel +- dgram0 +- gfyoung +- hack-c +- hcontrast +- jfoo +- kaustuv deolal +- llllllllll +- ranarag +- rockg +- scls19fr +- seales +- sinhrks +- srib +- surveymedia.ca +- tworec + pandas 0.17.1 ------------- diff --git a/doc/source/text.rst b/doc/source/text.rst index 53567ea25aeac..655df5c5e566c 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -9,7 +9,7 @@ randn = np.random.randn np.set_printoptions(precision=4, suppress=True) from pandas.compat import lrange - options.display.max_rows=15 + pd.options.display.max_rows=15 ====================== Working with Text Data @@ -256,7 +256,7 @@ It raises ``ValueError`` if ``expand=False``. .. code-block:: python >>> s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False) - ValueError: This pattern contains no groups to capture. + ValueError: only one regex group is supported with Index The table below summarizes the behavior of ``extract(expand=False)`` (input subject in first column, number of groups in regex in @@ -375,53 +375,54 @@ Method Summary .. csv-table:: :header: "Method", "Description" :widths: 20, 80 - - :meth:`~Series.str.cat`,Concatenate strings - :meth:`~Series.str.split`,Split strings on delimiter - :meth:`~Series.str.rsplit`,Split strings on delimiter working from the end of the string - :meth:`~Series.str.get`,Index into each element (retrieve i-th element) - :meth:`~Series.str.join`,Join strings in each element of the Series with passed separator - :meth:`~Series.str.get_dummies`,Split strings on delimiter, returning DataFrame of dummy variables - :meth:`~Series.str.contains`,Return boolean array if each string contains pattern/regex - :meth:`~Series.str.replace`,Replace occurrences of pattern/regex with some other string - :meth:`~Series.str.repeat`,Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``) - :meth:`~Series.str.pad`,"Add whitespace to left, right, or both sides of strings" - :meth:`~Series.str.center`,Equivalent to ``str.center`` - :meth:`~Series.str.ljust`,Equivalent to ``str.ljust`` - :meth:`~Series.str.rjust`,Equivalent to ``str.rjust`` - :meth:`~Series.str.zfill`,Equivalent to ``str.zfill`` - :meth:`~Series.str.wrap`,Split long strings into lines with length less than a given width - :meth:`~Series.str.slice`,Slice each string in the Series - :meth:`~Series.str.slice_replace`,Replace slice in each string with passed value - :meth:`~Series.str.count`,Count occurrences of pattern - :meth:`~Series.str.startswith`,Equivalent to ``str.startswith(pat)`` for each element - :meth:`~Series.str.endswith`,Equivalent to ``str.endswith(pat)`` for each element - :meth:`~Series.str.findall`,Compute list of all occurrences of pattern/regex for each string - :meth:`~Series.str.match`,"Call ``re.match`` on each element, returning matched groups as list" - :meth:`~Series.str.extract`,"Call ``re.search`` on each element, returning DataFrame with one row for each element and one column for each regex capture group" - :meth:`~Series.str.extractall`,"Call ``re.findall`` on each element, returning DataFrame with one row for each match and one column for each regex capture group" - :meth:`~Series.str.len`,Compute string lengths - :meth:`~Series.str.strip`,Equivalent to ``str.strip`` - :meth:`~Series.str.rstrip`,Equivalent to ``str.rstrip`` - :meth:`~Series.str.lstrip`,Equivalent to ``str.lstrip`` - :meth:`~Series.str.partition`,Equivalent to ``str.partition`` - :meth:`~Series.str.rpartition`,Equivalent to ``str.rpartition`` - :meth:`~Series.str.lower`,Equivalent to ``str.lower`` - :meth:`~Series.str.upper`,Equivalent to ``str.upper`` - :meth:`~Series.str.find`,Equivalent to ``str.find`` - :meth:`~Series.str.rfind`,Equivalent to ``str.rfind`` - :meth:`~Series.str.index`,Equivalent to ``str.index`` - :meth:`~Series.str.rindex`,Equivalent to ``str.rindex`` - :meth:`~Series.str.capitalize`,Equivalent to ``str.capitalize`` - :meth:`~Series.str.swapcase`,Equivalent to ``str.swapcase`` - :meth:`~Series.str.normalize`,Return Unicode normal form. Equivalent to ``unicodedata.normalize`` - :meth:`~Series.str.translate`,Equivalent to ``str.translate`` - :meth:`~Series.str.isalnum`,Equivalent to ``str.isalnum`` - :meth:`~Series.str.isalpha`,Equivalent to ``str.isalpha`` - :meth:`~Series.str.isdigit`,Equivalent to ``str.isdigit`` - :meth:`~Series.str.isspace`,Equivalent to ``str.isspace`` - :meth:`~Series.str.islower`,Equivalent to ``str.islower`` - :meth:`~Series.str.isupper`,Equivalent to ``str.isupper`` - :meth:`~Series.str.istitle`,Equivalent to ``str.istitle`` - :meth:`~Series.str.isnumeric`,Equivalent to ``str.isnumeric`` - :meth:`~Series.str.isdecimal`,Equivalent to ``str.isdecimal`` + :delim: ; + + :meth:`~Series.str.cat`;Concatenate strings + :meth:`~Series.str.split`;Split strings on delimiter + :meth:`~Series.str.rsplit`;Split strings on delimiter working from the end of the string + :meth:`~Series.str.get`;Index into each element (retrieve i-th element) + :meth:`~Series.str.join`;Join strings in each element of the Series with passed separator + :meth:`~Series.str.get_dummies`;Split strings on the delimiter returning DataFrame of dummy variables + :meth:`~Series.str.contains`;Return boolean array if each string contains pattern/regex + :meth:`~Series.str.replace`;Replace occurrences of pattern/regex with some other string + :meth:`~Series.str.repeat`;Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``) + :meth:`~Series.str.pad`;"Add whitespace to left, right, or both sides of strings" + :meth:`~Series.str.center`;Equivalent to ``str.center`` + :meth:`~Series.str.ljust`;Equivalent to ``str.ljust`` + :meth:`~Series.str.rjust`;Equivalent to ``str.rjust`` + :meth:`~Series.str.zfill`;Equivalent to ``str.zfill`` + :meth:`~Series.str.wrap`;Split long strings into lines with length less than a given width + :meth:`~Series.str.slice`;Slice each string in the Series + :meth:`~Series.str.slice_replace`;Replace slice in each string with passed value + :meth:`~Series.str.count`;Count occurrences of pattern + :meth:`~Series.str.startswith`;Equivalent to ``str.startswith(pat)`` for each element + :meth:`~Series.str.endswith`;Equivalent to ``str.endswith(pat)`` for each element + :meth:`~Series.str.findall`;Compute list of all occurrences of pattern/regex for each string + :meth:`~Series.str.match`;"Call ``re.match`` on each element, returning matched groups as list" + :meth:`~Series.str.extract`;"Call ``re.search`` on each element, returning DataFrame with one row for each element and one column for each regex capture group" + :meth:`~Series.str.extractall`;"Call ``re.findall`` on each element, returning DataFrame with one row for each match and one column for each regex capture group" + :meth:`~Series.str.len`;Compute string lengths + :meth:`~Series.str.strip`;Equivalent to ``str.strip`` + :meth:`~Series.str.rstrip`;Equivalent to ``str.rstrip`` + :meth:`~Series.str.lstrip`;Equivalent to ``str.lstrip`` + :meth:`~Series.str.partition`;Equivalent to ``str.partition`` + :meth:`~Series.str.rpartition`;Equivalent to ``str.rpartition`` + :meth:`~Series.str.lower`;Equivalent to ``str.lower`` + :meth:`~Series.str.upper`;Equivalent to ``str.upper`` + :meth:`~Series.str.find`;Equivalent to ``str.find`` + :meth:`~Series.str.rfind`;Equivalent to ``str.rfind`` + :meth:`~Series.str.index`;Equivalent to ``str.index`` + :meth:`~Series.str.rindex`;Equivalent to ``str.rindex`` + :meth:`~Series.str.capitalize`;Equivalent to ``str.capitalize`` + :meth:`~Series.str.swapcase`;Equivalent to ``str.swapcase`` + :meth:`~Series.str.normalize`;Return Unicode normal form. Equivalent to ``unicodedata.normalize`` + :meth:`~Series.str.translate`;Equivalent to ``str.translate`` + :meth:`~Series.str.isalnum`;Equivalent to ``str.isalnum`` + :meth:`~Series.str.isalpha`;Equivalent to ``str.isalpha`` + :meth:`~Series.str.isdigit`;Equivalent to ``str.isdigit`` + :meth:`~Series.str.isspace`;Equivalent to ``str.isspace`` + :meth:`~Series.str.islower`;Equivalent to ``str.islower`` + :meth:`~Series.str.isupper`;Equivalent to ``str.isupper`` + :meth:`~Series.str.istitle`;Equivalent to ``str.istitle`` + :meth:`~Series.str.isnumeric`;Equivalent to ``str.isnumeric`` + :meth:`~Series.str.isdecimal`;Equivalent to ``str.isdecimal`` diff --git a/doc/source/whatsnew/v0.10.0.txt b/doc/source/whatsnew/v0.10.0.txt index 48ce09f32b12b..f409be7dd0f41 100644 --- a/doc/source/whatsnew/v0.10.0.txt +++ b/doc/source/whatsnew/v0.10.0.txt @@ -292,6 +292,7 @@ Updated PyTables Support store.select('df') .. ipython:: python + :okwarning: wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'], major_axis=date_range('1/1/2000', periods=5), diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt index 9651c1efeff4a..3d992206cb426 100644 --- a/doc/source/whatsnew/v0.15.0.txt +++ b/doc/source/whatsnew/v0.15.0.txt @@ -420,7 +420,7 @@ Rolling/Expanding Moments improvements New behavior - .. ipython:: python + .. code-block:: python In [10]: pd.rolling_window(s, window=3, win_type='triang', center=True) Out[10]: diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index bd878db08a3ed..79efa2b278ae7 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -110,19 +110,18 @@ API changes .. code-block:: python - In [8]: s.loc[3.5:1.5] - KeyError: 3.5 + In [8]: s.loc[3.5:1.5] + KeyError: 3.5 current behavior: .. ipython:: python - s.loc[3.5:1.5] - + s.loc[3.5:1.5] - ``io.data.Options`` has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`), (:issue:`8741`) - .. note:: + .. note:: As a result of a change in Yahoo's option page layout, when an expiry date is given, ``Options`` methods now return data for a single expiry date. Previously, methods returned all @@ -146,6 +145,7 @@ API changes Current behavior: .. ipython:: python + :okwarning: from pandas.io.data import Options aapl = Options('aapl','yahoo') @@ -274,4 +274,3 @@ Bug Fixes - Bug in Setting by indexer to a scalar value with a mixed-dtype `Panel4d` was failing (:issue:`8702`) - Bug where ``DataReader``'s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (:issue:`8494`) - Bug in ``get_quote_yahoo`` that wouldn't allow non-float return values (:issue:`5229`). - diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index 9f943fa68e639..92eafdac387fa 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -723,6 +723,7 @@ be broadcast: or it can return False if broadcasting can not be done: .. ipython:: python + :okwarning: np.array([1, 2, 3]) == np.array([1, 2]) diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 8f6525f2cb6a5..dd1884efe5806 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1,7 +1,7 @@ .. _whatsnew_0180: -v0.18.0 (February ??, 2016) ---------------------------- +v0.18.0 (March 13, 2016) +------------------------ This is a major release from 0.17.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all @@ -12,6 +12,10 @@ users upgrade to this version. pandas >= 0.18.0 no longer supports compatibility with Python version 2.6 and 3.3 (:issue:`7718`, :issue:`11273`) +.. warning:: + + ``numexpr`` version 2.4.4 will now show a warning and not be used as a computation back-end for pandas because of some buggy behavior. This does not affect other versions (>= 2.1 and >= 2.4.6). (:issue:`12489`) + Highlights include: - Moving and expanding window functions are now methods on Series and DataFrame, @@ -46,12 +50,13 @@ New features Window functions are now methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Window functions have been refactored to be methods on ``Series/DataFrame`` objects, rather than top-level functions, which are now deprecated. This allows these window-type functions, to have a similar API to that of ``.groupby``. See the full documentation :ref:`here <stats.moments>` (:issue:`11603`) +Window functions have been refactored to be methods on ``Series/DataFrame`` objects, rather than top-level functions, which are now deprecated. This allows these window-type functions, to have a similar API to that of ``.groupby``. See the full documentation :ref:`here <stats.moments>` (:issue:`11603`, :issue:`12373`) + .. ipython:: python np.random.seed(1234) - df = DataFrame({'A' : range(10), 'B' : np.random.randn(10)}) + df = pd.DataFrame({'A' : range(10), 'B' : np.random.randn(10)}) df Previous Behavior: @@ -148,7 +153,7 @@ Previous Behavior: .. code-block:: python - In [3]: s = Series(range(1000)) + In [3]: s = pd.Series(range(1000)) In [4]: s.index Out[4]: @@ -164,7 +169,7 @@ New Behavior: .. ipython:: python - s = Series(range(1000)) + s = pd.Series(range(1000)) s.index s.index.nbytes @@ -186,9 +191,17 @@ In v0.18.0, the ``expand`` argument was added to Currently the default is ``expand=None`` which gives a ``FutureWarning`` and uses ``expand=False``. To avoid this warning, please explicitly specify ``expand``. -.. ipython:: python +.. code-block:: python + + In [1]: pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None) + FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) + but in a future version of pandas this will be changed to expand=True (return DataFrame) - pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None) + Out[1]: + 0 1 + 1 2 + 2 NaN + dtype: object Extracting a regular expression with one group returns a Series if ``expand=False``. @@ -209,7 +222,7 @@ returns an ``Index`` if ``expand=False``. .. ipython:: python s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"]) - s + s.index s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False) It returns a ``DataFrame`` with one column if ``expand=True``. @@ -242,8 +255,8 @@ Addition of str.extractall ^^^^^^^^^^^^^^^^^^^^^^^^^^ The :ref:`.str.extractall <text.extractall>` method was added -(:issue:`11386`). Unlike ``extract`` (which returns only the first -match), +(:issue:`11386`). Unlike ``extract``, which returns only the first +match. .. ipython:: python @@ -251,7 +264,7 @@ match), s s.str.extract("(?P<letter>[ab])(?P<digit>\d)", expand=False) -the ``extractall`` method returns all matches. +The ``extractall`` method returns all matches. .. ipython:: python @@ -268,12 +281,12 @@ A new, friendlier ``ValueError`` is added to protect against the mistake of supp .. ipython:: python - Series(['a','b',np.nan,'c']).str.cat(sep=' ') - Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?') + pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ') + pd.Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?') .. code-block:: python - In [2]: Series(['a','b',np.nan,'c']).str.cat(' ') + In [2]: pd.Series(['a','b',np.nan,'c']).str.cat(' ') ValueError: Did you mean to supply a `sep` keyword? @@ -321,21 +334,21 @@ In addition, ``.round()``, ``.floor()`` and ``.ceil()`` will be available thru t .. ipython:: python - s = Series(dr) + s = pd.Series(dr) s s.dt.round('D') -Formatting of integer in FloatIndex -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Formatting of Integers in FloatIndex +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Integers in ``FloatIndex``, e.g. 1., are now formatted with a decimal point and a ``0`` digit, e.g. ``1.0`` (:issue:`11713`) -This change not only affects the display in a jupyter notebook, but also the output of IO methods like ``.to_csv`` or ``.to_html`` +This change not only affects the display to the console, but also the output of IO methods like ``.to_csv`` or ``.to_html``. Previous Behavior: .. code-block:: python - In [2]: s = Series([1,2,3], index=np.arange(3.)) + In [2]: s = pd.Series([1,2,3], index=np.arange(3.)) In [3]: s Out[3]: @@ -357,11 +370,87 @@ New Behavior: .. ipython:: python - s = Series([1,2,3], index=np.arange(3.)) + s = pd.Series([1,2,3], index=np.arange(3.)) s s.index print(s.to_csv(path=None)) +Changes to dtype assignment behaviors +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When a DataFrame's slice is updated with a new slice of the same dtype, the dtype of the DataFrame will now remain the same. (:issue:`10503`) + +Previous Behavior: + +.. code-block:: python + + In [5]: df = pd.DataFrame({'a': [0, 1, 1], + 'b': pd.Series([100, 200, 300], dtype='uint32')}) + + In [7]: df.dtypes + Out[7]: + a int64 + b uint32 + dtype: object + + In [8]: ix = df['a'] == 1 + + In [9]: df.loc[ix, 'b'] = df.loc[ix, 'b'] + + In [11]: df.dtypes + Out[11]: + a int64 + b int64 + dtype: object + +New Behavior: + +.. ipython:: python + + df = pd.DataFrame({'a': [0, 1, 1], + 'b': pd.Series([100, 200, 300], dtype='uint32')}) + df.dtypes + ix = df['a'] == 1 + df.loc[ix, 'b'] = df.loc[ix, 'b'] + df.dtypes + +When a DataFrame's integer slice is partially updated with a new slice of floats that could potentially be downcasted to integer without losing precision, the dtype of the slice will be set to float instead of integer. + +Previous Behavior: + +.. code-block:: python + + In [4]: df = pd.DataFrame(np.array(range(1,10)).reshape(3,3), + columns=list('abc'), + index=[[4,4,8], [8,10,12]]) + + In [5]: df + Out[5]: + a b c + 4 8 1 2 3 + 10 4 5 6 + 8 12 7 8 9 + + In [7]: df.ix[4, 'c'] = np.array([0., 1.]) + + In [8]: df + Out[8]: + a b c + 4 8 1 2 0 + 10 4 5 1 + 8 12 7 8 9 + +New Behavior: + +.. ipython:: python + + df = pd.DataFrame(np.array(range(1,10)).reshape(3,3), + columns=list('abc'), + index=[[4,4,8], [8,10,12]]) + df + df.ix[4, 'c'] = np.array([0., 1.]) + df + .. _whatsnew_0180.enhancements.xarray: to_xarray @@ -369,7 +458,7 @@ to_xarray In a future version of pandas, we will be deprecating ``Panel`` and other > 2 ndim objects. In order to provide for continuity, all ``NDFrame`` objects have gained the ``.to_xarray()`` method in order to convert to ``xarray`` objects, which has -a pandas-like interface for > 2 ndim. +a pandas-like interface for > 2 ndim. (:issue:`11972`) See the `xarray full-documentation here <http://xarray.pydata.org/en/stable/>`__. @@ -397,17 +486,17 @@ Latex Representation ``DataFrame`` has gained a ``._repr_latex_()`` method in order to allow for conversion to latex in a ipython/jupyter notebook using nbconvert. (:issue:`11778`) -Note that this must be activated by setting the option ``display.latex.repr`` to ``True`` (issue:`12182`) +Note that this must be activated by setting the option ``pd.display.latex.repr=True`` (:issue:`12182`) -For example, if you have a jupyter notebook you plan to convert to latex using nbconvert, place the statement ``pd.set_option('display.latex.repr', True)`` in the first cell to have the contained DataFrame output also stored as latex. +For example, if you have a jupyter notebook you plan to convert to latex using nbconvert, place the statement ``pd.display.latex.repr=True`` in the first cell to have the contained DataFrame output also stored as latex. -Options ``display.latex.escape`` and ``display.latex.longtable`` have also been added to the configuration and are used automatically by the ``to_latex`` -method. See the :ref:`options documentation<options>` for more info. +The options ``display.latex.escape`` and ``display.latex.longtable`` have also been added to the configuration and are used automatically by the ``to_latex`` +method. See the :ref:`available options docs <options.available>` for more info. .. _whatsnew_0180.enhancements.sas: -read_sas changes -^^^^^^^^^^^^^^^^ +``pd.read_sas()`` changes +^^^^^^^^^^^^^^^^^^^^^^^^^ ``read_sas`` has gained the ability to read SAS7BDAT files, including compressed files. The files can be read in entirety, or incrementally. For full details see :ref:`here <io.sas>`. (:issue:`4052`) @@ -429,8 +518,9 @@ Other enhancements - Added ``DataFrame.style.format`` for more flexible formatting of cell values (:issue:`11692`) - ``DataFrame.select_dtypes`` now allows the ``np.float16`` typecode (:issue:`11990`) - ``pivot_table()`` now accepts most iterables for the ``values`` parameter (:issue:`12017`) -- Added Google ``BigQuery`` service account authentication support, which enables authentication on remote servers. (:issue:`11881`). For further details see :ref:`here <io.bigquery_authentication>` +- Added Google ``BigQuery`` service account authentication support, which enables authentication on remote servers. (:issue:`11881`, :issue:`12572`). For further details see :ref:`here <io.bigquery_authentication>` - ``HDFStore`` is now iterable: ``for k in store`` is equivalent to ``for k in store.keys()`` (:issue:`12221`). +- Add missing methods/fields to ``.dt`` for ``Period`` (:issue:`8848`) - The entire codebase has been ``PEP``-ified (:issue:`12096`) .. _whatsnew_0180.api_breaking: @@ -524,14 +614,12 @@ Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`1 Changes to msgpack ^^^^^^^^^^^^^^^^^^ -Forward incompatible changes in ``msgpack`` writing format were made over 0.17.0 and 0.18.0; older versions of pandas cannot read files packed by newer versions (:issue:`12129`, `10527`) +Forward incompatible changes in ``msgpack`` writing format were made over 0.17.0 and 0.18.0; older versions of pandas cannot read files packed by newer versions (:issue:`12129`, :issue:`10527`) -Bug in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.18.0, caused files packed in Python 2 unreadable by Python 3 (:issue:`12142`) +Bugs in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.18.0, caused files packed in Python 2 unreadable by Python 3 (:issue:`12142`). The following table describes the backward and forward compat of msgpacks. .. warning:: - As a result of a number of issues: - +----------------------+------------------------+ | Packed with | Can be unpacked with | +======================+========================+ @@ -539,13 +627,14 @@ Bug in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.1 +----------------------+------------------------+ | pre-0.17 / Python 3 | any | +----------------------+------------------------+ - | 0.17 / Python 2 | - 0.17 / Python 2 | + | 0.17 / Python 2 | - ==0.17 / Python 2 | | | - >=0.18 / any Python | +----------------------+------------------------+ | 0.17 / Python 3 | >=0.18 / any Python | +----------------------+------------------------+ | 0.18 | >= 0.18 | - +======================+========================+ + +----------------------+------------------------+ + 0.18.0 is backward-compatible for reading files packed by older versions, except for files packed with 0.17 in Python 2, in which case only they can only be unpacked in Python 2. @@ -623,7 +712,7 @@ other anchored offsets like ``MonthBegin`` and ``YearBegin``. Resample API ^^^^^^^^^^^^ -Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12334`, :issue:`12348`). +Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12334`, :issue:`12348`, :issue:`12448`). .. ipython:: python @@ -654,7 +743,7 @@ You could also specify a ``how`` directly .. code-block:: python - In [7]: df.resample('2s',how='sum') + In [7]: df.resample('2s', how='sum') Out[7]: A B C D 2010-01-01 09:00:00 0.971495 0.894701 0.714192 1.587231 @@ -663,42 +752,13 @@ You could also specify a ``how`` directly 2010-01-01 09:00:06 1.249976 1.219477 1.266330 1.224904 2010-01-01 09:00:08 1.020940 1.068634 1.146402 1.613897 -.. warning:: - - This new API for resample includes some internal changes for the prior-to-0.18.0 API, to work with a deprecation warning in most cases, as the resample operation returns a deferred object. We can intercept operations and just do what the (pre 0.18.0) API did (with a warning). Here is a typical use case: - - .. code-block:: python - - In [4]: r = df.resample('2s') - - In [6]: r*10 - pandas/tseries/resample.py:80: FutureWarning: .resample() is now a deferred operation - use .resample(...).mean() instead of .resample(...) - - Out[6]: - A B C D - 2010-01-01 09:00:00 4.857476 4.473507 3.570960 7.936154 - 2010-01-01 09:00:02 8.208011 7.943173 3.640340 5.310957 - 2010-01-01 09:00:04 4.339846 3.145823 4.241039 6.257326 - 2010-01-01 09:00:06 6.249881 6.097384 6.331650 6.124518 - 2010-01-01 09:00:08 5.104699 5.343172 5.732009 8.069486 - - However, getting and assignment operations directly on a ``Resampler`` will raise a ``ValueError``: - - .. code-block:: python - - In [7]: r.iloc[0] = 5 - ValueError: .resample() is now a deferred operation - use .resample(...).mean() instead of .resample(...) - assignment will have no effect as you are working on a copy - **New API**: -Now, you can write ``.resample`` as a 2-stage operation like groupby, which +Now, you can write ``.resample(..)`` as a 2-stage operation like ``.groupby(...)``, which yields a ``Resampler``. .. ipython:: python - + :okwarning: r = df.resample('2s') r @@ -707,7 +767,7 @@ Downsampling '''''''''''' You can then use this object to perform operations. -These are downsampling operations (going from a lower frequency to a higher one). +These are downsampling operations (going from a higher frequency to a lower one). .. ipython:: python @@ -740,7 +800,7 @@ Upsampling .. currentmodule:: pandas.tseries.resample -Upsampling operations take you from a higher frequency to a lower frequency. These are now +Upsampling operations take you from a lower frequency to a higher frequency. These are now performed with the ``Resampler`` objects with :meth:`~Resampler.backfill`, :meth:`~Resampler.ffill`, :meth:`~Resampler.fillna` and :meth:`~Resampler.asfreq` methods. @@ -781,6 +841,65 @@ New API In the new API, you can either downsample OR upsample. The prior implementation would allow you to pass an aggregator function (like ``mean``) even though you were upsampling, providing a bit of confusion. +Previous API will work but with deprecations +'''''''''''''''''''''''''''''''''''''''''''' + +.. warning:: + + This new API for resample includes some internal changes for the prior-to-0.18.0 API, to work with a deprecation warning in most cases, as the resample operation returns a deferred object. We can intercept operations and just do what the (pre 0.18.0) API did (with a warning). Here is a typical use case: + + .. code-block:: python + + In [4]: r = df.resample('2s') + + In [6]: r*10 + pandas/tseries/resample.py:80: FutureWarning: .resample() is now a deferred operation + use .resample(...).mean() instead of .resample(...) + + Out[6]: + A B C D + 2010-01-01 09:00:00 4.857476 4.473507 3.570960 7.936154 + 2010-01-01 09:00:02 8.208011 7.943173 3.640340 5.310957 + 2010-01-01 09:00:04 4.339846 3.145823 4.241039 6.257326 + 2010-01-01 09:00:06 6.249881 6.097384 6.331650 6.124518 + 2010-01-01 09:00:08 5.104699 5.343172 5.732009 8.069486 + + However, getting and assignment operations directly on a ``Resampler`` will raise a ``ValueError``: + + .. code-block:: python + + In [7]: r.iloc[0] = 5 + ValueError: .resample() is now a deferred operation + use .resample(...).mean() instead of .resample(...) + + There is a situation where the new API can not perform all the operations when using original code. + This code is intending to resample every 2s, take the ``mean`` AND then take the ``min`` of those results. + + .. code-block:: python + + In [4]: df.resample('2s').min() + Out[4]: + A 0.433985 + B 0.314582 + C 0.357096 + D 0.531096 + dtype: float64 + + The new API will: + + .. ipython:: python + + df.resample('2s').min() + + The good news is the return dimensions will differ between the new API and the old API, so this should loudly raise + an exception. + + To replicate the original operation + + .. ipython:: python + + df.resample('2s').mean().min() + Changes to eval ^^^^^^^^^^^^^^^ @@ -790,9 +909,28 @@ in an inplace change to the ``DataFrame``. (:issue:`9297`) .. ipython:: python df = pd.DataFrame({'a': np.linspace(0, 10, 5), 'b': range(5)}) - df.eval('c = a + b') df +.. ipython:: python + :suppress: + + df.eval('c = a + b', inplace=True) + +.. code-block:: python + + In [12]: df.eval('c = a + b') + FutureWarning: eval expressions containing an assignment currentlydefault to operating inplace. + This will change in a future version of pandas, use inplace=True to avoid this warning. + + In [13]: df + Out[13]: + a b c + 0 0.0 0 0.0 + 1 2.5 1 3.5 + 2 5.0 2 7.0 + 3 7.5 3 10.5 + 4 10.0 4 14.0 + In version 0.18.0, a new ``inplace`` keyword was added to choose whether the assignment should be done inplace or return a copy. @@ -855,22 +993,18 @@ Other API Changes In [2]: s.between_time('20150101 07:00:00','20150101 09:00:00') ValueError: Cannot convert arg ['20150101 07:00:00'] to a time. -- ``.memory_usage()`` now includes values in the index, as does memory_usage in ``.info`` (:issue:`11597`) - -- ``DataFrame.to_latex()`` now supports non-ascii encodings (eg utf-8) in Python 2 with the parameter ``encoding`` (:issue:`7061`) - +- ``.memory_usage()`` now includes values in the index, as does memory_usage in ``.info()`` (:issue:`11597`) +- ``DataFrame.to_latex()`` now supports non-ascii encodings (eg ``utf-8``) in Python 2 with the parameter ``encoding`` (:issue:`7061`) - ``pandas.merge()`` and ``DataFrame.merge()`` will show a specific error message when trying to merge with an object that is not of type ``DataFrame`` or a subclass (:issue:`12081`) - - ``DataFrame.unstack`` and ``Series.unstack`` now take ``fill_value`` keyword to allow direct replacement of missing values when an unstack results in missing values in the resulting ``DataFrame``. As an added benefit, specifying ``fill_value`` will preserve the data type of the original stacked data. (:issue:`9746`) - -- As part of the new API for :ref:`window functions <whatsnew_0180.enhancements.moments>` and :ref:`resampling <whatsnew_0180.breaking.resample>`, aggregation functions have been clarified, raising more informative error messages on invalid aggregations. (:issue:`9052`). A full set of examples are presented in :ref:`groupby <groupby.aggregation>`. - -- Statistical functions for ``NDFrame`` objects will now raise if non-numpy-compatible arguments are passed in for ``**kwargs`` (:issue:`12301`) - +- As part of the new API for :ref:`window functions <whatsnew_0180.enhancements.moments>` and :ref:`resampling <whatsnew_0180.breaking.resample>`, aggregation functions have been clarified, raising more informative error messages on invalid aggregations. (:issue:`9052`). A full set of examples are presented in :ref:`groupby <groupby.aggregate>`. +- Statistical functions for ``NDFrame`` objects (like ``sum(), mean(), min()``) will now raise if non-numpy-compatible arguments are passed in for ``**kwargs`` (:issue:`12301`) - ``.to_latex`` and ``.to_html`` gain a ``decimal`` parameter like ``.to_csv``; the default is ``'.'`` (:issue:`12031`) - - More helpful error message when constructing a ``DataFrame`` with empty data but with indices (:issue:`8020`) - +- ``.describe()`` will now properly handle bool dtype as a categorical (:issue:`6625`) +- More helpful error message with an invalid ``.transform`` with user defined input (:issue:`10165`) +- Exponentially weighted functions now allow specifying alpha directly (:issue:`10789`) and raise ``ValueError`` if parameters violate ``0 < alpha <= 1`` (:issue:`12492`) +- ``Rolling.min`` and ``Rolling.max`` now take an as_float argument, that when False, will cause those functions to return output that has the same value as the input type. This is .. _whatsnew_0180.deprecations: @@ -908,7 +1042,7 @@ Deprecations - The the ``freq`` and ``how`` arguments to the ``.rolling``, ``.expanding``, and ``.ewm`` (new) functions are deprecated, and will be removed in a future version. You can simply resample the input prior to creating a window function. (:issue:`11603`). - For example, instead of ``s.rolling(window=5,freq='D').max()`` to get the max value on a rolling 5 Day window, one could use ``s.resample('D',how='max').rolling(window=5).max()``, which first resamples the data to daily data, then provides a rolling 5 day window. + For example, instead of ``s.rolling(window=5,freq='D').max()`` to get the max value on a rolling 5 Day window, one could use ``s.resample('D').mean().rolling(window=5).max()``, which first resamples the data to daily data, then provides a rolling 5 day window. - ``pd.tseries.frequencies.get_offset_name`` function is deprecated. Use offset's ``.freqstr`` property as alternative (:issue:`11192`) - ``pandas.stats.fama_macbeth`` routines are deprecated and will be removed in a future version (:issue:`6077`) @@ -928,11 +1062,11 @@ Removal of deprecated float indexers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In :issue:`4892` indexing with floating point numbers on a non-``Float64Index`` was deprecated (in version 0.14.0). -In 0.18.0, this deprecation warning is removed and these will now raise a ``TypeError``. (:issue:`12165`) +In 0.18.0, this deprecation warning is removed and these will now raise a ``TypeError``. (:issue:`12165`, :issue:`12333`) .. ipython:: python - s = pd.Series([1,2,3]) + s = pd.Series([1, 2, 3], index=[4, 5, 6]) s s2 = pd.Series([1, 2, 3], index=list('abc')) s2 @@ -941,15 +1075,18 @@ Previous Behavior: .. code-block:: python - In [2]: s[1.0] + # this is label indexing + In [2]: s[5.0] FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[2]: 2 + # this is positional indexing In [3]: s.iloc[1.0] FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[3]: 2 - In [4]: s.loc[1.0] + # this is label indexing + In [4]: s.loc[5.0] FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[4]: 2 @@ -966,33 +1103,61 @@ Previous Behavior: New Behavior: +For iloc, getting & setting via a float scalar will always raise. + .. code-block:: python - In [2]: s[1.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> + In [3]: s.iloc[2.0] + TypeError: cannot do label indexing on <class 'pandas.indexes.numeric.Int64Index'> with these indexers [2.0] of <type 'float'> - In [3]: s.iloc[1.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> +Other indexers will coerce to a like integer for both getting and setting. The ``FutureWarning`` has been dropped for ``.loc``, ``.ix`` and ``[]``. - In [4]: s.loc[1.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> +.. ipython:: python - # .ix will now cause this to be a label lookup and coerce to and Index - In [5]: s2.ix[1.0] = 10 + s[5.0] + s.loc[5.0] + s.ix[5.0] - In [6]: s2 - Out[3]: - a 1 - b 2 - c 3 - 1.0 10 - dtype: int64 +and setting + +.. ipython:: python + + s_copy = s.copy() + s_copy[5.0] = 10 + s_copy + s_copy = s.copy() + s_copy.loc[5.0] = 10 + s_copy + s_copy = s.copy() + s_copy.ix[5.0] = 10 + s_copy + +Positional setting with ``.ix`` and a float indexer will ADD this value to the index, rather than previously setting the value by position. + +.. ipython:: python + + s2.ix[1.0] = 10 + s2 + +Slicing will also coerce integer-like floats to integers for a non-``Float64Index``. + +.. ipython:: python + + s.loc[5.0:6] + s.ix[5.0:6] + +Note that for floats that are NOT coercible to ints, the label based bounds will be excluded + +.. ipython:: python + + s.loc[5.1:6] + s.ix[5.1:6] Float indexing on a ``Float64Index`` is unchanged. .. ipython:: python - s = pd.Series([1,2,3],index=np.arange(3.)) + s = pd.Series([1, 2, 3], index=np.arange(3.)) s[1.0] s[1.0:2.5] @@ -1050,10 +1215,10 @@ Bug Fixes - Bug in ``Series.resample`` using a frequency of ``Nano`` when the index is a ``DatetimeIndex`` and contains non-zero nanosecond parts (:issue:`12037`) - Bug in resampling with ``.nunique`` and a sparse index (:issue:`12352`) - Removed some compiler warnings (:issue:`12471`) - +- Work around compat issues with ``boto`` in python 3.5 (:issue:`11915`) - Bug in ``NaT`` subtraction from ``Timestamp`` or ``DatetimeIndex`` with timezones (:issue:`11718`) - Bug in subtraction of ``Series`` of a single tz-aware ``Timestamp`` (:issue:`12290`) - +- Use compat iterators in PY2 to support ``.next()`` (:issue:`12299`) - Bug in ``Timedelta.round`` with negative values (:issue:`11690`) - Bug in ``.loc`` against ``CategoricalIndex`` may result in normal ``Index`` (:issue:`11586`) - Bug in ``DataFrame.info`` when duplicated column names exist (:issue:`11761`) @@ -1111,12 +1276,12 @@ Bug Fixes - Removed ``millisecond`` property of ``DatetimeIndex``. This would always raise a ``ValueError`` (:issue:`12019`). - Bug in ``Series`` constructor with read-only data (:issue:`11502`) - +- Removed ``pandas.util.testing.choice()``. Should use ``np.random.choice()``, instead. (:issue:`12386`) - Bug in ``.loc`` setitem indexer preventing the use of a TZ-aware DatetimeIndex (:issue:`12050`) - Bug in ``.style`` indexes and multi-indexes not appearing (:issue:`11655`) - Bug in ``to_msgpack`` and ``from_msgpack`` which did not correctly serialize or deserialize ``NaT`` (:issue:`12307`). - Bug in ``.skew`` and ``.kurt`` due to roundoff error for highly similar values (:issue:`11974`) - +- Bug in ``Timestamp`` constructor where microsecond resolution was lost if HHMMSS were not separated with ':' (:issue:`10041`) - Bug in ``buffer_rd_bytes`` src->buffer could be freed more than once if reading failed, causing a segfault (:issue:`12098`) - Bug in ``crosstab`` where arguments with non-overlapping indexes would return a ``KeyError`` (:issue:`10291`) @@ -1124,3 +1289,6 @@ Bug Fixes - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) +- Bug when increasing the buffer size of CSV reader in ``read_csv`` (:issue:`12494`) +- Bug when setting columns of a ``DataFrame`` with duplicate column names (:issue:`12344`) +- Bug in ``.rolling.min`` and ``.rolling.max`` where passing columns of type float32 raised a Value error (:issue:`12373`) diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 8c66cd0c1566d..70a1ad4a335ea 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -43,4 +43,3 @@ Performance Improvements Bug Fixes ~~~~~~~~~ - diff --git a/pandas/algos.pyx b/pandas/algos.pyx index 0f9ceba48e608..6097b85877b97 100644 --- a/pandas/algos.pyx +++ b/pandas/algos.pyx @@ -1625,29 +1625,38 @@ def roll_median_c(ndarray[float64_t] arg, int win, int minp): # of its Simplified BSD license # https://github.com/kwgoodman/bottleneck -cdef struct pairs: - double value - int death - from libc cimport stdlib @cython.boundscheck(False) @cython.wraparound(False) -def roll_max(ndarray[float64_t] a, int window, int minp): - "Moving max of 1d array of dtype=float64 along axis=0 ignoring NaNs." - cdef np.float64_t ai, aold +def roll_max(ndarray[numeric] a, int window, int minp): + "Moving max of 1d array of any numeric type along axis=0 ignoring NaNs." + return _roll_min_max(a, window, minp, 1) + +@cython.boundscheck(False) +@cython.wraparound(False) +def roll_min(ndarray[numeric] a, int window, int minp): + "Moving min of 1d array of any numeric type along axis=0 ignoring NaNs." + return _roll_min_max(a, window, minp, 0) + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef _roll_min_max(ndarray[numeric] a, int window, int minp, bint is_max): + "Moving min/max of 1d array of any numeric type along axis=0 ignoring NaNs." + cdef numeric ai, aold cdef Py_ssize_t count - cdef pairs* ring - cdef pairs* minpair - cdef pairs* end - cdef pairs* last + cdef Py_ssize_t* death + cdef numeric* ring + cdef numeric* minpair + cdef numeric* end + cdef numeric* last cdef Py_ssize_t i0 cdef np.npy_intp *dim dim = PyArray_DIMS(a) cdef Py_ssize_t n0 = dim[0] cdef np.npy_intp *dims = [n0] - cdef np.ndarray[np.float64_t, ndim=1] y = PyArray_EMPTY(1, dims, - NPY_float64, 0) + cdef bint should_replace + cdef np.ndarray[numeric, ndim=1] y = PyArray_EMPTY(1, dims, PyArray_TYPE(a), 0) if window < 1: raise ValueError('Invalid window size %d' @@ -1659,146 +1668,85 @@ def roll_max(ndarray[float64_t] a, int window, int minp): minp = _check_minp(window, minp, n0) with nogil: - ring = <pairs*>stdlib.malloc(window * sizeof(pairs)) + ring = <numeric*>stdlib.malloc(window * sizeof(numeric)) + death = <Py_ssize_t*>stdlib.malloc(window * sizeof(Py_ssize_t)) end = ring + window last = ring - minpair = ring + minvalue = ring ai = a[0] - if ai == ai: - minpair.value = ai + if numeric in cython.floating: + if ai == ai: + minvalue[0] = ai + elif is_max: + minvalue[0] = MINfloat64 + else: + minvalue[0] = MAXfloat64 else: - minpair.value = MINfloat64 - minpair.death = window + minvalue[0] = ai + death[0] = window count = 0 for i0 in range(n0): ai = a[i0] - if ai == ai: - count += 1 + if numeric in cython.floating: + if ai == ai: + count += 1 + elif is_max: + ai = MINfloat64 + else: + ai = MAXfloat64 else: - ai = MINfloat64 + count += 1 if i0 >= window: aold = a[i0 - window] if aold == aold: count -= 1 - if minpair.death == i0: - minpair += 1 - if minpair >= end: - minpair = ring - if ai >= minpair.value: - minpair.value = ai - minpair.death = i0 + window - last = minpair + if death[minvalue-ring] == i0: + minvalue += 1 + if minvalue >= end: + minvalue = ring + should_replace = ai >= minvalue[0] if is_max else ai <= minvalue[0] + if should_replace: + minvalue[0] = ai + death[minvalue-ring] = i0 + window + last = minvalue else: - while last.value <= ai: + should_replace = last[0] <= ai if is_max else last[0] >= ai + while should_replace: if last == ring: last = end last -= 1 + should_replace = last[0] <= ai if is_max else last[0] >= ai last += 1 if last == end: last = ring - last.value = ai - last.death = i0 + window - if count >= minp: - y[i0] = minpair.value + last[0] = ai + death[last - ring] = i0 + window + if numeric in cython.floating: + if count >= minp: + y[i0] = minvalue[0] + else: + y[i0] = NaN else: - y[i0] = NaN + y[i0] = minvalue[0] for i0 in range(minp - 1): - y[i0] = NaN + if numeric in cython.floating: + y[i0] = NaN + else: + y[i0] = 0 stdlib.free(ring) + stdlib.free(death) return y - cdef double_t _get_max(object skiplist, int nobs, int minp): if nobs >= minp: return <IndexableSkiplist> skiplist.get(nobs - 1) else: return NaN - -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_min(np.ndarray[np.float64_t, ndim=1] a, int window, int minp): - "Moving min of 1d array of dtype=float64 along axis=0 ignoring NaNs." - cdef np.float64_t ai, aold - cdef Py_ssize_t count - cdef pairs* ring - cdef pairs* minpair - cdef pairs* end - cdef pairs* last - cdef Py_ssize_t i0 - cdef np.npy_intp *dim - dim = PyArray_DIMS(a) - cdef Py_ssize_t n0 = dim[0] - cdef np.npy_intp *dims = [n0] - cdef np.ndarray[np.float64_t, ndim=1] y = PyArray_EMPTY(1, dims, - NPY_float64, 0) - - if window < 1: - raise ValueError('Invalid window size %d' - % (window)) - - if minp > window: - raise ValueError('Invalid min_periods size %d greater than window %d' - % (minp, window)) - - minp = _check_minp(window, minp, n0) - with nogil: - ring = <pairs*>stdlib.malloc(window * sizeof(pairs)) - end = ring + window - last = ring - - minpair = ring - ai = a[0] - if ai == ai: - minpair.value = ai - else: - minpair.value = MAXfloat64 - minpair.death = window - - count = 0 - for i0 in range(n0): - ai = a[i0] - if ai == ai: - count += 1 - else: - ai = MAXfloat64 - if i0 >= window: - aold = a[i0 - window] - if aold == aold: - count -= 1 - if minpair.death == i0: - minpair += 1 - if minpair >= end: - minpair = ring - if ai <= minpair.value: - minpair.value = ai - minpair.death = i0 + window - last = minpair - else: - while last.value >= ai: - if last == ring: - last = end - last -= 1 - last += 1 - if last == end: - last = ring - last.value = ai - last.death = i0 + window - if count >= minp: - y[i0] = minpair.value - else: - y[i0] = NaN - - for i0 in range(minp - 1): - y[i0] = NaN - - stdlib.free(ring) - return y - cdef double_t _get_min(object skiplist, int nobs, int minp): if nobs >= minp: return <IndexableSkiplist> skiplist.get(0) diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index cbdb69d1df8c3..aade3b8411bb9 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -153,31 +153,28 @@ def signature(f): lfilter = builtins.filter -def iteritems(obj, **kwargs): - """replacement for six's iteritems for Python2/3 compat - uses 'iteritems' if available and otherwise uses 'items'. +if PY2: + def iteritems(obj, **kw): + return obj.iteritems(**kw) - Passes kwargs to method. - """ - func = getattr(obj, "iteritems", None) - if not func: - func = obj.items - return func(**kwargs) + def iterkeys(obj, **kw): + return obj.iterkeys(**kw) + def itervalues(obj, **kw): + return obj.itervalues(**kw) -def iterkeys(obj, **kwargs): - func = getattr(obj, "iterkeys", None) - if not func: - func = obj.keys - return func(**kwargs) + next = lambda it : it.next() +else: + def iteritems(obj, **kw): + return iter(obj.items(**kw)) + def iterkeys(obj, **kw): + return iter(obj.keys(**kw)) -def itervalues(obj, **kwargs): - func = getattr(obj, "itervalues", None) - if not func: - func = obj.values - return func(**kwargs) + def itervalues(obj, **kw): + return iter(obj.values(**kw)) + next = next def bind_method(cls, name, func): """Bind a method to class, python 2 and python 3 compatible. diff --git a/pandas/computation/__init__.py b/pandas/computation/__init__.py index e69de29bb2d1d..9e94215eecf62 100644 --- a/pandas/computation/__init__.py +++ b/pandas/computation/__init__.py @@ -0,0 +1,30 @@ + +import warnings +from distutils.version import LooseVersion + +_NUMEXPR_INSTALLED = False + +try: + import numexpr as ne + ver = ne.__version__ + _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1') + + # we specifically disallow 2.4.4 as + # has some hard-to-diagnose bugs + if ver == LooseVersion('2.4.4'): + _NUMEXPR_INSTALLED = False + warnings.warn( + "The installed version of numexpr {ver} is not supported " + "in pandas and will be not be used\n".format(ver=ver), + UserWarning) + + elif not _NUMEXPR_INSTALLED: + warnings.warn( + "The installed version of numexpr {ver} is not supported " + "in pandas and will be not be used\nThe minimum supported " + "version is 2.1\n".format(ver=ver), UserWarning) + +except ImportError: # pragma: no cover + pass + +__all__ = ['_NUMEXPR_INSTALLED'] diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index d2d16acc27fb6..c3300ffca468e 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -6,11 +6,11 @@ import warnings import tokenize from pandas.core import common as com +from pandas.computation import _NUMEXPR_INSTALLED from pandas.computation.expr import Expr, _parsers, tokenize_string from pandas.computation.scope import _ensure_scope from pandas.compat import string_types from pandas.computation.engines import _engines -from distutils.version import LooseVersion def _check_engine(engine): @@ -35,17 +35,11 @@ def _check_engine(engine): # that won't necessarily be import-able) # Could potentially be done on engine instantiation if engine == 'numexpr': - try: - import numexpr - except ImportError: - raise ImportError("'numexpr' not found. Cannot use " + if not _NUMEXPR_INSTALLED: + raise ImportError("'numexpr' is not installed or an " + "unsupported version. Cannot use " "engine='numexpr' for query/eval " "if 'numexpr' is not installed") - else: - ne_version = numexpr.__version__ - if ne_version < LooseVersion('2.1'): - raise ImportError("'numexpr' version is %s, " - "must be >= 2.1" % ne_version) def _check_parser(parser): diff --git a/pandas/computation/expressions.py b/pandas/computation/expressions.py index 6e33250010c2b..086e92dbde1a0 100644 --- a/pandas/computation/expressions.py +++ b/pandas/computation/expressions.py @@ -9,20 +9,10 @@ import warnings import numpy as np from pandas.core.common import _values_from_object -from distutils.version import LooseVersion +from pandas.computation import _NUMEXPR_INSTALLED -try: +if _NUMEXPR_INSTALLED: import numexpr as ne - ver = ne.__version__ - _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1') - if not _NUMEXPR_INSTALLED: - warnings.warn( - "The installed version of numexpr {ver} is not supported " - "in pandas and will be not be used\nThe minimum supported " - "version is 2.1\n".format(ver=ver), UserWarning) - -except ImportError: # pragma: no cover - _NUMEXPR_INSTALLED = False _TEST_MODE = None _TEST_RESULT = None diff --git a/pandas/computation/tests/test_compat.py b/pandas/computation/tests/test_compat.py new file mode 100644 index 0000000000000..80b415739c647 --- /dev/null +++ b/pandas/computation/tests/test_compat.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python + +# flake8: noqa + +import nose +from itertools import product +from distutils.version import LooseVersion + +import pandas as pd +from pandas.util import testing as tm + +from pandas.computation.engines import _engines +import pandas.computation.expr as expr + +ENGINES_PARSERS = list(product(_engines, expr._parsers)) + + +def test_compat(): + # test we have compat with our version of nu + + from pandas.computation import _NUMEXPR_INSTALLED + try: + import numexpr as ne + ver = ne.__version__ + if ver == LooseVersion('2.4.4'): + assert not _NUMEXPR_INSTALLED + elif ver < LooseVersion('2.1'): + with tm.assert_produces_warning(UserWarning, + check_stacklevel=False): + assert not _NUMEXPR_INSTALLED + else: + assert _NUMEXPR_INSTALLED + + except ImportError: + raise nose.SkipTest("not testing numexpr version compat") + + +def test_invalid_numexpr_version(): + for engine, parser in ENGINES_PARSERS: + yield check_invalid_numexpr_version, engine, parser + + +def check_invalid_numexpr_version(engine, parser): + def testit(): + a, b = 1, 2 + res = pd.eval('a + b', engine=engine, parser=parser) + tm.assert_equal(res, 3) + + if engine == 'numexpr': + try: + import numexpr as ne + except ImportError: + raise nose.SkipTest("no numexpr") + else: + if ne.__version__ < LooseVersion('2.1'): + with tm.assertRaisesRegexp(ImportError, "'numexpr' version is " + ".+, must be >= 2.1"): + testit() + elif ne.__version__ == LooseVersion('2.4.4'): + raise nose.SkipTest("numexpr version==2.4.4") + else: + testit() + else: + testit() + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index b70252ed9f35b..97db171312557 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -1633,8 +1633,8 @@ def test_result_types(self): self.check_result_type(np.float64, np.float64) def test_result_types2(self): - # xref https://github.com/pydata/pandas/issues/12293 - tm._skip_if_windows() + # xref https://github.com/pydata/pandas/issues/12293 + raise nose.SkipTest("unreliable tests on complex128") # Did not test complex64 because DataFrame is converting it to # complex128. Due to https://github.com/pydata/pandas/issues/10952 @@ -1782,33 +1782,6 @@ def test_name_error_exprs(): yield check_name_error_exprs, engine, parser -def check_invalid_numexpr_version(engine, parser): - def testit(): - a, b = 1, 2 - res = pd.eval('a + b', engine=engine, parser=parser) - tm.assert_equal(res, 3) - - if engine == 'numexpr': - try: - import numexpr as ne - except ImportError: - raise nose.SkipTest("no numexpr") - else: - if ne.__version__ < LooseVersion('2.1'): - with tm.assertRaisesRegexp(ImportError, "'numexpr' version is " - ".+, must be >= 2.1"): - testit() - else: - testit() - else: - testit() - - -def test_invalid_numexpr_version(): - for engine, parser in ENGINES_PARSERS: - yield check_invalid_numexpr_version, engine, parser - - def check_invalid_local_variable_reference(engine, parser): tm.skip_if_no_ne(engine) diff --git a/pandas/core/datetools.py b/pandas/core/datetools.py index 91b33d30004b6..79718c79f9bdd 100644 --- a/pandas/core/datetools.py +++ b/pandas/core/datetools.py @@ -1,8 +1,10 @@ """A collection of random tools for dealing with dates in Python""" -from pandas.tseries.tools import * # noqa -from pandas.tseries.offsets import * # noqa -from pandas.tseries.frequencies import * # noqa +# flake8: noqa + +from pandas.tseries.tools import * +from pandas.tseries.offsets import * +from pandas.tseries.frequencies import * day = DateOffset() bday = BDay() diff --git a/pandas/core/format.py b/pandas/core/format.py index 101a5e64b65b5..1f1ff73869f73 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -10,7 +10,7 @@ from pandas.core.index import Index, MultiIndex, _ensure_index from pandas import compat from pandas.compat import (StringIO, lzip, range, map, zip, reduce, u, - OrderedDict) + OrderedDict, unichr) from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option, set_option from pandas.io.common import _get_handle, UnicodeWriter, _expand_user diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 6c2d4f7919ac6..01156252fcd6d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1627,6 +1627,7 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, human-readable units (base-2 representation). null_counts : boolean, default None Whether to show the non-null counts + - If None, then only show if the frame is smaller than max_info_rows and max_info_columns. - If True, always show counts. @@ -2016,7 +2017,7 @@ def _getitem_array(self, key): # with all other indexing behavior if isinstance(key, Series) and not key.index.equals(self.index): warnings.warn("Boolean Series key will be reindexed to match " - "DataFrame index.", UserWarning) + "DataFrame index.", UserWarning, stacklevel=3) elif len(key) != len(self.index): raise ValueError('Item wrong length %d instead of %d.' % (len(key), len(self.index))) @@ -4932,6 +4933,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, 0 or 'index' for row-wise, 1 or 'columns' for column-wise interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} .. versionadded:: 0.18.0 + This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: @@ -4945,11 +4947,12 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, Returns ------- quantiles : Series or DataFrame - If ``q`` is an array, a DataFrame will be returned where the - index is ``q``, the columns are the columns of self, and the - values are the quantiles. - If ``q`` is a float, a Series will be returned where the - index is the columns of self and the values are the quantiles. + + - If ``q`` is an array, a DataFrame will be returned where the + index is ``q``, the columns are the columns of self, and the + values are the quantiles. + - If ``q`` is a float, a Series will be returned where the + index is the columns of self and the values are the quantiles. Examples -------- @@ -4965,6 +4968,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, 0.1 1.3 3.7 0.5 2.5 55.0 """ + self._check_percentile(q) per = np.asarray(q) * 100 diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 13e4de0e2c5f0..963c953154b57 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2041,11 +2041,13 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. - * default: don't fill gaps - * pad / ffill: propagate last valid observation forward to next - valid - * backfill / bfill: use next valid observation to fill gap - * nearest: use nearest valid observations to fill gap + + * default: don't fill gaps + * pad / ffill: propagate last valid observation forward to next + valid + * backfill / bfill: use next valid observation to fill gap + * nearest: use nearest valid observations to fill gap + copy : boolean, default True Return a new object, even if the passed indexes are the same level : int or name @@ -2265,11 +2267,13 @@ def _reindex_multi(self, axes, copy, fill_value): axis : %(axes_single_arg)s method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional Method to use for filling holes in reindexed DataFrame: - * default: don't fill gaps - * pad / ffill: propagate last valid observation forward to next - valid - * backfill / bfill: use next valid observation to fill gap - * nearest: use nearest valid observations to fill gap + + * default: don't fill gaps + * pad / ffill: propagate last valid observation forward to next + valid + * backfill / bfill: use next valid observation to fill gap + * nearest: use nearest valid observations to fill gap + copy : boolean, default True Return a new object, even if the passed indexes are the same level : int or name @@ -3119,7 +3123,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, # fill in 2d chunks result = dict([(col, s.fillna(method=method, value=value)) - for col, s in compat.iteritems(self)]) + for col, s in self.iteritems()]) return self._constructor.from_dict(result).__finalize__(self) # 2d or less @@ -3932,57 +3936,18 @@ def resample(self, rule, how=None, axis=0, fill_method=None, closed=None, Freq: 3T, dtype: int64 """ - from pandas.tseries.resample import resample + from pandas.tseries.resample import (resample, + _maybe_process_deprecations) axis = self._get_axis_number(axis) r = resample(self, freq=rule, label=label, closed=closed, axis=axis, kind=kind, loffset=loffset, - fill_method=fill_method, convention=convention, - limit=limit, base=base) - - # deprecation warnings - # but call methods anyhow - - if how is not None: - - # .resample(..., how='sum') - if isinstance(how, compat.string_types): - method = "{0}()".format(how) - - # .resample(..., how=lambda x: ....) - else: - method = ".apply(<func>)" - - # if we have both a how and fill_method, then show - # the following warning - if fill_method is None: - warnings.warn("how in .resample() is deprecated\n" - "the new syntax is " - ".resample(...).{method}".format( - method=method), - FutureWarning, stacklevel=2) - r = r.aggregate(how) - - if fill_method is not None: - - # show the prior function call - method = '.' + method if how is not None else '' - - args = "limit={0}".format(limit) if limit is not None else "" - warnings.warn("fill_method is deprecated to .resample()\n" - "the new syntax is .resample(...){method}" - ".{fill_method}({args})".format( - method=method, - fill_method=fill_method, - args=args), - FutureWarning, stacklevel=2) - - if how is not None: - r = getattr(r, fill_method)(limit=limit) - else: - r = r.aggregate(fill_method, limit=limit) - - return r + convention=convention, + base=base) + return _maybe_process_deprecations(r, + how=how, + fill_method=fill_method, + limit=limit) def first(self, offset): """ @@ -4875,26 +4840,27 @@ def describe_numeric_1d(series, percentiles): def describe_categorical_1d(data): names = ['count', 'unique'] objcounts = data.value_counts() - result = [data.count(), len(objcounts[objcounts != 0])] + count_unique = len(objcounts[objcounts != 0]) + result = [data.count(), count_unique] if result[1] > 0: top, freq = objcounts.index[0], objcounts.iloc[0] - if (data.dtype == object or - com.is_categorical_dtype(data.dtype)): - names += ['top', 'freq'] - result += [top, freq] - - elif com.is_datetime64_dtype(data): + if com.is_datetime64_dtype(data): asint = data.dropna().values.view('i8') names += ['top', 'freq', 'first', 'last'] result += [lib.Timestamp(top), freq, lib.Timestamp(asint.min()), lib.Timestamp(asint.max())] + else: + names += ['top', 'freq'] + result += [top, freq] return pd.Series(result, index=names, name=data.name) def describe_1d(data, percentiles): - if com.is_numeric_dtype(data): + if com.is_bool_dtype(data): + return describe_categorical_1d(data) + elif com.is_numeric_dtype(data): return describe_numeric_1d(data, percentiles) elif com.is_timedelta64_dtype(data): return describe_numeric_1d(data, percentiles) @@ -4906,7 +4872,7 @@ def describe_1d(data, percentiles): elif (include is None) and (exclude is None): if len(self._get_numeric_data()._info_axis) > 0: # when some numerics are found, keep only numerics - data = self.select_dtypes(include=[np.number, np.bool]) + data = self.select_dtypes(include=[np.number]) else: data = self elif include == 'all': @@ -5164,11 +5130,12 @@ def expanding(self, min_periods=1, freq=None, center=False, axis=0): cls.expanding = expanding @Appender(rwindow.ewm.__doc__) - def ewm(self, com=None, span=None, halflife=None, min_periods=0, - freq=None, adjust=True, ignore_na=False, axis=0): + def ewm(self, com=None, span=None, halflife=None, alpha=None, + min_periods=0, freq=None, adjust=True, ignore_na=False, + axis=0): axis = self._get_axis_number(axis) return rwindow.ewm(self, com=com, span=span, halflife=halflife, - min_periods=min_periods, freq=freq, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, ignore_na=ignore_na, axis=axis) cls.ewm = ewm diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 06f3e0409600e..c8598639d9fad 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -1044,27 +1044,71 @@ def ohlc(self): @Substitution(name='groupby') @Appender(_doc_template) - def resample(self, rule, **kwargs): + def resample(self, rule, how=None, fill_method=None, limit=None, **kwargs): """ Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended """ - from pandas.tseries.resample import TimeGrouper + from pandas.tseries.resample import (TimeGrouper, + _maybe_process_deprecations) gpr = TimeGrouper(axis=self.axis, freq=rule, **kwargs) # we by definition have at least 1 key as we are already a grouper groupings = list(self.grouper.groupings) groupings.append(gpr) - return self.__class__(self.obj, - keys=groupings, - axis=self.axis, - level=self.level, - as_index=self.as_index, - sort=self.sort, - group_keys=self.group_keys, - squeeze=self.squeeze, - selection=self._selection) + result = self.__class__(self.obj, + keys=groupings, + axis=self.axis, + level=self.level, + as_index=self.as_index, + sort=self.sort, + group_keys=self.group_keys, + squeeze=self.squeeze, + selection=self._selection) + + return _maybe_process_deprecations(result, + how=how, + fill_method=fill_method, + limit=limit) + + @Substitution(name='groupby') + @Appender(_doc_template) + def pad(self, limit=None): + """ + Forward fill the values + + Parameters + ---------- + limit : integer, optional + limit of how many values to fill + + See Also + -------- + Series.fillna + DataFrame.fillna + """ + return self.apply(lambda x: x.ffill(limit=limit)) + ffill = pad + + @Substitution(name='groupby') + @Appender(_doc_template) + def backfill(self, limit=None): + """ + Backward fill the values + + Parameters + ---------- + limit : integer, optional + limit of how many values to fill + + See Also + -------- + Series.fillna + DataFrame.fillna + """ + return self.apply(lambda x: x.bfill(limit=limit)) + bfill = backfill @Substitution(name='groupby') @Appender(_doc_template) @@ -3346,9 +3390,9 @@ def _transform_general(self, func, *args, **kwargs): path, res = self._choose_path(fast_path, slow_path, group) except TypeError: return self._transform_item_by_item(obj, fast_path) - except Exception: # pragma: no cover - res = fast_path(group) - path = fast_path + except ValueError: + msg = 'transform must return a scalar value for each group' + raise ValueError(msg) else: res = path(group) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index f0f5507bc3e85..b0dd2596fccd5 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -541,7 +541,7 @@ def can_do_equal_len(): if (len(indexer) > info_axis and is_integer(indexer[info_axis]) and all(is_null_slice(idx) for i, idx in enumerate(indexer) - if i != info_axis)): + if i != info_axis) and item_labels.is_unique): self.obj[item_labels[indexer[info_axis]]] = value return @@ -995,6 +995,10 @@ def _getitem_axis(self, key, axis=0): return self._getitem_iterable(key, axis=axis) else: + + # maybe coerce a float scalar to integer + key = labels._maybe_cast_indexer(key) + if is_integer(key): if axis == 0 and isinstance(labels, MultiIndex): try: diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 0d9eccb882d03..484cb6afa77b2 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -702,7 +702,10 @@ def _is_empty_indexer(indexer): values[indexer] = value # coerce and try to infer the dtypes of the result - if lib.isscalar(value): + if hasattr(value, 'dtype') and is_dtype_equal(values.dtype, + value.dtype): + dtype = value.dtype + elif lib.isscalar(value): dtype, _ = _infer_dtype_from_scalar(value) else: dtype = 'infer' @@ -714,8 +717,23 @@ def _is_empty_indexer(indexer): block = block.convert(numeric=False) return block - except (ValueError, TypeError): + except ValueError: raise + except TypeError: + + # cast to the passed dtype if possible + # otherwise raise the original error + try: + # e.g. we are uint32 and our value is uint64 + # this is for compat with older numpies + block = self.make_block(transf(values.astype(value.dtype))) + return block.setitem(indexer=indexer, value=value, mgr=mgr) + + except: + pass + + raise + except Exception: pass diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 0abc154f467ab..adfbd6646b048 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -398,7 +398,7 @@ def to_sparse(self, fill_value=None, kind='block'): y : SparseDataFrame """ from pandas.core.sparse import SparsePanel - frames = dict(compat.iteritems(self)) + frames = dict(self.iteritems()) return SparsePanel(frames, items=self.items, major_axis=self.major_axis, minor_axis=self.minor_axis, default_kind=kind, @@ -450,7 +450,7 @@ def to_excel(self, path, na_rep='', engine=None, **kwargs): writer = path kwargs['na_rep'] = na_rep - for item, df in compat.iteritems(self): + for item, df in self.iteritems(): name = str(item) df.to_excel(writer, name, **kwargs) writer.save() diff --git a/pandas/core/series.py b/pandas/core/series.py index 5eb1ab0f14ecf..d339a93a3ed9b 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1289,8 +1289,10 @@ def quantile(self, q=0.5, interpolation='linear'): 0 <= q <= 1, the quantile(s) to compute interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} .. versionadded:: 0.18.0 + This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: + * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. @@ -1306,15 +1308,15 @@ def quantile(self, q=0.5, interpolation='linear'): Examples -------- - >>> s = Series([1, 2, 3, 4]) >>> s.quantile(.5) - 2.5 + 2.5 >>> s.quantile([.25, .5, .75]) 0.25 1.75 0.50 2.50 0.75 3.25 dtype: float64 + """ self._check_percentile(q) diff --git a/pandas/core/strings.py b/pandas/core/strings.py index c1ab46956c25f..a7ed1ba0c0be0 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -604,7 +604,7 @@ def str_extract(arr, pat, flags=0, expand=None): return _str_extract_frame(arr._orig, pat, flags=flags) else: result, name = _str_extract_noexpand(arr._data, pat, flags=flags) - return arr._wrap_result(result, name=name) + return arr._wrap_result(result, name=name, expand=expand) def str_extractall(arr, pat, flags=0): @@ -1292,7 +1292,10 @@ def __iter__(self): i += 1 g = self.get(i) - def _wrap_result(self, result, use_codes=True, name=None): + def _wrap_result(self, result, use_codes=True, + name=None, expand=None): + + from pandas.core.index import Index, MultiIndex # for category, we do the stuff on the categories, so blow it up # to the full series again @@ -1302,48 +1305,42 @@ def _wrap_result(self, result, use_codes=True, name=None): if use_codes and self._is_categorical: result = take_1d(result, self._orig.cat.codes) - # leave as it is to keep extract and get_dummies results - # can be merged to _wrap_result_expand in v0.17 - from pandas.core.series import Series - from pandas.core.frame import DataFrame - from pandas.core.index import Index - - if not hasattr(result, 'ndim'): + if not hasattr(result, 'ndim') or not hasattr(result, 'dtype'): return result + assert result.ndim < 3 - if result.ndim == 1: - # Wait until we are sure result is a Series or Index before - # checking attributes (GH 12180) - name = name or getattr(result, 'name', None) or self._orig.name - if isinstance(self._orig, Index): - # if result is a boolean np.array, return the np.array - # instead of wrapping it into a boolean Index (GH 8875) - if is_bool_dtype(result): - return result - return Index(result, name=name) - return Series(result, index=self._orig.index, name=name) - else: - assert result.ndim < 3 - return DataFrame(result, index=self._orig.index) + if expand is None: + # infer from ndim if expand is not specified + expand = False if result.ndim == 1 else True + + elif expand is True and not isinstance(self._orig, Index): + # required when expand=True is explicitly specified + # not needed when infered + + def cons_row(x): + if is_list_like(x): + return x + else: + return [x] + + result = [cons_row(x) for x in result] - def _wrap_result_expand(self, result, expand=False): if not isinstance(expand, bool): raise ValueError("expand must be True or False") - # for category, we do the stuff on the categories, so blow it up - # to the full series again - if self._is_categorical: - result = take_1d(result, self._orig.cat.codes) - - from pandas.core.index import Index, MultiIndex - if not hasattr(result, 'ndim'): - return result + if name is None: + name = getattr(result, 'name', None) + if name is None: + # do not use logical or, _orig may be a DataFrame + # which has "name" column + name = self._orig.name + # Wait until we are sure result is a Series or Index before + # checking attributes (GH 12180) if isinstance(self._orig, Index): - name = getattr(result, 'name', None) # if result is a boolean np.array, return the np.array # instead of wrapping it into a boolean Index (GH 8875) - if hasattr(result, 'dtype') and is_bool_dtype(result): + if is_bool_dtype(result): return result if expand: @@ -1354,18 +1351,10 @@ def _wrap_result_expand(self, result, expand=False): else: index = self._orig.index if expand: - - def cons_row(x): - if is_list_like(x): - return x - else: - return [x] - cons = self._orig._constructor_expanddim - data = [cons_row(x) for x in result] - return cons(data, index=index) + return cons(result, index=index) else: - name = getattr(result, 'name', None) + # Must a Series cons = self._orig._constructor return cons(result, name=name, index=index) @@ -1380,12 +1369,12 @@ def cat(self, others=None, sep=None, na_rep=None): @copy(str_split) def split(self, pat=None, n=-1, expand=False): result = str_split(self._data, pat, n=n) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) @copy(str_rsplit) def rsplit(self, pat=None, n=-1, expand=False): result = str_rsplit(self._data, pat, n=n) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) _shared_docs['str_partition'] = (""" Split the string at the %(side)s occurrence of `sep`, and return 3 elements @@ -1440,7 +1429,7 @@ def rsplit(self, pat=None, n=-1, expand=False): def partition(self, pat=' ', expand=True): f = lambda x: x.partition(pat) result = _na_map(f, self._data) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) @Appender(_shared_docs['str_partition'] % { 'side': 'last', @@ -1451,7 +1440,7 @@ def partition(self, pat=' ', expand=True): def rpartition(self, pat=' ', expand=True): f = lambda x: x.rpartition(pat) result = _na_map(f, self._data) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) @copy(str_get) def get(self, i): @@ -1597,7 +1586,8 @@ def get_dummies(self, sep='|'): # methods available for making the dummies... data = self._orig.astype(str) if self._is_categorical else self._data result = str_get_dummies(data, sep) - return self._wrap_result(result, use_codes=(not self._is_categorical)) + return self._wrap_result(result, use_codes=(not self._is_categorical), + expand=True) @copy(str_translate) def translate(self, table, deletechars=None): diff --git a/pandas/core/style.py b/pandas/core/style.py index 15fcec118e7d4..f66ac7485c76e 100644 --- a/pandas/core/style.py +++ b/pandas/core/style.py @@ -215,7 +215,7 @@ def _translate(self): "class": " ".join(cs)}) head.append(row_es) - if self.data.index.names: + if self.data.index.names and self.data.index.names != [None]: index_header_row = [] for c, name in enumerate(self.data.index.names): @@ -281,7 +281,7 @@ def format(self, formatter, subset=None): ---------- formatter: str, callable, or dict subset: IndexSlice - A argument to DataFrame.loc that restricts which elements + An argument to ``DataFrame.loc`` that restricts which elements ``formatter`` is applied to. Returns @@ -352,7 +352,7 @@ def render(self): ``Styler`` objects have defined the ``_repr_html_`` method which automatically calls ``self.render()`` when it's the last item in a Notebook cell. When calling ``Styler.render()`` - directly, wrap the resul in ``IPython.display.HTML`` to view + directly, wrap the result in ``IPython.display.HTML`` to view the rendered HTML in the notebook. """ self._compute() diff --git a/pandas/core/window.py b/pandas/core/window.py index 9c8490f608996..9964580b5b09b 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -124,13 +124,17 @@ def _dir_additions(self): def _get_window(self, other=None): return self.window + @property + def _window_type(self): + return self.__class__.__name__ + def __unicode__(self): """ provide a nice str repr of our rolling object """ attrs = ["{k}={v}".format(k=k, v=getattr(self, k)) for k in self._attributes if getattr(self, k, None) is not None] - return "{klass} [{attrs}]".format(klass=self.__class__.__name__, + return "{klass} [{attrs}]".format(klass=self._window_type, attrs=','.join(attrs)) def _shallow_copy(self, obj=None, **kwargs): @@ -144,21 +148,27 @@ def _shallow_copy(self, obj=None, **kwargs): kwargs[attr] = getattr(self, attr) return self._constructor(obj, **kwargs) - def _prep_values(self, values=None, kill_inf=True, how=None): + def _prep_values(self, values=None, kill_inf=True, how=None, + as_float=True): if values is None: values = getattr(self._selected_obj, 'values', self._selected_obj) - # coerce dtypes as appropriate - if com.is_float_dtype(values.dtype): - pass - elif com.is_integer_dtype(values.dtype): - values = values.astype(float) - elif com.is_timedelta64_dtype(values.dtype): - values = values.view('i8').astype(float) - else: + # GH #12373 : rolling functions error on float32 data + # make sure the data is coerced to float64 + if com.is_float_dtype(values.dtype) and as_float: + values = com._ensure_float64(values) + elif com.is_integer_dtype(values.dtype) and as_float: + values = com._ensure_float64(values) + elif com.needs_i8_conversion(values.dtype): + raise NotImplementedError("ops for {action} for this " + "dtype {dtype} are not " + "implemented".format( + action=self._window_type, + dtype=values.dtype)) + elif as_float: try: - values = values.astype(float) + values = com._ensure_float64(values) except (ValueError, TypeError): raise TypeError("cannot handle this type -> {0}" "".format(values.dtype)) @@ -198,6 +208,7 @@ def _wrap_results(self, results, blocks, obj): results : list of ndarrays blocks : list of blocks obj : conformed data (may be resampled) + as_float: bool, cast results to float """ final = [] @@ -408,7 +419,7 @@ def _constructor(self): return Rolling def _apply(self, func, window=None, center=None, check_minp=None, how=None, - **kwargs): + as_float=True, **kwargs): """ Rolling statistical measure using supplied function. Designed to be used with passed-in Cython array-based functions. @@ -421,6 +432,8 @@ def _apply(self, func, window=None, center=None, check_minp=None, how=None, check_minp : function, default to _use_window how : string, default to None (DEPRECATED) how to resample + as_float: bool, default to True + Cast result to float, otherwise return as original type Returns ------- @@ -438,7 +451,7 @@ def _apply(self, func, window=None, center=None, check_minp=None, how=None, results = [] for b in blocks: try: - values = self._prep_values(b.values) + values = self._prep_values(b.values, as_float=as_float) except TypeError: results.append(b.values.copy()) continue @@ -457,7 +470,9 @@ def _apply(self, func, window=None, center=None, check_minp=None, how=None, def func(arg, window, min_periods=None): minp = check_minp(min_periods, window) - return cfunc(arg, window, minp, **kwargs) + # GH #12373: rolling functions error on float32 data + return cfunc(com._ensure_float64(arg), + window, minp, **kwargs) # calculation function if center: @@ -494,15 +509,26 @@ def count(self): obj = self._convert_freq() window = self._get_window() window = min(window, len(obj)) if not self.center else window - try: - converted = np.isfinite(obj).astype(float) - except TypeError: - converted = np.isfinite(obj.astype(float)).astype(float) - result = self._constructor(converted, window=window, min_periods=0, - center=self.center).sum() - - result[result.isnull()] = 0 - return result + + blocks, obj = self._create_blocks(how=None) + results = [] + for b in blocks: + + if com.needs_i8_conversion(b.values): + result = b.notnull().astype(int) + else: + try: + result = np.isfinite(b).astype(float) + except TypeError: + result = np.isfinite(b.astype(float)).astype(float) + + result[pd.isnull(result)] = 0 + + result = self._constructor(result, window=window, min_periods=0, + center=self.center).sum() + results.append(result) + + return self._wrap_results(results, blocks, obj) _shared_docs['apply'] = dedent(""" %(name)s function apply @@ -535,12 +561,14 @@ def sum(self, **kwargs): Parameters ---------- how : string, default 'max' (DEPRECATED) - Method for down- or re-sampling""") + Method for down- or re-sampling + as_float : bool, default True + Cast to float, otherwise return as original type""") - def max(self, how=None, **kwargs): + def max(self, how=None, as_float=True, **kwargs): if self.freq is not None and how is None: how = 'max' - return self._apply('roll_max', how=how, **kwargs) + return self._apply('roll_max', how=how, as_float=as_float, **kwargs) _shared_docs['min'] = dedent(""" %(name)s minimum @@ -548,12 +576,14 @@ def max(self, how=None, **kwargs): Parameters ---------- how : string, default 'min' (DEPRECATED) - Method for down- or re-sampling""") + Method for down- or re-sampling + as_float : bool, default True + Cast to float, otherwise return as original type""") - def min(self, how=None, **kwargs): + def min(self, how=None, as_float=True, **kwargs): if self.freq is not None and how is None: how = 'min' - return self._apply('roll_min', how=how, **kwargs) + return self._apply('roll_min', how=how, as_float=as_float, **kwargs) def mean(self, **kwargs): return self._apply('roll_mean', **kwargs) @@ -657,6 +687,10 @@ def cov(self, other=None, pairwise=None, ddof=1, **kwargs): window = self._get_window(other) def _get_cov(X, Y): + # GH #12373 : rolling functions error on float32 data + # to avoid potential overflow, cast the data to float64 + X = X.astype('float64') + Y = Y.astype('float64') mean = lambda x: x.rolling(window, self.min_periods, center=self.center).mean(**kwargs) count = (X + Y).rolling(window=window, @@ -1012,13 +1046,21 @@ class EWM(_Rolling): Parameters ---------- - com : float. optional - Center of mass: :math:`\alpha = 1 / (1 + com)`, + com : float, optional + Specify decay in terms of center of mass, + :math:`\alpha = 1 / (1 + com),\text{ for } com \geq 0` span : float, optional - Specify decay in terms of span, :math:`\alpha = 2 / (span + 1)` + Specify decay in terms of span, + :math:`\alpha = 2 / (span + 1),\text{ for } span \geq 1` halflife : float, optional - Specify decay in terms of halflife, - :math:`\alpha = 1 - exp(log(0.5) / halflife)` + Specify decay in terms of half-life, + :math:`\alpha = 1 - exp(log(0.5) / halflife),\text{ for } halflife > 0` + alpha : float, optional + Specify smoothing factor :math:`\alpha` directly, + :math:`0 < \alpha \leq 1` + + .. versionadded:: 0.18.0 + min_periods : int, default 0 Minimum number of observations in window required to have a value (otherwise result is NA). @@ -1037,16 +1079,10 @@ class EWM(_Rolling): Notes ----- - Either center of mass, span or halflife must be specified - - EWMA is sometimes specified using a "span" parameter `s`, we have that the - decay parameter :math:`\alpha` is related to the span as - :math:`\alpha = 2 / (s + 1) = 1 / (1 + c)` - - where `c` is the center of mass. Given a span, the associated center of - mass is :math:`c = (s - 1) / 2` - - So a "20-day EWMA" would have center 9.5. + Exactly one of center of mass, span, half-life, and alpha must be provided. + Allowed values and relationship between the parameters are specified in the + parameter descriptions above; see the link at the end of this section for + a detailed explanation. The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters @@ -1070,14 +1106,15 @@ class EWM(_Rolling): (if adjust is True), and 1-alpha and alpha (if adjust is False). More details can be found at - http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions + http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows """ _attributes = ['com', 'min_periods', 'freq', 'adjust', 'ignore_na', 'axis'] - def __init__(self, obj, com=None, span=None, halflife=None, min_periods=0, - freq=None, adjust=True, ignore_na=False, axis=0): + def __init__(self, obj, com=None, span=None, halflife=None, alpha=None, + min_periods=0, freq=None, adjust=True, ignore_na=False, + axis=0): self.obj = obj - self.com = _get_center_of_mass(com, span, halflife) + self.com = _get_center_of_mass(com, span, halflife, alpha) self.min_periods = min_periods self.freq = freq self.adjust = adjust @@ -1294,20 +1331,32 @@ def dataframe_from_int_dict(data, frame_template): return _flex_binary_moment(arg2, arg1, f) -def _get_center_of_mass(com, span, halflife): - valid_count = len([x for x in [com, span, halflife] if x is not None]) +def _get_center_of_mass(com, span, halflife, alpha): + valid_count = len([x for x in [com, span, halflife, alpha] + if x is not None]) if valid_count > 1: - raise Exception("com, span, and halflife are mutually exclusive") - - if span is not None: - # convert span to center of mass + raise ValueError("com, span, halflife, and alpha " + "are mutually exclusive") + + # Convert to center of mass; domain checks ensure 0 < alpha <= 1 + if com is not None: + if com < 0: + raise ValueError("com must satisfy: com >= 0") + elif span is not None: + if span < 1: + raise ValueError("span must satisfy: span >= 1") com = (span - 1) / 2. elif halflife is not None: - # convert halflife to center of mass + if halflife <= 0: + raise ValueError("halflife must satisfy: halflife > 0") decay = 1 - np.exp(np.log(0.5) / halflife) com = 1 / decay - 1 - elif com is None: - raise Exception("Must pass one of com, span, or halflife") + elif alpha is not None: + if alpha <= 0 or alpha > 1: + raise ValueError("alpha must satisfy: 0 < alpha <= 1") + com = (1.0 - alpha) / alpha + else: + raise ValueError("Must pass one of com, span, halflife, or alpha") return float(com) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 8a679b1575e26..852cddc456213 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -902,6 +902,7 @@ def _mpl_repr(self): _na_value = np.nan """The expected NA value to use with this index.""" + # introspection @property def is_monotonic(self): """ alias for is_monotonic_increasing (deprecated) """ @@ -954,11 +955,12 @@ def is_categorical(self): return self.inferred_type in ['categorical'] def is_mixed(self): - return 'mixed' in self.inferred_type + return self.inferred_type in ['mixed'] def holds_integer(self): return self.inferred_type in ['integer', 'mixed-integer'] + # validate / convert indexers def _convert_scalar_indexer(self, key, kind=None): """ convert a scalar indexer @@ -966,44 +968,42 @@ def _convert_scalar_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) - - right now we are converting + kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] + if kind == 'iloc': - if is_integer(key): - return key - return self._invalid_indexer('positional', key) - else: + return self._validate_indexer('positional', key, kind) - if len(self): - - # we can safely disallow - # if we are not a MultiIndex - # or a Float64Index - # or have mixed inferred type (IOW we have the possiblity - # of a float in with say strings) - if is_float(key): - if not (isinstance(self, ABCMultiIndex,) or - self.is_floating() or self.is_mixed()): - return self._invalid_indexer('label', key) - - # we can disallow integers with loc - # if could not contain and integer - elif is_integer(key) and kind == 'loc': - if not (isinstance(self, ABCMultiIndex,) or - self.holds_integer() or self.is_mixed()): - return self._invalid_indexer('label', key) + if len(self) and not isinstance(self, ABCMultiIndex,): - return key + # we can raise here if we are definitive that this + # is positional indexing (eg. .ix on with a float) + # or label indexing if we are using a type able + # to be represented in the index - def _convert_slice_indexer_getitem(self, key, is_index_slice=False): - """ called from the getitem slicers, determine how to treat the key - whether positional or not """ - if self.is_integer() or is_index_slice: - return key - return self._convert_slice_indexer(key) + if kind in ['getitem', 'ix'] and is_float(key): + if not self.is_floating(): + return self._invalid_indexer('label', key) + + elif kind in ['loc'] and is_float(key): + + # we want to raise KeyError on string/mixed here + # technically we *could* raise a TypeError + # on anything but mixed though + if self.inferred_type not in ['floating', + 'mixed-integer-float', + 'string', + 'unicode', + 'mixed']: + return self._invalid_indexer('label', key) + + elif kind in ['loc'] and is_integer(key): + if not self.holds_integer(): + return self._invalid_indexer('label', key) + + return key def _convert_slice_indexer(self, key, kind=None): """ @@ -1012,8 +1012,9 @@ def _convert_slice_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) + kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] # if we are not a slice, then we are done if not isinstance(key, slice): @@ -1021,38 +1022,14 @@ def _convert_slice_indexer(self, key, kind=None): # validate iloc if kind == 'iloc': + return slice(self._validate_indexer('slice', key.start, kind), + self._validate_indexer('slice', key.stop, kind), + self._validate_indexer('slice', key.step, kind)) - # need to coerce to_int if needed - def f(c): - v = getattr(key, c) - if v is None or is_integer(v): - return v - self._invalid_indexer('slice {0} value'.format(c), v) - - return slice(*[f(c) for c in ['start', 'stop', 'step']]) - - # validate slicers - def validate(v): - if v is None or is_integer(v): - return True - - # dissallow floats (except for .ix) - elif is_float(v): - if kind == 'ix': - return True - - return False - - return True - - for c in ['start', 'stop', 'step']: - v = getattr(key, c) - if not validate(v): - self._invalid_indexer('slice {0} value'.format(c), v) - - # figure out if this is a positional indexer + # potentially cast the bounds to integers start, stop, step = key.start, key.stop, key.step + # figure out if this is a positional indexer def is_int(v): return v is None or is_integer(v) @@ -1061,8 +1038,14 @@ def is_int(v): is_positional = is_index_slice and not self.is_integer() if kind == 'getitem': - return self._convert_slice_indexer_getitem( - key, is_index_slice=is_index_slice) + """ + called from the getitem slicers, validate that we are in fact + integers + """ + if self.is_integer() or is_index_slice: + return slice(self._validate_indexer('slice', key.start, kind), + self._validate_indexer('slice', key.stop, kind), + self._validate_indexer('slice', key.step, kind)) # convert the slice to an indexer here @@ -1889,7 +1872,10 @@ def get_loc(self, key, method=None, tolerance=None): raise ValueError('tolerance argument only valid if using pad, ' 'backfill or nearest lookups') key = _values_from_object(key) - return self._engine.get_loc(key) + try: + return self._engine.get_loc(key) + except KeyError: + return self._engine.get_loc(self._maybe_cast_indexer(key)) indexer = self.get_indexer([key], method=method, tolerance=tolerance) if indexer.ndim > 1 or indexer.size > 1: @@ -2721,6 +2707,37 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): return slice(start_slice, end_slice, step) + def _maybe_cast_indexer(self, key): + """ + If we have a float key and are not a floating index + then try to cast to an int if equivalent + """ + + if is_float(key) and not self.is_floating(): + try: + ckey = int(key) + if ckey == key: + key = ckey + except (ValueError, TypeError): + pass + return key + + def _validate_indexer(self, form, key, kind): + """ + if we are positional indexer + validate that we have appropriate typed bounds + must be an integer + """ + assert kind in ['ix', 'loc', 'getitem', 'iloc'] + + if key is None: + pass + elif is_integer(key): + pass + elif kind in ['iloc', 'getitem']: + self._invalid_indexer(form, key) + return key + def _maybe_cast_slice_bound(self, label, side, kind): """ This function should be overloaded in subclasses that allow non-trivial @@ -2731,7 +2748,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -2742,6 +2759,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem', None] # We are a plain index here (sub-class override this method if they # wish to have special treatment for floats/ints, e.g. Float64Index and @@ -2783,9 +2801,11 @@ def get_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None, the type of indexer + kind : {'ix', 'loc', 'getitem'} """ + assert kind in ['ix', 'loc', 'getitem', None] + if side not in ('left', 'right'): raise ValueError("Invalid value for side kwarg," " must be either 'left' or 'right': %s" % @@ -2841,7 +2861,7 @@ def slice_locs(self, start=None, end=None, step=None, kind=None): If None, defaults to the end step : int, defaults None If None, defaults to 1 - kind : string, defaults None + kind : {'ix', 'loc', 'getitem'} or None Returns ------- diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index fea153b2de391..d14568ceca258 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1409,6 +1409,7 @@ def _tuple_index(self): return Index(self._values) def get_slice_bound(self, label, side, kind): + if not isinstance(label, tuple): label = label, return self._partial_tup_index(label, side=side) @@ -1743,7 +1744,7 @@ def convert_indexer(start, stop, step, indexer=indexer, labels=labels): # we have a partial slice (like looking up a partial date # string) start = stop = level_index.slice_indexer(key.start, key.stop, - key.step) + key.step, kind='loc') step = start.step if isinstance(start, slice) or isinstance(stop, slice): diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 0c102637ab70d..4b021c51456b9 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -7,6 +7,7 @@ from pandas.indexes.base import Index, InvalidIndexError from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com +from pandas.core.common import is_dtype_equal, isnull import pandas.indexes.base as ibase @@ -29,7 +30,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -40,18 +41,10 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem', None] - # we are a numeric index, so we accept - # integer directly - if com.is_integer(label): - pass - - # disallow floats only if we not-strict - elif com.is_float(label): - if not (self.is_floating() or kind in ['ix']): - self._invalid_indexer('slice', label) - - return label + # we will try to coerce to integers + return self._maybe_cast_indexer(label) def _convert_tolerance(self, tolerance): try: @@ -140,6 +133,24 @@ def is_all_dates(self): """ return False + def _convert_scalar_indexer(self, key, kind=None): + """ + convert a scalar indexer + + Parameters + ---------- + key : label of the slice bound + kind : {'ix', 'loc', 'getitem'} or None + """ + + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] + + # don't coerce ilocs to integers + if kind != 'iloc': + key = self._maybe_cast_indexer(key) + return (super(Int64Index, self) + ._convert_scalar_indexer(key, kind=kind)) + def equals(self, other): """ Determines if two Index objects contain the same elements. @@ -247,18 +258,13 @@ def _convert_scalar_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) - - right now we are converting - floats -> ints if the index supports it + kind : {'ix', 'loc', 'getitem'} or None """ - if kind == 'iloc': - if com.is_integer(key): - return key + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] - return (super(Float64Index, self) - ._convert_scalar_indexer(key, kind=kind)) + if kind == 'iloc': + return self._validate_indexer('positional', key, kind) return key @@ -282,7 +288,7 @@ def _convert_slice_indexer(self, key, kind=None): kind=kind) # translate to locations - return self.slice_indexer(key.start, key.stop, key.step) + return self.slice_indexer(key.start, key.stop, key.step, kind=kind) def _format_native_types(self, na_rep='', float_format=None, decimal='.', quoting=None, **kwargs): @@ -324,7 +330,7 @@ def equals(self, other): try: if not isinstance(other, Float64Index): other = self._constructor(other) - if (not com.is_dtype_equal(self.dtype, other.dtype) or + if (not is_dtype_equal(self.dtype, other.dtype) or self.shape != other.shape): return False left, right = self._values, other._values @@ -380,7 +386,7 @@ def isin(self, values, level=None): if level is not None: self._validate_index_level(level) return lib.ismember_nans(np.array(self), value_set, - com.isnull(list(value_set)).any()) + isnull(list(value_set)).any()) Float64Index._add_numeric_methods() diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 0bed2ec231dbe..4b06af9240436 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -487,8 +487,8 @@ def __getitem__(self, key): stop = l # delegate non-integer slices - if (start != int(start) and - stop != int(stop) and + if (start != int(start) or + stop != int(stop) or step != int(step)): return super_getitem(key) diff --git a/pandas/io/common.py b/pandas/io/common.py index 8c9c348b9a11c..be8c3ccfe08e6 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -146,6 +146,10 @@ def readline(self): except ImportError: # boto is only needed for reading from S3. pass +except TypeError: + # boto/boto3 issues + # GH11915 + pass def _is_url(url): diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index c7481a953e47b..e706434f29dc5 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -50,7 +50,6 @@ def _test_google_api_imports(): from apiclient.errors import HttpError # noqa from oauth2client.client import AccessTokenRefreshError # noqa from oauth2client.client import OAuth2WebServerFlow # noqa - from oauth2client.client import SignedJwtAssertionCredentials # noqa from oauth2client.file import Storage # noqa from oauth2client.tools import run_flow, argparser # noqa except ImportError as e: @@ -179,7 +178,30 @@ def get_user_account_credentials(self): return credentials def get_service_account_credentials(self): - from oauth2client.client import SignedJwtAssertionCredentials + # Bug fix for https://github.com/pydata/pandas/issues/12572 + # We need to know that a supported version of oauth2client is installed + # Test that either of the following is installed: + # - SignedJwtAssertionCredentials from oauth2client.client + # - ServiceAccountCredentials from oauth2client.service_account + # SignedJwtAssertionCredentials is available in oauthclient < 2.0.0 + # ServiceAccountCredentials is available in oauthclient >= 2.0.0 + oauth2client_v1 = True + oauth2client_v2 = True + + try: + from oauth2client.client import SignedJwtAssertionCredentials + except ImportError: + oauth2client_v1 = False + + try: + from oauth2client.service_account import ServiceAccountCredentials + except ImportError: + oauth2client_v2 = False + + if not oauth2client_v1 and not oauth2client_v2: + raise ImportError("Missing oauth2client required for BigQuery " + "service account support") + from os.path import isfile try: @@ -197,11 +219,16 @@ def get_service_account_credentials(self): json_key['private_key'] = bytes( json_key['private_key'], 'UTF-8') - return SignedJwtAssertionCredentials( - json_key['client_email'], - json_key['private_key'], - self.scope, - ) + if oauth2client_v1: + return SignedJwtAssertionCredentials( + json_key['client_email'], + json_key['private_key'], + self.scope, + ) + else: + return ServiceAccountCredentials.from_json_keyfile_dict( + json_key, + self.scope) except (KeyError, ValueError, TypeError, AttributeError): raise InvalidPrivateKeyFormat( "Private key is missing or invalid. It should be service " diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index f7b38c75a24b9..2604b6e0784cf 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -121,6 +121,7 @@ class ParserWarning(Warning): If True, skip over blank lines rather than interpreting as NaN values parse_dates : boolean or list of ints or names or list of lists or dict, \ default False + * boolean. If True -> try parsing the index. * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. @@ -128,6 +129,7 @@ class ParserWarning(Warning): a single date column. * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo' + Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : boolean, default False If True and parse_dates is enabled for a column, attempt to infer diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index c94b387f7554a..14881e0fb5a54 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -2726,7 +2726,7 @@ def write(self, obj, **kwargs): self.attrs.default_kind = obj.default_kind self.write_index('items', obj.items) - for name, sdf in compat.iteritems(obj): + for name, sdf in obj.iteritems(): key = 'sparse_frame_%s' % name if key not in self.group._v_children: node = self._handle.create_group(self.group, key) diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index c293ef5c9c2f6..d9c09fa788332 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -304,6 +304,7 @@ def setUpClass(cls): super(TestYahooOptions, cls).setUpClass() _skip_if_no_lxml() _skip_if_no_bs() + raise nose.SkipTest('unreliable test') # aapl has monthlies cls.aapl = web.Options('aapl', 'yahoo') @@ -370,6 +371,7 @@ def test_get_expiry_dates(self): @network def test_get_all_data(self): + try: data = self.aapl.get_all_data(put=True) except RemoteDataError as e: diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 5a1c2d63af365..865b7e8d689c0 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -77,7 +77,6 @@ def _test_imports(): from oauth2client.client import OAuth2WebServerFlow # noqa from oauth2client.client import AccessTokenRefreshError # noqa - from oauth2client.client import SignedJwtAssertionCredentials # noqa from oauth2client.file import Storage # noqa from oauth2client.tools import run_flow # noqa @@ -115,6 +114,30 @@ def _test_imports(): raise ImportError( "pandas requires httplib2 for Google BigQuery support") + # Bug fix for https://github.com/pydata/pandas/issues/12572 + # We need to know that a supported version of oauth2client is installed + # Test that either of the following is installed: + # - SignedJwtAssertionCredentials from oauth2client.client + # - ServiceAccountCredentials from oauth2client.service_account + # SignedJwtAssertionCredentials is available in oauthclient < 2.0.0 + # ServiceAccountCredentials is available in oauthclient >= 2.0.0 + oauth2client_v1 = True + oauth2client_v2 = True + + try: + from oauth2client.client import SignedJwtAssertionCredentials # noqa + except ImportError: + oauth2client_v1 = False + + try: + from oauth2client.service_account import ServiceAccountCredentials # noqa + except ImportError: + oauth2client_v2 = False + + if not oauth2client_v1 and not oauth2client_v2: + raise ImportError("Missing oauth2client required for BigQuery " + "service account support") + def test_requirements(): try: diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index d3020e337322b..f32dfd37e837c 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2635,6 +2635,26 @@ def test_eof_states(self): self.assertRaises(Exception, self.read_csv, StringIO(data), escapechar='\\') + def test_grow_boundary_at_cap(self): + # See gh-12494 + # + # Cause of error was the fact that pandas + # was not increasing the buffer size when + # the desired space would fill the buffer + # to capacity, which later would cause a + # buffer overflow error when checking the + # EOF terminator of the CSV stream + def test_empty_header_read(count): + s = StringIO(',' * count) + expected = DataFrame(columns=[ + 'Unnamed: {i}'.format(i=i) + for i in range(count + 1)]) + df = read_csv(s) + tm.assert_frame_equal(df, expected) + + for count in range(1, 101): + test_empty_header_read(count) + class TestPythonParser(ParserTests, tm.TestCase): diff --git a/pandas/rpy/__init__.py b/pandas/rpy/__init__.py index 8c92ce5842e15..b771a3d8374a3 100644 --- a/pandas/rpy/__init__.py +++ b/pandas/rpy/__init__.py @@ -2,6 +2,8 @@ # GH9602 # deprecate rpy to instead directly use rpy2 +# flake8: noqa + import warnings warnings.warn("The pandas.rpy module is deprecated and will be " "removed in a future version. We refer to external packages " diff --git a/pandas/rpy/base.py b/pandas/rpy/base.py index 4cd86d3c3f4e3..ac339dd366b0b 100644 --- a/pandas/rpy/base.py +++ b/pandas/rpy/base.py @@ -1,3 +1,5 @@ +# flake8: noqa + import pandas.rpy.util as util diff --git a/pandas/rpy/common.py b/pandas/rpy/common.py index 55adad3610816..95a072154dc68 100644 --- a/pandas/rpy/common.py +++ b/pandas/rpy/common.py @@ -2,6 +2,9 @@ Utilities for making working with rpy2 more user- and developer-friendly. """ + +# flake8: noqa + from __future__ import print_function from distutils.version import LooseVersion diff --git a/pandas/rpy/tests/test_common.py b/pandas/rpy/tests/test_common.py index 4b579e9263742..c3f09e21b1545 100644 --- a/pandas/rpy/tests/test_common.py +++ b/pandas/rpy/tests/test_common.py @@ -2,6 +2,8 @@ Testing that functions from rpy work as expected """ +# flake8: noqa + import pandas as pd import numpy as np import unittest diff --git a/pandas/rpy/vars.py b/pandas/rpy/vars.py index 4756b2779224c..2073b47483141 100644 --- a/pandas/rpy/vars.py +++ b/pandas/rpy/vars.py @@ -1,3 +1,5 @@ +# flake8: noqa + import pandas.rpy.util as util diff --git a/pandas/sandbox/qtpandas.py b/pandas/sandbox/qtpandas.py index 2655aa5a452c8..4f4d77bcdf268 100644 --- a/pandas/sandbox/qtpandas.py +++ b/pandas/sandbox/qtpandas.py @@ -4,6 +4,8 @@ @author: Jev Kuznetsov ''' +# flake8: noqa + # GH9615 import warnings diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index be4ce716a5a37..25b0e11448e97 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -393,7 +393,7 @@ def _combine(self, other, func, axis=0): return self._combinePanel(other, func) elif lib.isscalar(other): new_frames = dict((k, func(v, other)) - for k, v in compat.iteritems(self)) + for k, v in self.iteritems()) return self._new_like(new_frames) def _combineFrame(self, other, func, axis=0): @@ -470,7 +470,7 @@ def major_xs(self, key): y : DataFrame index -> minor axis, columns -> items """ - slices = dict((k, v.xs(key)) for k, v in compat.iteritems(self)) + slices = dict((k, v.xs(key)) for k, v in self.iteritems()) return DataFrame(slices, index=self.minor_axis, columns=self.items) def minor_xs(self, key): @@ -487,7 +487,7 @@ def minor_xs(self, key): y : SparseDataFrame index -> major axis, columns -> items """ - slices = dict((k, v[key]) for k, v in compat.iteritems(self)) + slices = dict((k, v[key]) for k, v in self.iteritems()) return SparseDataFrame(slices, index=self.major_axis, columns=self.items, default_fill_value=self.default_fill_value, diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index 24a73b3825a70..dc66e01ac3f78 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -104,7 +104,7 @@ def assert_sp_frame_equal(left, right, exact_indices=True): def assert_sp_panel_equal(left, right, exact_indices=True): - for item, frame in compat.iteritems(left): + for item, frame in left.iteritems(): assert (item in right) # trade-off? assert_sp_frame_equal(frame, right[item], exact_indices=exact_indices) diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c index 33ddc6c6e1f27..3a1d37f86cc28 100644 --- a/pandas/src/datetime/np_datetime_strings.c +++ b/pandas/src/datetime/np_datetime_strings.c @@ -355,6 +355,8 @@ convert_datetimestruct_local_to_utc(pandas_datetimestruct *out_dts_utc, * + Doesn't handle 24:00:00 as synonym for midnight (00:00:00) tomorrow * + Accepts special values "NaT" (not a time), "Today", (current * day according to local time) and "Now" (current time in UTC). + * + ':' separator between hours, minutes, and seconds is optional. When + * omitted, each component must be 2 digits if it appears. (GH-10041) * * 'str' must be a NULL-terminated string, and 'len' must be its length. * 'unit' should contain -1 if the unit is unknown, or the unit @@ -394,15 +396,21 @@ parse_iso_8601_datetime(char *str, int len, char *substr, sublen; PANDAS_DATETIMEUNIT bestunit; - /* if date components in are separated by one of valid separators - * months/days without leadings 0s will be parsed + /* If year-month-day are separated by a valid separator, + * months/days without leading zeroes will be parsed * (though not iso8601). If the components aren't separated, - * an error code will be retuned because the date is ambigous + * 4 (YYYY) or 8 (YYYYMMDD) digits are expected. 6 digits are + * forbidden here (but parsed as YYMMDD elsewhere). */ - int has_sep = 0; - char sep = '\0'; - char valid_sep[] = {'-', '.', '/', '\\', ' '}; - int valid_sep_len = 5; + int has_ymd_sep = 0; + char ymd_sep = '\0'; + char valid_ymd_sep[] = {'-', '.', '/', '\\', ' '}; + int valid_ymd_sep_len = sizeof(valid_ymd_sep); + + /* hour-minute-second may or may not separated by ':'. If not, then + * each component must be 2 digits. */ + int has_hms_sep = 0; + int hour_was_2_digits = 0; /* Initialize the output to all zeros */ memset(out, 0, sizeof(pandas_datetimestruct)); @@ -550,7 +558,7 @@ parse_iso_8601_datetime(char *str, int len, /* Check whether it's a leap-year */ year_leap = is_leapyear(out->year); - /* Next character must be a separator, start of month or end */ + /* Next character must be a separator, start of month, or end of string */ if (sublen == 0) { if (out_local != NULL) { *out_local = 0; @@ -558,59 +566,50 @@ parse_iso_8601_datetime(char *str, int len, bestunit = PANDAS_FR_Y; goto finish; } - else if (!isdigit(*substr)) { - for (i = 0; i < valid_sep_len; ++i) { - if (*substr == valid_sep[i]) { - has_sep = 1; - sep = valid_sep[i]; - ++substr; - --sublen; + + if (!isdigit(*substr)) { + for (i = 0; i < valid_ymd_sep_len; ++i) { + if (*substr == valid_ymd_sep[i]) { break; } } - if (i == valid_sep_len) { + if (i == valid_ymd_sep_len) { goto parse_error; } - } - - /* Can't have a trailing sep */ - if (sublen == 0) { - goto parse_error; - } - - - /* PARSE THE MONTH (2 digits) */ - if (has_sep && ((sublen >= 2 && isdigit(substr[0]) && !isdigit(substr[1])) - || (sublen == 1 && isdigit(substr[0])))) { - out->month = (substr[0] - '0'); - - if (out->month < 1) { - PyErr_Format(PyExc_ValueError, - "Month out of range in datetime string \"%s\"", str); - goto error; - } + has_ymd_sep = 1; + ymd_sep = valid_ymd_sep[i]; ++substr; --sublen; + /* Cannot have trailing separator */ + if (sublen == 0 || !isdigit(*substr)) { + goto parse_error; + } } - else if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->month = 10 * (substr[0] - '0') + (substr[1] - '0'); - if (out->month < 1 || out->month > 12) { - PyErr_Format(PyExc_ValueError, - "Month out of range in datetime string \"%s\"", str); - goto error; - } - substr += 2; - sublen -= 2; + /* PARSE THE MONTH */ + /* First digit required */ + out->month = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->month = 10 * out->month + (*substr - '0'); + ++substr; + --sublen; } - else { + else if (!has_ymd_sep) { goto parse_error; } + if (out->month < 1 || out->month > 12) { + PyErr_Format(PyExc_ValueError, + "Month out of range in datetime string \"%s\"", str); + goto error; + } - /* Next character must be a '-' or the end of the string */ + /* Next character must be the separator, start of day, or end of string */ if (sublen == 0) { - /* dates of form YYYYMM are not valid */ - if (!has_sep) { + /* Forbid YYYYMM. Parsed instead as YYMMDD by someone else. */ + if (!has_ymd_sep) { goto parse_error; } if (out_local != NULL) { @@ -619,47 +618,40 @@ parse_iso_8601_datetime(char *str, int len, bestunit = PANDAS_FR_M; goto finish; } - else if (has_sep && *substr == sep) { + + if (has_ymd_sep) { + /* Must have separator, but cannot be trailing */ + if (*substr != ymd_sep || sublen == 1) { + goto parse_error; + } ++substr; --sublen; } - else if (!isdigit(*substr)) { - goto parse_error; - } - /* Can't have a trailing '-' */ - if (sublen == 0) { - goto parse_error; + /* PARSE THE DAY */ + /* First digit required */ + if (!isdigit(*substr)) { + goto parse_error; } - - /* PARSE THE DAY (2 digits) */ - if (has_sep && ((sublen >= 2 && isdigit(substr[0]) && !isdigit(substr[1])) - || (sublen == 1 && isdigit(substr[0])))) { - out->day = (substr[0] - '0'); - - if (out->day < 1) { - PyErr_Format(PyExc_ValueError, - "Day out of range in datetime string \"%s\"", str); - goto error; - } + out->day = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->day = 10 * out->day + (*substr - '0'); ++substr; --sublen; } - else if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->day = 10 * (substr[0] - '0') + (substr[1] - '0'); - - if (out->day < 1 || - out->day > days_per_month_table[year_leap][out->month-1]) { - PyErr_Format(PyExc_ValueError, - "Day out of range in datetime string \"%s\"", str); - goto error; - } - substr += 2; - sublen -= 2; - } - else { + else if (!has_ymd_sep) { goto parse_error; } + if (out->day < 1 || + out->day > days_per_month_table[year_leap][out->month-1]) + { + PyErr_Format(PyExc_ValueError, + "Day out of range in datetime string \"%s\"", str); + goto error; + } /* Next character must be a 'T', ' ', or end of string */ if (sublen == 0) { @@ -669,104 +661,119 @@ parse_iso_8601_datetime(char *str, int len, bestunit = PANDAS_FR_D; goto finish; } - else if (*substr != 'T' && *substr != ' ') { + + if ((*substr != 'T' && *substr != ' ') || sublen == 1) { goto parse_error; } - else { + ++substr; + --sublen; + + /* PARSE THE HOURS */ + /* First digit required */ + if (!isdigit(*substr)) { + goto parse_error; + } + out->hour = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional */ + if (isdigit(*substr)) { + hour_was_2_digits = 1; + out->hour = 10 * out->hour + (*substr - '0'); ++substr; --sublen; - } - - /* PARSE THE HOURS (2 digits) */ - if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->hour = 10 * (substr[0] - '0') + (substr[1] - '0'); - if (out->hour >= 24) { PyErr_Format(PyExc_ValueError, "Hours out of range in datetime string \"%s\"", str); goto error; } - substr += 2; - sublen -= 2; - } - else if (sublen >= 1 && isdigit(substr[0])) { - out->hour = substr[0] - '0'; - ++substr; - --sublen; - } - else { - goto parse_error; } /* Next character must be a ':' or the end of the string */ - if (sublen > 0 && *substr == ':') { + if (sublen == 0) { + if (!hour_was_2_digits) { + goto parse_error; + } + bestunit = PANDAS_FR_h; + goto finish; + } + + if (*substr == ':') { + has_hms_sep = 1; ++substr; --sublen; + /* Cannot have a trailing separator */ + if (sublen == 0 || !isdigit(*substr)) { + goto parse_error; + } } - else { + else if (!isdigit(*substr)) { + if (!hour_was_2_digits) { + goto parse_error; + } bestunit = PANDAS_FR_h; goto parse_timezone; } - /* Can't have a trailing ':' */ - if (sublen == 0) { - goto parse_error; - } - - /* PARSE THE MINUTES (2 digits) */ - if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->min = 10 * (substr[0] - '0') + (substr[1] - '0'); - + /* PARSE THE MINUTES */ + /* First digit required */ + out->min = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->min = 10 * out->min + (*substr - '0'); + ++substr; + --sublen; if (out->min >= 60) { PyErr_Format(PyExc_ValueError, - "Minutes out of range in datetime string \"%s\"", str); + "Minutes out of range in datetime string \"%s\"", str); goto error; } - substr += 2; - sublen -= 2; - } - else if (sublen >= 1 && isdigit(substr[0])) { - out->min = substr[0] - '0'; - ++substr; - --sublen; } - else { + else if (!has_hms_sep) { goto parse_error; } - /* Next character must be a ':' or the end of the string */ - if (sublen > 0 && *substr == ':') { + if (sublen == 0) { + bestunit = PANDAS_FR_m; + goto finish; + } + + /* If we make it through this condition block, then the next + * character is a digit. */ + if (has_hms_sep && *substr == ':') { ++substr; --sublen; + /* Cannot have a trailing ':' */ + if (sublen == 0 || !isdigit(*substr)) { + goto parse_error; + } + } + else if (!has_hms_sep && isdigit(*substr)) { } else { bestunit = PANDAS_FR_m; goto parse_timezone; } - /* Can't have a trailing ':' */ - if (sublen == 0) { - goto parse_error; - } - - /* PARSE THE SECONDS (2 digits) */ - if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->sec = 10 * (substr[0] - '0') + (substr[1] - '0'); - + /* PARSE THE SECONDS */ + /* First digit required */ + out->sec = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->sec = 10 * out->sec + (*substr - '0'); + ++substr; + --sublen; if (out->sec >= 60) { PyErr_Format(PyExc_ValueError, - "Seconds out of range in datetime string \"%s\"", str); + "Seconds out of range in datetime string \"%s\"", str); goto error; } - substr += 2; - sublen -= 2; - } - else if (sublen >= 1 && isdigit(substr[0])) { - out->sec = substr[0] - '0'; - ++substr; - --sublen; } - else { + else if (!has_hms_sep) { goto parse_error; } diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index a19930a5cef30..dae15215929b7 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -111,7 +111,7 @@ static void *grow_buffer(void *buffer, int length, int *capacity, void *newbuffer = buffer; // Can we fit potentially nbytes tokens (+ null terminators) in the stream? - while ( (length + space > cap) && (newbuffer != NULL) ){ + while ( (length + space >= cap) && (newbuffer != NULL) ){ cap = cap? cap << 1 : 2; buffer = newbuffer; newbuffer = safe_realloc(newbuffer, elsize * cap); diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index c875a9d49039b..46d30ab7fe313 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -67,13 +67,21 @@ """ -_ewm_kw = r"""com : float. optional - Center of mass: :math:`\alpha = 1 / (1 + com)`, +_ewm_kw = r"""com : float, optional + Specify decay in terms of center of mass, + :math:`\alpha = 1 / (1 + com),\text{ for } com \geq 0` span : float, optional - Specify decay in terms of span, :math:`\alpha = 2 / (span + 1)` + Specify decay in terms of span, + :math:`\alpha = 2 / (span + 1),\text{ for } span \geq 1` halflife : float, optional - Specify decay in terms of halflife, - :math:`\alpha = 1 - exp(log(0.5) / halflife)` + Specify decay in terms of half-life, + :math:`\alpha = 1 - exp(log(0.5) / halflife),\text{ for } halflife > 0` +alpha : float, optional + Specify smoothing factor :math:`\alpha` directly, + :math:`0 < \alpha \leq 1` + + .. versionadded:: 0.18.0 + min_periods : int, default 0 Minimum number of observations in window required to have a value (otherwise result is NA). @@ -92,16 +100,10 @@ _ewm_notes = r""" Notes ----- -Either center of mass, span or halflife must be specified - -EWMA is sometimes specified using a "span" parameter `s`, we have that the -decay parameter :math:`\alpha` is related to the span as -:math:`\alpha = 2 / (s + 1) = 1 / (1 + c)` - -where `c` is the center of mass. Given a span, the associated center of mass is -:math:`c = (s - 1) / 2` - -So a "20-day EWMA" would have center 9.5. +Exactly one of center of mass, span, half-life, and alpha must be provided. +Allowed values and relationship between the parameters are specified in the +parameter descriptions above; see the link at the end of this section for +a detailed explanation. When adjust is True (default), weighted averages are calculated using weights (1-alpha)**(n-1), (1-alpha)**(n-2), ..., 1-alpha, 1. @@ -121,7 +123,7 @@ True), and 1-alpha and alpha (if adjust is False). More details can be found at -http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions +http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows """ _expanding_kw = """min_periods : int, default None @@ -323,14 +325,15 @@ def rolling_corr(arg1, arg2=None, window=None, pairwise=None, **kwargs): @Substitution("Exponentially-weighted moving average", _unary_arg, _ewm_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewma(arg, com=None, span=None, halflife=None, min_periods=0, freq=None, - adjust=True, how=None, ignore_na=False): +def ewma(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, + freq=None, adjust=True, how=None, ignore_na=False): return ensure_compat('ewm', 'mean', arg, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, @@ -341,14 +344,15 @@ def ewma(arg, com=None, span=None, halflife=None, min_periods=0, freq=None, @Substitution("Exponentially-weighted moving variance", _unary_arg, _ewm_kw + _bias_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmvar(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, - freq=None, how=None, ignore_na=False, adjust=True): +def ewmvar(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, + bias=False, freq=None, how=None, ignore_na=False, adjust=True): return ensure_compat('ewm', 'var', arg, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, @@ -361,14 +365,15 @@ def ewmvar(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, @Substitution("Exponentially-weighted moving std", _unary_arg, _ewm_kw + _bias_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, - freq=None, how=None, ignore_na=False, adjust=True): +def ewmstd(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, + bias=False, freq=None, how=None, ignore_na=False, adjust=True): return ensure_compat('ewm', 'std', arg, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, @@ -383,9 +388,9 @@ def ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, @Substitution("Exponentially-weighted moving covariance", _binary_arg_flex, _ewm_kw + _pairwise_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, - bias=False, freq=None, pairwise=None, how=None, ignore_na=False, - adjust=True): +def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, alpha=None, + min_periods=0, bias=False, freq=None, pairwise=None, how=None, + ignore_na=False, adjust=True): if arg2 is None: arg2 = arg1 pairwise = True if pairwise is None else pairwise @@ -401,6 +406,7 @@ def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, bias=bias, freq=freq, @@ -414,8 +420,9 @@ def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, @Substitution("Exponentially-weighted moving correlation", _binary_arg_flex, _ewm_kw + _pairwise_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, - freq=None, pairwise=None, how=None, ignore_na=False, adjust=True): +def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, alpha=None, + min_periods=0, freq=None, pairwise=None, how=None, ignore_na=False, + adjust=True): if arg2 is None: arg2 = arg1 pairwise = True if pairwise is None else pairwise @@ -430,6 +437,7 @@ def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, how=how, diff --git a/pandas/stats/tests/test_fama_macbeth.py b/pandas/stats/tests/test_fama_macbeth.py index deff392d6a16c..2c69eb64fd61d 100644 --- a/pandas/stats/tests/test_fama_macbeth.py +++ b/pandas/stats/tests/test_fama_macbeth.py @@ -44,7 +44,7 @@ def checkFamaMacBethExtended(self, window_type, x, y, **kwds): end = index[i + window - 1] x2 = {} - for k, v in compat.iteritems(x): + for k, v in x.iteritems(): x2[k] = v.truncate(start, end) y2 = y.truncate(start, end) diff --git a/pandas/stats/tests/test_ols.py b/pandas/stats/tests/test_ols.py index 175ad9dc33dc2..8e659d42bab25 100644 --- a/pandas/stats/tests/test_ols.py +++ b/pandas/stats/tests/test_ols.py @@ -573,7 +573,7 @@ def test_wls_panel(self): stack_y = y.stack() stack_x = DataFrame(dict((k, v.stack()) - for k, v in compat.iteritems(x))) + for k, v in x.iteritems())) weights = x.std('items') stack_weights = weights.stack() diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index 4154c24f227f9..8d0ddc678a11f 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -241,24 +241,21 @@ def test_bool_describe_in_mixed_frame(self): 'int_data': [10, 20, 30, 40, 50], }) - # Boolean data and integer data is included in .describe() output, - # string data isn't - self.assert_numpy_array_equal(df.describe().columns, [ - 'bool_data', 'int_data']) - - bool_describe = df.describe()['bool_data'] - - # Both the min and the max values should stay booleans - self.assertEqual(bool_describe['min'].dtype, np.bool_) - self.assertEqual(bool_describe['max'].dtype, np.bool_) + # Integer data are included in .describe() output, + # Boolean and string data are not. + result = df.describe() + expected = DataFrame({'int_data': [5, 30, df.int_data.std(), + 10, 20, 30, 40, 50]}, + index=['count', 'mean', 'std', 'min', '25%', + '50%', '75%', 'max']) + assert_frame_equal(result, expected) - self.assertFalse(bool_describe['min']) - self.assertTrue(bool_describe['max']) + # Top value is a boolean value that is False + result = df.describe(include=['bool']) - # For numeric operations, like mean or median, the values True/False - # are cast to the integer values 1 and 0 - assert_almost_equal(bool_describe['mean'], 0.4) - assert_almost_equal(bool_describe['50%'], 0) + expected = DataFrame({'bool_data': [5, 2, False, 3]}, + index=['count', 'unique', 'top', 'freq']) + assert_frame_equal(result, expected) def test_reduce_mixed_frame(self): # GH 6806 diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 264302866b023..2a3ee774af6e5 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -216,7 +216,7 @@ def test_getitem_boolean(self): # we are producing a warning that since the passed boolean # key is not the same as the given index, we will reindex # not sure this is really necessary - with tm.assert_produces_warning(UserWarning): + with tm.assert_produces_warning(UserWarning, check_stacklevel=False): indexer_obj = indexer_obj.reindex(self.tsframe.index[::-1]) subframe_obj = self.tsframe[indexer_obj] assert_frame_equal(subframe_obj, subframe) diff --git a/pandas/tests/frame/test_nonunique_indexes.py b/pandas/tests/frame/test_nonunique_indexes.py index 1b24e829088f2..77974718714f8 100644 --- a/pandas/tests/frame/test_nonunique_indexes.py +++ b/pandas/tests/frame/test_nonunique_indexes.py @@ -452,3 +452,19 @@ def test_as_matrix_duplicates(self): dtype=object) self.assertTrue(np.array_equal(result, expected)) + + def test_set_value_by_index(self): + # See gh-12344 + df = DataFrame(np.arange(9).reshape(3, 3).T) + df.columns = list('AAA') + expected = df.iloc[:, 2] + + df.iloc[:, 0] = 3 + assert_series_equal(df.iloc[:, 2], expected) + + df = DataFrame(np.arange(9).reshape(3, 3).T) + df.columns = [2, float(2), str(2)] + expected = df.iloc[:, 1] + + df.iloc[:, 0] = 3 + assert_series_equal(df.iloc[:, 1], expected) diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py index 6db507f0e4151..a52cb018c7bae 100644 --- a/pandas/tests/frame/test_query_eval.py +++ b/pandas/tests/frame/test_query_eval.py @@ -96,8 +96,8 @@ class TestDataFrameQueryWithMultiIndex(tm.TestCase): def check_query_with_named_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) - b = tm.choice(['eggs', 'ham'], size=10) + a = np.random.choice(['red', 'green'], size=10) + b = np.random.choice(['eggs', 'ham'], size=10) index = MultiIndex.from_arrays([a, b], names=['color', 'food']) df = DataFrame(randn(10, 2), index=index) ind = Series(df.index.get_level_values('color').values, index=index, @@ -149,8 +149,8 @@ def test_query_with_named_multiindex(self): def check_query_with_unnamed_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) - b = tm.choice(['eggs', 'ham'], size=10) + a = np.random.choice(['red', 'green'], size=10) + b = np.random.choice(['eggs', 'ham'], size=10) index = MultiIndex.from_arrays([a, b]) df = DataFrame(randn(10, 2), index=index) ind = Series(df.index.get_level_values(0).values, index=index) @@ -243,7 +243,7 @@ def test_query_with_unnamed_multiindex(self): def check_query_with_partially_named_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) + a = np.random.choice(['red', 'green'], size=10) b = np.arange(10) index = MultiIndex.from_arrays([a, b]) index.names = [None, 'rating'] @@ -975,7 +975,7 @@ def check_query_lex_compare_strings(self, parser, engine): tm.skip_if_no_ne(engine=engine) import operator as opr - a = Series(tm.choice(list('abcde'), 20)) + a = Series(np.random.choice(list('abcde'), 20)) b = Series(np.arange(a.size)) df = DataFrame({'X': a, 'Y': b}) diff --git a/pandas/tests/indexing/__init__.py b/pandas/tests/indexing/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/tests/indexing/test_categorical.py b/pandas/tests/indexing/test_categorical.py new file mode 100644 index 0000000000000..4e31fb350f6ee --- /dev/null +++ b/pandas/tests/indexing/test_categorical.py @@ -0,0 +1,339 @@ +# -*- coding: utf-8 -*- + +import pandas as pd +import numpy as np +from pandas import Series, DataFrame +from pandas.util.testing import assert_series_equal, assert_frame_equal +from pandas.util import testing as tm + + +class TestCategoricalIndex(tm.TestCase): + + def setUp(self): + + self.df = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': Series(list('aabbca')).astype( + 'category', categories=list( + 'cab'))}).set_index('B') + self.df2 = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': Series(list('aabbca')).astype( + 'category', categories=list( + 'cabe'))}).set_index('B') + self.df3 = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': (Series([1, 1, 2, 1, 3, 2]) + .astype('category', categories=[3, 2, 1], + ordered=True))}).set_index('B') + self.df4 = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': (Series([1, 1, 2, 1, 3, 2]) + .astype('category', categories=[3, 2, 1], + ordered=False))}).set_index('B') + + def test_loc_scalar(self): + result = self.df.loc['a'] + expected = (DataFrame({'A': [0, 1, 5], + 'B': (Series(list('aaa')) + .astype('category', + categories=list('cab')))}) + .set_index('B')) + assert_frame_equal(result, expected) + + df = self.df.copy() + df.loc['a'] = 20 + expected = (DataFrame({'A': [20, 20, 2, 3, 4, 20], + 'B': (Series(list('aabbca')) + .astype('category', + categories=list('cab')))}) + .set_index('B')) + assert_frame_equal(df, expected) + + # value not in the categories + self.assertRaises(KeyError, lambda: df.loc['d']) + + def f(): + df.loc['d'] = 10 + + self.assertRaises(TypeError, f) + + def f(): + df.loc['d', 'A'] = 10 + + self.assertRaises(TypeError, f) + + def f(): + df.loc['d', 'C'] = 10 + + self.assertRaises(TypeError, f) + + def test_loc_listlike(self): + + # list of labels + result = self.df.loc[['c', 'a']] + expected = self.df.iloc[[4, 0, 1, 5]] + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.loc[['a', 'b', 'e']] + exp_index = pd.CategoricalIndex( + list('aaabbe'), categories=list('cabe'), name='B') + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) + assert_frame_equal(result, expected, check_index_type=True) + + # element in the categories but not in the values + self.assertRaises(KeyError, lambda: self.df2.loc['e']) + + # assign is ok + df = self.df2.copy() + df.loc['e'] = 20 + result = df.loc[['a', 'b', 'e']] + exp_index = pd.CategoricalIndex( + list('aaabbe'), categories=list('cabe'), name='B') + expected = DataFrame({'A': [0, 1, 5, 2, 3, 20]}, index=exp_index) + assert_frame_equal(result, expected) + + df = self.df2.copy() + result = df.loc[['a', 'b', 'e']] + exp_index = pd.CategoricalIndex( + list('aaabbe'), categories=list('cabe'), name='B') + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) + assert_frame_equal(result, expected, check_index_type=True) + + # not all labels in the categories + self.assertRaises(KeyError, lambda: self.df2.loc[['a', 'd']]) + + def test_loc_listlike_dtypes(self): + # GH 11586 + + # unique categories and codes + index = pd.CategoricalIndex(['a', 'b', 'c']) + df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) + + # unique slice + res = df.loc[['a', 'b']] + exp = DataFrame({'A': [1, 2], + 'B': [4, 5]}, index=pd.CategoricalIndex(['a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[['a', 'a', 'b']] + exp = DataFrame({'A': [1, 1, 2], + 'B': [4, 4, 5]}, + index=pd.CategoricalIndex(['a', 'a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with tm.assertRaisesRegexp( + KeyError, + 'a list-indexer must only include values that are ' + 'in the categories'): + df.loc[['a', 'x']] + + # duplicated categories and codes + index = pd.CategoricalIndex(['a', 'b', 'a']) + df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) + + # unique slice + res = df.loc[['a', 'b']] + exp = DataFrame({'A': [1, 3, 2], + 'B': [4, 6, 5]}, + index=pd.CategoricalIndex(['a', 'a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[['a', 'a', 'b']] + exp = DataFrame( + {'A': [1, 3, 1, 3, 2], + 'B': [4, 6, 4, 6, 5 + ]}, index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with tm.assertRaisesRegexp( + KeyError, + 'a list-indexer must only include values ' + 'that are in the categories'): + df.loc[['a', 'x']] + + # contains unused category + index = pd.CategoricalIndex( + ['a', 'b', 'a', 'c'], categories=list('abcde')) + df = DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=index) + + res = df.loc[['a', 'b']] + exp = DataFrame({'A': [1, 3, 2], + 'B': [5, 7, 6]}, index=pd.CategoricalIndex( + ['a', 'a', 'b'], categories=list('abcde'))) + tm.assert_frame_equal(res, exp, check_index_type=True) + + res = df.loc[['a', 'e']] + exp = DataFrame({'A': [1, 3, np.nan], 'B': [5, 7, np.nan]}, + index=pd.CategoricalIndex(['a', 'a', 'e'], + categories=list('abcde'))) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[['a', 'a', 'b']] + exp = DataFrame({'A': [1, 3, 1, 3, 2], 'B': [5, 7, 5, 7, 6]}, + index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'], + categories=list('abcde'))) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with tm.assertRaisesRegexp( + KeyError, + 'a list-indexer must only include values ' + 'that are in the categories'): + df.loc[['a', 'x']] + + def test_read_only_source(self): + # GH 10043 + rw_array = np.eye(10) + rw_df = DataFrame(rw_array) + + ro_array = np.eye(10) + ro_array.setflags(write=False) + ro_df = DataFrame(ro_array) + + assert_frame_equal(rw_df.iloc[[1, 2, 3]], ro_df.iloc[[1, 2, 3]]) + assert_frame_equal(rw_df.iloc[[1]], ro_df.iloc[[1]]) + assert_series_equal(rw_df.iloc[1], ro_df.iloc[1]) + assert_frame_equal(rw_df.iloc[1:3], ro_df.iloc[1:3]) + + assert_frame_equal(rw_df.loc[[1, 2, 3]], ro_df.loc[[1, 2, 3]]) + assert_frame_equal(rw_df.loc[[1]], ro_df.loc[[1]]) + assert_series_equal(rw_df.loc[1], ro_df.loc[1]) + assert_frame_equal(rw_df.loc[1:3], ro_df.loc[1:3]) + + def test_reindexing(self): + + # reindexing + # convert to a regular index + result = self.df2.reindex(['a', 'b', 'e']) + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], + 'B': Series(list('aaabbe'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['a', 'b']) + expected = DataFrame({'A': [0, 1, 5, 2, 3], + 'B': Series(list('aaabb'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['e']) + expected = DataFrame({'A': [np.nan], + 'B': Series(['e'])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['d']) + expected = DataFrame({'A': [np.nan], + 'B': Series(['d'])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + # since we are actually reindexing with a Categorical + # then return a Categorical + cats = list('cabe') + + result = self.df2.reindex(pd.Categorical(['a', 'd'], categories=cats)) + expected = DataFrame({'A': [0, 1, 5, np.nan], + 'B': Series(list('aaad')).astype( + 'category', categories=cats)}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(pd.Categorical(['a'], categories=cats)) + expected = DataFrame({'A': [0, 1, 5], + 'B': Series(list('aaa')).astype( + 'category', categories=cats)}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['a', 'b', 'e']) + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], + 'B': Series(list('aaabbe'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['a', 'b']) + expected = DataFrame({'A': [0, 1, 5, 2, 3], + 'B': Series(list('aaabb'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['e']) + expected = DataFrame({'A': [np.nan], + 'B': Series(['e'])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + # give back the type of categorical that we received + result = self.df2.reindex(pd.Categorical( + ['a', 'd'], categories=cats, ordered=True)) + expected = DataFrame( + {'A': [0, 1, 5, np.nan], + 'B': Series(list('aaad')).astype('category', categories=cats, + ordered=True)}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(pd.Categorical( + ['a', 'd'], categories=['a', 'd'])) + expected = DataFrame({'A': [0, 1, 5, np.nan], + 'B': Series(list('aaad')).astype( + 'category', categories=['a', 'd' + ])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + # passed duplicate indexers are not allowed + self.assertRaises(ValueError, lambda: self.df2.reindex(['a', 'a'])) + + # args NotImplemented ATM + self.assertRaises(NotImplementedError, + lambda: self.df2.reindex(['a'], method='ffill')) + self.assertRaises(NotImplementedError, + lambda: self.df2.reindex(['a'], level=1)) + self.assertRaises(NotImplementedError, + lambda: self.df2.reindex(['a'], limit=2)) + + def test_loc_slice(self): + # slicing + # not implemented ATM + # GH9748 + + self.assertRaises(TypeError, lambda: self.df.loc[1:5]) + + # result = df.loc[1:5] + # expected = df.iloc[[1,2,3,4]] + # assert_frame_equal(result, expected) + + def test_boolean_selection(self): + + df3 = self.df3 + df4 = self.df4 + + result = df3[df3.index == 'a'] + expected = df3.iloc[[]] + assert_frame_equal(result, expected) + + result = df4[df4.index == 'a'] + expected = df4.iloc[[]] + assert_frame_equal(result, expected) + + result = df3[df3.index == 1] + expected = df3.iloc[[0, 1, 3]] + assert_frame_equal(result, expected) + + result = df4[df4.index == 1] + expected = df4.iloc[[0, 1, 3]] + assert_frame_equal(result, expected) + + # since we have an ordered categorical + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=True, + # name=u'B') + result = df3[df3.index < 2] + expected = df3.iloc[[4]] + assert_frame_equal(result, expected) + + result = df3[df3.index > 1] + expected = df3.iloc[[]] + assert_frame_equal(result, expected) + + # unordered + # cannot be compared + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=False, + # name=u'B') + self.assertRaises(TypeError, lambda: df4[df4.index < 2]) + self.assertRaises(TypeError, lambda: df4[df4.index > 1]) diff --git a/pandas/tests/indexing/test_floats.py b/pandas/tests/indexing/test_floats.py new file mode 100644 index 0000000000000..2a2f8678694de --- /dev/null +++ b/pandas/tests/indexing/test_floats.py @@ -0,0 +1,676 @@ +# -*- coding: utf-8 -*- + +import numpy as np +from pandas import Series, DataFrame, Index, Float64Index +from pandas.util.testing import assert_series_equal, assert_almost_equal +import pandas.util.testing as tm + + +class TestFloatIndexers(tm.TestCase): + + def check(self, result, original, indexer, getitem): + """ + comparator for results + we need to take care if we are indexing on a + Series or a frame + """ + if isinstance(original, Series): + expected = original.iloc[indexer] + else: + if getitem: + expected = original.iloc[:, indexer] + else: + expected = original.iloc[indexer] + + assert_almost_equal(result, expected) + + def test_scalar_error(self): + + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + # this duplicates the code below + # but is spefically testing for the error + # message + + for index in [tm.makeStringIndex, tm.makeUnicodeIndex, + tm.makeCategoricalIndex, + tm.makeDateIndex, tm.makeTimedeltaIndex, + tm.makePeriodIndex, tm.makeIntIndex, + tm.makeRangeIndex]: + + i = index(5) + + s = Series(np.arange(len(i)), index=i) + + def f(): + s.iloc[3.0] + self.assertRaisesRegexp(TypeError, + 'cannot do positional indexing', + f) + + def f(): + s.iloc[3.0] = 0 + self.assertRaises(TypeError, f) + + def test_scalar_non_numeric(self): + + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + + for index in [tm.makeStringIndex, tm.makeUnicodeIndex, + tm.makeCategoricalIndex, + tm.makeDateIndex, tm.makeTimedeltaIndex, + tm.makePeriodIndex]: + + i = index(5) + + for s in [Series( + np.arange(len(i)), index=i), DataFrame( + np.random.randn( + len(i), len(i)), index=i, columns=i)]: + + # getting + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.iloc, False), + (lambda x: x, True)]: + + def f(): + idxr(s)[3.0] + + # gettitem on a DataFrame is a KeyError as it is indexing + # via labels on the columns + if getitem and isinstance(s, DataFrame): + error = KeyError + else: + error = TypeError + self.assertRaises(error, f) + + # label based can be a TypeError or KeyError + def f(): + s.loc[3.0] + + if s.index.inferred_type in ['string', 'unicode', 'mixed']: + error = KeyError + else: + error = TypeError + self.assertRaises(error, f) + + # contains + self.assertFalse(3.0 in s) + + # setting with a float fails with iloc + def f(): + s.iloc[3.0] = 0 + self.assertRaises(TypeError, f) + + # setting with an indexer + if s.index.inferred_type in ['categorical']: + # Value or Type Error + pass + elif s.index.inferred_type in ['datetime64', 'timedelta64', + 'period']: + + # these should prob work + # and are inconsisten between series/dataframe ATM + # for idxr in [lambda x: x.ix, + # lambda x: x]: + # s2 = s.copy() + # def f(): + # idxr(s2)[3.0] = 0 + # self.assertRaises(TypeError, f) + pass + + else: + + s2 = s.copy() + s2.loc[3.0] = 10 + self.assertTrue(s2.index.is_object()) + + for idxr in [lambda x: x.ix, + lambda x: x]: + s2 = s.copy() + idxr(s2)[3.0] = 0 + self.assertTrue(s2.index.is_object()) + + # fallsback to position selection, series only + s = Series(np.arange(len(i)), index=i) + s[3] + self.assertRaises(TypeError, lambda: s[3.0]) + + def test_scalar_with_mixed(self): + + s2 = Series([1, 2, 3], index=['a', 'b', 'c']) + s3 = Series([1, 2, 3], index=['a', 'b', 1.5]) + + # lookup in a pure string index + # with an invalid indexer + for idxr in [lambda x: x.ix, + lambda x: x, + lambda x: x.iloc]: + + def f(): + idxr(s2)[1.0] + + self.assertRaises(TypeError, f) + + self.assertRaises(KeyError, lambda: s2.loc[1.0]) + + result = s2.loc['b'] + expected = 2 + self.assertEqual(result, expected) + + # mixed index so we have label + # indexing + for idxr in [lambda x: x.ix, + lambda x: x]: + + def f(): + idxr(s3)[1.0] + + self.assertRaises(TypeError, f) + + result = idxr(s3)[1] + expected = 2 + self.assertEqual(result, expected) + + self.assertRaises(TypeError, lambda: s3.iloc[1.0]) + self.assertRaises(KeyError, lambda: s3.loc[1.0]) + + result = s3.loc[1.5] + expected = 3 + self.assertEqual(result, expected) + + def test_scalar_integer(self): + + # test how scalar float indexers work on int indexes + + # integer index + for index in [tm.makeIntIndex, tm.makeRangeIndex]: + + i = index(5) + for s in [Series(np.arange(len(i))), + DataFrame(np.random.randn(len(i), len(i)), + index=i, columns=i)]: + + # coerce to equal int + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.loc, False), + (lambda x: x, True)]: + + result = idxr(s)[3.0] + self.check(result, s, 3, getitem) + + # coerce to equal int + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.loc, False), + (lambda x: x, True)]: + + if isinstance(s, Series): + compare = self.assertEqual + expected = 100 + else: + compare = tm.assert_series_equal + if getitem: + expected = Series(100, + index=range(len(s)), name=3) + else: + expected = Series(100., + index=range(len(s)), name=3) + + s2 = s.copy() + idxr(s2)[3.0] = 100 + + result = idxr(s2)[3.0] + compare(result, expected) + + result = idxr(s2)[3] + compare(result, expected) + + # contains + # coerce to equal int + self.assertTrue(3.0 in s) + + def test_scalar_float(self): + + # scalar float indexers work on a float index + index = Index(np.arange(5.)) + for s in [Series(np.arange(len(index)), index=index), + DataFrame(np.random.randn(len(index), len(index)), + index=index, columns=index)]: + + # assert all operations except for iloc are ok + indexer = index[3] + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.loc, False), + (lambda x: x, True)]: + + # getting + result = idxr(s)[indexer] + self.check(result, s, 3, getitem) + + # setting + s2 = s.copy() + + def f(): + idxr(s2)[indexer] = expected + result = idxr(s2)[indexer] + self.check(result, s, 3, getitem) + + # random integer is a KeyError + self.assertRaises(KeyError, lambda: idxr(s)[3.5]) + + # contains + self.assertTrue(3.0 in s) + + # iloc succeeds with an integer + expected = s.iloc[3] + s2 = s.copy() + + s2.iloc[3] = expected + result = s2.iloc[3] + self.check(result, s, 3, False) + + # iloc raises with a float + self.assertRaises(TypeError, lambda: s.iloc[3.0]) + + def g(): + s2.iloc[3.0] = 0 + self.assertRaises(TypeError, g) + + def test_slice_non_numeric(self): + + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + + for index in [tm.makeStringIndex, tm.makeUnicodeIndex, + tm.makeDateIndex, tm.makeTimedeltaIndex, + tm.makePeriodIndex]: + + index = index(5) + for s in [Series(range(5), index=index), + DataFrame(np.random.randn(5, 2), index=index)]: + + # getitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + def f(): + s.iloc[l] + self.assertRaises(TypeError, f) + + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x.iloc, + lambda x: x]: + + def f(): + idxr(s)[l] + self.assertRaises(TypeError, f) + + # setitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + def f(): + s.iloc[l] = 0 + self.assertRaises(TypeError, f) + + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x.iloc, + lambda x: x]: + def f(): + idxr(s)[l] = 0 + self.assertRaises(TypeError, f) + + def test_slice_integer(self): + + # same as above, but for Integer based indexes + # these coerce to a like integer + # oob indiciates if we are out of bounds + # of positional indexing + for index, oob in [(tm.makeIntIndex(5), False), + (tm.makeRangeIndex(5), False), + (tm.makeIntIndex(5) + 10, True)]: + + # s is an in-range index + s = Series(range(5), index=index) + + # getitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + + result = idxr(s)[l] + + # these are all label indexing + # except getitem which is positional + # empty + if oob: + indexer = slice(0, 0) + else: + indexer = slice(3, 5) + self.check(result, s, indexer, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # getitem out-of-bounds + for l in [slice(-6, 6), + slice(-6.0, 6.0)]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + result = idxr(s)[l] + + # these are all label indexing + # except getitem which is positional + # empty + if oob: + indexer = slice(0, 0) + else: + indexer = slice(-6, 6) + self.check(result, s, indexer, False) + + # positional indexing + def f(): + s[slice(-6.0, 6.0)] + + self.assertRaises(TypeError, f) + + # getitem odd floats + for l, res1 in [(slice(2.5, 4), slice(3, 5)), + (slice(2, 3.5), slice(2, 4)), + (slice(2.5, 3.5), slice(3, 4))]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + + result = idxr(s)[l] + if oob: + res = slice(0, 0) + else: + res = res1 + + self.check(result, s, res, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # setitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + sc = s.copy() + idxr(sc)[l] = 0 + result = idxr(sc)[l].values.ravel() + self.assertTrue((result == 0).all()) + + # positional indexing + def f(): + s[l] = 0 + + self.assertRaises(TypeError, f) + + def test_integer_positional_indexing(self): + """ make sure that we are raising on positional indexing + w.r.t. an integer index """ + + s = Series(range(2, 6), index=range(2, 6)) + + result = s[2:4] + expected = s.iloc[2:4] + assert_series_equal(result, expected) + + for idxr in [lambda x: x, + lambda x: x.iloc]: + + for l in [slice(2, 4.0), + slice(2.0, 4), + slice(2.0, 4.0)]: + + def f(): + idxr(s)[l] + + self.assertRaises(TypeError, f) + + def test_slice_integer_frame_getitem(self): + + # similar to above, but on the getitem dim (of a DataFrame) + for index in [tm.makeIntIndex, tm.makeRangeIndex]: + + index = index(5) + s = DataFrame(np.random.randn(5, 2), index=index) + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + + # getitem + for l in [slice(0.0, 1), + slice(0, 1.0), + slice(0.0, 1.0)]: + + result = idxr(s)[l] + indexer = slice(0, 2) + self.check(result, s, indexer, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # getitem out-of-bounds + for l in [slice(-10, 10), + slice(-10.0, 10.0)]: + + result = idxr(s)[l] + self.check(result, s, slice(-10, 10), True) + + # positional indexing + def f(): + s[slice(-10.0, 10.0)] + + self.assertRaises(TypeError, f) + + # getitem odd floats + for l, res in [(slice(0.5, 1), slice(1, 2)), + (slice(0, 0.5), slice(0, 1)), + (slice(0.5, 1.5), slice(1, 2))]: + + result = idxr(s)[l] + self.check(result, s, res, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # setitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + sc = s.copy() + idxr(sc)[l] = 0 + result = idxr(sc)[l].values.ravel() + self.assertTrue((result == 0).all()) + + # positional indexing + def f(): + s[l] = 0 + + self.assertRaises(TypeError, f) + + def test_slice_float(self): + + # same as above, but for floats + index = Index(np.arange(5.)) + 0.1 + for s in [Series(range(5), index=index), + DataFrame(np.random.randn(5, 2), index=index)]: + + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + expected = s.iloc[3:4] + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x]: + + # getitem + result = idxr(s)[l] + self.assertTrue(result.equals(expected)) + + # setitem + s2 = s.copy() + idxr(s2)[l] = 0 + result = idxr(s2)[l].values.ravel() + self.assertTrue((result == 0).all()) + + def test_floating_index_doc_example(self): + + index = Index([1.5, 2, 3, 4.5, 5]) + s = Series(range(5), index=index) + self.assertEqual(s[3], 2) + self.assertEqual(s.ix[3], 2) + self.assertEqual(s.loc[3], 2) + self.assertEqual(s.iloc[3], 3) + + def test_floating_misc(self): + + # related 236 + # scalar/slicing of a float index + s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64) + + # label based slicing + result1 = s[1.0:3.0] + result2 = s.ix[1.0:3.0] + result3 = s.loc[1.0:3.0] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + + # exact indexing when found + result1 = s[5.0] + result2 = s.loc[5.0] + result3 = s.ix[5.0] + self.assertEqual(result1, result2) + self.assertEqual(result1, result3) + + result1 = s[5] + result2 = s.loc[5] + result3 = s.ix[5] + self.assertEqual(result1, result2) + self.assertEqual(result1, result3) + + self.assertEqual(s[5.0], s[5]) + + # value not found (and no fallbacking at all) + + # scalar integers + self.assertRaises(KeyError, lambda: s.loc[4]) + self.assertRaises(KeyError, lambda: s.ix[4]) + self.assertRaises(KeyError, lambda: s[4]) + + # fancy floats/integers create the correct entry (as nan) + # fancy tests + expected = Series([2, 0], index=Float64Index([5.0, 0.0])) + for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float + assert_series_equal(s[fancy_idx], expected) + assert_series_equal(s.loc[fancy_idx], expected) + assert_series_equal(s.ix[fancy_idx], expected) + + expected = Series([2, 0], index=Index([5, 0], dtype='int64')) + for fancy_idx in [[5, 0], np.array([5, 0])]: # int + assert_series_equal(s[fancy_idx], expected) + assert_series_equal(s.loc[fancy_idx], expected) + assert_series_equal(s.ix[fancy_idx], expected) + + # all should return the same as we are slicing 'the same' + result1 = s.loc[2:5] + result2 = s.loc[2.0:5.0] + result3 = s.loc[2.0:5] + result4 = s.loc[2.1:5] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + # previously this did fallback indexing + result1 = s[2:5] + result2 = s[2.0:5.0] + result3 = s[2.0:5] + result4 = s[2.1:5] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + result1 = s.ix[2:5] + result2 = s.ix[2.0:5.0] + result3 = s.ix[2.0:5] + result4 = s.ix[2.1:5] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + # combined test + result1 = s.loc[2:5] + result2 = s.ix[2:5] + result3 = s[2:5] + + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + + # list selection + result1 = s[[0.0, 5, 10]] + result2 = s.loc[[0.0, 5, 10]] + result3 = s.ix[[0.0, 5, 10]] + result4 = s.iloc[[0, 2, 4]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + result1 = s[[1.6, 5, 10]] + result2 = s.loc[[1.6, 5, 10]] + result3 = s.ix[[1.6, 5, 10]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, Series( + [np.nan, 2, 4], index=[1.6, 5, 10])) + + result1 = s[[0, 1, 2]] + result2 = s.ix[[0, 1, 2]] + result3 = s.loc[[0, 1, 2]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, Series( + [0.0, np.nan, np.nan], index=[0, 1, 2])) + + result1 = s.loc[[2.5, 5]] + result2 = s.ix[[2.5, 5]] + assert_series_equal(result1, result2) + assert_series_equal(result1, Series([1, 2], index=[2.5, 5.0])) + + result1 = s[[2.5]] + result2 = s.ix[[2.5]] + result3 = s.loc[[2.5]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, Series([1], index=[2.5])) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/indexing/test_indexing.py similarity index 84% rename from pandas/tests/test_indexing.py rename to pandas/tests/indexing/test_indexing.py index 6a904c67fffeb..89552ab776608 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -17,7 +17,7 @@ from pandas import option_context from pandas.core.indexing import _non_reducing_slice, _maybe_numeric_slice from pandas.core.api import (DataFrame, Index, Series, Panel, isnull, - MultiIndex, Float64Index, Timestamp, Timedelta) + MultiIndex, Timestamp, Timedelta) from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_attr_equal) @@ -699,6 +699,29 @@ def test_iloc_setitem(self): expected = Series([0, 1, 0], index=[4, 5, 6]) assert_series_equal(s, expected) + def test_loc_setitem_slice(self): + # GH10503 + + # assigning the same type should not change the type + df1 = DataFrame({'a': [0, 1, 1], + 'b': Series([100, 200, 300], dtype='uint32')}) + ix = df1['a'] == 1 + newb1 = df1.loc[ix, 'b'] + 1 + df1.loc[ix, 'b'] = newb1 + expected = DataFrame({'a': [0, 1, 1], + 'b': Series([100, 201, 301], dtype='uint32')}) + assert_frame_equal(df1, expected) + + # assigning a new type should get the inferred type + df2 = DataFrame({'a': [0, 1, 1], 'b': [100, 200, 300]}, + dtype='uint64') + ix = df1['a'] == 1 + newb2 = df2.loc[ix, 'b'] + df1.loc[ix, 'b'] = newb2 + expected = DataFrame({'a': [0, 1, 1], 'b': [100, 200, 300]}, + dtype='uint64') + assert_frame_equal(df2, expected) + def test_ix_loc_setitem_consistency(self): # GH 5771 @@ -3256,12 +3279,12 @@ def test_multiindex_assignment(self): df.ix[4, 'c'] = arr assert_series_equal(df.ix[4, 'c'], Series(arr, index=[8, 10], name='c', - dtype='int64')) + dtype='float64')) # scalar ok df.ix[4, 'c'] = 10 assert_series_equal(df.ix[4, 'c'], Series(10, index=[8, 10], name='c', - dtype='int64')) + dtype='float64')) # invalid assignments def f(): @@ -3495,29 +3518,29 @@ def test_iloc_mask(self): 'integer type is not available'), } - warnings.filterwarnings(action='ignore', category=UserWarning) - result = dict() - for idx in [None, 'index', 'locs']: - mask = (df.nums > 2).values - if idx: - mask = Series(mask, list(reversed(getattr(df, idx)))) - for method in ['', '.loc', '.iloc']: - try: - if method: - accessor = getattr(df, method[1:]) - else: - accessor = df - ans = str(bin(accessor[mask]['nums'].sum())) - except Exception as e: - ans = str(e) - - key = tuple([idx, method]) - r = expected.get(key) - if r != ans: - raise AssertionError( - "[%s] does not match [%s], received [%s]" - % (key, ans, r)) - warnings.filterwarnings(action='always', category=UserWarning) + # UserWarnings from reindex of a boolean mask + with warnings.catch_warnings(record=True): + result = dict() + for idx in [None, 'index', 'locs']: + mask = (df.nums > 2).values + if idx: + mask = Series(mask, list(reversed(getattr(df, idx)))) + for method in ['', '.loc', '.iloc']: + try: + if method: + accessor = getattr(df, method[1:]) + else: + accessor = df + ans = str(bin(accessor[mask]['nums'].sum())) + except Exception as e: + ans = str(e) + + key = tuple([idx, method]) + r = expected.get(key) + if r != ans: + raise AssertionError( + "[%s] does not match [%s], received [%s]" + % (key, ans, r)) def test_ix_slicing_strings(self): # GH3836 @@ -4956,324 +4979,6 @@ def test_float64index_slicing_bug(self): result = s.value_counts() str(result) - def test_floating_index_doc_example(self): - - index = Index([1.5, 2, 3, 4.5, 5]) - s = Series(range(5), index=index) - self.assertEqual(s[3], 2) - self.assertEqual(s.ix[3], 2) - self.assertEqual(s.loc[3], 2) - self.assertEqual(s.iloc[3], 3) - - def test_floating_index(self): - - # related 236 - # scalar/slicing of a float index - s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64) - - # label based slicing - result1 = s[1.0:3.0] - result2 = s.ix[1.0:3.0] - result3 = s.loc[1.0:3.0] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # exact indexing when found - result1 = s[5.0] - result2 = s.loc[5.0] - result3 = s.ix[5.0] - self.assertEqual(result1, result2) - self.assertEqual(result1, result3) - - result1 = s[5] - result2 = s.loc[5] - result3 = s.ix[5] - self.assertEqual(result1, result2) - self.assertEqual(result1, result3) - - self.assertEqual(s[5.0], s[5]) - - # value not found (and no fallbacking at all) - - # scalar integers - self.assertRaises(KeyError, lambda: s.loc[4]) - self.assertRaises(KeyError, lambda: s.ix[4]) - self.assertRaises(KeyError, lambda: s[4]) - - # fancy floats/integers create the correct entry (as nan) - # fancy tests - expected = Series([2, 0], index=Float64Index([5.0, 0.0])) - for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float - assert_series_equal(s[fancy_idx], expected) - assert_series_equal(s.loc[fancy_idx], expected) - assert_series_equal(s.ix[fancy_idx], expected) - - expected = Series([2, 0], index=Index([5, 0], dtype='int64')) - for fancy_idx in [[5, 0], np.array([5, 0])]: # int - assert_series_equal(s[fancy_idx], expected) - assert_series_equal(s.loc[fancy_idx], expected) - assert_series_equal(s.ix[fancy_idx], expected) - - # all should return the same as we are slicing 'the same' - result1 = s.loc[2:5] - result2 = s.loc[2.0:5.0] - result3 = s.loc[2.0:5] - result4 = s.loc[2.1:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - # previously this did fallback indexing - result1 = s[2:5] - result2 = s[2.0:5.0] - result3 = s[2.0:5] - result4 = s[2.1:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - result1 = s.ix[2:5] - result2 = s.ix[2.0:5.0] - result3 = s.ix[2.0:5] - result4 = s.ix[2.1:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - # combined test - result1 = s.loc[2:5] - result2 = s.ix[2:5] - result3 = s[2:5] - - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # list selection - result1 = s[[0.0, 5, 10]] - result2 = s.loc[[0.0, 5, 10]] - result3 = s.ix[[0.0, 5, 10]] - result4 = s.iloc[[0, 2, 4]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - result1 = s[[1.6, 5, 10]] - result2 = s.loc[[1.6, 5, 10]] - result3 = s.ix[[1.6, 5, 10]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, Series( - [np.nan, 2, 4], index=[1.6, 5, 10])) - - result1 = s[[0, 1, 2]] - result2 = s.ix[[0, 1, 2]] - result3 = s.loc[[0, 1, 2]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, Series( - [0.0, np.nan, np.nan], index=[0, 1, 2])) - - result1 = s.loc[[2.5, 5]] - result2 = s.ix[[2.5, 5]] - assert_series_equal(result1, result2) - assert_series_equal(result1, Series([1, 2], index=[2.5, 5.0])) - - result1 = s[[2.5]] - result2 = s.ix[[2.5]] - result3 = s.loc[[2.5]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, Series([1], index=[2.5])) - - def test_scalar_indexer(self): - # float indexing checked above - - def check_invalid(index, loc=None, iloc=None, ix=None, getitem=None): - - # related 236/4850 - # trying to access with a float index - s = Series(np.arange(len(index)), index=index) - - if iloc is None: - iloc = TypeError - self.assertRaises(iloc, lambda: s.iloc[3.5]) - if loc is None: - loc = TypeError - self.assertRaises(loc, lambda: s.loc[3.5]) - if ix is None: - ix = TypeError - self.assertRaises(ix, lambda: s.ix[3.5]) - if getitem is None: - getitem = TypeError - self.assertRaises(getitem, lambda: s[3.5]) - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeIntIndex, tm.makeRangeIndex, tm.makeDateIndex, - tm.makePeriodIndex]: - check_invalid(index()) - check_invalid(Index(np.arange(5) * 2.5), - loc=KeyError, - ix=KeyError, - getitem=KeyError) - - def check_index(index, error): - index = index() - s = Series(np.arange(len(index)), index=index) - - # positional selection - result1 = s[5] - self.assertRaises(TypeError, lambda: s[5.0]) - result3 = s.iloc[5] - self.assertRaises(TypeError, lambda: s.iloc[5.0]) - - # by value - self.assertRaises(TypeError, lambda: s.loc[5]) - self.assertRaises(TypeError, lambda: s.loc[5.0]) - - # this is fallback, so it works - result5 = s.ix[5] - self.assertRaises(error, lambda: s.ix[5.0]) - - self.assertEqual(result1, result3) - self.assertEqual(result1, result5) - - # string-like - for index in [tm.makeStringIndex, tm.makeUnicodeIndex]: - check_index(index, TypeError) - - # datetimelike - for index in [tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - check_index(index, TypeError) - - # exact indexing when found on IntIndex - s = Series(np.arange(10), dtype='int64') - - self.assertRaises(TypeError, lambda: s[5.0]) - self.assertRaises(TypeError, lambda: s.loc[5.0]) - self.assertRaises(TypeError, lambda: s.ix[5.0]) - result4 = s[5] - result5 = s.loc[5] - result6 = s.ix[5] - self.assertEqual(result4, result5) - self.assertEqual(result4, result6) - - def test_slice_indexer(self): - def check_iloc_compat(s): - # these are exceptions - self.assertRaises(TypeError, lambda: s.iloc[6.0:8]) - self.assertRaises(TypeError, lambda: s.iloc[6.0:8.0]) - self.assertRaises(TypeError, lambda: s.iloc[6:8.0]) - - def check_slicing_positional(index): - - s = Series(np.arange(len(index)) + 10, index=index) - - # these are all positional - result1 = s[2:5] - result2 = s.ix[2:5] - result3 = s.iloc[2:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # loc will fail - self.assertRaises(TypeError, lambda: s.loc[2:5]) - - # make all float slicing fail - self.assertRaises(TypeError, lambda: s[2.0:5]) - self.assertRaises(TypeError, lambda: s[2.0:5.0]) - self.assertRaises(TypeError, lambda: s[2:5.0]) - - self.assertRaises(TypeError, lambda: s.ix[2.0:5]) - self.assertRaises(TypeError, lambda: s.ix[2.0:5.0]) - self.assertRaises(TypeError, lambda: s.ix[2:5.0]) - - self.assertRaises(TypeError, lambda: s.loc[2.0:5]) - self.assertRaises(TypeError, lambda: s.loc[2.0:5.0]) - self.assertRaises(TypeError, lambda: s.loc[2:5.0]) - - check_iloc_compat(s) - - # all index types except int, float - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - check_slicing_positional(index()) - - ############ - # IntIndex # - ############ - for index in [tm.makeIntIndex(), tm.makeRangeIndex()]: - - s = Series(np.arange(len(index), dtype='int64') + 10, index + 5) - - # this is positional - result1 = s[2:5] - result4 = s.iloc[2:5] - assert_series_equal(result1, result4) - - # these are all label based - result2 = s.ix[2:5] - result3 = s.loc[2:5] - assert_series_equal(result2, result3) - - # float slicers on an int index with ix - expected = Series([11, 12, 13], index=[6, 7, 8]) - result = s.ix[6.0:8.5] - assert_series_equal(result, expected) - - result = s.ix[5.5:8.5] - assert_series_equal(result, expected) - - result = s.ix[5.5:8.0] - assert_series_equal(result, expected) - - for method in ['loc', 'iloc']: - # make all float slicing fail for .loc with an int index - self.assertRaises(TypeError, - lambda: getattr(s, method)[6.0:8]) - self.assertRaises(TypeError, - lambda: getattr(s, method)[6.0:8.0]) - self.assertRaises(TypeError, - lambda: getattr(s, method)[6:8.0]) - - # make all float slicing fail for [] with an int index - self.assertRaises(TypeError, lambda: s[6.0:8]) - self.assertRaises(TypeError, lambda: s[6.0:8.0]) - self.assertRaises(TypeError, lambda: s[6:8.0]) - - check_iloc_compat(s) - - ############## - # FloatIndex # - ############## - s.index = s.index.astype('float64') - - # these are all value based - result1 = s[6:8] - result2 = s.ix[6:8] - result3 = s.loc[6:8] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # these are valid for all methods - # these are treated like labels (e.g. the rhs IS included) - def compare(slicers, expected): - for method in [lambda x: x, lambda x: x.loc, lambda x: x.ix]: - for slices in slicers: - - result = method(s)[slices] - assert_series_equal(result, expected) - - compare([slice(6.0, 8), slice(6.0, 8.0), slice(6, 8.0)], - s[(s.index >= 6.0) & (s.index <= 8)]) - compare([slice(6.5, 8), slice(6.5, 8.5)], - s[(s.index >= 6.5) & (s.index <= 8.5)]) - compare([slice(6, 8.5)], s[(s.index >= 6.0) & (s.index <= 8.5)]) - compare([slice(6.5, 6.5)], s[(s.index >= 6.5) & (s.index <= 6.5)]) - - check_iloc_compat(s) - def test_set_ix_out_of_bounds_axis_0(self): df = pd.DataFrame( randn(2, 5), index=["row%s" % i for i in range(2)], @@ -5339,347 +5044,46 @@ def test_index_type_coercion(self): self.assertTrue(s.index.is_integer()) - for attr in ['ix', 'loc']: + for indexer in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x]: s2 = s.copy() - getattr(s2, attr)[0.1] = 0 + indexer(s2)[0.1] = 0 self.assertTrue(s2.index.is_floating()) - self.assertTrue(getattr(s2, attr)[0.1] == 0) + self.assertTrue(indexer(s2)[0.1] == 0) s2 = s.copy() - getattr(s2, attr)[0.0] = 0 + indexer(s2)[0.0] = 0 exp = s.index if 0 not in s: exp = Index(s.index.tolist() + [0]) tm.assert_index_equal(s2.index, exp) s2 = s.copy() - getattr(s2, attr)['0'] = 0 + indexer(s2)['0'] = 0 self.assertTrue(s2.index.is_object()) - # setitem - s2 = s.copy() - s2[0.1] = 0 - self.assertTrue(s2.index.is_floating()) - self.assertTrue(s2[0.1] == 0) - - s2 = s.copy() - s2[0.0] = 0 - exp = s.index - if 0 not in s: - exp = Index(s.index.tolist() + [0]) - tm.assert_index_equal(s2.index, exp) - - s2 = s.copy() - s2['0'] = 0 - self.assertTrue(s2.index.is_object()) - for s in [Series(range(5), index=np.arange(5.))]: self.assertTrue(s.index.is_floating()) - for attr in ['ix', 'loc']: + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x]: s2 = s.copy() - getattr(s2, attr)[0.1] = 0 + idxr(s2)[0.1] = 0 self.assertTrue(s2.index.is_floating()) - self.assertTrue(getattr(s2, attr)[0.1] == 0) + self.assertTrue(idxr(s2)[0.1] == 0) s2 = s.copy() - getattr(s2, attr)[0.0] = 0 + idxr(s2)[0.0] = 0 tm.assert_index_equal(s2.index, s.index) s2 = s.copy() - getattr(s2, attr)['0'] = 0 + idxr(s2)['0'] = 0 self.assertTrue(s2.index.is_object()) - # setitem - s2 = s.copy() - s2[0.1] = 0 - self.assertTrue(s2.index.is_floating()) - self.assertTrue(s2[0.1] == 0) - - s2 = s.copy() - s2[0.0] = 0 - tm.assert_index_equal(s2.index, s.index) - - s2 = s.copy() - s2['0'] = 0 - self.assertTrue(s2.index.is_object()) - - def test_invalid_scalar_float_indexers_error(self): - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeCategoricalIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - - i = index(5) - - s = Series(np.arange(len(i)), index=i) - - def f(): - s.iloc[3.0] - self.assertRaisesRegexp(TypeError, - 'cannot do positional indexing', - f) - - def test_invalid_scalar_float_indexers(self): - - # GH 4892 - # float_indexers should raise exceptions - # on appropriate Index types & accessors - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeCategoricalIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - - i = index(5) - - for s in [Series( - np.arange(len(i)), index=i), DataFrame( - np.random.randn( - len(i), len(i)), index=i, columns=i)]: - - for attr in ['iloc', 'loc', 'ix', '__getitem__']: - def f(): - getattr(s, attr)()[3.0] - self.assertRaises(TypeError, f) - - # setting only fails with iloc as - # the others expand the index - def f(): - s.iloc[3.0] = 0 - self.assertRaises(TypeError, f) - - # fallsback to position selection ,series only - s = Series(np.arange(len(i)), index=i) - s[3] - self.assertRaises(TypeError, lambda: s[3.0]) - - # integer index - for index in [tm.makeIntIndex, tm.makeRangeIndex]: - - i = index(5) - for s in [Series(np.arange(len(i))), - DataFrame(np.random.randn(len(i), len(i)), - index=i, columns=i)]: - - # any kind of get access should fail - for attr in ['iloc', 'loc', 'ix']: - def f(): - getattr(s, attr)[3.0] - self.assertRaises(TypeError, f) - error = KeyError if isinstance(s, DataFrame) else TypeError - self.assertRaises(error, lambda: s[3.0]) - - # setting only fails with iloc as - def f(): - s.iloc[3.0] = 0 - self.assertRaises(TypeError, f) - - # other indexers will coerce to an object index - # tested explicity in: test_invalid_scalar_float_indexers - # above - - # floats index - index = tm.makeFloatIndex(5) - for s in [Series(np.arange(len(index)), index=index), - DataFrame(np.random.randn(len(index), len(index)), - index=index, columns=index)]: - - # assert all operations except for iloc are ok - indexer = index[3] - expected = s.iloc[3] - - if isinstance(s, Series): - compare = self.assertEqual - else: - compare = tm.assert_series_equal - - for attr in ['loc', 'ix']: - - # getting - result = getattr(s, attr)[indexer] - compare(result, expected) - - # setting - s2 = s.copy() - - def f(): - getattr(s2, attr)[indexer] = expected - result = getattr(s2, attr)[indexer] - compare(result, expected) - - # random integer is a KeyError - self.assertRaises(KeyError, lambda: getattr(s, attr)[3]) - - # iloc succeeds with an integer - result = s.iloc[3] - compare(result, expected) - - s2 = s.copy() - - def f(): - s2.iloc[3] = expected - result = s2.iloc[3] - compare(result, expected) - - # iloc raises with a float - self.assertRaises(TypeError, lambda: s.iloc[3.0]) - - def f(): - s.iloc[3.0] = 0 - self.assertRaises(TypeError, f) - - # getitem - - # getting - if isinstance(s, DataFrame): - expected = s.iloc[:, 3] - result = s[indexer] - compare(result, expected) - - # setting - s2 = s.copy() - - def f(): - s2[indexer] = expected - result = s2[indexer] - compare(result, expected) - - # random integer is a KeyError - result = self.assertRaises(KeyError, lambda: s[3]) - - def test_invalid_slice_float_indexers(self): - - # GH 4892 - # float_indexers should raise exceptions - # on appropriate Index types & accessors - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - - index = index(5) - for s in [Series(range(5), index=index), - DataFrame(np.random.randn(5, 2), index=index)]: - - # getitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] - self.assertRaises(TypeError, f) - - def f(): - s[l] - self.assertRaises(TypeError, f) - - def f(): - s.ix[l] - self.assertRaises(TypeError, f) - - # setitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s.ix[l] = 0 - self.assertRaises(TypeError, f) - - # same as above, but for Integer based indexes - for index in [tm.makeIntIndex, tm.makeRangeIndex]: - - index = index(5) - for s in [Series(range(5), index=index), - DataFrame(np.random.randn(5, 2), index=index)]: - - # getitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] - self.assertRaises(TypeError, f) - - def f(): - s[l] - self.assertRaises(TypeError, f) - - # ix allows float slicing - s.ix[l] - - # setitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s[l] = 0 - self.assertRaises(TypeError, f) - - # ix allows float slicing - s.ix[l] = 0 - - # same as above, but for floats - index = tm.makeFloatIndex(5) - for s in [Series(range(5), index=index), - DataFrame(np.random.randn(5, 2), index=index)]: - - # getitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - # ix is ok - result1 = s.ix[3:4] - result2 = s.ix[3.0:4] - result3 = s.ix[3.0:4.0] - result4 = s.ix[3:4.0] - self.assertTrue(result1.equals(result2)) - self.assertTrue(result1.equals(result3)) - self.assertTrue(result1.equals(result4)) - - # setitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - pass - def test_float_index_to_mixed(self): df = DataFrame({0.0: np.random.rand(10), 1.0: np.random.rand(10)}) df['a'] = 10 @@ -5906,338 +5310,6 @@ def test_maybe_numeric_slice(self): self.assertEqual(result, expected) -class TestCategoricalIndex(tm.TestCase): - - def setUp(self): - - self.df = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': Series(list('aabbca')).astype( - 'category', categories=list( - 'cab'))}).set_index('B') - self.df2 = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': Series(list('aabbca')).astype( - 'category', categories=list( - 'cabe'))}).set_index('B') - self.df3 = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': (Series([1, 1, 2, 1, 3, 2]) - .astype('category', categories=[3, 2, 1], - ordered=True))}).set_index('B') - self.df4 = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': (Series([1, 1, 2, 1, 3, 2]) - .astype('category', categories=[3, 2, 1], - ordered=False))}).set_index('B') - - def test_loc_scalar(self): - result = self.df.loc['a'] - expected = (DataFrame({'A': [0, 1, 5], - 'B': (Series(list('aaa')) - .astype('category', - categories=list('cab')))}) - .set_index('B')) - assert_frame_equal(result, expected) - - df = self.df.copy() - df.loc['a'] = 20 - expected = (DataFrame({'A': [20, 20, 2, 3, 4, 20], - 'B': (Series(list('aabbca')) - .astype('category', - categories=list('cab')))}) - .set_index('B')) - assert_frame_equal(df, expected) - - # value not in the categories - self.assertRaises(KeyError, lambda: df.loc['d']) - - def f(): - df.loc['d'] = 10 - - self.assertRaises(TypeError, f) - - def f(): - df.loc['d', 'A'] = 10 - - self.assertRaises(TypeError, f) - - def f(): - df.loc['d', 'C'] = 10 - - self.assertRaises(TypeError, f) - - def test_loc_listlike(self): - - # list of labels - result = self.df.loc[['c', 'a']] - expected = self.df.iloc[[4, 0, 1, 5]] - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.loc[['a', 'b', 'e']] - exp_index = pd.CategoricalIndex( - list('aaabbe'), categories=list('cabe'), name='B') - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) - assert_frame_equal(result, expected, check_index_type=True) - - # element in the categories but not in the values - self.assertRaises(KeyError, lambda: self.df2.loc['e']) - - # assign is ok - df = self.df2.copy() - df.loc['e'] = 20 - result = df.loc[['a', 'b', 'e']] - exp_index = pd.CategoricalIndex( - list('aaabbe'), categories=list('cabe'), name='B') - expected = DataFrame({'A': [0, 1, 5, 2, 3, 20]}, index=exp_index) - assert_frame_equal(result, expected) - - df = self.df2.copy() - result = df.loc[['a', 'b', 'e']] - exp_index = pd.CategoricalIndex( - list('aaabbe'), categories=list('cabe'), name='B') - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) - assert_frame_equal(result, expected, check_index_type=True) - - # not all labels in the categories - self.assertRaises(KeyError, lambda: self.df2.loc[['a', 'd']]) - - def test_loc_listlike_dtypes(self): - # GH 11586 - - # unique categories and codes - index = pd.CategoricalIndex(['a', 'b', 'c']) - df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) - - # unique slice - res = df.loc[['a', 'b']] - exp = DataFrame({'A': [1, 2], - 'B': [4, 5]}, index=pd.CategoricalIndex(['a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - # duplicated slice - res = df.loc[['a', 'a', 'b']] - exp = DataFrame({'A': [1, 1, 2], - 'B': [4, 4, 5]}, - index=pd.CategoricalIndex(['a', 'a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - with tm.assertRaisesRegexp( - KeyError, - 'a list-indexer must only include values that are ' - 'in the categories'): - df.loc[['a', 'x']] - - # duplicated categories and codes - index = pd.CategoricalIndex(['a', 'b', 'a']) - df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) - - # unique slice - res = df.loc[['a', 'b']] - exp = DataFrame({'A': [1, 3, 2], - 'B': [4, 6, 5]}, - index=pd.CategoricalIndex(['a', 'a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - # duplicated slice - res = df.loc[['a', 'a', 'b']] - exp = DataFrame( - {'A': [1, 3, 1, 3, 2], - 'B': [4, 6, 4, 6, 5 - ]}, index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - with tm.assertRaisesRegexp( - KeyError, - 'a list-indexer must only include values ' - 'that are in the categories'): - df.loc[['a', 'x']] - - # contains unused category - index = pd.CategoricalIndex( - ['a', 'b', 'a', 'c'], categories=list('abcde')) - df = DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=index) - - res = df.loc[['a', 'b']] - exp = DataFrame({'A': [1, 3, 2], - 'B': [5, 7, 6]}, index=pd.CategoricalIndex( - ['a', 'a', 'b'], categories=list('abcde'))) - tm.assert_frame_equal(res, exp, check_index_type=True) - - res = df.loc[['a', 'e']] - exp = DataFrame({'A': [1, 3, np.nan], 'B': [5, 7, np.nan]}, - index=pd.CategoricalIndex(['a', 'a', 'e'], - categories=list('abcde'))) - tm.assert_frame_equal(res, exp, check_index_type=True) - - # duplicated slice - res = df.loc[['a', 'a', 'b']] - exp = DataFrame({'A': [1, 3, 1, 3, 2], 'B': [5, 7, 5, 7, 6]}, - index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'], - categories=list('abcde'))) - tm.assert_frame_equal(res, exp, check_index_type=True) - - with tm.assertRaisesRegexp( - KeyError, - 'a list-indexer must only include values ' - 'that are in the categories'): - df.loc[['a', 'x']] - - def test_read_only_source(self): - # GH 10043 - rw_array = np.eye(10) - rw_df = DataFrame(rw_array) - - ro_array = np.eye(10) - ro_array.setflags(write=False) - ro_df = DataFrame(ro_array) - - assert_frame_equal(rw_df.iloc[[1, 2, 3]], ro_df.iloc[[1, 2, 3]]) - assert_frame_equal(rw_df.iloc[[1]], ro_df.iloc[[1]]) - assert_series_equal(rw_df.iloc[1], ro_df.iloc[1]) - assert_frame_equal(rw_df.iloc[1:3], ro_df.iloc[1:3]) - - assert_frame_equal(rw_df.loc[[1, 2, 3]], ro_df.loc[[1, 2, 3]]) - assert_frame_equal(rw_df.loc[[1]], ro_df.loc[[1]]) - assert_series_equal(rw_df.loc[1], ro_df.loc[1]) - assert_frame_equal(rw_df.loc[1:3], ro_df.loc[1:3]) - - def test_reindexing(self): - - # reindexing - # convert to a regular index - result = self.df2.reindex(['a', 'b', 'e']) - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], - 'B': Series(list('aaabbe'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['a', 'b']) - expected = DataFrame({'A': [0, 1, 5, 2, 3], - 'B': Series(list('aaabb'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['e']) - expected = DataFrame({'A': [np.nan], - 'B': Series(['e'])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['d']) - expected = DataFrame({'A': [np.nan], - 'B': Series(['d'])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - # since we are actually reindexing with a Categorical - # then return a Categorical - cats = list('cabe') - - result = self.df2.reindex(pd.Categorical(['a', 'd'], categories=cats)) - expected = DataFrame({'A': [0, 1, 5, np.nan], - 'B': Series(list('aaad')).astype( - 'category', categories=cats)}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(pd.Categorical(['a'], categories=cats)) - expected = DataFrame({'A': [0, 1, 5], - 'B': Series(list('aaa')).astype( - 'category', categories=cats)}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['a', 'b', 'e']) - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], - 'B': Series(list('aaabbe'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['a', 'b']) - expected = DataFrame({'A': [0, 1, 5, 2, 3], - 'B': Series(list('aaabb'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['e']) - expected = DataFrame({'A': [np.nan], - 'B': Series(['e'])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - # give back the type of categorical that we received - result = self.df2.reindex(pd.Categorical( - ['a', 'd'], categories=cats, ordered=True)) - expected = DataFrame( - {'A': [0, 1, 5, np.nan], - 'B': Series(list('aaad')).astype('category', categories=cats, - ordered=True)}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(pd.Categorical( - ['a', 'd'], categories=['a', 'd'])) - expected = DataFrame({'A': [0, 1, 5, np.nan], - 'B': Series(list('aaad')).astype( - 'category', categories=['a', 'd' - ])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - # passed duplicate indexers are not allowed - self.assertRaises(ValueError, lambda: self.df2.reindex(['a', 'a'])) - - # args NotImplemented ATM - self.assertRaises(NotImplementedError, - lambda: self.df2.reindex(['a'], method='ffill')) - self.assertRaises(NotImplementedError, - lambda: self.df2.reindex(['a'], level=1)) - self.assertRaises(NotImplementedError, - lambda: self.df2.reindex(['a'], limit=2)) - - def test_loc_slice(self): - # slicing - # not implemented ATM - # GH9748 - - self.assertRaises(TypeError, lambda: self.df.loc[1:5]) - - # result = df.loc[1:5] - # expected = df.iloc[[1,2,3,4]] - # assert_frame_equal(result, expected) - - def test_boolean_selection(self): - - df3 = self.df3 - df4 = self.df4 - - result = df3[df3.index == 'a'] - expected = df3.iloc[[]] - assert_frame_equal(result, expected) - - result = df4[df4.index == 'a'] - expected = df4.iloc[[]] - assert_frame_equal(result, expected) - - result = df3[df3.index == 1] - expected = df3.iloc[[0, 1, 3]] - assert_frame_equal(result, expected) - - result = df4[df4.index == 1] - expected = df4.iloc[[0, 1, 3]] - assert_frame_equal(result, expected) - - # since we have an ordered categorical - - # CategoricalIndex([1, 1, 2, 1, 3, 2], - # categories=[3, 2, 1], - # ordered=True, - # name=u'B') - result = df3[df3.index < 2] - expected = df3.iloc[[4]] - assert_frame_equal(result, expected) - - result = df3[df3.index > 1] - expected = df3.iloc[[]] - assert_frame_equal(result, expected) - - # unordered - # cannot be compared - - # CategoricalIndex([1, 1, 2, 1, 3, 2], - # categories=[3, 2, 1], - # ordered=False, - # name=u'B') - self.assertRaises(TypeError, lambda: df4[df4.index < 2]) - self.assertRaises(TypeError, lambda: df4[df4.index > 1]) - - class TestSeriesNoneCoercion(tm.TestCase): EXPECTED_RESULTS = [ # For numeric series, we should coerce to NaN. diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index d4121fb86de79..6e9df1661d139 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -32,8 +32,8 @@ def test_dt_namespace_accessor(self): 'weekofyear', 'week', 'dayofweek', 'weekday', 'dayofyear', 'quarter', 'freq', 'days_in_month', 'daysinmonth'] - ok_for_period = ok_for_base + ['qyear'] - ok_for_period_methods = ['strftime'] + ok_for_period = ok_for_base + ['qyear', 'start_time', 'end_time'] + ok_for_period_methods = ['strftime', 'to_timestamp', 'asfreq'] ok_for_dt = ok_for_base + ['date', 'time', 'microsecond', 'nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', diff --git a/pandas/tests/test_compat.py b/pandas/tests/test_compat.py index 2ea95b4e0b300..68c0b81eb18ce 100644 --- a/pandas/tests/test_compat.py +++ b/pandas/tests/test_compat.py @@ -4,7 +4,8 @@ """ from pandas.compat import (range, zip, map, filter, lrange, lzip, lmap, - lfilter, builtins) + lfilter, builtins, iterkeys, itervalues, iteritems, + next) import pandas.util.testing as tm @@ -61,3 +62,8 @@ def test_zip(self): expected = list(builtins.zip(*lst)), lengths = 10, self.check_result(actual, expected, lengths) + + def test_dict_iterators(self): + self.assertEqual(next(itervalues({1: 2})), 2) + self.assertEqual(next(iterkeys({1: 2})), 1) + self.assertEqual(next(iteritems({1: 2})), (1, 2)) diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index a309d88e62857..1198d6b194c60 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -956,7 +956,8 @@ def test_describe_objects(self): s = Series(['a', 'b', 'b', np.nan, np.nan, np.nan, 'c', 'd', 'a', 'a']) result = s.describe() expected = Series({'count': 7, 'unique': 4, - 'top': 'a', 'freq': 3}, index=result.index) + 'top': 'a', 'freq': 3, 'second': 'b', + 'second_freq': 2}, index=result.index) assert_series_equal(result, expected) dt = list(self.ts.index) @@ -1487,9 +1488,8 @@ def test_describe_typefiltering_category_bool(self): 'D_num': np.arange(24.) + .5, 'E_ts': tm.makeTimeSeries()[:24].index}) - # bool is considered numeric in describe, although not an np.number desc = df.describe() - expected_cols = ['C_bool', 'D_num'] + expected_cols = ['D_num'] expected = DataFrame(dict((k, df[k].describe()) for k in expected_cols), columns=expected_cols) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index b339d25cd6c45..45d3fd0dad855 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -17,7 +17,9 @@ from pandas.util.decorators import cache_readonly import pandas.core.common as com import pandas.util.testing as tm -from pandas.util.testing import ensure_clean +from pandas.util.testing import (ensure_clean, + assert_is_valid_plot_return_object) + from pandas.core.config import set_option import numpy as np @@ -60,8 +62,8 @@ def setUp(self): n = 100 with tm.RNGContext(42): - gender = tm.choice(['Male', 'Female'], size=n) - classroom = tm.choice(['A', 'B', 'C'], size=n) + gender = np.random.choice(['Male', 'Female'], size=n) + classroom = np.random.choice(['A', 'B', 'C'], size=n) self.hist_df = DataFrame({'gender': gender, 'classroom': classroom, @@ -3861,7 +3863,7 @@ def test_series_groupby_plotting_nominally_works(self): weight = Series(np.random.normal(166, 20, size=n)) height = Series(np.random.normal(60, 10, size=n)) with tm.RNGContext(42): - gender = tm.choice(['male', 'female'], size=n) + gender = np.random.choice(['male', 'female'], size=n) weight.groupby(gender).plot() tm.close() @@ -3916,21 +3918,6 @@ def test_plot_kwargs(self): self.assertEqual(len(res['a'].collections), 1) -def assert_is_valid_plot_return_object(objs): - import matplotlib.pyplot as plt - if isinstance(objs, np.ndarray): - for el in objs.flat: - assert isinstance(el, plt.Axes), ('one of \'objs\' is not a ' - 'matplotlib Axes instance, ' - 'type encountered {0!r}' - ''.format(el.__class__.__name__)) - else: - assert isinstance(objs, (plt.Artist, tuple, dict)), \ - ('objs is neither an ndarray of Artist instances nor a ' - 'single Artist instance, tuple, or dict, "objs" is a {0!r} ' - ''.format(objs.__class__.__name__)) - - def _check_plot_works(f, filterwarnings='always', **kwargs): import matplotlib.pyplot as plt ret = None diff --git a/pandas/tests/test_graphics_others.py b/pandas/tests/test_graphics_others.py index 983d0c310f71d..b032ce196c113 100644 --- a/pandas/tests/test_graphics_others.py +++ b/pandas/tests/test_graphics_others.py @@ -641,7 +641,7 @@ def test_grouped_plot_fignums(self): weight = Series(np.random.normal(166, 20, size=n)) height = Series(np.random.normal(60, 10, size=n)) with tm.RNGContext(42): - gender = tm.choice(['male', 'female'], size=n) + gender = np.random.choice(['male', 'female'], size=n) df = DataFrame({'height': height, 'weight': weight, 'gender': gender}) gb = df.groupby('gender') @@ -715,7 +715,7 @@ def test_grouped_hist_legacy2(self): weight = Series(np.random.normal(166, 20, size=n)) height = Series(np.random.normal(60, 10, size=n)) with tm.RNGContext(42): - gender_int = tm.choice([0, 1], size=n) + gender_int = np.random.choice([0, 1], size=n) df_int = DataFrame({'height': height, 'weight': weight, 'gender': gender_int}) gb = df_int.groupby('gender') diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 7ee40a7758011..947daab2017d3 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -3993,11 +3993,13 @@ def test_groupby_groups_datetimeindex_tz(self): df['datetime'] = df['datetime'].apply( lambda d: Timestamp(d, tz='US/Pacific')) - exp_idx1 = pd.DatetimeIndex( - ['2011-07-19 07:00:00', '2011-07-19 07:00:00', - '2011-07-19 08:00:00', '2011-07-19 08:00:00', - '2011-07-19 09:00:00', '2011-07-19 09:00:00'], - tz='US/Pacific', name='datetime') + exp_idx1 = pd.DatetimeIndex(['2011-07-19 07:00:00', + '2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00', + '2011-07-19 09:00:00'], + tz='US/Pacific', name='datetime') exp_idx2 = Index(['a', 'b'] * 3, name='label') exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], @@ -4013,9 +4015,9 @@ def test_groupby_groups_datetimeindex_tz(self): 'value2': [1, 2, 3, 1, 2, 3]}, index=didx) - exp_idx = pd.DatetimeIndex( - ['2011-07-19 07:00:00', '2011-07-19 08:00:00', - '2011-07-19 09:00:00'], tz='Asia/Tokyo') + exp_idx = pd.DatetimeIndex(['2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00'], tz='Asia/Tokyo') expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, index=exp_idx, columns=['value1', 'value2']) @@ -4032,8 +4034,8 @@ def test_groupby_multi_timezone(self): 3,2000-01-31 16:50:00,America/Chicago 4,2000-01-01 16:50:00,America/New_York""" - df = pd.read_csv( - StringIO(data), header=None, names=['value', 'date', 'tz']) + df = pd.read_csv(StringIO(data), header=None, + names=['value', 'date', 'tz']) result = df.groupby('tz').date.apply( lambda x: pd.to_datetime(x).dt.tz_localize(x.name)) @@ -4051,14 +4053,54 @@ def test_groupby_multi_timezone(self): assert_series_equal(result, expected) tz = 'America/Chicago' - result = pd.to_datetime(df.groupby('tz').date.get_group( - tz)).dt.tz_localize(tz) - expected = pd.to_datetime(Series( - ['2000-01-28 16:47:00', '2000-01-29 16:48:00', - '2000-01-31 16:50:00'], index=[0, 1, 3 - ], name='date')).dt.tz_localize(tz) + res_values = df.groupby('tz').date.get_group(tz) + result = pd.to_datetime(res_values).dt.tz_localize(tz) + exp_values = Series(['2000-01-28 16:47:00', '2000-01-29 16:48:00', + '2000-01-31 16:50:00'], + index=[0, 1, 3], name='date') + expected = pd.to_datetime(exp_values).dt.tz_localize(tz) assert_series_equal(result, expected) + def test_groupby_groups_periods(self): + dates = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', + '2011-07-19 09:00:00', '2011-07-19 07:00:00', + '2011-07-19 08:00:00', '2011-07-19 09:00:00'] + df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'], + 'period': [pd.Period(d, freq='H') for d in dates], + 'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2] * 3}) + + exp_idx1 = pd.PeriodIndex(['2011-07-19 07:00:00', + '2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00', + '2011-07-19 09:00:00'], + freq='H', name='period') + exp_idx2 = Index(['a', 'b'] * 3, name='label') + exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) + expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], + 'value2': [1, 2, 2, 1, 1, 2]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(['period', 'label']).sum() + assert_frame_equal(result, expected) + + # by level + didx = pd.PeriodIndex(dates, freq='H') + df = DataFrame({'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2, 3, 1, 2, 3]}, + index=didx) + + exp_idx = pd.PeriodIndex(['2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00'], freq='H') + expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(level=0).sum() + assert_frame_equal(result, expected) + def test_groupby_reindex_inside_function(self): from pandas.tseries.api import DatetimeIndex @@ -5568,7 +5610,8 @@ def test_tab_completion(self): 'cumprod', 'tail', 'resample', 'cummin', 'fillna', 'cumsum', 'cumcount', 'all', 'shift', 'skew', 'bfill', 'ffill', 'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith', 'cov', - 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin']) + 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin', + 'ffill', 'bfill', 'pad', 'backfill']) self.assertEqual(results, expected) def test_lexsort_indexer(self): @@ -6104,6 +6147,21 @@ def test_nunique_with_object(self): expected = pd.Series([1] * 5, name='name', index=index) tm.assert_series_equal(result, expected) + def test_transform_with_non_scalar_group(self): + # GH 10165 + cols = pd.MultiIndex.from_tuples([ + ('syn', 'A'), ('mis', 'A'), ('non', 'A'), + ('syn', 'C'), ('mis', 'C'), ('non', 'C'), + ('syn', 'T'), ('mis', 'T'), ('non', 'T'), + ('syn', 'G'), ('mis', 'G'), ('non', 'G')]) + df = pd.DataFrame(np.random.randint(1, 10, (4, 12)), + columns=cols, + index=['A', 'C', 'G', 'T']) + self.assertRaisesRegexp(ValueError, 'transform must return a scalar ' + 'value for each group.*', df.groupby + (axis=1, level=1).transform, + lambda z: z.div(z.sum(axis=1), axis=0)) + def assert_fp_equal(a, b): assert (np.abs(a - b) < 1e-12).all() diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index dd7468723c9c7..0a1e15921dad7 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -318,10 +318,10 @@ def test_keys(self): def test_iteritems(self): # Test panel.iteritems(), aka panel.iteritems() # just test that it works - for k, v in compat.iteritems(self.panel): + for k, v in self.panel.iteritems(): pass - self.assertEqual(len(list(compat.iteritems(self.panel))), + self.assertEqual(len(list(self.panel.iteritems())), len(self.panel.items)) @ignore_sparse_panel_future_warning @@ -1105,7 +1105,7 @@ def test_ctor_dict(self): assert_panel_equal(result, expected) def test_constructor_dict_mixed(self): - data = dict((k, v.values) for k, v in compat.iteritems(self.panel)) + data = dict((k, v.values) for k, v in self.panel.iteritems()) result = Panel(data) exp_major = Index(np.arange(len(self.panel.major_axis))) self.assertTrue(result.major_axis.equals(exp_major)) @@ -1872,7 +1872,7 @@ def test_shift(self): # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel(dict((i, f.shift(-1)[:-1]) - for i, f in compat.iteritems(self.panel))) + for i, f in self.panel.iteritems())) assert_panel_equal(result, expected) # mixed dtypes #6959 @@ -2072,7 +2072,7 @@ def test_to_excel(self): except ImportError: raise nose.SkipTest("need xlwt xlrd openpyxl") - for item, df in compat.iteritems(self.panel): + for item, df in self.panel.iteritems(): recdf = reader.parse(str(item), index_col=0) assert_frame_equal(df, recdf) @@ -2092,7 +2092,7 @@ def test_to_excel_xlsxwriter(self): except ImportError as e: raise nose.SkipTest("cannot write excel file: %s" % e) - for item, df in compat.iteritems(self.panel): + for item, df in self.panel.iteritems(): recdf = reader.parse(str(item), index_col=0) assert_frame_equal(df, recdf) diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 6238f13864552..40447fffdebbd 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -12,7 +12,6 @@ from pandas.core.panel4d import Panel4D from pandas.core.series import remove_na import pandas.core.common as com -from pandas import compat from pandas.util.testing import (assert_panel_equal, assert_panel4d_equal, @@ -232,7 +231,7 @@ def test_keys(self): def test_iteritems(self): """Test panel4d.iteritems()""" - self.assertEqual(len(list(compat.iteritems(self.panel4d))), + self.assertEqual(len(list(self.panel4d.iteritems())), len(self.panel4d.labels)) def test_combinePanel4d(self): @@ -731,7 +730,7 @@ def test_ctor_dict(self): # assert_panel_equal(result, expected) def test_constructor_dict_mixed(self): - data = dict((k, v.values) for k, v in compat.iteritems(self.panel4d)) + data = dict((k, v.values) for k, v in self.panel4d.iteritems()) result = Panel4D(data) exp_major = Index(np.arange(len(self.panel4d.major_axis))) self.assertTrue(result.major_axis.equals(exp_major)) diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py index ef5a966d65545..bfabaab8ad2f5 100644 --- a/pandas/tests/test_style.py +++ b/pandas/tests/test_style.py @@ -17,9 +17,12 @@ if job_name == '27_slow_nnet_LOCALE': raise SkipTest("No jinja") try: - from pandas.core.style import Styler + # Do try except on just jinja, so the only reason + # We skip is if jinja can't import, not something else + import jinja2 # noqa except ImportError: raise SkipTest("No Jinja2") +from pandas.core.style import Styler # noqa class TestStyler(TestCase): @@ -129,6 +132,27 @@ def test_set_properties_subset(self): expected = {(0, 0): ['color: white']} self.assertEqual(result, expected) + def test_empty_index_name_doesnt_display(self): + # https://github.com/pydata/pandas/pull/12090#issuecomment-180695902 + df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}) + result = df.style._translate() + + expected = [[{'class': 'blank', 'type': 'th', 'value': ''}, + {'class': 'col_heading level0 col0', + 'display_value': 'A', + 'type': 'th', + 'value': 'A'}, + {'class': 'col_heading level0 col1', + 'display_value': 'B', + 'type': 'th', + 'value': 'B'}, + {'class': 'col_heading level0 col2', + 'display_value': 'C', + 'type': 'th', + 'value': 'C'}]] + + self.assertEqual(result['head'], expected) + def test_index_name(self): # https://github.com/pydata/pandas/issues/11655 df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}) diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index cc4a6ba61306d..d5647d1b5f822 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -3,6 +3,7 @@ import sys import warnings +from nose.tools import assert_raises from datetime import datetime from numpy.random import randn from numpy.testing.decorators import slow @@ -14,7 +15,7 @@ notnull, concat) from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, - assert_index_equal) + assert_index_equal, assert_numpy_array_equal) import pandas.core.datetools as datetools import pandas.stats.moments as mom import pandas.core.window as rwindow @@ -98,19 +99,6 @@ def tests_skip_nuisance(self): result = r.sum() assert_frame_equal(result, expected) - def test_timedeltas(self): - - df = DataFrame({'A': range(5), - 'B': pd.timedelta_range('1 day', periods=5)}) - r = df.rolling(window=3) - result = r.sum() - expected = DataFrame({'A': [np.nan, np.nan, 3, 6, 9], - 'B': pd.to_timedelta([pd.NaT, pd.NaT, - '6 days', '9 days', - '12 days'])}, - columns=list('AB')) - assert_frame_equal(result, expected) - def test_agg(self): df = DataFrame({'A': range(5), 'B': range(0, 10, 2)}) @@ -289,6 +277,218 @@ def test_deprecations(self): mom.rolling_mean(Series(np.ones(10)), 3, center=True, axis=0) +# GH #12373 : rolling functions error on float32 data +# make sure rolling functions works for different dtypes +# +# NOTE that these are yielded tests and so _create_data is +# explicity called, nor do these inherit from unittest.TestCase +# +# further note that we are only checking rolling for fully dtype +# compliance (though both expanding and ewm inherit) +class Dtype(object): + window = 2 + + funcs = { + 'count': lambda v: v.count(), + 'max': lambda v: v.max(), + 'min': lambda v: v.min(), + 'sum': lambda v: v.sum(), + 'mean': lambda v: v.mean(), + 'std': lambda v: v.std(), + 'var': lambda v: v.var(), + 'median': lambda v: v.median() + } + + def get_expects(self): + expects = { + 'sr1': { + 'count': Series([1, 2, 2, 2, 2], dtype='float64'), + 'max': Series([np.nan, 1, 2, 3, 4], dtype='float64'), + 'min': Series([np.nan, 0, 1, 2, 3], dtype='float64'), + 'sum': Series([np.nan, 1, 3, 5, 7], dtype='float64'), + 'mean': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64'), + 'std': Series([np.nan] + [np.sqrt(.5)] * 4, dtype='float64'), + 'var': Series([np.nan, .5, .5, .5, .5], dtype='float64'), + 'median': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64') + }, + 'sr2': { + 'count': Series([1, 2, 2, 2, 2], dtype='float64'), + 'max': Series([np.nan, 10, 8, 6, 4], dtype='float64'), + 'min': Series([np.nan, 8, 6, 4, 2], dtype='float64'), + 'sum': Series([np.nan, 18, 14, 10, 6], dtype='float64'), + 'mean': Series([np.nan, 9, 7, 5, 3], dtype='float64'), + 'std': Series([np.nan] + [np.sqrt(2)] * 4, dtype='float64'), + 'var': Series([np.nan, 2, 2, 2, 2], dtype='float64'), + 'median': Series([np.nan, 9, 7, 5, 3], dtype='float64') + }, + 'df': { + 'count': DataFrame({0: Series([1, 2, 2, 2, 2]), + 1: Series([1, 2, 2, 2, 2])}, + dtype='float64'), + 'max': DataFrame({0: Series([np.nan, 2, 4, 6, 8]), + 1: Series([np.nan, 3, 5, 7, 9])}, + dtype='float64'), + 'min': DataFrame({0: Series([np.nan, 0, 2, 4, 6]), + 1: Series([np.nan, 1, 3, 5, 7])}, + dtype='float64'), + 'sum': DataFrame({0: Series([np.nan, 2, 6, 10, 14]), + 1: Series([np.nan, 4, 8, 12, 16])}, + dtype='float64'), + 'mean': DataFrame({0: Series([np.nan, 1, 3, 5, 7]), + 1: Series([np.nan, 2, 4, 6, 8])}, + dtype='float64'), + 'std': DataFrame({0: Series([np.nan] + [np.sqrt(2)] * 4), + 1: Series([np.nan] + [np.sqrt(2)] * 4)}, + dtype='float64'), + 'var': DataFrame({0: Series([np.nan, 2, 2, 2, 2]), + 1: Series([np.nan, 2, 2, 2, 2])}, + dtype='float64'), + 'median': DataFrame({0: Series([np.nan, 1, 3, 5, 7]), + 1: Series([np.nan, 2, 4, 6, 8])}, + dtype='float64'), + } + } + return expects + + def _create_dtype_data(self, dtype): + sr1 = Series(range(5), dtype=dtype) + sr2 = Series(range(10, 0, -2), dtype=dtype) + df = DataFrame(np.arange(10).reshape((5, 2)), dtype=dtype) + + data = { + 'sr1': sr1, + 'sr2': sr2, + 'df': df + } + + return data + + def _create_data(self): + self.data = self._create_dtype_data(self.dtype) + self.expects = self.get_expects() + + def test_dtypes(self): + self._create_data() + for f_name, d_name in product(self.funcs.keys(), self.data.keys()): + f = self.funcs[f_name] + d = self.data[d_name] + exp = self.expects[d_name][f_name] + yield self.check_dtypes, f, f_name, d, d_name, exp + + def check_dtypes(self, f, f_name, d, d_name, exp): + roll = d.rolling(window=self.window) + result = f(roll) + assert_almost_equal(result, exp) + + +class TestDtype_object(Dtype): + dtype = object + + +class Dtype_integer(Dtype): + pass + + +class TestDtype_int8(Dtype_integer): + dtype = np.int8 + + +class TestDtype_int16(Dtype_integer): + dtype = np.int16 + + +class TestDtype_int32(Dtype_integer): + dtype = np.int32 + + +class TestDtype_int64(Dtype_integer): + dtype = np.int64 + + +class Dtype_uinteger(Dtype): + pass + + +class TestDtype_uint8(Dtype_uinteger): + dtype = np.uint8 + + +class TestDtype_uint16(Dtype_uinteger): + dtype = np.uint16 + + +class TestDtype_uint32(Dtype_uinteger): + dtype = np.uint32 + + +class TestDtype_uint64(Dtype_uinteger): + dtype = np.uint64 + + +class Dtype_float(Dtype): + pass + + +class TestDtype_float16(Dtype_float): + dtype = np.float16 + + +class TestDtype_float32(Dtype_float): + dtype = np.float32 + + +class TestDtype_float64(Dtype_float): + dtype = np.float64 + + +class TestDtype_category(Dtype): + dtype = 'category' + include_df = False + + def _create_dtype_data(self, dtype): + sr1 = Series(range(5), dtype=dtype) + sr2 = Series(range(10, 0, -2), dtype=dtype) + + data = { + 'sr1': sr1, + 'sr2': sr2 + } + + return data + + +class DatetimeLike(Dtype): + + def check_dtypes(self, f, f_name, d, d_name, exp): + + roll = d.rolling(window=self.window) + + if f_name == 'count': + result = f(roll) + assert_almost_equal(result, exp) + + else: + + # other methods not Implemented ATM + assert_raises(NotImplementedError, f, roll) + + +class TestDtype_timedelta(DatetimeLike): + dtype = np.dtype('m8[ns]') + + +class TestDtype_datetime(DatetimeLike): + dtype = np.dtype('M8[ns]') + + +class TestDtype_datetime64UTC(DatetimeLike): + dtype = 'datetime64[ns, UTC]' + + def _create_data(self): + raise nose.SkipTest("direct creation of extension dtype " + "datetime64[ns, UTC] is not supported ATM") + + class TestMoments(Base): def setUp(self): @@ -1049,8 +1249,8 @@ def test_ewma_span_com_args(self): B = mom.ewma(self.arr, span=20) assert_almost_equal(A, B) - self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20) - self.assertRaises(Exception, mom.ewma, self.arr) + self.assertRaises(ValueError, mom.ewma, self.arr, com=9.5, span=20) + self.assertRaises(ValueError, mom.ewma, self.arr) def test_ewma_halflife_arg(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): @@ -1058,13 +1258,78 @@ def test_ewma_halflife_arg(self): B = mom.ewma(self.arr, halflife=10.0) assert_almost_equal(A, B) - self.assertRaises(Exception, mom.ewma, self.arr, span=20, + self.assertRaises(ValueError, mom.ewma, self.arr, span=20, halflife=50) - self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, + self.assertRaises(ValueError, mom.ewma, self.arr, com=9.5, halflife=50) - self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20, + self.assertRaises(ValueError, mom.ewma, self.arr, com=9.5, span=20, halflife=50) - self.assertRaises(Exception, mom.ewma, self.arr) + self.assertRaises(ValueError, mom.ewma, self.arr) + + def test_ewma_alpha_old_api(self): + # GH 10789 + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + a = mom.ewma(self.arr, alpha=0.61722699889169674) + b = mom.ewma(self.arr, com=0.62014947789973052) + c = mom.ewma(self.arr, span=2.240298955799461) + d = mom.ewma(self.arr, halflife=0.721792864318) + assert_numpy_array_equal(a, b) + assert_numpy_array_equal(a, c) + assert_numpy_array_equal(a, d) + + def test_ewma_alpha_arg_old_api(self): + # GH 10789 + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + self.assertRaises(ValueError, mom.ewma, self.arr) + self.assertRaises(ValueError, mom.ewma, self.arr, + com=10.0, alpha=0.5) + self.assertRaises(ValueError, mom.ewma, self.arr, + span=10.0, alpha=0.5) + self.assertRaises(ValueError, mom.ewma, self.arr, + halflife=10.0, alpha=0.5) + + def test_ewm_alpha(self): + # GH 10789 + s = Series(self.arr) + a = s.ewm(alpha=0.61722699889169674).mean() + b = s.ewm(com=0.62014947789973052).mean() + c = s.ewm(span=2.240298955799461).mean() + d = s.ewm(halflife=0.721792864318).mean() + assert_series_equal(a, b) + assert_series_equal(a, c) + assert_series_equal(a, d) + + def test_ewm_alpha_arg(self): + # GH 10789 + s = Series(self.arr) + self.assertRaises(ValueError, s.ewm) + self.assertRaises(ValueError, s.ewm, com=10.0, alpha=0.5) + self.assertRaises(ValueError, s.ewm, span=10.0, alpha=0.5) + self.assertRaises(ValueError, s.ewm, halflife=10.0, alpha=0.5) + + def test_ewm_domain_checks(self): + # GH 12492 + s = Series(self.arr) + # com must satisfy: com >= 0 + self.assertRaises(ValueError, s.ewm, com=-0.1) + s.ewm(com=0.0) + s.ewm(com=0.1) + # span must satisfy: span >= 1 + self.assertRaises(ValueError, s.ewm, span=-0.1) + self.assertRaises(ValueError, s.ewm, span=0.0) + self.assertRaises(ValueError, s.ewm, span=0.9) + s.ewm(span=1.0) + s.ewm(span=1.1) + # halflife must satisfy: halflife > 0 + self.assertRaises(ValueError, s.ewm, halflife=-0.1) + self.assertRaises(ValueError, s.ewm, halflife=0.0) + s.ewm(halflife=0.1) + # alpha must satisfy: 0 < alpha <= 1 + self.assertRaises(ValueError, s.ewm, alpha=-0.1) + self.assertRaises(ValueError, s.ewm, alpha=0.0) + s.ewm(alpha=0.1) + s.ewm(alpha=1.0) + self.assertRaises(ValueError, s.ewm, alpha=1.1) def test_ew_empty_arrays(self): arr = np.array([], dtype=np.float64) @@ -2432,3 +2697,24 @@ def test_rolling_median_memory_error(self): n = 20000 Series(np.random.randn(n)).rolling(window=2, center=False).median() Series(np.random.randn(n)).rolling(window=2, center=False).median() + + def test_rolling_min_max_numeric_types(self): + # GH12373 + types_test = [np.dtype("f{}".format(width)) for width in [4, 8]] + types_test.extend([np.dtype("{}{}".format(sign, width)) + for width in [1, 2, 4, 8] for sign in "ui"]) + for data_type in types_test: + # Just testing that these don't throw exceptions and that + # the types match. Other tests will cover quantitative + # correctness + for convert_to_float in [True]: + if not convert_to_float: + expected_type = data_type + else: + expected_type = np.dtype(float) + result = (DataFrame(np.arange(20, dtype=data_type)) + .rolling(window=5).max(as_float=convert_to_float)) + self.assertEqual(result.dtypes[0], expected_type) + result = (DataFrame(np.arange(20, dtype=data_type)) + .rolling(window=5).min(as_float=convert_to_float)) + self.assertEqual(result.dtypes[0], expected_type) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 046d2322165b5..d5ddfe624e240 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -236,27 +236,27 @@ def test_join_on(self): def test_join_on_fails_with_different_right_index(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}, index=tm.makeCustomIndex(10, 2)) merge(df, df2, left_on='a', right_index=True) def test_join_on_fails_with_different_left_index(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}, index=tm.makeCustomIndex(10, 2)) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}) merge(df, df2, right_on='b', left_index=True) def test_join_on_fails_with_different_column_counts(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}, index=tm.makeCustomIndex(10, 2)) merge(df, df2, right_on='a', left_on=['a', 'b']) @@ -1031,6 +1031,36 @@ def test_merge_on_datetime64tz(self): result = pd.merge(left, right, on='key', how='outer') assert_frame_equal(result, expected) + def test_merge_on_periods(self): + left = pd.DataFrame({'key': pd.period_range('20151010', periods=2, + freq='D'), + 'value': [1, 2]}) + right = pd.DataFrame({'key': pd.period_range('20151011', periods=3, + freq='D'), + 'value': [1, 2, 3]}) + + expected = DataFrame({'key': pd.period_range('20151010', periods=4, + freq='D'), + 'value_x': [1, 2, np.nan, np.nan], + 'value_y': [np.nan, 1, 2, 3]}) + result = pd.merge(left, right, on='key', how='outer') + assert_frame_equal(result, expected) + + left = pd.DataFrame({'value': pd.period_range('20151010', periods=2, + freq='D'), + 'key': [1, 2]}) + right = pd.DataFrame({'value': pd.period_range('20151011', periods=2, + freq='D'), + 'key': [2, 3]}) + + exp_x = pd.period_range('20151010', periods=2, freq='D') + exp_y = pd.period_range('20151011', periods=2, freq='D') + expected = DataFrame({'value_x': list(exp_x) + [pd.NaT], + 'value_y': [pd.NaT] + list(exp_y), + 'key': [1., 2, 3]}) + result = pd.merge(left, right, on='key', how='outer') + assert_frame_equal(result, expected) + def test_concat_NaT_series(self): # GH 11693 # test for merging NaT series with datetime series. @@ -1131,6 +1161,39 @@ def test_concat_tz_series(self): result = pd.concat([first, second]) self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + def test_concat_period_series(self): + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # different freq + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # non-period + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(['A', 'B']) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [ @@ -2671,7 +2734,7 @@ def test_panel_join_many(self): data_dict = {} for p in panels: - data_dict.update(compat.iteritems(p)) + data_dict.update(p.iteritems()) joined = panels[0].join(panels[1:], how='inner') expected = Panel.from_dict(data_dict, intersect=True) diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 845f50aa65d70..994269d36cd85 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -240,6 +240,39 @@ def test_pivot_with_tz(self): pv = df.pivot(index='dt1', columns='dt2', values='data1') tm.assert_frame_equal(pv, expected) + def test_pivot_periods(self): + df = DataFrame({'p1': [pd.Period('2013-01-01', 'D'), + pd.Period('2013-01-02', 'D'), + pd.Period('2013-01-01', 'D'), + pd.Period('2013-01-02', 'D')], + 'p2': [pd.Period('2013-01', 'M'), + pd.Period('2013-01', 'M'), + pd.Period('2013-02', 'M'), + pd.Period('2013-02', 'M')], + 'data1': np.arange(4, dtype='int64'), + 'data2': np.arange(4, dtype='int64')}) + + exp_col1 = Index(['data1', 'data1', 'data2', 'data2']) + exp_col2 = pd.PeriodIndex(['2013-01', '2013-02'] * 2, + name='p2', freq='M') + exp_col = pd.MultiIndex.from_arrays([exp_col1, exp_col2]) + expected = DataFrame([[0, 2, 0, 2], [1, 3, 1, 3]], + index=pd.PeriodIndex(['2013-01-01', '2013-01-02'], + name='p1', freq='D'), + columns=exp_col) + + pv = df.pivot(index='p1', columns='p2') + tm.assert_frame_equal(pv, expected) + + expected = DataFrame([[0, 2], [1, 3]], + index=pd.PeriodIndex(['2013-01-01', '2013-01-02'], + name='p1', freq='D'), + columns=pd.PeriodIndex(['2013-01', '2013-02'], + name='p2', freq='M')) + + pv = df.pivot(index='p1', columns='p2', values='data1') + tm.assert_frame_equal(pv, expected) + def test_margins(self): def _check_output(result, values_col, index=['A', 'B'], columns=['C'], diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 62a7ad078da70..7584b99dbdb97 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -453,12 +453,20 @@ def _convert_scalar_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) + kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ - if (kind in ['loc'] and lib.isscalar(key) and - (is_integer(key) or is_float(key))): - self._invalid_indexer('index', key) + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] + + # we don't allow integer/float indexing for loc + # we don't allow float indexing for ix/getitem + if lib.isscalar(key): + is_int = is_integer(key) + is_flt = is_float(key) + if kind in ['loc'] and (is_int or is_flt): + self._invalid_indexer('index', key) + elif kind in ['ix', 'getitem'] and is_flt: + self._invalid_indexer('index', key) return (super(DatetimeIndexOpsMixin, self) ._convert_scalar_indexer(key, kind=kind)) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index c745f1b2eddf9..b3b43e1a5babb 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1443,7 +1443,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -1454,6 +1454,8 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem', None] + if is_float(label) or isinstance(label, time) or is_integer(label): self._invalid_indexer('slice', label) @@ -1500,7 +1502,7 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): raise KeyError('Cannot mix time and non-time slice keys') try: - return Index.slice_indexer(self, start, end, step) + return Index.slice_indexer(self, start, end, step, kind=kind) except KeyError: # For historical reasons DatetimeIndex by default supports # value-based partial (aka string) slices on non-monotonic arrays, diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index f34936f9c7b82..df04984bcb582 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -156,7 +156,8 @@ class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin, Int64Index): _datetimelike_ops = ['year', 'month', 'day', 'hour', 'minute', 'second', 'weekofyear', 'week', 'dayofweek', 'weekday', 'dayofyear', 'quarter', 'qyear', 'freq', - 'days_in_month', 'daysinmonth'] + 'days_in_month', 'daysinmonth', + 'to_timestamp', 'asfreq', 'start_time', 'end_time'] _is_numeric_dtype = False _infer_as_myclass = True @@ -498,6 +499,14 @@ def to_datetime(self, dayfirst=False): 'days_in_month', 11, "The number of days in the month") daysinmonth = days_in_month + @property + def start_time(self): + return self.to_timestamp(how='start') + + @property + def end_time(self): + return self.to_timestamp(how='end') + def _get_object_array(self): freq = self.freq return np.array([Period._from_ordinal(ordinal=x, freq=freq) @@ -683,7 +692,7 @@ def get_loc(self, key, method=None, tolerance=None): except ValueError: # we cannot construct the Period # as we have an invalid type - return self._invalid_indexer('label', key) + raise KeyError(key) try: return Index.get_loc(self, key.ordinal, method, tolerance) except KeyError: @@ -698,7 +707,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -709,6 +718,8 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem'] + if isinstance(label, datetime): return Period(label, freq=self.freq) elif isinstance(label, compat.string_types): diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index ba2eb3463d169..0ac10eb4fa15b 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -102,7 +102,7 @@ def _typ(self): def _deprecated(self): warnings.warn(".resample() is now a deferred operation\n" "use .resample(...).mean() instead of .resample(...)", - FutureWarning, stacklevel=2) + FutureWarning, stacklevel=3) return self.mean() def _make_deprecated_binop(op): @@ -154,9 +154,7 @@ def __getattr__(self, attr): if attr in self._deprecated_invalids: raise ValueError(".resample() is now a deferred operation\n" "\tuse .resample(...).mean() instead of " - ".resample(...)\n" - "\tassignment will have no effect as you " - "are working on a copy") + ".resample(...)") if attr not in self._deprecated_valids: self = self._deprecated() return object.__getattribute__(self, attr) @@ -167,6 +165,17 @@ def __setattr__(self, attr, value): self.__class__.__name__)) object.__setattr__(self, attr, value) + def __getitem__(self, key): + try: + return super(Resampler, self).__getitem__(key) + except (KeyError, com.AbstractMethodError): + + # compat for deprecated + if isinstance(self.obj, com.ABCSeries): + return self._deprecated()[key] + + raise + def __setitem__(self, attr, value): raise ValueError("cannot set items on {0}".format( self.__class__.__name__)) @@ -208,6 +217,11 @@ def _assure_grouper(self): """ make sure that we are creating our binner & grouper """ self._set_binner() + def plot(self, *args, **kwargs): + # for compat with prior versions, we want to + # have the warnings shown here and just have this work + return self._deprecated().plot(*args, **kwargs) + def aggregate(self, arg, *args, **kwargs): """ Apply aggregation function or functions to resampled groups, yielding @@ -400,6 +414,8 @@ def backfill(self, limit=None): def fillna(self, method, limit=None): """ + Fill missing values + Parameters ---------- method : str, method of resampling ('ffill', 'bfill') @@ -468,6 +484,52 @@ def f(self, _method=method): setattr(Resampler, method, f) +def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None): + """ potentially we might have a deprecation warning, show it + but call the appropriate methods anyhow """ + + if how is not None: + + # .resample(..., how='sum') + if isinstance(how, compat.string_types): + method = "{0}()".format(how) + + # .resample(..., how=lambda x: ....) + else: + method = ".apply(<func>)" + + # if we have both a how and fill_method, then show + # the following warning + if fill_method is None: + warnings.warn("how in .resample() is deprecated\n" + "the new syntax is " + ".resample(...).{method}".format( + method=method), + FutureWarning, stacklevel=3) + r = r.aggregate(how) + + if fill_method is not None: + + # show the prior function call + method = '.' + method if how is not None else '' + + args = "limit={0}".format(limit) if limit is not None else "" + warnings.warn("fill_method is deprecated to .resample()\n" + "the new syntax is .resample(...){method}" + ".{fill_method}({args})".format( + method=method, + fill_method=fill_method, + args=args), + FutureWarning, stacklevel=3) + + if how is not None: + r = getattr(r, fill_method)(limit=limit) + else: + r = r.aggregate(fill_method, limit=limit) + + return r + + class DatetimeIndexResampler(Resampler): def _get_binner_for_time(self): diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 9759d13fe4632..bea2aeb508358 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -710,13 +710,15 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- label : object """ + assert kind in ['ix', 'loc', 'getitem', None] + if isinstance(label, compat.string_types): parsed = _coerce_scalar_to_timedelta_type(label, box=True) lbound = parsed.round(parsed.resolution) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 8a876272dfdef..95d84bba4b5db 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -2030,6 +2030,16 @@ def test_to_timestamp_pi_mult(self): ['2011-02-28', 'NaT', '2011-03-31'], name='idx') self.assert_index_equal(result, expected) + def test_start_time(self): + index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') + expected_index = date_range('2016-01-01', end='2016-05-31', freq='MS') + self.assertTrue(index.start_time.equals(expected_index)) + + def test_end_time(self): + index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') + expected_index = date_range('2016-01-01', end='2016-05-31', freq='M') + self.assertTrue(index.end_time.equals(expected_index)) + def test_as_frame_columns(self): rng = period_range('1/1/2000', periods=5) df = DataFrame(randn(10, 5), columns=rng) @@ -2867,6 +2877,17 @@ def test_union(self): index3 = period_range('1/1/2000', '1/20/2000', freq='2D') self.assertRaises(ValueError, index.join, index3) + def test_union_dataframe_index(self): + rng1 = pd.period_range('1/1/1999', '1/1/2012', freq='M') + s1 = pd.Series(np.random.randn(len(rng1)), rng1) + + rng2 = pd.period_range('1/1/1980', '12/1/2001', freq='M') + s2 = pd.Series(np.random.randn(len(rng2)), rng2) + df = pd.DataFrame({'s1': s1, 's2': s2}) + + exp = pd.period_range('1/1/1980', '1/1/2012', freq='M') + self.assert_index_equal(df.index, exp) + def test_intersection(self): index = period_range('1/1/2000', '1/20/2000', freq='D') @@ -2887,6 +2908,63 @@ def test_intersection(self): index3 = period_range('1/1/2000', '1/20/2000', freq='2D') self.assertRaises(ValueError, index.intersection, index3) + def test_intersection_cases(self): + base = period_range('6/1/2000', '6/30/2000', freq='D', name='idx') + + # if target has the same name, it is preserved + rng2 = period_range('5/15/2000', '6/20/2000', freq='D', name='idx') + expected2 = period_range('6/1/2000', '6/20/2000', freq='D', + name='idx') + + # if target name is different, it will be reset + rng3 = period_range('5/15/2000', '6/20/2000', freq='D', name='other') + expected3 = period_range('6/1/2000', '6/20/2000', freq='D', + name=None) + + rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = PeriodIndex([], name='idx', freq='D') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, expected.freq) + + # non-monotonic + base = PeriodIndex(['2011-01-05', '2011-01-04', '2011-01-02', + '2011-01-03'], freq='D', name='idx') + + rng2 = PeriodIndex(['2011-01-04', '2011-01-02', + '2011-02-02', '2011-02-03'], + freq='D', name='idx') + expected2 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', + name='idx') + + rng3 = PeriodIndex(['2011-01-04', '2011-01-02', '2011-02-02', + '2011-02-03'], + freq='D', name='other') + expected3 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', + name=None) + + rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = PeriodIndex([], freq='D', name='idx') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, 'D') + + # empty same freq + rng = date_range('6/1/2000', '6/15/2000', freq='T') + result = rng[0:0].intersection(rng) + self.assertEqual(len(result), 0) + + result = rng.intersection(rng[0:0]) + self.assertEqual(len(result), 0) + def test_fields(self): # year, month, day, hour, minute # second, weekofyear, week, dayofweek, weekday, dayofyear, quarter @@ -3724,6 +3802,86 @@ def test_pi_nat_comp(self): idx1 == diff +class TestSeriesPeriod(tm.TestCase): + + def setUp(self): + self.series = Series(period_range('2000-01-01', periods=10, freq='D')) + + def test_auto_conversion(self): + series = Series(list(period_range('2000-01-01', periods=10, freq='D'))) + self.assertEqual(series.dtype, 'object') + + def test_constructor_cant_cast_period(self): + with tm.assertRaises(TypeError): + Series(period_range('2000-01-01', periods=10, freq='D'), + dtype=float) + + def test_series_comparison_scalars(self): + val = pd.Period('2000-01-04', freq='D') + result = self.series > val + expected = np.array([x > val for x in self.series]) + self.assert_numpy_array_equal(result, expected) + + val = self.series[5] + result = self.series > val + expected = np.array([x > val for x in self.series]) + self.assert_numpy_array_equal(result, expected) + + def test_between(self): + left, right = self.series[[2, 7]] + result = self.series.between(left, right) + expected = (self.series >= left) & (self.series <= right) + assert_series_equal(result, expected) + + # --------------------------------------------------------------------- + # NaT support + + """ + # ToDo: Enable when support period dtype + def test_NaT_scalar(self): + series = Series([0, 1000, 2000, iNaT], dtype='period[D]') + + val = series[3] + self.assertTrue(com.isnull(val)) + + series[2] = val + self.assertTrue(com.isnull(series[2])) + + def test_NaT_cast(self): + result = Series([np.nan]).astype('period[D]') + expected = Series([NaT]) + assert_series_equal(result, expected) + """ + + def test_set_none_nan(self): + # currently Period is stored as object dtype, not as NaT + self.series[3] = None + self.assertIs(self.series[3], None) + + self.series[3:5] = None + self.assertIs(self.series[4], None) + + self.series[5] = np.nan + self.assertTrue(np.isnan(self.series[5])) + + self.series[5:7] = np.nan + self.assertTrue(np.isnan(self.series[6])) + + def test_intercept_astype_object(self): + expected = self.series.astype('object') + + df = DataFrame({'a': self.series, + 'b': np.random.randn(len(self.series))}) + + result = df.values.squeeze() + self.assertTrue((result[:, 0] == expected.values).all()) + + df = DataFrame({'a': self.series, 'b': ['foo'] * len(self.series)}) + + result = df.values.squeeze() + self.assertTrue((result[:, 0] == expected.values).all()) + + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index b0e315ead2acb..4ddfc6ac573e4 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -151,6 +151,74 @@ def f(): check_stacklevel=False): self.assertIsInstance(getattr(r, op)(2), pd.Series) + # getitem compat + df = self.series.to_frame('foo') + + # same as prior versions for DataFrame + self.assertRaises(KeyError, lambda: df.resample('H')[0]) + + # compat for Series + # but we cannot be sure that we need a warning here + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = self.series.resample('H')[0] + expected = self.series.resample('H').mean()[0] + self.assertEqual(result, expected) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = self.series.resample('H')['2005-01-09 23:00:00'] + expected = self.series.resample('H').mean()['2005-01-09 23:00:00'] + self.assertEqual(result, expected) + + def test_groupby_resample_api(self): + + # GH 12448 + # .groupby(...).resample(...) hitting warnings + # when appropriate + df = DataFrame({'date': pd.date_range(start='2016-01-01', + periods=4, + freq='W'), + 'group': [1, 1, 2, 2], + 'val': [5, 6, 7, 8]}).set_index('date') + + # replication step + i = pd.date_range('2016-01-03', periods=8).tolist() + \ + pd.date_range('2016-01-17', periods=8).tolist() + index = pd.MultiIndex.from_arrays([[1] * 8 + [2] * 8, i], + names=['group', 'date']) + expected = DataFrame({'val': [5] * 7 + [6] + [7] * 7 + [8]}, + index=index) + result = df.groupby('group').apply( + lambda x: x.resample('1D').ffill())[['val']] + assert_frame_equal(result, expected) + + # deferred operations are currently disabled + # GH 12486 + # + # with tm.assert_produces_warning(FutureWarning, + # check_stacklevel=False): + # result = df.groupby('group').resample('1D').ffill() + # assert_frame_equal(result, expected) + + def test_plot_api(self): + tm._skip_if_no_mpl() + + # .resample(....).plot(...) + # hitting warnings + # GH 12448 + s = Series(np.random.randn(60), + index=date_range('2016-01-01', periods=60, freq='1min')) + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = s.resample('15min').plot() + tm.assert_is_valid_plot_return_object(result) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = s.resample('15min', how='sum').plot() + tm.assert_is_valid_plot_return_object(result) + def test_getitem(self): r = self.frame.resample('H') diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index b83c51b6a3ab6..6167edd9499ab 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1325,6 +1325,47 @@ def test_date_range_negative_freq(self): self.assert_index_equal(rng, exp) self.assertEqual(rng.freq, '-2M') + def test_date_range_bms_bug(self): + # #1645 + rng = date_range('1/1/2000', periods=10, freq='BMS') + + ex_first = Timestamp('2000-01-03') + self.assertEqual(rng[0], ex_first) + + def test_date_range_businesshour(self): + idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', + '2014-07-04 11:00', + '2014-07-04 12:00', '2014-07-04 13:00', + '2014-07-04 14:00', + '2014-07-04 15:00', '2014-07-04 16:00'], + freq='BH') + rng = date_range('2014-07-04 09:00', '2014-07-04 16:00', freq='BH') + tm.assert_index_equal(idx, rng) + + idx = DatetimeIndex( + ['2014-07-04 16:00', '2014-07-07 09:00'], freq='BH') + rng = date_range('2014-07-04 16:00', '2014-07-07 09:00', freq='BH') + tm.assert_index_equal(idx, rng) + + idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', + '2014-07-04 11:00', + '2014-07-04 12:00', '2014-07-04 13:00', + '2014-07-04 14:00', + '2014-07-04 15:00', '2014-07-04 16:00', + '2014-07-07 09:00', '2014-07-07 10:00', + '2014-07-07 11:00', + '2014-07-07 12:00', '2014-07-07 13:00', + '2014-07-07 14:00', + '2014-07-07 15:00', '2014-07-07 16:00', + '2014-07-08 09:00', '2014-07-08 10:00', + '2014-07-08 11:00', + '2014-07-08 12:00', '2014-07-08 13:00', + '2014-07-08 14:00', + '2014-07-08 15:00', '2014-07-08 16:00'], + freq='BH') + rng = date_range('2014-07-04 09:00', '2014-07-08 16:00', freq='BH') + tm.assert_index_equal(idx, rng) + def test_first_subset(self): ts = _simple_ts('1/1/2000', '1/1/2010', freq='12h') result = ts.first('10d') @@ -2716,6 +2757,26 @@ def test_union_bug_4564(self): exp = DatetimeIndex(sorted(set(list(left)) | set(list(right)))) self.assertTrue(result.equals(exp)) + def test_union_freq_both_none(self): + # GH11086 + expected = bdate_range('20150101', periods=10) + expected.freq = None + + result = expected.union(expected) + tm.assert_index_equal(result, expected) + self.assertIsNone(result.freq) + + def test_union_dataframe_index(self): + rng1 = date_range('1/1/1999', '1/1/2012', freq='MS') + s1 = Series(np.random.randn(len(rng1)), rng1) + + rng2 = date_range('1/1/1980', '12/1/2001', freq='MS') + s2 = Series(np.random.randn(len(rng2)), rng2) + df = DataFrame({'s1': s1, 's2': s2}) + + exp = pd.date_range('1/1/1980', '1/1/2012', freq='MS') + self.assert_index_equal(df.index, exp) + def test_intersection_bug_1708(self): from pandas import DateOffset index_1 = date_range('1/1/2012', periods=4, freq='12H') @@ -2724,14 +2785,80 @@ def test_intersection_bug_1708(self): result = index_1 & index_2 self.assertEqual(len(result), 0) - def test_union_freq_both_none(self): - # GH11086 - expected = bdate_range('20150101', periods=10) - expected.freq = None + def test_intersection(self): + # GH 4690 (with tz) + for tz in [None, 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Pacific']: + base = date_range('6/1/2000', '6/30/2000', freq='D', name='idx') - result = expected.union(expected) - tm.assert_index_equal(result, expected) - self.assertIsNone(result.freq) + # if target has the same name, it is preserved + rng2 = date_range('5/15/2000', '6/20/2000', freq='D', name='idx') + expected2 = date_range('6/1/2000', '6/20/2000', freq='D', + name='idx') + + # if target name is different, it will be reset + rng3 = date_range('5/15/2000', '6/20/2000', freq='D', name='other') + expected3 = date_range('6/1/2000', '6/20/2000', freq='D', + name=None) + + rng4 = date_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = DatetimeIndex([], name='idx') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, expected.freq) + self.assertEqual(result.tz, expected.tz) + + # non-monotonic + base = DatetimeIndex(['2011-01-05', '2011-01-04', + '2011-01-02', '2011-01-03'], + tz=tz, name='idx') + + rng2 = DatetimeIndex(['2011-01-04', '2011-01-02', + '2011-02-02', '2011-02-03'], + tz=tz, name='idx') + expected2 = DatetimeIndex( + ['2011-01-04', '2011-01-02'], tz=tz, name='idx') + + rng3 = DatetimeIndex(['2011-01-04', '2011-01-02', + '2011-02-02', '2011-02-03'], + tz=tz, name='other') + expected3 = DatetimeIndex( + ['2011-01-04', '2011-01-02'], tz=tz, name=None) + + # GH 7880 + rng4 = date_range('7/1/2000', '7/31/2000', freq='D', tz=tz, + name='idx') + expected4 = DatetimeIndex([], tz=tz, name='idx') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertIsNone(result.freq) + self.assertEqual(result.tz, expected.tz) + + # empty same freq GH2129 + rng = date_range('6/1/2000', '6/15/2000', freq='T') + result = rng[0:0].intersection(rng) + self.assertEqual(len(result), 0) + + result = rng.intersection(rng[0:0]) + self.assertEqual(len(result), 0) + + def test_string_index_series_name_converted(self): + # #1644 + df = DataFrame(np.random.randn(10, 4), + index=date_range('1/1/2000', periods=10)) + + result = df.ix['1/3/2000'] + self.assertEqual(result.name, df.index[2]) + + result = df.T['1/3/2000'] + self.assertEqual(result.name, df.index[2]) # GH 10699 def test_datetime64_with_DateOffset(self): @@ -3823,131 +3950,6 @@ def test_intercept_astype_object(self): result = df.values.squeeze() self.assertTrue((result[:, 0] == expected.values).all()) - def test_union(self): - rng1 = date_range('1/1/1999', '1/1/2012', freq='MS') - s1 = Series(np.random.randn(len(rng1)), rng1) - - rng2 = date_range('1/1/1980', '12/1/2001', freq='MS') - s2 = Series(np.random.randn(len(rng2)), rng2) - df = DataFrame({'s1': s1, 's2': s2}) - self.assertEqual(df.index.values.dtype, np.dtype('M8[ns]')) - - def test_intersection(self): - # GH 4690 (with tz) - for tz in [None, 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Pacific']: - base = date_range('6/1/2000', '6/30/2000', freq='D', name='idx') - - # if target has the same name, it is preserved - rng2 = date_range('5/15/2000', '6/20/2000', freq='D', name='idx') - expected2 = date_range('6/1/2000', '6/20/2000', freq='D', - name='idx') - - # if target name is different, it will be reset - rng3 = date_range('5/15/2000', '6/20/2000', freq='D', name='other') - expected3 = date_range('6/1/2000', '6/20/2000', freq='D', - name=None) - - rng4 = date_range('7/1/2000', '7/31/2000', freq='D', name='idx') - expected4 = DatetimeIndex([], name='idx') - - for (rng, expected) in [(rng2, expected2), (rng3, expected3), - (rng4, expected4)]: - result = base.intersection(rng) - self.assertTrue(result.equals(expected)) - self.assertEqual(result.name, expected.name) - self.assertEqual(result.freq, expected.freq) - self.assertEqual(result.tz, expected.tz) - - # non-monotonic - base = DatetimeIndex(['2011-01-05', '2011-01-04', - '2011-01-02', '2011-01-03'], - tz=tz, name='idx') - - rng2 = DatetimeIndex(['2011-01-04', '2011-01-02', - '2011-02-02', '2011-02-03'], - tz=tz, name='idx') - expected2 = DatetimeIndex( - ['2011-01-04', '2011-01-02'], tz=tz, name='idx') - - rng3 = DatetimeIndex(['2011-01-04', '2011-01-02', - '2011-02-02', '2011-02-03'], - tz=tz, name='other') - expected3 = DatetimeIndex( - ['2011-01-04', '2011-01-02'], tz=tz, name=None) - - # GH 7880 - rng4 = date_range('7/1/2000', '7/31/2000', freq='D', tz=tz, - name='idx') - expected4 = DatetimeIndex([], tz=tz, name='idx') - - for (rng, expected) in [(rng2, expected2), (rng3, expected3), - (rng4, expected4)]: - result = base.intersection(rng) - self.assertTrue(result.equals(expected)) - self.assertEqual(result.name, expected.name) - self.assertIsNone(result.freq) - self.assertEqual(result.tz, expected.tz) - - # empty same freq GH2129 - rng = date_range('6/1/2000', '6/15/2000', freq='T') - result = rng[0:0].intersection(rng) - self.assertEqual(len(result), 0) - - result = rng.intersection(rng[0:0]) - self.assertEqual(len(result), 0) - - def test_date_range_bms_bug(self): - # #1645 - rng = date_range('1/1/2000', periods=10, freq='BMS') - - ex_first = Timestamp('2000-01-03') - self.assertEqual(rng[0], ex_first) - - def test_date_range_businesshour(self): - idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', - '2014-07-04 11:00', - '2014-07-04 12:00', '2014-07-04 13:00', - '2014-07-04 14:00', - '2014-07-04 15:00', '2014-07-04 16:00'], - freq='BH') - rng = date_range('2014-07-04 09:00', '2014-07-04 16:00', freq='BH') - tm.assert_index_equal(idx, rng) - - idx = DatetimeIndex( - ['2014-07-04 16:00', '2014-07-07 09:00'], freq='BH') - rng = date_range('2014-07-04 16:00', '2014-07-07 09:00', freq='BH') - tm.assert_index_equal(idx, rng) - - idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', - '2014-07-04 11:00', - '2014-07-04 12:00', '2014-07-04 13:00', - '2014-07-04 14:00', - '2014-07-04 15:00', '2014-07-04 16:00', - '2014-07-07 09:00', '2014-07-07 10:00', - '2014-07-07 11:00', - '2014-07-07 12:00', '2014-07-07 13:00', - '2014-07-07 14:00', - '2014-07-07 15:00', '2014-07-07 16:00', - '2014-07-08 09:00', '2014-07-08 10:00', - '2014-07-08 11:00', - '2014-07-08 12:00', '2014-07-08 13:00', - '2014-07-08 14:00', - '2014-07-08 15:00', '2014-07-08 16:00'], - freq='BH') - rng = date_range('2014-07-04 09:00', '2014-07-08 16:00', freq='BH') - tm.assert_index_equal(idx, rng) - - def test_string_index_series_name_converted(self): - # #1644 - df = DataFrame(np.random.randn(10, 4), - index=date_range('1/1/2000', periods=10)) - - result = df.ix['1/3/2000'] - self.assertEqual(result.name, df.index[2]) - - result = df.T['1/3/2000'] - self.assertEqual(result.name, df.index[2]) - class TestTimestamp(tm.TestCase): def test_class_ops_pytz(self): diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 381b106b17eb0..937a8fa340348 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -519,7 +519,12 @@ def test_parsers(self): '2014-06': datetime.datetime(2014, 6, 1), '06-2014': datetime.datetime(2014, 6, 1), '2014-6': datetime.datetime(2014, 6, 1), - '6-2014': datetime.datetime(2014, 6, 1), } + '6-2014': datetime.datetime(2014, 6, 1), + + '20010101 12': datetime.datetime(2001, 1, 1, 12), + '20010101 1234': datetime.datetime(2001, 1, 1, 12, 34), + '20010101 123456': datetime.datetime(2001, 1, 1, 12, 34, 56), + } for date_str, expected in compat.iteritems(cases): result1, _, _ = tools.parse_time_string(date_str) @@ -713,11 +718,22 @@ def test_parsers_iso8601(self): self.assertEqual(actual, exp) # seperators must all match - YYYYMM not valid - invalid_cases = ['2011-01/02', '2011^11^11', '201401', - '201111', '200101'] + invalid_cases = ['2011-01/02', '2011^11^11', + '201401', '201111', '200101', + # mixed separated and unseparated + '2005-0101', '200501-01', + '20010101 12:3456', '20010101 1234:56', + # HHMMSS must have two digits in each component + # if unseparated + '20010101 1', '20010101 123', '20010101 12345', + '20010101 12345Z', + # wrong separator for HHMMSS + '2001-01-01 12-34-56'] for date_str in invalid_cases: with tm.assertRaises(ValueError): tslib._test_parse_iso8601(date_str) + # If no ValueError raised, let me know which case failed. + raise Exception(date_str) class TestArrayToDatetime(tm.TestCase): @@ -881,6 +897,11 @@ def test_nanosecond_string_parsing(self): self.assertEqual(ts.value, expected_value + 4 * 3600 * 1000000000) self.assertIn(expected_repr, repr(ts)) + # GH 10041 + ts = Timestamp('20130501T071545.123456789') + self.assertEqual(ts.value, expected_value) + self.assertIn(expected_repr, repr(ts)) + def test_nanosecond_timestamp(self): # GH 7610 expected = 1293840000000000005 diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index 8f127e28e28a9..d92cfef5280fc 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -190,6 +190,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, ---------- arg : string, datetime, list, tuple, 1-d array, or Series errors : {'ignore', 'raise', 'coerce'}, default 'raise' + - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaT - If 'ignore', then invalid parsing will return the input @@ -201,10 +202,12 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, with day first (this is a known bug, based on dateutil behavior). yearfirst : boolean, default False Specify a date parse order if `arg` is str or its list-likes. + - If True parses dates with the year first, eg 10/11/12 is parsed as 2010-11-12. - If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil). + Warning: yearfirst=True is not strict, but will prefer to parse with year first (this is a known bug, based on dateutil beahavior). @@ -214,14 +217,17 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well). box : boolean, default True + - If True returns a DatetimeIndex - If False returns ndarray of values. format : string, default None strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse all the way up to nanoseconds. exact : boolean, True by default + - If True, require an exact format match. - If False, allow the format to match anywhere in the target string. + unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch (e.g. a unix timestamp), which is an integer/float number. infer_datetime_format : boolean, default False @@ -273,6 +279,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, datetime.datetime(1300, 1, 1, 0, 0) >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT + """ return _to_datetime(arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst, diff --git a/pandas/util/clipboard.py b/pandas/util/clipboard.py index 026f13aad0bf3..02da0d5b8159f 100644 --- a/pandas/util/clipboard.py +++ b/pandas/util/clipboard.py @@ -45,6 +45,7 @@ # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# flake8: noqa import platform import os diff --git a/pandas/util/nosetester.py b/pandas/util/nosetester.py index 445cb79978fc1..1bdaaff99fd50 100644 --- a/pandas/util/nosetester.py +++ b/pandas/util/nosetester.py @@ -122,6 +122,45 @@ def _get_custom_doctester(self): """ return None + def _test_argv(self, label, verbose, extra_argv): + """ + Generate argv for nosetest command + + Parameters + ---------- + label : {'fast', 'full', '', attribute identifier}, optional + see ``test`` docstring + verbose : int, optional + Verbosity value for test outputs, in the range 1-10. Default is 1. + extra_argv : list, optional + List with any extra arguments to pass to nosetests. + + Returns + ------- + argv : list + command line arguments that will be passed to nose + """ + + argv = [__file__, self.package_path] + if label and label != 'full': + if not isinstance(label, string_types): + raise TypeError('Selection label should be a string') + if label == 'fast': + label = 'not slow and not network and not disabled' + argv += ['-A', label] + argv += ['--verbosity', str(verbose)] + + # When installing with setuptools, and also in some other cases, the + # test_*.py files end up marked +x executable. Nose, by default, does + # not run files marked with +x as they might be scripts. However, in + # our case nose only looks for test_*.py files under the package + # directory, which should be safe. + argv += ['--exe'] + + if extra_argv: + argv += extra_argv + return argv + def test(self, label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None): """ @@ -133,6 +172,7 @@ def test(self, label='fast', verbose=1, extra_argv=None, Identifies the tests to run. This can be a string to pass to the nosetests executable with the '-A' option, or one of several special values. Special values are: + * 'fast' - the default - which corresponds to the ``nosetests -A`` option of 'not slow'. * 'full' - fast (as above) and slow tests as in the @@ -140,6 +180,7 @@ def test(self, label='fast', verbose=1, extra_argv=None, * None or '' - run all tests. * attribute_identifier - string passed directly to nosetests as '-A'. + verbose : int, optional Verbosity value for test outputs, in the range 1-10. Default is 1. extra_argv : list, optional @@ -154,14 +195,15 @@ def test(self, label='fast', verbose=1, extra_argv=None, This specifies which warnings to configure as 'raise' instead of 'warn' during the test execution. Valid strings are: - - "develop" : equals ``(DeprecationWarning, RuntimeWarning)`` - - "release" : equals ``()``, don't raise on any warnings. + - 'develop' : equals ``(DeprecationWarning, RuntimeWarning)`` + - 'release' : equals ``()``, don't raise on any warnings. Returns ------- result : object Returns the result of running the tests as a ``nose.result.TextTestResult`` object. + """ # cap verbosity at 3 because nose becomes *very* verbose beyond that diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index 80c10b53d37b5..c972caad5d74c 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -91,7 +91,8 @@ def show_versions(as_json=False): ("sqlalchemy", lambda mod: mod.__version__), ("pymysql", lambda mod: mod.__version__), ("psycopg2", lambda mod: mod.__version__), - ("jinja2", lambda mod: mod.__version__) + ("jinja2", lambda mod: mod.__version__), + ("boto", lambda mod: mod.__version__) ] deps_blob = list() diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 35a615db444e9..ba869efbc5837 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -133,7 +133,7 @@ def randbool(size=(), p=0.5): def rands_array(nchars, size, dtype='O'): """Generate an array of byte strings.""" - retval = (choice(RANDS_CHARS, size=nchars * np.prod(size)) + retval = (np.random.choice(RANDS_CHARS, size=nchars * np.prod(size)) .view((np.str_, nchars)).reshape(size)) if dtype is None: return retval @@ -143,7 +143,7 @@ def rands_array(nchars, size, dtype='O'): def randu_array(nchars, size, dtype='O'): """Generate an array of unicode strings.""" - retval = (choice(RANDU_CHARS, size=nchars * np.prod(size)) + retval = (np.random.choice(RANDU_CHARS, size=nchars * np.prod(size)) .view((np.unicode_, nchars)).reshape(size)) if dtype is None: return retval @@ -158,7 +158,7 @@ def rands(nchars): See `rands_array` if you want to create an array of random strings. """ - return ''.join(choice(RANDS_CHARS, nchars)) + return ''.join(np.random.choice(RANDS_CHARS, nchars)) def randu(nchars): @@ -171,14 +171,6 @@ def randu(nchars): return ''.join(choice(RANDU_CHARS, nchars)) -def choice(x, size=10): - """sample with replacement; uniform over the input""" - try: - return np.random.choice(x, size=size) - except AttributeError: - return np.random.randint(len(x), size=size).choose(x) - - def close(fignum=None): from matplotlib.pyplot import get_fignums, close as _close @@ -209,6 +201,12 @@ def setUpClass(cls): cls.setUpClass = setUpClass return cls +def _skip_if_no_mpl(): + try: + import matplotlib + except ImportError: + import nose + raise nose.SkipTest("matplotlib not installed") def _skip_if_mpl_1_5(): import matplotlib @@ -217,7 +215,6 @@ def _skip_if_mpl_1_5(): import nose raise nose.SkipTest("matplotlib 1.5") - def _skip_if_no_scipy(): try: import scipy.stats @@ -775,6 +772,21 @@ def assert_attr_equal(attr, left, right, obj='Attributes'): left_attr, right_attr) +def assert_is_valid_plot_return_object(objs): + import matplotlib.pyplot as plt + if isinstance(objs, np.ndarray): + for el in objs.flat: + assert isinstance(el, plt.Axes), ('one of \'objs\' is not a ' + 'matplotlib Axes instance, ' + 'type encountered {0!r}' + ''.format(el.__class__.__name__)) + else: + assert isinstance(objs, (plt.Artist, tuple, dict)), \ + ('objs is neither an ndarray of Artist instances nor a ' + 'single Artist instance, tuple, or dict, "objs" is a {0!r} ' + ''.format(objs.__class__.__name__)) + + def isiterable(obj): return hasattr(obj, '__iter__') diff --git a/setup.py b/setup.py index f33b01b24c165..e3fb5a007aad3 100755 --- a/setup.py +++ b/setup.py @@ -14,6 +14,15 @@ import platform from distutils.version import LooseVersion +def is_platform_windows(): + return sys.platform == 'win32' or sys.platform == 'cygwin' + +def is_platform_linux(): + return sys.platform == 'linux2' + +def is_platform_mac(): + return sys.platform == 'darwin' + # versioning import versioneer cmdclass = versioneer.get_cmdclass() @@ -375,6 +384,11 @@ def srcpath(name=None, suffix='.pyx', subdir='src'): def pxd(name): return os.path.abspath(pjoin('pandas', name + '.pxd')) +# args to ignore warnings +if is_platform_windows(): + extra_compile_args=[] +else: + extra_compile_args=['-Wno-unused-function'] lib_depends = lib_depends + ['pandas/src/numpy_helper.h', 'pandas/src/parse_helper.h'] @@ -386,7 +400,7 @@ def pxd(name): # some linux distros require it -libraries = ['m'] if 'win32' not in sys.platform else [] +libraries = ['m'] if not is_platform_windows() else [] ext_data = dict( lib={'pyxfile': 'lib', @@ -439,7 +453,8 @@ def pxd(name): obj = Extension('pandas.%s' % name, sources=sources, depends=data.get('depends', []), - include_dirs=include) + include_dirs=include, + extra_compile_args=extra_compile_args) extensions.append(obj) @@ -447,14 +462,16 @@ def pxd(name): sparse_ext = Extension('pandas._sparse', sources=[srcpath('sparse', suffix=suffix)], include_dirs=[], - libraries=libraries) + libraries=libraries, + extra_compile_args=extra_compile_args) extensions.extend([sparse_ext]) testing_ext = Extension('pandas._testing', sources=[srcpath('testing', suffix=suffix)], include_dirs=[], - libraries=libraries) + libraries=libraries, + extra_compile_args=extra_compile_args) extensions.extend([testing_ext]) @@ -474,7 +491,8 @@ def pxd(name): subdir='msgpack')], language='c++', include_dirs=['pandas/src/msgpack'] + common_include, - define_macros=macros) + define_macros=macros, + extra_compile_args=extra_compile_args) unpacker_ext = Extension('pandas.msgpack._unpacker', depends=['pandas/src/msgpack/unpack.h', 'pandas/src/msgpack/unpack_define.h', @@ -484,7 +502,8 @@ def pxd(name): subdir='msgpack')], language='c++', include_dirs=['pandas/src/msgpack'] + common_include, - define_macros=macros) + define_macros=macros, + extra_compile_args=extra_compile_args) extensions.append(packer_ext) extensions.append(unpacker_ext) @@ -508,7 +527,7 @@ def pxd(name): include_dirs=['pandas/src/ujson/python', 'pandas/src/ujson/lib', 'pandas/src/datetime'] + common_include, - extra_compile_args=['-D_GNU_SOURCE']) + extra_compile_args=['-D_GNU_SOURCE'] + extra_compile_args) extensions.append(ujson_ext) diff --git a/vb_suite/groupby.py b/vb_suite/groupby.py index bc21372225322..268d71f864823 100644 --- a/vb_suite/groupby.py +++ b/vb_suite/groupby.py @@ -143,7 +143,7 @@ def f(): value2 = np.random.randn(n) value2[np.random.rand(n) > 0.5] = np.nan -obj = tm.choice(list('ab'), size=n).astype(object) +obj = np.random.choice(list('ab'), size=n).astype(object) obj[np.random.randn(n) > 0.5] = np.nan df = DataFrame({'key1': np.random.randint(0, 500, size=n),
- Closes #12373 - Added test_rolling_min_max_types to test_window.py - passes `git diff upstream/master | flake8 --diff` Ran the performance tests with no changes detected: $ asv continuous -b 'stat_op.*rolling' upstream/master GH12373 · Creating environments · Discovering benchmarks ·· Uninstalling from py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt ·· Building for py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.................................................. ·· Installing into py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.. · Running 18 total benchmarks (2 commits \* 1 environments \* 9 benchmarks) [ 0.00%] · For pandas commit hash 170fb27a: [ 0.00%] ·· Building for py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt....................................................... [ 0.00%] ·· Benchmarking py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt [ 5.56%] ··· Running stat_ops.stats_rolling_mean.time_rolling_kurt 11.39ms [ 11.11%] ··· Running stat_ops.stats_rolling_mean.time_rolling_max 7.48ms [ 16.67%] ··· Running stat_ops.stats_rolling_mean.time_rolling_mean 7.22ms [ 22.22%] ··· Running stat_ops.stats_rolling_mean.time_rolling_median 114.21ms [ 27.78%] ··· Running stat_ops.stats_rolling_mean.time_rolling_min 7.55ms [ 33.33%] ··· Running stat_ops.stats_rolling_mean.time_rolling_skew 11.35ms [ 38.89%] ··· Running stat_ops.stats_rolling_mean.time_rolling_std 9.13ms [ 44.44%] ··· Running stat_ops.stats_rolling_mean.time_rolling_sum 6.93ms [ 50.00%] ··· Running stat_ops.stats_rolling_mean.time_rolling_var 7.98ms [ 50.00%] · For pandas commit hash 56e285a6: [ 50.00%] ·· Building for py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt....................................................... [ 50.00%] ·· Benchmarking py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt [ 55.56%] ··· Running stat_ops.stats_rolling_mean.time_rolling_kurt 11.66ms [ 61.11%] ··· Running stat_ops.stats_rolling_mean.time_rolling_max 8.68ms [ 66.67%] ··· Running stat_ops.stats_rolling_mean.time_rolling_mean 7.48ms [ 72.22%] ··· Running stat_ops.stats_rolling_mean.time_rolling_median 107.71ms [ 77.78%] ··· Running stat_ops.stats_rolling_mean.time_rolling_min 8.74ms [ 83.33%] ··· Running stat_ops.stats_rolling_mean.time_rolling_skew 11.53ms [ 88.89%] ··· Running stat_ops.stats_rolling_mean.time_rolling_std 9.25ms [ 94.44%] ··· Running stat_ops.stats_rolling_mean.time_rolling_sum 6.97ms [100.00%] ··· Running stat_ops.stats_rolling_mean.time_rolling_var 8.24msBENCHMARKS NOT SIGNIFICANTLY CHANGED.
https://api.github.com/repos/pandas-dev/pandas/pulls/12481
2016-02-27T15:27:12Z
2016-04-18T17:52:32Z
null
2016-04-18T18:04:39Z
TST: Add name vaidation for dt accessor
diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index c6593d403ffcc..d4121fb86de79 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -52,7 +52,7 @@ def get_expected(s, name): result = result.astype('int64') elif not com.is_list_like(result): return result - return Series(result, index=s.index) + return Series(result, index=s.index, name=s.name) def compare(s, name): a = getattr(s.dt, prop) @@ -63,10 +63,12 @@ def compare(s, name): tm.assert_series_equal(a, b) # datetimeindex - for s in [Series(date_range('20130101', periods=5)), - Series(date_range('20130101', periods=5, freq='s')), - Series(date_range('20130101 00:00:00', periods=5, freq='ms')) - ]: + cases = [Series(date_range('20130101', periods=5), name='xxx'), + Series(date_range('20130101', periods=5, freq='s'), + name='xxx'), + Series(date_range('20130101 00:00:00', periods=5, freq='ms'), + name='xxx')] + for s in cases: for prop in ok_for_dt: # we test freq below if prop != 'freq': @@ -80,9 +82,8 @@ def compare(s, name): self.assertTrue(result.dtype == object) result = s.dt.tz_localize('US/Eastern') - expected = Series( - DatetimeIndex(s.values).tz_localize('US/Eastern'), - index=s.index) + exp_values = DatetimeIndex(s.values).tz_localize('US/Eastern') + expected = Series(exp_values, index=s.index, name='xxx') tm.assert_series_equal(result, expected) tz_result = result.dt.tz @@ -93,48 +94,50 @@ def compare(s, name): # let's localize, then convert result = s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern') - expected = Series( - DatetimeIndex(s.values).tz_localize('UTC').tz_convert( - 'US/Eastern'), index=s.index) + exp_values = (DatetimeIndex(s.values).tz_localize('UTC') + .tz_convert('US/Eastern')) + expected = Series(exp_values, index=s.index, name='xxx') tm.assert_series_equal(result, expected) # round - s = Series(pd.to_datetime( - ['2012-01-01 13:00:00', '2012-01-01 12:01:00', - '2012-01-01 08:00:00'])) + s = Series(pd.to_datetime(['2012-01-01 13:00:00', + '2012-01-01 12:01:00', + '2012-01-01 08:00:00']), name='xxx') result = s.dt.round('D') expected = Series(pd.to_datetime(['2012-01-02', '2012-01-02', - '2012-01-01'])) + '2012-01-01']), name='xxx') tm.assert_series_equal(result, expected) # round with tz - result = s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern').dt.round( - 'D') - expected = Series(pd.to_datetime(['2012-01-01', '2012-01-01', - '2012-01-01']).tz_localize( - 'US/Eastern')) + result = (s.dt.tz_localize('UTC') + .dt.tz_convert('US/Eastern') + .dt.round('D')) + exp_values = pd.to_datetime(['2012-01-01', '2012-01-01', + '2012-01-01']).tz_localize('US/Eastern') + expected = Series(exp_values, name='xxx') tm.assert_series_equal(result, expected) # floor - s = Series(pd.to_datetime( - ['2012-01-01 13:00:00', '2012-01-01 12:01:00', - '2012-01-01 08:00:00'])) + s = Series(pd.to_datetime(['2012-01-01 13:00:00', + '2012-01-01 12:01:00', + '2012-01-01 08:00:00']), name='xxx') result = s.dt.floor('D') expected = Series(pd.to_datetime(['2012-01-01', '2012-01-01', - '2012-01-01'])) + '2012-01-01']), name='xxx') tm.assert_series_equal(result, expected) # ceil - s = Series(pd.to_datetime( - ['2012-01-01 13:00:00', '2012-01-01 12:01:00', - '2012-01-01 08:00:00'])) + s = Series(pd.to_datetime(['2012-01-01 13:00:00', + '2012-01-01 12:01:00', + '2012-01-01 08:00:00']), name='xxx') result = s.dt.ceil('D') expected = Series(pd.to_datetime(['2012-01-02', '2012-01-02', - '2012-01-02'])) + '2012-01-02']), name='xxx') tm.assert_series_equal(result, expected) # datetimeindex with tz - s = Series(date_range('20130101', periods=5, tz='US/Eastern')) + s = Series(date_range('20130101', periods=5, tz='US/Eastern'), + name='xxx') for prop in ok_for_dt: # we test freq below @@ -149,7 +152,8 @@ def compare(s, name): self.assertTrue(result.dtype == object) result = s.dt.tz_convert('CET') - expected = Series(s._values.tz_convert('CET'), index=s.index) + expected = Series(s._values.tz_convert('CET'), + index=s.index, name='xxx') tm.assert_series_equal(result, expected) tz_result = result.dt.tz @@ -159,11 +163,13 @@ def compare(s, name): freq='infer').freq) # timedeltaindex - for s in [Series( - timedelta_range('1 day', periods=5), index=list('abcde')), - Series(timedelta_range('1 day 01:23:45', periods=5, freq='s')), - Series(timedelta_range('2 days 01:23:45.012345', periods=5, - freq='ms'))]: + cases = [Series(timedelta_range('1 day', periods=5), + index=list('abcde'), name='xxx'), + Series(timedelta_range('1 day 01:23:45', periods=5, + freq='s'), name='xxx'), + Series(timedelta_range('2 days 01:23:45.012345', periods=5, + freq='ms'), name='xxx')] + for s in cases: for prop in ok_for_td: # we test freq below if prop != 'freq': @@ -190,21 +196,27 @@ def compare(s, name): # both index = date_range('20130101', periods=3, freq='D') - s = Series(date_range('20140204', periods=3, freq='s'), index=index) - tm.assert_series_equal(s.dt.year, Series( - np.array( - [2014, 2014, 2014], dtype='int64'), index=index)) - tm.assert_series_equal(s.dt.month, Series( - np.array( - [2, 2, 2], dtype='int64'), index=index)) - tm.assert_series_equal(s.dt.second, Series( - np.array( - [0, 1, 2], dtype='int64'), index=index)) - tm.assert_series_equal(s.dt.normalize(), pd.Series( - [s[0]] * 3, index=index)) + s = Series(date_range('20140204', periods=3, freq='s'), + index=index, name='xxx') + exp = Series(np.array([2014, 2014, 2014], dtype='int64'), + index=index, name='xxx') + tm.assert_series_equal(s.dt.year, exp) + + exp = Series(np.array([2, 2, 2], dtype='int64'), + index=index, name='xxx') + tm.assert_series_equal(s.dt.month, exp) + + exp = Series(np.array([0, 1, 2], dtype='int64'), + index=index, name='xxx') + tm.assert_series_equal(s.dt.second, exp) + + exp = pd.Series([s[0]] * 3, index=index, name='xxx') + tm.assert_series_equal(s.dt.normalize(), exp) # periodindex - for s in [Series(period_range('20130101', periods=5, freq='D'))]: + cases = [Series(period_range('20130101', periods=5, freq='D'), + name='xxx')] + for s in cases: for prop in ok_for_period: # we test freq below if prop != 'freq': @@ -221,30 +233,32 @@ def get_dir(s): results = [r for r in s.dt.__dir__() if not r.startswith('_')] return list(sorted(set(results))) - s = Series(date_range('20130101', periods=5, freq='D')) + s = Series(date_range('20130101', periods=5, freq='D'), name='xxx') results = get_dir(s) tm.assert_almost_equal( results, list(sorted(set(ok_for_dt + ok_for_dt_methods)))) - s = Series(period_range('20130101', periods=5, freq='D').asobject) + s = Series(period_range('20130101', periods=5, + freq='D', name='xxx').asobject) results = get_dir(s) tm.assert_almost_equal( results, list(sorted(set(ok_for_period + ok_for_period_methods)))) # 11295 # ambiguous time error on the conversions - s = Series(pd.date_range('2015-01-01', '2016-01-01', freq='T')) + s = Series(pd.date_range('2015-01-01', '2016-01-01', + freq='T'), name='xxx') s = s.dt.tz_localize('UTC').dt.tz_convert('America/Chicago') results = get_dir(s) tm.assert_almost_equal( results, list(sorted(set(ok_for_dt + ok_for_dt_methods)))) - expected = Series(pd.date_range('2015-01-01', '2016-01-01', freq='T', - tz='UTC').tz_convert( - 'America/Chicago')) + exp_values = pd.date_range('2015-01-01', '2016-01-01', freq='T', + tz='UTC').tz_convert('America/Chicago') + expected = Series(exp_values, name='xxx') tm.assert_series_equal(s, expected) # no setting allowed - s = Series(date_range('20130101', periods=5, freq='D')) + s = Series(date_range('20130101', periods=5, freq='D'), name='xxx') with tm.assertRaisesRegexp(ValueError, "modifications"): s.dt.hour = 5
Closes #10712. This is already fixed in other PR, thus added explicit tests.
https://api.github.com/repos/pandas-dev/pandas/pulls/12479
2016-02-27T06:09:33Z
2016-02-27T15:09:04Z
null
2016-02-27T18:17:52Z
BUG: indexing operation changes dtype, #10503
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 8a48314de5f77..9e62ba22d8f96 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -362,6 +362,89 @@ New Behavior: s.index print(s.to_csv(path=None)) +Changes to dtype assignment behaviors +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When a DataFrame's slice is updated with a new slice of the same +dtype, the dtype of the DataFrame will now remain the same. + +Previous Behavior: + +.. code-block:: python + + In [2]: df = pd.DataFrame({'a':[0, 1, 1], 'b':[100, 200, 300]}, dtype='uint32') + + In [3]: df.info() + <class 'pandas.core.frame.DataFrame'> + RangeIndex: 3 entries, 0 to 2 + Data columns (total 2 columns): + a 3 non-null uint32 + b 3 non-null uint32 + dtypes: uint32(2) + memory usage: 96.0 bytes + + In [4]: ix = df['a'] == 1 + + In [5]: df.loc[ix, 'b'] = df.loc[ix, 'b'] + + In [6]: df.info() + <class 'pandas.core.frame.DataFrame'> + RangeIndex: 3 entries, 0 to 2 + Data columns (total 2 columns): + a 3 non-null int64 + b 3 non-null int64 + dtypes: int64(2) + +New Behavior: + +.. ipython:: python + + df = pd.DataFrame({'a':[0, 1, 1], 'b':[100, 200, 300]}, dtype='uint32') + df.info() + ix = df['a'] == 1 + df.loc[ix, 'b'] = df.loc[ix, 'b'] + df.info() + + +When a DataFrame's integer slice is partially updated with a new slice of floats that +could potentially be downcasted to integer without losing precision, +the dtype of the slice will be set to float instead of integer. + +Previous Behavior: + +.. code-block:: python + + In [4]: df = pd.DataFrame(np.array(range(1,10)).reshape(3,3), + ...: columns=list('abc'), + ...: index=[[4,4,8], [8,10,12]]) + + In [5]: df + Out[5]: + a b c + 4 8 1 2 3 + 10 4 5 6 + 8 12 7 8 9 + + In [6]: df.ix[4, 'c'] = np.array([0., 1.]) + + In [7]: df + Out[7]: + a b c + 4 8 1 2 0 + 10 4 5 1 + 8 12 7 8 9 + +New Behavior: + +.. ipython:: python + + df = pd.DataFrame(np.array(range(1,10)).reshape(3,3), + columns=list('abc'), + index=[[4,4,8], [8,10,12]]) + df + df.ix[4, 'c'] = np.array([0., 1.]) + df + .. _whatsnew_0180.enhancements.xarray: to_xarray @@ -1120,3 +1203,4 @@ Bug Fixes - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) +- Bug when modifying a slice of a ``DataFrame`` with the same ``dtype``, the ``dtype`` of the ``DataFrame`` could unexpected changed. (:issue:`10503`). diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 8563481c8564d..51bd9fd0e952c 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -702,7 +702,10 @@ def _is_empty_indexer(indexer): values[indexer] = value # coerce and try to infer the dtypes of the result - if np.isscalar(value): + if hasattr(value, 'dtype') and is_dtype_equal(values.dtype, + value.dtype): + dtype = value.dtype + elif np.isscalar(value): dtype, _ = _infer_dtype_from_scalar(value) else: dtype = 'infer' diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 4c7510783eda0..591ffc9a68c7a 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -361,6 +361,24 @@ def test_head_tail(self): self._compare(o.head(-3), o.head(7)) self._compare(o.tail(-3), o.tail(7)) + def test_dtype_after_slice_update(self): + # GH10503 + + # assigning the same type should not change the type + df1 = pd.DataFrame({'a': [0, 1, 1], 'b': [100, 200, 300]}, + dtype='uint32') + ix = df1['a'] == 1 + newb1 = df1.loc[ix, 'b'] + 1 + df1.loc[ix, 'b'] = newb1 + assert_equal(df1['a'].dtype, newb1.dtype) + + # assigning a new type should get the inferred type + df2 = pd.DataFrame({'a': [0, 1, 1], 'b': [100, 200, 300]}, + dtype='uint64') + newb2 = df2.loc[ix, 'b'] + df1.loc[ix, 'b'] = newb2 + assert_equal(df1['a'].dtype, np.dtype('int64')) + def test_sample(self): # Fixes issue: 2419 diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 1c0986b025acc..9497dbc6ddeef 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -3256,12 +3256,12 @@ def test_multiindex_assignment(self): df.ix[4, 'c'] = arr assert_series_equal(df.ix[4, 'c'], Series(arr, index=[8, 10], name='c', - dtype='int64')) + dtype='float64')) # scalar ok df.ix[4, 'c'] = 10 assert_series_equal(df.ix[4, 'c'], Series(10, index=[8, 10], name='c', - dtype='int64')) + dtype='float64')) # invalid assignments def f():
- [ ] closes #10503 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12477
2016-02-27T01:00:05Z
2016-02-27T17:52:43Z
null
2016-02-27T17:53:27Z
Add a free-form summary function to describe a dataframe (in the vein of R)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index f850bfba4f90e..c2420a36ad9e7 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3,6 +3,7 @@ import operator import weakref import gc +from collections import OrderedDict import numpy as np import pandas.lib as lib @@ -4837,6 +4838,7 @@ def abs(self): See Also -------- DataFrame.select_dtypes + DataFrame.summary """ @Appender(_shared_docs['describe'] % _shared_doc_kwargs) @@ -4943,6 +4945,159 @@ def _check_percentile(self, q): raise ValueError(msg.format(q / 100.0)) return q + _shared_docs['summary'] = """ + Human readable tabular description of a dataset and its columns. + + Returns + ------- + summary: tabular representation of summary statistics + + Notes + ----- + As compared to describe(), the summary function + will always return information about all columns. + + See Also + -------- + DataFrame.describe + """ + + @Appender(_shared_docs['summary'] % _shared_doc_kwargs) + def summary( + self, + value_count_limit=1000000, + significant_value_threshold=0.2, + significant_percentage=5, + ): + def summary_1d(series, percentiles, std, mean, count): + count_all = len(series) + ret = OrderedDict() + + if count_all != count: + ret['count valid'] = count + ret['NAs'] = count_all - count + else: + ret['count'] = count + + if mean is not None: + ret['mean'] = mean + if std is not None: + ret['std'] = std + + if percentiles is not None: + def percentile_to_label(p): + if p == 0: + return 'min' + if p == 50: + return 'median' + if p == 100: + return 'max' + return 'p' + str(p) + + def maybe_output_range(start_p, end_p, value): + output = False + for p in [0, 25, 50, 75, 100]: + if start_p <= p and end_p >= p: + output = True + break + if end_p - start_p >= significant_percentage: + output = True + + if output: + label = percentile_to_label(start_p) + if start_p != end_p: + label += ':' + percentile_to_label(end_p) + ret[label] = series.dtype.type(value) + + start_p = 0 + last_p = -1 + value = None + for p in range(len(percentiles)): + v = percentiles[p] + if value != v: + maybe_output_range(start_p, last_p, value) + start_p = p + value = v + last_p = p + maybe_output_range(start_p, last_p, value) + + if count < value_count_limit: + # for small sets, report on object counts + objcounts = series.value_counts() + cardinality = len(objcounts) + ret['cardinality'] = cardinality + threshold = significant_value_threshold * count + + for i in range(len(objcounts)): + if objcounts.iloc[i] > threshold: + ret['value:' + str(objcounts.index[i])] =\ + '{:,} ({:.0%})'.format( + objcounts.iloc[i], (1. * objcounts.iloc[i]) / + count_all) + + ret_as_list = [] + for k, v in ret.iteritems(): + def pretty_str(v): + # this could be much improved + if com.is_integer(v): + return "{:,}".format(v) + elif com.is_number(v): + return "%.3g" % v + else: + return str(v) + ret_as_list.append(str(k) + '=' + pretty_str(v)) + + return ret_as_list + + def process_dataframe_of_same_dtype(df): + percentiles = [None] * len(df.columns) + means = [None] * len(df.columns) + stds = [None] * len(df.columns) + + if com.is_numeric_dtype(df[df.columns[0]].dtype): + try: + percentiles = np.nanpercentile( + df, + range(101), + axis=0, + interpolation='nearest', + ) + except AttributeError: + # old NumPy before 0.10 does not support interpolation + # settings or skipping NaNs + percentiles = np.percentile(df, range(101), axis=0) + + percentiles = np.transpose(percentiles) + + means = df.mean(axis=0) + stds = df.std(axis=0) + + counts = df.count(axis=0) + + results = [] + for i, (colname, series) in enumerate(df.iteritems()): + results.append( + pd.Series( + summary_1d( + series, + std=stds[i], + mean=means[i], + count=counts[i], + percentiles=percentiles[i], + ), + name=colname) + ) + + ret = pd.concat(results, axis=1) + + return ret + + result = [] + for k, df in self.as_blocks(copy=False).items(): + result.append(process_dataframe_of_same_dtype(df)) + return pd.concat(result, axis=1)\ + .reindex(columns=self.columns).fillna('') + _shared_docs['pct_change'] = """ Percent change over given number of periods. @@ -5003,6 +5158,7 @@ def _add_numeric_operations(cls): cls.any = _make_logical_function( 'any', name, name2, axis_descr, + 'Return whether any element is True over requested axis', nanops.nanany) cls.all = _make_logical_function(
First cut. Please let me know your thoughts and thanks again to @jreback for teaching me. Looks like this ``` A_cat B_str C_bool D_num \ 0 count=24 count=24 count=24 count=24 1 cardinality=2 cardinality=4 mean=0.5 mean=12 2 value:foo=16 (67%) value:d=6 (25%) std=0.511 std=7.07 3 value:bar=8 (33%) value:b=6 (25%) min:p47=False min=0.5 4 value:c=6 (25%) median=True p25=6.25 5 value:a=6 (25%) p53:max=True median=12 6 cardinality=2 p75=17.8 7 value:True=12 (50%) max=23.5 8 value:False=12 (50%) cardinality=24 E_regular 0 count=24 1 mean=4.17 2 std=3.14 3 min:p65=2 4 p70:p82=8 5 p87:max=9 6 cardinality=3 7 value:2=16 (67%) 8 ``` for input ``` df = pd.DataFrame({'A_cat': pd.Categorical(['foo', 'foo', 'bar'] * 8), 'B_str': ['a', 'b', 'c', 'd'] * 6, 'C_bool': [True] * 12 + [False] * 12, 'D_num': np.arange(24.) + .5, 'E_regular': [9,8,2,2,2,2]*4, }) ``` - [ ] closes #12460
https://api.github.com/repos/pandas-dev/pandas/pulls/12476
2016-02-27T00:40:15Z
2016-05-07T19:07:35Z
null
2016-05-07T19:07:36Z
ERR: Better error reporting with .transform and an invalid output per GH 10165
diff --git a/.travis.yml b/.travis.yml index a408f40f5dc3c..e08bd3b0413c9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,7 +14,7 @@ env: git: # for cloning - depth: 200 + depth: 300 matrix: fast_finish: true diff --git a/appveyor.yml b/appveyor.yml index f6bf947892093..650137b995121 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -9,7 +9,7 @@ matrix: fast_finish: true # immediately finish build once one of the jobs fails. # set clone depth -clone_depth: 200 +clone_depth: 300 environment: global: diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 70a1ad4a335ea..57a50064ecac7 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -43,3 +43,5 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + +- Better error reporting on invalid .transform with user defined input (:issue:`10165`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 8c277568c9e35..ec6a8003c9681 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -3345,9 +3345,9 @@ def _transform_general(self, func, *args, **kwargs): path, res = self._choose_path(fast_path, slow_path, group) except TypeError: return self._transform_item_by_item(obj, fast_path) - except Exception: # pragma: no cover - res = fast_path(group) - path = fast_path + except ValueError: + msg = 'transform must return a scalar value for each group' + raise ValueError(msg) else: res = path(group) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 7ee40a7758011..89ef4decfda39 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -6104,6 +6104,21 @@ def test_nunique_with_object(self): expected = pd.Series([1] * 5, name='name', index=index) tm.assert_series_equal(result, expected) + def test_transform_with_non_scalar_group(self): + # GH 10165 + cols = pd.MultiIndex.from_tuples([ + ('syn', 'A'), ('mis', 'A'), ('non', 'A'), + ('syn', 'C'), ('mis', 'C'), ('non', 'C'), + ('syn', 'T'), ('mis', 'T'), ('non', 'T'), + ('syn', 'G'), ('mis', 'G'), ('non', 'G')]) + df = pd.DataFrame(np.random.randint(1, 10, (4, 12)), + columns=cols, + index=['A', 'C', 'G', 'T']) + self.assertRaisesRegexp(ValueError, 'transform must return a scalar ' + 'value for each group.*', df.groupby + (axis=1, level=1).transform, + lambda z: z.div(z.sum(axis=1), axis=0)) + def assert_fp_equal(a, b): assert (np.abs(a - b) < 1e-12).all()
- [ ] closes #10165 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12474
2016-02-26T22:59:24Z
2016-02-27T16:10:01Z
null
2016-02-27T16:10:12Z
DOC: expanding comparison with R section
diff --git a/doc/source/comparison_with_r.rst b/doc/source/comparison_with_r.rst index 0841f3354d160..fad3d034c8d17 100644 --- a/doc/source/comparison_with_r.rst +++ b/doc/source/comparison_with_r.rst @@ -31,6 +31,79 @@ For transfer of ``DataFrame`` objects from ``pandas`` to R, one option is to use HDF5 files, see :ref:`io.external_compatibility` for an example. + +Quick Reference +--------------- + +We'll start off with a quick reference guide pairing some common R +operations using `dplyr +<http://cran.r-project.org/web/packages/dplyr/index.html>`__ with +pandas equivalents. + + +Querying, Filtering, Sampling +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +=========================================== =========================================== +R pandas +=========================================== =========================================== +``dim(df)`` ``df.shape`` +``head(df)`` ``df.head()`` +``slice(df, 1:10)`` ``df.iloc[:9]`` +``filter(df, col1 == 1, col2 == 1)`` ``df.query('col1 == 1 & col2 == 1')`` +``df[df$col1 == 1 & df$col2 == 1,]`` ``df[(df.col1 == 1) & (df.col2 == 1)]`` +``select(df, col1, col2)`` ``df[['col1', 'col2']]`` +``select(df, col1:col3)`` ``df.loc[:, 'col1':'col3']`` +``select(df, -(col1:col3))`` ``df.drop(cols_to_drop, axis=1)`` but see [#select_range]_ +``distinct(select(df, col1))`` ``df[['col1']].drop_duplicates()`` +``distinct(select(df, col1, col2))`` ``df[['col1', 'col2']].drop_duplicates()`` +``sample_n(df, 10)`` ``df.sample(n=10)`` +``sample_frac(df, 0.01)`` ``df.sample(frac=0.01)`` +=========================================== =========================================== + +.. [#select_range] R's shorthand for a subrange of columns + (``select(df, col1:col3)``) can be approached + cleanly in pandas, if you have the list of columns, + for example ``df[cols[1:3]]`` or + ``df.drop(cols[1:3])``, but doing this by column + name is a bit messy. + + +Sorting +~~~~~~~ + +=========================================== =========================================== +R pandas +=========================================== =========================================== +``arrange(df, col1, col2)`` ``df.sort_values(['col1', 'col2'])`` +``arrange(df, desc(col1))`` ``df.sort_values('col1', ascending=False)`` +=========================================== =========================================== + +Transforming +~~~~~~~~~~~~ + +=========================================== =========================================== +R pandas +=========================================== =========================================== +``select(df, col_one = col1)`` ``df.rename(columns={'col1': 'col_one'})['col_one']`` +``rename(df, col_one = col1)`` ``df.rename(columns={'col1': 'col_one'})`` +``mutate(df, c=a-b)`` ``df.assign(c=df.a-df.b)`` +=========================================== =========================================== + + +Grouping and Summarizing +~~~~~~~~~~~~~~~~~~~~~~~~ + +============================================== =========================================== +R pandas +============================================== =========================================== +``summary(df)`` ``df.describe()`` +``gdf <- group_by(df, col1)`` ``gdf = df.groupby('col1')`` +``summarise(gdf, avg=mean(col1, na.rm=TRUE))`` ``df.groupby('col1').agg({'col1': 'mean'})`` +``summarise(gdf, total=sum(col1))`` ``df.groupby('col1').sum()`` +============================================== =========================================== + + Base R ------
- [ ] closes #9815 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry This is the beginning of a quick reference section. It's incomplete, just did a rough translation of http://nbviewer.jupyter.org/urls/gist.githubusercontent.com/TomAugspurger/6e052140eaa5fdb6e8c0/raw/811585624e843f3f80b9b6fe89e18119d7d2d73c/dplyr_pandas.ipynb into tables. Should try to get some R experts to comment, and it would be nice to have the pandas versions link to docs for the functions being used, but I'm terrible at reStructuredText and gave up for the moment.
https://api.github.com/repos/pandas-dev/pandas/pulls/12472
2016-02-26T20:54:16Z
2016-04-27T14:01:13Z
null
2016-04-27T14:02:18Z
CLN/BUILD: Fix warnings on build
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 70a1ad4a335ea..309e8b35e2df1 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -43,3 +43,5 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + +- Removed some compiler warnings (:issue:`5385`) diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c index 1e59b31da1e65..33ddc6c6e1f27 100644 --- a/pandas/src/datetime/np_datetime_strings.c +++ b/pandas/src/datetime/np_datetime_strings.c @@ -400,7 +400,7 @@ parse_iso_8601_datetime(char *str, int len, * an error code will be retuned because the date is ambigous */ int has_sep = 0; - char sep; + char sep = '\0'; char valid_sep[] = {'-', '.', '/', '\\', ' '}; int valid_sep_len = 5; diff --git a/pandas/src/klib/khash.h b/pandas/src/klib/khash.h index 10c437c22fe1d..0f1a17c6333f4 100644 --- a/pandas/src/klib/khash.h +++ b/pandas/src/klib/khash.h @@ -152,7 +152,7 @@ typedef khint_t khiter_t; #define __ac_set_isempty_false(flag, i) (flag[i>>5]&=~(1ul<<(i&0x1fU))) #define __ac_set_isempty_true(flag, i) (flag[i>>5]|=(1ul<<(i&0x1fU))) #define __ac_set_isboth_false(flag, i) __ac_set_isempty_false(flag, i) -#define __ac_set_isdel_true(flag, i) (0) +#define __ac_set_isdel_true(flag, i) ((void)0) #ifdef KHASH_LINEAR #define __ac_inc(k, m) 1 diff --git a/pandas/src/msgpack/unpack.h b/pandas/src/msgpack/unpack.h index 3f9d0f1b64895..591fad1ae4661 100644 --- a/pandas/src/msgpack/unpack.h +++ b/pandas/src/msgpack/unpack.h @@ -265,9 +265,9 @@ static inline int unpack_callback_ext(unpack_user* u, const char* base, const ch } // length also includes the typecode, so the actual data is length-1 #if PY_MAJOR_VERSION == 2 - py = PyObject_CallFunction(u->ext_hook, "(is#)", typecode, pos, (Py_ssize_t)length-1); + py = PyObject_CallFunction(u->ext_hook, (char*)"(is#)", typecode, pos, (Py_ssize_t)length-1); #else - py = PyObject_CallFunction(u->ext_hook, "(iy#)", typecode, pos, (Py_ssize_t)length-1); + py = PyObject_CallFunction(u->ext_hook, (char*)"(iy#)", typecode, pos, (Py_ssize_t)length-1); #endif if (!py) return -1; diff --git a/pandas/src/msgpack/unpack_template.h b/pandas/src/msgpack/unpack_template.h index d34eceda6ab69..95af6735520fc 100644 --- a/pandas/src/msgpack/unpack_template.h +++ b/pandas/src/msgpack/unpack_template.h @@ -89,7 +89,7 @@ static inline int unpack_execute(unpack_context* ctx, const char* data, size_t l */ unpack_user* user = &ctx->user; - PyObject* obj; + PyObject* obj = NULL; unpack_stack* c = NULL; int ret; diff --git a/pandas/src/parser/io.h b/pandas/src/parser/io.h index f5831ad9971a1..2ae72ff8a7fe0 100644 --- a/pandas/src/parser/io.h +++ b/pandas/src/parser/io.h @@ -29,7 +29,7 @@ typedef struct _file_source { #define FS(source) ((file_source *)source) -#if !defined(_WIN32) +#if !defined(_WIN32) && !defined(HAVE_MMAP) #define HAVE_MMAP #endif diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index 8fd3674047301..a19930a5cef30 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -2185,7 +2185,7 @@ double xstrtod(const char *str, char **endptr, char decimal, p++; num_digits++; - p += (tsep != '\0' & *p == tsep); + p += (tsep != '\0' && *p == tsep); } // Process decimal part @@ -2358,7 +2358,7 @@ double precise_xstrtod(const char *str, char **endptr, char decimal, ++exponent; p++; - p += (tsep != '\0' & *p == tsep); + p += (tsep != '\0' && *p == tsep); } // Process decimal part diff --git a/pandas/src/parser/tokenizer.h b/pandas/src/parser/tokenizer.h index 6aac34ecce41e..a2d7925df08e2 100644 --- a/pandas/src/parser/tokenizer.h +++ b/pandas/src/parser/tokenizer.h @@ -267,7 +267,7 @@ double xstrtod(const char *p, char **q, char decimal, char sci, char tsep, int s double precise_xstrtod(const char *p, char **q, char decimal, char sci, char tsep, int skip_trailing); double round_trip(const char *p, char **q, char decimal, char sci, char tsep, int skip_trailing); //int P_INLINE to_complex(char *item, double *p_real, double *p_imag, char sci, char decimal); -int P_INLINE to_longlong(char *item, long long *p_value); +//int P_INLINE to_longlong(char *item, long long *p_value); //int P_INLINE to_longlong_thousands(char *item, long long *p_value, char tsep); int to_boolean(const char *item, uint8_t *val); diff --git a/pandas/src/period_helper.c b/pandas/src/period_helper.c index 86672e1a753ea..6078be6fc3d19 100644 --- a/pandas/src/period_helper.c +++ b/pandas/src/period_helper.c @@ -30,8 +30,6 @@ static int floordiv(int x, int divisor) { } } -static asfreq_info NULL_AF_INFO; - /* Table with day offsets for each month (0-based, without and with leap) */ static int month_offset[2][13] = { { 0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365 }, @@ -299,7 +297,7 @@ PANDAS_INLINE int get_freq_group_index(int freq) { return freq/1000; } -static int calc_conversion_factors_matrix_size() { +static int calc_conversion_factors_matrix_size(void) { int matrix_size = 0; int index; for (index=0;; index++) { @@ -348,7 +346,7 @@ static npy_int64 calculate_conversion_factor(int start_value, int end_value) { return conversion_factor; } -static void populate_conversion_factors_matrix() { +static void populate_conversion_factors_matrix(void) { int row_index_index; int row_value, row_index; int column_index_index; diff --git a/pandas/src/ujson/lib/ultrajsondec.c b/pandas/src/ujson/lib/ultrajsondec.c index 3e316eb26e6e1..9a4d5972b101b 100644 --- a/pandas/src/ujson/lib/ultrajsondec.c +++ b/pandas/src/ujson/lib/ultrajsondec.c @@ -76,12 +76,6 @@ static JSOBJ SetError( struct DecoderState *ds, int offset, const char *message) return NULL; } -static void ClearError( struct DecoderState *ds) -{ - ds->dec->errorOffset = 0; - ds->dec->errorStr = NULL; -} - double createDouble(double intNeg, double intValue, double frcValue, int frcDecimalCount) { static const double g_pow10[] = {1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001,0.0000001, 0.00000001, 0.000000001, 0.0000000001, 0.00000000001, 0.000000000001, 0.0000000000001, 0.00000000000001, 0.000000000000001};
- [x] progress towards closing #5385 - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12471
2016-02-26T20:43:54Z
2016-02-27T14:55:55Z
null
2016-02-27T14:56:53Z
CLN: consolidate Series.quantile and DataFrame.quantile
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 449068eff4f43..79db9f259aff4 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -62,7 +62,8 @@ import pandas.algos as _algos from pandas.core.config import get_option -from pandas import _np_version_under1p9 + +from textwrap import dedent # --------------------------------------------------------------------- # Docstring templates @@ -4919,108 +4920,20 @@ def f(s): return data.apply(f, axis=axis) - def quantile(self, q=0.5, axis=0, numeric_only=True, - interpolation='linear'): - """ - Return values at the given quantile over requested axis, a la - numpy.percentile. - - Parameters - ---------- - q : float or array-like, default 0.5 (50% quantile) - 0 <= q <= 1, the quantile(s) to compute - axis : {0, 1, 'index', 'columns'} (default 0) - 0 or 'index' for row-wise, 1 or 'columns' for column-wise - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} - .. versionadded:: 0.18.0 - This optional parameter specifies the interpolation method to use, - when the desired quantile lies between two data points `i` and `j`: - - * linear: `i + (j - i) * fraction`, where `fraction` is the - fractional part of the index surrounded by `i` and `j`. - * lower: `i`. - * higher: `j`. - * nearest: `i` or `j` whichever is nearest. - * midpoint: (`i` + `j`) / 2. - - Returns - ------- + @Substitution(dedent(""" quantiles : Series or DataFrame If ``q`` is an array, a DataFrame will be returned where the index is ``q``, the columns are the columns of self, and the values are the quantiles. If ``q`` is a float, a Series will be returned where the index is the columns of self and the values are the quantiles. - - Examples - -------- - - >>> df = DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), - columns=['a', 'b']) - >>> df.quantile(.1) - a 1.3 - b 3.7 - dtype: float64 - >>> df.quantile([.1, .5]) - a b - 0.1 1.3 3.7 - 0.5 2.5 55.0 - """ - self._check_percentile(q) - per = np.asarray(q) * 100 - - if not com.is_list_like(per): - per = [per] - q = [q] - squeeze = True - else: - squeeze = False - - if _np_version_under1p9: - if interpolation != 'linear': - raise ValueError("Interpolation methods other than linear " - "are not supported in numpy < 1.9") - - def f(arr, per, interpolation): - if arr._is_datelike_mixed_type: - values = _values_from_object(arr).view('i8') - else: - values = arr.astype(float) - values = values[notnull(values)] - if len(values) == 0: - return NA - else: - if _np_version_under1p9: - return _quantile(values, per) - else: - return _quantile(values, per, interpolation=interpolation) - - data = self._get_numeric_data() if numeric_only else self - - axis = self._get_axis_number(axis) - - if axis == 1: - data = data.T - - # need to know which cols are timestamp going in so that we can - # map timestamp over them after getting the quantile. - is_dt_col = data.dtypes.map(com.is_datetime64_dtype) - is_dt_col = is_dt_col[is_dt_col].index - - quantiles = [[f(vals, x, interpolation) for x in per] - for (_, vals) in data.iteritems()] - - result = self._constructor(quantiles, index=data._info_axis, - columns=q).T - if len(is_dt_col) > 0: - result[is_dt_col] = result[is_dt_col].applymap(lib.Timestamp) - if squeeze: - if result.shape == (1, 1): - result = result.T.iloc[:, 0] # don't want scalar - else: - result = result.T.squeeze() - result.name = None # For groupby, so it can set an index name - return result + """)) + @Appender(_shared_docs['quantile']) + def quantile(self, q=0.5, axis=0, numeric_only=True, + interpolation='linear'): + return super(DataFrame, + self).quantile(q=q, axis=axis, numeric_only=numeric_only, + interpolation=interpolation) def to_timestamp(self, freq=None, how='start', axis=0, copy=True): """ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index f850bfba4f90e..3c843ab42fa90 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -27,8 +27,10 @@ SettingWithCopyError, SettingWithCopyWarning, AbstractMethodError) import pandas.core.nanops as nanops +from numpy import percentile as _quantile from pandas.util.decorators import Appender, Substitution, deprecate_kwarg from pandas.core import config +from pandas import _np_version_under1p9 # goal is to be able to define the docs close to function, while still being # able to share @@ -842,43 +844,7 @@ def __contains__(self, key): @property def empty(self): - """True if NDFrame is entirely empty [no items], meaning any of the - axes are of length 0. - - Notes - ----- - If NDFrame contains only NaNs, it is still not considered empty. See - the example below. - - Examples - -------- - An example of an actual empty DataFrame. Notice the index is empty: - - >>> df_empty = pd.DataFrame({'A' : []}) - >>> df_empty - Empty DataFrame - Columns: [A] - Index: [] - >>> df_empty.empty - True - - If we only have NaNs in our DataFrame, it is not considered empty! We - will need to drop the NaNs to make the DataFrame empty: - - >>> df = pd.DataFrame({'A' : [np.nan]}) - >>> df - A - 0 NaN - >>> df.empty - False - >>> df.dropna().empty - True - - See also - -------- - pandas.Series.dropna - pandas.DataFrame.dropna - """ + """True if NDFrame is entirely empty [no items]""" return not all(len(self._get_axis(a)) > 0 for a in self._AXIS_ORDERS) def __nonzero__(self): @@ -4110,6 +4076,125 @@ def ranker(data): return ranker(data) + _shared_docs['quantile'] = (""" + Return values at the given quantile over requested axis, a la + numpy.percentile. + + Parameters + ---------- + q : float or array-like, default 0.5 (50 percentile) + 0 <= q <= 1, the quantile(s) to compute + axis : {0, 1, 'index', 'columns'} (default 0) + 0 or 'index' for row-wise, 1 or 'columns' for column-wise + numeric_only : boolean, default None + Include only float, int, boolean data. If None, will attempt to use + everything, then use only numeric data + interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} + .. versionadded:: 0.18.0 + This optional parameter specifies the interpolation method to use, + when the desired quantile lies between two data points `i` and `j`: + + * linear: `i + (j - i) * fraction`, where `fraction` is the + fractional part of the index surrounded by `i` and `j`. + * lower: `i`. + * higher: `j`. + * nearest: `i` or `j` whichever is nearest. + * midpoint: (`i` + `j`) / 2. + + Returns + ------- + %s + + Examples + -------- + + >>> s = Series([1, 2, 3, 4]) + >>> s.quantile(.5) + 2.5 + >>> s.quantile([.25, .5, .75]) + 0.25 1.75 + 0.50 2.50 + 0.75 3.25 + dtype: float64 + >>> df = DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), + columns=['a', 'b']) + >>> df.quantile(.1) + a 1.3 + b 3.7 + dtype: float64 + >>> df.quantile([.1, .5]) + a b + 0.1 1.3 3.7 + 0.5 2.5 55.0 + """) + + @Appender(_shared_docs['quantile'] % '') + def quantile(self, q=0.5, axis=0, numeric_only=None, + interpolation='linear'): + if self.ndim >= 3: + msg = "quantile is not implemented on on Panel or PanelND objects." + raise NotImplementedError(msg) + elif self.ndim == 1: + result = self.to_frame().quantile(q=q, axis=axis, + numeric_only=numeric_only, + interpolation=interpolation) + if not com.is_list_like(q): + return result.iloc[0] + else: + return result[result.columns[0]] + + self._check_percentile(q) + per = np.asarray(q) * 100 + + if not com.is_list_like(per): + per = [per] + q = [q] + squeeze = True + else: + squeeze = False + + if _np_version_under1p9: + if interpolation != 'linear': + raise ValueError("Interpolation methods other than linear " + "are not supported in numpy < 1.9") + + def f(arr, per, interpolation): + boxer = com.i8_boxer(arr) \ + if com.needs_i8_conversion(arr) else lambda x: x + if arr._is_datelike_mixed_type: + values = _values_from_object(arr).view('i8') + else: + values = arr.astype(float) + values = values[notnull(values)] + if len(values) == 0: + return boxer(np.nan) + else: + if _np_version_under1p9: + return boxer(_quantile(values, per)) + else: + return boxer(_quantile(values, per, + interpolation=interpolation)) + + data = self._get_numeric_data() if numeric_only else self + + axis = self._get_axis_number(axis) + + if axis == 1: + data = data.T + + quantiles = [[f(vals, x, interpolation) for x in per] + for (_, vals) in data.iteritems()] + + result = self._constructor(quantiles, index=data._info_axis, + columns=q).T + if squeeze: + if result.shape == (1, 1): + result = result.T.iloc[:, 0] # don't want scalar + else: + result = result.T.squeeze() + result.name = None # For groupby, so it can set an index name + return result + _shared_docs['align'] = (""" Align two object on their axes with the specified join method for each axis Index diff --git a/pandas/core/series.py b/pandas/core/series.py index 1e5e0f6fb4553..479e356022bf6 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -50,16 +50,15 @@ import pandas.core.datetools as datetools import pandas.core.format as fmt import pandas.core.nanops as nanops -from pandas.util.decorators import Appender, deprecate_kwarg +from pandas.util.decorators import Appender, Substitution, deprecate_kwarg import pandas.lib as lib import pandas.tslib as tslib import pandas.index as _index -from numpy import percentile as _quantile from pandas.core.config import get_option -from pandas import _np_version_under1p9 +from textwrap import dedent __all__ = ['Series'] @@ -1279,66 +1278,17 @@ def round(self, decimals=0): return result - def quantile(self, q=0.5, interpolation='linear'): - """ - Return value at the given quantile, a la numpy.percentile. - - Parameters - ---------- - q : float or array-like, default 0.5 (50% quantile) - 0 <= q <= 1, the quantile(s) to compute - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} - .. versionadded:: 0.18.0 - This optional parameter specifies the interpolation method to use, - when the desired quantile lies between two data points `i` and `j`: - * linear: `i + (j - i) * fraction`, where `fraction` is the - fractional part of the index surrounded by `i` and `j`. - * lower: `i`. - * higher: `j`. - * nearest: `i` or `j` whichever is nearest. - * midpoint: (`i` + `j`) / 2. - - Returns - ------- + @Substitution(dedent(""" quantile : float or Series if ``q`` is an array, a Series will be returned where the index is ``q`` and the values are the quantiles. - - Examples - -------- - - >>> s = Series([1, 2, 3, 4]) - >>> s.quantile(.5) - 2.5 - >>> s.quantile([.25, .5, .75]) - 0.25 1.75 - 0.50 2.50 - 0.75 3.25 - dtype: float64 - """ - - self._check_percentile(q) - - if _np_version_under1p9: - if interpolation != 'linear': - raise ValueError("Interpolation methods other than linear " - "are not supported in numpy < 1.9.") - - def multi(values, qs, **kwargs): - if com.is_list_like(qs): - values = [_quantile(values, x * 100, **kwargs) for x in qs] - # let empty result to be Float64Index - qs = Float64Index(qs) - return self._constructor(values, index=qs, name=self.name) - else: - return _quantile(values, qs * 100, **kwargs) - - kwargs = dict() - if not _np_version_under1p9: - kwargs.update({'interpolation': interpolation}) - - return self._maybe_box(lambda values: multi(values, q, **kwargs), - dropna=True) + """)) + @Appender(generic._shared_docs['quantile']) + def quantile(self, q=0.5, axis=0, numeric_only=None, + interpolation='linear'): + return super(Series, + self).quantile(q=q, axis=axis, numeric_only=numeric_only, + interpolation=interpolation) def corr(self, other, method='pearson', min_periods=None): """ diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 455e5cda03307..dcb074e274d87 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -572,8 +572,7 @@ def test_quantile_multi(self): assert_series_equal(result, expected) result = self.ts.quantile([]) - expected = pd.Series([], name=self.ts.name, index=Index( - [], dtype=float)) + expected = pd.Series([], name=self.ts.name, index=[]) assert_series_equal(result, expected) def test_quantile_interpolation(self):
- closes #10207 - Slightly modified test in tests/series/tests_analytics.py:575 to standardize behavior across Series and DataFrame - passes `git diff upstream/master | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/12469
2016-02-26T20:02:49Z
2016-04-03T17:03:18Z
null
2016-04-03T17:03:18Z
MAINT: Nicely inform users if they're missing hard dependencies.
diff --git a/pandas/__init__.py b/pandas/__init__.py index b8c1f082b6cb3..b34edb1688e5a 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -4,8 +4,21 @@ __docformat__ = 'restructuredtext' +# Let users know if they're missing any of our hard dependencies, ie. don't fail fast. +hard_dependencies = ("numpy", "pytz", "dateutil") +missing_dependencies = [] + +for dependency in hard_dependencies: + try: + __import__(dependency) + except ImportError as e: + missing_dependencies.append(dependency) + +if missing_dependencies: + raise ImportError("Missing required dependencies {0}".format(missing_dependencies)) + + # numpy compat -import numpy as np from pandas.compat.numpy_compat import * try:
Closes #12176 - [✓] tests pass Manually tested with condas environments w/ all and no dependencies. - [✓] passes `git diff upstream/master | flake8 --diff` - [✗] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12468
2016-02-26T19:58:05Z
2016-02-27T14:28:05Z
null
2016-02-27T14:28:15Z
Confirm timedelta algos work
diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 9ba4915d235ee..c26fd8255df53 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -219,7 +219,8 @@ For Python 2.7, you can install the ``mingw`` compiler which will work equivalen or use the `Microsoft Visual Studio VC++ compiler for Python <https://www.microsoft.com/en-us/download/details.aspx?id=44266>`__. Note that you have to check the ``x64`` box to install the ``x64`` extension building capability as this is not installed by default. -For Python 3.4, you can download and install the `Windows 7.1 SDK <https://www.microsoft.com/en-us/download/details.aspx?id=8279>`__ +For Python 3.4, you can download and install the `Windows 7.1 SDK <https://www.microsoft.com/en-us/download/details.aspx?id=8279>`__. Read the references below as there may be various gotchas during the installation. + For Python 3.5, you can download and install the `Visual Studio 2015 Community Edition <https://www.visualstudio.com/en-us/downloads/visual-studio-2015-downloads-vs.aspx>`__. Here are some references: diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 455e5cda03307..6c3df944637f9 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -160,6 +160,13 @@ def test_mode(self): assert_series_equal(s.mode(), Series(['2011-01-03', '2013-01-02'], dtype='M8[ns]')) + # GH 5986 + s = Series(['1 days', '-1 days', '0 days'], dtype='timedelta64[ns]') + assert_series_equal(s.mode(), Series([], dtype='timedelta64[ns]')) + + s = Series(['1 day', '1 day', '-1 day', '-1 day 2 min', '2 min', '2 min'], dtype='timedelta64[ns]') + assert_series_equal(s.mode(), Series(['2 min', '1 day'], dtype='timedelta64[ns]')) + def test_prod(self): self._check_stat_op('prod', np.prod) @@ -1015,6 +1022,12 @@ def test_rank(self): iranks = iseries.rank() assert_series_equal(iranks, exp) + # GH 5968 + iseries = Series([1e-50, 1e-100, 1e-20, 1e-2, 1e-20 + 1e-30, 1e-1, pd.NaT], dtype='timedelta64[ns]') + exp = Series([2, 1, 3, 5, 4, 6, 7]) + iranks = iseries.rank() + assert_series_equal(iranks, exp) + values = np.array( [-50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40 ], dtype='float64') diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index ef3f6210f0c21..380e0a581e781 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -143,6 +143,20 @@ def test_datelike(self): [0, 0, 0, 1, 1, 0], dtype=np.int64)) self.assert_numpy_array_equal(uniques, pd.PeriodIndex([v1, v2])) + # GH 5986 + v1 = pd.to_timedelta('1 day 1 min') + v2 = pd.to_timedelta('1 day') + x = Series([v1, v2, v1, v1, v2, v2, v1]) + labels, uniques = algos.factorize(x) + self.assert_numpy_array_equal(labels, np.array( + [0, 1, 0, 0, 1, 1, 0], dtype=np.int64)) + self.assert_numpy_array_equal(uniques, pd.to_timedelta([v1, v2])) + + labels, uniques = algos.factorize(x, sort=True) + self.assert_numpy_array_equal(labels, np.array( + [1, 0, 1, 1, 0, 0, 1], dtype=np.int64)) + self.assert_numpy_array_equal(uniques, pd.to_timedelta([v2, v1])) + def test_factorize_nan(self): # nan should map to na_sentinel, not reverse_indexer[na_sentinel] # rizer.factorize should not raise an exception if na_sentinel indexes
- closes #5986 - 3 tests added / passed: rank, factorize, mode - confirm the other 3 tests already exist: unique, quantile, value_counts
https://api.github.com/repos/pandas-dev/pandas/pulls/12465
2016-02-26T18:51:34Z
2016-02-27T15:22:59Z
null
2016-02-27T15:23:16Z
ENH: add missing methods to .dt for Period, resolves #8848
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 8a48314de5f77..6e9574b6ee4f7 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -431,6 +431,7 @@ Other enhancements - ``pivot_table()`` now accepts most iterables for the ``values`` parameter (:issue:`12017`) - Added Google ``BigQuery`` service account authentication support, which enables authentication on remote servers. (:issue:`11881`). For further details see :ref:`here <io.bigquery_authentication>` - ``HDFStore`` is now iterable: ``for k in store`` is equivalent to ``for k in store.keys()`` (:issue:`12221`). +- Add missing methods/fields to .dt for Period (:issue:`8848`) - The entire codebase has been ``PEP``-ified (:issue:`12096`) .. _whatsnew_0180.api_breaking: diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index c6593d403ffcc..cd1fd92f241cf 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -32,8 +32,8 @@ def test_dt_namespace_accessor(self): 'weekofyear', 'week', 'dayofweek', 'weekday', 'dayofyear', 'quarter', 'freq', 'days_in_month', 'daysinmonth'] - ok_for_period = ok_for_base + ['qyear'] - ok_for_period_methods = ['strftime'] + ok_for_period = ok_for_base + ['qyear', 'start_time', 'end_time'] + ok_for_period_methods = ['strftime', 'to_timestamp', 'asfreq'] ok_for_dt = ok_for_base + ['date', 'time', 'microsecond', 'nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index a25bb525c9970..e14996517fa65 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -156,7 +156,8 @@ class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin, Int64Index): _datetimelike_ops = ['year', 'month', 'day', 'hour', 'minute', 'second', 'weekofyear', 'week', 'dayofweek', 'weekday', 'dayofyear', 'quarter', 'qyear', 'freq', - 'days_in_month', 'daysinmonth'] + 'days_in_month', 'daysinmonth', + 'to_timestamp', 'asfreq', 'start_time', 'end_time'] _is_numeric_dtype = False _infer_as_myclass = True @@ -498,6 +499,14 @@ def to_datetime(self, dayfirst=False): 'days_in_month', 11, "The number of days in the month") daysinmonth = days_in_month + @property + def start_time(self): + return self.to_timestamp(how='start') + + @property + def end_time(self): + return self.to_timestamp(how='end') + def _get_object_array(self): freq = self.freq return np.array([Period._from_ordinal(ordinal=x, freq=freq) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 8a876272dfdef..ee6f1a8b0e2db 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -2030,6 +2030,16 @@ def test_to_timestamp_pi_mult(self): ['2011-02-28', 'NaT', '2011-03-31'], name='idx') self.assert_index_equal(result, expected) + def test_start_time(self): + index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') + expected_index = date_range('2016-01-01', end='2016-05-31', freq='MS') + self.assertTrue(index.start_time.equals(expected_index)) + + def test_end_time(self): + index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31') + expected_index = date_range('2016-01-01', end='2016-05-31', freq='M') + self.assertTrue(index.end_time.equals(expected_index)) + def test_as_frame_columns(self): rng = period_range('1/1/2000', periods=5) df = DataFrame(randn(10, 5), columns=rng)
- [x] closes #8848 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Not 100% complete, but putting this in as a place to house comments: - I am not sure where to put the whatsnew entry - 0.18.0 or 0.18.1? - Also not sure if you'd like tests on the actual usage in the .dt accessor, there are actually no tests anywhere in pandas (as far as I can tell) on period_series.dt.\* (as opposed to PeriodIndex). I just added a couple PeriodIndex tests.
https://api.github.com/repos/pandas-dev/pandas/pulls/12463
2016-02-26T18:42:14Z
2016-02-27T16:02:50Z
null
2016-02-27T18:05:01Z
BUG: fix df concat containing mix of localized and non-localized Timestamps
diff --git a/pandas/tests/frame/test_combine_concat.py b/pandas/tests/frame/test_combine_concat.py index 77077440ea301..2600b8c870ba7 100644 --- a/pandas/tests/frame/test_combine_concat.py +++ b/pandas/tests/frame/test_combine_concat.py @@ -46,6 +46,28 @@ def test_combine_multiple_frames_dtypes(self): expected = Series(dict(float64=2, float32=2)) assert_series_equal(results, expected) + def test_combine_multiple_tzs(self): + # GH 12467 + ts1 = Timestamp('2015-01-01', tz=None) + ts2 = Timestamp('2015-01-01', tz='UTC') + ts3 = Timestamp('2015-01-01', tz='EST') + + df1 = DataFrame(dict(time=[ts1])) + df2 = DataFrame(dict(time=[ts2])) + df3 = DataFrame(dict(time=[ts3])) + + results = pd.concat([df1, df2]).reset_index(drop=True) + expected = DataFrame(dict(time=[ts1, ts2]), dtype=object) + assert_frame_equal(results, expected) + + results = pd.concat([df1, df3]).reset_index(drop=True) + expected = DataFrame(dict(time=[ts1, ts3]), dtype=object) + assert_frame_equal(results, expected) + + results = pd.concat([df2, df3]).reset_index(drop=True) + expected = DataFrame(dict(time=[ts2, ts3])) + assert_frame_equal(results, expected) + def test_append_series_dict(self): df = DataFrame(np.random.randn(5, 4), columns=['foo', 'bar', 'baz', 'qux']) diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 95ac985bd2334..11a5fdc062e22 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -260,7 +260,7 @@ def convert_to_pydatetime(x, axis): # if dtype is of datetimetz or timezone if x.dtype.kind == _NS_DTYPE.kind: if getattr(x, 'tz', None) is not None: - x = x.asobject + x = x.asobject.values else: shape = x.shape x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) @@ -271,6 +271,8 @@ def convert_to_pydatetime(x, axis): x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) x = x.reshape(shape) + if axis == 1: + x = np.atleast_2d(x) return x if typs is None:
BUG: fix issue with concat of localized timestamps TST: test concat of dataframes with non-None timezone columns
https://api.github.com/repos/pandas-dev/pandas/pulls/12462
2016-02-26T18:23:29Z
2016-04-06T13:00:10Z
null
2016-04-06T13:00:19Z
ENH: Improve error message for empty data constructor. #8020
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 51fa0482accca..e15a5de23f79a 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -869,6 +869,8 @@ Other API Changes - ``.to_latex`` and ``.to_html`` gain a ``decimal`` parameter like ``.to_csv``; the default is ``'.'`` (:issue:`12031`) +- The error message when constructing a DataFrame with empty data and indices specified has been updated. + .. _whatsnew_0180.deprecations: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 449068eff4f43..6c2d4f7919ac6 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -411,7 +411,6 @@ def _get_axes(N, K, index=index, columns=columns): values = _prep_ndarray(values, copy=copy) if dtype is not None: - if values.dtype != dtype: try: values = values.astype(dtype) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 8563481c8564d..c945e2560b587 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3875,6 +3875,8 @@ def construction_error(tot_items, block_shape, axes, e=None): implied = tuple(map(int, [len(ax) for ax in axes])) if passed == implied and e is not None: raise e + if block_shape[0] == 0: + raise ValueError("Empty data passed with indices specified.") raise ValueError("Shape of passed values is {0}, indices imply {1}".format( passed, implied)) diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 70e8d3bcf4582..ec18844354f6b 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -285,6 +285,11 @@ def test_constructor_multi_index(self): self.assertTrue(pd.isnull(df).values.ravel().all()) def test_constructor_error_msgs(self): + msg = "Empty data passed with indices specified." + # passing an empty array with columns specified. + with assertRaisesRegexp(ValueError, msg): + DataFrame(np.empty(0), columns=list('abc')) + msg = "Mixing dicts with non-Series may lead to ambiguous ordering." # mix dict and array, wrong size with assertRaisesRegexp(ValueError, msg):
- [X] closes #8020 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12461
2016-02-26T18:21:08Z
2016-02-27T14:46:28Z
null
2016-02-27T14:46:40Z
ENH: replace uses of np.isscalar with pd.lib.isscalar
diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index 0d528de9f55b6..b80823de6de05 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -10,6 +10,7 @@ import pandas as pd from pandas.compat import PY3, string_types, text_type import pandas.core.common as com +import pandas.lib as lib from pandas.core.base import StringMixin from pandas.computation.common import _ensure_decoded, _result_type_many from pandas.computation.scope import _DEFAULT_GLOBALS @@ -98,7 +99,7 @@ def update(self, value): @property def isscalar(self): - return np.isscalar(self._value) + return lib.isscalar(self._value) @property def type(self): diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index 43d2faac36c2f..b70252ed9f35b 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -30,6 +30,7 @@ import pandas.computation.expr as expr import pandas.util.testing as tm +import pandas.lib as lib from pandas.util.testing import (assert_frame_equal, randbool, assertRaisesRegexp, assert_numpy_array_equal, assert_produces_warning, assert_series_equal) @@ -196,7 +197,7 @@ def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2): ex = '(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)'.format(cmp1=cmp1, binop=binop, cmp2=cmp2) - scalar_with_in_notin = (np.isscalar(rhs) and (cmp1 in skip_these or + scalar_with_in_notin = (lib.isscalar(rhs) and (cmp1 in skip_these or cmp2 in skip_these)) if scalar_with_in_notin: with tm.assertRaises(TypeError): @@ -327,7 +328,7 @@ def check_pow(self, lhs, arith1, rhs): expected = self.get_expected_pow_result(lhs, rhs) result = pd.eval(ex, engine=self.engine, parser=self.parser) - if (np.isscalar(lhs) and np.isscalar(rhs) and + if (lib.isscalar(lhs) and lib.isscalar(rhs) and _is_py3_complex_incompat(result, expected)): self.assertRaises(AssertionError, tm.assert_numpy_array_equal, result, expected) @@ -360,16 +361,16 @@ def check_compound_invert_op(self, lhs, cmp1, rhs): skip_these = 'in', 'not in' ex = '~(lhs {0} rhs)'.format(cmp1) - if np.isscalar(rhs) and cmp1 in skip_these: + if lib.isscalar(rhs) and cmp1 in skip_these: self.assertRaises(TypeError, pd.eval, ex, engine=self.engine, parser=self.parser, local_dict={'lhs': lhs, 'rhs': rhs}) else: # compound - if np.isscalar(lhs) and np.isscalar(rhs): + if lib.isscalar(lhs) and lib.isscalar(rhs): lhs, rhs = map(lambda x: np.array([x]), (lhs, rhs)) expected = _eval_single_bin(lhs, cmp1, rhs, self.engine) - if np.isscalar(expected): + if lib.isscalar(expected): expected = not expected else: expected = ~expected @@ -639,17 +640,17 @@ def test_identical(self): x = 1 result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, 1) - self.assertTrue(np.isscalar(result)) + self.assertTrue(lib.isscalar(result)) x = 1.5 result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, 1.5) - self.assertTrue(np.isscalar(result)) + self.assertTrue(lib.isscalar(result)) x = False result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, False) - self.assertTrue(np.isscalar(result)) + self.assertTrue(lib.isscalar(result)) x = np.array([1]) result = pd.eval('x', engine=self.engine, parser=self.parser) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 2c70da413bb8c..ad1efa21e8280 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -468,7 +468,7 @@ def _get_score(at): return score - if np.isscalar(q): + if lib.isscalar(q): return _get_score(q) else: q = np.asarray(q, np.float64) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 23740f1983b43..35fa06ce5009c 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -1901,7 +1901,7 @@ def _convert_to_list_like(list_like): if (is_sequence(list_like) or isinstance(list_like, tuple) or isinstance(list_like, types.GeneratorType)): return list(list_like) - elif np.isscalar(list_like): + elif lib.isscalar(list_like): return [list_like] else: # is this reached? diff --git a/pandas/core/common.py b/pandas/core/common.py index b95b44a0394bc..05bcb53d85dd0 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -333,7 +333,7 @@ def notnull(obj): pandas.isnull : boolean inverse of pandas.notnull """ res = isnull(obj) - if np.isscalar(res): + if lib.isscalar(res): return not res return ~res @@ -343,7 +343,7 @@ def is_null_datelike_scalar(other): but guard against passing a non-scalar """ if other is pd.NaT or other is None: return True - elif np.isscalar(other): + elif lib.isscalar(other): # a timedelta if hasattr(other, 'dtype'): @@ -489,7 +489,7 @@ def mask_missing(arr, values_to_mask): # if x is a string and arr is not, then we get False and we must # expand the mask to size arr.shape - if np.isscalar(mask): + if lib.isscalar(mask): mask = np.zeros(arr.shape, dtype=bool) else: @@ -1276,7 +1276,7 @@ def changeit(): # we have a scalar or len 0 ndarray # and its nan and we are changing some values - if (np.isscalar(other) or + if (lib.isscalar(other) or (isinstance(other, np.ndarray) and other.ndim < 1)): if isnull(other): return changeit() @@ -1336,7 +1336,7 @@ def _possibly_downcast_to_dtype(result, dtype): or could be an astype of float64->float32 """ - if np.isscalar(result): + if lib.isscalar(result): return result def trans(x): diff --git a/pandas/core/generic.py b/pandas/core/generic.py index f850bfba4f90e..13e4de0e2c5f0 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -898,7 +898,7 @@ def bool(self): v = self.squeeze() if isinstance(v, (bool, np.bool_)): return bool(v) - elif np.isscalar(v): + elif lib.isscalar(v): raise ValueError("bool cannot act on a non-boolean single element " "{0}".format(self.__class__.__name__)) @@ -1750,10 +1750,10 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): else: return self.take(loc, axis=axis, convert=True) - if not np.isscalar(loc): + if not lib.isscalar(loc): new_index = self.index[loc] - if np.isscalar(loc): + if lib.isscalar(loc): from pandas import Series new_values = self._data.fast_xs(loc) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 8c277568c9e35..7dfba83b7419f 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -712,7 +712,7 @@ def _try_cast(self, result, obj): else: dtype = obj.dtype - if not np.isscalar(result): + if not lib.isscalar(result): result = _possibly_downcast_to_dtype(result, dtype) return result @@ -2384,7 +2384,8 @@ def is_in_obj(gpr): def _is_label_like(val): - return isinstance(val, compat.string_types) or np.isscalar(val) + return isinstance(val, compat.string_types) or\ + (not val is None and lib.isscalar(val)) def _convert_grouper(axis, grouper): diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 22185c5676593..f0f5507bc3e85 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -4,6 +4,7 @@ from pandas.compat import range, zip import pandas.compat as compat import pandas.core.common as com +import pandas.lib as lib from pandas.core.common import (is_bool_indexer, is_integer_dtype, _asarray_tuplesafe, is_list_like, isnull, is_null_slice, is_full_slice, ABCSeries, @@ -67,7 +68,7 @@ def __getitem__(self, key): if type(key) is tuple: try: values = self.obj.get_value(*key) - if np.isscalar(values): + if lib.isscalar(values): return values except Exception: pass @@ -677,7 +678,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False): return ser - elif np.isscalar(indexer): + elif lib.isscalar(indexer): ax = self.obj._get_axis(1) if ser.index.equals(ax): @@ -753,7 +754,7 @@ def _align_frame(self, indexer, df): val = df.reindex(index=ax)._values return val - elif np.isscalar(indexer) and is_panel: + elif lib.isscalar(indexer) and is_panel: idx = self.obj.axes[1] cols = self.obj.axes[2] @@ -960,7 +961,7 @@ def _getitem_nested_tuple(self, tup): axis += 1 # if we have a scalar, we are done - if np.isscalar(obj) or not hasattr(obj, 'ndim'): + if lib.isscalar(obj) or not hasattr(obj, 'ndim'): break # has the dim of the obj changed? diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 8563481c8564d..4bc8e081aacdb 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -665,7 +665,7 @@ def _is_scalar_indexer(indexer): if arr_value.ndim == 1: if not isinstance(indexer, tuple): indexer = tuple([indexer]) - return all([np.isscalar(idx) for idx in indexer]) + return all([lib.isscalar(idx) for idx in indexer]) return False def _is_empty_indexer(indexer): @@ -702,7 +702,7 @@ def _is_empty_indexer(indexer): values[indexer] = value # coerce and try to infer the dtypes of the result - if np.isscalar(value): + if lib.isscalar(value): dtype, _ = _infer_dtype_from_scalar(value) else: dtype = 'infer' @@ -3209,7 +3209,7 @@ def get(self, item, fastpath=True): indexer = np.arange(len(self.items))[isnull(self.items)] # allow a single nan location indexer - if not np.isscalar(indexer): + if not lib.isscalar(indexer): if len(indexer) == 1: loc = indexer.item() else: diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index db6488b1f9142..f390e3f04a6c3 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -351,7 +351,7 @@ def _get_counts_nanvar(mask, axis, ddof, dtype=float): d = count - dtype.type(ddof) # always return NaN, never inf - if np.isscalar(count): + if lib.isscalar(count): if count <= ddof: count = np.nan d = np.nan @@ -623,7 +623,7 @@ def _get_counts(mask, axis, dtype=float): return dtype.type(mask.size - mask.sum()) count = mask.shape[axis] - mask.sum(axis) - if np.isscalar(count): + if lib.isscalar(count): return dtype.type(count) try: return count.astype(dtype) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index e0989574d79e1..0abc154f467ab 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -576,7 +576,7 @@ def __setitem__(self, key, value): 'object was {1}'.format( shape[1:], tuple(map(int, value.shape)))) mat = np.asarray(value) - elif np.isscalar(value): + elif lib.isscalar(value): dtype, value = _infer_dtype_from_scalar(value) mat = np.empty(shape[1:], dtype=dtype) mat.fill(value) @@ -703,7 +703,7 @@ def _combine(self, other, func, axis=0): return self._combine_panel(other, func) elif isinstance(other, DataFrame): return self._combine_frame(other, func, axis=axis) - elif np.isscalar(other): + elif lib.isscalar(other): return self._combine_const(other, func) else: raise NotImplementedError("%s is not supported in combine " diff --git a/pandas/core/series.py b/pandas/core/series.py index 1e5e0f6fb4553..5eb1ab0f14ecf 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -559,7 +559,7 @@ def __getitem__(self, key): try: result = self.index.get_value(self, key) - if not np.isscalar(result): + if not lib.isscalar(result): if is_list_like(result) and not isinstance(result, Series): # we need to box if we have a non-unique index here diff --git a/pandas/core/strings.py b/pandas/core/strings.py index ad39755403a07..c1ab46956c25f 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -331,7 +331,7 @@ def str_repeat(arr, repeats): ------- repeated : Series/Index of objects """ - if np.isscalar(repeats): + if lib.isscalar(repeats): def rep(x): try: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index e6b5271d88856..8a679b1575e26 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -260,7 +260,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, elif hasattr(data, '__array__'): return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs) - elif data is None or np.isscalar(data): + elif data is None or lib.isscalar(data): cls._scalar_data_error(data) else: if (tupleize_cols and isinstance(data, list) and data and @@ -486,7 +486,7 @@ def _coerce_to_ndarray(cls, data): """ if not isinstance(data, (np.ndarray, Index)): - if data is None or np.isscalar(data): + if data is None or lib.isscalar(data): cls._scalar_data_error(data) # other iterable of some kind @@ -1269,7 +1269,7 @@ def __getitem__(self, key): getitem = self._data.__getitem__ promote = self._shallow_copy - if np.isscalar(key): + if lib.isscalar(key): return getitem(key) if isinstance(key, slice): @@ -1282,7 +1282,7 @@ def __getitem__(self, key): key = _values_from_object(key) result = getitem(key) - if not np.isscalar(result): + if not lib.isscalar(result): return promote(result) else: return result @@ -1941,7 +1941,7 @@ def get_value(self, series, key): raise e1 except TypeError: # python 3 - if np.isscalar(key): # pragma: no cover + if lib.isscalar(key): # pragma: no cover raise IndexError(key) raise InvalidIndexError(key) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 8138f2deb534f..fea153b2de391 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -978,7 +978,7 @@ def __setstate__(self, state): self._reset_identity() def __getitem__(self, key): - if np.isscalar(key): + if lib.isscalar(key): retval = [] for lev, lab in zip(self.levels, self.labels): if lab[key] == -1: diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index c1817129116e2..0c102637ab70d 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -295,7 +295,7 @@ def _format_native_types(self, na_rep='', float_format=None, decimal='.', def get_value(self, series, key): """ we always want to get an index value, never a value """ - if not np.isscalar(key): + if not lib.isscalar(key): raise InvalidIndexError from pandas.core.indexing import maybe_droplevels @@ -305,7 +305,7 @@ def get_value(self, series, key): loc = self.get_loc(k) new_values = com._values_from_object(series)[loc] - if np.isscalar(new_values) or new_values is None: + if lib.isscalar(new_values) or new_values is None: return new_values new_index = self[loc] diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index ca12a06b4888e..0bed2ec231dbe 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -10,6 +10,7 @@ from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com import pandas.indexes.base as ibase +import pandas.lib as lib from pandas.indexes.numeric import Int64Index @@ -440,7 +441,7 @@ def __getitem__(self, key): """ super_getitem = super(RangeIndex, self).__getitem__ - if np.isscalar(key): + if lib.isscalar(key): n = int(key) if n != key: return super_getitem(key) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index d39540af2ed06..f7b38c75a24b9 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -856,7 +856,7 @@ def _should_parse_dates(self, i): name = self.index_names[i] j = self.index_col[i] - if np.isscalar(self.parse_dates): + if lib.isscalar(self.parse_dates): return (j == self.parse_dates) or (name == self.parse_dates) else: return (j in self.parse_dates) or (name in self.parse_dates) @@ -2144,7 +2144,7 @@ def _isindex(colspec): if isinstance(parse_spec, list): # list of column lists for colspec in parse_spec: - if np.isscalar(colspec): + if lib.isscalar(colspec): if isinstance(colspec, int) and colspec not in data_dict: colspec = orig_names[colspec] if _isindex(colspec): diff --git a/pandas/lib.pyx b/pandas/lib.pyx index f7978c4791538..a6d6ea50caa0b 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -312,6 +312,8 @@ def isscalar(object val): return (np.PyArray_IsAnyScalar(val) # As of numpy-1.9, PyArray_IsAnyScalar misses bytearrays on Py3. or PyBytes_Check(val) + # We differ from numpy (as of 1.10), which claims that None is + # not scalar in np.isscalar(). or val is None or PyDate_Check(val) or PyDelta_Check(val) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index a370bcf42fbaa..4d8ec61e84c85 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -37,7 +37,7 @@ def wrapper(self, other): return _sparse_array_op(other, self, op, name[1:]) else: return _sparse_array_op(self, other, op, name) - elif np.isscalar(other): + elif lib.isscalar(other): new_fill_value = op(np.float64(self.fill_value), np.float64(other)) return SparseArray(op(self.sp_values, other), @@ -121,7 +121,7 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', if index is not None: if data is None: data = np.nan - if not np.isscalar(data): + if not lib.isscalar(data): raise Exception("must only pass scalars with an index ") values = np.empty(len(index), dtype='float64') values.fill(data) @@ -362,7 +362,7 @@ def __setslice__(self, i, j, value): j = 0 slobj = slice(i, j) # noqa - # if not np.isscalar(value): + # if not lib.isscalar(value): # raise Exception("SparseArray does not support seting non-scalars # via slices") @@ -504,7 +504,7 @@ def make_sparse(arr, kind='block', fill_value=nan): if hasattr(arr, 'values'): arr = arr.values else: - if np.isscalar(arr): + if lib.isscalar(arr): arr = [arr] arr = np.asarray(arr) diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py index bfc4ab9d3eb48..6cfe1bc6a79a3 100644 --- a/pandas/sparse/list.py +++ b/pandas/sparse/list.py @@ -4,6 +4,7 @@ from pandas.sparse.array import SparseArray import pandas._sparse as splib +import pandas.lib as lib class SparseList(PandasObject): @@ -120,7 +121,7 @@ def append(self, value): ---------- value: scalar or array-like """ - if np.isscalar(value): + if lib.isscalar(value): value = [value] sparr = SparseArray(value, fill_value=self.fill_value) diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index 524508b828980..be4ce716a5a37 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -18,6 +18,7 @@ import pandas.core.common as com import pandas.core.ops as ops +import pandas.lib as lib class SparsePanelAxis(object): @@ -390,7 +391,7 @@ def _combine(self, other, func, axis=0): return self._combineFrame(other, func, axis=axis) elif isinstance(other, Panel): return self._combinePanel(other, func) - elif np.isscalar(other): + elif lib.isscalar(other): new_frames = dict((k, func(v, other)) for k, v in compat.iteritems(self)) return self._new_like(new_frames) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 1a2fc9698da2f..25a6671594dab 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -19,6 +19,7 @@ import pandas.core.common as com import pandas.core.ops as ops import pandas.index as _index +import pandas.lib as lib from pandas.sparse.array import (make_sparse, _sparse_array_op, SparseArray) from pandas._sparse import BlockIndex, IntIndex @@ -48,7 +49,7 @@ def wrapper(self, other): return _sparse_series_op(self, other, op, name) elif isinstance(other, DataFrame): return NotImplemented - elif np.isscalar(other): + elif lib.isscalar(other): if isnull(other) or isnull(self.fill_value): new_fill_value = np.nan else: diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 43509857c8f70..264302866b023 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -26,6 +26,7 @@ from pandas.core.indexing import IndexingError import pandas.util.testing as tm +import pandas.lib as lib from pandas.tests.frame.common import TestData @@ -2218,7 +2219,7 @@ def _check_align(df, cond, other, check_dtypes=True): d = df[k].values c = cond[k].reindex(df[k].index).fillna(False).values - if np.isscalar(other): + if lib.isscalar(other): o = other else: if isinstance(other, np.ndarray): diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index c5ebbca67dc0e..058fb430b9c87 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -15,6 +15,7 @@ import pandas.core.common as com import pandas.core.datetools as datetools +import pandas.lib as lib from pandas.compat import lrange, range from pandas import compat @@ -364,7 +365,7 @@ def test_getitem_ambiguous_keyerror(self): def test_getitem_unordered_dup(self): obj = Series(lrange(5), index=['c', 'a', 'a', 'b', 'b']) - self.assertTrue(np.isscalar(obj['c'])) + self.assertTrue(lib.isscalar(obj['c'])) self.assertEqual(obj['c'], 0) def test_getitem_dups_with_missing(self): diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 4c7510783eda0..a309d88e62857 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -13,6 +13,7 @@ from pandas.core.index import MultiIndex import pandas.core.common as com +import pandas.lib as lib from pandas.compat import range, zip from pandas import compat @@ -60,7 +61,7 @@ def _construct(self, shape, value=None, dtype=None, **kwargs): if isinstance(shape, int): shape = tuple([shape] * self._ndim) if value is not None: - if np.isscalar(value): + if lib.isscalar(value): if value == 'empty': arr = None diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 1c0986b025acc..6a904c67fffeb 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -199,7 +199,7 @@ def _print(result, error=None): return try: - if np.isscalar(rs) and np.isscalar(xp): + if lib.isscalar(rs) and lib.isscalar(xp): self.assertEqual(rs, xp) elif xp.ndim == 1: assert_series_equal(rs, xp) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index b88aeb310752e..611ce462385fa 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -9,6 +9,7 @@ from pandas.compat import range, lrange, zip from pandas import compat import pandas.core.common as com +import pandas.lib as lib import numpy as np @@ -356,7 +357,7 @@ def _all_key(): def _convert_by(by): if by is None: by = [] - elif (np.isscalar(by) or isinstance(by, (np.ndarray, Index, + elif (lib.isscalar(by) or isinstance(by, (np.ndarray, Index, Series, Grouper)) or hasattr(by, '__call__')): by = [by] diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index f66ace14ccf50..b0bbf8ba70354 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -7,6 +7,7 @@ import pandas.core.algorithms as algos import pandas.core.common as com import pandas.core.nanops as nanops +import pandas.lib as lib from pandas.compat import zip import numpy as np @@ -79,7 +80,7 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3, """ # NOTE: this binning code is changed a bit from histogram for var(x) == 0 if not np.iterable(bins): - if np.isscalar(bins) and bins < 1: + if lib.isscalar(bins) and bins < 1: raise ValueError("`bins` should be a positive integer.") try: # for array-like sz = x.size diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index d82a229f48de8..62a7ad078da70 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -145,13 +145,13 @@ def _format_with_header(self, header, **kwargs): def __contains__(self, key): try: res = self.get_loc(key) - return np.isscalar(res) or type(res) == slice or np.any(res) + return lib.isscalar(res) or type(res) == slice or np.any(res) except (KeyError, TypeError, ValueError): return False def __getitem__(self, key): getitem = self._data.__getitem__ - if np.isscalar(key): + if lib.isscalar(key): val = getitem(key) return self._box_func(val) else: diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 84a62cb5fdf23..c745f1b2eddf9 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -261,7 +261,7 @@ def __new__(cls, data=None, ambiguous=ambiguous) if not isinstance(data, (np.ndarray, Index, ABCSeries)): - if np.isscalar(data): + if lib.isscalar(data): raise ValueError('DatetimeIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index a25bb525c9970..f34936f9c7b82 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -211,7 +211,7 @@ def _generate_range(cls, start, end, periods, freq, fields): def _from_arraylike(cls, data, freq, tz): if not isinstance(data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): - if np.isscalar(data) or isinstance(data, Period): + if lib.isscalar(data) or isinstance(data, Period): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) @@ -805,7 +805,7 @@ def _apply_meta(self, rawarr): def __getitem__(self, key): getitem = self._data.__getitem__ - if np.isscalar(key): + if lib.isscalar(key): val = getitem(key) return Period(ordinal=val, freq=self.freq) else: diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index e74879602fa64..9759d13fe4632 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -165,7 +165,7 @@ def __new__(cls, data=None, unit=None, data = to_timedelta(data, unit=unit, box=False) if not isinstance(data, (np.ndarray, Index, ABCSeries)): - if np.isscalar(data): + if lib.isscalar(data): raise ValueError('TimedeltaIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index d434b19317dfc..35a615db444e9 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -31,6 +31,7 @@ needs_i8_conversion) import pandas.compat as compat +import pandas.lib as lib from pandas.compat import( filter, map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter, raise_with_traceback, httplib, is_platform_windows, is_platform_32bit @@ -885,7 +886,7 @@ def assert_numpy_array_equal(left, right, if err_msg is None: # show detailed error - if np.isscalar(left) and np.isscalar(right): + if lib.isscalar(left) and lib.isscalar(right): # show scalar comparison error assert_equal(left, right) elif is_list_like(left) and is_list_like(right):
Closes #8708 Added note that pd.lib.isscalar() returns True for None, which is different from np.isscalar().
https://api.github.com/repos/pandas-dev/pandas/pulls/12459
2016-02-26T18:06:59Z
2016-02-27T14:41:27Z
null
2016-02-27T16:30:24Z
describe() outputs bool as categorical
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index f850bfba4f90e..46e8a0a5b2d3f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4875,26 +4875,27 @@ def describe_numeric_1d(series, percentiles): def describe_categorical_1d(data): names = ['count', 'unique'] objcounts = data.value_counts() - result = [data.count(), len(objcounts[objcounts != 0])] + count_unique = len(objcounts[objcounts != 0]) + result = [data.count(), count_unique] if result[1] > 0: top, freq = objcounts.index[0], objcounts.iloc[0] - if (data.dtype == object or - com.is_categorical_dtype(data.dtype)): - names += ['top', 'freq'] - result += [top, freq] - - elif com.is_datetime64_dtype(data): + if com.is_datetime64_dtype(data): asint = data.dropna().values.view('i8') names += ['top', 'freq', 'first', 'last'] result += [lib.Timestamp(top), freq, lib.Timestamp(asint.min()), lib.Timestamp(asint.max())] + else: + names += ['top', 'freq'] + result += [top, freq] return pd.Series(result, index=names, name=data.name) def describe_1d(data, percentiles): - if com.is_numeric_dtype(data): + if com.is_bool_dtype(data): + return describe_categorical_1d(data) + elif com.is_numeric_dtype(data): return describe_numeric_1d(data, percentiles) elif com.is_timedelta64_dtype(data): return describe_numeric_1d(data, percentiles) @@ -4906,7 +4907,7 @@ def describe_1d(data, percentiles): elif (include is None) and (exclude is None): if len(self._get_numeric_data()._info_axis) > 0: # when some numerics are found, keep only numerics - data = self.select_dtypes(include=[np.number, np.bool]) + data = self.select_dtypes(include=[np.number]) else: data = self elif include == 'all': diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index 4154c24f227f9..fed845827bf92 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -241,24 +241,16 @@ def test_bool_describe_in_mixed_frame(self): 'int_data': [10, 20, 30, 40, 50], }) - # Boolean data and integer data is included in .describe() output, - # string data isn't - self.assert_numpy_array_equal(df.describe().columns, [ - 'bool_data', 'int_data']) + # Integer data are included in .describe() output, + # Boolean and string data are not. + self.assert_numpy_array_equal(df.describe().columns, ['int_data']) - bool_describe = df.describe()['bool_data'] + bool_describe = df.describe(include='all')['bool_data'] - # Both the min and the max values should stay booleans - self.assertEqual(bool_describe['min'].dtype, np.bool_) - self.assertEqual(bool_describe['max'].dtype, np.bool_) + # Top value is a boolean value that is False + self.assertTrue(isinstance(bool_describe['top'] , bool)) + self.assertFalse(bool_describe['top']) - self.assertFalse(bool_describe['min']) - self.assertTrue(bool_describe['max']) - - # For numeric operations, like mean or median, the values True/False - # are cast to the integer values 1 and 0 - assert_almost_equal(bool_describe['mean'], 0.4) - assert_almost_equal(bool_describe['50%'], 0) def test_reduce_mixed_frame(self): # GH 6806 diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 4c7510783eda0..375902b782098 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -955,7 +955,7 @@ def test_describe_objects(self): s = Series(['a', 'b', 'b', np.nan, np.nan, np.nan, 'c', 'd', 'a', 'a']) result = s.describe() expected = Series({'count': 7, 'unique': 4, - 'top': 'a', 'freq': 3}, index=result.index) + 'top': 'a', 'freq': 3,'second':'b', 'second_freq': 2}, index=result.index) assert_series_equal(result, expected) dt = list(self.ts.index) @@ -1486,9 +1486,8 @@ def test_describe_typefiltering_category_bool(self): 'D_num': np.arange(24.) + .5, 'E_ts': tm.makeTimeSeries()[:24].index}) - # bool is considered numeric in describe, although not an np.number desc = df.describe() - expected_cols = ['C_bool', 'D_num'] + expected_cols = ['D_num'] expected = DataFrame(dict((k, df[k].describe()) for k in expected_cols), columns=expected_cols)
- [ ] closes #6625 - [ ] tests passed so far - [ ] passes `git diff upstream/master | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/12458
2016-02-26T17:58:41Z
2016-02-27T15:33:36Z
null
2016-02-27T15:33:54Z
Update what's new page with relevant issues for df.query() with in-pl…
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 8a48314de5f77..30b8b05e04d5f 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -785,7 +785,7 @@ Changes to eval ^^^^^^^^^^^^^^^ In prior versions, new columns assignments in an ``eval`` expression resulted -in an inplace change to the ``DataFrame``. (:issue:`9297`) +in an inplace change to the ``DataFrame``. (:issue:`9297`, :issue:`8664`, :issue:`10486`) .. ipython:: python
- [x] closes #10486 - [N/A] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry …ace operator The relevant issues added are : BUG: query with invalid dtypes should fallback to python engine #10486 BUG: query modifies the frame when you compare with `=` #8664
https://api.github.com/repos/pandas-dev/pandas/pulls/12456
2016-02-26T15:36:40Z
2016-05-07T17:12:30Z
null
2016-05-07T17:12:30Z
BUG: resample fixes
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index bdc20d964a06a..4dde6bcdb5038 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -705,7 +705,7 @@ other anchored offsets like ``MonthBegin`` and ``YearBegin``. Resample API ^^^^^^^^^^^^ -Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12334`, :issue:`12348`). +Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12334`, :issue:`12348`, :issue:`12448`). .. ipython:: python @@ -774,6 +774,29 @@ You could also specify a ``how`` directly use .resample(...).mean() instead of .resample(...) assignment will have no effect as you are working on a copy + There is a situation where the new API can not perform all the operations when using original code. + This code is intending to resample every 2s, take the ``mean`` AND then take the ``min` of those results. + + .. code-block:: python + + In [4]: df.resample('2s').min() + Out[4]: + A 0.433985 + B 0.314582 + C 0.357096 + D 0.531096 + dtype: float64 + + The new API will: + + .. ipython: python + + df.resample('2s').min() + + Good news is the return dimensions will differ (between the new API and the old API), so this should loudly raise + an exception. + + **New API**: Now, you can write ``.resample`` as a 2-stage operation like groupby, which diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 1684768eec2c4..bc92733e7c2fd 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3932,57 +3932,18 @@ def resample(self, rule, how=None, axis=0, fill_method=None, closed=None, Freq: 3T, dtype: int64 """ - from pandas.tseries.resample import resample + from pandas.tseries.resample import (resample, + _maybe_process_deprecations) axis = self._get_axis_number(axis) r = resample(self, freq=rule, label=label, closed=closed, axis=axis, kind=kind, loffset=loffset, - fill_method=fill_method, convention=convention, - limit=limit, base=base) - - # deprecation warnings - # but call methods anyhow - - if how is not None: - - # .resample(..., how='sum') - if isinstance(how, compat.string_types): - method = "{0}()".format(how) - - # .resample(..., how=lambda x: ....) - else: - method = ".apply(<func>)" - - # if we have both a how and fill_method, then show - # the following warning - if fill_method is None: - warnings.warn("how in .resample() is deprecated\n" - "the new syntax is " - ".resample(...).{method}".format( - method=method), - FutureWarning, stacklevel=2) - r = r.aggregate(how) - - if fill_method is not None: - - # show the prior function call - method = '.' + method if how is not None else '' - - args = "limit={0}".format(limit) if limit is not None else "" - warnings.warn("fill_method is deprecated to .resample()\n" - "the new syntax is .resample(...){method}" - ".{fill_method}({args})".format( - method=method, - fill_method=fill_method, - args=args), - FutureWarning, stacklevel=2) - - if how is not None: - r = getattr(r, fill_method)(limit=limit) - else: - r = r.aggregate(fill_method, limit=limit) - - return r + convention=convention, + base=base) + return _maybe_process_deprecations(r, + how=how, + fill_method=fill_method, + limit=limit) def first(self, offset): """ diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 442f2132847ee..c8598639d9fad 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -1044,27 +1044,71 @@ def ohlc(self): @Substitution(name='groupby') @Appender(_doc_template) - def resample(self, rule, **kwargs): + def resample(self, rule, how=None, fill_method=None, limit=None, **kwargs): """ Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended """ - from pandas.tseries.resample import TimeGrouper + from pandas.tseries.resample import (TimeGrouper, + _maybe_process_deprecations) gpr = TimeGrouper(axis=self.axis, freq=rule, **kwargs) # we by definition have at least 1 key as we are already a grouper groupings = list(self.grouper.groupings) groupings.append(gpr) - return self.__class__(self.obj, - keys=groupings, - axis=self.axis, - level=self.level, - as_index=self.as_index, - sort=self.sort, - group_keys=self.group_keys, - squeeze=self.squeeze, - selection=self._selection) + result = self.__class__(self.obj, + keys=groupings, + axis=self.axis, + level=self.level, + as_index=self.as_index, + sort=self.sort, + group_keys=self.group_keys, + squeeze=self.squeeze, + selection=self._selection) + + return _maybe_process_deprecations(result, + how=how, + fill_method=fill_method, + limit=limit) + + @Substitution(name='groupby') + @Appender(_doc_template) + def pad(self, limit=None): + """ + Forward fill the values + + Parameters + ---------- + limit : integer, optional + limit of how many values to fill + + See Also + -------- + Series.fillna + DataFrame.fillna + """ + return self.apply(lambda x: x.ffill(limit=limit)) + ffill = pad + + @Substitution(name='groupby') + @Appender(_doc_template) + def backfill(self, limit=None): + """ + Backward fill the values + + Parameters + ---------- + limit : integer, optional + limit of how many values to fill + + See Also + -------- + Series.fillna + DataFrame.fillna + """ + return self.apply(lambda x: x.bfill(limit=limit)) + bfill = backfill @Substitution(name='groupby') @Appender(_doc_template) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 2fdfc7ccc37ef..45d3fd0dad855 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -17,7 +17,9 @@ from pandas.util.decorators import cache_readonly import pandas.core.common as com import pandas.util.testing as tm -from pandas.util.testing import ensure_clean +from pandas.util.testing import (ensure_clean, + assert_is_valid_plot_return_object) + from pandas.core.config import set_option import numpy as np @@ -3916,21 +3918,6 @@ def test_plot_kwargs(self): self.assertEqual(len(res['a'].collections), 1) -def assert_is_valid_plot_return_object(objs): - import matplotlib.pyplot as plt - if isinstance(objs, np.ndarray): - for el in objs.flat: - assert isinstance(el, plt.Axes), ('one of \'objs\' is not a ' - 'matplotlib Axes instance, ' - 'type encountered {0!r}' - ''.format(el.__class__.__name__)) - else: - assert isinstance(objs, (plt.Artist, tuple, dict)), \ - ('objs is neither an ndarray of Artist instances nor a ' - 'single Artist instance, tuple, or dict, "objs" is a {0!r} ' - ''.format(objs.__class__.__name__)) - - def _check_plot_works(f, filterwarnings='always', **kwargs): import matplotlib.pyplot as plt ret = None diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index d301016aa1316..947daab2017d3 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -5610,7 +5610,8 @@ def test_tab_completion(self): 'cumprod', 'tail', 'resample', 'cummin', 'fillna', 'cumsum', 'cumcount', 'all', 'shift', 'skew', 'bfill', 'ffill', 'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith', 'cov', - 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin']) + 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin', + 'ffill', 'bfill', 'pad', 'backfill']) self.assertEqual(results, expected) def test_lexsort_indexer(self): diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index ba2eb3463d169..4e1ca42710cc5 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -102,7 +102,7 @@ def _typ(self): def _deprecated(self): warnings.warn(".resample() is now a deferred operation\n" "use .resample(...).mean() instead of .resample(...)", - FutureWarning, stacklevel=2) + FutureWarning, stacklevel=3) return self.mean() def _make_deprecated_binop(op): @@ -154,9 +154,7 @@ def __getattr__(self, attr): if attr in self._deprecated_invalids: raise ValueError(".resample() is now a deferred operation\n" "\tuse .resample(...).mean() instead of " - ".resample(...)\n" - "\tassignment will have no effect as you " - "are working on a copy") + ".resample(...)") if attr not in self._deprecated_valids: self = self._deprecated() return object.__getattribute__(self, attr) @@ -167,6 +165,17 @@ def __setattr__(self, attr, value): self.__class__.__name__)) object.__setattr__(self, attr, value) + def __getitem__(self, key): + try: + return super(Resampler, self).__getitem__(key) + except (KeyError, com.AbstractMethodError): + + # compat for deprecated + if isinstance(self.obj, com.ABCSeries): + return self._deprecated()[key] + + raise + def __setitem__(self, attr, value): raise ValueError("cannot set items on {0}".format( self.__class__.__name__)) @@ -208,6 +217,11 @@ def _assure_grouper(self): """ make sure that we are creating our binner & grouper """ self._set_binner() + def plot(self, *args, **kwargs): + # for compat with prior versions, we want to + # have the warnings shown here and just have this work + return self._deprecated().plot(*args, **kwargs) + def aggregate(self, arg, *args, **kwargs): """ Apply aggregation function or functions to resampled groups, yielding @@ -468,6 +482,52 @@ def f(self, _method=method): setattr(Resampler, method, f) +def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None): + """ potentially we might have a deprecation warning, show it + but call the appropriate methods anyhow """ + + if how is not None: + + # .resample(..., how='sum') + if isinstance(how, compat.string_types): + method = "{0}()".format(how) + + # .resample(..., how=lambda x: ....) + else: + method = ".apply(<func>)" + + # if we have both a how and fill_method, then show + # the following warning + if fill_method is None: + warnings.warn("how in .resample() is deprecated\n" + "the new syntax is " + ".resample(...).{method}".format( + method=method), + FutureWarning, stacklevel=3) + r = r.aggregate(how) + + if fill_method is not None: + + # show the prior function call + method = '.' + method if how is not None else '' + + args = "limit={0}".format(limit) if limit is not None else "" + warnings.warn("fill_method is deprecated to .resample()\n" + "the new syntax is .resample(...){method}" + ".{fill_method}({args})".format( + method=method, + fill_method=fill_method, + args=args), + FutureWarning, stacklevel=3) + + if how is not None: + r = getattr(r, fill_method)(limit=limit) + else: + r = r.aggregate(fill_method, limit=limit) + + return r + + class DatetimeIndexResampler(Resampler): def _get_binner_for_time(self): diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index b0e315ead2acb..4ddfc6ac573e4 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -151,6 +151,74 @@ def f(): check_stacklevel=False): self.assertIsInstance(getattr(r, op)(2), pd.Series) + # getitem compat + df = self.series.to_frame('foo') + + # same as prior versions for DataFrame + self.assertRaises(KeyError, lambda: df.resample('H')[0]) + + # compat for Series + # but we cannot be sure that we need a warning here + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = self.series.resample('H')[0] + expected = self.series.resample('H').mean()[0] + self.assertEqual(result, expected) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = self.series.resample('H')['2005-01-09 23:00:00'] + expected = self.series.resample('H').mean()['2005-01-09 23:00:00'] + self.assertEqual(result, expected) + + def test_groupby_resample_api(self): + + # GH 12448 + # .groupby(...).resample(...) hitting warnings + # when appropriate + df = DataFrame({'date': pd.date_range(start='2016-01-01', + periods=4, + freq='W'), + 'group': [1, 1, 2, 2], + 'val': [5, 6, 7, 8]}).set_index('date') + + # replication step + i = pd.date_range('2016-01-03', periods=8).tolist() + \ + pd.date_range('2016-01-17', periods=8).tolist() + index = pd.MultiIndex.from_arrays([[1] * 8 + [2] * 8, i], + names=['group', 'date']) + expected = DataFrame({'val': [5] * 7 + [6] + [7] * 7 + [8]}, + index=index) + result = df.groupby('group').apply( + lambda x: x.resample('1D').ffill())[['val']] + assert_frame_equal(result, expected) + + # deferred operations are currently disabled + # GH 12486 + # + # with tm.assert_produces_warning(FutureWarning, + # check_stacklevel=False): + # result = df.groupby('group').resample('1D').ffill() + # assert_frame_equal(result, expected) + + def test_plot_api(self): + tm._skip_if_no_mpl() + + # .resample(....).plot(...) + # hitting warnings + # GH 12448 + s = Series(np.random.randn(60), + index=date_range('2016-01-01', periods=60, freq='1min')) + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = s.resample('15min').plot() + tm.assert_is_valid_plot_return_object(result) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + result = s.resample('15min', how='sum').plot() + tm.assert_is_valid_plot_return_object(result) + def test_getitem(self): r = self.frame.resample('H') diff --git a/pandas/util/testing.py b/pandas/util/testing.py index c32239daaaf12..ba869efbc5837 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -201,6 +201,12 @@ def setUpClass(cls): cls.setUpClass = setUpClass return cls +def _skip_if_no_mpl(): + try: + import matplotlib + except ImportError: + import nose + raise nose.SkipTest("matplotlib not installed") def _skip_if_mpl_1_5(): import matplotlib @@ -209,7 +215,6 @@ def _skip_if_mpl_1_5(): import nose raise nose.SkipTest("matplotlib 1.5") - def _skip_if_no_scipy(): try: import scipy.stats @@ -767,6 +772,21 @@ def assert_attr_equal(attr, left, right, obj='Attributes'): left_attr, right_attr) +def assert_is_valid_plot_return_object(objs): + import matplotlib.pyplot as plt + if isinstance(objs, np.ndarray): + for el in objs.flat: + assert isinstance(el, plt.Axes), ('one of \'objs\' is not a ' + 'matplotlib Axes instance, ' + 'type encountered {0!r}' + ''.format(el.__class__.__name__)) + else: + assert isinstance(objs, (plt.Artist, tuple, dict)), \ + ('objs is neither an ndarray of Artist instances nor a ' + 'single Artist instance, tuple, or dict, "objs" is a {0!r} ' + ''.format(objs.__class__.__name__)) + + def isiterable(obj): return hasattr(obj, '__iter__')
make sure .resample(...).plot() warns and returns a correct plotting object make sure that .groupby(...).resample(....) is hitting warnings when appropriate closes #12448
https://api.github.com/repos/pandas-dev/pandas/pulls/12449
2016-02-26T02:00:01Z
2016-03-08T23:39:51Z
null
2016-04-19T13:00:54Z
DOC: Add examples and notes to empty documentation
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 14d788fdded7e..f850bfba4f90e 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -842,7 +842,43 @@ def __contains__(self, key): @property def empty(self): - """True if NDFrame is entirely empty [no items]""" + """True if NDFrame is entirely empty [no items], meaning any of the + axes are of length 0. + + Notes + ----- + If NDFrame contains only NaNs, it is still not considered empty. See + the example below. + + Examples + -------- + An example of an actual empty DataFrame. Notice the index is empty: + + >>> df_empty = pd.DataFrame({'A' : []}) + >>> df_empty + Empty DataFrame + Columns: [A] + Index: [] + >>> df_empty.empty + True + + If we only have NaNs in our DataFrame, it is not considered empty! We + will need to drop the NaNs to make the DataFrame empty: + + >>> df = pd.DataFrame({'A' : [np.nan]}) + >>> df + A + 0 NaN + >>> df.empty + False + >>> df.dropna().empty + True + + See also + -------- + pandas.Series.dropna + pandas.DataFrame.dropna + """ return not all(len(self._get_axis(a)) > 0 for a in self._AXIS_ORDERS) def __nonzero__(self):
- [x] closes #12393 - [x] passes `git diff upstream/master | flake8 --diff` Added notes and examples to provide clarity around the issue discussed in #12393. This is my first contribution to pandas, so let me know if I need to fix anything.
https://api.github.com/repos/pandas-dev/pandas/pulls/12442
2016-02-24T20:16:00Z
2016-02-26T02:13:43Z
null
2016-02-26T02:14:46Z
DOC: fix links in contributing.rst
diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 573e83f5e1d5f..9ba4915d235ee 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -217,10 +217,10 @@ For Python 2.7, you can install the ``mingw`` compiler which will work equivalen conda install -n pandas_dev libpython -or use the [Microsoft Visual Studio VC++ compiler for Python](https://www.microsoft.com/en-us/download/details.aspx?id=44266). Note that you have to check the ``x64`` box to install the ``x64`` extension building capability as this is not installed by default. +or use the `Microsoft Visual Studio VC++ compiler for Python <https://www.microsoft.com/en-us/download/details.aspx?id=44266>`__. Note that you have to check the ``x64`` box to install the ``x64`` extension building capability as this is not installed by default. -For Python 3.4, you can download and install the [Windows 7.1 SDK](https://www.microsoft.com/en-us/download/details.aspx?id=8279) -For Python 3.5, you can download and install the [Visual Studio 2015 Community Edition](https://www.visualstudio.com/en-us/downloads/visual-studio-2015-downloads-vs.aspx). +For Python 3.4, you can download and install the `Windows 7.1 SDK <https://www.microsoft.com/en-us/download/details.aspx?id=8279>`__ +For Python 3.5, you can download and install the `Visual Studio 2015 Community Edition <https://www.visualstudio.com/en-us/downloads/visual-studio-2015-downloads-vs.aspx>`__. Here are some references:
fixup for #12428
https://api.github.com/repos/pandas-dev/pandas/pulls/12439
2016-02-24T15:59:05Z
2016-02-24T16:23:18Z
null
2016-02-24T16:23:18Z
DOC:Fix micro-typo
diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 4378d182b3128..e2d80fbd9eb94 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -1641,7 +1641,7 @@ We import the rplot API: Examples ~~~~~~~~ -RPlot was an API for producing Trellis plots. These plots allow you toµ +RPlot was an API for producing Trellis plots. These plots allow you to arrange data in a rectangular grid by values of certain attributes. In the example below, data from the tips data set is arranged by the attributes 'sex' and 'smoker'. Since both of those attributes can take on one of two
Removes a extraneous µ from the docs.
https://api.github.com/repos/pandas-dev/pandas/pulls/12431
2016-02-23T21:30:23Z
2016-02-23T21:59:56Z
null
2016-02-23T21:59:59Z
DOC: improve documentation for building extensions on windows
diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 05d5eb67d7a32..573e83f5e1d5f 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -169,10 +169,9 @@ For a python 3 environment:: conda create -n pandas_dev python=3 --file ci/requirements_dev.txt +.. warning:: -If you are on Windows, then you will also need to install the compiler linkages:: - - conda install -n pandas_dev libpython + If you are on Windows, see :ref:`here for a fully compliant Windows environment <contributing.windows>`. This will create the new environment, and not touch any of your existing environments, nor any existing python installation. It will install all of the basic dependencies of @@ -207,6 +206,29 @@ See the full conda docs `here <http://conda.pydata.org/docs>`__. At this point you can easily do an *in-place* install, as detailed in the next section. +.. _contributing.windows: + +Creating a Windows development environment +------------------------------------------ + +To build on Windows, you need to have compilers installed to build the extensions. You will need to install the appropriate Visual Studio compilers, VS 2008 for Python 2.7, VS 2010 for 3.4, and VS 2015 for Python 3.5. + +For Python 2.7, you can install the ``mingw`` compiler which will work equivalently to VS 2008:: + + conda install -n pandas_dev libpython + +or use the [Microsoft Visual Studio VC++ compiler for Python](https://www.microsoft.com/en-us/download/details.aspx?id=44266). Note that you have to check the ``x64`` box to install the ``x64`` extension building capability as this is not installed by default. + +For Python 3.4, you can download and install the [Windows 7.1 SDK](https://www.microsoft.com/en-us/download/details.aspx?id=8279) +For Python 3.5, you can download and install the [Visual Studio 2015 Community Edition](https://www.visualstudio.com/en-us/downloads/visual-studio-2015-downloads-vs.aspx). + +Here are some references: + +- https://github.com/conda/conda-recipes/wiki/Building-from-Source-on-Windows-32-bit-and-64-bit +- https://cowboyprogrammer.org/building-python-wheels-for-windows/ +- https://blog.ionelmc.ro/2014/12/21/compiling-python-extensions-on-windows/ +- https://support.enthought.com/hc/en-us/articles/204469260-Building-Python-extensions-with-Canopy + .. _contributing.getting_source: Making changes @@ -231,6 +253,7 @@ just checked out. There are two primary methods of doing this. from your development directory. Thus, you can always be using the development version on your system without being inside the clone directory. + .. _contributing.documentation: Contributing to the documentation
https://api.github.com/repos/pandas-dev/pandas/pulls/12428
2016-02-23T16:39:12Z
2016-02-24T13:33:21Z
null
2016-02-24T15:56:38Z
Notes about unique vs cat.categories #12278
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index 317641f1b3eea..bb8a896a5b91c 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -182,6 +182,18 @@ It's also possible to pass in the categories in a specific order: indicate an ordered ``Categorical``. +.. note:: + + The result of ``Series.unique()`` is not always the same as ``Series.cat.categories``, + because ``Series.unique()`` has a couple of guarantees, namely that it returns categories + in the order of appearance, and it only includes values that are actually present. + + .. ipython:: python + + s = pd.Series(list('babc')).astype('category', categories=list('abcd')) + s.unique() + s.cat.categories + Renaming categories ~~~~~~~~~~~~~~~~~~~
- [x] closes #12278 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] adding notes about .unique() and .cat.categories (as suggested in [#12278 (comment)](https://github.com/pydata/pandas/issues/12278#issuecomment-182445522))
https://api.github.com/repos/pandas-dev/pandas/pulls/12421
2016-02-22T18:48:30Z
2016-02-23T14:57:16Z
null
2016-02-23T14:57:23Z
DOC: xray -> xarray
diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index 683cb671bca9d..dd3e86577d228 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -172,10 +172,10 @@ Geopandas extends pandas data objects to include geographic information which su geometric operations. If your work entails maps and geographical coordinates, and you love pandas, you should take a close look at Geopandas. -`xray <https://github.com/xray/xray>`__ -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +`xarray <https://github.com/pydata/xarray>`__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -xray brings the labeled data power of pandas to the physical sciences by +xarray brings the labeled data power of pandas to the physical sciences by providing N-dimensional variants of the core pandas data structures. It aims to provide a pandas-like and pandas-compatible toolkit for analytics on multi- dimensional arrays, rather than the tabular data for which pandas excels. diff --git a/doc/source/io.rst b/doc/source/io.rst index 002fdb6e32b87..acb8bc1eceda5 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4599,11 +4599,11 @@ into and from pandas, we recommend these packages from the broader community. netCDF '''''' -xray_ provides data structures inspired by the pandas DataFrame for working +xarray_ provides data structures inspired by the pandas DataFrame for working with multi-dimensional datasets, with a focus on the netCDF file format and easy conversion to and from pandas. -.. _xray: http://xray.readthedocs.org/ +.. _xarray: http://xarray.pydata.org/ .. _io.perf:
@shoyer just came across this in the docs. These should be the only (remaining) mentions of xray
https://api.github.com/repos/pandas-dev/pandas/pulls/12419
2016-02-22T14:45:12Z
2016-02-22T16:44:53Z
null
2016-02-22T17:04:02Z
COMPAT: fixes for numpy compat
diff --git a/pandas/src/sparse.pyx b/pandas/src/sparse.pyx index 579d473cae1b3..6744c6e5a4e07 100644 --- a/pandas/src/sparse.pyx +++ b/pandas/src/sparse.pyx @@ -7,6 +7,15 @@ import numpy as np import operator import sys +from distutils.version import LooseVersion + +# numpy versioning +_np_version = np.version.short_version +_np_version_under1p8 = LooseVersion(_np_version) < '1.8' +_np_version_under1p9 = LooseVersion(_np_version) < '1.9' +_np_version_under1p10 = LooseVersion(_np_version) < '1.10' +_np_version_under1p11 = LooseVersion(_np_version) < '1.11' + np.import_array() np.import_ufunc() @@ -1001,12 +1010,14 @@ cdef inline float64_t __rdiv(float64_t a, float64_t b): cdef inline float64_t __floordiv(float64_t a, float64_t b): if b == 0: - if a > 0: - return INF - elif a < 0: - return -INF - else: - return NaN + # numpy >= 1.11 returns NaN + # for a // 0, rather than +-inf + if _np_version_under1p11: + if a > 0: + return INF + elif a < 0: + return -INF + return NaN else: return a // b diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 6912712cf90e2..8a876272dfdef 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -25,7 +25,8 @@ from pandas.compat import range, lrange, lmap, zip, text_type, PY3 from pandas.compat.numpy_compat import np_datetime64_compat -from pandas import Series, DataFrame, _np_version_under1p9 +from pandas import (Series, DataFrame, + _np_version_under1p9, _np_version_under1p11) from pandas import tslib from pandas.util.testing import (assert_series_equal, assert_almost_equal, assertRaisesRegexp) @@ -2580,12 +2581,15 @@ def test_range_slice_day(self): didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400) pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400) + # changed to TypeError in 1.11 + exc = IndexError if _np_version_under1p11 else TypeError + for idx in [didx, pidx]: # slices against index should raise IndexError values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', '2013/02/01 09:00'] for v in values: - with tm.assertRaises(IndexError): + with tm.assertRaises(exc): idx[v:] s = Series(np.random.rand(len(idx)), index=idx) @@ -2597,7 +2601,7 @@ def test_range_slice_day(self): invalid = ['2013/02/01 9H', '2013/02/01 09:00'] for v in invalid: - with tm.assertRaises(IndexError): + with tm.assertRaises(exc): idx[v:] def test_getitem_seconds(self): @@ -2634,12 +2638,15 @@ def test_range_slice_seconds(self): periods=4000) pidx = PeriodIndex(start='2013/01/01 09:00:00', freq='S', periods=4000) + # changed to TypeError in 1.11 + exc = IndexError if _np_version_under1p11 else TypeError + for idx in [didx, pidx]: # slices against index should raise IndexError values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H', '2013/02/01 09:00'] for v in values: - with tm.assertRaises(IndexError): + with tm.assertRaises(exc): idx[v:] s = Series(np.random.rand(len(idx)), index=idx)
test_period and slicing exception with nan compat in sparse for floordiv with 0, now returns NaN rather than inf closes #12406
https://api.github.com/repos/pandas-dev/pandas/pulls/12418
2016-02-22T13:47:14Z
2016-02-22T16:44:03Z
null
2016-02-22T16:44:03Z
ENH decimal parameter to to_latex and to_html, #12031
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index b3bbc5cf5ef8c..71cf61c8f8f82 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -867,6 +867,9 @@ Other API Changes - Statistical functions for ``NDFrame`` objects will now raise if non-numpy-compatible arguments are passed in for ``**kwargs`` (:issue:`12301`) +- ``.to_latex`` and ``.to_html`` gain a ``decimal`` parameter like ``.to_csv``, default ``'.'`` (:issue:`12031`) + + .. _whatsnew_0180.deprecations: Deprecations diff --git a/pandas/core/format.py b/pandas/core/format.py index d5b95abb6ad08..101a5e64b65b5 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -338,7 +338,7 @@ def __init__(self, frame, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, justify=None, float_format=None, sparsify=None, index_names=True, line_width=None, max_rows=None, - max_cols=None, show_dimensions=False, **kwds): + max_cols=None, show_dimensions=False, decimal='.', **kwds): self.frame = frame self.buf = _expand_user(buf) if buf is not None else StringIO() self.show_index_names = index_names @@ -351,6 +351,7 @@ def __init__(self, frame, buf=None, columns=None, col_space=None, self.float_format = float_format self.formatters = formatters if formatters is not None else {} self.na_rep = na_rep + self.decimal = decimal self.col_space = col_space self.header = header self.index = index @@ -648,7 +649,7 @@ def _format_col(self, i): formatter = self._get_formatter(i) return format_array(frame.iloc[:, i]._values, formatter, float_format=self.float_format, na_rep=self.na_rep, - space=self.col_space) + space=self.col_space, decimal=self.decimal) def to_html(self, classes=None, notebook=False): """ @@ -1970,7 +1971,7 @@ def get_formatted_cells(self): def format_array(values, formatter, float_format=None, na_rep='NaN', - digits=None, space=None, justify='right'): + digits=None, space=None, justify='right', decimal='.'): if com.is_categorical_dtype(values): fmt_klass = CategoricalArrayFormatter @@ -2000,7 +2001,7 @@ def format_array(values, formatter, float_format=None, na_rep='NaN', fmt_obj = fmt_klass(values, digits=digits, na_rep=na_rep, float_format=float_format, formatter=formatter, - space=space, justify=justify) + space=space, justify=justify, decimal=decimal) return fmt_obj.get_result() diff --git a/pandas/core/frame.py b/pandas/core/frame.py index cd32ff2133cae..449068eff4f43 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1497,7 +1497,7 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, float_format=None, sparsify=None, index_names=True, justify=None, bold_rows=True, classes=None, escape=True, max_rows=None, max_cols=None, show_dimensions=False, - notebook=False): + notebook=False, decimal='.'): """ Render a DataFrame as an HTML table. @@ -1515,7 +1515,10 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, max_cols : int, optional Maximum number of columns to show before truncating. If None, show all. + decimal : string, default '.' + Character recognized as decimal separator, e.g. ',' in Europe + .. versionadded:: 0.18.0 """ if colSpace is not None: # pragma: no cover @@ -1533,7 +1536,8 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, bold_rows=bold_rows, escape=escape, max_rows=max_rows, max_cols=max_cols, - show_dimensions=show_dimensions) + show_dimensions=show_dimensions, + decimal=decimal) # TODO: a generic formatter wld b in DataFrameFormatter formatter.to_html(classes=classes, notebook=notebook) @@ -1545,7 +1549,7 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=True, column_format=None, longtable=None, - escape=None, encoding=None): + escape=None, encoding=None, decimal='.'): """ Render a DataFrame to a tabular environment table. You can splice this into a LaTeX document. Requires \\usepackage{booktabs}. @@ -1568,6 +1572,11 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, characters in column names. encoding : str, default None Default encoding is ascii in Python 2 and utf-8 in Python 3 + decimal : string, default '.' + Character recognized as decimal separator, e.g. ',' in Europe + + .. versionadded:: 0.18.0 + """ if colSpace is not None: # pragma: no cover @@ -1588,7 +1597,7 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, bold_rows=bold_rows, sparsify=sparsify, index_names=index_names, - escape=escape) + escape=escape, decimal=decimal) formatter.to_latex(column_format=column_format, longtable=longtable, encoding=encoding) diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index cb5241c205d03..6772c1ee4b1ee 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -760,6 +760,34 @@ def test_to_html_unicode(self): expected = u'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>A</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\u03c3</td>\n </tr>\n </tbody>\n</table>' self.assertEqual(df.to_html(), expected) + def test_to_html_decimal(self): + # GH 12031 + df = DataFrame({'A': [6.0, 3.1, 2.2]}) + result = df.to_html(decimal=',') + expected = ('<table border="1" class="dataframe">\n' + ' <thead>\n' + ' <tr style="text-align: right;">\n' + ' <th></th>\n' + ' <th>A</th>\n' + ' </tr>\n' + ' </thead>\n' + ' <tbody>\n' + ' <tr>\n' + ' <th>0</th>\n' + ' <td>6,0</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>1</th>\n' + ' <td>3,1</td>\n' + ' </tr>\n' + ' <tr>\n' + ' <th>2</th>\n' + ' <td>2,2</td>\n' + ' </tr>\n' + ' </tbody>\n' + '</table>') + self.assertEqual(result, expected) + def test_to_html_escaped(self): a = 'str<ing1 &amp;' b = 'stri>ng2 &amp;' @@ -2897,6 +2925,24 @@ def test_to_latex_no_header(self): self.assertEqual(withoutindex_result, withoutindex_expected) + def test_to_latex_decimal(self): + # GH 12031 + self.frame.to_latex() + df = DataFrame({'a': [1.0, 2.1], 'b': ['b1', 'b2']}) + withindex_result = df.to_latex(decimal=',') + print("WHAT THE") + withindex_expected = r"""\begin{tabular}{lrl} +\toprule +{} & a & b \\ +\midrule +0 & 1,0 & b1 \\ +1 & 2,1 & b2 \\ +\bottomrule +\end{tabular} +""" + + self.assertEqual(withindex_result, withindex_expected) + def test_to_csv_quotechar(self): df = DataFrame({'col': [1, 2]}) expected = """\
- [x] closes #12031 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry I just pass on the `decimal` parameter. All the machinery was already in place.
https://api.github.com/repos/pandas-dev/pandas/pulls/12417
2016-02-22T11:12:07Z
2016-02-23T18:50:26Z
null
2016-02-23T19:29:43Z
MAINT: More informative TypeError for IndexEngine.get_loc
diff --git a/pandas/index.pyx b/pandas/index.pyx index a7e613ee867c7..dad2b26e13412 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -136,7 +136,7 @@ cdef class IndexEngine: cpdef get_loc(self, object val): if is_definitely_invalid_key(val): - raise TypeError + raise TypeError("'{val}' is an invalid key".format(val=val)) if self.over_size_threshold and self.is_monotonic_increasing: if not self.is_unique: diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 7c5ec8f35bf44..43509857c8f70 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -2590,3 +2590,30 @@ def test_head_tail(self): empty_df = DataFrame() assert_frame_equal(empty_df.tail(), empty_df) assert_frame_equal(empty_df.head(), empty_df) + + def test_type_error_multiindex(self): + # See gh-12218 + df = DataFrame(columns=['i', 'c', 'x', 'y'], + data=[[0, 0, 1, 2], [1, 0, 3, 4], + [0, 1, 1, 2], [1, 1, 3, 4]]) + dg = df.pivot_table(index='i', columns='c', + values=['x', 'y']) + + with assertRaisesRegexp(TypeError, "is an invalid key"): + str(dg[:, 0]) + + index = Index(range(2), name='i') + columns = MultiIndex(levels=[['x', 'y'], [0, 1]], + labels=[[0, 1], [0, 0]], + names=[None, 'c']) + expected = DataFrame([[1, 2], [3, 4]], columns=columns, index=index) + + result = dg.loc[:, (slice(None), 0)] + assert_frame_equal(result, expected) + + name = ('x', 0) + index = Index(range(2), name='i') + expected = Series([1, 3], index=index, name=name) + + result = dg['x', 0] + assert_series_equal(result, expected)
Title is self-explanatory. Closes #12218.
https://api.github.com/repos/pandas-dev/pandas/pulls/12414
2016-02-22T07:14:23Z
2016-02-24T00:05:40Z
null
2016-02-24T03:21:50Z
BUG: Matched SearchedSorted Signature with NumPy's
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 0118dea6f8867..d9c226176c30e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -43,6 +43,7 @@ Enhancements API changes ~~~~~~~~~~~ +- ``searchsorted`` for ``Index`` and ``TimedeltaIndex`` now accept a ``sorter`` argument to maintain compatibility with numpy's ``searchsorted`` function (:issue:`12238`) - ``Period`` and ``PeriodIndex`` now raises ``IncompatibleFrequency`` error which inherits ``ValueError`` rather than raw ``ValueError`` (:issue:`12615`) diff --git a/pandas/core/base.py b/pandas/core/base.py index 168310b6d7da0..3ebd60d45b48d 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -7,7 +7,8 @@ from pandas.core import common as com import pandas.core.nanops as nanops import pandas.lib as lib -from pandas.util.decorators import Appender, cache_readonly, deprecate_kwarg +from pandas.util.decorators import (Appender, cache_readonly, + deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError _shared_docs = dict() @@ -990,13 +991,73 @@ def factorize(self, sort=False, na_sentinel=-1): from pandas.core.algorithms import factorize return factorize(self, sort=sort, na_sentinel=na_sentinel) - def searchsorted(self, key, side='left'): - """ np.ndarray searchsorted compat """ + _shared_docs['searchsorted'] = ( + """Find indices where elements should be inserted to maintain order. - # FIXME in GH7447 - # needs coercion on the key (DatetimeIndex does alreay) - # needs tests/doc-string - return self.values.searchsorted(key, side=side) + Find the indices into a sorted %(klass)s `self` such that, if the + corresponding elements in `v` were inserted before the indices, the + order of `self` would be preserved. + + Parameters + ---------- + %(value)s : array_like + Values to insert into `self`. + side : {'left', 'right'}, optional + If 'left', the index of the first suitable location found is given. + If 'right', return the last such index. If there is no suitable + index, return either 0 or N (where N is the length of `self`). + sorter : 1-D array_like, optional + Optional array of integer indices that sort `self` into ascending + order. They are typically the result of ``np.argsort``. + + Returns + ------- + indices : array of ints + Array of insertion points with the same shape as `v`. + + See Also + -------- + numpy.searchsorted + + Notes + ----- + Binary search is used to find the required insertion points. + + Examples + -------- + >>> x = pd.Series([1, 2, 3]) + >>> x + 0 1 + 1 2 + 2 3 + dtype: int64 + >>> x.searchsorted(4) + array([3]) + >>> x.searchsorted([0, 4]) + array([0, 3]) + >>> x.searchsorted([1, 3], side='left') + array([0, 2]) + >>> x.searchsorted([1, 3], side='right') + array([1, 3]) + >>> + >>> x = pd.Categorical(['apple', 'bread', 'bread', 'cheese', 'milk' ]) + [apple, bread, bread, cheese, milk] + Categories (4, object): [apple < bread < cheese < milk] + >>> x.searchsorted('bread') + array([1]) # Note: an array, not a scalar + >>> x.searchsorted(['bread']) + array([1]) + >>> x.searchsorted(['bread', 'eggs']) + array([1, 4]) + >>> x.searchsorted(['bread', 'eggs'], side='right') + array([3, 4]) # eggs before milk + """) + + @Substitution(klass='IndexOpsMixin', value='key') + @Appender(_shared_docs['searchsorted']) + def searchsorted(self, key, side='left', sorter=None): + # needs coercion on the key (DatetimeIndex does already) + return self.values.searchsorted(key, side=side, sorter=sorter) _shared_docs['drop_duplicates'] = ( """Return %(klass)s with duplicate values removed diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 35fa06ce5009c..69c1adbfae574 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -8,10 +8,12 @@ from pandas.compat import u from pandas.core.algorithms import factorize -from pandas.core.base import PandasObject, PandasDelegate, NoNewAttributesMixin +from pandas.core.base import (PandasObject, PandasDelegate, + NoNewAttributesMixin, _shared_docs) import pandas.core.common as com from pandas.core.missing import interpolate_2d -from pandas.util.decorators import cache_readonly, deprecate_kwarg +from pandas.util.decorators import (Appender, cache_readonly, + deprecate_kwarg, Substitution) from pandas.core.common import ( ABCSeries, ABCIndexClass, ABCCategoricalIndex, isnull, notnull, @@ -1003,59 +1005,9 @@ def memory_usage(self, deep=False): """ return self._codes.nbytes + self._categories.memory_usage(deep=deep) + @Substitution(klass='Categorical', value='v') + @Appender(_shared_docs['searchsorted']) def searchsorted(self, v, side='left', sorter=None): - """Find indices where elements should be inserted to maintain order. - - Find the indices into a sorted Categorical `self` such that, if the - corresponding elements in `v` were inserted before the indices, the - order of `self` would be preserved. - - Parameters - ---------- - v : array_like - Array-like values or a scalar value, to insert/search for in - `self`. - side : {'left', 'right'}, optional - If 'left', the index of the first suitable location found is given. - If 'right', return the last such index. If there is no suitable - index, return either 0 or N (where N is the length of `a`). - sorter : 1-D array_like, optional - Optional array of integer indices that sort `self` into ascending - order. They are typically the result of ``np.argsort``. - - Returns - ------- - indices : array of ints - Array of insertion points with the same shape as `v`. - - See Also - -------- - Series.searchsorted - numpy.searchsorted - - Notes - ----- - Binary search is used to find the required insertion points. - - Examples - -------- - >>> x = pd.Categorical(['apple', 'bread', 'bread', 'cheese', 'milk' ]) - [apple, bread, bread, cheese, milk] - Categories (4, object): [apple < bread < cheese < milk] - >>> x.searchsorted('bread') - array([1]) # Note: an array, not a scalar - >>> x.searchsorted(['bread']) - array([1]) - >>> x.searchsorted(['bread', 'eggs']) - array([1, 4]) - >>> x.searchsorted(['bread', 'eggs'], side='right') - array([3, 4]) # eggs before milk - >>> x = pd.Categorical(['apple', 'bread', 'bread', 'cheese', 'milk', - 'donuts' ]) - >>> x.searchsorted(['bread', 'eggs'], side='right', - sorter=[0, 1, 2, 3, 5, 4]) - array([3, 5]) # eggs after donuts, after switching milk and donuts - """ if not self.ordered: raise ValueError("Categorical not ordered\nyou can use " ".as_ordered() to change the Categorical to an " @@ -1063,7 +1015,8 @@ def searchsorted(self, v, side='left', sorter=None): from pandas.core.series import Series values_as_codes = self.categories.values.searchsorted( - Series(v).values, side) + Series(v).values, side=side) + return self.codes.searchsorted(values_as_codes, sorter=sorter) def isnull(self): diff --git a/pandas/core/series.py b/pandas/core/series.py index 80154065f0c8f..ffbea0d5704e3 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -49,7 +49,7 @@ import pandas.core.datetools as datetools import pandas.core.format as fmt import pandas.core.nanops as nanops -from pandas.util.decorators import Appender, deprecate_kwarg +from pandas.util.decorators import Appender, deprecate_kwarg, Substitution import pandas.lib as lib import pandas.tslib as tslib @@ -1464,63 +1464,11 @@ def dot(self, other): else: # pragma: no cover raise TypeError('unsupported type: %s' % type(other)) + @Substitution(klass='Series', value='v') + @Appender(base._shared_docs['searchsorted']) def searchsorted(self, v, side='left', sorter=None): - """Find indices where elements should be inserted to maintain order. - - Find the indices into a sorted Series `self` such that, if the - corresponding elements in `v` were inserted before the indices, the - order of `self` would be preserved. - - Parameters - ---------- - v : array_like - Values to insert into `a`. - side : {'left', 'right'}, optional - If 'left', the index of the first suitable location found is given. - If 'right', return the last such index. If there is no suitable - index, return either 0 or N (where N is the length of `a`). - sorter : 1-D array_like, optional - Optional array of integer indices that sort `self` into ascending - order. They are typically the result of ``np.argsort``. - - Returns - ------- - indices : array of ints - Array of insertion points with the same shape as `v`. - - See Also - -------- - Series.sort_values - numpy.searchsorted - - Notes - ----- - Binary search is used to find the required insertion points. - - Examples - -------- - >>> x = pd.Series([1, 2, 3]) - >>> x - 0 1 - 1 2 - 2 3 - dtype: int64 - >>> x.searchsorted(4) - array([3]) - >>> x.searchsorted([0, 4]) - array([0, 3]) - >>> x.searchsorted([1, 3], side='left') - array([0, 2]) - >>> x.searchsorted([1, 3], side='right') - array([1, 3]) - >>> x.searchsorted([1, 2], side='right', sorter=[0, 2, 1]) - array([1, 3]) - """ - if sorter is not None: - sorter = com._ensure_platform_int(sorter) - - return self._values.searchsorted(Series(v)._values, side=side, - sorter=sorter) + return self._values.searchsorted(Series(v)._values, + side=side, sorter=sorter) # ------------------------------------------------------------------- # Combination diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 99f894bfd3320..0a64bb058fbb4 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -972,6 +972,15 @@ def test_memory_usage(self): diff = res_deep - sys.getsizeof(o) self.assertTrue(abs(diff) < 100) + def test_searchsorted(self): + # See gh-12238 + for o in self.objs: + index = np.searchsorted(o, max(o)) + self.assertTrue(0 <= index <= len(o)) + + index = np.searchsorted(o, max(o), sorter=range(len(o))) + self.assertTrue(0 <= index <= len(o)) + class TestFloat64HashTable(tm.TestCase): diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index b3b43e1a5babb..8381273873dcf 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -5,6 +5,7 @@ from datetime import time, datetime from datetime import timedelta import numpy as np +from pandas.core.base import _shared_docs from pandas.core.common import (_NS_DTYPE, _INT64_DTYPE, _values_from_object, _maybe_box, is_object_dtype, is_datetime64_dtype, @@ -22,7 +23,8 @@ from pandas.tseries.offsets import DateOffset, generate_range, Tick, CDay from pandas.tseries.tools import parse_time_string, normalize_date, to_time from pandas.tseries.timedeltas import to_timedelta -from pandas.util.decorators import cache_readonly, deprecate_kwarg +from pandas.util.decorators import (Appender, cache_readonly, + deprecate_kwarg, Substitution) import pandas.core.common as com import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools @@ -1629,7 +1631,9 @@ def normalize(self): return DatetimeIndex(new_values, freq='infer', name=self.name, tz=self.tz) - def searchsorted(self, key, side='left'): + @Substitution(klass='DatetimeIndex', value='key') + @Appender(_shared_docs['searchsorted']) + def searchsorted(self, key, side='left', sorter=None): if isinstance(key, (np.ndarray, Index)): key = np.array(key, dtype=_NS_DTYPE, copy=False) else: diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 798df0b9e31bd..b34af4e62845b 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -13,13 +13,14 @@ get_period_field_arr, _validate_end_alias, _quarter_to_myear) +from pandas.core.base import _shared_docs + import pandas.core.common as com from pandas.core.common import (isnull, _INT64_DTYPE, _maybe_box, _values_from_object, ABCSeries, is_integer, is_float, is_object_dtype) from pandas import compat -from pandas.util.decorators import cache_readonly - +from pandas.util.decorators import Appender, cache_readonly, Substitution from pandas.lib import Timedelta import pandas.lib as lib import pandas.tslib as tslib @@ -385,7 +386,9 @@ def astype(self, dtype): return Index(self.values, dtype) raise ValueError('Cannot cast PeriodIndex to dtype %s' % dtype) - def searchsorted(self, key, side='left'): + @Substitution(klass='PeriodIndex', value='key') + @Appender(_shared_docs['searchsorted']) + def searchsorted(self, key, side='left', sorter=None): if isinstance(key, Period): if key.freq != self.freq: msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, key.freqstr) @@ -394,7 +397,7 @@ def searchsorted(self, key, side='left'): elif isinstance(key, compat.string_types): key = Period(key, freq=self.freq).ordinal - return self.values.searchsorted(key, side=side) + return self.values.searchsorted(key, side=side, sorter=sorter) @property def is_all_dates(self): diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index bea2aeb508358..6e54f1fde8a8f 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -10,7 +10,9 @@ import pandas.compat as compat from pandas.compat import u from pandas.tseries.frequencies import to_offset +from pandas.core.base import _shared_docs import pandas.core.common as com +from pandas.util.decorators import Appender, Substitution from pandas.tseries.base import TimelikeOps, DatetimeIndexOpsMixin from pandas.tseries.timedeltas import (to_timedelta, _coerce_scalar_to_timedelta_type) @@ -786,13 +788,15 @@ def _partial_td_slice(self, key, freq, use_lhs=True, use_rhs=True): # # try to find a the dates # return (lhs_mask & rhs_mask).nonzero()[0] - def searchsorted(self, key, side='left'): + @Substitution(klass='TimedeltaIndex', value='key') + @Appender(_shared_docs['searchsorted']) + def searchsorted(self, key, side='left', sorter=None): if isinstance(key, (np.ndarray, Index)): key = np.array(key, dtype=_TD_DTYPE, copy=False) else: key = _to_m8(key) - return self.values.searchsorted(key, side=side) + return self.values.searchsorted(key, side=side, sorter=sorter) def is_type_compatible(self, typ): return typ == self.inferred_type or typ == 'timedelta'
Title is self-explanatory. Closes #12238.
https://api.github.com/repos/pandas-dev/pandas/pulls/12413
2016-02-22T06:50:35Z
2016-03-17T23:46:50Z
null
2016-03-17T23:55:10Z
BUG: date_range breaks with tz-aware start/end dates and closed intervals #12409
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index e944189e2a338..97b3ef7a22b22 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1022,7 +1022,7 @@ Bug Fixes - Bug in ``GroupBy.size`` when data-frame is empty. (:issue:`11699`) - Bug in ``Period.end_time`` when a multiple of time period is requested (:issue:`11738`) - Regression in ``.clip`` with tz-aware datetimes (:issue:`11838`) -- Bug in ``date_range`` when the boundaries fell on the frequency (:issue:`11804`) +- Bug in ``date_range`` when the boundaries fell on the frequency (:issue:`11804`, :issue:`12409`) - Bug in consistency of passing nested dicts to ``.groupby(...).agg(...)`` (:issue:`9052`) - Accept unicode in ``Timedelta`` constructor (:issue:`11995`) - Bug in value label reading for ``StataReader`` when reading incrementally (:issue:`12014`) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 9faf6f174115c..84a62cb5fdf23 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -528,12 +528,12 @@ def _generate(cls, start, end, periods, name, offset, ambiguous=ambiguous) index = index.view(_NS_DTYPE) - index = cls._simple_new(index, name=name, freq=offset, tz=tz) if not left_closed and len(index) and index[0] == start: index = index[1:] if not right_closed and len(index) and index[-1] == end: index = index[:-1] + index = cls._simple_new(index, name=name, freq=offset, tz=tz) return index @property diff --git a/pandas/tseries/tests/test_daterange.py b/pandas/tseries/tests/test_daterange.py index ec3cb0bf08a26..6e572289a3cae 100644 --- a/pandas/tseries/tests/test_daterange.py +++ b/pandas/tseries/tests/test_daterange.py @@ -497,6 +497,36 @@ def test_range_closed(self): self.assertTrue(expected_left.equals(left)) self.assertTrue(expected_right.equals(right)) + def test_range_closed_with_tz_aware_start_end(self): + # GH12409 + begin = Timestamp('2011/1/1', tz='US/Eastern') + end = Timestamp('2014/1/1', tz='US/Eastern') + + for freq in ["3D", "2M", "7W", "3H", "A"]: + closed = date_range(begin, end, closed=None, freq=freq) + left = date_range(begin, end, closed="left", freq=freq) + right = date_range(begin, end, closed="right", freq=freq) + expected_left = left + expected_right = right + + if end == closed[-1]: + expected_left = closed[:-1] + if begin == closed[0]: + expected_right = closed[1:] + + self.assertTrue(expected_left.equals(left)) + self.assertTrue(expected_right.equals(right)) + + # test with default frequency, UTC + begin = Timestamp('2011/1/1', tz='UTC') + end = Timestamp('2014/1/1', tz='UTC') + + intervals = ['left', 'right', None] + for i in intervals: + result = date_range(start=begin, end=end, closed=i) + self.assertEqual(result[0], begin) + self.assertEqual(result[-1], end) + def test_range_closed_boundary(self): # GH 11804 for closed in ['right', 'left', None]:
- [X] closes #12409 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry - not added since this fixes a pre-release bug do comparison for closed intervals on UTC basis prior to tz conversion added tests for date_range covering case of tz-aware start/end dates with closed ranges
https://api.github.com/repos/pandas-dev/pandas/pulls/12410
2016-02-21T19:51:40Z
2016-02-23T14:54:21Z
null
2016-02-23T14:54:21Z
Doc fix for sas7bdat
diff --git a/doc/source/io.rst b/doc/source/io.rst index bbfa711eb4445..002fdb6e32b87 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4542,24 +4542,6 @@ whether imported ``Categorical`` variables are ordered. a ``Categorical`` with string categories for the values that are labeled and numeric categories for values with no label. -.. _io.other: - -Other file formats ------------------- - -pandas itself only supports IO with a limited set of file formats that map -cleanly to its tabular data model. For reading and writing other file formats -into and from pandas, we recommend these packages from the broader community. - -netCDF -'''''' - -xray_ provides data structures inspired by the pandas DataFrame for working -with multi-dimensional datasets, with a focus on the netCDF file format and -easy conversion to and from pandas. - -.. _xray: http://xray.readthedocs.org/ - .. _io.sas: .. _io.sas_reader: @@ -4584,11 +4566,11 @@ objects (``XportReader`` or ``SAS7BDATReader``) for incrementally reading the file. The reader objects also have attributes that contain additional information about the file and its variables. -Read a SAS XPORT file: +Read a SAS7BDAT file: .. code-block:: python - df = pd.read_sas('sas_xport.xpt') + df = pd.read_sas('sas_data.sas7bdat') Obtain an iterator and read an XPORT file 100,000 lines at a time: @@ -4605,6 +4587,24 @@ web site. No official documentation is available for the SAS7BDAT format. +.. _io.other: + +Other file formats +------------------ + +pandas itself only supports IO with a limited set of file formats that map +cleanly to its tabular data model. For reading and writing other file formats +into and from pandas, we recommend these packages from the broader community. + +netCDF +'''''' + +xray_ provides data structures inspired by the pandas DataFrame for working +with multi-dimensional datasets, with a focus on the netCDF file format and +easy conversion to and from pandas. + +.. _xray: http://xray.readthedocs.org/ + .. _io.perf: Performance Considerations
Minor doc fixes following merge of PR #12015.
https://api.github.com/repos/pandas-dev/pandas/pulls/12407
2016-02-21T16:20:54Z
2016-02-22T11:18:32Z
null
2016-02-22T11:18:41Z
ENH: allow index to be referenced by name
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index b3bbc5cf5ef8c..0f0c925b366a5 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -432,6 +432,7 @@ Other enhancements - Added Google ``BigQuery`` service account authentication support, which enables authentication on remote servers. (:issue:`11881`). For further details see :ref:`here <io.bigquery_authentication>` - ``HDFStore`` is now iterable: ``for k in store`` is equivalent to ``for k in store.keys()`` (:issue:`12221`). - The entire codebase has been ``PEP``-ified (:issue:`12096`) +- Index (or index levels, with a MultiIndex) can now be referenced like column names (:issue:`8162`, :issue:`10816`). .. _whatsnew_0180.api_breaking: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index cd32ff2133cae..5ddceeee7e548 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2018,7 +2018,14 @@ def _getitem_array(self, key): indexer = key.nonzero()[0] return self.take(indexer, axis=0, convert=False) else: - indexer = self.ix._convert_to_indexer(key, axis=1) + try: + indexer = self.ix._convert_to_indexer(key, axis=1) + except KeyError: + if (hasattr(self, 'index') and + any(item in self.index.names for item in key)): + return self._getitem_array_with_index_name(key) + raise + return self.take(indexer, axis=1, convert=True) def _getitem_multilevel(self, key): @@ -2054,6 +2061,15 @@ def _getitem_frame(self, key): raise ValueError('Must pass DataFrame with boolean values only') return self.where(key) + def _getitem_array_with_index_name(self, key): + ix_ix, ix_name = next((i, k) for i, k in enumerate(key) + if k in self.index.names) + key.remove(ix_name) + ix_col = self[ix_name] + result = self[key] + result.insert(ix_ix, ix_name, ix_col) + return result + def query(self, expr, inplace=False, **kwargs): """Query the columns of a frame with a boolean expression. diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 14d788fdded7e..bd9cfc28ebc33 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1306,8 +1306,14 @@ def _get_item_cache(self, item): cache = self._item_cache res = cache.get(item) if res is None: - values = self._data.get(item) - res = self._box_item_values(item, values) + try: + values = self._data.get(item) + res = self._box_item_values(item, values) + except KeyError: + if hasattr(self, 'index') and item in self.index.names: + res = self._get_item_index_name(item) + else: + raise cache[item] = res res._set_as_cached(item, self) @@ -1315,6 +1321,10 @@ def _get_item_cache(self, item): res.is_copy = self.is_copy return res + def _get_item_index_name(self, item): + return pd.Series(self.index.get_level_values(item), + index=self.index, name=item) + def _set_as_cached(self, item, cacher): """Set the _cacher attribute on the calling object with a weakref to cacher. @@ -2623,10 +2633,9 @@ def __getattr__(self, name): if (name in self._internal_names_set or name in self._metadata or name in self._accessors): return object.__getattribute__(self, name) - else: - if name in self._info_axis: + elif name in self._info_axis or name in self.index.names: return self[name] - return object.__getattribute__(self, name) + return object.__getattribute__(self, name) def __setattr__(self, name, value): """After regular attribute access, try setting the name diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 4c7510783eda0..b80522e2f76f0 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -1853,6 +1853,81 @@ def test_to_xarray(self): expected, check_index_type=False) + def test_getitem_index(self): + # GH8162 + idx = pd.Index(list('abc'), name='idx') + df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=idx) + expected = pd.Series(['a', 'b', 'c'], index=idx, name='idx') + + assert_series_equal(df['idx'], expected) + assert_series_equal(df.idx, expected) + + def test_getitem_index_listlike(self): + idx = pd.Index(list('abc'), name='idx') + df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=idx) + assert_frame_equal( + df[['idx', 'B']], + pd.DataFrame([ + ['a', 4], + ['b', 5], + ['c', 6], + ], + columns=['idx', 'B'], + index=idx) + ) + assert_frame_equal( + df[['idx', 'A', 'B']], + pd.DataFrame([ + ['a', 1, 4], + ['b', 2, 5], + ['c', 3, 6], + ], + columns=['idx', 'A', 'B'], + index=idx) + ) + + def test_getitem_multiindex_level(self): + # GH10816 + idx = pd.MultiIndex.from_product([list('abc'), list('fg')], + names=['lev0', 'lev1']) + df = pd.DataFrame({'A': range(6), 'B': range(10, 16)}, index=idx) + expected = pd.Series(list('aabbcc'), index=idx, name='lev0') + + assert_series_equal(df['lev0'], expected) + assert_series_equal(df.lev0, expected) + + def test_getitem_multiindex_level_listlike(self): + idx = pd.MultiIndex.from_product([list('abc'), list('fg')], + names=['lev0', 'lev1']) + df = pd.DataFrame({'A': range(6), 'B': range(10, 16)}, index=idx) + assert_frame_equal( + df[['A', 'lev1']], + pd.DataFrame([ + [0, 'f'], + [1, 'g'], + [2, 'f'], + [3, 'g'], + [4, 'f'], + [5, 'g'], + ], + columns=['A', 'lev1'], + index=idx) + ) + + assert_frame_equal( + df[['A', 'B', 'lev1', 'lev0']], + pd.DataFrame([ + [0, 10, 'f', 'a'], + [1, 11, 'g', 'a'], + [2, 12, 'f', 'b'], + [3, 13, 'g', 'b'], + [4, 14, 'f', 'c'], + [5, 15, 'g', 'c'], + ], + columns=['A', 'B', 'lev1', 'lev0'], + index=idx) + ) + class TestPanel(tm.TestCase, Generic): _typ = Panel
- [x] closes #8162 and #10816 - [x] tests added - [x] tests passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Still missing are groupby support (#5677) and `.loc` support. Also, I wasn't sure if this deserves more than just a line in whatsnew, so I kept it small for now. With a standard index: ``` idx = pd.Index(list('abc'), name='idx') df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=idx) df['idx'] Out[4]: idx a a b b c c Name: idx, dtype: object df.idx Out[5]: idx a a b b c c Name: idx, dtype: object df[['idx', 'B']] Out[6]: idx B idx a a 4 b b 5 c c 6 ``` and with a MultiIndex: ``` idx = pd.MultiIndex.from_product([list('abc'), list('fg')], names=['lev0', 'lev1']) df = pd.DataFrame({'A': range(6), 'B': range(10, 16)}, index=idx) df['lev0'] Out[9]: lev0 lev1 a f a g a b f b g b c f c g c Name: lev0, dtype: object df.lev0 Out[10]: lev0 lev1 a f a g a b f b g b c f c g c Name: lev0, dtype: object df[['A', 'lev1']] Out[11]: A lev1 lev0 lev1 a f 0 f g 1 g b f 2 f g 3 g c f 4 f g 5 g ```
https://api.github.com/repos/pandas-dev/pandas/pulls/12404
2016-02-21T07:16:43Z
2016-05-07T17:45:31Z
null
2016-05-07T20:42:54Z
BUG: Concatenation of DFs with all NaT columns and TZ-aware ones breaks #12396
diff --git a/pandas/core/internals.py b/pandas/core/internals.py index abfc5c989056e..188b00348b4e1 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -4905,8 +4905,12 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): if len(values) and values[0] is None: fill_value = None - if getattr(self.block, 'is_datetimetz', False): - pass + if getattr(self.block, 'is_datetimetz', False) \ + or com.is_datetimetz(empty_dtype): + # handle timezone-aware all NaT cases + num_elements = np.prod(self.shape) + return DatetimeIndex([fill_value] * num_elements, + dtype=empty_dtype) elif getattr(self.block, 'is_categorical', False): pass elif getattr(self.block, 'is_sparse', False): diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 13f00afb5a489..a44db54e36f38 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -12,6 +12,7 @@ from pandas.compat import range, lrange, lzip, StringIO from pandas import compat from pandas.tseries.index import DatetimeIndex +from pandas.types.dtypes import DatetimeTZDtype from pandas.tools.merge import merge, concat, ordered_merge, MergeError from pandas import Categorical, Timestamp from pandas.util.testing import (assert_frame_equal, assert_series_equal, @@ -2522,6 +2523,112 @@ def test_concat_multiindex_with_tz(self): result = concat([df, df]) tm.assert_frame_equal(result, expected) + def test_concat_NaT_dataframes_all_NaT_axis_0(self): + # GH 12396 + expect = pd.DataFrame([pd.NaT, pd.NaT, pd.NaT], index=[0, 1, 0]) + + # non-timezone aware + first = pd.DataFrame([[pd.NaT], [pd.NaT]]) + second = pd.DataFrame([[pd.NaT]]) + + result = pd.concat([first, second], axis=0) + assert_frame_equal(result, expect) + + # one side timezone-aware + dtype = DatetimeTZDtype('ns', tz='UTC') + first = pd.DataFrame([[pd.NaT], [pd.NaT]], dtype=dtype) + + result = pd.concat([first, second], axis=0) + # upcasts for mixed case + expect = expect.apply(lambda x: x.astype(dtype)) + assert_frame_equal(result, expect) + + # both sides timezone-aware + second = pd.DataFrame([[pd.NaT]], dtype=dtype) + + # upcasts to tz-aware + result = pd.concat([first, second], axis=0) + assert_frame_equal(result, expect) + + def test_concat_NaT_dataframes_all_NaT_axis_1(self): + # GH 12396 + expect = pd.DataFrame([[pd.NaT, pd.NaT], [pd.NaT, pd.NaT]], + columns=[0, 1]) + + # non-timezone aware + first = pd.DataFrame([[pd.NaT], [pd.NaT]]) + second = pd.DataFrame([[pd.NaT]], columns=[1]) + + result = pd.concat([first, second], axis=1) + assert_frame_equal(result, expect) + + # one side timezone-aware + dtype = DatetimeTZDtype('ns', tz='UTC') + first = pd.DataFrame([[pd.NaT], [pd.NaT]], dtype=dtype) + + # upcasts result to tz-aware + expect.loc[:, 0] = expect.loc[:, 0].astype(dtype) + result = pd.concat([first, second], axis=1) + assert_frame_equal(result, expect) + + # both sides timezone-aware + second = pd.DataFrame([[pd.NaT]], dtype=dtype, columns=[1]) + + # upcasts to tz-aware + expect = expect.apply(lambda x: x.astype(dtype)) + result = pd.concat([first, second], axis=1) + assert_frame_equal(result, expect) + + def test_concat_NaT_dataframes_mixed_timestamps_and_NaT(self): + # GH 12396 + + # non-timezone aware + first = pd.DataFrame([[pd.NaT], [pd.NaT]]) + second = pd.DataFrame([[pd.Timestamp('2015/01/01')], + [pd.Timestamp('2016/01/01')]]) + + expect = pd.DataFrame([pd.NaT, pd.NaT, second.iloc[0, 0], + second.iloc[1, 0]], index=[0, 1, 0, 1]) + + result = pd.concat([first, second], axis=0) + assert_frame_equal(result, expect) + + # one side timezone-aware + dtype = DatetimeTZDtype('ns', tz='UTC') + second = second.apply(lambda x: x.astype(dtype)) + + result = pd.concat([first, second], axis=0) + expect = expect.apply(lambda x: x.astype(dtype)) + assert_frame_equal(result, expect) + + def test_concat_NaT_series_dataframe_all_NaT(self): + # GH 12396 + + # non-timezone aware + first = pd.Series([pd.NaT, pd.NaT]) + second = pd.DataFrame([[pd.Timestamp('2015/01/01')], + [pd.Timestamp('2016/01/01')]]) + + expect = pd.DataFrame([pd.NaT, pd.NaT, second.iloc[0, 0], + second.iloc[1, 0]], index=[0, 1, 0, 1]) + + result = pd.concat([first, second]) + assert_frame_equal(result, expect) + + # one side timezone-aware + dtype = DatetimeTZDtype('ns', tz='UTC') + second = second.apply(lambda x: x.astype(dtype)) + + result = pd.concat([first, second]) + + expect = expect.apply(lambda x: x.astype(dtype)) + assert_frame_equal(result, expect) + + # both sides timezone-aware + first = first.astype(dtype) + result = pd.concat([first, second]) + assert_frame_equal(result, expect) + def test_concat_keys_and_levels(self): df = DataFrame(np.random.randn(1, 3)) df2 = DataFrame(np.random.randn(1, 4))
- [X] closes #12396 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry - `concat` to handle tz-aware all NaT/mixed cases - added unit tests
https://api.github.com/repos/pandas-dev/pandas/pulls/12403
2016-02-20T23:06:11Z
2016-11-16T22:22:21Z
null
2018-01-20T02:21:29Z
ENH: Series between inclusive pair (GH: 12398)
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index e944189e2a338..4cb0627b623aa 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -417,6 +417,7 @@ Other enhancements - ``sys.getsizeof(obj)`` returns the memory usage of a pandas object, including the values it contains (:issue:`11597`) - ``Series`` gained an ``is_unique`` attribute (:issue:`11946`) +- ``Series`` method ``between`` now provides ability to set different inclusive endpoints, e.g. series.between(2, 5, inclusive=(True, False)). (:issue:`12398`) - ``DataFrame.quantile`` and ``Series.quantile`` now accept ``interpolation`` keyword (:issue:`10174`). - Added ``DataFrame.style.format`` for more flexible formatting of cell values (:issue:`11692`) - ``DataFrame.select_dtypes`` now allows the ``np.float16`` typecode (:issue:`11990`) diff --git a/pandas/core/series.py b/pandas/core/series.py index 1e5e0f6fb4553..f708adc88df0c 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2475,14 +2475,25 @@ def between(self, left, right, inclusive=True): Left boundary right : scalar Right boundary + inclusive : Boolean to indicate whether or not to include both the left + and right endpoints (True: a <= series <= b, False: a < series < b) + or an iterable boolean pair to set them separately, e.g + inclusive=(False, True) for a < series <= b. Returns ------- is_between : Series """ if inclusive: - lmask = self >= left - rmask = self <= right + try: + pair = iter(inclusive) + left_inclusive = pair.next() + rigt_inclusive = pair.next() + lmask = self >= left if left_inclusive else self > left + rmask = self <= right if rigt_inclusive else self < right + except TypeError: + lmask = self >= left + rmask = self <= right else: lmask = self > left rmask = self < right diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index c6593d403ffcc..d158c7cb44526 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -393,3 +393,11 @@ def test_between(self): result = s[s.between(s[3], s[17], inclusive=False)] expected = s[5:16].dropna() assert_series_equal(result, expected) + + result = s[s.between(s[3], s[17], inclusive=(True, False))] + expected = s[3:16].dropna() + assert_series_equal(result, expected) + + result = s[s.between(s[3], s[17], inclusive=(False, True))] + expected = s[5:18].dropna() + assert_series_equal(result, expected)
- [ X ] closes #12398 - [ X ] tests added / passed - [ X ] passes `git diff upstream/master | flake8 --diff` - [ X ] whatsnew entry Amends pandas.Series.between method to set different inclusive/exclusive endpoints. inclusive : Boolean to indicate whether or not to include both the left and right endpoints (True: a <= series <= b, False: a < series < b), or an iterable boolean pair to set them separately, e.g inclusive=(False, True) for a < series <= b.
https://api.github.com/repos/pandas-dev/pandas/pulls/12402
2016-02-20T20:34:31Z
2016-04-29T22:08:57Z
null
2023-05-11T01:13:23Z
COMPAT: Objects construction compat with xarray.Dataset
diff --git a/pandas/core/common.py b/pandas/core/common.py index 341bd3b4cc845..50392e2f5b947 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -10,6 +10,8 @@ import numpy as np import pandas.lib as lib import pandas.tslib as tslib + +import pandas as pd from pandas import compat from pandas.compat import long, zip, iteritems from pandas.core.config import get_option @@ -159,7 +161,6 @@ def _get_info_slice(obj, indexer): def _maybe_box(indexer, values, obj, key): - # if we have multiples coming back, box em if isinstance(values, np.ndarray): return obj[indexer.get_loc(key)] diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 501f4e443b1fc..3f803fc7326ef 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -54,7 +54,8 @@ is_list_like, is_iterator, is_sequence, - is_named_tuple) + is_named_tuple, + is_dict_like) from pandas.types.missing import isnull, notnull from pandas.core.common import (PandasError, _try_sort, @@ -64,11 +65,11 @@ _dict_compat) from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index -from pandas.core.indexing import (maybe_droplevels, convert_to_index_sliceable, - check_bool_indexer) -from pandas.core.internals import (BlockManager, - create_block_manager_from_arrays, - create_block_manager_from_blocks) +from pandas.core.indexing import ( + maybe_droplevels, convert_to_index_sliceable, check_bool_indexer) +from pandas.core.internals import ( + BlockManager, create_block_manager_from_arrays, + create_block_manager_from_blocks) from pandas.core.series import Series from pandas.core.categorical import Categorical import pandas.computation.expressions as expressions @@ -259,11 +260,16 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, if isinstance(data, DataFrame): data = data._data + if hasattr(data, 'to_dataframe'): # xr.Dataset + if index or columns or dtype or copy: + raise ValueError("Supply only a Dataset if supplying a " + "Dataset") + data = data.to_dataframe()._data + if isinstance(data, BlockManager): mgr = self._init_mgr(data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy) - elif isinstance(data, dict): - mgr = self._init_dict(data, index, columns, dtype=dtype) + elif isinstance(data, ma.MaskedArray): import numpy.ma.mrecords as mrecords # masked recarray @@ -295,6 +301,8 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, else: mgr = self._init_ndarray(data, index, columns, dtype=dtype, copy=copy) + elif is_dict_like(data): + mgr = self._init_dict(data, index, columns, dtype=dtype) elif isinstance(data, (list, types.GeneratorType)): if isinstance(data, types.GeneratorType): data = list(data) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index b2f318d825db6..cbfa282c904e2 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -11,7 +11,7 @@ from pandas.types.cast import (_infer_dtype_from_scalar, _possibly_cast_item) from pandas.types.common import (is_integer, is_list_like, - is_string_like, is_scalar) + is_string_like, is_scalar, is_dict_like) from pandas.types.missing import notnull import pandas.computation.expressions as expressions @@ -164,7 +164,7 @@ def _init_data(self, data, copy, dtype, **kwargs): axes = [x if x is not None else y for x, y in zip(passed_axes, data.axes)] mgr = data - elif isinstance(data, dict): + elif is_dict_like(data): mgr = self._init_dict(data, passed_axes, dtype=dtype) copy = False dtype = None @@ -200,9 +200,8 @@ def _init_dict(self, data, axes, dtype=None): ks = _try_sort(ks) haxis = Index(ks) - for k, v in compat.iteritems(data): - if isinstance(v, dict): - data[k] = self._constructor_sliced(v) + data = {k: self._constructor_sliced(v) + for k, v in compat.iteritems(data) if is_dict_like(v)} # extract axis for remaining axes & create the slicemap raxes = [self._extract_axis(self, data, axis=i) if a is None else a diff --git a/pandas/core/series.py b/pandas/core/series.py index 7979a230eed84..d6c255fc88790 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -92,12 +92,13 @@ def wrapper(self): return wrapper + # ---------------------------------------------------------------------- # Series class class Series(base.IndexOpsMixin, strings.StringAccessorMixin, - generic.NDFrame,): + generic.NDFrame): """ One-dimensional ndarray with axis labels (including time series). @@ -174,7 +175,7 @@ def __init__(self, data=None, index=None, dtype=None, name=None, else: data = data.reindex(index, copy=copy) data = data._data - elif isinstance(data, dict): + elif is_dict_like(data): if index is None: if isinstance(data, OrderedDict): index = Index(data) @@ -2127,10 +2128,9 @@ def map_f(values, f): else: map_f = lib.map_infer - if isinstance(arg, (dict, Series)): - if isinstance(arg, dict): - arg = self._constructor(arg, index=arg.keys()) - + if is_dict_like(arg): + arg = self._constructor(arg, index=arg.keys()) + if isinstance(arg, Series): indexer = arg.index.get_indexer(values) new_values = algos.take_1d(arg._values, indexer) else: @@ -2737,6 +2737,7 @@ def _dir_additions(self): Series._add_series_or_dataframe_operations() _INDEX_TYPES = ndarray, Index, list, tuple + # ----------------------------------------------------------------------------- # Supplementary functions @@ -2928,6 +2929,7 @@ def __init__(self, *args, **kwargs): super(TimeSeries, self).__init__(*args, **kwargs) + # ---------------------------------------------------------------------- # Add plotting methods to Series diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index d21db5ba52a45..5b9ca3a1bed45 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -123,7 +123,7 @@ def _make_mixed_dtypes_df(typ, ad=None): zipper = lzip(dtypes, arrays) for d, a in zipper: - assert(a.dtype == d) + assert (a.dtype == d) if ad is None: ad = dict() ad.update(dict([(d, a) for d, a in zipper])) @@ -134,7 +134,7 @@ def _check_mixed_dtypes(df, dtypes=None): dtypes = MIXED_FLOAT_DTYPES + MIXED_INT_DTYPES for d in dtypes: if d in df: - assert(df.dtypes[d] == d) + assert (df.dtypes[d] == d) # mixed floating and integer coexinst in the same frame df = _make_mixed_dtypes_df('float') @@ -516,6 +516,15 @@ def test_nested_dict_frame_constructor(self): result = DataFrame(data, index=rng).T tm.assert_frame_equal(result, df) + def test_constructor_mapping(self): + + mapping = tm.MappingMock(base=Series([0, 1, 2])) + + result = DataFrame(mapping) + expected = DataFrame({4: [0, 4, 8], 5: [0, 5, 10]}) + + tm.assert_frame_equal(result, expected) + def _check_basic_constructor(self, empty): # mat: 2d matrix with shpae (3, 2) to input. empty - makes sized # objects @@ -826,7 +835,6 @@ def test_constructor_sequence_like(self): import collections class DummyContainer(collections.Sequence): - def __init__(self, lst): self._lst = lst @@ -988,6 +996,7 @@ def test_constructor_list_of_series(self): def test_constructor_list_of_derived_dicts(self): class CustomDict(dict): pass + d = {'a': 1.5, 'b': 3} data_custom = [CustomDict(d)] @@ -1473,6 +1482,7 @@ def check(df): def f(): df.loc[:, np.nan] + self.assertRaises(TypeError, f) df = DataFrame([[1, 2, 3], [4, 5, 6]], index=[1, np.nan]) @@ -1624,6 +1634,7 @@ def test_from_records_set_index_name(self): def create_dict(order_id): return {'order_id': order_id, 'quantity': np.random.randint(1, 10), 'price': np.random.randint(1, 10)} + documents = [create_dict(i) for i in range(10)] # demo missing data documents.append({'order_id': 10, 'quantity': 5}) @@ -1849,7 +1860,6 @@ def test_from_records_bad_index_column(self): def test_from_records_non_tuple(self): class Record(object): - def __init__(self, *args): self.args = args @@ -1875,6 +1885,18 @@ def test_from_records_len0_with_columns(self): self.assertEqual(len(result), 0) self.assertEqual(result.index.name, 'foo') + def test_constructor_xarray_dataset(self): + tm._skip_if_no_xarray() + + index = pd.Index(['x', 'y'], name='z') + expected = DataFrame( + dict(a=[4, 5], b=[8, 10]), + index=index) + + result = DataFrame(expected.to_xarray()) + + tm.assert_frame_equal(result, expected) + class TestDataFrameConstructorWithDatetimeTZ(tm.TestCase, TestData): diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index ed7b0fda19cb7..af9f5780b6743 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -596,6 +596,15 @@ def test_constructor_subclass_dict(self): refseries = Series(dict(compat.iteritems(data))) assert_series_equal(refseries, series) + def test_constructor_mapping(self): + + mapping = tm.MappingMock(base=2) + + result = Series(mapping) + expected = pd.Series([8, 10], index=[4, 5]) + + assert_series_equal(result, expected) + def test_constructor_dict_datetime64_index(self): # GH 9456 @@ -769,6 +778,27 @@ def f(): s = Series([pd.NaT, np.nan, '1 Day']) self.assertEqual(s.dtype, 'timedelta64[ns]') + def test_constructor_dict_numpy_0d_arrays(self): + + data = [np.asarray(i) for i in range(4)] + + result = Series(data) + expected = Series(range(4)) + + # disabled for the moment (will remove from PR) + # assert_series_equal(result, expected) + + def test_constructor_xarray_dataset(self): + tm._skip_if_no_xarray() + import xarray as xr + + d = {'a': 5, 'b': 10} + result = Series(xr.Dataset(d)) + expected = Series(d) + + # disabled for the moment (will remove from PR) + # assert_series_equal(result, expected) + def test_constructor_name_hashable(self): for n in [777, 777., 'name', datetime(2001, 11, 11), (1, ), u"\u05D0"]: for data in [[1, 2, 3], np.ones(3), {'a': 0, 'b': 1}]: diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 10a6693525590..71aae7e814932 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1081,9 +1081,21 @@ def test_constructor_dict_mixed(self): data['ItemB'] = self.panel['ItemB'].values[:, :-1] self.assertRaises(Exception, Panel, data) + def test_constructor_mapping(self): + + mapping = tm.MappingMock(base=DataFrame({1: [0, 1], 2: [0, 1]})) + + result = Panel(mapping) + expected = Panel({ + 4: DataFrame({1: [0, 4], 2: [0, 4]}), + 5: DataFrame({1: [0, 5], 2: [0, 5]}) + }) + + assert_panel_equal(result, expected) + def test_ctor_orderedDict(self): - keys = list(set(np.random.randint(0, 5000, 100)))[ - :50] # unique random int keys + # unique random int keys + keys = list(set(np.random.randint(0, 5000, 100)))[:50] d = OrderedDict([(k, mkdf(10, 5)) for k in keys]) p = Panel(d) self.assertTrue(list(p.items) == keys) @@ -2147,6 +2159,7 @@ def check_drop(drop_val, axis_number, aliases, expected): pprint_thing("Failed with axis_number %d and aliases: %s" % (axis_number, aliases)) raise + # Items expected = Panel({"One": df}) check_drop('Two', 0, ['items'], expected) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 2d1d88b69941b..44728f8fbde01 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -17,6 +17,7 @@ from functools import wraps, partial from contextlib import contextmanager from distutils.version import LooseVersion +from collections import Mapping from numpy.random import randn, rand from numpy.testing.decorators import slow # noqa @@ -1960,9 +1961,7 @@ def add_nans_panel4d(panel4d): class TestSubDict(dict): - - def __init__(self, *args, **kwargs): - dict.__init__(self, *args, **kwargs) + pass # Dependency checks. Copied this from Nipy/Nipype (Copyright of @@ -2726,6 +2725,25 @@ def patch(ob, attr, value): setattr(ob, attr, old) +class MappingMock(Mapping): + """ + Mock class to represent a Mapping + Takes a base, and returns that multiplied by whatever key is passed in + """ + + def __init__(self, base): + self.base = base + + def __getitem__(self, key): + return key * self.base + + def __iter__(self): + return iter([4, 5]) + + def __len__(self): + return 2 + + @contextmanager def set_timezone(tz): """Context manager for temporarily setting a timezone.
- [x] closes #12353 - [x] tests added - [ ] tests passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry I'm changed the dict / mapping issue, but I can't get this part working (so the tests fail at the moment): > But pd.Series(dict(xr.Dataset({'a': 2, 'b': 3}))) doesn't work either, because the values in the dict are 0-th dimension arrays, and Series doesn't unpack them (list(dict(xr.Dataset({'a': 2, 'b': 3})).values())[0] for the full code) > https://github.com/pydata/pandas/issues/12353#issuecomment-184968747 Is that a reasonable goal? Where would be a good place to do the conversion?
https://api.github.com/repos/pandas-dev/pandas/pulls/12400
2016-02-20T06:59:54Z
2017-08-17T10:28:43Z
null
2017-08-17T10:28:43Z
Add example usage to DataFrame.filter
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 93c76bc80684f..875ba907823c6 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -537,6 +537,7 @@ Backwards incompatible API changes - the order of keyword arguments to text file parsing functions (``.read_csv()``, ``.read_table()``, ``.read_fwf()``) changed to group related arguments. (:issue:`11555`) - ``NaTType.isoformat`` now returns the string ``'NaT`` to allow the result to be passed to the constructor of ``Timestamp``. (:issue:`12300`) +- ``DataFrame.filter()`` enforces mutual exclusion of keyword arguments. (:issue:`12399`) NaT and Timedelta operations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 9ecaaebc2b523..95529512edd40 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2357,7 +2357,10 @@ def _reindex_axis(self, new_index, fill_method, axis, copy): def filter(self, items=None, like=None, regex=None, axis=None): """ - Restrict the info axis to set of items or wildcard + Subset rows or columns of dataframe according to labels in the index. + + Note that this routine does not filter a dataframe on its + contents. The filter is applied to the labels of the index. Parameters ---------- @@ -2367,19 +2370,57 @@ def filter(self, items=None, like=None, regex=None, axis=None): Keep info axis where "arg in col == True" regex : string (regular expression) Keep info axis with re.search(regex, col) == True - axis : int or None - The axis to filter on. By default this is the info axis. The "info - axis" is the axis that is used when indexing with ``[]``. For - example, ``df = DataFrame({'a': [1, 2, 3, 4]]}); df['a']``. So, - the ``DataFrame`` columns are the info axis. + axis : int or string axis name + The axis to filter on. By default this is the info axis. The "info + axis" is the axis that is used when indexing with ``[]`` + + Returns + ------- + same type as input object with filtered info axis + + Examples + -------- + >>> df + one two three + mouse 1 2 3 + rabbit 4 5 6 + + >>> # select columns by name + >>> df.filter(items=['one', 'three']) + one three + mouse 1 3 + rabbit 4 6 + + >>> # select columns by regular expression + >>> df.filter(regex='e$', axis=1) + one three + mouse 1 3 + rabbit 4 6 + + >>> # select rows containing 'bbi' + >>> df.filter(like='bbi', axis=0) + one two three + rabbit 4 5 6 + + See Also + -------- + pandas.DataFrame.select Notes ----- - Arguments are mutually exclusive, but this is not checked for + The ``items``, ``like``, and ``regex`` parameters are + enforced to be mutually exclusive. + ``axis`` defaults to the info axis that is used when indexing + with ``[]``. """ import re + nkw = sum([x is not None for x in [items, like, regex]]) + if nkw > 1: + raise TypeError('Keyword arguments `items`, `like`, or `regex` ' + 'are mutually exclusive') + if axis is None: axis = self._info_axis_name axis_name = self._get_axis_name(axis) diff --git a/pandas/tests/frame/test_axis_select_reindex.py b/pandas/tests/frame/test_axis_select_reindex.py index 07fe28f13b7d0..9da1b31d259c5 100644 --- a/pandas/tests/frame/test_axis_select_reindex.py +++ b/pandas/tests/frame/test_axis_select_reindex.py @@ -661,8 +661,24 @@ def test_filter(self): assert_frame_equal(filtered, expected) # pass in None + with assertRaisesRegexp(TypeError, 'Must pass'): + self.frame.filter() with assertRaisesRegexp(TypeError, 'Must pass'): self.frame.filter(items=None) + with assertRaisesRegexp(TypeError, 'Must pass'): + self.frame.filter(axis=1) + + # test mutually exclusive arguments + with assertRaisesRegexp(TypeError, 'mutually exclusive'): + self.frame.filter(items=['one', 'three'], regex='e$', like='bbi') + with assertRaisesRegexp(TypeError, 'mutually exclusive'): + self.frame.filter(items=['one', 'three'], regex='e$', axis=1) + with assertRaisesRegexp(TypeError, 'mutually exclusive'): + self.frame.filter(items=['one', 'three'], regex='e$') + with assertRaisesRegexp(TypeError, 'mutually exclusive'): + self.frame.filter(items=['one', 'three'], like='bbi', axis=0) + with assertRaisesRegexp(TypeError, 'mutually exclusive'): + self.frame.filter(items=['one', 'three'], like='bbi') # objects filtered = self.mixed_frame.filter(like='foo')
Updates doc comments for `DataFrame.filter` and adds usage examples. Fixed errors identified by `flake8` and correctly rebase my branch before issuing PR. <dl class="method"> <dt id="pandas.DataFrame.filter"> <code class="descclassname">DataFrame.</code><code class="descname">filter</code><span class="sig-paren">(</span><em>items=None</em>, <em>like=None</em>, <em>regex=None</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pandas.DataFrame.filter" title="Permalink to this definition">¶</a></dt> <dd><p>Subset rows or columns of dataframe according to labels in the index.</p> <p>Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. This method is a thin veneer on top of <span class="xref std std-ref">DateFrame Select</span></p> <table class="docutils field-list" frame="void" rules="none"> <colgroup><col class="field-name"> <col class="field-body"> </colgroup><tbody valign="top"> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>items</strong> : list-like</p> <blockquote> <div><p>List of info axis to restrict to (must not all be present)</p> </div></blockquote> <p><strong>like</strong> : string</p> <blockquote> <div><p>Keep info axis where “arg in col == True”</p> </div></blockquote> <p><strong>regex</strong> : string (regular expression)</p> <blockquote> <div><p>Keep info axis with re.search(regex, col) == True</p> </div></blockquote> <p><strong>axis</strong> : int or None</p> <blockquote> <div><p>The axis to filter on.</p> </div></blockquote> </td> </tr> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">same type as input object with filtered info axis</p> </td> </tr> </tbody> </table> <p class="rubric">Notes</p> <p>The <code class="docutils literal"><span class="pre">items</span></code>, <code class="docutils literal"><span class="pre">like</span></code>, and <code class="docutils literal"><span class="pre">regex</span></code> parameters should be mutually exclusive, but this is not checked.</p> <p><code class="docutils literal"><span class="pre">axis</span></code> defaults to the info axis that is used when indexing with <code class="docutils literal"><span class="pre">[]</span></code>.</p> <p class="rubric">Examples</p> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="go"> one two three</span> <span class="go">mouse 1 2 3</span> <span class="go">rabbit 4 5 6</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="c1"># select columns by name</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">items</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span> <span class="go"> one three</span> <span class="go">mouse 1 3</span> <span class="go">rabbit 4 6</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="c1"># select columns by regular expression</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">regex</span><span class="o">=</span><span class="s1">'e$'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="go"> one three</span> <span class="go">mouse 1 3</span> <span class="go">rabbit 4 6</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="c1"># select rows containing 'bbi'</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'bbi'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="go"> one two three</span> <span class="go">rabbit 4 5 6</span> </pre></div> </div> </dd></dl>
https://api.github.com/repos/pandas-dev/pandas/pulls/12399
2016-02-20T00:49:44Z
2016-06-03T21:01:24Z
null
2016-06-03T21:01:39Z
Doc/df filter
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 85f23b988778f..f395b0a034332 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -15,6 +15,7 @@ from pandas.tseries.index import DatetimeIndex from pandas.tseries.period import PeriodIndex from pandas.core.internals import BlockManager +import pandas.core.algorithms as algos import pandas.core.common as com import pandas.core.missing as mis import pandas.core.datetools as datetools @@ -32,11 +33,10 @@ # goal is to be able to define the docs close to function, while still being # able to share _shared_docs = dict() -_shared_doc_kwargs = dict(axes='keywords for axes', - klass='NDFrame', +_shared_doc_kwargs = dict(axes='keywords for axes', klass='NDFrame', axes_single_arg='int or labels for object', args_transpose='axes to permute (int or label for' - ' object)') + ' object)') def is_dictlike(x): @@ -69,7 +69,6 @@ def _single_replace(self, to_replace, method, inplace, limit): class NDFrame(PandasObject): - """ N-dimensional analogue of DataFrame. Store multi-dimensional in a size-mutable, labeled data structure @@ -80,10 +79,10 @@ class NDFrame(PandasObject): axes : list copy : boolean, default False """ - _internal_names = ['_data', '_cacher', '_item_cache', '_cache', - 'is_copy', '_subtyp', '_index', - '_default_kind', '_default_fill_value', '_metadata', - '__array_struct__', '__array_interface__'] + _internal_names = ['_data', '_cacher', '_item_cache', '_cache', 'is_copy', + '_subtyp', '_index', '_default_kind', + '_default_fill_value', '_metadata', '__array_struct__', + '__array_interface__'] _internal_names_set = set(_internal_names) _accessors = frozenset([]) _metadata = [] @@ -123,8 +122,9 @@ def _init_mgr(self, mgr, axes=None, dtype=None, copy=False): """ passed a manager and a axes dict """ for a, axe in axes.items(): if axe is not None: - mgr = mgr.reindex_axis( - axe, axis=self._get_block_manager_axis(a), copy=False) + mgr = mgr.reindex_axis(axe, + axis=self._get_block_manager_axis(a), + copy=False) # make a copy if explicitly requested if copy: @@ -135,7 +135,7 @@ def _init_mgr(self, mgr, axes=None, dtype=None, copy=False): mgr = mgr.astype(dtype=dtype) return mgr - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Construction @property @@ -154,7 +154,7 @@ def __unicode__(self): def _dir_additions(self): """ add the string-like attributes from the info_axis """ return set([c for c in self._info_axis - if isinstance(c, string_types) and isidentifier(c)]) + if isinstance(c, string_types) and isidentifier(c)]) @property def _constructor_sliced(self): @@ -170,31 +170,32 @@ def _constructor_expanddim(self): """ raise NotImplementedError - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Axis @classmethod - def _setup_axes( - cls, axes, info_axis=None, stat_axis=None, aliases=None, slicers=None, - axes_are_reversed=False, build_axes=True, ns=None): - """ provide axes setup for the major PandasObjects - - axes : the names of the axes in order (lowest to highest) - info_axis_num : the axis of the selector dimension (int) - stat_axis_num : the number of axis for the default stats (int) - aliases : other names for a single axis (dict) - slicers : how axes slice to others (dict) - axes_are_reversed : boolean whether to treat passed axes as - reversed (DataFrame) - build_axes : setup the axis properties (default True) - """ + def _setup_axes(cls, axes, info_axis=None, stat_axis=None, aliases=None, + slicers=None, axes_are_reversed=False, build_axes=True, + ns=None): + """Provide axes setup for the major PandasObjects. + + Parameters + ---------- + axes : the names of the axes in order (lowest to highest) + info_axis_num : the axis of the selector dimension (int) + stat_axis_num : the number of axis for the default stats (int) + aliases : other names for a single axis (dict) + slicers : how axes slice to others (dict) + axes_are_reversed : boolean whether to treat passed axes as + reversed (DataFrame) + build_axes : setup the axis properties (default True) + """ cls._AXIS_ORDERS = axes cls._AXIS_NUMBERS = dict((a, i) for i, a in enumerate(axes)) cls._AXIS_LEN = len(axes) cls._AXIS_ALIASES = aliases or dict() - cls._AXIS_IALIASES = dict((v, k) - for k, v in cls._AXIS_ALIASES.items()) + cls._AXIS_IALIASES = dict((v, k) for k, v in cls._AXIS_ALIASES.items()) cls._AXIS_NAMES = dict(enumerate(axes)) cls._AXIS_SLICEMAP = slicers or None cls._AXIS_REVERSED = axes_are_reversed @@ -234,29 +235,31 @@ def set_axis(a, i): setattr(cls, k, v) def _construct_axes_dict(self, axes=None, **kwargs): - """ return an axes dictionary for myself """ + """Return an axes dictionary for myself.""" d = dict([(a, self._get_axis(a)) for a in (axes or self._AXIS_ORDERS)]) d.update(kwargs) return d @staticmethod def _construct_axes_dict_from(self, axes, **kwargs): - """ return an axes dictionary for the passed axes """ + """Return an axes dictionary for the passed axes.""" d = dict([(a, ax) for a, ax in zip(self._AXIS_ORDERS, axes)]) d.update(kwargs) return d def _construct_axes_dict_for_slice(self, axes=None, **kwargs): - """ return an axes dictionary for myself """ + """Return an axes dictionary for myself.""" d = dict([(self._AXIS_SLICEMAP[a], self._get_axis(a)) - for a in (axes or self._AXIS_ORDERS)]) + for a in (axes or self._AXIS_ORDERS)]) d.update(kwargs) return d def _construct_axes_from_arguments(self, args, kwargs, require_all=False): - """ construct and returns axes if supplied in args/kwargs - if require_all, raise if all axis arguments are not supplied - return a tuple of (axes, kwargs) """ + """Construct and returns axes if supplied in args/kwargs. + + If require_all, raise if all axis arguments are not supplied + return a tuple of (axes, kwargs). + """ # construct the args args = list(args) @@ -267,10 +270,8 @@ def _construct_axes_from_arguments(self, args, kwargs, require_all=False): if alias is not None: if a in kwargs: if alias in kwargs: - raise TypeError( - "arguments are mutually exclusive for [%s,%s]" % - (a, alias) - ) + raise TypeError("arguments are mutually exclusive " + "for [%s,%s]" % (a, alias)) continue if alias in kwargs: kwargs[a] = kwargs.pop(alias) @@ -280,10 +281,10 @@ def _construct_axes_from_arguments(self, args, kwargs, require_all=False): if a not in kwargs: try: kwargs[a] = args.pop(0) - except (IndexError): + except IndexError: if require_all: - raise TypeError( - "not enough/duplicate arguments specified!") + raise TypeError("not enough/duplicate arguments " + "specified!") axes = dict([(a, kwargs.pop(a, None)) for a in self._AXIS_ORDERS]) return axes, kwargs @@ -331,7 +332,7 @@ def _get_axis(self, axis): return getattr(self, name) def _get_block_manager_axis(self, axis): - """ map the axis to the block_manager axis """ + """Map the axis to the block_manager axis.""" axis = self._get_axis_number(axis) if self._AXIS_REVERSED: m = self._AXIS_LEN - 1 @@ -384,24 +385,24 @@ def _stat_axis(self): @property def shape(self): - "Return a tuple of axis dimensions" + """Return a tuple of axis dimensions""" return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS) @property def axes(self): - "Return index label(s) of the internal NDFrame" + """Return index label(s) of the internal NDFrame""" # we do it this way because if we have reversed axes, then # the block manager shows then reversed return [self._get_axis(a) for a in self._AXIS_ORDERS] @property def ndim(self): - "Number of axes / array dimensions" + """Number of axes / array dimensions""" return self._data.ndim @property def size(self): - "number of elements in the NDFrame" + """number of elements in the NDFrame""" return np.prod(self.shape) def _expand_axes(self, key): @@ -418,7 +419,7 @@ def _expand_axes(self, key): def set_axis(self, axis, labels): """ public verson of axis assignment """ - setattr(self,self._get_axis_name(axis),labels) + setattr(self, self._get_axis_name(axis), labels) def _set_axis(self, axis, labels): self._data.set_axis(axis, labels) @@ -448,26 +449,26 @@ def _set_axis(self, axis, labels): def transpose(self, *args, **kwargs): # construct the args - axes, kwargs = self._construct_axes_from_arguments( - args, kwargs, require_all=True) + axes, kwargs = self._construct_axes_from_arguments(args, kwargs, + require_all=True) axes_names = tuple([self._get_axis_name(axes[a]) for a in self._AXIS_ORDERS]) axes_numbers = tuple([self._get_axis_number(axes[a]) - for a in self._AXIS_ORDERS]) + for a in self._AXIS_ORDERS]) # we must have unique axes if len(axes) != len(set(axes)): raise ValueError('Must specify %s unique axes' % self._AXIS_LEN) - new_axes = self._construct_axes_dict_from( - self, [self._get_axis(x) for x in axes_names]) + new_axes = self._construct_axes_dict_from(self, [self._get_axis(x) + for x in axes_names]) new_values = self.values.transpose(axes_numbers) if kwargs.pop('copy', None) or (len(args) and args[-1]): new_values = new_values.copy() if kwargs: raise TypeError('transpose() got an unexpected keyword ' - 'argument "{0}"'.format(list(kwargs.keys())[0])) + 'argument "{0}"'.format(list(kwargs.keys())[0])) return self._constructor(new_values, **new_axes).__finalize__(self) @@ -511,10 +512,10 @@ def pop(self, item): return result def squeeze(self): - """ squeeze length 1 dimensions """ + """Squeeze length 1 dimensions.""" try: return self.iloc[tuple([0 if len(a) == 1 else slice(None) - for a in self.axes])] + for a in self.axes])] except: return self @@ -537,7 +538,7 @@ def swaplevel(self, i, j, axis=0): result._data.set_axis(axis, labels.swaplevel(i, j)) return result - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Rename # TODO: define separate funcs for DataFrame, Series and Panel so you can @@ -545,13 +546,16 @@ def swaplevel(self, i, j, axis=0): _shared_docs['rename'] = """ Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left - as-is. + as-is. Alternatively, change ``Series.name`` with a scalar + value (Series only). Parameters ---------- - %(axes)s : dict-like or function, optional - Transformation to apply to that axis values - + %(axes)s : scalar, list-like, dict-like or function, optional + Scalar or list-like will alter the ``Series.name`` attribute, + and raise on DataFrame or Panel. + dict-like or functions are transformations to apply to + that axis' values copy : boolean, default True Also copy underlying data inplace : boolean, default False @@ -561,6 +565,43 @@ def swaplevel(self, i, j, axis=0): Returns ------- renamed : %(klass)s (new object) + + See Also + -------- + pandas.NDFrame.rename_axis + + Examples + -------- + >>> s = pd.Series([1, 2, 3]) + >>> s + 0 1 + 1 2 + 2 3 + dtype: int64 + >>> s.rename("my_name") # scalar, changes Series.name + 0 1 + 1 2 + 2 3 + Name: my_name, dtype: int64 + >>> s.rename(lambda x: x ** 2) # function, changes labels + 0 1 + 1 2 + 4 3 + dtype: int64 + >>> s.rename({1: 3, 2: 5}) # mapping, changes labels + 0 1 + 3 2 + 5 3 + dtype: int64 + >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + >>> df.rename(2) + ... + TypeError: 'int' object is not callable + >>> df.rename(index=str, columns={"A": "a", "B": "c"}) + a c + 0 1 4 + 1 2 5 + 2 3 6 """ @Appender(_shared_docs['rename'] % dict(axes='axes keywords for this' @@ -573,14 +614,15 @@ def rename(self, *args, **kwargs): if kwargs: raise TypeError('rename() got an unexpected keyword ' - 'argument "{0}"'.format(list(kwargs.keys())[0])) + 'argument "{0}"'.format(list(kwargs.keys())[0])) - if (com._count_not_none(*axes.values()) == 0): + if com._count_not_none(*axes.values()) == 0: raise TypeError('must pass an index to rename') # renamer function if passed a dict def _get_rename_function(mapper): if isinstance(mapper, (dict, ABCSeries)): + def f(x): if x in mapper: return mapper[x] @@ -615,12 +657,15 @@ def f(x): def rename_axis(self, mapper, axis=0, copy=True, inplace=False): """ Alter index and / or columns using input function or functions. + A scaler or list-like for ``mapper`` will alter the ``Index.name`` + or ``MultiIndex.names`` attribute. + A function or dict for ``mapper`` will alter the labels. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Parameters ---------- - mapper : dict-like or function, optional + mapper : scalar, list-like, dict-like or function, optional axis : int or string, default 0 copy : boolean, default True Also copy underlying data @@ -629,13 +674,90 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): Returns ------- renamed : type of caller + + See Also + -------- + pandas.NDFrame.rename + pandas.Index.rename + + Examples + -------- + >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + >>> df.rename_axis("foo") # scalar, alters df.index.name + A B + foo + 0 1 4 + 1 2 5 + 2 3 6 + >>> df.rename_axis(lambda x: 2 * x) # function: alters labels + A B + 0 1 4 + 2 2 5 + 4 3 6 + >>> df.rename_axis({"A": "ehh", "C": "see"}, axis="columns") # mapping + ehh B + 0 1 4 + 1 2 5 + 2 3 6 + """ + is_scalar_or_list = ( + (not com.is_sequence(mapper) and not callable(mapper)) or + (com.is_list_like(mapper) and not com.is_dict_like(mapper)) + ) + + if is_scalar_or_list: + return self._set_axis_name(mapper, axis=axis) + else: + axis = self._get_axis_name(axis) + d = {'copy': copy, 'inplace': inplace} + d[axis] = mapper + return self.rename(**d) + + def _set_axis_name(self, name, axis=0): """ - axis = self._get_axis_name(axis) - d = {'copy': copy, 'inplace': inplace} - d[axis] = mapper - return self.rename(**d) + Alter the name or names of the axis, returning self. + + Parameters + ---------- + name : str or list of str + Name for the Index, or list of names for the MultiIndex + axis : int or str + 0 or 'index' for the index; 1 or 'columns' for the columns + + Returns + ------- + renamed : type of caller + + See Also + -------- + pandas.DataFrame.rename + pandas.Series.rename + pandas.Index.rename - #---------------------------------------------------------------------- + Examples + -------- + >>> df._set_axis_name("foo") + A + foo + 0 1 + 1 2 + 2 3 + >>> df.index = pd.MultiIndex.from_product([['A'], ['a', 'b', 'c']]) + >>> df._set_axis_name(["bar", "baz"]) + A + bar baz + A a 1 + b 2 + c 3 + """ + axis = self._get_axis_number(axis) + idx = self._get_axis(axis).set_names(name) + + renamed = self.copy(deep=True) + renamed.set_axis(axis, idx) + return renamed + + # ---------------------------------------------------------------------- # Comparisons def _indexed_same(self, other): @@ -664,14 +786,14 @@ def __invert__(self): def equals(self, other): """ - Determines if two NDFrame objects contain the same elements. NaNs in the - same location are considered equal. + Determines if two NDFrame objects contain the same elements. NaNs in + the same location are considered equal. """ if not isinstance(other, self._constructor): return False return self._data.equals(other._data) - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Iteration def __hash__(self): @@ -679,9 +801,7 @@ def __hash__(self): ' hashed'.format(self.__class__.__name__)) def __iter__(self): - """ - Iterate over infor axis - """ + """Iterate over infor axis""" return iter(self._info_axis) # can we get a better explanation of this? @@ -689,7 +809,8 @@ def keys(self): """Get the 'info axis' (see Indexing for more) This is index for Series, columns for DataFrame and major_axis for - Panel.""" + Panel. + """ return self._info_axis def iteritems(self): @@ -707,21 +828,21 @@ def iteritems(self): def iterkv(self, *args, **kwargs): "iteritems alias used to get around 2to3. Deprecated" warnings.warn("iterkv is deprecated and will be removed in a future " - "release, use ``iteritems`` instead.", - FutureWarning, stacklevel=2) + "release, use ``iteritems`` instead.", FutureWarning, + stacklevel=2) return self.iteritems(*args, **kwargs) def __len__(self): - """Returns length of info axis """ + """Returns length of info axis""" return len(self._info_axis) def __contains__(self, key): - """True if the key is in the info axis """ + """True if the key is in the info axis""" return key in self._info_axis @property def empty(self): - "True if NDFrame is entirely empty [no items]" + """True if NDFrame is entirely empty [no items]""" return not all(len(self._get_axis(a)) > 0 for a in self._AXIS_ORDERS) def __nonzero__(self): @@ -732,11 +853,12 @@ def __nonzero__(self): __bool__ = __nonzero__ def bool(self): - """ Return the bool of a single element PandasObject - This must be a boolean scalar value, either True or False + """Return the bool of a single element PandasObject. - Raise a ValueError if the PandasObject does not have exactly - 1 element, or that element is not boolean """ + This must be a boolean scalar value, either True or False. Raise a + ValueError if the PandasObject does not have exactly 1 element, or that + element is not boolean + """ v = self.squeeze() if isinstance(v, (bool, np.bool_)): return bool(v) @@ -749,10 +871,10 @@ def bool(self): def __abs__(self): return self.abs() - def __round__(self,decimals=0): + def __round__(self, decimals=0): return self.round(decimals) - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Array Interface def __array__(self, dtype=None): @@ -764,24 +886,24 @@ def __array_wrap__(self, result, context=None): # ideally we would define this to avoid the getattr checks, but # is slower - #@property - #def __array_interface__(self): + # @property + # def __array_interface__(self): # """ provide numpy array interface method """ # values = self.values # return dict(typestr=values.dtype.str,shape=values.shape,data=values) def to_dense(self): - "Return dense representation of NDFrame (as opposed to sparse)" + """Return dense representation of NDFrame (as opposed to sparse)""" # compat return self - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Picklability def __getstate__(self): meta = dict((k, getattr(self, k, None)) for k in self._metadata) - return dict(_data=self._data, _typ=self._typ, - _metadata=self._metadata, **meta) + return dict(_data=self._data, _typ=self._typ, _metadata=self._metadata, + **meta) def __setstate__(self, state): @@ -822,10 +944,10 @@ def __setstate__(self, state): self._item_cache = {} - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # IO - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # I/O Methods def to_json(self, path_or_buf=None, orient=None, date_format='epoch', @@ -886,17 +1008,14 @@ def to_json(self, path_or_buf=None, orient=None, date_format='epoch', """ from pandas.io import json - return json.to_json( - path_or_buf=path_or_buf, - obj=self, orient=orient, - date_format=date_format, - double_precision=double_precision, - force_ascii=force_ascii, - date_unit=date_unit, - default_handler=default_handler) + return json.to_json(path_or_buf=path_or_buf, obj=self, orient=orient, + date_format=date_format, + double_precision=double_precision, + force_ascii=force_ascii, date_unit=date_unit, + default_handler=default_handler) def to_hdf(self, path_or_buf, key, **kwargs): - """ activate the HDFStore + """Activate the HDFStore. Parameters ---------- @@ -940,7 +1059,7 @@ def to_hdf(self, path_or_buf, key, **kwargs): from pandas.io import pytables return pytables.to_hdf(path_or_buf, key, self, **kwargs) - def to_msgpack(self, path_or_buf=None, **kwargs): + def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs): """ msgpack (serialize) object to input file path @@ -958,7 +1077,8 @@ def to_msgpack(self, path_or_buf=None, **kwargs): """ from pandas.io import packers - return packers.to_msgpack(path_or_buf, self, **kwargs) + return packers.to_msgpack(path_or_buf, self, encoding=encoding, + **kwargs) def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None): @@ -975,8 +1095,8 @@ def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', If a DBAPI2 object, only sqlite3 is supported. flavor : {'sqlite', 'mysql'}, default 'sqlite' The flavor of SQL to use. Ignored when using SQLAlchemy engine. - 'mysql' is deprecated and will be removed in future versions, but it - will be further supported through SQLAlchemy engines. + 'mysql' is deprecated and will be removed in future versions, but + it will be further supported through SQLAlchemy engines. schema : string, default None Specify the schema (if database flavor supports this). If None, use default schema. @@ -999,14 +1119,13 @@ def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', """ from pandas.io import sql - sql.to_sql( - self, name, con, flavor=flavor, schema=schema, if_exists=if_exists, - index=index, index_label=index_label, chunksize=chunksize, - dtype=dtype) + sql.to_sql(self, name, con, flavor=flavor, schema=schema, + if_exists=if_exists, index=index, index_label=index_label, + chunksize=chunksize, dtype=dtype) def to_pickle(self, path): """ - Pickle (serialize) object to input file path + Pickle (serialize) object to input file path. Parameters ---------- @@ -1041,12 +1160,109 @@ def to_clipboard(self, excel=None, sep=None, **kwargs): from pandas.io import clipboard clipboard.to_clipboard(self, excel=excel, sep=sep, **kwargs) - #---------------------------------------------------------------------- + def to_xarray(self): + """ + Return an xarray object from the pandas object. + + Returns + ------- + a DataArray for a Series + a Dataset for a DataFrame + a DataArray for higher dims + + Examples + -------- + >>> df = pd.DataFrame({'A' : [1, 1, 2], + 'B' : ['foo', 'bar', 'foo'], + 'C' : np.arange(4.,7)}) + >>> df + A B C + 0 1 foo 4.0 + 1 1 bar 5.0 + 2 2 foo 6.0 + + >>> df.to_xarray() + <xarray.Dataset> + Dimensions: (index: 3) + Coordinates: + * index (index) int64 0 1 2 + Data variables: + A (index) int64 1 1 2 + B (index) object 'foo' 'bar' 'foo' + C (index) float64 4.0 5.0 6.0 + + >>> df = pd.DataFrame({'A' : [1, 1, 2], + 'B' : ['foo', 'bar', 'foo'], + 'C' : np.arange(4.,7)} + ).set_index(['B','A']) + >>> df + C + B A + foo 1 4.0 + bar 1 5.0 + foo 2 6.0 + + >>> df.to_xarray() + <xarray.Dataset> + Dimensions: (A: 2, B: 2) + Coordinates: + * B (B) object 'bar' 'foo' + * A (A) int64 1 2 + Data variables: + C (B, A) float64 5.0 nan 4.0 6.0 + + >>> p = pd.Panel(np.arange(24).reshape(4,3,2), + items=list('ABCD'), + major_axis=pd.date_range('20130101', periods=3), + minor_axis=['first', 'second']) + >>> p + <class 'pandas.core.panel.Panel'> + Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis) + Items axis: A to D + Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00 + Minor_axis axis: first to second + + >>> p.to_xarray() + <xarray.DataArray (items: 4, major_axis: 3, minor_axis: 2)> + array([[[ 0, 1], + [ 2, 3], + [ 4, 5]], + [[ 6, 7], + [ 8, 9], + [10, 11]], + [[12, 13], + [14, 15], + [16, 17]], + [[18, 19], + [20, 21], + [22, 23]]]) + Coordinates: + * items (items) object 'A' 'B' 'C' 'D' + * major_axis (major_axis) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03 # noqa + * minor_axis (minor_axis) object 'first' 'second' + + Notes + ----- + See the `xarray docs <http://xarray.pydata.org/en/stable/>`__ + """ + import xarray + if self.ndim == 1: + return xarray.DataArray.from_series(self) + elif self.ndim == 2: + return xarray.Dataset.from_dataframe(self) + + # > 2 dims + coords = [(a, self._get_axis(a)) for a in self._AXIS_ORDERS] + return xarray.DataArray(self, + coords=coords, + ) + + # ---------------------------------------------------------------------- # Fancy Indexing @classmethod def _create_indexer(cls, name, indexer): - """ create an indexer like _name in the class """ + """Create an indexer like _name in the class.""" if getattr(cls, name, None) is None: iname = '_%s' % name @@ -1067,7 +1283,7 @@ def _indexer(self): def get(self, key, default=None): """ Get item from object for given key (DataFrame column, Panel slice, - etc.). Returns default value if not found + etc.). Returns default value if not found. Parameters ---------- @@ -1086,7 +1302,7 @@ def __getitem__(self, item): return self._get_item_cache(item) def _get_item_cache(self, item): - """ return the cached item, item represents a label indexer """ + """Return the cached item, item represents a label indexer.""" cache = self._item_cache res = cache.get(item) if res is None: @@ -1100,17 +1316,18 @@ def _get_item_cache(self, item): return res def _set_as_cached(self, item, cacher): - """ set the _cacher attribute on the calling object with - a weakref to cacher """ + """Set the _cacher attribute on the calling object with a weakref to + cacher. + """ self._cacher = (item, weakref.ref(cacher)) def _reset_cacher(self): - """ reset the cacher """ - if hasattr(self,'_cacher'): + """Reset the cacher.""" + if hasattr(self, '_cacher'): del self._cacher def _iget_item_cache(self, item): - """ return the cached item, item represents a positional indexer """ + """Return the cached item, item represents a positional indexer.""" ax = self._info_axis if ax.is_unique: lower = self._get_item_cache(ax[item]) @@ -1122,9 +1339,7 @@ def _box_item_values(self, key, values): raise AbstractMethodError(self) def _maybe_cache_changed(self, item, value): - """ - the object has called back to us saying - maybe it has changed + """The object has called back to us saying maybe it has changed. numpy < 1.8 has an issue with object arrays and aliasing GH6026 @@ -1133,11 +1348,11 @@ def _maybe_cache_changed(self, item, value): @property def _is_cached(self): - """ boolean : return if I am cached """ + """Return boolean indicating if self is cached or not.""" return getattr(self, '_cacher', None) is not None def _get_cacher(self): - """ return my cacher or None """ + """return my cacher or None""" cacher = getattr(self, '_cacher', None) if cacher is not None: cacher = cacher[1]() @@ -1145,14 +1360,13 @@ def _get_cacher(self): @property def _is_view(self): - """ boolean : return if I am a view of another array """ + """Return boolean indicating if self is view of another array """ return self._data.is_view def _maybe_update_cacher(self, clear=False, verify_is_copy=True): """ - - see if we need to update our parent cacher - if clear, then clear our cache + See if we need to update our parent cacher if clear, then clear our + cache. Parameters ---------- @@ -1194,7 +1408,6 @@ def _slice(self, slobj, axis=0, kind=None): Construct a slice of this container. kind parameter is maintained for compatibility with Series slicing. - """ axis = self._get_block_manager_axis(axis) result = self._constructor(self._data.get_slice(slobj, axis=axis)) @@ -1202,7 +1415,7 @@ def _slice(self, slobj, axis=0, kind=None): # this could be a view # but only in a single-dtyped view slicable case - is_copy = axis!=0 or result._is_view + is_copy = axis != 0 or result._is_view result._set_is_copy(self, copy=is_copy) return result @@ -1221,18 +1434,20 @@ def _set_is_copy(self, ref=None, copy=True): def _check_is_chained_assignment_possible(self): """ - check if we are a view, have a cacher, and are of mixed type - if so, then force a setitem_copy check + Check if we are a view, have a cacher, and are of mixed type. + If so, then force a setitem_copy check. - should be called just near setting a value + Should be called just near setting a value - will return a boolean if it we are a view and are cached, but a single-dtype - meaning that the cacher should be updated following setting + Will return a boolean if it we are a view and are cached, but a + single-dtype meaning that the cacher should be updated following + setting. """ if self._is_view and self._is_cached: ref = self._get_cacher() if ref is not None and ref._is_mixed_type: - self._check_setitem_copy(stacklevel=4, t='referant', force=True) + self._check_setitem_copy(stacklevel=4, t='referant', + force=True) return True elif self.is_copy: self._check_setitem_copy(stacklevel=4, t='referant') @@ -1255,16 +1470,16 @@ def _check_setitem_copy(self, stacklevel=4, t='setting', force=False): user will see the error *at the level of setting* It is technically possible to figure out that we are setting on - a copy even WITH a multi-dtyped pandas object. In other words, some blocks - may be views while other are not. Currently _is_view will ALWAYS return False - for multi-blocks to avoid having to handle this case. + a copy even WITH a multi-dtyped pandas object. In other words, some + blocks may be views while other are not. Currently _is_view will ALWAYS + return False for multi-blocks to avoid having to handle this case. df = DataFrame(np.arange(0,9), columns=['count']) df['group'] = 'b' - # this technically need not raise SettingWithCopy if both are view (which is not - # generally guaranteed but is usually True - # however, this is in general not a good practice and we recommend using .loc + # This technically need not raise SettingWithCopy if both are view + # (which is not # generally guaranteed but is usually True. However, + # this is in general not a good practice and we recommend using .loc. df.iloc[0:5]['group'] = 'a' """ @@ -1302,15 +1517,19 @@ def _check_setitem_copy(self, stacklevel=4, t='setting', force=False): "A value is trying to be set on a copy of a slice from a " "DataFrame\n\n" "See the caveats in the documentation: " - "http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy") + "http://pandas.pydata.org/pandas-docs/stable/" + "indexing.html#indexing-view-versus-copy" + ) else: t = ("\n" "A value is trying to be set on a copy of a slice from a " "DataFrame.\n" - "Try using .loc[row_indexer,col_indexer] = value instead\n\n" - "See the caveats in the documentation: " - "http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy") + "Try using .loc[row_indexer,col_indexer] = value " + "instead\n\nSee the caveats in the documentation: " + "http://pandas.pydata.org/pandas-docs/stable/" + "indexing.html#indexing-view-versus-copy" + ) if value == 'raise': raise SettingWithCopyError(t) @@ -1334,7 +1553,7 @@ def __delitem__(self, key): # Allow shorthand to delete all columns whose first len(key) # elements match key: if not isinstance(key, tuple): - key = (key,) + key = (key, ) for col in self.columns: if isinstance(col, tuple) and col[:len(key)] == key: del self[col] @@ -1382,8 +1601,8 @@ def take(self, indices, axis=0, convert=True, is_copy=True): def xs(self, key, axis=0, level=None, copy=None, drop_level=True): """ - Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. - Defaults to cross-section on the rows (axis=0). + Returns a cross-section (row(s) or column(s)) from the + Series/DataFrame. Defaults to cross-section on the rows (axis=0). Parameters ---------- @@ -1446,8 +1665,9 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): ----- xs is only for getting, not setting values. - MultiIndex Slicers is a generic way to get/set values on any level or levels - it is a superset of xs functionality, see :ref:`MultiIndex Slicers <advanced.mi_slicers>` + MultiIndex Slicers is a generic way to get/set values on any level or + levels. It is a superset of xs functionality, see + :ref:`MultiIndex Slicers <advanced.mi_slicers>` """ if copy is not None: @@ -1509,10 +1729,8 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): if not is_list_like(new_values) or self.ndim == 1: return _maybe_box_datetimelike(new_values) - result = Series(new_values, - index=self.columns, - name=self.index[loc], - copy=copy, + result = Series(new_values, index=self.columns, + name=self.index[loc], copy=copy, dtype=new_values.dtype) else: @@ -1555,7 +1773,7 @@ def select(self, crit, axis=0): def reindex_like(self, other, method=None, copy=True, limit=None, tolerance=None): - """ return an object with matching indicies to myself + """Return an object with matching indices to myself. Parameters ---------- @@ -1579,15 +1797,15 @@ def reindex_like(self, other, method=None, copy=True, limit=None, ------- reindexed : same as input """ - d = other._construct_axes_dict(axes=self._AXIS_ORDERS, - method=method, copy=copy, limit=limit, - tolerance=tolerance) + d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method, + copy=copy, limit=limit, + tolerance=tolerance) return self.reindex(**d) def drop(self, labels, axis=0, level=None, inplace=False, errors='raise'): """ - Return new object with labels in requested axis removed + Return new object with labels in requested axis removed. Parameters ---------- @@ -1629,8 +1847,8 @@ def drop(self, labels, axis=0, level=None, inplace=False, errors='raise'): if level is not None: if not isinstance(axis, MultiIndex): raise AssertionError('axis must be a MultiIndex') - indexer = ~lib.ismember(axis.get_level_values(level).values, - set(labels)) + indexer = ~lib.ismember( + axis.get_level_values(level).values, set(labels)) else: indexer = ~axis.isin(labels) @@ -1646,7 +1864,7 @@ def drop(self, labels, axis=0, level=None, inplace=False, errors='raise'): def _update_inplace(self, result, verify_is_copy=True): """ - replace self internals with result. + Replace self internals with result. Parameters ---------- @@ -1659,7 +1877,7 @@ def _update_inplace(self, result, verify_is_copy=True): self._reset_cache() self._clear_item_cache() - self._data = getattr(result,'_data',result) + self._data = getattr(result, '_data', result) self._maybe_update_cacher(verify_is_copy=verify_is_copy) def add_prefix(self, prefix): @@ -1679,7 +1897,7 @@ def add_prefix(self, prefix): def add_suffix(self, suffix): """ - Concatenate suffix string with panel items names + Concatenate suffix string with panel items names. Parameters ---------- @@ -1702,14 +1920,16 @@ def add_suffix(self, suffix): by : string name or list of names which refer to the axis items axis : %(axes)s to direct sorting ascending : bool or list of bool - Sort ascending vs. descending. Specify list for multiple sort orders. - If this is a list of bools, must match the length of the by + Sort ascending vs. descending. Specify list for multiple sort + orders. If this is a list of bools, must match the length of + the by. inplace : bool if True, perform operation in-place kind : {`quicksort`, `mergesort`, `heapsort`} - Choice of sorting algorithm. See also ndarray.np.sort for more information. - `mergesort` is the only stable algorithm. For DataFrames, this option is - only applied when sorting on a single column or label. + Choice of sorting algorithm. See also ndarray.np.sort for more + information. `mergesort` is the only stable algorithm. For + DataFrames, this option is only applied when sorting on a single + column or label. na_position : {'first', 'last'} `first` puts NaNs at the beginning, `last` puts NaNs at the end @@ -1717,6 +1937,7 @@ def add_suffix(self, suffix): ------- sorted_obj : %(klass)s """ + def sort_values(self, by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last'): raise AbstractMethodError(self) @@ -1734,14 +1955,15 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False, inplace : bool if True, perform operation in-place kind : {`quicksort`, `mergesort`, `heapsort`} - Choice of sorting algorithm. See also ndarray.np.sort for more information. - `mergesort` is the only stable algorithm. For DataFrames, this option is - only applied when sorting on a single column or label. + Choice of sorting algorithm. See also ndarray.np.sort for more + information. `mergesort` is the only stable algorithm. For + DataFrames, this option is only applied when sorting on a single + column or label. na_position : {'first', 'last'} `first` puts NaNs at the beginning, `last` puts NaNs at the end sort_remaining : bool - if true and sorting by level and index is multilevel, sort by other levels - too (in order) after sorting by specified level + if true and sorting by level and index is multilevel, sort by other + levels too (in order) after sorting by specified level Returns ------- @@ -1784,7 +2006,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * default: don't fill gaps - * pad / ffill: propagate last valid observation forward to next valid + * pad / ffill: propagate last valid observation forward to next + valid * backfill / bfill: use next valid observation to fill gap * nearest: use nearest valid observations to fill gap copy : boolean, default True @@ -1923,6 +2146,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, ------- reindexed : %(klass)s """ + # TODO: Decide if we care about having different examples for different # kinds @@ -1940,7 +2164,7 @@ def reindex(self, *args, **kwargs): if kwargs: raise TypeError('reindex() got an unexpected keyword ' - 'argument "{0}"'.format(list(kwargs.keys())[0])) + 'argument "{0}"'.format(list(kwargs.keys())[0])) self._consolidate_inplace() @@ -1960,12 +2184,12 @@ def reindex(self, *args, **kwargs): pass # perform the reindex on the axes - return self._reindex_axes(axes, level, limit, tolerance, - method, fill_value, copy).__finalize__(self) + return self._reindex_axes(axes, level, limit, tolerance, method, + fill_value, copy).__finalize__(self) - def _reindex_axes(self, axes, level, limit, tolerance, method, - fill_value, copy): - """ perform the reinxed for all the axes """ + def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, + copy): + """Perform the reindex for all the axes.""" obj = self for a in self._AXIS_ORDERS: labels = axes[a] @@ -1973,30 +2197,29 @@ def _reindex_axes(self, axes, level, limit, tolerance, method, continue ax = self._get_axis(a) - new_index, indexer = ax.reindex( - labels, level=level, limit=limit, tolerance=tolerance, - method=method) + new_index, indexer = ax.reindex(labels, level=level, limit=limit, + tolerance=tolerance, method=method) axis = self._get_axis_number(a) - obj = obj._reindex_with_indexers( - {axis: [new_index, indexer]}, - fill_value=fill_value, copy=copy, allow_dups=False) + obj = obj._reindex_with_indexers({axis: [new_index, indexer]}, + fill_value=fill_value, + copy=copy, allow_dups=False) return obj def _needs_reindex_multi(self, axes, method, level): - """ check if we do need a multi reindex """ + """Check if we do need a multi reindex.""" return ((com._count_not_none(*axes.values()) == self._AXIS_LEN) and method is None and level is None and not self._is_mixed_type) def _reindex_multi(self, axes, copy, fill_value): return NotImplemented - _shared_docs['reindex_axis'] = ( - """Conform input object to new index with optional filling logic, - placing NA/NaN in locations having no value in the previous index. A - new object is produced unless the new index is equivalent to the - current one and copy=False + _shared_docs[ + 'reindex_axis'] = ("""Conform input object to new index with optional + filling logic, placing NA/NaN in locations having no value in the + previous index. A new object is produced unless the new index is + equivalent to the current one and copy=False Parameters ---------- @@ -2007,7 +2230,8 @@ def _reindex_multi(self, axes, copy, fill_value): method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional Method to use for filling holes in reindexed DataFrame: * default: don't fill gaps - * pad / ffill: propagate last valid observation forward to next valid + * pad / ffill: propagate last valid observation forward to next + valid * backfill / bfill: use next valid observation to fill gap * nearest: use nearest valid observations to fill gap copy : boolean, default True @@ -2028,7 +2252,7 @@ def _reindex_multi(self, axes, copy, fill_value): -------- >>> df.reindex_axis(['A', 'B', 'C'], axis=1) - See also + See Also -------- reindex, reindex_like @@ -2047,15 +2271,14 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, method = mis._clean_reindex_fill_method(method) new_index, indexer = axis_values.reindex(labels, method, level, limit=limit) - return self._reindex_with_indexers( - {axis: [new_index, indexer]}, fill_value=fill_value, copy=copy) + return self._reindex_with_indexers({axis: [new_index, indexer]}, + fill_value=fill_value, copy=copy) - def _reindex_with_indexers(self, reindexers, - fill_value=np.nan, copy=False, + def _reindex_with_indexers(self, reindexers, fill_value=np.nan, copy=False, allow_dups=False): - """ allow_dups indicates an internal call here """ + """allow_dups indicates an internal call here """ - # reindex doing multiple operations on different axes if indiciated + # reindex doing multiple operations on different axes if indicated new_data = self._data for axis in sorted(reindexers.keys()): index, indexer = reindexers[axis] @@ -2089,28 +2312,63 @@ def _reindex_axis(self, new_index, fill_method, axis, copy): return self._constructor(new_data).__finalize__(self) def filter(self, items=None, like=None, regex=None, axis=None): + """ - Restrict the info axis to set of items or wildcard + + Subset rows or columns of dataframe according to labels in the index. + + Note that this routine does not filter a dataframe on its + contents. The filter is applied to the labels of the index. + This method is a thin veneer on top of :ref:`DateFrame Select + <DataFrame.select>` Parameters ---------- items : list-like - List of info axis to restrict to (must not all be present) + List of info axis to restrict to (must not all be present) like : string - Keep info axis where "arg in col == True" + Keep info axis where "arg in col == True" regex : string (regular expression) - Keep info axis with re.search(regex, col) == True + Keep info axis with re.search(regex, col) == True axis : int or None - The axis to filter on. By default this is the info axis. The "info - axis" is the axis that is used when indexing with ``[]``. For - example, ``df = DataFrame({'a': [1, 2, 3, 4]]}); df['a']``. So, - the ``DataFrame`` columns are the info axis. + The axis to filter on. + + Examples + -------- + >>> df + one two three + mouse 1 2 3 + rabbit 4 5 6 + + >>> # select columns by name + >>> df.filter(items=['one', 'three']) + one three + mouse 1 3 + rabbit 4 6 + + >>> # select columns by regular expression + >>> df.filter(regex='e$', axis=1) + one three + mouse 1 3 + rabbit 4 6 + + >>> # select rows containing 'bbi' + >>> df.filter(like='bbi', axis=0) + one two three + rabbit 4 5 6 + + Returns + ------- + same type as input object with filtered info axis Notes ----- - Arguments are mutually exclusive, but this is not checked for + The ``items``, ``like``, and ``regex`` parameters should be + mutually exclusive, but this is not checked. - """ + ``axis`` defaults to the info axis that is used when indexing + with ``[]``. +""" import re if axis is None: @@ -2119,11 +2377,11 @@ def filter(self, items=None, like=None, regex=None, axis=None): axis_values = self._get_axis(axis_name) if items is not None: - return self.reindex(**{axis_name: [r for r in items - if r in axis_values]}) + return self.reindex(**{axis_name: + [r for r in items if r in axis_values]}) elif like: - matchf = lambda x: (like in x if isinstance(x, string_types) - else like in str(x)) + matchf = lambda x: (like in x if isinstance(x, string_types) else + like in str(x)) return self.select(matchf, axis=axis_name) elif regex: matcher = re.compile(regex) @@ -2136,22 +2394,18 @@ def head(self, n=5): """ Returns first n rows """ - l = len(self) - if l == 0 or n==0: - return self return self.iloc[:n] def tail(self, n=5): """ Returns last n rows """ - l = len(self) - if l == 0 or n == 0: - return self + if n == 0: + return self.iloc[0:0] return self.iloc[-n:] - - def sample(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None): + def sample(self, n=None, frac=None, replace=False, weights=None, + random_state=None, axis=None): """ Returns a random sample of items from an axis of object. @@ -2256,22 +2510,28 @@ def sample(self, n=None, frac=None, replace=False, weights=None, random_state=No try: weights = self[weights] except KeyError: - raise KeyError("String passed to weights not a valid column") + raise KeyError("String passed to weights not a " + "valid column") else: - raise ValueError("Strings can only be passed to weights when sampling from rows on a DataFrame") + raise ValueError("Strings can only be passed to " + "weights when sampling from rows on " + "a DataFrame") else: - raise ValueError("Strings cannot be passed as weights when sampling from a Series or Panel.") + raise ValueError("Strings cannot be passed as weights " + "when sampling from a Series or Panel.") weights = pd.Series(weights, dtype='float64') if len(weights) != axis_length: - raise ValueError("Weights and axis to be sampled must be of same length") + raise ValueError("Weights and axis to be sampled must be of " + "same length") if (weights == np.inf).any() or (weights == -np.inf).any(): raise ValueError("weight vector may not include `inf` values") if (weights < 0).any(): - raise ValueError("weight vector many not include negative values") + raise ValueError("weight vector many not include negative " + "values") # If has nan, set to zero. weights = weights.fillna(0) @@ -2293,16 +2553,17 @@ def sample(self, n=None, frac=None, replace=False, weights=None, random_state=No elif n is None and frac is not None: n = int(round(frac * axis_length)) elif n is not None and frac is not None: - raise ValueError('Please enter a value for `frac` OR `n`, not both') + raise ValueError('Please enter a value for `frac` OR `n`, not ' + 'both') # Check for negative sizes if n < 0: - raise ValueError("A negative number of rows requested. Please provide positive value.") + raise ValueError("A negative number of rows requested. Please " + "provide positive value.") locs = rs.choice(axis_length, size=n, replace=replace, p=weights) return self.take(locs, axis=axis, is_copy=False) - _shared_docs['pipe'] = (""" Apply func(self, \*args, \*\*kwargs) @@ -2352,26 +2613,26 @@ def sample(self, n=None, frac=None, replace=False, weights=None, random_state=No pandas.DataFrame.apply pandas.DataFrame.applymap pandas.Series.map - """ - ) + """) + @Appender(_shared_docs['pipe'] % _shared_doc_kwargs) def pipe(self, func, *args, **kwargs): if isinstance(func, tuple): func, target = func if target in kwargs: - msg = '%s is both the pipe target and a keyword argument' % target - raise ValueError(msg) + raise ValueError('%s is both the pipe target and a keyword ' + 'argument' % target) kwargs[target] = self return func(*args, **kwargs) else: return func(self, *args, **kwargs) - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Attribute access def __finalize__(self, other, method=None, **kwargs): """ - propagate metadata from other to self + Propagate metadata from other to self. Parameters ---------- @@ -2390,12 +2651,12 @@ def __getattr__(self, name): """After regular attribute access, try looking up the name This allows simpler access to columns for interactive use. """ + # Note: obj.x will always call obj.__getattribute__('x') prior to # calling obj.__getattr__('x'). - if (name in self._internal_names_set - or name in self._metadata - or name in self._accessors): + if (name in self._internal_names_set or name in self._metadata or + name in self._accessors): return object.__getattribute__(self, name) else: if name in self._info_axis: @@ -2404,7 +2665,9 @@ def __getattr__(self, name): def __setattr__(self, name, value): """After regular attribute access, try setting the name - This allows simpler access to columns for interactive use.""" + This allows simpler access to columns for interactive use. + """ + # first try regular attribute access via __getattribute__, so that # e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify # the same attribute. @@ -2433,14 +2696,16 @@ def __setattr__(self, name, value): except (AttributeError, TypeError): object.__setattr__(self, name, value) - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Getting and setting elements - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Consolidation of internals def _protect_consolidate(self, f): - """ consolidate _data. if the blocks have changed, then clear the cache """ + """Consolidate _data -- if the blocks have changed, then clear the + cache + """ blocks_before = len(self._data.blocks) result = f() if len(self._data.blocks) != blocks_before: @@ -2448,9 +2713,11 @@ def _protect_consolidate(self, f): return result def _consolidate_inplace(self): - """ we are inplace consolidating; return None """ + """Consolidate data in place and return None""" + def f(): self._data = self._data.consolidate() + self._protect_consolidate(f) def consolidate(self, inplace=False): @@ -2503,8 +2770,8 @@ def _check_inplace_setting(self, value): except: pass - raise TypeError( - 'Cannot do inplace boolean setting on mixed-types with a non np.nan value') + raise TypeError('Cannot do inplace boolean setting on ' + 'mixed-types with a non np.nan value') return True @@ -2515,7 +2782,7 @@ def _get_numeric_data(self): def _get_bool_data(self): return self._constructor(self._data.get_bool_data()).__finalize__(self) - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Internal Interface Methods def as_matrix(self, columns=None): @@ -2578,7 +2845,7 @@ def values(self): @property def _values(self): - """ internal implementation """ + """internal implementation""" return self.values @property @@ -2587,22 +2854,22 @@ def _get_values(self): return self.as_matrix() def get_values(self): - """ same as values (but handles sparseness conversions) """ + """same as values (but handles sparseness conversions)""" return self.as_matrix() def get_dtype_counts(self): - """ Return the counts of dtypes in this object """ + """Return the counts of dtypes in this object.""" from pandas import Series return Series(self._data.get_dtype_counts()) def get_ftype_counts(self): - """ Return the counts of ftypes in this object """ + """Return the counts of ftypes in this object.""" from pandas import Series return Series(self._data.get_ftype_counts()) @property def dtypes(self): - """ Return the dtypes in this object """ + """Return the dtypes in this object.""" from pandas import Series return Series(self._data.get_dtypes(), index=self._info_axis, dtype=np.object_) @@ -2652,7 +2919,7 @@ def as_blocks(self, copy=True): @property def blocks(self): - "Internal property, property synonym for as_blocks()" + """Internal property, property synonym for as_blocks()""" return self.as_blocks() def astype(self, dtype, copy=True, raise_on_error=True, **kwargs): @@ -2671,8 +2938,8 @@ def astype(self, dtype, copy=True, raise_on_error=True, **kwargs): casted : type of caller """ - mgr = self._data.astype( - dtype=dtype, copy=copy, raise_on_error=raise_on_error, **kwargs) + mgr = self._data.astype(dtype=dtype, copy=copy, + raise_on_error=raise_on_error, **kwargs) return self._constructor(mgr).__finalize__(self) def copy(self, deep=True): @@ -2718,16 +2985,16 @@ def _convert(self, datetime=False, numeric=False, timedelta=False, converted : same as input object """ return self._constructor( - self._data.convert(datetime=datetime, - numeric=numeric, - timedelta=timedelta, - coerce=coerce, - copy=copy)).__finalize__(self) + self._data.convert(datetime=datetime, numeric=numeric, + timedelta=timedelta, coerce=coerce, + copy=copy)).__finalize__(self) # TODO: Remove in 0.18 or 2017, which ever is sooner def convert_objects(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True): """ + Deprecated. + Attempt to infer better dtype for object columns Parameters @@ -2746,6 +3013,13 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, conversion was done). Note: This is meant for internal use, and should not be confused with inplace. + See Also + -------- + pandas.to_datetime : Convert argument to datetime. + pandas.to_timedelta : Convert argument to timedelta. + pandas.to_numeric : Return a fixed frequency timedelta index, + with day as the default. + Returns ------- converted : same as input object @@ -2761,20 +3035,20 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, convert_timedeltas=convert_timedeltas, copy=copy)).__finalize__(self) - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Filling NA's - _shared_docs['fillna'] = ( - """ + _shared_docs['fillna'] = (""" Fill NA/NaN values using the specified method Parameters ---------- value : scalar, dict, Series, or DataFrame - Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of - values specifying which value to use for each index (for a Series) or - column (for a DataFrame). (values not in the dict/Series/DataFrame will not be - filled). This value cannot be a list. + Value to use to fill holes (e.g. 0), alternately a + dict/Series/DataFrame of values specifying which value to use for + each index (for a Series) or column (for a DataFrame). (values not + in the dict/Series/DataFrame will not be filled). This value cannot + be a list. method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid @@ -2796,15 +3070,14 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible) - See also + See Also -------- reindex, asfreq Returns ------- filled : %(klass)s - """ - ) + """) @Appender(_shared_docs['fillna'] % _shared_doc_kwargs) def fillna(self, value=None, method=None, axis=None, inplace=False, @@ -2837,9 +3110,8 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, # > 3d if self.ndim > 3: - raise NotImplementedError( - 'Cannot fillna with a method for > 3dims' - ) + raise NotImplementedError('Cannot fillna with a method for > ' + '3dims') # 3d elif self.ndim == 3: @@ -2851,12 +3123,9 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, # 2d or less method = mis._clean_fill_method(method) - new_data = self._data.interpolate(method=method, - axis=axis, - limit=limit, - inplace=inplace, - coerce=True, - downcast=downcast) + new_data = self._data.interpolate(method=method, axis=axis, + limit=limit, inplace=inplace, + coerce=True, downcast=downcast) else: if method is not None: raise ValueError('cannot specify both a fill method and value') @@ -2871,10 +3140,10 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, elif not com.is_list_like(value): pass else: - raise ValueError("invalid fill value with a %s" % type(value)) + raise ValueError("invalid fill value with a %s" % + type(value)) - new_data = self._data.fillna(value=value, - limit=limit, + new_data = self._data.fillna(value=value, limit=limit, inplace=inplace, downcast=downcast) @@ -2892,8 +3161,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, obj.fillna(v, limit=limit, inplace=True) return result elif not com.is_list_like(value): - new_data = self._data.fillna(value=value, - limit=limit, + new_data = self._data.fillna(value=value, limit=limit, inplace=inplace, downcast=downcast) elif isinstance(value, DataFrame) and self.ndim == 2: @@ -2907,12 +3175,12 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, return self._constructor(new_data).__finalize__(self) def ffill(self, axis=None, inplace=False, limit=None, downcast=None): - "Synonym for NDFrame.fillna(method='ffill')" + """Synonym for NDFrame.fillna(method='ffill')""" return self.fillna(method='ffill', axis=axis, inplace=inplace, limit=limit, downcast=downcast) def bfill(self, axis=None, inplace=False, limit=None, downcast=None): - "Synonym for NDFrame.fillna(method='bfill')" + """Synonym for NDFrame.fillna(method='bfill')""" return self.fillna(method='bfill', axis=axis, inplace=inplace, limit=limit, downcast=downcast) @@ -2982,7 +3250,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, The method to use when for replacement, when ``to_replace`` is a ``list``. - See also + See Also -------- NDFrame.reindex NDFrame.asfreq @@ -3089,8 +3357,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, if c in value and c in self: res[c] = res[c].replace(to_replace=src, value=value[c], - inplace=False, - regex=regex) + inplace=False, regex=regex) return None if inplace else res # {'A': NA} -> 0 @@ -3120,13 +3387,11 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, else: # [NA, ''] -> 0 new_data = self._data.replace(to_replace=to_replace, - value=value, - inplace=inplace, + value=value, inplace=inplace, regex=regex) elif to_replace is None: if not (com.is_re_compilable(regex) or - com.is_list_like(regex) or - is_dictlike(regex)): + com.is_list_like(regex) or is_dictlike(regex)): raise TypeError("'regex' must be a string or a compiled " "regular expression or a list or dict of " "strings or regular expressions, you " @@ -3143,14 +3408,14 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, for k, v in compat.iteritems(value): if k in self: new_data = new_data.replace(to_replace=to_replace, - value=v, - filter=[k], + value=v, filter=[k], inplace=inplace, regex=regex) elif not com.is_list_like(value): # NA -> 0 - new_data = self._data.replace(to_replace=to_replace, value=value, - inplace=inplace, regex=regex) + new_data = self._data.replace(to_replace=to_replace, + value=value, inplace=inplace, + regex=regex) else: msg = ('Invalid "to_replace" type: ' '{0!r}').format(type(to_replace).__name__) @@ -3166,8 +3431,8 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, """ Interpolate values according to different methods. - Please note that only ``method='linear'`` is supported for DataFrames/Series - with a MultiIndex. + Please note that only ``method='linear'`` is supported for + DataFrames/Series with a MultiIndex. Parameters ---------- @@ -3191,8 +3456,8 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, wrappers around the scipy interpolation methods of similar names. These use the actual numerical values of the index. See the scipy documentation for more on their behavior - `here <http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ - `and here <http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__ + `here <http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ # noqa + `and here <http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__ # noqa axis : {0, 1}, default 0 * 0: fill column-by-column @@ -3252,16 +3517,19 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, else: alt_ax = ax - if isinstance(_maybe_transposed_self.index, MultiIndex) and method != 'linear': + if (isinstance(_maybe_transposed_self.index, MultiIndex) and + method != 'linear'): raise ValueError("Only `method=linear` interpolation is supported " "on MultiIndexes.") - if _maybe_transposed_self._data.get_dtype_counts().get('object') == len(_maybe_transposed_self.T): + if _maybe_transposed_self._data.get_dtype_counts().get( + 'object') == len(_maybe_transposed_self.T): raise TypeError("Cannot interpolate with all NaNs.") # create/use the index if method == 'linear': - index = np.arange(len(_maybe_transposed_self._get_axis(alt_ax))) # prior default + # prior default + index = np.arange(len(_maybe_transposed_self._get_axis(alt_ax))) else: index = _maybe_transposed_self._get_axis(alt_ax) @@ -3269,17 +3537,13 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, raise NotImplementedError("Interpolation with NaNs in the index " "has not been implemented. Try filling " "those NaNs before interpolating.") - new_data = _maybe_transposed_self._data.interpolate( - method=method, - axis=ax, - index=index, - values=_maybe_transposed_self, - limit=limit, - limit_direction=limit_direction, - inplace=inplace, - downcast=downcast, - **kwargs - ) + data = _maybe_transposed_self._data + new_data = data.interpolate(method=method, axis=ax, index=index, + values=_maybe_transposed_self, limit=limit, + limit_direction=limit_direction, + inplace=inplace, downcast=downcast, + **kwargs) + if inplace: if axis == 1: new_data = self._constructor(new_data).T._data @@ -3290,14 +3554,14 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, res = res.T return res - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Action Methods def isnull(self): """ - Return a boolean same-sized object indicating if the values are null + Return a boolean same-sized object indicating if the values are null. - See also + See Also -------- notnull : boolean inverse of isnull """ @@ -3305,9 +3569,9 @@ def isnull(self): def notnull(self): """Return a boolean same-sized object indicating if the values are - not null + not null. - See also + See Also -------- isnull : boolean inverse of notnull """ @@ -3315,7 +3579,7 @@ def notnull(self): def clip(self, lower=None, upper=None, out=None, axis=None): """ - Trim values at input threshold(s) + Trim values at input threshold(s). Parameters ---------- @@ -3377,7 +3641,7 @@ def clip(self, lower=None, upper=None, out=None, axis=None): def clip_upper(self, threshold, axis=None): """ - Return copy of input with values above given value(s) truncated + Return copy of input with values above given value(s) truncated. Parameters ---------- @@ -3385,7 +3649,7 @@ def clip_upper(self, threshold, axis=None): axis : int or string axis name, optional Align object with threshold along the given axis. - See also + See Also -------- clip @@ -3401,7 +3665,7 @@ def clip_upper(self, threshold, axis=None): def clip_lower(self, threshold, axis=None): """ - Return copy of the input with values below given value(s) truncated + Return copy of the input with values below given value(s) truncated. Parameters ---------- @@ -3409,7 +3673,7 @@ def clip_lower(self, threshold, axis=None): axis : int or string axis name, optional Align object with threshold along the given axis. - See also + See Also -------- clip @@ -3427,7 +3691,7 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False): """ Group series using mapper (dict or key function, apply given function - to group, return result as series) or by a series of columns + to group, return result as series) or by a series of columns. Parameters ---------- @@ -3446,8 +3710,8 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, effectively "SQL-style" grouped output sort : boolean, default True Sort group keys. Get better performance by turning this off. - Note this does not influence the order of observations within each group. - groupby preserves the order of rows within each group. + Note this does not influence the order of observations within each + group. groupby preserves the order of rows within each group. group_keys : boolean, default True When calling apply, add group keys to index to identify pieces squeeze : boolean, default False @@ -3500,12 +3764,11 @@ def asfreq(self, freq, method=None, how=None, normalize=False): converted : type of caller """ from pandas.tseries.resample import asfreq - return asfreq(self, freq, method=method, how=how, - normalize=normalize) + return asfreq(self, freq, method=method, how=how, normalize=normalize) def at_time(self, time, asof=False): """ - Select values at particular time of day (e.g. 9:30AM) + Select values at particular time of day (e.g. 9:30AM). Parameters ---------- @@ -3524,7 +3787,7 @@ def at_time(self, time, asof=False): def between_time(self, start_time, end_time, include_start=True, include_end=True): """ - Select values between particular times of the day (e.g., 9:00-9:30 AM) + Select values between particular times of the day (e.g., 9:00-9:30 AM). Parameters ---------- @@ -3545,9 +3808,9 @@ def between_time(self, start_time, end_time, include_start=True, except AttributeError: raise TypeError('Index must be DatetimeIndex') - def resample(self, rule, how=None, axis=0, fill_method=None, - closed=None, label=None, convention='start', - kind=None, loffset=None, limit=None, base=0): + def resample(self, rule, how=None, axis=0, fill_method=None, closed=None, + label=None, convention='start', kind=None, loffset=None, + limit=None, base=0): """ Convenience method for frequency conversion and resampling of regular time-series data. @@ -3556,22 +3819,14 @@ def resample(self, rule, how=None, axis=0, fill_method=None, ---------- rule : string the offset string or object representing target conversion - how : string - method for down- or re-sampling, default to 'mean' for - downsampling axis : int, optional, default 0 - fill_method : string, default None - fill_method for upsampling closed : {'right', 'left'} Which side of bin interval is closed label : {'right', 'left'} Which bin edge label to label bucket with convention : {'start', 'end', 's', 'e'} - kind : "period"/"timestamp" loffset : timedelta Adjust the resampled time labels - limit : int, default None - Maximum size gap to when reindexing with fill_method base : int, default 0 For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for '5min' frequency, base could @@ -3600,7 +3855,7 @@ def resample(self, rule, how=None, axis=0, fill_method=None, Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin. - >>> series.resample('3T', how='sum') + >>> series.resample('3T').sum() 2000-01-01 00:00:00 3 2000-01-01 00:03:00 12 2000-01-01 00:06:00 21 @@ -3616,7 +3871,7 @@ def resample(self, rule, how=None, axis=0, fill_method=None, To include this value close the right side of the bin interval as illustrated in the example below this one. - >>> series.resample('3T', how='sum', label='right') + >>> series.resample('3T', label='right').sum() 2000-01-01 00:03:00 3 2000-01-01 00:06:00 12 2000-01-01 00:09:00 21 @@ -3625,7 +3880,7 @@ def resample(self, rule, how=None, axis=0, fill_method=None, Downsample the series into 3 minute bins as above, but close the right side of the bin interval. - >>> series.resample('3T', how='sum', label='right', closed='right') + >>> series.resample('3T', label='right', closed='right').sum() 2000-01-01 00:00:00 0 2000-01-01 00:03:00 6 2000-01-01 00:06:00 15 @@ -3634,7 +3889,7 @@ def resample(self, rule, how=None, axis=0, fill_method=None, Upsample the series into 30 second bins. - >>> series.resample('30S')[0:5] #select first 5 rows + >>> series.resample('30S').asfreq()[0:5] #select first 5 rows 2000-01-01 00:00:00 0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 1 @@ -3645,7 +3900,7 @@ def resample(self, rule, how=None, axis=0, fill_method=None, Upsample the series into 30 second bins and fill the ``NaN`` values using the ``pad`` method. - >>> series.resample('30S', fill_method='pad')[0:5] + >>> series.resample('30S').pad()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 0 2000-01-01 00:01:00 1 @@ -3656,7 +3911,7 @@ def resample(self, rule, how=None, axis=0, fill_method=None, Upsample the series into 30 second bins and fill the ``NaN`` values using the ``bfill`` method. - >>> series.resample('30S', fill_method='bfill')[0:5] + >>> series.resample('30S').bfill()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 1 2000-01-01 00:01:00 1 @@ -3664,31 +3919,74 @@ def resample(self, rule, how=None, axis=0, fill_method=None, 2000-01-01 00:02:00 2 Freq: 30S, dtype: int64 - Pass a custom function to ``how``. + Pass a custom function via ``apply`` >>> def custom_resampler(array_like): ... return np.sum(array_like)+5 - >>> series.resample('3T', how=custom_resampler) + >>> series.resample('3T').apply(custom_resampler) 2000-01-01 00:00:00 8 2000-01-01 00:03:00 17 2000-01-01 00:06:00 26 Freq: 3T, dtype: int64 """ + from pandas.tseries.resample import resample - from pandas.tseries.resample import TimeGrouper axis = self._get_axis_number(axis) - sampler = TimeGrouper(rule, label=label, closed=closed, how=how, - axis=axis, kind=kind, loffset=loffset, - fill_method=fill_method, convention=convention, - limit=limit, base=base) - return sampler.resample(self).__finalize__(self) + r = resample(self, freq=rule, label=label, closed=closed, + axis=axis, kind=kind, loffset=loffset, + fill_method=fill_method, convention=convention, + limit=limit, base=base) + + # deprecation warnings + # but call methods anyhow + + if how is not None: + + # .resample(..., how='sum') + if isinstance(how, compat.string_types): + method = "{0}()".format(how) + + # .resample(..., how=lambda x: ....) + else: + method = ".apply(<func>)" + + # if we have both a how and fill_method, then show + # the following warning + if fill_method is None: + warnings.warn("how in .resample() is deprecated\n" + "the new syntax is " + ".resample(...).{method}".format( + method=method), + FutureWarning, stacklevel=2) + r = r.aggregate(how) + + if fill_method is not None: + + # show the prior function call + method = '.' + method if how is not None else '' + + args = "limit={0}".format(limit) if limit is not None else "" + warnings.warn("fill_method is deprecated to .resample()\n" + "the new syntax is .resample(...){method}" + ".{fill_method}({args})".format( + method=method, + fill_method=fill_method, + args=args), + FutureWarning, stacklevel=2) + + if how is not None: + r = getattr(r, fill_method)(limit=limit) + else: + r = r.aggregate(fill_method, limit=limit) + + return r def first(self, offset): """ Convenience method for subsetting initial periods of time series data - based on a date offset + based on a date offset. Parameters ---------- @@ -3696,7 +3994,7 @@ def first(self, offset): Examples -------- - ts.last('10D') -> First 10 days + ts.first('10D') -> First 10 days Returns ------- @@ -3723,7 +4021,7 @@ def first(self, offset): def last(self, offset): """ Convenience method for subsetting final periods of time series data - based on a date offset + based on a date offset. Parameters ---------- @@ -3751,8 +4049,67 @@ def last(self, offset): start = self.index.searchsorted(start_date, side='right') return self.ix[start:] - _shared_docs['align'] = ( + def rank(self, axis=0, method='average', numeric_only=None, + na_option='keep', ascending=True, pct=False): """ + Compute numerical data ranks (1 through n) along axis. Equal values are + assigned a rank that is the average of the ranks of those values + + Parameters + ---------- + axis: {0 or 'index', 1 or 'columns'}, default 0 + index to direct ranking + method : {'average', 'min', 'max', 'first', 'dense'} + * average: average rank of group + * min: lowest rank in group + * max: highest rank in group + * first: ranks assigned in order they appear in the array + * dense: like 'min', but rank always increases by 1 between groups + numeric_only : boolean, default None + Include only float, int, boolean data. Valid only for DataFrame or + Panel objects + na_option : {'keep', 'top', 'bottom'} + * keep: leave NA values where they are + * top: smallest rank if ascending + * bottom: smallest rank if descending + ascending : boolean, default True + False for ranks by high (1) to low (N) + pct : boolean, default False + Computes percentage rank of data + + Returns + ------- + ranks : same type as caller + """ + axis = self._get_axis_number(axis) + + if self.ndim > 2: + msg = "rank does not make sense when ndim > 2" + raise NotImplementedError(msg) + + def ranker(data): + ranks = algos.rank(data.values, axis=axis, method=method, + ascending=ascending, na_option=na_option, + pct=pct) + ranks = self._constructor(ranks, **data._construct_axes_dict()) + return ranks.__finalize__(self) + + # if numeric_only is None, and we can't get anything, we try with + # numeric_only=True + if numeric_only is None: + try: + return ranker(self) + except TypeError: + numeric_only = True + + if numeric_only: + data = self._get_numeric_data() + else: + data = self + + return ranker(data) + + _shared_docs['align'] = (""" Align two object on their axes with the specified join method for each axis Index @@ -3785,8 +4142,7 @@ def last(self, offset): ------- (left, right) : (%(klass)s, type of other) Aligned objects - """ - ) + """) @Appender(_shared_docs['align'] % _shared_doc_kwargs) def align(self, other, join='outer', axis=None, level=None, copy=True, @@ -3797,15 +4153,18 @@ def align(self, other, join='outer', axis=None, level=None, copy=True, if broadcast_axis == 1 and self.ndim != other.ndim: if isinstance(self, Series): - # this means other is a DataFrame, and we need to broadcast self - df = DataFrame(dict((c, self) for c in other.columns), - **other._construct_axes_dict()) - return df._align_frame(other, join=join, axis=axis, level=level, - copy=copy, fill_value=fill_value, - method=method, limit=limit, - fill_axis=fill_axis) + # this means other is a DataFrame, and we need to broadcast + # self + df = DataFrame( + dict((c, self) for c in other.columns), + **other._construct_axes_dict()) + return df._align_frame(other, join=join, axis=axis, + level=level, copy=copy, + fill_value=fill_value, method=method, + limit=limit, fill_axis=fill_axis) elif isinstance(other, Series): - # this means self is a DataFrame, and we need to broadcast other + # this means self is a DataFrame, and we need to broadcast + # other df = DataFrame(dict((c, other) for c in self.columns), **self._construct_axes_dict()) return self._align_frame(df, join=join, axis=axis, level=level, @@ -3838,15 +4197,13 @@ def _align_frame(self, other, join='outer', axis=None, level=None, if axis is None or axis == 0: if not self.index.equals(other.index): - join_index, ilidx, iridx = \ - self.index.join(other.index, how=join, level=level, - return_indexers=True) + join_index, ilidx, iridx = self.index.join( + other.index, how=join, level=level, return_indexers=True) if axis is None or axis == 1: if not self.columns.equals(other.columns): - join_columns, clidx, cridx = \ - self.columns.join(other.columns, how=join, level=level, - return_indexers=True) + join_columns, clidx, cridx = self.columns.join( + other.columns, how=join, level=level, return_indexers=True) left = self._reindex_with_indexers({0: [join_index, ilidx], 1: [join_columns, clidx]}, @@ -3875,7 +4232,7 @@ def _align_series(self, other, join='outer', axis=None, level=None, 'axis 0') # equal - if self.index.equals(other.index): + if self.index.equals(other.index): join_index, lidx, ridx = None, None, None else: join_index, lidx, ridx = self.index.join(other.index, how=join, @@ -3892,9 +4249,9 @@ def _align_series(self, other, join='outer', axis=None, level=None, join_index = self.index lidx, ridx = None, None if not self.index.equals(other.index): - join_index, lidx, ridx = \ - self.index.join(other.index, how=join, level=level, - return_indexers=True) + join_index, lidx, ridx = self.index.join( + other.index, how=join, level=level, + return_indexers=True) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=1) @@ -3903,9 +4260,9 @@ def _align_series(self, other, join='outer', axis=None, level=None, join_index = self.columns lidx, ridx = None, None if not self.columns.equals(other.index): - join_index, lidx, ridx = \ - self.columns.join(other.index, how=join, level=level, - return_indexers=True) + join_index, lidx, ridx = self.columns.join( + other.index, how=join, level=level, + return_indexers=True) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=0) @@ -3925,13 +4282,15 @@ def _align_series(self, other, join='outer', axis=None, level=None, # fill fill_na = notnull(fill_value) or (method is not None) if fill_na: - left = left.fillna(fill_value, method=method, limit=limit, axis=fill_axis) + left = left.fillna(fill_value, method=method, limit=limit, + axis=fill_axis) right = right.fillna(fill_value, method=method, limit=limit) - return (left.__finalize__(self), right.__finalize__(other)) + return left.__finalize__(self), right.__finalize__(other) _shared_docs['where'] = (""" Return an object of same shape as self and whose corresponding - entries are from self where cond is %(cond)s and otherwise are from other. + entries are from self where cond is %(cond)s and otherwise are from + other. Parameters ---------- @@ -3951,6 +4310,7 @@ def _align_series(self, other, join='outer', axis=None, level=None, ------- wh : same type as caller """) + @Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="True")) def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True): @@ -3962,8 +4322,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, raise ValueError('where requires an ndarray like object for ' 'its condition') if cond.shape != self.shape: - raise ValueError( - 'Array conditional must be same shape as self') + raise ValueError('Array conditional must be same shape as ' + 'self') cond = self._constructor(cond, **self._construct_axes_dict()) if inplace: @@ -3978,9 +4338,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, # align with me if other.ndim <= self.ndim: - _, other = self.align(other, join='left', - axis=axis, level=level, - fill_value=np.nan) + _, other = self.align(other, join='left', axis=axis, + level=level, fill_value=np.nan) # if we are NOT aligned, raise as we cannot where index if (axis is None and @@ -3990,9 +4349,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, # slice me out of the other else: - raise NotImplemented( - "cannot align with a higher dimensional NDFrame" - ) + raise NotImplemented("cannot align with a higher dimensional " + "NDFrame") elif is_list_like(other): @@ -4022,7 +4380,9 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, other = np.array(other) else: other = np.asarray(other) - other = np.asarray(other, dtype=np.common_type(other, new_other)) + other = np.asarray(other, + dtype=np.common_type(other, + new_other)) # we need to use the new dtype try_quick = False @@ -4070,8 +4430,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, other = new_other else: - raise ValueError( - 'Length of replacements must equal series length') + raise ValueError('Length of replacements must equal ' + 'series length') else: raise ValueError('other must be the same shape as self ' @@ -4113,7 +4473,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True): return self.where(~cond, other=other, inplace=inplace, axis=axis, - level=level, try_cast=try_cast, raise_on_error=raise_on_error) + level=level, try_cast=try_cast, + raise_on_error=raise_on_error) _shared_docs['shift'] = (""" Shift index by desired number of periods with an optional time freq @@ -4137,6 +4498,7 @@ def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None, ------- shifted : %(klass)s """) + @Appender(_shared_docs['shift'] % _shared_doc_kwargs) def shift(self, periods=1, freq=None, axis=0): if periods == 0: @@ -4188,7 +4550,7 @@ def slice_shift(self, periods=1, axis=0): def tshift(self, periods=1, freq=None, axis=0): """ - Shift the time index, using the index's frequency if available + Shift the time index, using the index's frequency if available. Parameters ---------- @@ -4321,10 +4683,10 @@ def _tz_convert(ax, tz): if not hasattr(ax, 'tz_convert'): if len(ax) > 0: ax_name = self._get_axis_name(axis) - raise TypeError('%s is not a valid DatetimeIndex or PeriodIndex' % - ax_name) + raise TypeError('%s is not a valid DatetimeIndex or ' + 'PeriodIndex' % ax_name) else: - ax = DatetimeIndex([],tz=tz) + ax = DatetimeIndex([], tz=tz) else: ax = ax.tz_convert(tz) return ax @@ -4338,18 +4700,19 @@ def _tz_convert(ax, tz): else: if level not in (None, 0, ax.name): raise ValueError("The level {0} is not valid".format(level)) - ax = _tz_convert(ax, tz) + ax = _tz_convert(ax, tz) result = self._constructor(self._data, copy=copy) - result.set_axis(axis,ax) + result.set_axis(axis, ax) return result.__finalize__(self) @deprecate_kwarg(old_arg_name='infer_dst', new_arg_name='ambiguous', - mapping={True: 'infer', False: 'raise'}) + mapping={True: 'infer', + False: 'raise'}) def tz_localize(self, tz, axis=0, level=None, copy=True, ambiguous='raise'): """ - Localize tz-naive TimeSeries to target time zone + Localize tz-naive TimeSeries to target time zone. Parameters ---------- @@ -4361,11 +4724,14 @@ def tz_localize(self, tz, axis=0, level=None, copy=True, copy : boolean, default True Also make a copy of the underlying data ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' - - 'infer' will attempt to infer fall dst-transition hours based on order + - 'infer' will attempt to infer fall dst-transition hours based on + order - bool-ndarray where True signifies a DST time, False designates - a non-DST time (note that this flag is only applicable for ambiguous times) + a non-DST time (note that this flag is only applicable for + ambiguous times) - 'NaT' will return NaT where there are ambiguous times - - 'raise' will raise an AmbiguousTimeError if there are ambiguous times + - 'raise' will raise an AmbiguousTimeError if there are ambiguous + times infer_dst : boolean, default False (DEPRECATED) Attempt to infer fall dst-transition hours based on order @@ -4384,10 +4750,10 @@ def _tz_localize(ax, tz, ambiguous): if not hasattr(ax, 'tz_localize'): if len(ax) > 0: ax_name = self._get_axis_name(axis) - raise TypeError('%s is not a valid DatetimeIndex or PeriodIndex' % - ax_name) + raise TypeError('%s is not a valid DatetimeIndex or ' + 'PeriodIndex' % ax_name) else: - ax = DatetimeIndex([],tz=tz) + ax = DatetimeIndex([], tz=tz) else: ax = ax.tz_localize(tz, ambiguous=ambiguous) return ax @@ -4401,18 +4767,18 @@ def _tz_localize(ax, tz, ambiguous): else: if level not in (None, 0, ax.name): raise ValueError("The level {0} is not valid".format(level)) - ax = _tz_localize(ax, tz, ambiguous) + ax = _tz_localize(ax, tz, ambiguous) result = self._constructor(self._data, copy=copy) - result.set_axis(axis,ax) + result.set_axis(axis, ax) return result.__finalize__(self) - #---------------------------------------------------------------------- + # ---------------------------------------------------------------------- # Numeric Methods def abs(self): """ - Return an object with absolute value taken. Only applicable to objects - that are all numeric + Return an object with absolute value taken--only applicable to objects + that are all numeric. Returns ------- @@ -4432,8 +4798,8 @@ def abs(self): include, exclude : list-like, 'all', or None (default) Specify the form of the returned result. Either: - - None to both (default). The result will include only numeric-typed - columns or, if none are, only categorical columns. + - None to both (default). The result will include only + numeric-typed columns or, if none are, only categorical columns. - A list of dtypes or strings to be included/excluded. To select all numeric types use numpy numpy.number. To select categorical objects use type object. See also the select_dtypes @@ -4467,13 +4833,13 @@ def abs(self): The include, exclude arguments are ignored for Series. - See also + See Also -------- DataFrame.select_dtypes """ @Appender(_shared_docs['describe'] % _shared_doc_kwargs) - def describe(self, percentiles=None, include=None, exclude=None ): + def describe(self, percentiles=None, include=None, exclude=None): if self.ndim >= 3: msg = "describe is not implemented on on Panel or PanelND objects." raise NotImplementedError(msg) @@ -4500,20 +4866,20 @@ def pretty_name(x): def describe_numeric_1d(series, percentiles): stat_index = (['count', 'mean', 'std', 'min'] + - [pretty_name(x) for x in percentiles] + ['max']) + [pretty_name(x) for x in percentiles] + ['max']) d = ([series.count(), series.mean(), series.std(), series.min()] + [series.quantile(x) for x in percentiles] + [series.max()]) return pd.Series(d, index=stat_index, name=series.name) - def describe_categorical_1d(data): names = ['count', 'unique'] objcounts = data.value_counts() - result = [data.count(), len(objcounts[objcounts!=0])] + result = [data.count(), len(objcounts[objcounts != 0])] if result[1] > 0: top, freq = objcounts.index[0], objcounts.iloc[0] - if data.dtype == object or com.is_categorical_dtype(data.dtype): + if (data.dtype == object or + com.is_categorical_dtype(data.dtype)): names += ['top', 'freq'] result += [top, freq] @@ -4563,7 +4929,7 @@ def describe_1d(data, percentiles): return d def _check_percentile(self, q): - """ Validate percentiles. Used by describe and quantile """ + """Validate percentiles (used by describe and quantile).""" msg = ("percentiles should all be in the interval [0, 1]. " "Try {0} instead.") @@ -4612,8 +4978,8 @@ def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, else: data = self.fillna(method=fill_method, limit=limit, axis=axis) - rs = (data.div(data.shift(periods=periods, freq=freq, - axis=axis, **kwargs)) - 1) + rs = (data.div(data.shift(periods=periods, freq=freq, axis=axis, + **kwargs)) - 1) if freq is None: mask = com.isnull(_values_from_object(self)) np.putmask(rs.values, mask, np.nan) @@ -4630,7 +4996,7 @@ def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs): @classmethod def _add_numeric_operations(cls): - """ add the operations to the cls; evaluate the doc strings again """ + """Add the operations to the cls; evaluate the doc strings again""" axis_descr, name, name2 = _doc_parms(cls) @@ -4646,11 +5012,9 @@ def _add_numeric_operations(cls): @Substitution(outname='mad', desc="Return the mean absolute deviation of the values " "for the requested axis", - name1=name, - name2=name2, - axis_descr=axis_descr) + name1=name, name2=name2, axis_descr=axis_descr) @Appender(_num_doc) - def mad(self, axis=None, skipna=None, level=None): + def mad(self, axis=None, skipna=None, level=None): if skipna is None: skipna = True if axis is None: @@ -4665,58 +5029,51 @@ def mad(self, axis=None, skipna=None, level=None): else: demeaned = data.sub(data.mean(axis=1), axis=0) return np.abs(demeaned).mean(axis=axis, skipna=skipna) + cls.mad = mad cls.sem = _make_stat_function_ddof( 'sem', name, name2, axis_descr, - "Return unbiased standard error of the mean over " - "requested axis.\n\nNormalized by N-1 by default. " - "This can be changed using the ddof argument", + "Return unbiased standard error of the mean over requested " + "axis.\n\nNormalized by N-1 by default. This can be changed " + "using the ddof argument", nanops.nansem) cls.var = _make_stat_function_ddof( 'var', name, name2, axis_descr, - "Return unbiased variance over requested " - "axis.\n\nNormalized by N-1 by default. " - "This can be changed using the ddof argument", + "Return unbiased variance over requested axis.\n\nNormalized by " + "N-1 by default. This can be changed using the ddof argument", nanops.nanvar) cls.std = _make_stat_function_ddof( 'std', name, name2, axis_descr, - "Return unbiased standard deviation over requested " - "axis.\n\nNormalized by N-1 by default. " - "This can be changed using the ddof argument", + "Return sample standard deviation over requested axis." + "\n\nNormalized by N-1 by default. This can be changed using the " + "ddof argument", nanops.nanstd) @Substitution(outname='compounded', desc="Return the compound percentage of the values for " - "the requested axis", - name1=name, - name2=name2, + "the requested axis", name1=name, name2=name2, axis_descr=axis_descr) @Appender(_num_doc) def compound(self, axis=None, skipna=None, level=None): if skipna is None: skipna = True return (1 + self).prod(axis=axis, skipna=skipna, level=level) - 1 + cls.compound = compound cls.cummin = _make_cum_function( - 'min', name, name2, axis_descr, - "cumulative minimum", - lambda y, axis: np.minimum.accumulate(y, axis), - np.inf, np.nan) + 'min', name, name2, axis_descr, "cumulative minimum", + lambda y, axis: np.minimum.accumulate(y, axis), np.inf, np.nan) cls.cumsum = _make_cum_function( - 'sum', name, name2, axis_descr, - "cumulative sum", + 'sum', name, name2, axis_descr, "cumulative sum", lambda y, axis: y.cumsum(axis), 0., np.nan) cls.cumprod = _make_cum_function( - 'prod', name, name2, axis_descr, - "cumulative product", + 'prod', name, name2, axis_descr, "cumulative product", lambda y, axis: y.cumprod(axis), 1., np.nan) cls.cummax = _make_cum_function( - 'max', name, name2, axis_descr, - "cumulative max", - lambda y, axis: np.maximum.accumulate(y, axis), - -np.inf, np.nan) + 'max', name, name2, axis_descr, "cumulative max", + lambda y, axis: np.maximum.accumulate(y, axis), -np.inf, np.nan) cls.sum = _make_stat_function( 'sum', name, name2, axis_descr, @@ -4732,9 +5089,9 @@ def compound(self, axis=None, skipna=None, level=None): nanops.nanskew) cls.kurt = _make_stat_function( 'kurt', name, name2, axis_descr, - 'Return unbiased kurtosis over requested axis using Fisher''s ' - 'definition of\nkurtosis (kurtosis of normal == 0.0). Normalized ' - 'by N-1\n', + "Return unbiased kurtosis over requested axis using Fisher's " + "definition of\nkurtosis (kurtosis of normal == 0.0). Normalized " + "by N-1\n", nanops.nankurt) cls.kurtosis = cls.kurt cls.prod = _make_stat_function( @@ -4746,20 +5103,24 @@ def compound(self, axis=None, skipna=None, level=None): 'median', name, name2, axis_descr, 'Return the median of the values for the requested axis', nanops.nanmedian) - cls.max = _make_stat_function('max', name, name2, axis_descr, - """This method returns the maximum of the values in the object. If you - want the *index* of the maximum, use ``idxmax``. This is the - equivalent of the ``numpy.ndarray`` method ``argmax``.""", - nanops.nanmax) - cls.min = _make_stat_function('min', name, name2, axis_descr, - """This method returns the minimum of the values in the object. If you - want the *index* of the minimum, use ``idxmin``. This is the - equivalent of the ``numpy.ndarray`` method ``argmin``.""", - nanops.nanmin) + cls.max = _make_stat_function( + 'max', name, name2, axis_descr, + """This method returns the maximum of the values in the object. + If you want the *index* of the maximum, use ``idxmax``. This is + the equivalent of the ``numpy.ndarray`` method ``argmax``.""", + nanops.nanmax) + cls.min = _make_stat_function( + 'min', name, name2, axis_descr, + """This method returns the minimum of the values in the object. + If you want the *index* of the minimum, use ``idxmin``. This is + the equivalent of the ``numpy.ndarray`` method ``argmin``.""", + nanops.nanmin) @classmethod def _add_series_only_operations(cls): - """ add the series only operations to the cls; evaluate the doc strings again """ + """Add the series only operations to the cls; evaluate the doc + strings again. + """ axis_descr, name, name2 = _doc_parms(cls) @@ -4768,16 +5129,18 @@ def nanptp(values, axis=0, skipna=True): nmin = nanops.nanmin(values, axis, skipna) return nmax - nmin - cls.ptp = _make_stat_function('ptp', name, name2, axis_descr, - """ - Returns the difference between the maximum value and the minimum - value in the object. This is the equivalent of the ``numpy.ndarray`` - method ``ptp``.""", nanptp) - + cls.ptp = _make_stat_function( + 'ptp', name, name2, axis_descr, + """Returns the difference between the maximum value and the + minimum value in the object. This is the equivalent of the + ``numpy.ndarray`` method ``ptp``.""", + nanptp) @classmethod def _add_series_or_dataframe_operations(cls): - """ add the series or dataframe only operations to the cls; evaluate the doc strings again """ + """Add the series or dataframe only operations to the cls; evaluate + the doc strings again. + """ from pandas.core import window as rwindow @@ -4785,35 +5148,41 @@ def _add_series_or_dataframe_operations(cls): def rolling(self, window, min_periods=None, freq=None, center=False, win_type=None, axis=0): axis = self._get_axis_number(axis) - return rwindow.rolling(self, window=window, min_periods=min_periods, freq=freq, center=center, - win_type=win_type, axis=axis) + return rwindow.rolling(self, window=window, + min_periods=min_periods, freq=freq, + center=center, win_type=win_type, axis=axis) + cls.rolling = rolling @Appender(rwindow.expanding.__doc__) def expanding(self, min_periods=1, freq=None, center=False, axis=0): axis = self._get_axis_number(axis) - return rwindow.expanding(self, min_periods=min_periods, freq=freq, center=center, - axis=axis) + return rwindow.expanding(self, min_periods=min_periods, freq=freq, + center=center, axis=axis) + cls.expanding = expanding @Appender(rwindow.ewm.__doc__) - def ewm(self, com=None, span=None, halflife=None, min_periods=0, freq=None, - adjust=True, ignore_na=False, axis=0): + def ewm(self, com=None, span=None, halflife=None, min_periods=0, + freq=None, adjust=True, ignore_na=False, axis=0): axis = self._get_axis_number(axis) - return rwindow.ewm(self, com=com, span=span, halflife=halflife, min_periods=min_periods, - freq=freq, adjust=adjust, ignore_na=ignore_na, axis=axis) + return rwindow.ewm(self, com=com, span=span, halflife=halflife, + min_periods=min_periods, freq=freq, + adjust=adjust, ignore_na=ignore_na, axis=axis) + cls.ewm = ewm + def _doc_parms(cls): - """ return a tuple of the doc parms """ - axis_descr = "{%s}" % ', '.join([ - "{0} ({1})".format(a, i) for i, a in enumerate(cls._AXIS_ORDERS) - ]) + """Return a tuple of the doc parms.""" + axis_descr = "{%s}" % ', '.join(["{0} ({1})".format(a, i) + for i, a in enumerate(cls._AXIS_ORDERS)]) name = (cls._constructor_sliced.__name__ if cls._AXIS_LEN > 1 else 'scalar') name2 = cls.__name__ return axis_descr, name, name2 + _num_doc = """ %(desc)s @@ -4892,12 +5261,30 @@ def _doc_parms(cls): ------- %(outname)s : %(name1)s\n""" -def _make_stat_function(name, name1, name2, axis_descr, desc, f): - @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) +def _validate_kwargs(fname, kwargs, *compat_args): + """ + Checks whether parameters passed to the + **kwargs argument in a 'stat' function 'fname' + are valid parameters as specified in *compat_args + + """ + list(map(kwargs.__delitem__, filter( + kwargs.__contains__, compat_args))) + if kwargs: + bad_arg = list(kwargs)[0] # first 'key' element + raise TypeError(("{fname}() got an unexpected " + "keyword argument '{arg}'". + format(fname=fname, arg=bad_arg))) + + +def _make_stat_function(name, name1, name2, axis_descr, desc, f): + @Substitution(outname=name, desc=desc, name1=name1, name2=name2, + axis_descr=axis_descr) @Appender(_num_doc) - def stat_func(self, axis=None, skipna=None, level=None, - numeric_only=None, **kwargs): + def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, + **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: @@ -4905,17 +5292,20 @@ def stat_func(self, axis=None, skipna=None, level=None, if level is not None: return self._agg_by_level(name, axis=axis, level=level, skipna=skipna) - return self._reduce(f, name, axis=axis, - skipna=skipna, numeric_only=numeric_only) + return self._reduce(f, name, axis=axis, skipna=skipna, + numeric_only=numeric_only) + stat_func.__name__ = name return stat_func -def _make_stat_function_ddof(name, name1, name2, axis_descr, desc, f): - @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) +def _make_stat_function_ddof(name, name1, name2, axis_descr, desc, f): + @Substitution(outname=name, desc=desc, name1=name1, name2=name2, + axis_descr=axis_descr) @Appender(_num_ddof_doc) def stat_func(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: @@ -4923,19 +5313,21 @@ def stat_func(self, axis=None, skipna=None, level=None, ddof=1, if level is not None: return self._agg_by_level(name, axis=axis, level=level, skipna=skipna, ddof=ddof) - return self._reduce(f, name, axis=axis, - numeric_only=numeric_only, + return self._reduce(f, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof) + stat_func.__name__ = name return stat_func -def _make_cum_function(name, name1, name2, axis_descr, desc, accum_func, mask_a, mask_b): - @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) - @Appender("Return cumulative {0} over requested axis.".format(name) - + _cnum_doc) - def func(self, axis=None, dtype=None, out=None, skipna=True, - **kwargs): +def _make_cum_function(name, name1, name2, axis_descr, desc, accum_func, + mask_a, mask_b): + @Substitution(outname=name, desc=desc, name1=name1, name2=name2, + axis_descr=axis_descr) + @Appender("Return cumulative {0} over requested axis.".format(name) + + _cnum_doc) + def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if axis is None: axis = self._stat_axis_number else: @@ -4943,8 +5335,8 @@ def func(self, axis=None, dtype=None, out=None, skipna=True, y = _values_from_object(self).copy() - if skipna and issubclass(y.dtype.type, - (np.datetime64, np.timedelta64)): + if (skipna and + issubclass(y.dtype.type, (np.datetime64, np.timedelta64))): result = accum_func(y, axis) mask = isnull(self) np.putmask(result, mask, pd.tslib.iNaT) @@ -4963,26 +5355,28 @@ def func(self, axis=None, dtype=None, out=None, skipna=True, func.__name__ = name return func -def _make_logical_function(name, name1, name2, axis_descr, desc, f): - @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) +def _make_logical_function(name, name1, name2, axis_descr, desc, f): + @Substitution(outname=name, desc=desc, name1=name1, name2=name2, + axis_descr=axis_descr) @Appender(_bool_doc) - def logical_func(self, axis=None, bool_only=None, skipna=None, - level=None, **kwargs): + def logical_func(self, axis=None, bool_only=None, skipna=None, level=None, + **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: if bool_only is not None: - raise NotImplementedError( - "Option bool_only is not implemented with option " - "level.") + raise NotImplementedError("Option bool_only is not " + "implemented with option level.") return self._agg_by_level(name, axis=axis, level=level, - skipna=skipna) + skipna=skipna) return self._reduce(f, axis=axis, skipna=skipna, numeric_only=bool_only, filter_type='bool', name=name) + logical_func.__name__ = name return logical_func
Updates doc comments for `DataFrame.filter` and adds usage examples. <dl class="method"> <dt id="pandas.DataFrame.filter"> <code class="descclassname">DataFrame.</code><code class="descname">filter</code><span class="sig-paren">(</span><em>items=None</em>, <em>like=None</em>, <em>regex=None</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pandas.DataFrame.filter" title="Permalink to this definition">¶</a></dt> <dd><p>Subset rows or columns of dataframe according to labels in the index.</p> <p>Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. This method is a thin veneer on top of <span class="xref std std-ref">DateFrame Select</span></p> <table class="docutils field-list" frame="void" rules="none"> <colgroup><col class="field-name"> <col class="field-body"> </colgroup><tbody valign="top"> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>items</strong> : list-like</p> <blockquote> <div><p>List of info axis to restrict to (must not all be present)</p> </div></blockquote> <p><strong>like</strong> : string</p> <blockquote> <div><p>Keep info axis where “arg in col == True”</p> </div></blockquote> <p><strong>regex</strong> : string (regular expression)</p> <blockquote> <div><p>Keep info axis with re.search(regex, col) == True</p> </div></blockquote> <p><strong>axis</strong> : int or None</p> <blockquote> <div><p>The axis to filter on.</p> </div></blockquote> </td> </tr> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">same type as input object with filtered info axis</p> </td> </tr> </tbody> </table> <p class="rubric">Notes</p> <p>The <code class="docutils literal"><span class="pre">items</span></code>, <code class="docutils literal"><span class="pre">like</span></code>, and <code class="docutils literal"><span class="pre">regex</span></code> parameters should be mutually exclusive, but this is not checked.</p> <p><code class="docutils literal"><span class="pre">axis</span></code> defaults to the info axis that is used when indexing with <code class="docutils literal"><span class="pre">[]</span></code>.</p> <p class="rubric">Examples</p> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="go"> one two three</span> <span class="go">mouse 1 2 3</span> <span class="go">rabbit 4 5 6</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="c1"># select columns by name</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">items</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span> <span class="go"> one three</span> <span class="go">mouse 1 3</span> <span class="go">rabbit 4 6</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="c1"># select columns by regular expression</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">regex</span><span class="o">=</span><span class="s1">'e$'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="go"> one three</span> <span class="go">mouse 1 3</span> <span class="go">rabbit 4 6</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="c1"># select rows containing 'bbi'</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'bbi'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="go"> one two three</span> <span class="go">rabbit 4 5 6</span> </pre></div> </div> </dd></dl>
https://api.github.com/repos/pandas-dev/pandas/pulls/12395
2016-02-19T18:44:32Z
2016-02-19T23:59:24Z
null
2016-02-20T18:19:05Z
BUG: Return UTC DateTimeIndex when specified in to_datetime
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index e944189e2a338..aab9e6125732a 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1108,3 +1108,4 @@ Bug Fixes - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) - Bug when initializing categorical series with a scalar value. (:issue:`12336`) +- Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:11934) diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 2c5620769d1b4..b83c51b6a3ab6 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1143,6 +1143,16 @@ def test_to_datetime_tz_pytz(self): dtype='datetime64[ns, UTC]', freq=None) tm.assert_index_equal(result, expected) + def test_to_datetime_utc_is_true(self): + # See gh-11934 + start = pd.Timestamp('2014-01-01', tz='utc') + end = pd.Timestamp('2014-01-03', tz='utc') + date_range = pd.bdate_range(start, end) + + result = pd.to_datetime(date_range, utc=True) + expected = pd.DatetimeIndex(data=date_range) + tm.assert_index_equal(result, expected) + def test_to_datetime_tz_psycopg2(self): # xref 8260 @@ -1175,7 +1185,8 @@ def test_to_datetime_tz_psycopg2(self): tm.assert_index_equal(result, i) result = pd.to_datetime(i, errors='coerce', utc=True) - expected = pd.DatetimeIndex(['2000-01-01 13:00:00']) + expected = pd.DatetimeIndex(['2000-01-01 13:00:00'], + dtype='datetime64[ns, UTC]') tm.assert_index_equal(result, expected) def test_index_to_datetime(self): diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index 2c7f1ba8c6787..8f127e28e28a9 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -310,7 +310,7 @@ def _convert_listlike(arg, box, format, name=None): if not isinstance(arg, DatetimeIndex): return DatetimeIndex(arg, tz='utc' if utc else None) if utc: - arg = arg.tz_convert(None) + arg = arg.tz_convert(None).tz_localize('UTC') return arg elif format is None and com.is_integer_dtype(arg) and unit == 'ns':
Title is self-explanatory. Closes #11934.
https://api.github.com/repos/pandas-dev/pandas/pulls/12391
2016-02-19T03:36:30Z
2016-02-20T18:27:34Z
null
2016-02-21T00:41:34Z
TST: skip bool_ops_scalar tests for numexpr if numpy >= 1.11
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index 43d2faac36c2f..fb4d56a5eaf99 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -1871,6 +1871,8 @@ def test_more_than_one_expression_raises(): def check_bool_ops_fails_on_scalars(gen, lhs, cmp, rhs, engine, parser): tm.skip_if_no_ne(engine) + if not pd._np_version_under1p11: + raise nose.SkipTest("numpy >= 1.11, bool_ops_fails_on_scalars") mid = gen[type(lhs)]() ex1 = 'lhs {0} mid {1} rhs'.format(cmp, cmp) ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp, cmp)
numpy >= 1.11failing on these
https://api.github.com/repos/pandas-dev/pandas/pulls/12389
2016-02-19T02:25:02Z
2016-02-19T13:06:42Z
null
2016-02-19T13:06:42Z
Update groupby.rst
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 1d1dd59efff52..02d4a924a92fc 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -217,7 +217,7 @@ the length of the ``groups`` dict, so it is largely just a convenience: weight = np.random.normal(166, 20, size=n) height = np.random.normal(60, 10, size=n) time = pd.date_range('1/1/2000', periods=n) - gender = tm.choice(['male', 'female'], size=n) + gender = np.random.choice(['male', 'female'], size=n) df = pd.DataFrame({'height': height, 'weight': weight, 'gender': gender}, index=time)
Fixed typo (replaced tm with np.random as there is no tm module in this doc) so that the choice method at line 220 will work. @ellisonbg
https://api.github.com/repos/pandas-dev/pandas/pulls/12385
2016-02-18T21:31:40Z
2016-02-18T21:55:07Z
null
2016-02-18T21:55:16Z
Use correct random choice function in code snippet
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 1d1dd59efff52..02d4a924a92fc 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -217,7 +217,7 @@ the length of the ``groups`` dict, so it is largely just a convenience: weight = np.random.normal(166, 20, size=n) height = np.random.normal(60, 10, size=n) time = pd.date_range('1/1/2000', periods=n) - gender = tm.choice(['male', 'female'], size=n) + gender = np.random.choice(['male', 'female'], size=n) df = pd.DataFrame({'height': height, 'weight': weight, 'gender': gender}, index=time)
A hidden code snippet uses `tm.choice()` when it should use `np.random.choice()`
https://api.github.com/repos/pandas-dev/pandas/pulls/12384
2016-02-18T21:25:40Z
2016-02-18T21:42:50Z
null
2016-02-18T22:01:41Z
Fix #12373: rolling functions raise ValueError on float32 data
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 7f253ae437d9f..320520582f1e2 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1200,3 +1200,5 @@ Bug Fixes - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) - Bug when increasing the buffer size of CSV reader in ``read_csv`` (:issue:`12494`) + +- Bug in ``.rolling`` in which apply on float32 data will raise a ``ValueError`` (:issue:`12373`) diff --git a/pandas/core/window.py b/pandas/core/window.py index 9c8490f608996..fc71c59afcdac 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -149,16 +149,17 @@ def _prep_values(self, values=None, kill_inf=True, how=None): if values is None: values = getattr(self._selected_obj, 'values', self._selected_obj) - # coerce dtypes as appropriate + # GH #12373 : rolling functions error on float32 data + # make sure the data is coerced to float64 if com.is_float_dtype(values.dtype): - pass + values = com._ensure_float64(values) elif com.is_integer_dtype(values.dtype): - values = values.astype(float) + values = com._ensure_float64(values) elif com.is_timedelta64_dtype(values.dtype): - values = values.view('i8').astype(float) + values = com._ensure_float64(values.view('i8')) else: try: - values = values.astype(float) + values = com._ensure_float64(values) except (ValueError, TypeError): raise TypeError("cannot handle this type -> {0}" "".format(values.dtype)) @@ -457,7 +458,9 @@ def _apply(self, func, window=None, center=None, check_minp=None, how=None, def func(arg, window, min_periods=None): minp = check_minp(min_periods, window) - return cfunc(arg, window, minp, **kwargs) + # GH #12373: rolling functions error on float32 data + return cfunc(com._ensure_float64(arg), + window, minp, **kwargs) # calculation function if center: @@ -494,6 +497,7 @@ def count(self): obj = self._convert_freq() window = self._get_window() window = min(window, len(obj)) if not self.center else window + try: converted = np.isfinite(obj).astype(float) except TypeError: @@ -657,6 +661,10 @@ def cov(self, other=None, pairwise=None, ddof=1, **kwargs): window = self._get_window(other) def _get_cov(X, Y): + # GH #12373 : rolling functions error on float32 data + # to avoid potential overflow, cast the data to float64 + X = X.astype('float64') + Y = Y.astype('float64') mean = lambda x: x.rolling(window, self.min_periods, center=self.center).mean(**kwargs) count = (X + Y).rolling(window=window, diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index cc4a6ba61306d..223879ab1cdba 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -289,6 +289,193 @@ def test_deprecations(self): mom.rolling_mean(Series(np.ones(10)), 3, center=True, axis=0) +# GH #12373 : rolling functions error on float32 data +# make sure rolling functions works for different dtypes +class TestDtype(Base): + dtype = None + window = 2 + + funcs = { + 'count': lambda v: v.count(), + 'max': lambda v: v.max(), + 'min': lambda v: v.min(), + 'sum': lambda v: v.sum(), + 'mean': lambda v: v.mean(), + 'std': lambda v: v.std(), + 'var': lambda v: v.var(), + 'median': lambda v: v.median() + } + + def get_expects(self): + expects = { + 'sr1': { + 'count': Series([1, 2, 2, 2, 2], dtype='float64'), + 'max': Series([np.nan, 1, 2, 3, 4], dtype='float64'), + 'min': Series([np.nan, 0, 1, 2, 3], dtype='float64'), + 'sum': Series([np.nan, 1, 3, 5, 7], dtype='float64'), + 'mean': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64'), + 'std': Series([np.nan] + [np.sqrt(.5)] * 4, dtype='float64'), + 'var': Series([np.nan, .5, .5, .5, .5], dtype='float64'), + 'median': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64') + }, + 'sr2': { + 'count': Series([1, 2, 2, 2, 2], dtype='float64'), + 'max': Series([np.nan, 10, 8, 6, 4], dtype='float64'), + 'min': Series([np.nan, 8, 6, 4, 2], dtype='float64'), + 'sum': Series([np.nan, 18, 14, 10, 6], dtype='float64'), + 'mean': Series([np.nan, 9, 7, 5, 3], dtype='float64'), + 'std': Series([np.nan] + [np.sqrt(2)] * 4, dtype='float64'), + 'var': Series([np.nan, 2, 2, 2, 2], dtype='float64'), + 'median': Series([np.nan, 9, 7, 5, 3], dtype='float64') + }, + 'df': { + 'count': DataFrame({0: Series([1, 2, 2, 2, 2]), + 1: Series([1, 2, 2, 2, 2])}, + dtype='float64'), + 'max': DataFrame({0: Series([np.nan, 2, 4, 6, 8]), + 1: Series([np.nan, 3, 5, 7, 9])}, + dtype='float64'), + 'min': DataFrame({0: Series([np.nan, 0, 2, 4, 6]), + 1: Series([np.nan, 1, 3, 5, 7])}, + dtype='float64'), + 'sum': DataFrame({0: Series([np.nan, 2, 6, 10, 14]), + 1: Series([np.nan, 4, 8, 12, 16])}, + dtype='float64'), + 'mean': DataFrame({0: Series([np.nan, 1, 3, 5, 7]), + 1: Series([np.nan, 2, 4, 6, 8])}, + dtype='float64'), + 'std': DataFrame({0: Series([np.nan] + [np.sqrt(2)] * 4), + 1: Series([np.nan] + [np.sqrt(2)] * 4)}, + dtype='float64'), + 'var': DataFrame({0: Series([np.nan, 2, 2, 2, 2]), + 1: Series([np.nan, 2, 2, 2, 2])}, + dtype='float64'), + 'median': DataFrame({0: Series([np.nan, 1, 3, 5, 7]), + 1: Series([np.nan, 2, 4, 6, 8])}, + dtype='float64'), + } + } + return expects + + def _create_dtype_data(self, dtype): + sr1 = Series(range(5), dtype=dtype) + sr2 = Series(range(10, 0, -2), dtype=dtype) + df = DataFrame(np.arange(10).reshape((5, 2)), dtype=dtype) + + data = { + 'sr1': sr1, + 'sr2': sr2, + 'df': df + } + + return data + + def _create_data(self): + super(TestDtype, self)._create_data() + self.data = self._create_dtype_data(self.dtype) + self.expects = self.get_expects() + + def setUp(self): + self._create_data() + + def test_dtypes(self): + for f_name, d_name in product(self.funcs.keys(), self.data.keys()): + f = self.funcs[f_name] + d = self.data[d_name] + assert_equal = assert_series_equal if isinstance( + d, Series) else assert_frame_equal + exp = self.expects[d_name][f_name] + + roll = d.rolling(window=self.window) + result = f(roll) + + assert_equal(result, exp) + + +class TestDtype_object(TestDtype): + dtype = object + + +class TestDtype_int8(TestDtype): + dtype = np.int8 + + +class TestDtype_int16(TestDtype): + dtype = np.int16 + + +class TestDtype_int32(TestDtype): + dtype = np.int32 + + +class TestDtype_int64(TestDtype): + dtype = np.int64 + + +class TestDtype_uint8(TestDtype): + dtype = np.uint8 + + +class TestDtype_uint16(TestDtype): + dtype = np.uint16 + + +class TestDtype_uint32(TestDtype): + dtype = np.uint32 + + +class TestDtype_uint64(TestDtype): + dtype = np.uint64 + + +class TestDtype_float16(TestDtype): + dtype = np.float16 + + +class TestDtype_float32(TestDtype): + dtype = np.float32 + + +class TestDtype_float64(TestDtype): + dtype = np.float64 + + +class TestDtype_category(TestDtype): + dtype = 'category' + include_df = False + + def _create_dtype_data(self, dtype): + sr1 = Series(range(5), dtype=dtype) + sr2 = Series(range(10, 0, -2), dtype=dtype) + + data = { + 'sr1': sr1, + 'sr2': sr2 + } + + return data + + +class TestDatetimeLikeDtype(TestDtype): + dtype = np.dtype('M8[ns]') + + # GH #12373: rolling functions raise ValueError on float32 data + def setUp(self): + raise nose.SkipTest("Skip rolling on DatetimeLike dtypes.") + + def test_dtypes(self): + with tm.assertRaises(TypeError): + super(TestDatetimeLikeDtype, self).test_dtypes() + + +class TestDtype_timedelta(TestDatetimeLikeDtype): + dtype = np.dtype('m8[ns]') + + +class TestDtype_datetime64UTC(TestDatetimeLikeDtype): + dtype = 'datetime64[ns, UTC]' + + class TestMoments(Base): def setUp(self):
- [x] closes #12373 - [x] tests added - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry added - [x] add test for various other dtypes
https://api.github.com/repos/pandas-dev/pandas/pulls/12376
2016-02-18T04:52:09Z
2016-03-06T00:45:44Z
null
2016-03-06T01:11:31Z
API: correctly provide __name__ for cum functions
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c14a0c0961a2d..87525e6edfba0 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -103,6 +103,7 @@ API changes - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) +- Provide a proper ``__name__`` and ``__qualname__`` attributes for generic functions (:issue:`12021`) .. _whatsnew_0181.apply_resample: @@ -170,6 +171,7 @@ Performance Improvements +- Bug in ``__name__`` of ``.cum*`` functions (:issue:`12021`) diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index a364a7ffa1c32..e9f8e9757cbae 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -242,6 +242,15 @@ def import_lzma(): import lzma return lzma + def set_function_name(f, name, cls): + """ Bind the name/qualname attributes of the function """ + f.__name__ = name + f.__qualname__ = '{klass}.{name}'.format( + klass=cls.__name__, + name=name) + f.__module__ = cls.__module__ + return f + else: string_types = basestring, integer_types = (int, long) @@ -284,6 +293,11 @@ def import_lzma(): from backports import lzma return lzma + def set_function_name(f, name, cls): + """ Bind the name attributes of the function """ + f.__name__ = name + return f + string_and_binary_types = string_types + (binary_type,) @@ -369,6 +383,10 @@ def __reduce__(self): # optional, for pickle support # https://github.com/pydata/pandas/pull/9123 +def is_platform_little_endian(): + """ am I little endian """ + return sys.byteorder == 'little' + def is_platform_windows(): return sys.platform == 'win32' or sys.platform == 'cygwin' diff --git a/pandas/core/common.py b/pandas/core/common.py index 6de6da4afedc8..4275870cb8543 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -5,7 +5,6 @@ import re import collections import numbers -import types from datetime import datetime, timedelta from functools import partial @@ -130,31 +129,6 @@ def __instancecheck__(cls, inst): ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) -def bind_method(cls, name, func): - """Bind a method to class, python 2 and python 3 compatible. - - Parameters - ---------- - - cls : type - class to receive bound method - name : basestring - name of method on class instance - func : function - function to be bound as method - - - Returns - ------- - None - """ - # only python 2 has bound/unbound method issue - if not compat.PY3: - setattr(cls, name, types.MethodType(func, None, cls)) - else: - setattr(cls, name, func) - - def isnull(obj): """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) @@ -1466,60 +1440,6 @@ def _lcd_dtypes(a_dtype, b_dtype): return np.object -def _fill_zeros(result, x, y, name, fill): - """ - if this is a reversed op, then flip x,y - - if we have an integer value (or array in y) - and we have 0's, fill them with the fill, - return the result - - mask the nan's from x - """ - if fill is None or is_float_dtype(result): - return result - - if name.startswith(('r', '__r')): - x, y = y, x - - is_typed_variable = (hasattr(y, 'dtype') or hasattr(y, 'type')) - is_scalar = lib.isscalar(y) - - if not is_typed_variable and not is_scalar: - return result - - if is_scalar: - y = np.array(y) - - if is_integer_dtype(y): - - if (y == 0).any(): - - # GH 7325, mask and nans must be broadcastable (also: PR 9308) - # Raveling and then reshaping makes np.putmask faster - mask = ((y == 0) & ~np.isnan(result)).ravel() - - shape = result.shape - result = result.astype('float64', copy=False).ravel() - - np.putmask(result, mask, fill) - - # if we have a fill of inf, then sign it correctly - # (GH 6178 and PR 9308) - if np.isinf(fill): - signs = np.sign(y if name.startswith(('r', '__r')) else x) - negative_inf_mask = (signs.ravel() < 0) & mask - np.putmask(result, negative_inf_mask, -fill) - - if "floordiv" in name: # (PR 9308) - nan_mask = ((y == 0) & (x == 0)).ravel() - np.putmask(result, nan_mask, np.nan) - - result = result.reshape(shape) - - return result - - def _consensus_name_attr(objs): name = objs[0].name for obj in objs[1:]: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 848ed7c3baa94..d8ee85df58e11 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -17,10 +17,11 @@ from pandas.core.internals import BlockManager import pandas.core.algorithms as algos import pandas.core.common as com -import pandas.core.missing as mis +import pandas.core.missing as missing import pandas.core.datetools as datetools from pandas import compat -from pandas.compat import map, zip, lrange, string_types, isidentifier +from pandas.compat import (map, zip, lrange, string_types, + isidentifier, set_function_name) from pandas.core.common import (isnull, notnull, is_list_like, _values_from_object, _maybe_promote, _maybe_box_datetimelike, ABCSeries, @@ -51,7 +52,7 @@ def _single_replace(self, to_replace, method, inplace, limit): orig_dtype = self.dtype result = self if inplace else self.copy() - fill_f = mis._get_fill_func(method) + fill_f = missing.get_fill_func(method) mask = com.mask_missing(result.values, to_replace) values = fill_f(result.values, limit=limit, mask=mask) @@ -2189,7 +2190,7 @@ def reindex(self, *args, **kwargs): # construct the args axes, kwargs = self._construct_axes_from_arguments(args, kwargs) - method = mis._clean_reindex_fill_method(kwargs.pop('method', None)) + method = missing.clean_reindex_fill_method(kwargs.pop('method', None)) level = kwargs.pop('level', None) copy = kwargs.pop('copy', True) limit = kwargs.pop('limit', None) @@ -2304,7 +2305,7 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, axis_name = self._get_axis_name(axis) axis_values = self._get_axis(axis_name) - method = mis._clean_reindex_fill_method(method) + method = missing.clean_reindex_fill_method(method) new_index, indexer = axis_values.reindex(labels, method, level, limit=limit) return self._reindex_with_indexers({axis: [new_index, indexer]}, @@ -3099,7 +3100,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, if axis is None: axis = 0 axis = self._get_axis_number(axis) - method = mis._clean_fill_method(method) + method = missing.clean_fill_method(method) from pandas import DataFrame if value is None: @@ -3132,7 +3133,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, else: # 2d or less - method = mis._clean_fill_method(method) + method = missing.clean_fill_method(method) new_data = self._data.interpolate(method=method, axis=axis, limit=limit, inplace=inplace, coerce=True, @@ -4121,7 +4122,7 @@ def align(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None): from pandas import DataFrame, Series - method = mis._clean_fill_method(method) + method = missing.clean_fill_method(method) if broadcast_axis == 1 and self.ndim != other.ndim: if isinstance(self, Series): @@ -4976,11 +4977,11 @@ def _add_numeric_operations(cls): axis_descr, name, name2 = _doc_parms(cls) cls.any = _make_logical_function( - 'any', name, name2, axis_descr, + cls, 'any', name, name2, axis_descr, 'Return whether any element is True over requested axis', nanops.nanany) cls.all = _make_logical_function( - 'all', name, name2, axis_descr, + cls, 'all', name, name2, axis_descr, 'Return whether all elements are True over requested axis', nanops.nanall) @@ -5008,18 +5009,18 @@ def mad(self, axis=None, skipna=None, level=None): cls.mad = mad cls.sem = _make_stat_function_ddof( - 'sem', name, name2, axis_descr, + cls, 'sem', name, name2, axis_descr, "Return unbiased standard error of the mean over requested " "axis.\n\nNormalized by N-1 by default. This can be changed " "using the ddof argument", nanops.nansem) cls.var = _make_stat_function_ddof( - 'var', name, name2, axis_descr, + cls, 'var', name, name2, axis_descr, "Return unbiased variance over requested axis.\n\nNormalized by " "N-1 by default. This can be changed using the ddof argument", nanops.nanvar) cls.std = _make_stat_function_ddof( - 'std', name, name2, axis_descr, + cls, 'std', name, name2, axis_descr, "Return sample standard deviation over requested axis." "\n\nNormalized by N-1 by default. This can be changed using the " "ddof argument", @@ -5038,54 +5039,54 @@ def compound(self, axis=None, skipna=None, level=None): cls.compound = compound cls.cummin = _make_cum_function( - 'min', name, name2, axis_descr, "cumulative minimum", + cls, 'cummin', name, name2, axis_descr, "cumulative minimum", lambda y, axis: np.minimum.accumulate(y, axis), np.inf, np.nan) cls.cumsum = _make_cum_function( - 'sum', name, name2, axis_descr, "cumulative sum", + cls, 'cumsum', name, name2, axis_descr, "cumulative sum", lambda y, axis: y.cumsum(axis), 0., np.nan) cls.cumprod = _make_cum_function( - 'prod', name, name2, axis_descr, "cumulative product", + cls, 'cumprod', name, name2, axis_descr, "cumulative product", lambda y, axis: y.cumprod(axis), 1., np.nan) cls.cummax = _make_cum_function( - 'max', name, name2, axis_descr, "cumulative max", + cls, 'cummax', name, name2, axis_descr, "cumulative max", lambda y, axis: np.maximum.accumulate(y, axis), -np.inf, np.nan) cls.sum = _make_stat_function( - 'sum', name, name2, axis_descr, + cls, 'sum', name, name2, axis_descr, 'Return the sum of the values for the requested axis', nanops.nansum) cls.mean = _make_stat_function( - 'mean', name, name2, axis_descr, + cls, 'mean', name, name2, axis_descr, 'Return the mean of the values for the requested axis', nanops.nanmean) cls.skew = _make_stat_function( - 'skew', name, name2, axis_descr, + cls, 'skew', name, name2, axis_descr, 'Return unbiased skew over requested axis\nNormalized by N-1', nanops.nanskew) cls.kurt = _make_stat_function( - 'kurt', name, name2, axis_descr, + cls, 'kurt', name, name2, axis_descr, "Return unbiased kurtosis over requested axis using Fisher's " "definition of\nkurtosis (kurtosis of normal == 0.0). Normalized " "by N-1\n", nanops.nankurt) cls.kurtosis = cls.kurt cls.prod = _make_stat_function( - 'prod', name, name2, axis_descr, + cls, 'prod', name, name2, axis_descr, 'Return the product of the values for the requested axis', nanops.nanprod) cls.product = cls.prod cls.median = _make_stat_function( - 'median', name, name2, axis_descr, + cls, 'median', name, name2, axis_descr, 'Return the median of the values for the requested axis', nanops.nanmedian) cls.max = _make_stat_function( - 'max', name, name2, axis_descr, + cls, 'max', name, name2, axis_descr, """This method returns the maximum of the values in the object. If you want the *index* of the maximum, use ``idxmax``. This is the equivalent of the ``numpy.ndarray`` method ``argmax``.""", nanops.nanmax) cls.min = _make_stat_function( - 'min', name, name2, axis_descr, + cls, 'min', name, name2, axis_descr, """This method returns the minimum of the values in the object. If you want the *index* of the minimum, use ``idxmin``. This is the equivalent of the ``numpy.ndarray`` method ``argmin``.""", @@ -5105,7 +5106,7 @@ def nanptp(values, axis=0, skipna=True): return nmax - nmin cls.ptp = _make_stat_function( - 'ptp', name, name2, axis_descr, + cls, 'ptp', name, name2, axis_descr, """Returns the difference between the maximum value and the minimum value in the object. This is the equivalent of the ``numpy.ndarray`` method ``ptp``.""", @@ -5238,7 +5239,7 @@ def _doc_parms(cls): %(outname)s : %(name1)s\n""" -def _make_stat_function(name, name1, name2, axis_descr, desc, f): +def _make_stat_function(cls, name, name1, name2, axis_descr, desc, f): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender(_num_doc) @@ -5255,11 +5256,10 @@ def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, return self._reduce(f, name, axis=axis, skipna=skipna, numeric_only=numeric_only) - stat_func.__name__ = name - return stat_func + return set_function_name(stat_func, name, cls) -def _make_stat_function_ddof(name, name1, name2, axis_descr, desc, f): +def _make_stat_function_ddof(cls, name, name1, name2, axis_descr, desc, f): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender(_num_ddof_doc) @@ -5276,17 +5276,16 @@ def stat_func(self, axis=None, skipna=None, level=None, ddof=1, return self._reduce(f, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof) - stat_func.__name__ = name - return stat_func + return set_function_name(stat_func, name, cls) -def _make_cum_function(name, name1, name2, axis_descr, desc, accum_func, +def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func, mask_a, mask_b): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) - def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): + def cum_func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): validate_kwargs(name, kwargs, 'out', 'dtype') if axis is None: axis = self._stat_axis_number @@ -5312,11 +5311,10 @@ def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): d['copy'] = False return self._constructor(result, **d).__finalize__(self) - func.__name__ = name - return func + return set_function_name(cum_func, name, cls) -def _make_logical_function(name, name1, name2, axis_descr, desc, f): +def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender(_bool_doc) @@ -5337,8 +5335,8 @@ def logical_func(self, axis=None, bool_only=None, skipna=None, level=None, numeric_only=bool_only, filter_type='bool', name=name) - logical_func.__name__ = name - return logical_func + return set_function_name(logical_func, name, cls) + # install the indexes for _name, _indexer in indexing.get_indexers_list(): diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 613140e242239..c5353f6fef6dc 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -27,7 +27,7 @@ from pandas.core.categorical import Categorical, maybe_to_categorical from pandas.tseries.index import DatetimeIndex import pandas.core.common as com -import pandas.core.missing as mis +import pandas.core.missing as missing import pandas.core.convert as convert from pandas.sparse.array import _maybe_to_sparse, SparseArray import pandas.lib as lib @@ -872,7 +872,7 @@ def check_int_bool(self, inplace): # a fill na type method try: - m = mis._clean_fill_method(method) + m = missing.clean_fill_method(method) except: m = None @@ -887,7 +887,7 @@ def check_int_bool(self, inplace): downcast=downcast, mgr=mgr) # try an interp method try: - m = mis._clean_interp_method(method, **kwargs) + m = missing.clean_interp_method(method, **kwargs) except: m = None @@ -920,9 +920,9 @@ def _interpolate_with_fill(self, method='pad', axis=0, inplace=False, values = self.values if inplace else self.values.copy() values, _, fill_value, _ = self._try_coerce_args(values, fill_value) values = self._try_operate(values) - values = mis.interpolate_2d(values, method=method, axis=axis, - limit=limit, fill_value=fill_value, - dtype=self.dtype) + values = missing.interpolate_2d(values, method=method, axis=axis, + limit=limit, fill_value=fill_value, + dtype=self.dtype) values = self._try_coerce_result(values) blocks = [self.make_block(values, klass=self.__class__, fastpath=True)] @@ -955,11 +955,11 @@ def func(x): # process a 1-d slice, returning it # should the axis argument be handled below in apply_along_axis? - # i.e. not an arg to mis.interpolate_1d - return mis.interpolate_1d(index, x, method=method, limit=limit, - limit_direction=limit_direction, - fill_value=fill_value, - bounds_error=False, **kwargs) + # i.e. not an arg to missing.interpolate_1d + return missing.interpolate_1d(index, x, method=method, limit=limit, + limit_direction=limit_direction, + fill_value=fill_value, + bounds_error=False, **kwargs) # interp each column independently interp_values = np.apply_along_axis(func, axis, data) @@ -2414,8 +2414,8 @@ def make_block_same_class(self, values, placement, sparse_index=None, def interpolate(self, method='pad', axis=0, inplace=False, limit=None, fill_value=None, **kwargs): - values = mis.interpolate_2d(self.values.to_dense(), method, axis, - limit, fill_value) + values = missing.interpolate_2d(self.values.to_dense(), method, axis, + limit, fill_value) return self.make_block_same_class(values=values, placement=self.mgr_locs) @@ -3851,8 +3851,10 @@ def reindex(self, new_axis, indexer=None, method=None, fill_value=None, # fill if needed if method is not None or limit is not None: - new_values = mis.interpolate_2d(new_values, method=method, - limit=limit, fill_value=fill_value) + new_values = missing.interpolate_2d(new_values, + method=method, + limit=limit, + fill_value=fill_value) if self._block.is_sparse: make_block = self._block.make_block_same_class diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 86640cffc136e..a8ca5e452c7ac 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -10,7 +10,7 @@ from pandas.compat import range -def _clean_fill_method(method, allow_nearest=False): +def clean_fill_method(method, allow_nearest=False): if method is None: return None method = method.lower() @@ -31,7 +31,7 @@ def _clean_fill_method(method, allow_nearest=False): return method -def _clean_interp_method(method, **kwargs): +def clean_interp_method(method, **kwargs): order = kwargs.get('order') valid = ['linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial', 'krogh', @@ -241,7 +241,7 @@ def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, else: # todo create faster fill func without masking mask = com.mask_missing(transf(values), fill_value) - method = _clean_fill_method(method) + method = clean_fill_method(method) if method == 'pad': values = transf(pad_2d( transf(values), limit=limit, mask=mask, dtype=dtype)) @@ -385,10 +385,64 @@ def backfill_2d(values, limit=None, mask=None, dtype=None): _fill_methods = {'pad': pad_1d, 'backfill': backfill_1d} -def _get_fill_func(method): - method = _clean_fill_method(method) +def get_fill_func(method): + method = clean_fill_method(method) return _fill_methods[method] -def _clean_reindex_fill_method(method): - return _clean_fill_method(method, allow_nearest=True) +def clean_reindex_fill_method(method): + return clean_fill_method(method, allow_nearest=True) + + +def fill_zeros(result, x, y, name, fill): + """ + if this is a reversed op, then flip x,y + + if we have an integer value (or array in y) + and we have 0's, fill them with the fill, + return the result + + mask the nan's from x + """ + if fill is None or com.is_float_dtype(result): + return result + + if name.startswith(('r', '__r')): + x, y = y, x + + is_typed_variable = (hasattr(y, 'dtype') or hasattr(y, 'type')) + is_scalar = lib.isscalar(y) + + if not is_typed_variable and not is_scalar: + return result + + if is_scalar: + y = np.array(y) + + if com.is_integer_dtype(y): + + if (y == 0).any(): + + # GH 7325, mask and nans must be broadcastable (also: PR 9308) + # Raveling and then reshaping makes np.putmask faster + mask = ((y == 0) & ~np.isnan(result)).ravel() + + shape = result.shape + result = result.astype('float64', copy=False).ravel() + + np.putmask(result, mask, fill) + + # if we have a fill of inf, then sign it correctly + # (GH 6178 and PR 9308) + if np.isinf(fill): + signs = np.sign(y if name.startswith(('r', '__r')) else x) + negative_inf_mask = (signs.ravel() < 0) & mask + np.putmask(result, negative_inf_mask, -fill) + + if "floordiv" in name: # (PR 9308) + nan_mask = ((y == 0) & (x == 0)).ravel() + np.putmask(result, nan_mask, np.nan) + + result = result.reshape(shape) + + return result diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 01df9218c1936..11161d8a5d186 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -18,6 +18,7 @@ from pandas.lib import isscalar from pandas.tslib import iNaT from pandas.compat import bind_method +import pandas.core.missing as missing from pandas.core.common import (is_list_like, notnull, isnull, _values_from_object, _maybe_match_name, needs_i8_conversion, is_datetimelike_v_numeric, @@ -595,7 +596,7 @@ def na_op(x, y): result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) - result = com._fill_zeros(result, x, y, name, fill_zeros) + result = missing.fill_zeros(result, x, y, name, fill_zeros) return result def wrapper(left, right, name=name, na_op=na_op): @@ -1004,7 +1005,7 @@ def na_op(x, y): result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) result = result.reshape(x.shape) - result = com._fill_zeros(result, x, y, name, fill_zeros) + result = missing.fill_zeros(result, x, y, name, fill_zeros) return result @@ -1207,7 +1208,7 @@ def na_op(x, y): result[mask] = op(x[mask], y) result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) - result = com._fill_zeros(result, x, y, name, fill_zeros) + result = missing.fill_zeros(result, x, y, name, fill_zeros) return result # work only for scalars diff --git a/pandas/core/panel.py b/pandas/core/panel.py index adfbd6646b048..f0f3803c62566 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -11,6 +11,7 @@ import pandas.computation.expressions as expressions import pandas.core.common as com import pandas.core.ops as ops +import pandas.core.missing as missing from pandas import compat from pandas import lib from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict) @@ -1505,7 +1506,7 @@ def na_op(x, y): # handles discrepancy between numpy and numexpr on division/mod # by 0 though, given that these are generally (always?) # non-scalars, I'm not sure whether it's worth it at the moment - result = com._fill_zeros(result, x, y, name, fill_zeros) + result = missing.fill_zeros(result, x, y, name, fill_zeros) return result if name in _op_descriptions: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index e1bc843eb5d88..dedabd1126b09 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -18,7 +18,7 @@ from pandas.util.decorators import (Appender, Substitution, cache_readonly, deprecate, deprecate_kwarg) import pandas.core.common as com -from pandas.core.missing import _clean_reindex_fill_method +import pandas.core.missing as missing from pandas.core.common import (isnull, array_equivalent, is_object_dtype, is_datetimetz, ABCSeries, ABCPeriodIndex, ABCMultiIndex, @@ -2034,7 +2034,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): positions matches the corresponding target values. Missing values in the target are marked by -1. """ - method = _clean_reindex_fill_method(method) + method = missing.clean_reindex_fill_method(method) target = _ensure_index(target) if tolerance is not None: tolerance = self._convert_tolerance(tolerance) diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 5844c69c57f0e..16b8fd8df4e2a 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -5,11 +5,11 @@ from pandas import compat from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg) -from pandas.core.missing import _clean_reindex_fill_method from pandas.core.config import get_option from pandas.indexes.base import Index, _index_shared_docs import pandas.core.base as base import pandas.core.common as com +import pandas.core.missing as missing import pandas.indexes.base as ibase @@ -415,7 +415,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): ------- (indexer, mask) : (ndarray, ndarray) """ - method = _clean_reindex_fill_method(method) + method = missing.clean_reindex_fill_method(method) target = ibase._ensure_index(target) if isinstance(target, CategoricalIndex): diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index de3a67ebc1abf..773852f986fe1 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -17,7 +17,7 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate, deprecate_kwarg) import pandas.core.common as com -from pandas.core.missing import _clean_reindex_fill_method +import pandas.core.missing as missing from pandas.core.common import (isnull, array_equivalent, is_object_dtype, _values_from_object, @@ -1334,8 +1334,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): ------- (indexer, mask) : (ndarray, ndarray) """ - method = _clean_reindex_fill_method(method) - + method = missing.clean_reindex_fill_method(method) target = _ensure_index(target) target_index = target diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index e8218ca5950ba..2d3adce236b40 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -9,8 +9,7 @@ import pandas as pd from pandas import Index -from pandas.compat import u -from pandas.util.misc import is_little_endian +from pandas.compat import u, is_platform_little_endian import pandas import pandas.util.testing as tm from pandas.tseries.offsets import Day, MonthEnd @@ -97,7 +96,7 @@ def compare_frame_dt_mixed_tzs(self, result, expected, typ, version): tm.assert_frame_equal(result, expected) def read_pickles(self, version): - if not is_little_endian(): + if not is_platform_little_endian(): raise nose.SkipTest("known failure on non-little endian") pth = tm.get_data_path('legacy_pickle/{0}'.format(str(version))) diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 861be35f6a2b4..4a7c5c3b79de8 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -15,12 +15,11 @@ import numpy.ma.mrecords as mrecords from pandas.compat import (lmap, long, zip, range, lrange, lzip, - OrderedDict) + OrderedDict, is_platform_little_endian) from pandas import compat from pandas import (DataFrame, Index, Series, notnull, isnull, MultiIndex, Timedelta, Timestamp, date_range) -from pandas.util.misc import is_little_endian from pandas.core.common import PandasError import pandas as pd import pandas.core.common as com @@ -1835,7 +1834,7 @@ def test_from_records_with_datetimes(self): # this may fail on certain platforms because of a numpy issue # related GH6140 - if not is_little_endian(): + if not is_platform_little_endian(): raise nose.SkipTest("known failure of test on non-little endian") # construction with a null in a recarray diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 68cc74e010781..71f2551e89ccf 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -15,7 +15,7 @@ import pandas.core.common as com import pandas.lib as lib -from pandas.compat import range, zip +from pandas.compat import range, zip, PY3 from pandas import compat from pandas.util.testing import (assertRaisesRegexp, assert_series_equal, @@ -549,6 +549,18 @@ def test_stat_unexpected_keyword(self): with assertRaisesRegexp(TypeError, 'unexpected keyword'): obj.any(epic=starwars) # logical_function + def test_api_compat(self): + + # GH 12021 + # compat for __name__, __qualname__ + + obj = self._construct(5) + for func in ['sum', 'cumsum', 'any', 'var']: + f = getattr(obj, func) + self.assertEqual(f.__name__, func) + if PY3: + self.assertTrue(f.__qualname__.endswith(func)) + class TestSeries(tm.TestCase, Generic): _typ = Series diff --git a/pandas/util/misc.py b/pandas/util/misc.py deleted file mode 100644 index 2dd59043b5f63..0000000000000 --- a/pandas/util/misc.py +++ /dev/null @@ -1,12 +0,0 @@ -""" various miscellaneous utilities """ - - -def is_little_endian(): - """ am I little endian """ - import sys - return sys.byteorder == 'little' - - -def exclusive(*args): - count = sum([arg is not None for arg in args]) - return count == 1
closes #12021 ``` (py3.5)[Tue Apr 05 08:16:18 ~/miniconda/envs/py3.5/pandas]$ ipython Python 3.5.1 |Continuum Analytics, Inc.| (default, Dec 7 2015, 11:24:55) In [1]: pd.Series.sum.__qualname__ Out[1]: 'Series.sum' In [2]: pd.Series.cumsum.__qualname__ Out[2]: 'Series.cumsum' In [3]: pd.Series.cumsum.__module__ Out[3]: 'pandas.core.series' In [4]: pd.Series.cumsum.__name__ Out[4]: 'cumsum' ```
https://api.github.com/repos/pandas-dev/pandas/pulls/12372
2016-02-17T22:34:50Z
2016-04-05T16:17:48Z
null
2016-04-05T16:35:49Z
API: allow scalar setting/getting via float indexer on integer indexes
diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index beb803282ebe3..4d1354a515b1c 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -717,6 +717,10 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index. Int64Index and RangeIndex ~~~~~~~~~~~~~~~~~~~~~~~~~ +.. warning:: + + Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`. + ``Int64Index`` is a fundamental basic index in *pandas*. This is an Immutable array implementing an ordered, sliceable set. Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``NDFrame`` objects. @@ -736,7 +740,6 @@ Float64Index operations by about 30x and boolean indexing operations on the ``Float64Index`` itself are about 2x as fast. - .. versionadded:: 0.13.0 By default a ``Float64Index`` will be automatically created when passing floating, or mixed-integer-floating values in index creation. @@ -797,12 +800,12 @@ In non-float indexes, slicing using floats will raise a ``TypeError`` .. warning:: - Using a scalar float indexer has been removed in 0.18.0, so the following will raise a ``TypeError`` + Using a scalar float indexer for ``.iloc`` has been removed in 0.18.0, so the following will raise a ``TypeError`` .. code-block:: python - In [3]: pd.Series(range(5))[3.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'> + In [3]: pd.Series(range(5)).iloc[3.0] + TypeError: cannot do positional indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'> Further the treatment of ``.ix`` with a float indexer on a non-float index, will be label based, and thus coerce the index. diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 98bc50bae9260..7494f8ae88307 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -53,6 +53,10 @@ advanced indexing. but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This should be a transparent change with only very limited API implications (See the :ref:`Internal Refactoring <whatsnew_0150.refactoring>`) +.. warning:: + + Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`. + See the :ref:`MultiIndex / Advanced Indexing <advanced>` for ``MultiIndex`` and more advanced indexing documentation. See the :ref:`cookbook<cookbook.selection>` for some advanced strategies diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index bdc20d964a06a..d4a5224f6f775 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1024,11 +1024,11 @@ Removal of deprecated float indexers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In :issue:`4892` indexing with floating point numbers on a non-``Float64Index`` was deprecated (in version 0.14.0). -In 0.18.0, this deprecation warning is removed and these will now raise a ``TypeError``. (:issue:`12165`) +In 0.18.0, this deprecation warning is removed and these will now raise a ``TypeError``. (:issue:`12165`, :issue:`12333`) .. ipython:: python - s = pd.Series([1,2,3]) + s = pd.Series([1, 2, 3], index=[4, 5, 6]) s s2 = pd.Series([1, 2, 3], index=list('abc')) s2 @@ -1037,15 +1037,18 @@ Previous Behavior: .. code-block:: python - In [2]: s[1.0] + # this is label indexing + In [2]: s[5.0] FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[2]: 2 + # this is positional indexing In [3]: s.iloc[1.0] FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[3]: 2 - In [4]: s.loc[1.0] + # this is label indexing + In [4]: s.loc[5.0] FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[4]: 2 @@ -1062,33 +1065,54 @@ Previous Behavior: New Behavior: +For iloc, getting & setting via a float scalar will always raise. + .. code-block:: python - In [2]: s[1.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> + In [3]: s.iloc[2.0] + TypeError: cannot do label indexing on <class 'pandas.indexes.numeric.Int64Index'> with these indexers [2.0] of <type 'float'> - In [3]: s.iloc[1.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> +Other indexers will coerce to a like integer for both getting and setting. The ``FutureWarning`` has been dropped for ``.loc``, ``.ix`` and ``[]``. - In [4]: s.loc[1.0] - TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> +.. ipython:: python - # .ix will now cause this to be a label lookup and coerce to and Index - In [5]: s2.ix[1.0] = 10 + s[5.0] + s.loc[5.0] + s.ix[5.0] - In [6]: s2 - Out[3]: - a 1 - b 2 - c 3 - 1.0 10 - dtype: int64 +and setting + +.. ipython:: python + + s_copy = s.copy() + s_copy[5.0] = 10 + s_copy + s_copy = s.copy() + s_copy.loc[5.0] = 10 + s_copy + s_copy = s.copy() + s_copy.ix[5.0] = 10 + s_copy + +Slicing will also coerce integer-like floats to integers for a non-``Float64Index``. + +.. ipython:: python + + s.loc[5.0:6] + s.ix[5.0:6] + +Note that for floats that are NOT coercible to ints, the label based bounds will be excluded + +.. ipython:: python + + s.loc[5.1:6] + s.ix[5.1:6] Float indexing on a ``Float64Index`` is unchanged. .. ipython:: python - s = pd.Series([1,2,3],index=np.arange(3.)) + s = pd.Series([1, 2, 3], index=np.arange(3.)) s[1.0] s[1.0:2.5] diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 6c2d4f7919ac6..80f3d0d66ca9a 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2016,7 +2016,7 @@ def _getitem_array(self, key): # with all other indexing behavior if isinstance(key, Series) and not key.index.equals(self.index): warnings.warn("Boolean Series key will be reindexed to match " - "DataFrame index.", UserWarning) + "DataFrame index.", UserWarning, stacklevel=3) elif len(key) != len(self.index): raise ValueError('Item wrong length %d instead of %d.' % (len(key), len(self.index))) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 03fa072db83da..b0dd2596fccd5 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -995,6 +995,10 @@ def _getitem_axis(self, key, axis=0): return self._getitem_iterable(key, axis=axis) else: + + # maybe coerce a float scalar to integer + key = labels._maybe_cast_indexer(key) + if is_integer(key): if axis == 0 and isinstance(labels, MultiIndex): try: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 8a679b1575e26..852cddc456213 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -902,6 +902,7 @@ def _mpl_repr(self): _na_value = np.nan """The expected NA value to use with this index.""" + # introspection @property def is_monotonic(self): """ alias for is_monotonic_increasing (deprecated) """ @@ -954,11 +955,12 @@ def is_categorical(self): return self.inferred_type in ['categorical'] def is_mixed(self): - return 'mixed' in self.inferred_type + return self.inferred_type in ['mixed'] def holds_integer(self): return self.inferred_type in ['integer', 'mixed-integer'] + # validate / convert indexers def _convert_scalar_indexer(self, key, kind=None): """ convert a scalar indexer @@ -966,44 +968,42 @@ def _convert_scalar_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) - - right now we are converting + kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] + if kind == 'iloc': - if is_integer(key): - return key - return self._invalid_indexer('positional', key) - else: + return self._validate_indexer('positional', key, kind) - if len(self): - - # we can safely disallow - # if we are not a MultiIndex - # or a Float64Index - # or have mixed inferred type (IOW we have the possiblity - # of a float in with say strings) - if is_float(key): - if not (isinstance(self, ABCMultiIndex,) or - self.is_floating() or self.is_mixed()): - return self._invalid_indexer('label', key) - - # we can disallow integers with loc - # if could not contain and integer - elif is_integer(key) and kind == 'loc': - if not (isinstance(self, ABCMultiIndex,) or - self.holds_integer() or self.is_mixed()): - return self._invalid_indexer('label', key) + if len(self) and not isinstance(self, ABCMultiIndex,): - return key + # we can raise here if we are definitive that this + # is positional indexing (eg. .ix on with a float) + # or label indexing if we are using a type able + # to be represented in the index - def _convert_slice_indexer_getitem(self, key, is_index_slice=False): - """ called from the getitem slicers, determine how to treat the key - whether positional or not """ - if self.is_integer() or is_index_slice: - return key - return self._convert_slice_indexer(key) + if kind in ['getitem', 'ix'] and is_float(key): + if not self.is_floating(): + return self._invalid_indexer('label', key) + + elif kind in ['loc'] and is_float(key): + + # we want to raise KeyError on string/mixed here + # technically we *could* raise a TypeError + # on anything but mixed though + if self.inferred_type not in ['floating', + 'mixed-integer-float', + 'string', + 'unicode', + 'mixed']: + return self._invalid_indexer('label', key) + + elif kind in ['loc'] and is_integer(key): + if not self.holds_integer(): + return self._invalid_indexer('label', key) + + return key def _convert_slice_indexer(self, key, kind=None): """ @@ -1012,8 +1012,9 @@ def _convert_slice_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) + kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] # if we are not a slice, then we are done if not isinstance(key, slice): @@ -1021,38 +1022,14 @@ def _convert_slice_indexer(self, key, kind=None): # validate iloc if kind == 'iloc': + return slice(self._validate_indexer('slice', key.start, kind), + self._validate_indexer('slice', key.stop, kind), + self._validate_indexer('slice', key.step, kind)) - # need to coerce to_int if needed - def f(c): - v = getattr(key, c) - if v is None or is_integer(v): - return v - self._invalid_indexer('slice {0} value'.format(c), v) - - return slice(*[f(c) for c in ['start', 'stop', 'step']]) - - # validate slicers - def validate(v): - if v is None or is_integer(v): - return True - - # dissallow floats (except for .ix) - elif is_float(v): - if kind == 'ix': - return True - - return False - - return True - - for c in ['start', 'stop', 'step']: - v = getattr(key, c) - if not validate(v): - self._invalid_indexer('slice {0} value'.format(c), v) - - # figure out if this is a positional indexer + # potentially cast the bounds to integers start, stop, step = key.start, key.stop, key.step + # figure out if this is a positional indexer def is_int(v): return v is None or is_integer(v) @@ -1061,8 +1038,14 @@ def is_int(v): is_positional = is_index_slice and not self.is_integer() if kind == 'getitem': - return self._convert_slice_indexer_getitem( - key, is_index_slice=is_index_slice) + """ + called from the getitem slicers, validate that we are in fact + integers + """ + if self.is_integer() or is_index_slice: + return slice(self._validate_indexer('slice', key.start, kind), + self._validate_indexer('slice', key.stop, kind), + self._validate_indexer('slice', key.step, kind)) # convert the slice to an indexer here @@ -1889,7 +1872,10 @@ def get_loc(self, key, method=None, tolerance=None): raise ValueError('tolerance argument only valid if using pad, ' 'backfill or nearest lookups') key = _values_from_object(key) - return self._engine.get_loc(key) + try: + return self._engine.get_loc(key) + except KeyError: + return self._engine.get_loc(self._maybe_cast_indexer(key)) indexer = self.get_indexer([key], method=method, tolerance=tolerance) if indexer.ndim > 1 or indexer.size > 1: @@ -2721,6 +2707,37 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): return slice(start_slice, end_slice, step) + def _maybe_cast_indexer(self, key): + """ + If we have a float key and are not a floating index + then try to cast to an int if equivalent + """ + + if is_float(key) and not self.is_floating(): + try: + ckey = int(key) + if ckey == key: + key = ckey + except (ValueError, TypeError): + pass + return key + + def _validate_indexer(self, form, key, kind): + """ + if we are positional indexer + validate that we have appropriate typed bounds + must be an integer + """ + assert kind in ['ix', 'loc', 'getitem', 'iloc'] + + if key is None: + pass + elif is_integer(key): + pass + elif kind in ['iloc', 'getitem']: + self._invalid_indexer(form, key) + return key + def _maybe_cast_slice_bound(self, label, side, kind): """ This function should be overloaded in subclasses that allow non-trivial @@ -2731,7 +2748,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -2742,6 +2759,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem', None] # We are a plain index here (sub-class override this method if they # wish to have special treatment for floats/ints, e.g. Float64Index and @@ -2783,9 +2801,11 @@ def get_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None, the type of indexer + kind : {'ix', 'loc', 'getitem'} """ + assert kind in ['ix', 'loc', 'getitem', None] + if side not in ('left', 'right'): raise ValueError("Invalid value for side kwarg," " must be either 'left' or 'right': %s" % @@ -2841,7 +2861,7 @@ def slice_locs(self, start=None, end=None, step=None, kind=None): If None, defaults to the end step : int, defaults None If None, defaults to 1 - kind : string, defaults None + kind : {'ix', 'loc', 'getitem'} or None Returns ------- diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index fea153b2de391..d14568ceca258 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1409,6 +1409,7 @@ def _tuple_index(self): return Index(self._values) def get_slice_bound(self, label, side, kind): + if not isinstance(label, tuple): label = label, return self._partial_tup_index(label, side=side) @@ -1743,7 +1744,7 @@ def convert_indexer(start, stop, step, indexer=indexer, labels=labels): # we have a partial slice (like looking up a partial date # string) start = stop = level_index.slice_indexer(key.start, key.stop, - key.step) + key.step, kind='loc') step = start.step if isinstance(start, slice) or isinstance(stop, slice): diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 0c102637ab70d..4b021c51456b9 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -7,6 +7,7 @@ from pandas.indexes.base import Index, InvalidIndexError from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com +from pandas.core.common import is_dtype_equal, isnull import pandas.indexes.base as ibase @@ -29,7 +30,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -40,18 +41,10 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem', None] - # we are a numeric index, so we accept - # integer directly - if com.is_integer(label): - pass - - # disallow floats only if we not-strict - elif com.is_float(label): - if not (self.is_floating() or kind in ['ix']): - self._invalid_indexer('slice', label) - - return label + # we will try to coerce to integers + return self._maybe_cast_indexer(label) def _convert_tolerance(self, tolerance): try: @@ -140,6 +133,24 @@ def is_all_dates(self): """ return False + def _convert_scalar_indexer(self, key, kind=None): + """ + convert a scalar indexer + + Parameters + ---------- + key : label of the slice bound + kind : {'ix', 'loc', 'getitem'} or None + """ + + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] + + # don't coerce ilocs to integers + if kind != 'iloc': + key = self._maybe_cast_indexer(key) + return (super(Int64Index, self) + ._convert_scalar_indexer(key, kind=kind)) + def equals(self, other): """ Determines if two Index objects contain the same elements. @@ -247,18 +258,13 @@ def _convert_scalar_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) - - right now we are converting - floats -> ints if the index supports it + kind : {'ix', 'loc', 'getitem'} or None """ - if kind == 'iloc': - if com.is_integer(key): - return key + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] - return (super(Float64Index, self) - ._convert_scalar_indexer(key, kind=kind)) + if kind == 'iloc': + return self._validate_indexer('positional', key, kind) return key @@ -282,7 +288,7 @@ def _convert_slice_indexer(self, key, kind=None): kind=kind) # translate to locations - return self.slice_indexer(key.start, key.stop, key.step) + return self.slice_indexer(key.start, key.stop, key.step, kind=kind) def _format_native_types(self, na_rep='', float_format=None, decimal='.', quoting=None, **kwargs): @@ -324,7 +330,7 @@ def equals(self, other): try: if not isinstance(other, Float64Index): other = self._constructor(other) - if (not com.is_dtype_equal(self.dtype, other.dtype) or + if (not is_dtype_equal(self.dtype, other.dtype) or self.shape != other.shape): return False left, right = self._values, other._values @@ -380,7 +386,7 @@ def isin(self, values, level=None): if level is not None: self._validate_index_level(level) return lib.ismember_nans(np.array(self), value_set, - com.isnull(list(value_set)).any()) + isnull(list(value_set)).any()) Float64Index._add_numeric_methods() diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 0bed2ec231dbe..4b06af9240436 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -487,8 +487,8 @@ def __getitem__(self, key): stop = l # delegate non-integer slices - if (start != int(start) and - stop != int(stop) and + if (start != int(start) or + stop != int(stop) or step != int(step)): return super_getitem(key) diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 264302866b023..2a3ee774af6e5 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -216,7 +216,7 @@ def test_getitem_boolean(self): # we are producing a warning that since the passed boolean # key is not the same as the given index, we will reindex # not sure this is really necessary - with tm.assert_produces_warning(UserWarning): + with tm.assert_produces_warning(UserWarning, check_stacklevel=False): indexer_obj = indexer_obj.reindex(self.tsframe.index[::-1]) subframe_obj = self.tsframe[indexer_obj] assert_frame_equal(subframe_obj, subframe) diff --git a/pandas/tests/indexing/__init__.py b/pandas/tests/indexing/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/tests/indexing/test_categorical.py b/pandas/tests/indexing/test_categorical.py new file mode 100644 index 0000000000000..4e31fb350f6ee --- /dev/null +++ b/pandas/tests/indexing/test_categorical.py @@ -0,0 +1,339 @@ +# -*- coding: utf-8 -*- + +import pandas as pd +import numpy as np +from pandas import Series, DataFrame +from pandas.util.testing import assert_series_equal, assert_frame_equal +from pandas.util import testing as tm + + +class TestCategoricalIndex(tm.TestCase): + + def setUp(self): + + self.df = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': Series(list('aabbca')).astype( + 'category', categories=list( + 'cab'))}).set_index('B') + self.df2 = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': Series(list('aabbca')).astype( + 'category', categories=list( + 'cabe'))}).set_index('B') + self.df3 = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': (Series([1, 1, 2, 1, 3, 2]) + .astype('category', categories=[3, 2, 1], + ordered=True))}).set_index('B') + self.df4 = DataFrame({'A': np.arange(6, dtype='int64'), + 'B': (Series([1, 1, 2, 1, 3, 2]) + .astype('category', categories=[3, 2, 1], + ordered=False))}).set_index('B') + + def test_loc_scalar(self): + result = self.df.loc['a'] + expected = (DataFrame({'A': [0, 1, 5], + 'B': (Series(list('aaa')) + .astype('category', + categories=list('cab')))}) + .set_index('B')) + assert_frame_equal(result, expected) + + df = self.df.copy() + df.loc['a'] = 20 + expected = (DataFrame({'A': [20, 20, 2, 3, 4, 20], + 'B': (Series(list('aabbca')) + .astype('category', + categories=list('cab')))}) + .set_index('B')) + assert_frame_equal(df, expected) + + # value not in the categories + self.assertRaises(KeyError, lambda: df.loc['d']) + + def f(): + df.loc['d'] = 10 + + self.assertRaises(TypeError, f) + + def f(): + df.loc['d', 'A'] = 10 + + self.assertRaises(TypeError, f) + + def f(): + df.loc['d', 'C'] = 10 + + self.assertRaises(TypeError, f) + + def test_loc_listlike(self): + + # list of labels + result = self.df.loc[['c', 'a']] + expected = self.df.iloc[[4, 0, 1, 5]] + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.loc[['a', 'b', 'e']] + exp_index = pd.CategoricalIndex( + list('aaabbe'), categories=list('cabe'), name='B') + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) + assert_frame_equal(result, expected, check_index_type=True) + + # element in the categories but not in the values + self.assertRaises(KeyError, lambda: self.df2.loc['e']) + + # assign is ok + df = self.df2.copy() + df.loc['e'] = 20 + result = df.loc[['a', 'b', 'e']] + exp_index = pd.CategoricalIndex( + list('aaabbe'), categories=list('cabe'), name='B') + expected = DataFrame({'A': [0, 1, 5, 2, 3, 20]}, index=exp_index) + assert_frame_equal(result, expected) + + df = self.df2.copy() + result = df.loc[['a', 'b', 'e']] + exp_index = pd.CategoricalIndex( + list('aaabbe'), categories=list('cabe'), name='B') + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) + assert_frame_equal(result, expected, check_index_type=True) + + # not all labels in the categories + self.assertRaises(KeyError, lambda: self.df2.loc[['a', 'd']]) + + def test_loc_listlike_dtypes(self): + # GH 11586 + + # unique categories and codes + index = pd.CategoricalIndex(['a', 'b', 'c']) + df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) + + # unique slice + res = df.loc[['a', 'b']] + exp = DataFrame({'A': [1, 2], + 'B': [4, 5]}, index=pd.CategoricalIndex(['a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[['a', 'a', 'b']] + exp = DataFrame({'A': [1, 1, 2], + 'B': [4, 4, 5]}, + index=pd.CategoricalIndex(['a', 'a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with tm.assertRaisesRegexp( + KeyError, + 'a list-indexer must only include values that are ' + 'in the categories'): + df.loc[['a', 'x']] + + # duplicated categories and codes + index = pd.CategoricalIndex(['a', 'b', 'a']) + df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) + + # unique slice + res = df.loc[['a', 'b']] + exp = DataFrame({'A': [1, 3, 2], + 'B': [4, 6, 5]}, + index=pd.CategoricalIndex(['a', 'a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[['a', 'a', 'b']] + exp = DataFrame( + {'A': [1, 3, 1, 3, 2], + 'B': [4, 6, 4, 6, 5 + ]}, index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'])) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with tm.assertRaisesRegexp( + KeyError, + 'a list-indexer must only include values ' + 'that are in the categories'): + df.loc[['a', 'x']] + + # contains unused category + index = pd.CategoricalIndex( + ['a', 'b', 'a', 'c'], categories=list('abcde')) + df = DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=index) + + res = df.loc[['a', 'b']] + exp = DataFrame({'A': [1, 3, 2], + 'B': [5, 7, 6]}, index=pd.CategoricalIndex( + ['a', 'a', 'b'], categories=list('abcde'))) + tm.assert_frame_equal(res, exp, check_index_type=True) + + res = df.loc[['a', 'e']] + exp = DataFrame({'A': [1, 3, np.nan], 'B': [5, 7, np.nan]}, + index=pd.CategoricalIndex(['a', 'a', 'e'], + categories=list('abcde'))) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[['a', 'a', 'b']] + exp = DataFrame({'A': [1, 3, 1, 3, 2], 'B': [5, 7, 5, 7, 6]}, + index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'], + categories=list('abcde'))) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with tm.assertRaisesRegexp( + KeyError, + 'a list-indexer must only include values ' + 'that are in the categories'): + df.loc[['a', 'x']] + + def test_read_only_source(self): + # GH 10043 + rw_array = np.eye(10) + rw_df = DataFrame(rw_array) + + ro_array = np.eye(10) + ro_array.setflags(write=False) + ro_df = DataFrame(ro_array) + + assert_frame_equal(rw_df.iloc[[1, 2, 3]], ro_df.iloc[[1, 2, 3]]) + assert_frame_equal(rw_df.iloc[[1]], ro_df.iloc[[1]]) + assert_series_equal(rw_df.iloc[1], ro_df.iloc[1]) + assert_frame_equal(rw_df.iloc[1:3], ro_df.iloc[1:3]) + + assert_frame_equal(rw_df.loc[[1, 2, 3]], ro_df.loc[[1, 2, 3]]) + assert_frame_equal(rw_df.loc[[1]], ro_df.loc[[1]]) + assert_series_equal(rw_df.loc[1], ro_df.loc[1]) + assert_frame_equal(rw_df.loc[1:3], ro_df.loc[1:3]) + + def test_reindexing(self): + + # reindexing + # convert to a regular index + result = self.df2.reindex(['a', 'b', 'e']) + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], + 'B': Series(list('aaabbe'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['a', 'b']) + expected = DataFrame({'A': [0, 1, 5, 2, 3], + 'B': Series(list('aaabb'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['e']) + expected = DataFrame({'A': [np.nan], + 'B': Series(['e'])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['d']) + expected = DataFrame({'A': [np.nan], + 'B': Series(['d'])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + # since we are actually reindexing with a Categorical + # then return a Categorical + cats = list('cabe') + + result = self.df2.reindex(pd.Categorical(['a', 'd'], categories=cats)) + expected = DataFrame({'A': [0, 1, 5, np.nan], + 'B': Series(list('aaad')).astype( + 'category', categories=cats)}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(pd.Categorical(['a'], categories=cats)) + expected = DataFrame({'A': [0, 1, 5], + 'B': Series(list('aaa')).astype( + 'category', categories=cats)}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['a', 'b', 'e']) + expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], + 'B': Series(list('aaabbe'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['a', 'b']) + expected = DataFrame({'A': [0, 1, 5, 2, 3], + 'B': Series(list('aaabb'))}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(['e']) + expected = DataFrame({'A': [np.nan], + 'B': Series(['e'])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + # give back the type of categorical that we received + result = self.df2.reindex(pd.Categorical( + ['a', 'd'], categories=cats, ordered=True)) + expected = DataFrame( + {'A': [0, 1, 5, np.nan], + 'B': Series(list('aaad')).astype('category', categories=cats, + ordered=True)}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + result = self.df2.reindex(pd.Categorical( + ['a', 'd'], categories=['a', 'd'])) + expected = DataFrame({'A': [0, 1, 5, np.nan], + 'B': Series(list('aaad')).astype( + 'category', categories=['a', 'd' + ])}).set_index('B') + assert_frame_equal(result, expected, check_index_type=True) + + # passed duplicate indexers are not allowed + self.assertRaises(ValueError, lambda: self.df2.reindex(['a', 'a'])) + + # args NotImplemented ATM + self.assertRaises(NotImplementedError, + lambda: self.df2.reindex(['a'], method='ffill')) + self.assertRaises(NotImplementedError, + lambda: self.df2.reindex(['a'], level=1)) + self.assertRaises(NotImplementedError, + lambda: self.df2.reindex(['a'], limit=2)) + + def test_loc_slice(self): + # slicing + # not implemented ATM + # GH9748 + + self.assertRaises(TypeError, lambda: self.df.loc[1:5]) + + # result = df.loc[1:5] + # expected = df.iloc[[1,2,3,4]] + # assert_frame_equal(result, expected) + + def test_boolean_selection(self): + + df3 = self.df3 + df4 = self.df4 + + result = df3[df3.index == 'a'] + expected = df3.iloc[[]] + assert_frame_equal(result, expected) + + result = df4[df4.index == 'a'] + expected = df4.iloc[[]] + assert_frame_equal(result, expected) + + result = df3[df3.index == 1] + expected = df3.iloc[[0, 1, 3]] + assert_frame_equal(result, expected) + + result = df4[df4.index == 1] + expected = df4.iloc[[0, 1, 3]] + assert_frame_equal(result, expected) + + # since we have an ordered categorical + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=True, + # name=u'B') + result = df3[df3.index < 2] + expected = df3.iloc[[4]] + assert_frame_equal(result, expected) + + result = df3[df3.index > 1] + expected = df3.iloc[[]] + assert_frame_equal(result, expected) + + # unordered + # cannot be compared + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=False, + # name=u'B') + self.assertRaises(TypeError, lambda: df4[df4.index < 2]) + self.assertRaises(TypeError, lambda: df4[df4.index > 1]) diff --git a/pandas/tests/indexing/test_floats.py b/pandas/tests/indexing/test_floats.py new file mode 100644 index 0000000000000..2a2f8678694de --- /dev/null +++ b/pandas/tests/indexing/test_floats.py @@ -0,0 +1,676 @@ +# -*- coding: utf-8 -*- + +import numpy as np +from pandas import Series, DataFrame, Index, Float64Index +from pandas.util.testing import assert_series_equal, assert_almost_equal +import pandas.util.testing as tm + + +class TestFloatIndexers(tm.TestCase): + + def check(self, result, original, indexer, getitem): + """ + comparator for results + we need to take care if we are indexing on a + Series or a frame + """ + if isinstance(original, Series): + expected = original.iloc[indexer] + else: + if getitem: + expected = original.iloc[:, indexer] + else: + expected = original.iloc[indexer] + + assert_almost_equal(result, expected) + + def test_scalar_error(self): + + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + # this duplicates the code below + # but is spefically testing for the error + # message + + for index in [tm.makeStringIndex, tm.makeUnicodeIndex, + tm.makeCategoricalIndex, + tm.makeDateIndex, tm.makeTimedeltaIndex, + tm.makePeriodIndex, tm.makeIntIndex, + tm.makeRangeIndex]: + + i = index(5) + + s = Series(np.arange(len(i)), index=i) + + def f(): + s.iloc[3.0] + self.assertRaisesRegexp(TypeError, + 'cannot do positional indexing', + f) + + def f(): + s.iloc[3.0] = 0 + self.assertRaises(TypeError, f) + + def test_scalar_non_numeric(self): + + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + + for index in [tm.makeStringIndex, tm.makeUnicodeIndex, + tm.makeCategoricalIndex, + tm.makeDateIndex, tm.makeTimedeltaIndex, + tm.makePeriodIndex]: + + i = index(5) + + for s in [Series( + np.arange(len(i)), index=i), DataFrame( + np.random.randn( + len(i), len(i)), index=i, columns=i)]: + + # getting + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.iloc, False), + (lambda x: x, True)]: + + def f(): + idxr(s)[3.0] + + # gettitem on a DataFrame is a KeyError as it is indexing + # via labels on the columns + if getitem and isinstance(s, DataFrame): + error = KeyError + else: + error = TypeError + self.assertRaises(error, f) + + # label based can be a TypeError or KeyError + def f(): + s.loc[3.0] + + if s.index.inferred_type in ['string', 'unicode', 'mixed']: + error = KeyError + else: + error = TypeError + self.assertRaises(error, f) + + # contains + self.assertFalse(3.0 in s) + + # setting with a float fails with iloc + def f(): + s.iloc[3.0] = 0 + self.assertRaises(TypeError, f) + + # setting with an indexer + if s.index.inferred_type in ['categorical']: + # Value or Type Error + pass + elif s.index.inferred_type in ['datetime64', 'timedelta64', + 'period']: + + # these should prob work + # and are inconsisten between series/dataframe ATM + # for idxr in [lambda x: x.ix, + # lambda x: x]: + # s2 = s.copy() + # def f(): + # idxr(s2)[3.0] = 0 + # self.assertRaises(TypeError, f) + pass + + else: + + s2 = s.copy() + s2.loc[3.0] = 10 + self.assertTrue(s2.index.is_object()) + + for idxr in [lambda x: x.ix, + lambda x: x]: + s2 = s.copy() + idxr(s2)[3.0] = 0 + self.assertTrue(s2.index.is_object()) + + # fallsback to position selection, series only + s = Series(np.arange(len(i)), index=i) + s[3] + self.assertRaises(TypeError, lambda: s[3.0]) + + def test_scalar_with_mixed(self): + + s2 = Series([1, 2, 3], index=['a', 'b', 'c']) + s3 = Series([1, 2, 3], index=['a', 'b', 1.5]) + + # lookup in a pure string index + # with an invalid indexer + for idxr in [lambda x: x.ix, + lambda x: x, + lambda x: x.iloc]: + + def f(): + idxr(s2)[1.0] + + self.assertRaises(TypeError, f) + + self.assertRaises(KeyError, lambda: s2.loc[1.0]) + + result = s2.loc['b'] + expected = 2 + self.assertEqual(result, expected) + + # mixed index so we have label + # indexing + for idxr in [lambda x: x.ix, + lambda x: x]: + + def f(): + idxr(s3)[1.0] + + self.assertRaises(TypeError, f) + + result = idxr(s3)[1] + expected = 2 + self.assertEqual(result, expected) + + self.assertRaises(TypeError, lambda: s3.iloc[1.0]) + self.assertRaises(KeyError, lambda: s3.loc[1.0]) + + result = s3.loc[1.5] + expected = 3 + self.assertEqual(result, expected) + + def test_scalar_integer(self): + + # test how scalar float indexers work on int indexes + + # integer index + for index in [tm.makeIntIndex, tm.makeRangeIndex]: + + i = index(5) + for s in [Series(np.arange(len(i))), + DataFrame(np.random.randn(len(i), len(i)), + index=i, columns=i)]: + + # coerce to equal int + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.loc, False), + (lambda x: x, True)]: + + result = idxr(s)[3.0] + self.check(result, s, 3, getitem) + + # coerce to equal int + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.loc, False), + (lambda x: x, True)]: + + if isinstance(s, Series): + compare = self.assertEqual + expected = 100 + else: + compare = tm.assert_series_equal + if getitem: + expected = Series(100, + index=range(len(s)), name=3) + else: + expected = Series(100., + index=range(len(s)), name=3) + + s2 = s.copy() + idxr(s2)[3.0] = 100 + + result = idxr(s2)[3.0] + compare(result, expected) + + result = idxr(s2)[3] + compare(result, expected) + + # contains + # coerce to equal int + self.assertTrue(3.0 in s) + + def test_scalar_float(self): + + # scalar float indexers work on a float index + index = Index(np.arange(5.)) + for s in [Series(np.arange(len(index)), index=index), + DataFrame(np.random.randn(len(index), len(index)), + index=index, columns=index)]: + + # assert all operations except for iloc are ok + indexer = index[3] + for idxr, getitem in [(lambda x: x.ix, False), + (lambda x: x.loc, False), + (lambda x: x, True)]: + + # getting + result = idxr(s)[indexer] + self.check(result, s, 3, getitem) + + # setting + s2 = s.copy() + + def f(): + idxr(s2)[indexer] = expected + result = idxr(s2)[indexer] + self.check(result, s, 3, getitem) + + # random integer is a KeyError + self.assertRaises(KeyError, lambda: idxr(s)[3.5]) + + # contains + self.assertTrue(3.0 in s) + + # iloc succeeds with an integer + expected = s.iloc[3] + s2 = s.copy() + + s2.iloc[3] = expected + result = s2.iloc[3] + self.check(result, s, 3, False) + + # iloc raises with a float + self.assertRaises(TypeError, lambda: s.iloc[3.0]) + + def g(): + s2.iloc[3.0] = 0 + self.assertRaises(TypeError, g) + + def test_slice_non_numeric(self): + + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + + for index in [tm.makeStringIndex, tm.makeUnicodeIndex, + tm.makeDateIndex, tm.makeTimedeltaIndex, + tm.makePeriodIndex]: + + index = index(5) + for s in [Series(range(5), index=index), + DataFrame(np.random.randn(5, 2), index=index)]: + + # getitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + def f(): + s.iloc[l] + self.assertRaises(TypeError, f) + + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x.iloc, + lambda x: x]: + + def f(): + idxr(s)[l] + self.assertRaises(TypeError, f) + + # setitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + def f(): + s.iloc[l] = 0 + self.assertRaises(TypeError, f) + + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x.iloc, + lambda x: x]: + def f(): + idxr(s)[l] = 0 + self.assertRaises(TypeError, f) + + def test_slice_integer(self): + + # same as above, but for Integer based indexes + # these coerce to a like integer + # oob indiciates if we are out of bounds + # of positional indexing + for index, oob in [(tm.makeIntIndex(5), False), + (tm.makeRangeIndex(5), False), + (tm.makeIntIndex(5) + 10, True)]: + + # s is an in-range index + s = Series(range(5), index=index) + + # getitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + + result = idxr(s)[l] + + # these are all label indexing + # except getitem which is positional + # empty + if oob: + indexer = slice(0, 0) + else: + indexer = slice(3, 5) + self.check(result, s, indexer, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # getitem out-of-bounds + for l in [slice(-6, 6), + slice(-6.0, 6.0)]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + result = idxr(s)[l] + + # these are all label indexing + # except getitem which is positional + # empty + if oob: + indexer = slice(0, 0) + else: + indexer = slice(-6, 6) + self.check(result, s, indexer, False) + + # positional indexing + def f(): + s[slice(-6.0, 6.0)] + + self.assertRaises(TypeError, f) + + # getitem odd floats + for l, res1 in [(slice(2.5, 4), slice(3, 5)), + (slice(2, 3.5), slice(2, 4)), + (slice(2.5, 3.5), slice(3, 4))]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + + result = idxr(s)[l] + if oob: + res = slice(0, 0) + else: + res = res1 + + self.check(result, s, res, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # setitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + sc = s.copy() + idxr(sc)[l] = 0 + result = idxr(sc)[l].values.ravel() + self.assertTrue((result == 0).all()) + + # positional indexing + def f(): + s[l] = 0 + + self.assertRaises(TypeError, f) + + def test_integer_positional_indexing(self): + """ make sure that we are raising on positional indexing + w.r.t. an integer index """ + + s = Series(range(2, 6), index=range(2, 6)) + + result = s[2:4] + expected = s.iloc[2:4] + assert_series_equal(result, expected) + + for idxr in [lambda x: x, + lambda x: x.iloc]: + + for l in [slice(2, 4.0), + slice(2.0, 4), + slice(2.0, 4.0)]: + + def f(): + idxr(s)[l] + + self.assertRaises(TypeError, f) + + def test_slice_integer_frame_getitem(self): + + # similar to above, but on the getitem dim (of a DataFrame) + for index in [tm.makeIntIndex, tm.makeRangeIndex]: + + index = index(5) + s = DataFrame(np.random.randn(5, 2), index=index) + + for idxr in [lambda x: x.loc, + lambda x: x.ix]: + + # getitem + for l in [slice(0.0, 1), + slice(0, 1.0), + slice(0.0, 1.0)]: + + result = idxr(s)[l] + indexer = slice(0, 2) + self.check(result, s, indexer, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # getitem out-of-bounds + for l in [slice(-10, 10), + slice(-10.0, 10.0)]: + + result = idxr(s)[l] + self.check(result, s, slice(-10, 10), True) + + # positional indexing + def f(): + s[slice(-10.0, 10.0)] + + self.assertRaises(TypeError, f) + + # getitem odd floats + for l, res in [(slice(0.5, 1), slice(1, 2)), + (slice(0, 0.5), slice(0, 1)), + (slice(0.5, 1.5), slice(1, 2))]: + + result = idxr(s)[l] + self.check(result, s, res, False) + + # positional indexing + def f(): + s[l] + + self.assertRaises(TypeError, f) + + # setitem + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + sc = s.copy() + idxr(sc)[l] = 0 + result = idxr(sc)[l].values.ravel() + self.assertTrue((result == 0).all()) + + # positional indexing + def f(): + s[l] = 0 + + self.assertRaises(TypeError, f) + + def test_slice_float(self): + + # same as above, but for floats + index = Index(np.arange(5.)) + 0.1 + for s in [Series(range(5), index=index), + DataFrame(np.random.randn(5, 2), index=index)]: + + for l in [slice(3.0, 4), + slice(3, 4.0), + slice(3.0, 4.0)]: + + expected = s.iloc[3:4] + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x]: + + # getitem + result = idxr(s)[l] + self.assertTrue(result.equals(expected)) + + # setitem + s2 = s.copy() + idxr(s2)[l] = 0 + result = idxr(s2)[l].values.ravel() + self.assertTrue((result == 0).all()) + + def test_floating_index_doc_example(self): + + index = Index([1.5, 2, 3, 4.5, 5]) + s = Series(range(5), index=index) + self.assertEqual(s[3], 2) + self.assertEqual(s.ix[3], 2) + self.assertEqual(s.loc[3], 2) + self.assertEqual(s.iloc[3], 3) + + def test_floating_misc(self): + + # related 236 + # scalar/slicing of a float index + s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64) + + # label based slicing + result1 = s[1.0:3.0] + result2 = s.ix[1.0:3.0] + result3 = s.loc[1.0:3.0] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + + # exact indexing when found + result1 = s[5.0] + result2 = s.loc[5.0] + result3 = s.ix[5.0] + self.assertEqual(result1, result2) + self.assertEqual(result1, result3) + + result1 = s[5] + result2 = s.loc[5] + result3 = s.ix[5] + self.assertEqual(result1, result2) + self.assertEqual(result1, result3) + + self.assertEqual(s[5.0], s[5]) + + # value not found (and no fallbacking at all) + + # scalar integers + self.assertRaises(KeyError, lambda: s.loc[4]) + self.assertRaises(KeyError, lambda: s.ix[4]) + self.assertRaises(KeyError, lambda: s[4]) + + # fancy floats/integers create the correct entry (as nan) + # fancy tests + expected = Series([2, 0], index=Float64Index([5.0, 0.0])) + for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float + assert_series_equal(s[fancy_idx], expected) + assert_series_equal(s.loc[fancy_idx], expected) + assert_series_equal(s.ix[fancy_idx], expected) + + expected = Series([2, 0], index=Index([5, 0], dtype='int64')) + for fancy_idx in [[5, 0], np.array([5, 0])]: # int + assert_series_equal(s[fancy_idx], expected) + assert_series_equal(s.loc[fancy_idx], expected) + assert_series_equal(s.ix[fancy_idx], expected) + + # all should return the same as we are slicing 'the same' + result1 = s.loc[2:5] + result2 = s.loc[2.0:5.0] + result3 = s.loc[2.0:5] + result4 = s.loc[2.1:5] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + # previously this did fallback indexing + result1 = s[2:5] + result2 = s[2.0:5.0] + result3 = s[2.0:5] + result4 = s[2.1:5] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + result1 = s.ix[2:5] + result2 = s.ix[2.0:5.0] + result3 = s.ix[2.0:5] + result4 = s.ix[2.1:5] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + # combined test + result1 = s.loc[2:5] + result2 = s.ix[2:5] + result3 = s[2:5] + + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + + # list selection + result1 = s[[0.0, 5, 10]] + result2 = s.loc[[0.0, 5, 10]] + result3 = s.ix[[0.0, 5, 10]] + result4 = s.iloc[[0, 2, 4]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, result4) + + result1 = s[[1.6, 5, 10]] + result2 = s.loc[[1.6, 5, 10]] + result3 = s.ix[[1.6, 5, 10]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, Series( + [np.nan, 2, 4], index=[1.6, 5, 10])) + + result1 = s[[0, 1, 2]] + result2 = s.ix[[0, 1, 2]] + result3 = s.loc[[0, 1, 2]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, Series( + [0.0, np.nan, np.nan], index=[0, 1, 2])) + + result1 = s.loc[[2.5, 5]] + result2 = s.ix[[2.5, 5]] + assert_series_equal(result1, result2) + assert_series_equal(result1, Series([1, 2], index=[2.5, 5.0])) + + result1 = s[[2.5]] + result2 = s.ix[[2.5]] + result3 = s.loc[[2.5]] + assert_series_equal(result1, result2) + assert_series_equal(result1, result3) + assert_series_equal(result1, Series([1], index=[2.5])) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/indexing/test_indexing.py similarity index 84% rename from pandas/tests/test_indexing.py rename to pandas/tests/indexing/test_indexing.py index c95be009ee38d..89552ab776608 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -17,7 +17,7 @@ from pandas import option_context from pandas.core.indexing import _non_reducing_slice, _maybe_numeric_slice from pandas.core.api import (DataFrame, Index, Series, Panel, isnull, - MultiIndex, Float64Index, Timestamp, Timedelta) + MultiIndex, Timestamp, Timedelta) from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_attr_equal) @@ -3518,29 +3518,29 @@ def test_iloc_mask(self): 'integer type is not available'), } - warnings.filterwarnings(action='ignore', category=UserWarning) - result = dict() - for idx in [None, 'index', 'locs']: - mask = (df.nums > 2).values - if idx: - mask = Series(mask, list(reversed(getattr(df, idx)))) - for method in ['', '.loc', '.iloc']: - try: - if method: - accessor = getattr(df, method[1:]) - else: - accessor = df - ans = str(bin(accessor[mask]['nums'].sum())) - except Exception as e: - ans = str(e) - - key = tuple([idx, method]) - r = expected.get(key) - if r != ans: - raise AssertionError( - "[%s] does not match [%s], received [%s]" - % (key, ans, r)) - warnings.filterwarnings(action='always', category=UserWarning) + # UserWarnings from reindex of a boolean mask + with warnings.catch_warnings(record=True): + result = dict() + for idx in [None, 'index', 'locs']: + mask = (df.nums > 2).values + if idx: + mask = Series(mask, list(reversed(getattr(df, idx)))) + for method in ['', '.loc', '.iloc']: + try: + if method: + accessor = getattr(df, method[1:]) + else: + accessor = df + ans = str(bin(accessor[mask]['nums'].sum())) + except Exception as e: + ans = str(e) + + key = tuple([idx, method]) + r = expected.get(key) + if r != ans: + raise AssertionError( + "[%s] does not match [%s], received [%s]" + % (key, ans, r)) def test_ix_slicing_strings(self): # GH3836 @@ -4979,324 +4979,6 @@ def test_float64index_slicing_bug(self): result = s.value_counts() str(result) - def test_floating_index_doc_example(self): - - index = Index([1.5, 2, 3, 4.5, 5]) - s = Series(range(5), index=index) - self.assertEqual(s[3], 2) - self.assertEqual(s.ix[3], 2) - self.assertEqual(s.loc[3], 2) - self.assertEqual(s.iloc[3], 3) - - def test_floating_index(self): - - # related 236 - # scalar/slicing of a float index - s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64) - - # label based slicing - result1 = s[1.0:3.0] - result2 = s.ix[1.0:3.0] - result3 = s.loc[1.0:3.0] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # exact indexing when found - result1 = s[5.0] - result2 = s.loc[5.0] - result3 = s.ix[5.0] - self.assertEqual(result1, result2) - self.assertEqual(result1, result3) - - result1 = s[5] - result2 = s.loc[5] - result3 = s.ix[5] - self.assertEqual(result1, result2) - self.assertEqual(result1, result3) - - self.assertEqual(s[5.0], s[5]) - - # value not found (and no fallbacking at all) - - # scalar integers - self.assertRaises(KeyError, lambda: s.loc[4]) - self.assertRaises(KeyError, lambda: s.ix[4]) - self.assertRaises(KeyError, lambda: s[4]) - - # fancy floats/integers create the correct entry (as nan) - # fancy tests - expected = Series([2, 0], index=Float64Index([5.0, 0.0])) - for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float - assert_series_equal(s[fancy_idx], expected) - assert_series_equal(s.loc[fancy_idx], expected) - assert_series_equal(s.ix[fancy_idx], expected) - - expected = Series([2, 0], index=Index([5, 0], dtype='int64')) - for fancy_idx in [[5, 0], np.array([5, 0])]: # int - assert_series_equal(s[fancy_idx], expected) - assert_series_equal(s.loc[fancy_idx], expected) - assert_series_equal(s.ix[fancy_idx], expected) - - # all should return the same as we are slicing 'the same' - result1 = s.loc[2:5] - result2 = s.loc[2.0:5.0] - result3 = s.loc[2.0:5] - result4 = s.loc[2.1:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - # previously this did fallback indexing - result1 = s[2:5] - result2 = s[2.0:5.0] - result3 = s[2.0:5] - result4 = s[2.1:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - result1 = s.ix[2:5] - result2 = s.ix[2.0:5.0] - result3 = s.ix[2.0:5] - result4 = s.ix[2.1:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - # combined test - result1 = s.loc[2:5] - result2 = s.ix[2:5] - result3 = s[2:5] - - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # list selection - result1 = s[[0.0, 5, 10]] - result2 = s.loc[[0.0, 5, 10]] - result3 = s.ix[[0.0, 5, 10]] - result4 = s.iloc[[0, 2, 4]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, result4) - - result1 = s[[1.6, 5, 10]] - result2 = s.loc[[1.6, 5, 10]] - result3 = s.ix[[1.6, 5, 10]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, Series( - [np.nan, 2, 4], index=[1.6, 5, 10])) - - result1 = s[[0, 1, 2]] - result2 = s.ix[[0, 1, 2]] - result3 = s.loc[[0, 1, 2]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, Series( - [0.0, np.nan, np.nan], index=[0, 1, 2])) - - result1 = s.loc[[2.5, 5]] - result2 = s.ix[[2.5, 5]] - assert_series_equal(result1, result2) - assert_series_equal(result1, Series([1, 2], index=[2.5, 5.0])) - - result1 = s[[2.5]] - result2 = s.ix[[2.5]] - result3 = s.loc[[2.5]] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - assert_series_equal(result1, Series([1], index=[2.5])) - - def test_scalar_indexer(self): - # float indexing checked above - - def check_invalid(index, loc=None, iloc=None, ix=None, getitem=None): - - # related 236/4850 - # trying to access with a float index - s = Series(np.arange(len(index)), index=index) - - if iloc is None: - iloc = TypeError - self.assertRaises(iloc, lambda: s.iloc[3.5]) - if loc is None: - loc = TypeError - self.assertRaises(loc, lambda: s.loc[3.5]) - if ix is None: - ix = TypeError - self.assertRaises(ix, lambda: s.ix[3.5]) - if getitem is None: - getitem = TypeError - self.assertRaises(getitem, lambda: s[3.5]) - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeIntIndex, tm.makeRangeIndex, tm.makeDateIndex, - tm.makePeriodIndex]: - check_invalid(index()) - check_invalid(Index(np.arange(5) * 2.5), - loc=KeyError, - ix=KeyError, - getitem=KeyError) - - def check_index(index, error): - index = index() - s = Series(np.arange(len(index)), index=index) - - # positional selection - result1 = s[5] - self.assertRaises(TypeError, lambda: s[5.0]) - result3 = s.iloc[5] - self.assertRaises(TypeError, lambda: s.iloc[5.0]) - - # by value - self.assertRaises(TypeError, lambda: s.loc[5]) - self.assertRaises(TypeError, lambda: s.loc[5.0]) - - # this is fallback, so it works - result5 = s.ix[5] - self.assertRaises(error, lambda: s.ix[5.0]) - - self.assertEqual(result1, result3) - self.assertEqual(result1, result5) - - # string-like - for index in [tm.makeStringIndex, tm.makeUnicodeIndex]: - check_index(index, TypeError) - - # datetimelike - for index in [tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - check_index(index, TypeError) - - # exact indexing when found on IntIndex - s = Series(np.arange(10), dtype='int64') - - self.assertRaises(TypeError, lambda: s[5.0]) - self.assertRaises(TypeError, lambda: s.loc[5.0]) - self.assertRaises(TypeError, lambda: s.ix[5.0]) - result4 = s[5] - result5 = s.loc[5] - result6 = s.ix[5] - self.assertEqual(result4, result5) - self.assertEqual(result4, result6) - - def test_slice_indexer(self): - def check_iloc_compat(s): - # these are exceptions - self.assertRaises(TypeError, lambda: s.iloc[6.0:8]) - self.assertRaises(TypeError, lambda: s.iloc[6.0:8.0]) - self.assertRaises(TypeError, lambda: s.iloc[6:8.0]) - - def check_slicing_positional(index): - - s = Series(np.arange(len(index)) + 10, index=index) - - # these are all positional - result1 = s[2:5] - result2 = s.ix[2:5] - result3 = s.iloc[2:5] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # loc will fail - self.assertRaises(TypeError, lambda: s.loc[2:5]) - - # make all float slicing fail - self.assertRaises(TypeError, lambda: s[2.0:5]) - self.assertRaises(TypeError, lambda: s[2.0:5.0]) - self.assertRaises(TypeError, lambda: s[2:5.0]) - - self.assertRaises(TypeError, lambda: s.ix[2.0:5]) - self.assertRaises(TypeError, lambda: s.ix[2.0:5.0]) - self.assertRaises(TypeError, lambda: s.ix[2:5.0]) - - self.assertRaises(TypeError, lambda: s.loc[2.0:5]) - self.assertRaises(TypeError, lambda: s.loc[2.0:5.0]) - self.assertRaises(TypeError, lambda: s.loc[2:5.0]) - - check_iloc_compat(s) - - # all index types except int, float - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - check_slicing_positional(index()) - - ############ - # IntIndex # - ############ - for index in [tm.makeIntIndex(), tm.makeRangeIndex()]: - - s = Series(np.arange(len(index), dtype='int64') + 10, index + 5) - - # this is positional - result1 = s[2:5] - result4 = s.iloc[2:5] - assert_series_equal(result1, result4) - - # these are all label based - result2 = s.ix[2:5] - result3 = s.loc[2:5] - assert_series_equal(result2, result3) - - # float slicers on an int index with ix - expected = Series([11, 12, 13], index=[6, 7, 8]) - result = s.ix[6.0:8.5] - assert_series_equal(result, expected) - - result = s.ix[5.5:8.5] - assert_series_equal(result, expected) - - result = s.ix[5.5:8.0] - assert_series_equal(result, expected) - - for method in ['loc', 'iloc']: - # make all float slicing fail for .loc with an int index - self.assertRaises(TypeError, - lambda: getattr(s, method)[6.0:8]) - self.assertRaises(TypeError, - lambda: getattr(s, method)[6.0:8.0]) - self.assertRaises(TypeError, - lambda: getattr(s, method)[6:8.0]) - - # make all float slicing fail for [] with an int index - self.assertRaises(TypeError, lambda: s[6.0:8]) - self.assertRaises(TypeError, lambda: s[6.0:8.0]) - self.assertRaises(TypeError, lambda: s[6:8.0]) - - check_iloc_compat(s) - - ############## - # FloatIndex # - ############## - s.index = s.index.astype('float64') - - # these are all value based - result1 = s[6:8] - result2 = s.ix[6:8] - result3 = s.loc[6:8] - assert_series_equal(result1, result2) - assert_series_equal(result1, result3) - - # these are valid for all methods - # these are treated like labels (e.g. the rhs IS included) - def compare(slicers, expected): - for method in [lambda x: x, lambda x: x.loc, lambda x: x.ix]: - for slices in slicers: - - result = method(s)[slices] - assert_series_equal(result, expected) - - compare([slice(6.0, 8), slice(6.0, 8.0), slice(6, 8.0)], - s[(s.index >= 6.0) & (s.index <= 8)]) - compare([slice(6.5, 8), slice(6.5, 8.5)], - s[(s.index >= 6.5) & (s.index <= 8.5)]) - compare([slice(6, 8.5)], s[(s.index >= 6.0) & (s.index <= 8.5)]) - compare([slice(6.5, 6.5)], s[(s.index >= 6.5) & (s.index <= 6.5)]) - - check_iloc_compat(s) - def test_set_ix_out_of_bounds_axis_0(self): df = pd.DataFrame( randn(2, 5), index=["row%s" % i for i in range(2)], @@ -5362,347 +5044,46 @@ def test_index_type_coercion(self): self.assertTrue(s.index.is_integer()) - for attr in ['ix', 'loc']: + for indexer in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x]: s2 = s.copy() - getattr(s2, attr)[0.1] = 0 + indexer(s2)[0.1] = 0 self.assertTrue(s2.index.is_floating()) - self.assertTrue(getattr(s2, attr)[0.1] == 0) + self.assertTrue(indexer(s2)[0.1] == 0) s2 = s.copy() - getattr(s2, attr)[0.0] = 0 + indexer(s2)[0.0] = 0 exp = s.index if 0 not in s: exp = Index(s.index.tolist() + [0]) tm.assert_index_equal(s2.index, exp) s2 = s.copy() - getattr(s2, attr)['0'] = 0 + indexer(s2)['0'] = 0 self.assertTrue(s2.index.is_object()) - # setitem - s2 = s.copy() - s2[0.1] = 0 - self.assertTrue(s2.index.is_floating()) - self.assertTrue(s2[0.1] == 0) - - s2 = s.copy() - s2[0.0] = 0 - exp = s.index - if 0 not in s: - exp = Index(s.index.tolist() + [0]) - tm.assert_index_equal(s2.index, exp) - - s2 = s.copy() - s2['0'] = 0 - self.assertTrue(s2.index.is_object()) - for s in [Series(range(5), index=np.arange(5.))]: self.assertTrue(s.index.is_floating()) - for attr in ['ix', 'loc']: + for idxr in [lambda x: x.ix, + lambda x: x.loc, + lambda x: x]: s2 = s.copy() - getattr(s2, attr)[0.1] = 0 + idxr(s2)[0.1] = 0 self.assertTrue(s2.index.is_floating()) - self.assertTrue(getattr(s2, attr)[0.1] == 0) + self.assertTrue(idxr(s2)[0.1] == 0) s2 = s.copy() - getattr(s2, attr)[0.0] = 0 + idxr(s2)[0.0] = 0 tm.assert_index_equal(s2.index, s.index) s2 = s.copy() - getattr(s2, attr)['0'] = 0 + idxr(s2)['0'] = 0 self.assertTrue(s2.index.is_object()) - # setitem - s2 = s.copy() - s2[0.1] = 0 - self.assertTrue(s2.index.is_floating()) - self.assertTrue(s2[0.1] == 0) - - s2 = s.copy() - s2[0.0] = 0 - tm.assert_index_equal(s2.index, s.index) - - s2 = s.copy() - s2['0'] = 0 - self.assertTrue(s2.index.is_object()) - - def test_invalid_scalar_float_indexers_error(self): - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeCategoricalIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - - i = index(5) - - s = Series(np.arange(len(i)), index=i) - - def f(): - s.iloc[3.0] - self.assertRaisesRegexp(TypeError, - 'cannot do positional indexing', - f) - - def test_invalid_scalar_float_indexers(self): - - # GH 4892 - # float_indexers should raise exceptions - # on appropriate Index types & accessors - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeCategoricalIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - - i = index(5) - - for s in [Series( - np.arange(len(i)), index=i), DataFrame( - np.random.randn( - len(i), len(i)), index=i, columns=i)]: - - for attr in ['iloc', 'loc', 'ix', '__getitem__']: - def f(): - getattr(s, attr)()[3.0] - self.assertRaises(TypeError, f) - - # setting only fails with iloc as - # the others expand the index - def f(): - s.iloc[3.0] = 0 - self.assertRaises(TypeError, f) - - # fallsback to position selection ,series only - s = Series(np.arange(len(i)), index=i) - s[3] - self.assertRaises(TypeError, lambda: s[3.0]) - - # integer index - for index in [tm.makeIntIndex, tm.makeRangeIndex]: - - i = index(5) - for s in [Series(np.arange(len(i))), - DataFrame(np.random.randn(len(i), len(i)), - index=i, columns=i)]: - - # any kind of get access should fail - for attr in ['iloc', 'loc', 'ix']: - def f(): - getattr(s, attr)[3.0] - self.assertRaises(TypeError, f) - error = KeyError if isinstance(s, DataFrame) else TypeError - self.assertRaises(error, lambda: s[3.0]) - - # setting only fails with iloc as - def f(): - s.iloc[3.0] = 0 - self.assertRaises(TypeError, f) - - # other indexers will coerce to an object index - # tested explicity in: test_invalid_scalar_float_indexers - # above - - # floats index - index = tm.makeFloatIndex(5) - for s in [Series(np.arange(len(index)), index=index), - DataFrame(np.random.randn(len(index), len(index)), - index=index, columns=index)]: - - # assert all operations except for iloc are ok - indexer = index[3] - expected = s.iloc[3] - - if isinstance(s, Series): - compare = self.assertEqual - else: - compare = tm.assert_series_equal - - for attr in ['loc', 'ix']: - - # getting - result = getattr(s, attr)[indexer] - compare(result, expected) - - # setting - s2 = s.copy() - - def f(): - getattr(s2, attr)[indexer] = expected - result = getattr(s2, attr)[indexer] - compare(result, expected) - - # random integer is a KeyError - self.assertRaises(KeyError, lambda: getattr(s, attr)[3]) - - # iloc succeeds with an integer - result = s.iloc[3] - compare(result, expected) - - s2 = s.copy() - - def f(): - s2.iloc[3] = expected - result = s2.iloc[3] - compare(result, expected) - - # iloc raises with a float - self.assertRaises(TypeError, lambda: s.iloc[3.0]) - - def f(): - s.iloc[3.0] = 0 - self.assertRaises(TypeError, f) - - # getitem - - # getting - if isinstance(s, DataFrame): - expected = s.iloc[:, 3] - result = s[indexer] - compare(result, expected) - - # setting - s2 = s.copy() - - def f(): - s2[indexer] = expected - result = s2[indexer] - compare(result, expected) - - # random integer is a KeyError - result = self.assertRaises(KeyError, lambda: s[3]) - - def test_invalid_slice_float_indexers(self): - - # GH 4892 - # float_indexers should raise exceptions - # on appropriate Index types & accessors - - for index in [tm.makeStringIndex, tm.makeUnicodeIndex, - tm.makeDateIndex, tm.makeTimedeltaIndex, - tm.makePeriodIndex]: - - index = index(5) - for s in [Series(range(5), index=index), - DataFrame(np.random.randn(5, 2), index=index)]: - - # getitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] - self.assertRaises(TypeError, f) - - def f(): - s[l] - self.assertRaises(TypeError, f) - - def f(): - s.ix[l] - self.assertRaises(TypeError, f) - - # setitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s.ix[l] = 0 - self.assertRaises(TypeError, f) - - # same as above, but for Integer based indexes - for index in [tm.makeIntIndex, tm.makeRangeIndex]: - - index = index(5) - for s in [Series(range(5), index=index), - DataFrame(np.random.randn(5, 2), index=index)]: - - # getitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] - self.assertRaises(TypeError, f) - - def f(): - s[l] - self.assertRaises(TypeError, f) - - # ix allows float slicing - s.ix[l] - - # setitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - def f(): - s.iloc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s.loc[l] = 0 - self.assertRaises(TypeError, f) - - def f(): - s[l] = 0 - self.assertRaises(TypeError, f) - - # ix allows float slicing - s.ix[l] = 0 - - # same as above, but for floats - index = tm.makeFloatIndex(5) - for s in [Series(range(5), index=index), - DataFrame(np.random.randn(5, 2), index=index)]: - - # getitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - # ix is ok - result1 = s.ix[3:4] - result2 = s.ix[3.0:4] - result3 = s.ix[3.0:4.0] - result4 = s.ix[3:4.0] - self.assertTrue(result1.equals(result2)) - self.assertTrue(result1.equals(result3)) - self.assertTrue(result1.equals(result4)) - - # setitem - for l in [slice(3.0, 4), - slice(3, 4.0), - slice(3.0, 4.0)]: - - pass - def test_float_index_to_mixed(self): df = DataFrame({0.0: np.random.rand(10), 1.0: np.random.rand(10)}) df['a'] = 10 @@ -5929,338 +5310,6 @@ def test_maybe_numeric_slice(self): self.assertEqual(result, expected) -class TestCategoricalIndex(tm.TestCase): - - def setUp(self): - - self.df = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': Series(list('aabbca')).astype( - 'category', categories=list( - 'cab'))}).set_index('B') - self.df2 = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': Series(list('aabbca')).astype( - 'category', categories=list( - 'cabe'))}).set_index('B') - self.df3 = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': (Series([1, 1, 2, 1, 3, 2]) - .astype('category', categories=[3, 2, 1], - ordered=True))}).set_index('B') - self.df4 = DataFrame({'A': np.arange(6, dtype='int64'), - 'B': (Series([1, 1, 2, 1, 3, 2]) - .astype('category', categories=[3, 2, 1], - ordered=False))}).set_index('B') - - def test_loc_scalar(self): - result = self.df.loc['a'] - expected = (DataFrame({'A': [0, 1, 5], - 'B': (Series(list('aaa')) - .astype('category', - categories=list('cab')))}) - .set_index('B')) - assert_frame_equal(result, expected) - - df = self.df.copy() - df.loc['a'] = 20 - expected = (DataFrame({'A': [20, 20, 2, 3, 4, 20], - 'B': (Series(list('aabbca')) - .astype('category', - categories=list('cab')))}) - .set_index('B')) - assert_frame_equal(df, expected) - - # value not in the categories - self.assertRaises(KeyError, lambda: df.loc['d']) - - def f(): - df.loc['d'] = 10 - - self.assertRaises(TypeError, f) - - def f(): - df.loc['d', 'A'] = 10 - - self.assertRaises(TypeError, f) - - def f(): - df.loc['d', 'C'] = 10 - - self.assertRaises(TypeError, f) - - def test_loc_listlike(self): - - # list of labels - result = self.df.loc[['c', 'a']] - expected = self.df.iloc[[4, 0, 1, 5]] - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.loc[['a', 'b', 'e']] - exp_index = pd.CategoricalIndex( - list('aaabbe'), categories=list('cabe'), name='B') - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) - assert_frame_equal(result, expected, check_index_type=True) - - # element in the categories but not in the values - self.assertRaises(KeyError, lambda: self.df2.loc['e']) - - # assign is ok - df = self.df2.copy() - df.loc['e'] = 20 - result = df.loc[['a', 'b', 'e']] - exp_index = pd.CategoricalIndex( - list('aaabbe'), categories=list('cabe'), name='B') - expected = DataFrame({'A': [0, 1, 5, 2, 3, 20]}, index=exp_index) - assert_frame_equal(result, expected) - - df = self.df2.copy() - result = df.loc[['a', 'b', 'e']] - exp_index = pd.CategoricalIndex( - list('aaabbe'), categories=list('cabe'), name='B') - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan]}, index=exp_index) - assert_frame_equal(result, expected, check_index_type=True) - - # not all labels in the categories - self.assertRaises(KeyError, lambda: self.df2.loc[['a', 'd']]) - - def test_loc_listlike_dtypes(self): - # GH 11586 - - # unique categories and codes - index = pd.CategoricalIndex(['a', 'b', 'c']) - df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) - - # unique slice - res = df.loc[['a', 'b']] - exp = DataFrame({'A': [1, 2], - 'B': [4, 5]}, index=pd.CategoricalIndex(['a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - # duplicated slice - res = df.loc[['a', 'a', 'b']] - exp = DataFrame({'A': [1, 1, 2], - 'B': [4, 4, 5]}, - index=pd.CategoricalIndex(['a', 'a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - with tm.assertRaisesRegexp( - KeyError, - 'a list-indexer must only include values that are ' - 'in the categories'): - df.loc[['a', 'x']] - - # duplicated categories and codes - index = pd.CategoricalIndex(['a', 'b', 'a']) - df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=index) - - # unique slice - res = df.loc[['a', 'b']] - exp = DataFrame({'A': [1, 3, 2], - 'B': [4, 6, 5]}, - index=pd.CategoricalIndex(['a', 'a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - # duplicated slice - res = df.loc[['a', 'a', 'b']] - exp = DataFrame( - {'A': [1, 3, 1, 3, 2], - 'B': [4, 6, 4, 6, 5 - ]}, index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'])) - tm.assert_frame_equal(res, exp, check_index_type=True) - - with tm.assertRaisesRegexp( - KeyError, - 'a list-indexer must only include values ' - 'that are in the categories'): - df.loc[['a', 'x']] - - # contains unused category - index = pd.CategoricalIndex( - ['a', 'b', 'a', 'c'], categories=list('abcde')) - df = DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=index) - - res = df.loc[['a', 'b']] - exp = DataFrame({'A': [1, 3, 2], - 'B': [5, 7, 6]}, index=pd.CategoricalIndex( - ['a', 'a', 'b'], categories=list('abcde'))) - tm.assert_frame_equal(res, exp, check_index_type=True) - - res = df.loc[['a', 'e']] - exp = DataFrame({'A': [1, 3, np.nan], 'B': [5, 7, np.nan]}, - index=pd.CategoricalIndex(['a', 'a', 'e'], - categories=list('abcde'))) - tm.assert_frame_equal(res, exp, check_index_type=True) - - # duplicated slice - res = df.loc[['a', 'a', 'b']] - exp = DataFrame({'A': [1, 3, 1, 3, 2], 'B': [5, 7, 5, 7, 6]}, - index=pd.CategoricalIndex(['a', 'a', 'a', 'a', 'b'], - categories=list('abcde'))) - tm.assert_frame_equal(res, exp, check_index_type=True) - - with tm.assertRaisesRegexp( - KeyError, - 'a list-indexer must only include values ' - 'that are in the categories'): - df.loc[['a', 'x']] - - def test_read_only_source(self): - # GH 10043 - rw_array = np.eye(10) - rw_df = DataFrame(rw_array) - - ro_array = np.eye(10) - ro_array.setflags(write=False) - ro_df = DataFrame(ro_array) - - assert_frame_equal(rw_df.iloc[[1, 2, 3]], ro_df.iloc[[1, 2, 3]]) - assert_frame_equal(rw_df.iloc[[1]], ro_df.iloc[[1]]) - assert_series_equal(rw_df.iloc[1], ro_df.iloc[1]) - assert_frame_equal(rw_df.iloc[1:3], ro_df.iloc[1:3]) - - assert_frame_equal(rw_df.loc[[1, 2, 3]], ro_df.loc[[1, 2, 3]]) - assert_frame_equal(rw_df.loc[[1]], ro_df.loc[[1]]) - assert_series_equal(rw_df.loc[1], ro_df.loc[1]) - assert_frame_equal(rw_df.loc[1:3], ro_df.loc[1:3]) - - def test_reindexing(self): - - # reindexing - # convert to a regular index - result = self.df2.reindex(['a', 'b', 'e']) - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], - 'B': Series(list('aaabbe'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['a', 'b']) - expected = DataFrame({'A': [0, 1, 5, 2, 3], - 'B': Series(list('aaabb'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['e']) - expected = DataFrame({'A': [np.nan], - 'B': Series(['e'])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['d']) - expected = DataFrame({'A': [np.nan], - 'B': Series(['d'])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - # since we are actually reindexing with a Categorical - # then return a Categorical - cats = list('cabe') - - result = self.df2.reindex(pd.Categorical(['a', 'd'], categories=cats)) - expected = DataFrame({'A': [0, 1, 5, np.nan], - 'B': Series(list('aaad')).astype( - 'category', categories=cats)}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(pd.Categorical(['a'], categories=cats)) - expected = DataFrame({'A': [0, 1, 5], - 'B': Series(list('aaa')).astype( - 'category', categories=cats)}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['a', 'b', 'e']) - expected = DataFrame({'A': [0, 1, 5, 2, 3, np.nan], - 'B': Series(list('aaabbe'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['a', 'b']) - expected = DataFrame({'A': [0, 1, 5, 2, 3], - 'B': Series(list('aaabb'))}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(['e']) - expected = DataFrame({'A': [np.nan], - 'B': Series(['e'])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - # give back the type of categorical that we received - result = self.df2.reindex(pd.Categorical( - ['a', 'd'], categories=cats, ordered=True)) - expected = DataFrame( - {'A': [0, 1, 5, np.nan], - 'B': Series(list('aaad')).astype('category', categories=cats, - ordered=True)}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - result = self.df2.reindex(pd.Categorical( - ['a', 'd'], categories=['a', 'd'])) - expected = DataFrame({'A': [0, 1, 5, np.nan], - 'B': Series(list('aaad')).astype( - 'category', categories=['a', 'd' - ])}).set_index('B') - assert_frame_equal(result, expected, check_index_type=True) - - # passed duplicate indexers are not allowed - self.assertRaises(ValueError, lambda: self.df2.reindex(['a', 'a'])) - - # args NotImplemented ATM - self.assertRaises(NotImplementedError, - lambda: self.df2.reindex(['a'], method='ffill')) - self.assertRaises(NotImplementedError, - lambda: self.df2.reindex(['a'], level=1)) - self.assertRaises(NotImplementedError, - lambda: self.df2.reindex(['a'], limit=2)) - - def test_loc_slice(self): - # slicing - # not implemented ATM - # GH9748 - - self.assertRaises(TypeError, lambda: self.df.loc[1:5]) - - # result = df.loc[1:5] - # expected = df.iloc[[1,2,3,4]] - # assert_frame_equal(result, expected) - - def test_boolean_selection(self): - - df3 = self.df3 - df4 = self.df4 - - result = df3[df3.index == 'a'] - expected = df3.iloc[[]] - assert_frame_equal(result, expected) - - result = df4[df4.index == 'a'] - expected = df4.iloc[[]] - assert_frame_equal(result, expected) - - result = df3[df3.index == 1] - expected = df3.iloc[[0, 1, 3]] - assert_frame_equal(result, expected) - - result = df4[df4.index == 1] - expected = df4.iloc[[0, 1, 3]] - assert_frame_equal(result, expected) - - # since we have an ordered categorical - - # CategoricalIndex([1, 1, 2, 1, 3, 2], - # categories=[3, 2, 1], - # ordered=True, - # name=u'B') - result = df3[df3.index < 2] - expected = df3.iloc[[4]] - assert_frame_equal(result, expected) - - result = df3[df3.index > 1] - expected = df3.iloc[[]] - assert_frame_equal(result, expected) - - # unordered - # cannot be compared - - # CategoricalIndex([1, 1, 2, 1, 3, 2], - # categories=[3, 2, 1], - # ordered=False, - # name=u'B') - self.assertRaises(TypeError, lambda: df4[df4.index < 2]) - self.assertRaises(TypeError, lambda: df4[df4.index > 1]) - - class TestSeriesNoneCoercion(tm.TestCase): EXPECTED_RESULTS = [ # For numeric series, we should coerce to NaN. diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 62a7ad078da70..7584b99dbdb97 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -453,12 +453,20 @@ def _convert_scalar_indexer(self, key, kind=None): Parameters ---------- key : label of the slice bound - kind : optional, type of the indexing operation (loc/ix/iloc/None) + kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ - if (kind in ['loc'] and lib.isscalar(key) and - (is_integer(key) or is_float(key))): - self._invalid_indexer('index', key) + assert kind in ['ix', 'loc', 'getitem', 'iloc', None] + + # we don't allow integer/float indexing for loc + # we don't allow float indexing for ix/getitem + if lib.isscalar(key): + is_int = is_integer(key) + is_flt = is_float(key) + if kind in ['loc'] and (is_int or is_flt): + self._invalid_indexer('index', key) + elif kind in ['ix', 'getitem'] and is_flt: + self._invalid_indexer('index', key) return (super(DatetimeIndexOpsMixin, self) ._convert_scalar_indexer(key, kind=kind)) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index c745f1b2eddf9..b3b43e1a5babb 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1443,7 +1443,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -1454,6 +1454,8 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem', None] + if is_float(label) or isinstance(label, time) or is_integer(label): self._invalid_indexer('slice', label) @@ -1500,7 +1502,7 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): raise KeyError('Cannot mix time and non-time slice keys') try: - return Index.slice_indexer(self, start, end, step) + return Index.slice_indexer(self, start, end, step, kind=kind) except KeyError: # For historical reasons DatetimeIndex by default supports # value-based partial (aka string) slices on non-monotonic arrays, diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 2795dc846f6de..df04984bcb582 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -692,7 +692,7 @@ def get_loc(self, key, method=None, tolerance=None): except ValueError: # we cannot construct the Period # as we have an invalid type - return self._invalid_indexer('label', key) + raise KeyError(key) try: return Index.get_loc(self, key.ordinal, method, tolerance) except KeyError: @@ -707,7 +707,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- @@ -718,6 +718,8 @@ def _maybe_cast_slice_bound(self, label, side, kind): Value of `side` parameter should be validated in caller. """ + assert kind in ['ix', 'loc', 'getitem'] + if isinstance(label, datetime): return Period(label, freq=self.freq) elif isinstance(label, compat.string_types): diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 9759d13fe4632..bea2aeb508358 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -710,13 +710,15 @@ def _maybe_cast_slice_bound(self, label, side, kind): ---------- label : object side : {'left', 'right'} - kind : string / None + kind : {'ix', 'loc', 'getitem'} Returns ------- label : object """ + assert kind in ['ix', 'loc', 'getitem', None] + if isinstance(label, compat.string_types): parsed = _coerce_scalar_to_timedelta_type(label, box=True) lbound = parsed.round(parsed.resolution)
closes #12333
https://api.github.com/repos/pandas-dev/pandas/pulls/12370
2016-02-17T19:31:24Z
2016-03-08T23:38:19Z
null
2016-03-08T23:38:19Z
BUG #12336: Fix init categorical series with scalar value
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index fa959f651d135..e944189e2a338 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1107,3 +1107,4 @@ Bug Fixes - Bug in ``crosstab`` where arguments with non-overlapping indexes would return a ``KeyError`` (:issue:`10291`) - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) +- Bug when initializing categorical series with a scalar value. (:issue:`12336`) diff --git a/pandas/core/series.py b/pandas/core/series.py index 286a2ba79585a..1e5e0f6fb4553 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2926,6 +2926,8 @@ def create_from_value(value, index, dtype): if is_datetimetz(dtype): subarr = DatetimeIndex([value] * len(index), dtype=dtype) + elif is_categorical_dtype(dtype): + subarr = Categorical([value] * len(index)) else: if not isinstance(dtype, (np.dtype, type(np.dtype))): dtype = dtype.dtype diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 1289c0da14dd5..43e47ae121408 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -635,6 +635,14 @@ def test_fromValue(self): self.assertEqual(dates.dtype, 'M8[ns]') self.assertEqual(len(dates), len(self.ts)) + # GH12336 + # Test construction of categorical series from value + categorical = Series(0, index=self.ts.index, dtype="category") + expected = Series(0, index=self.ts.index).astype("category") + self.assertEqual(categorical.dtype, 'category') + self.assertEqual(len(categorical), len(self.ts)) + tm.assert_series_equal(categorical, expected) + def test_constructor_dtype_timedelta64(self): # basic
closes #12336 Changed the tests to match the other ones in `test_fromValue`.
https://api.github.com/repos/pandas-dev/pandas/pulls/12368
2016-02-17T16:36:01Z
2016-02-18T13:15:08Z
null
2016-02-18T13:15:13Z
fmt in TimeFormatter is now changeable
diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py index 8ccfdfa05e9b5..c7b663ba8bcc3 100644 --- a/pandas/tseries/converter.py +++ b/pandas/tseries/converter.py @@ -83,9 +83,9 @@ class TimeFormatter(Formatter): def __init__(self, locs): self.locs = locs + self.fmt = '%H:%M:%S' def __call__(self, x, pos=0): - fmt = '%H:%M:%S' s = int(x) ms = int((x - s) * 1e3) us = int((x - s) * 1e6 - ms) @@ -97,7 +97,7 @@ def __call__(self, x, pos=0): elif ms != 0: fmt += '.%3f' - return pydt.time(h, m, s, us).strftime(fmt) + return pydt.time(h, m, s, us).strftime(self.fmt) # Period Conversion
`datetime.time` objects are useful as index values, e.g. in a data frame containing data of an experiment lasting a few minutes or hours. When plotting such data frame, the x-axis ticks are formatted as `'%H:%M:%S'` as standard. In most cases, either `'%H:%M'` or `'%M:%S'` contains enough information, and the `'%H:%M:%S'` format is superfluous. The xticks were formatted based on the fmt variable in the `__call__` method. In this commit, the tick format is saved in the `TimeFormatter.fmt` variable. This is set in the `__init__` method to `'%H:%M:%S'`, but can later be changed, e.g.: ``` ax = df.plot() ax.xaxis.get_major_formatter().fmt = '%M:%S' ```
https://api.github.com/repos/pandas-dev/pandas/pulls/12367
2016-02-17T15:52:09Z
2016-02-18T13:22:04Z
null
2016-02-18T13:45:46Z
BUG #12336: Fix init categorical series with scalar value
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index fa959f651d135..e44ac4d900d9e 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1107,3 +1107,4 @@ Bug Fixes - Bug in ``crosstab`` where arguments with non-overlapping indexes would return a ``KeyError`` (:issue:`10291`) - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) +- Bug when initializing categorical series with a scalar value. (:issue:`12336`) \ No newline at end of file diff --git a/pandas/core/series.py b/pandas/core/series.py index 286a2ba79585a..1e5e0f6fb4553 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2926,6 +2926,8 @@ def create_from_value(value, index, dtype): if is_datetimetz(dtype): subarr = DatetimeIndex([value] * len(index), dtype=dtype) + elif is_categorical_dtype(dtype): + subarr = Categorical([value] * len(index)) else: if not isinstance(dtype, (np.dtype, type(np.dtype))): dtype = dtype.dtype diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 1289c0da14dd5..a8e592b5d13ce 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -635,6 +635,10 @@ def test_fromValue(self): self.assertEqual(dates.dtype, 'M8[ns]') self.assertEqual(len(dates), len(self.ts)) + categorical = Series(0, index=self.ts.index, dtype="category") + self.assertEqual(categorical.dtype, 'category') + self.assertEqual(len(categorical), len(self.ts)) + def test_constructor_dtype_timedelta64(self): # basic
closes #12336
https://api.github.com/repos/pandas-dev/pandas/pulls/12366
2016-02-17T14:54:21Z
2016-02-17T16:18:04Z
null
2016-02-17T16:18:04Z
BUG: Bug in DataFrame.set_index() with tz-aware Series
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index c749c4540013b..2baa366a29bb1 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1045,7 +1045,7 @@ Bug Fixes - Bug in ``DataFrame.info`` when duplicated column names exist (:issue:`11761`) - Bug in ``.copy`` of datetime tz-aware objects (:issue:`11794`) - Bug in ``Series.apply`` and ``Series.map`` where ``timedelta64`` was not boxed (:issue:`11349`) - +- Bug in ``DataFrame.set_index()`` with tz-aware ``Series`` (:issue:`12358`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 8a3ac4db37d2d..cd32ff2133cae 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2810,7 +2810,7 @@ def set_index(self, keys, drop=True, append=False, inplace=False, level = col.get_level_values(col.nlevels - 1) names.extend(col.names) elif isinstance(col, Series): - level = col.values + level = col._values names.append(col.name) elif isinstance(col, Index): level = col diff --git a/pandas/tests/frame/test_alter_axes.py b/pandas/tests/frame/test_alter_axes.py index 2d7ba6c704e41..1da5487aefc01 100644 --- a/pandas/tests/frame/test_alter_axes.py +++ b/pandas/tests/frame/test_alter_axes.py @@ -7,7 +7,8 @@ import numpy as np from pandas.compat import lrange -from pandas import DataFrame, Series, Index, MultiIndex, RangeIndex +from pandas import (DataFrame, Series, Index, MultiIndex, + RangeIndex) import pandas as pd from pandas.util.testing import (assert_series_equal, @@ -267,6 +268,15 @@ def test_set_index_cast_datetimeindex(self): lambda d: pd.Timestamp(d, tz=tz)) assert_frame_equal(df.reset_index(), expected) + # GH 12358 + # tz-aware Series should retain the tz + i = pd.to_datetime(["2014-01-01 10:10:10"], + utc=True).tz_convert('Europe/Rome') + df = DataFrame({'i': i}) + self.assertEqual(df.set_index(i).index[0].hour, 11) + self.assertEqual(pd.DatetimeIndex(pd.Series(df.i))[0].hour, 11) + self.assertEqual(df.set_index(df.i).index[0].hour, 11) + def test_set_index_multiindexcolumns(self): columns = MultiIndex.from_tuples([('foo', 1), ('foo', 2), ('bar', 1)]) df = DataFrame(np.random.randn(3, 3), columns=columns)
closes #12358
https://api.github.com/repos/pandas-dev/pandas/pulls/12365
2016-02-17T13:16:49Z
2016-02-17T14:29:42Z
null
2016-02-17T14:29:42Z
BUG: resample with nunique
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index c749c4540013b..abb8ef8d2970c 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1035,7 +1035,7 @@ Bug Fixes - Bug in ``pd.concat`` while concatenating tz-aware NaT series. (:issue:`11693`, :issue:`11755`, :issue:`12217`) - Bug in ``pd.read_stata`` with version <= 108 files (:issue:`12232`) - Bug in ``Series.resample`` using a frequency of ``Nano`` when the index is a ``DatetimeIndex`` and contains non-zero nanosecond parts (:issue:`12037`) - +- Bug in resampling with ``.nunique`` and a sparse index (:issue:`12352`) - Bug in ``NaT`` subtraction from ``Timestamp`` or ``DatetimeIndex`` with timezones (:issue:`11718`) - Bug in subtraction of ``Series`` of a single tz-aware ``Timestamp`` (:issue:`12290`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 963c6223730f3..c081671c6d8db 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2817,8 +2817,16 @@ def nunique(self, dropna=True): inc[idx] = 1 out = np.add.reduceat(inc, idx).astype('int64', copy=False) - return Series(out if ids[0] != -1 else out[1:], - index=self.grouper.result_index, + res = out if ids[0] != -1 else out[1:] + ri = self.grouper.result_index + + # we might have duplications among the bins + if len(res) != len(ri): + res, out = np.zeros(len(ri), dtype=out.dtype), res + res[ids] = out + + return Series(res, + index=ri, name=self.name) @deprecate_kwarg('take_last', 'keep', diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 68999ac143ea8..b0e315ead2acb 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -1526,6 +1526,34 @@ def test_resample_timegrouper(self): result = df.groupby(pd.Grouper(freq='M', key='A')).count() assert_frame_equal(result, expected) + def test_resample_nunique(self): + + # GH 12352 + df = DataFrame({ + 'ID': {pd.Timestamp('2015-06-05 00:00:00'): '0010100903', + pd.Timestamp('2015-06-08 00:00:00'): '0010150847'}, + 'DATE': {pd.Timestamp('2015-06-05 00:00:00'): '2015-06-05', + pd.Timestamp('2015-06-08 00:00:00'): '2015-06-08'}}) + r = df.resample('D') + g = df.groupby(pd.Grouper(freq='D')) + expected = df.groupby(pd.TimeGrouper('D')).ID.apply(lambda x: + x.nunique()) + self.assertEqual(expected.name, 'ID') + + for t in [r, g]: + result = r.ID.nunique() + assert_series_equal(result, expected) + + # TODO + # this should have name + # https://github.com/pydata/pandas/issues/12363 + expected.name = None + result = df.ID.resample('D').nunique() + assert_series_equal(result, expected) + + result = df.ID.groupby(pd.Grouper(freq='D')).nunique() + assert_series_equal(result, expected) + def test_resample_group_info(self): # GH10914 for n, k in product((10000, 100000), (10, 100, 1000)): dr = date_range(start='2015-08-27', periods=n // 10, freq='T')
closes #12352
https://api.github.com/repos/pandas-dev/pandas/pulls/12364
2016-02-17T02:49:16Z
2016-02-17T13:22:18Z
null
2016-02-17T13:22:18Z
ENH Consistent apply output when grouping with freq
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 1179a347e4c46..48ec3f8240bcd 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -72,15 +72,39 @@ API changes - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) -- ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) - - +- ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) +- Using ``apply`` on resampling groupby operations (e.g. ``df.groupby(pd.TimeGrouper(freq='M', key='date')).apply(...)``) now has the same output types as similar ``apply``s on other groupby operations (e.g. ``df.groupby(pd.Grouper(key='color')).apply(...)``). (:issue:`11742`). +New Behavior: +.. ipython:: python + df = pd.DataFrame({'date': pd.to_datetime(['10/10/2000', '11/10/2000']), 'value': [10, 13]}) + df + # Output is a Series + df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum()) + # Output is a DataFrame + df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x[['value']].sum()) + +Previous behavior: + +.. code-block:: python + + In [1]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum()) + Out[1]: + ... + TypeError: cannot concatenate a non-NDFrame object + + # Output is a Series + In [2]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x[['value']].sum()) + Out[2]: + date + 2000-10-31 value 10 + 2000-11-30 value 13 + dtype: int64 .. _whatsnew_0181.deprecations: diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 398e37d52d7ba..066afc55e442f 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2004,25 +2004,6 @@ def get_iterator(self, data, axis=0): if start < length: yield self.binlabels[-1], slicer(start, None) - def apply(self, f, data, axis=0): - result_keys = [] - result_values = [] - mutated = False - for key, group in self.get_iterator(data, axis=axis): - object.__setattr__(group, 'name', key) - - # group might be modified - group_axes = _get_axes(group) - res = f(group) - - if not _is_indexed_like(res, group_axes): - mutated = True - - result_keys.append(key) - result_values.append(res) - - return result_keys, result_values, mutated - @cache_readonly def indices(self): indices = collections.defaultdict(list) @@ -2071,8 +2052,8 @@ def names(self): @property def groupings(self): - # for compat - return None + return [Grouping(lvl, lvl, in_axis=False, level=None, name=name) + for lvl, name in zip(self.levels, self.names)] def agg_series(self, obj, func): dummy = obj[:0] diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index ff9fd7dfb5980..28038e02b64ca 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -4824,6 +4824,42 @@ def test_timegrouper_get_group(self): result = grouped.get_group(dt) assert_frame_equal(result, expected) + def test_timegrouper_apply_return_type_series(self): + # Using `apply` with the `TimeGrouper` should give the + # same return type as an `apply` with a `Grouper`. + # Issue #11742 + df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'], + 'value': [10, 13]}) + df_dt = df.copy() + df_dt['date'] = pd.to_datetime(df_dt['date']) + + def sumfunc_series(x): + return pd.Series([x['value'].sum()], ('sum',)) + + expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_series) + result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date')) + .apply(sumfunc_series)) + assert_frame_equal(result.reset_index(drop=True), + expected.reset_index(drop=True)) + + def test_timegrouper_apply_return_type_value(self): + # Using `apply` with the `TimeGrouper` should give the + # same return type as an `apply` with a `Grouper`. + # Issue #11742 + df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'], + 'value': [10, 13]}) + df_dt = df.copy() + df_dt['date'] = pd.to_datetime(df_dt['date']) + + def sumfunc_value(x): + return x.value.sum() + + expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_value) + result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date')) + .apply(sumfunc_value)) + assert_series_equal(result.reset_index(drop=True), + expected.reset_index(drop=True)) + def test_cumcount(self): df = DataFrame([['a'], ['a'], ['a'], ['b'], ['a']], columns=['A']) g = df.groupby('A')
The `BinGrouper.apply` (used by the `TimeGrouper`) and `BaseGrouper.apply` (used by the `Grouper`) have different output types. To make them consistent, remove `BinGrouper.apply` and let it use the same method as the superclass `BaseGrouper`. This requires changing `BinGrouper.groupings` to return an object which looks sufficiently like a `Grouping` object for `BaseGrouper.apply` to be happy. Use a namedtuple to create an object with the needed attributes. I think this change will only affect the output of `apply` with a custom function, and only when grouping by a `TimeGrouper` (although a `Grouper` with a `freq` specified turns into a `TimeGrouper`, so that counts). Closes #11742 .
https://api.github.com/repos/pandas-dev/pandas/pulls/12362
2016-02-17T02:04:45Z
2016-04-01T13:13:27Z
null
2016-04-01T13:13:36Z
TST: skip blosc tests on msgpacks if not installed
diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index d0e7d00d79cb0..9387a6069d974 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -725,7 +725,11 @@ def read_msgpacks(self, version): f.split('.')[-4][-1] == '2'): continue vf = os.path.join(pth, f) - self.compare(vf, version) + try: + self.compare(vf, version) + except ImportError: + # blosc not installed + continue n += 1 assert n > 0, 'Msgpack files are not tested'
https://api.github.com/repos/pandas-dev/pandas/pulls/12361
2016-02-17T01:05:48Z
2016-02-17T02:51:47Z
null
2016-02-17T02:51:47Z
COMPAT: invalid casting to nan
diff --git a/pandas/core/common.py b/pandas/core/common.py index 6165297c13048..b95b44a0394bc 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1098,6 +1098,33 @@ def _infer_dtype_from_scalar(val): return dtype, val +def _is_na_compat(arr, fill_value=np.nan): + """ + Parameters + ---------- + arr: a numpy array + fill_value: fill value, default to np.nan + + Returns + ------- + True if we can fill using this fill_value + """ + dtype = arr.dtype + if isnull(fill_value): + return not (is_bool_dtype(dtype) or + is_integer_dtype(dtype)) + return True + + +def _maybe_fill(arr, fill_value=np.nan): + """ + if we have a compatiable fill_value and arr dtype, then fill + """ + if _is_na_compat(arr, fill_value): + arr.fill(fill_value) + return arr + + def _maybe_promote(dtype, fill_value=np.nan): # if we passed an array here, determine the fill value by dtype @@ -1359,7 +1386,10 @@ def trans(x): # noqa # do a test on the first element, if it fails then we are done r = result.ravel() arr = np.array([r[0]]) - if not np.allclose(arr, trans(arr).astype(dtype)): + + # if we have any nulls, then we are done + if isnull(arr).any() or not np.allclose(arr, + trans(arr).astype(dtype)): return result # a comparable, e.g. a Decimal may slip in here diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 963c6223730f3..64329fc6a7664 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -31,7 +31,8 @@ is_timedelta64_dtype, is_datetime64_dtype, is_categorical_dtype, _values_from_object, is_datetime_or_timedelta_dtype, is_bool, - is_bool_dtype, AbstractMethodError) + is_bool_dtype, AbstractMethodError, + _maybe_fill) from pandas.core.config import option_context import pandas.lib as lib from pandas.lib import Timestamp @@ -1725,14 +1726,15 @@ def _cython_operation(self, kind, values, how, axis): labels, _, _ = self.group_info if kind == 'aggregate': - result = np.empty(out_shape, dtype=out_dtype) - result.fill(np.nan) + result = _maybe_fill(np.empty(out_shape, dtype=out_dtype), + fill_value=np.nan) counts = np.zeros(self.ngroups, dtype=np.int64) result = self._aggregate( result, counts, values, labels, func, is_numeric) elif kind == 'transform': - result = np.empty_like(values, dtype=out_dtype) - result.fill(np.nan) + result = _maybe_fill(np.empty_like(values, dtype=out_dtype), + fill_value=np.nan) + # temporary storange for running-total type tranforms accum = np.empty(out_shape, dtype=out_dtype) result = self._transform( diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c6b04757e201c..8563481c8564d 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -14,7 +14,7 @@ is_dtype_equal, is_null_datelike_scalar, _maybe_promote, is_timedelta64_dtype, is_datetime64_dtype, is_datetimetz, is_sparse, - array_equivalent, + array_equivalent, _is_na_compat, _maybe_convert_string_to_object, is_categorical, is_datetimelike_v_numeric, is_numeric_v_string_like, is_internal_type) @@ -4392,7 +4392,6 @@ def _putmask_smart(v, m, n): m : `mask`, applies to both sides (array like) n : `new values` either scalar or an array like aligned with `values` """ - # n should be the length of the mask or a scalar here if not is_list_like(n): n = np.array([n] * len(m)) @@ -4403,6 +4402,12 @@ def _putmask_smart(v, m, n): # will work in the current dtype try: nn = n[m] + + # make sure that we have a nullable type + # if we have nulls + if not _is_na_compat(v, nn[0]): + raise ValueError + nn_at = nn.astype(v.dtype) # avoid invalid dtype comparisons diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 2d038f8b16520..7ee40a7758011 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -5774,7 +5774,6 @@ def test_cython_group_transform_algos(self): data = np.array([np.timedelta64(1, 'ns')] * 5, dtype='m8[ns]')[:, None] accum = np.array([[0]], dtype='int64') actual = np.zeros_like(data, dtype='int64') - actual.fill(np.nan) pd.algos.group_cumsum(actual, data.view('int64'), labels, accum) expected = np.array([np.timedelta64(1, 'ns'), np.timedelta64( 2, 'ns'), np.timedelta64(3, 'ns'), np.timedelta64(4, 'ns'),
closes #12303
https://api.github.com/repos/pandas-dev/pandas/pulls/12360
2016-02-17T00:14:15Z
2016-02-17T13:18:51Z
null
2016-02-17T13:18:51Z
ENH: always make ndarrays from msgpack writable
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 5bb7dfe87562c..037639b51cd26 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -37,7 +37,11 @@ Enhancements +Other Enhancements +^^^^^^^^^^^^^^^^^^ +- ``from_msgpack`` now always gives writeable ndarrays even when compression is + used (:issue:`12359`). .. _whatsnew_0181.api: diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 701b78d2771fb..f3f3c26b88993 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -38,9 +38,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ -import os from datetime import datetime, date, timedelta from dateutil.parser import parse +import os +from textwrap import dedent +import warnings import numpy as np from pandas import compat @@ -52,12 +54,61 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame -from pandas.core.common import needs_i8_conversion, pandas_dtype +from pandas.core.common import ( + PerformanceWarning, + needs_i8_conversion, + pandas_dtype, +) from pandas.io.common import get_filepath_or_buffer from pandas.core.internals import BlockManager, make_block import pandas.core.internals as internals from pandas.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType +from pandas.util._move import ( + BadMove as _BadMove, + move_into_mutable_buffer as _move_into_mutable_buffer, +) + +# check whcih compression libs we have installed +try: + import zlib + + def _check_zlib(): + pass +except ImportError: + def _check_zlib(): + raise ValueError('zlib is not installed') + +_check_zlib.__doc__ = dedent( + """\ + Check if zlib is installed. + + Raises + ------ + ValueError + Raised when zlib is not installed. + """, +) + +try: + import blosc + + def _check_blosc(): + pass +except ImportError: + def _check_blosc(): + raise ValueError('blosc is not installed') + +_check_blosc.__doc__ = dedent( + """\ + Check if blosc is installed. + + Raises + ------ + ValueError + Raised when blosc is not installed. + """, +) # until we can pass this into our conversion functions, # this is pretty hacky @@ -215,6 +266,7 @@ def convert(values): return v.tolist() if compressor == 'zlib': + _check_zlib() # return string arrays like they are if dtype == np.object_: @@ -222,10 +274,10 @@ def convert(values): # convert to a bytes array v = v.tostring() - import zlib return ExtType(0, zlib.compress(v)) elif compressor == 'blosc': + _check_blosc() # return string arrays like they are if dtype == np.object_: @@ -233,7 +285,6 @@ def convert(values): # convert to a bytes array v = v.tostring() - import blosc return ExtType(0, blosc.compress(v, typesize=dtype.itemsize)) # ndarray (on original dtype) @@ -255,17 +306,40 @@ def unconvert(values, dtype, compress=None): if not as_is_ext: values = values.encode('latin1') - if compress == u'zlib': - import zlib - values = zlib.decompress(values) - return np.frombuffer(values, dtype=dtype) - - elif compress == u'blosc': - import blosc - values = blosc.decompress(values) - return np.frombuffer(values, dtype=dtype) + if compress: + if compress == u'zlib': + _check_zlib() + decompress = zlib.decompress + elif compress == u'blosc': + _check_blosc() + decompress = blosc.decompress + else: + raise ValueError("compress must be one of 'zlib' or 'blosc'") - # from a string + try: + return np.frombuffer( + _move_into_mutable_buffer(decompress(values)), + dtype=dtype, + ) + except _BadMove as e: + # Pull the decompressed data off of the `_BadMove` exception. + # We don't just store this in the locals because we want to + # minimize the risk of giving users access to a `bytes` object + # whose data is also given to a mutable buffer. + values = e.args[0] + if len(values) > 1: + # The empty string and single characters are memoized in many + # string creating functions in the capi. This case should not + # warn even though we need to make a copy because we are only + # copying at most 1 byte. + warnings.warn( + 'copying data after decompressing; this may mean that' + ' decompress is caching its result', + PerformanceWarning, + ) + # fall through to copying `np.fromstring` + + # Copy the string into a numpy array. return np.fromstring(values, dtype=dtype) diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 9387a6069d974..276763989d7cf 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -10,11 +10,13 @@ from pandas.compat import u from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range, date_range, period_range, Index) +from pandas.core.common import PerformanceWarning from pandas.io.packers import to_msgpack, read_msgpack import pandas.util.testing as tm from pandas.util.testing import (ensure_clean, assert_index_equal, assert_series_equal, - assert_frame_equal) + assert_frame_equal, + patch) from pandas.tests.test_panel import assert_panel_equal import pandas @@ -539,17 +541,126 @@ def test_plain(self): for k in self.frame.keys(): assert_frame_equal(self.frame[k], i_rec[k]) - def test_compression_zlib(self): - i_rec = self.encode_decode(self.frame, compress='zlib') + def _test_compression(self, compress): + i_rec = self.encode_decode(self.frame, compress=compress) for k in self.frame.keys(): - assert_frame_equal(self.frame[k], i_rec[k]) + value = i_rec[k] + expected = self.frame[k] + assert_frame_equal(value, expected) + # make sure that we can write to the new frames + for block in value._data.blocks: + self.assertTrue(block.values.flags.writeable) + + def test_compression_zlib(self): + if not _ZLIB_INSTALLED: + raise nose.SkipTest('no zlib') + self._test_compression('zlib') def test_compression_blosc(self): if not _BLOSC_INSTALLED: raise nose.SkipTest('no blosc') - i_rec = self.encode_decode(self.frame, compress='blosc') - for k in self.frame.keys(): - assert_frame_equal(self.frame[k], i_rec[k]) + self._test_compression('blosc') + + def _test_compression_warns_when_decompress_caches(self, compress): + not_garbage = [] + control = [] # copied data + + compress_module = globals()[compress] + real_decompress = compress_module.decompress + + def decompress(ob): + """mock decompress function that delegates to the real + decompress but caches the result and a copy of the result. + """ + res = real_decompress(ob) + not_garbage.append(res) # hold a reference to this bytes object + control.append(bytearray(res)) # copy the data here to check later + return res + + # types mapped to values to add in place. + rhs = { + np.dtype('float64'): 1.0, + np.dtype('int32'): 1, + np.dtype('object'): 'a', + np.dtype('datetime64[ns]'): np.timedelta64(1, 'ns'), + np.dtype('timedelta64[ns]'): np.timedelta64(1, 'ns'), + } + + with patch(compress_module, 'decompress', decompress), \ + tm.assert_produces_warning(PerformanceWarning) as ws: + + i_rec = self.encode_decode(self.frame, compress=compress) + for k in self.frame.keys(): + + value = i_rec[k] + expected = self.frame[k] + assert_frame_equal(value, expected) + # make sure that we can write to the new frames even though + # we needed to copy the data + for block in value._data.blocks: + self.assertTrue(block.values.flags.writeable) + # mutate the data in some way + block.values[0] += rhs[block.dtype] + + for w in ws: + # check the messages from our warnings + self.assertEqual( + str(w.message), + 'copying data after decompressing; this may mean that' + ' decompress is caching its result', + ) + + for buf, control_buf in zip(not_garbage, control): + # make sure none of our mutations above affected the + # original buffers + self.assertEqual(buf, control_buf) + + def test_compression_warns_when_decompress_caches_zlib(self): + if not _ZLIB_INSTALLED: + raise nose.SkipTest('no zlib') + self._test_compression_warns_when_decompress_caches('zlib') + + def test_compression_warns_when_decompress_caches_blosc(self): + if not _BLOSC_INSTALLED: + raise nose.SkipTest('no blosc') + self._test_compression_warns_when_decompress_caches('blosc') + + def _test_small_strings_no_warn(self, compress): + empty = np.array([], dtype='uint8') + with tm.assert_produces_warning(None): + empty_unpacked = self.encode_decode(empty, compress=compress) + + np.testing.assert_array_equal(empty_unpacked, empty) + self.assertTrue(empty_unpacked.flags.writeable) + + char = np.array([ord(b'a')], dtype='uint8') + with tm.assert_produces_warning(None): + char_unpacked = self.encode_decode(char, compress=compress) + + np.testing.assert_array_equal(char_unpacked, char) + self.assertTrue(char_unpacked.flags.writeable) + # if this test fails I am sorry because the interpreter is now in a + # bad state where b'a' points to 98 == ord(b'b'). + char_unpacked[0] = ord(b'b') + + # we compare the ord of bytes b'a' with unicode u'a' because the should + # always be the same (unless we were able to mutate the shared + # character singleton in which case ord(b'a') == ord(b'b'). + self.assertEqual(ord(b'a'), ord(u'a')) + np.testing.assert_array_equal( + char_unpacked, + np.array([ord(b'b')], dtype='uint8'), + ) + + def test_small_strings_no_warn_zlib(self): + if not _ZLIB_INSTALLED: + raise nose.SkipTest('no zlib') + self._test_small_strings_no_warn('zlib') + + def test_small_strings_no_warn_blosc(self): + if not _BLOSC_INSTALLED: + raise nose.SkipTest('no blosc') + self._test_small_strings_no_warn('blosc') def test_readonly_axis_blosc(self): # GH11880 diff --git a/pandas/tests/test_util.py b/pandas/tests/test_util.py index 367b8d21f95d0..e87e9770b770a 100644 --- a/pandas/tests/test_util.py +++ b/pandas/tests/test_util.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- import nose +from pandas.util._move import move_into_mutable_buffer, BadMove from pandas.util.decorators import deprecate_kwarg from pandas.util.validators import validate_args, validate_kwargs @@ -150,6 +151,38 @@ def test_validation(self): kwargs = {'f': 'foo', 'b': 'bar'} validate_kwargs('func', kwargs, *compat_args) + +class TestMove(tm.TestCase): + def test_more_than_one_ref(self): + """Test case for when we try to use ``move_into_mutable_buffer`` when + the object being moved has other references. + """ + b = b'testing' + + with tm.assertRaises(BadMove) as e: + def handle_success(type_, value, tb): + self.assertIs(value.args[0], b) + return type(e).handle_success(e, type_, value, tb) # super + + e.handle_success = handle_success + move_into_mutable_buffer(b) + + def test_exactly_one_ref(self): + """Test case for when the object being moved has exactly one reference. + """ + b = b'testing' + + # We need to pass an expression on the stack to ensure that there are + # not extra references hanging around. We cannot rewrite this test as + # buf = b[:-3] + # as_stolen_buf = move_into_mutable_buffer(buf) + # because then we would have more than one reference to buf. + as_stolen_buf = move_into_mutable_buffer(b[:-3]) + + # materialize as bytearray to show that it is mutable + self.assertEqual(bytearray(as_stolen_buf), b'test') + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/util/_move.c b/pandas/util/_move.c new file mode 100644 index 0000000000000..68fcad793e16c --- /dev/null +++ b/pandas/util/_move.c @@ -0,0 +1,274 @@ +#include <Python.h> + +#define COMPILING_IN_PY2 (PY_VERSION_HEX <= 0x03000000) + +#if !COMPILING_IN_PY2 +/* alias this because it is not aliased in Python 3 */ +#define PyString_CheckExact PyBytes_CheckExact +#define PyString_AS_STRING PyBytes_AS_STRING +#define PyString_GET_SIZE PyBytes_GET_SIZE +#endif /* !COMPILING_IN_PY2 */ + +#ifndef Py_TPFLAGS_HAVE_GETCHARBUFFER +#define Py_TPFLAGS_HAVE_GETCHARBUFFER 0 +#endif + +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER +#define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif + +PyObject *badmove; /* bad move exception class */ + +typedef struct { + PyObject_HEAD + /* the bytes that own the buffer we are mutating */ + PyObject *invalid_bytes; +} stolenbufobject; + +PyTypeObject stolenbuf_type; /* forward declare type */ + +static void +stolenbuf_dealloc(stolenbufobject *self) +{ + Py_DECREF(self->invalid_bytes); + PyObject_Del(self); +} + +static int +stolenbuf_getbuffer(stolenbufobject *self, Py_buffer *view, int flags) +{ + return PyBuffer_FillInfo(view, + (PyObject*) self, + (void*) PyString_AS_STRING(self->invalid_bytes), + PyString_GET_SIZE(self->invalid_bytes), + 0, /* not readonly */ + flags); +} + +#if COMPILING_IN_PY2 + +static Py_ssize_t +stolenbuf_getreadwritebuf(stolenbufobject *self, Py_ssize_t segment, void **out) +{ + if (segment != 0) { + PyErr_SetString(PyExc_SystemError, + "accessing non-existent string segment"); + return -1; + } + *out = PyString_AS_STRING(self->invalid_bytes); + return PyString_GET_SIZE(self->invalid_bytes); +} + +static Py_ssize_t +stolenbuf_getsegcount(stolenbufobject *self, Py_ssize_t *len) +{ + if (len) { + *len = PyString_GET_SIZE(self->invalid_bytes); + } + return 1; +} + +PyBufferProcs stolenbuf_as_buffer = { + (readbufferproc) stolenbuf_getreadwritebuf, + (writebufferproc) stolenbuf_getreadwritebuf, + (segcountproc) stolenbuf_getsegcount, + (charbufferproc) stolenbuf_getreadwritebuf, + (getbufferproc) stolenbuf_getbuffer, +}; + +#else /* Python 3 */ + +PyBufferProcs stolenbuf_as_buffer = { + (getbufferproc) stolenbuf_getbuffer, + NULL, +}; + +#endif /* COMPILING_IN_PY2 */ + +static PyObject * +stolenbuf_new(PyObject *self, PyObject *args, PyObject *kwargs) +{ + stolenbufobject *ret; + PyObject *bytes_rvalue; + + if (kwargs && PyDict_Size(kwargs)) { + PyErr_SetString(PyExc_TypeError, + "stolenbuf does not accept keyword arguments"); + return NULL; + } + + if (PyTuple_GET_SIZE(args) != 1) { + PyErr_SetString(PyExc_TypeError, + "stolenbuf requires exactly 1 positional argument"); + return NULL; + + } + + /* pull out the single, positional argument */ + bytes_rvalue = PyTuple_GET_ITEM(args, 0); + + if (!PyString_CheckExact(bytes_rvalue)) { + PyErr_SetString(PyExc_TypeError, + "stolenbuf can only steal from bytes objects"); + return NULL; + } + + if (Py_REFCNT(bytes_rvalue) != 1) { + /* there is a reference other than the caller's stack */ + PyErr_SetObject(badmove, bytes_rvalue); + return NULL; + } + + if (!(ret = PyObject_New(stolenbufobject, &stolenbuf_type))) { + return NULL; + } + + /* store the original bytes object in a field that is not + exposed to python */ + Py_INCREF(bytes_rvalue); + ret->invalid_bytes = bytes_rvalue; + return (PyObject*) ret; +} + +PyDoc_STRVAR( + stolenbuf_doc, + "Moves a bytes object that is about to be destroyed into a mutable buffer\n" + "without copying the data.\n" + "\n" + "Parameters\n" + "----------\n" + "bytes_rvalue : bytes with 1 refcount.\n" + " The bytes object that you want to move into a mutable buffer. This\n" + " cannot be a named object. It must only have a single reference.\n" + "\n" + "Returns\n" + "-------\n" + "buf : stolenbuf\n" + " An object that supports the buffer protocol which can give a mutable\n" + " view of the data that was previously owned by ``bytes_rvalue``.\n" + "\n" + "Raises\n" + "------\n" + "BadMove\n" + " Raised when a move is attempted on an object with more than one\n" + " reference.\n" + "\n" + "Notes\n" + "-----\n" + "If you want to use this function you are probably wrong.\n"); + +PyTypeObject stolenbuf_type = { + PyVarObject_HEAD_INIT(NULL, 0) + "pandas.util._move.stolenbuf", /* tp_name */ + sizeof(stolenbufobject), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor) stolenbuf_dealloc, /* tp_dealloc */ + 0, /* tp_print */ + 0, /* tp_getattr */ + 0, /* tp_setattr */ + 0, /* tp_reserved */ + 0, /* tp_repr */ + 0, /* tp_as_number */ + 0, /* tp_as_sequence */ + 0, /* tp_as_mapping */ + 0, /* tp_hash */ + 0, /* tp_call */ + 0, /* tp_str */ + 0, /* tp_getattro */ + 0, /* tp_setattro */ + &stolenbuf_as_buffer, /* tp_as_buffer */ + Py_TPFLAGS_DEFAULT | + Py_TPFLAGS_HAVE_NEWBUFFER | + Py_TPFLAGS_HAVE_GETCHARBUFFER, /* tp_flags */ + stolenbuf_doc, /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + 0, /* tp_richcompare */ + 0, /* tp_weaklistoffset */ + 0, /* tp_iter */ + 0, /* tp_iternext */ + 0, /* tp_methods */ + 0, /* tp_members */ + 0, /* tp_getset */ + 0, /* tp_base */ + 0, /* tp_dict */ + 0, /* tp_descr_get */ + 0, /* tp_descr_set */ + 0, /* tp_dictoffset */ + 0, /* tp_init */ + 0, /* tp_alloc */ + (newfunc) stolenbuf_new, /* tp_new */ +}; + +#define MODULE_NAME "pandas.util._move" + +#if !COMPILING_IN_PY2 +PyModuleDef _move_module = { + PyModuleDef_HEAD_INIT, + MODULE_NAME, + NULL, + -1, +}; +#endif /* !COMPILING_IN_PY2 */ + +PyDoc_STRVAR( + badmove_doc, + "Exception used to indicate that a move was attempted on a value with\n" + "more than a single reference.\n" + "\n" + "Parameters\n" + "----------\n" + "data : any\n" + " The data which was passed to ``_move_into_mutable_buffer``.\n" + "\n" + "See Also\n" + "--------\n" + "pandas.util._move.move_into_mutable_buffer\n"); + +PyMODINIT_FUNC +#if !COMPILING_IN_PY2 +#define ERROR_RETURN NULL +PyInit__move(void) +#else +#define ERROR_RETURN +init_move(void) +#endif /* !COMPILING_IN_PY2 */ +{ + PyObject *m; + + if (!(badmove = PyErr_NewExceptionWithDoc("pandas.util._move.BadMove", + badmove_doc, + NULL, + NULL))) { + return ERROR_RETURN; + } + + if (PyType_Ready(&stolenbuf_type)) { + return ERROR_RETURN; + } + +#if !COMPILING_IN_PY2 + if (!(m = PyModule_Create(&_move_module))) +#else + if (!(m = Py_InitModule(MODULE_NAME, NULL))) +#endif /* !COMPILING_IN_PY2 */ + { + return ERROR_RETURN; + } + + if (PyModule_AddObject(m, + "move_into_mutable_buffer", + (PyObject*) &stolenbuf_type)) { + Py_DECREF(m); + return ERROR_RETURN; + } + + if (PyModule_AddObject(m, "BadMove", badmove)) { + Py_DECREF(m); + return ERROR_RETURN; + } + +#if !COMPILING_IN_PY2 + return m; +#endif /* !COMPILING_IN_PY2 */ +} diff --git a/pandas/util/testing.py b/pandas/util/testing.py index ebd1f7d2c17f8..3aebbf527bcd2 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -2313,3 +2313,55 @@ class SubclassedDataFrame(DataFrame): @property def _constructor(self): return SubclassedDataFrame + + +@contextmanager +def patch(ob, attr, value): + """Temporarily patch an attribute of an object. + + Parameters + ---------- + ob : any + The object to patch. This must support attribute assignment for `attr`. + attr : str + The name of the attribute to patch. + value : any + The temporary attribute to assign. + + Examples + -------- + >>> class C(object): + ... attribute = 'original' + ... + >>> C.attribute + 'original' + >>> with patch(C, 'attribute', 'patched'): + ... in_context = C.attribute + ... + >>> in_context + 'patched' + >>> C.attribute # the value is reset when the context manager exists + 'original' + + Correctly replaces attribute when the manager exits with an exception. + >>> with patch(C, 'attribute', 'patched'): + ... in_context = C.attribute + ... raise ValueError() + Traceback (most recent call last): + ... + ValueError + >>> in_context + 'patched' + >>> C.attribute + 'original' + """ + noattr = object() # mark that the attribute never existed + old = getattr(ob, attr, noattr) + setattr(ob, attr, value) + try: + yield + finally: + if old is noattr: + delattr(ob, attr) + else: + setattr(ob, attr, old) diff --git a/setup.py b/setup.py index e3fb5a007aad3..848e8b724edad 100755 --- a/setup.py +++ b/setup.py @@ -532,6 +532,13 @@ def pxd(name): extensions.append(ujson_ext) +# extension for pseudo-safely moving bytes into mutable buffers +_move_ext = Extension('pandas.util._move', + depends=[], + sources=['pandas/util/_move.c']) +extensions.append(_move_ext) + + if _have_setuptools: setuptools_kwargs["test_suite"] = "nose.collector"
Addresses the case where 'compress' was not none. The old implementation would decompress the data and then call np.frombuffer on a bytes object. Because a bytes object is not a mutable buffer, the resulting ndarray had writeable=False. The new implementation ensures that the pandas is the only owner of this new buffer and then sets it to mutable without copying it. This means that we don't need to do a copy of the data coming in AND we can mutate it later. If we are not the only owner of this data then we just copy it with np.fromstring.
https://api.github.com/repos/pandas-dev/pandas/pulls/12359
2016-02-16T23:55:01Z
2016-03-18T00:01:28Z
null
2016-03-18T16:06:36Z
COMPAT: DataFrame construction compat with xarray.Dataset
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 8a3ac4db37d2d..3c9a5868b725e 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -28,7 +28,7 @@ is_sequence, _infer_dtype_from_scalar, _values_from_object, is_list_like, _maybe_box_datetimelike, is_categorical_dtype, is_object_dtype, is_internal_type, is_datetimetz, _possibly_infer_to_datetimelike, - _dict_compat) + _dict_compat, is_dict_like) from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import (maybe_droplevels, convert_to_index_sliceable, @@ -219,8 +219,6 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, if isinstance(data, BlockManager): mgr = self._init_mgr(data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy) - elif isinstance(data, dict): - mgr = self._init_dict(data, index, columns, dtype=dtype) elif isinstance(data, ma.MaskedArray): import numpy.ma.mrecords as mrecords # masked recarray @@ -252,6 +250,8 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, else: mgr = self._init_ndarray(data, index, columns, dtype=dtype, copy=copy) + elif is_dict_like(data): + mgr = self._init_dict(data, index, columns, dtype=dtype) elif isinstance(data, (list, types.GeneratorType)): if isinstance(data, types.GeneratorType): data = list(data) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index e0989574d79e1..7a3f85bf59e87 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -16,7 +16,8 @@ from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict) from pandas.core.categorical import Categorical from pandas.core.common import (PandasError, _try_sort, _default_index, - _infer_dtype_from_scalar, is_list_like) + _infer_dtype_from_scalar, is_list_like, + is_dict_like) from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import (Index, MultiIndex, _ensure_index, @@ -157,7 +158,7 @@ def _init_data(self, data, copy, dtype, **kwargs): axes = [x if x is not None else y for x, y in zip(passed_axes, data.axes)] mgr = data - elif isinstance(data, dict): + elif is_dict_like(data): mgr = self._init_dict(data, passed_axes, dtype=dtype) copy = False dtype = None diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 7983ac7fff834..52bb14af5bbeb 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -1034,6 +1034,8 @@ def testit(index, check_index_type=True): self.assertIsInstance(result, DataArray) assert_series_equal(result.to_series(), s) + #assert_series_equal(Series(result), s) + class TestDataFrame(tm.TestCase, Generic): _typ = DataFrame @@ -1832,6 +1834,10 @@ def test_to_xarray(self): expected, check_index_type=False) + assert_frame_equal(DataFrame(result).set_index('foo'), + expected, + check_index_type=False) + # not implemented df.index = pd.MultiIndex.from_product([['a'], range(3)], names=['one', 'two']) @@ -1859,6 +1865,8 @@ def test_to_xarray(self): # idempotency assert_panel_equal(result.to_pandas(), p) + #assert_panel_equal(Panel(result), p) + class TestPanel4D(tm.TestCase, Generic): _typ = Panel4D
closes #12353
https://api.github.com/repos/pandas-dev/pandas/pulls/12356
2016-02-16T20:06:40Z
2016-03-23T11:57:39Z
null
2016-03-23T11:57:39Z
docs: add hyperlinks to str.get_dummies
diff --git a/doc/source/text.rst b/doc/source/text.rst index a0cc32ecea531..53567ea25aeac 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -381,6 +381,7 @@ Method Summary :meth:`~Series.str.rsplit`,Split strings on delimiter working from the end of the string :meth:`~Series.str.get`,Index into each element (retrieve i-th element) :meth:`~Series.str.join`,Join strings in each element of the Series with passed separator + :meth:`~Series.str.get_dummies`,Split strings on delimiter, returning DataFrame of dummy variables :meth:`~Series.str.contains`,Return boolean array if each string contains pattern/regex :meth:`~Series.str.replace`,Replace occurrences of pattern/regex with some other string :meth:`~Series.str.repeat`,Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``) diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 0643250484839..5ee3e4f08d285 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -1035,8 +1035,10 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, 2 0 1 3 0 0 4 0 0 - See also ``Series.str.get_dummies``. + See Also + -------- + Series.str.get_dummies """ from pandas.tools.merge import concat from itertools import cycle
The description used in the summary table is the first sentence of str_get_dummies()'s docstring, edited to match the style of other summaries. I tried to build this branch locally – the _Method Summary_ section doesn't render with this change and I can't see why, it looks syntactically correct to me…
https://api.github.com/repos/pandas-dev/pandas/pulls/12350
2016-02-16T15:25:49Z
2016-02-17T01:09:18Z
null
2016-02-17T01:09:19Z
TST: confirming tests for #12348
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 47e78cf558a16..00c7d1d31047f 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -615,7 +615,7 @@ other anchored offsets like ``MonthBegin`` and ``YearBegin``. Resample API ^^^^^^^^^^^^ -Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`). +Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12348`). .. ipython:: python diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 262526f3f4c7c..91922ef5aa9e1 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -938,6 +938,18 @@ def test_resample_ohlc_result(self): b = s.loc[:'4-14-2000'].resample('30T').ohlc() self.assertIsInstance(b, DataFrame) + # GH12348 + # raising on odd period + rng = date_range('2013-12-30', '2014-01-07') + index = rng.drop([Timestamp('2014-01-01'), + Timestamp('2013-12-31'), + Timestamp('2014-01-04'), + Timestamp('2014-01-05')]) + df = DataFrame(data=np.arange(len(index)), index=index) + result = df.resample('B').mean() + expected = df.reindex(index=date_range(rng[0], rng[-1], freq='B')) + assert_frame_equal(result, expected) + def test_resample_ohlc_dataframe(self): df = ( pd.DataFrame({
xref #12348
https://api.github.com/repos/pandas-dev/pandas/pulls/12349
2016-02-16T15:24:36Z
2016-02-16T16:00:18Z
null
2016-02-16T16:00:18Z
ENH: gbq docs update
diff --git a/doc/source/io.rst b/doc/source/io.rst index e2f2301beb078..a42b5dab25578 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4153,7 +4153,7 @@ to existing tables. .. warning:: To use this module, you will need a valid BigQuery account. Refer to the - `BigQuery Documentation <https://developers.google.com/bigquery/>`__ for details on the service itself. + `BigQuery Documentation <https://cloud.google.com/bigquery/what-is-bigquery>`__ for details on the service itself. The key functions are: @@ -4173,17 +4173,18 @@ The key functions are: Authentication '''''''''''''' - -Authentication is possible with either user account credentials or service account credentials. +Authentication to the Google ``BigQuery`` service is via ``OAuth 2.0``. +Is possible to authenticate with either user account credentials or service account credentials. Authenticating with user account credentials is as simple as following the prompts in a browser window which will be automatically opened for you. You will be authenticated to the specified -``BigQuery`` account via Google's ``Oauth2`` mechanism. Additional information on the -authentication mechanism can be found `here <https://developers.google.com/identity/protocols/OAuth2#clientside/>`__. +``BigQuery`` account using the product name ``pandas GBQ``. It is only possible on local host. +The remote authentication using user account credentials is not currently supported in Pandas. +Additional information on the authentication mechanism can be found +`here <https://developers.google.com/identity/protocols/OAuth2#clientside/>`__. Authentication with service account credentials is possible via the `'private_key'` parameter. This method is particularly useful when working on remote servers (eg. jupyter iPython notebook on remote host). -The remote authentication using user account credentials is not currently supported in Pandas. Additional information on service accounts can be found `here <https://developers.google.com/identity/protocols/OAuth2#serviceaccount>`__. @@ -4314,13 +4315,13 @@ For example: .. note:: The BigQuery SQL query language has some oddities, see the - `BigQuery Query Reference Documentation <https://developers.google.com/bigquery/query-reference>`__. + `BigQuery Query Reference Documentation <https://cloud.google.com/bigquery/query-reference>`__. .. note:: While BigQuery uses SQL-like syntax, it has some important differences from traditional databases both in functionality, API limitations (size and quantity of queries or uploads), - and how Google charges for use of the service. You should refer to `Google BigQuery documentation <https://developers.google.com/bigquery/>`__ + and how Google charges for use of the service. You should refer to `Google BigQuery documentation <https://cloud.google.com/bigquery/what-is-bigquery>`__ often as the service seems to be changing and evolving. BiqQuery is best for analyzing large sets of data quickly, but it is not a direct replacement for a transactional database. diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 96e72cbf9f528..c7481a953e47b 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -525,12 +525,18 @@ def read_gbq(query, project_id=None, index_col=None, col_order=None, THIS IS AN EXPERIMENTAL LIBRARY - The main method a user calls to execute a Query in Google BigQuery and read - results into a pandas DataFrame using the v2 Google API client for Python. - Documentation for the API is available at - https://developers.google.com/api-client-library/python/. Authentication - to the Google BigQuery service is via OAuth 2.0 using the product name - 'pandas GBQ'. + The main method a user calls to execute a Query in Google BigQuery + and read results into a pandas DataFrame. + + Google BigQuery API Client Library v2 for Python is used. + Documentation is available at + https://developers.google.com/api-client-library/python/apis/bigquery/v2 + + Authentication to the Google BigQuery service is via OAuth 2.0. + By default user account credentials are used. You will be asked to + grant permissions for product name 'pandas GBQ'. It is also posible + to authenticate via service account credentials by using + private_key parameter. Parameters ---------- @@ -615,6 +621,19 @@ def to_gbq(dataframe, destination_table, project_id, chunksize=10000, THIS IS AN EXPERIMENTAL LIBRARY + The main method a user calls to export pandas DataFrame contents to + Google BigQuery table. + + Google BigQuery API Client Library v2 for Python is used. + Documentation is available at + https://developers.google.com/api-client-library/python/apis/bigquery/v2 + + Authentication to the Google BigQuery service is via OAuth 2.0. + By default user account credentials are used. You will be asked to + grant permissions for product name 'pandas GBQ'. It is also posible + to authenticate via service account credentials by using + private_key parameter. + Parameters ---------- dataframe : DataFrame
I've made small documentation update for gbq features.
https://api.github.com/repos/pandas-dev/pandas/pulls/12345
2016-02-16T10:59:35Z
2016-02-16T14:08:19Z
null
2016-02-17T11:21:10Z
BUG: Fix bug where sort_index if unsorted levels.
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 51ff9915fc589..f345c3d822b3e 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -992,3 +992,4 @@ Bug Fixes - Bug in ``crosstab`` where arguments with non-overlapping indexes would return a ``KeyError`` (:issue:`10291`) - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) +- Bug in ``MultiIndex.sort_index`` where if the levels were out of order, but the labels were monotonic, then the index would return unsorted. (:issue:`12261`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 8a3ac4db37d2d..1463034f38769 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3236,7 +3236,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, # make sure that the axis is lexsorted to start # if not we need to reconstruct to get the correct indexer - if not labels.is_lexsorted(): + if not labels.is_lexsorted() or not all([labels.get_level_values(i).is_monotonic for i in range(labels.nlevels)]): labels = MultiIndex.from_tuples(labels.values) indexer = _lexsort_indexer(labels.labels, orders=ascending, diff --git a/pandas/tests/frame/test_sorting.py b/pandas/tests/frame/test_sorting.py index ff2159f8b6f40..86542c08dccef 100644 --- a/pandas/tests/frame/test_sorting.py +++ b/pandas/tests/frame/test_sorting.py @@ -471,3 +471,18 @@ def test_frame_column_inplace_sort_exception(self): cp = s.copy() cp.sort_values() # it works! + + def test_sort_added_index(self): + # GH 12261 + df = DataFrame(0, columns=[], index=MultiIndex.from_product([[], []])) + + df.loc['b', '2'] = 1 + df.loc['a', '3'] = 1 + + expected = DataFrame([[1.0, np.nan], [np.nan, 1.0]], + index=MultiIndex.from_product([['b', 'a'], ['']]), + columns=['2', '3']).sort_index() + + actual = df.sort_index() + + assert_frame_equal(expected, actual)
Closes #12261
https://api.github.com/repos/pandas-dev/pandas/pulls/12342
2016-02-16T07:17:22Z
2016-02-16T14:40:53Z
null
2016-02-16T14:40:53Z
COMPAT: dateutil=2.2 compat
diff --git a/ci/requirements-3.4.build b/ci/requirements-3.4.build index 8e2a952b840f7..11901862776bc 100644 --- a/ci/requirements-3.4.build +++ b/ci/requirements-3.4.build @@ -1,5 +1,3 @@ -python-dateutil -pytz numpy=1.8.1 cython libgfortran diff --git a/ci/requirements-3.4.pip b/ci/requirements-3.4.pip index 47a049aac7632..62be867437af1 100644 --- a/ci/requirements-3.4.pip +++ b/ci/requirements-3.4.pip @@ -1,3 +1,4 @@ +python-dateutil==2.2 blosc httplib2 google-api-python-client diff --git a/ci/requirements-3.4.run b/ci/requirements-3.4.run index 902a2984d4b3d..7d4cdcd21595a 100644 --- a/ci/requirements-3.4.run +++ b/ci/requirements-3.4.run @@ -1,4 +1,3 @@ -python-dateutil pytz numpy=1.8.1 openpyxl diff --git a/pandas/core/common.py b/pandas/core/common.py index 70c02c5632d80..0a6a3097b2ff0 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1681,7 +1681,7 @@ def _possibly_cast_to_datetime(value, dtype, errors='raise'): errors=errors).tz_localize(dtype.tz) elif is_timedelta64: value = to_timedelta(value, errors=errors)._values - except (AttributeError, ValueError): + except (AttributeError, ValueError, TypeError): pass # coerce datetimelike to object
closes #12338
https://api.github.com/repos/pandas-dev/pandas/pulls/12339
2016-02-16T01:19:26Z
2016-02-16T01:47:48Z
null
2016-02-16T01:47:48Z
API: consistency in aggregate groupby results
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 00c7d1d31047f..c749c4540013b 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -615,7 +615,7 @@ other anchored offsets like ``MonthBegin`` and ``YearBegin``. Resample API ^^^^^^^^^^^^ -Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12348`). +Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12334`, :issue:`12348`). .. ipython:: python diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 1b43d3bd76f59..963c6223730f3 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2550,15 +2550,7 @@ def aggregate(self, func_or_funcs, *args, **kwargs): # _level handled at higher if not _level and isinstance(ret, dict): from pandas import concat - - # our result is a Series-like - if len(ret) == 1: - ret = concat([r for r in ret.values()], - axis=1) - - # our result is a DataFrame like - else: - ret = concat(ret, axis=1) + ret = concat(ret, axis=1) return ret agg = aggregate diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index cc619a998cdd8..2d038f8b16520 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1532,6 +1532,34 @@ def test_aggregate_api_consistency(self): ['D', 'C']]) assert_frame_equal(result, expected, check_like=True) + def test_agg_compat(self): + + # GH 12334 + + df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'two', + 'two', 'two', 'one', 'two'], + 'C': np.random.randn(8) + 1.0, + 'D': np.arange(8)}) + + g = df.groupby(['A', 'B']) + + expected = pd.concat([g['D'].sum(), + g['D'].std()], + axis=1) + expected.columns = MultiIndex.from_tuples([('C', 'sum'), + ('C', 'std')]) + result = g['D'].agg({'C': ['sum', 'std']}) + assert_frame_equal(result, expected, check_like=True) + + expected = pd.concat([g['D'].sum(), + g['D'].std()], + axis=1) + expected.columns = ['C', 'D'] + result = g['D'].agg({'C': 'sum', 'D': 'std'}) + assert_frame_equal(result, expected, check_like=True) + def test_agg_nested_dicts(self): # API change for disallowing these types of nested dicts diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 91922ef5aa9e1..68999ac143ea8 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -424,8 +424,8 @@ def test_agg_misc(self): expected = pd.concat([t['A'].sum(), t['A'].std()], axis=1) - expected.columns = ['sum', 'std'] - + expected.columns = pd.MultiIndex.from_tuples([('A', 'sum'), + ('A', 'std')]) assert_frame_equal(result, expected, check_like=True) expected = pd.concat([t['A'].agg(['sum', 'std']),
closes #12334
https://api.github.com/repos/pandas-dev/pandas/pulls/12335
2016-02-15T22:12:16Z
2016-02-17T01:12:08Z
null
2016-02-17T01:12:08Z
BUG: Common NumericIndex.__new__, with fixed name handling
diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 4ead02e5bd022..5f681ba618cd4 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -45,6 +45,9 @@ def __new__(cls, data=None, categories=None, ordered=None, dtype=None, if fastpath: return cls._simple_new(data, name=name) + if name is None and hasattr(data, 'name'): + name = data.name + if isinstance(data, com.ABCCategorical): data = cls._create_categorical(cls, data, categories, ordered) elif isinstance(data, CategoricalIndex): diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 8138f2deb534f..d29d089871bf8 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -66,8 +66,10 @@ def __new__(cls, levels=None, labels=None, sortorder=None, names=None, name=None, **kwargs): # compat with Index - if name is not None: - names = name + if name is None and hasattr(levels, 'name'): + name = levels.name + if isinstance(levels, MultiIndex): + return levels.copy(name=name, names=names, deep=copy) if levels is None or labels is None: raise TypeError("Must pass both levels and labels") if len(levels) != len(labels): @@ -77,8 +79,6 @@ def __new__(cls, levels=None, labels=None, sortorder=None, names=None, if len(levels) == 1: if names: name = names[0] - else: - name = None return Index(levels[0], name=name, copy=True).take(labels[0]) result = object.__new__(MultiIndex) @@ -333,7 +333,7 @@ def set_labels(self, labels, level=None, inplace=False, labels = property(fget=_get_labels, fset=__set_labels) def copy(self, names=None, dtype=None, levels=None, labels=None, - deep=False, _set_identity=False): + deep=False, name=None, _set_identity=False): """ Make a copy of this object. Names, dtype, levels and labels can be passed and will be set on new copy. @@ -344,6 +344,7 @@ def copy(self, names=None, dtype=None, levels=None, labels=None, dtype : numpy dtype or pandas type, optional levels : sequence, optional labels : sequence, optional + name : object, optional Returns ------- @@ -366,7 +367,7 @@ def copy(self, names=None, dtype=None, levels=None, labels=None, names = self.names return MultiIndex(levels=levels, labels=labels, names=names, sortorder=self.sortorder, verify_integrity=False, - _set_identity=_set_identity) + name=name, _set_identity=_set_identity) def __array__(self, dtype=None): """ the array interface, return my values """ diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index c1817129116e2..6af6c2aaaa356 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -19,6 +19,32 @@ class NumericIndex(Index): """ _is_numeric_dtype = True + def __new__(cls, data=None, dtype=None, copy=False, name=None, + fastpath=False, **kwargs): + + if fastpath: + return cls._simple_new(data, name=name) + + # isscalar, generators handled in coerce_to_ndarray + data = cls._coerce_to_ndarray(data) + + if issubclass(data.dtype.type, compat.string_types): + cls._string_data_error(data) + + if copy or data.dtype != cls._default_dtype: + try: + subarr = np.array(data, dtype=cls._default_dtype, copy=copy) + assert((subarr == data) | np.isnan(subarr)).all() + except: + raise TypeError('Unsafe NumPy casting, you must ' + 'explicitly cast') + else: + subarr = data + + if name is None and hasattr(data, 'name'): + name = data.name + return cls._simple_new(subarr, name=name) + def _maybe_cast_slice_bound(self, label, side, kind): """ This function should be overloaded in subclasses that allow non-trivial @@ -94,35 +120,7 @@ class Int64Index(NumericIndex): _can_hold_na = False _engine_type = _index.Int64Engine - - def __new__(cls, data=None, dtype=None, copy=False, name=None, - fastpath=False, **kwargs): - - if fastpath: - return cls._simple_new(data, name=name) - - # isscalar, generators handled in coerce_to_ndarray - data = cls._coerce_to_ndarray(data) - - if issubclass(data.dtype.type, compat.string_types): - cls._string_data_error(data) - - elif issubclass(data.dtype.type, np.integer): - # don't force the upcast as we may be dealing - # with a platform int - if (dtype is None or - not issubclass(np.dtype(dtype).type, np.integer)): - dtype = np.int64 - - subarr = np.array(data, dtype=dtype, copy=copy) - else: - subarr = np.array(data, dtype=np.int64, copy=copy) - if len(data) > 0: - if (subarr != data).any(): - raise TypeError('Unsafe NumPy casting to integer, you must' - ' explicitly cast') - - return cls._simple_new(subarr, name=name) + _default_dtype = np.int64 @property def inferred_type(self): @@ -192,42 +190,7 @@ class Float64Index(NumericIndex): _inner_indexer = _algos.inner_join_indexer_float64 _outer_indexer = _algos.outer_join_indexer_float64 - def __new__(cls, data=None, dtype=None, copy=False, name=None, - fastpath=False, **kwargs): - - if fastpath: - return cls._simple_new(data, name) - - data = cls._coerce_to_ndarray(data) - - if issubclass(data.dtype.type, compat.string_types): - cls._string_data_error(data) - - if dtype is None: - dtype = np.float64 - dtype = np.dtype(dtype) - - # allow integer / object dtypes to be passed, but coerce to float64 - if dtype.kind in ['i', 'O']: - dtype = np.float64 - - elif dtype.kind in ['f']: - pass - - else: - raise TypeError("cannot support {0} dtype in " - "Float64Index".format(dtype)) - - try: - subarr = np.array(data, dtype=dtype, copy=copy) - except: - raise TypeError('Unsafe NumPy casting, you must explicitly cast') - - # coerce to float64 for storage - if subarr.dtype != np.float64: - subarr = subarr.astype(np.float64) - - return cls._simple_new(subarr, name) + _default_dtype = np.float64 @property def inferred_type(self): diff --git a/pandas/tests/frame/test_block_internals.py b/pandas/tests/frame/test_block_internals.py index f337bf48c05ee..94d5307d797e1 100644 --- a/pandas/tests/frame/test_block_internals.py +++ b/pandas/tests/frame/test_block_internals.py @@ -372,11 +372,13 @@ def test_consolidate_datetime64(self): ser_starting.index = ser_starting.values ser_starting = ser_starting.tz_localize('US/Eastern') ser_starting = ser_starting.tz_convert('UTC') + ser_starting.index.name = 'starting' ser_ending = df.ending ser_ending.index = ser_ending.values ser_ending = ser_ending.tz_localize('US/Eastern') ser_ending = ser_ending.tz_convert('UTC') + ser_ending.index.name = 'ending' df.starting = ser_starting.index df.ending = ser_ending.index diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index f1824267d63d8..b5dd16bdca8a0 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -654,3 +654,19 @@ def test_fillna(self): expected[1] = True self.assert_numpy_array_equal(idx._isnan, expected) self.assertTrue(idx.hasnans) + + def test_copy(self): + # GH12309 + for name, index in compat.iteritems(self.indices): + first = index.__class__(index, copy=True, name='mario') + second = first.__class__(first, copy=False) + self.assertTrue(index.equals(first)) + # Even though "copy=False", we want a new object: + self.assertTrue(id(first) != id(second)) + + if isinstance(index, MultiIndex) and len(index.levels) > 1: + # No unique "name" attribute (each level has its own) + continue + + self.assertEqual(first.name, 'mario') + self.assertEqual(second.name, 'mario') diff --git a/pandas/tests/indexes/test_numeric.py b/pandas/tests/indexes/test_numeric.py index 325e0df14a07e..70f75adf04ea8 100644 --- a/pandas/tests/indexes/test_numeric.py +++ b/pandas/tests/indexes/test_numeric.py @@ -831,3 +831,11 @@ def test_ufunc_coercions(self): tm.assertIsInstance(result, Float64Index) exp = Float64Index([0.5, 1., 1.5, 2., 2.5], name='x') tm.assert_index_equal(result, exp) + + def test_ensure_copied_data(self): + data = np.array([1, 2, 3], dtype='int64') + idx = Int64Index(data, copy=True) + self.assert_numpy_array_equal(data, idx._data) + tm.assertIsNot(data, idx._data) + idx2 = Int64Index(data, copy=False) + tm.assertIs(data, idx2._data) diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index d24e1eab1cea8..6fbf2943e3c3f 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -725,9 +725,9 @@ def test_ensure_platform_int(): pi = com._ensure_platform_int(x) assert (pi.dtype == np.int_) - # int32 + # int32 - "dtype" argument is irrelevant x = Int64Index([1, 2, 3], dtype='int32') - assert (x.dtype == np.int32) + assert (x.dtype == np.int64) pi = com._ensure_platform_int(x) assert (pi.dtype == np.int_) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 9faf6f174115c..98a19a43f30fc 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -218,6 +218,9 @@ def __new__(cls, data=None, verify_integrity=True, normalize=False, closed=None, ambiguous='raise', dtype=None, **kwargs): + if name is None and hasattr(data, 'name'): + name = data.name + dayfirst = kwargs.pop('dayfirst', None) yearfirst = kwargs.pop('yearfirst', None) diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index a25bb525c9970..33fc6d6d80a18 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -179,6 +179,9 @@ def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None, raise ValueError('Periods must be a number, got %s' % str(periods)) + if name is None and hasattr(data, 'name'): + name = data.name + if data is None: if ordinal is not None: data = np.asarray(ordinal, dtype=np.int64) diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index e74879602fa64..0e4ceec12f24b 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -133,8 +133,9 @@ def __new__(cls, data=None, unit=None, if isinstance(data, TimedeltaIndex) and freq is None and name is None: if copy: - data = data.copy() - return data + return data.copy() + else: + return data._shallow_copy() freq_infer = False if not isinstance(freq, DateOffset): diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 4bdd0ed462852..d433c5a87f378 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -1649,7 +1649,7 @@ def test_join_self(self): kinds = 'outer', 'inner', 'left', 'right' for kind in kinds: joined = index.join(index, how=kind) - self.assertIs(index, joined) + tm.assert_index_equal(index, joined) def test_factorize(self): idx1 = TimedeltaIndex(['1 day', '1 day', '2 day', '2 day', '3 day',
closes #12309
https://api.github.com/repos/pandas-dev/pandas/pulls/12331
2016-02-15T17:46:31Z
2016-05-07T17:46:49Z
null
2016-05-17T12:42:29Z
DOC: add addtl docs for FloatIndexing changes in 0.18.0
diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 465fbf483b1cc..a28eb39f36917 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -795,12 +795,23 @@ In non-float indexes, slicing using floats will raise a ``TypeError`` In [1]: pd.Series(range(5))[3.5:4.5] TypeError: the slice start [3.5] is not a proper indexer for this index type (Int64Index) -Using a scalar float indexer will be deprecated in a future version, but is allowed for now. +.. warning:: -.. code-block:: python + Using a scalar float indexer has been removed in 0.18.0, so the following will raise a ``TypeError`` + + .. code-block:: python + + In [3]: pd.Series(range(5))[3.0] + TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'> + + Further the treatment of ``.ix`` with a float indexer on a non-float index, will be label based, and thus coerce the index. + + .. ipython:: python - In [3]: pd.Series(range(5))[3.0] - Out[3]: 3 + s2 = pd.Series([1, 2, 3], index=list('abc')) + s2 + s2.ix[1.0] = 10 + s2 Here is a typical use-case for using this type of indexing. Imagine that you have a somewhat irregular timedelta-like indexing scheme, but the data is recorded as floats. This could for diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index c6d02acf75477..a1056591abd01 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -704,8 +704,8 @@ performed with the ``Resampler`` objects with :meth:`~Resampler.backfill`, .. ipython:: python - s = Series(np.arange(5,dtype='int64'), - index=date_range('2010-01-01', periods=5, freq='Q')) + s = pd.Series(np.arange(5,dtype='int64'), + index=date_range('2010-01-01', periods=5, freq='Q')) s Previously @@ -838,7 +838,7 @@ Deprecations .. code-block:: python - In [1]: s = Series(range(3)) + In [1]: s = pd.Series(range(3)) In [2]: pd.rolling_mean(s,window=2,min_periods=1) FutureWarning: pd.rolling_mean is deprecated for Series and @@ -884,11 +884,17 @@ Removal of deprecated float indexers In :issue:`4892` indexing with floating point numbers on a non-``Float64Index`` was deprecated (in version 0.14.0). In 0.18.0, this deprecation warning is removed and these will now raise a ``TypeError``. (:issue:`12165`) +.. ipython:: python + + s = pd.Series([1,2,3]) + s + s2 = pd.Series([1, 2, 3], index=list('abc')) + s2 + Previous Behavior: .. code-block:: python - In [1]: s = Series([1,2,3]) In [2]: s[1.0] FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[2]: 2 @@ -901,24 +907,46 @@ Previous Behavior: FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[4]: 2 + # .ix would coerce 1.0 to the positional 1, and index + In [5]: s2.ix[1.0] = 10 + FutureWarning: scalar indexers for index type Index should be integers and not floating point + + In [6]: s2 + Out[6]: + a 1 + b 10 + c 3 + dtype: int64 + New Behavior: .. code-block:: python - In [4]: s[1.0] + In [2]: s[1.0] TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> - In [4]: s.iloc[1.0] + In [3]: s.iloc[1.0] TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> In [4]: s.loc[1.0] TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> + # .ix will now cause this to be a label lookup and coerce to and Index + In [5]: s2.ix[1.0] = 10 + + In [6]: s2 + Out[3]: + a 1 + b 2 + c 3 + 1.0 10 + dtype: int64 + Float indexing on a ``Float64Index`` is unchanged. .. ipython:: python - s = Series([1,2,3],index=np.arange(3.)) + s = pd.Series([1,2,3],index=np.arange(3.)) s[1.0] s[1.0:2.5] @@ -945,7 +973,7 @@ Performance Improvements - Improved huge ``DatetimeIndex``, ``PeriodIndex`` and ``TimedeltaIndex``'s ops performance including ``NaT`` (:issue:`10277`) - Improved performance of ``pandas.concat`` (:issue:`11958`) - Improved performance of ``StataReader`` (:issue:`11591`) -- Improved performance in construction of ``Categoricals`` with Series of datetimes containing ``NaT`` (:issue:`12077`) +- Improved performance in construction of ``Categoricals`` with ``Series`` of datetimes containing ``NaT`` (:issue:`12077`) - Improved performance of ISO 8601 date parsing for dates without separators (:issue:`11899`), leading zeros (:issue:`11871`) and with whitespace preceding the time zone (:issue:`9714`) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 172f81e5a6423..e6b5271d88856 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -974,7 +974,7 @@ def _convert_scalar_indexer(self, key, kind=None): if kind == 'iloc': if is_integer(key): return key - return self._invalid_indexer('label', key) + return self._invalid_indexer('positional', key) else: if len(self): diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 72fff3f82111e..1c0986b025acc 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -5406,6 +5406,23 @@ def test_index_type_coercion(self): s2['0'] = 0 self.assertTrue(s2.index.is_object()) + def test_invalid_scalar_float_indexers_error(self): + + for index in [tm.makeStringIndex, tm.makeUnicodeIndex, + tm.makeCategoricalIndex, + tm.makeDateIndex, tm.makeTimedeltaIndex, + tm.makePeriodIndex]: + + i = index(5) + + s = Series(np.arange(len(i)), index=i) + + def f(): + s.iloc[3.0] + self.assertRaisesRegexp(TypeError, + 'cannot do positional indexing', + f) + def test_invalid_scalar_float_indexers(self): # GH 4892
closes #12322
https://api.github.com/repos/pandas-dev/pandas/pulls/12330
2016-02-15T16:57:55Z
2016-02-15T20:05:56Z
null
2016-02-15T20:05:56Z
BUG: addtl fix for compat summary of groupby/resample with dicts
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index c6d02acf75477..b1cb94d131b5b 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -581,7 +581,7 @@ other anchored offsets like ``MonthBegin`` and ``YearBegin``. Resample API ^^^^^^^^^^^^ -Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`). +Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`). .. ipython:: python diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 698bbcb2538b9..1b43d3bd76f59 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2526,7 +2526,8 @@ def aggregate(self, func_or_funcs, *args, **kwargs): return getattr(self, func_or_funcs)(*args, **kwargs) if hasattr(func_or_funcs, '__iter__'): - ret = self._aggregate_multiple_funcs(func_or_funcs, _level) + ret = self._aggregate_multiple_funcs(func_or_funcs, + (_level or 0) + 1) else: cyfunc = self._is_cython_func(func_or_funcs) if cyfunc and not args and not kwargs: @@ -2546,6 +2547,18 @@ def aggregate(self, func_or_funcs, *args, **kwargs): if not self.as_index: # pragma: no cover print('Warning, ignoring as_index=True') + # _level handled at higher + if not _level and isinstance(ret, dict): + from pandas import concat + + # our result is a Series-like + if len(ret) == 1: + ret = concat([r for r in ret.values()], + axis=1) + + # our result is a DataFrame like + else: + ret = concat(ret, axis=1) return ret agg = aggregate @@ -2571,14 +2584,6 @@ def _aggregate_multiple_funcs(self, arg, _level): columns.append(com._get_callable_name(f)) arg = lzip(columns, arg) - # for a ndim=1, disallow a nested dict for an aggregator as - # this is a mis-specification of the aggregations, via a - # specificiation error - # e.g. g['A'].agg({'A': ..., 'B': ...}) - if self.name in columns and len(columns) > 1: - raise SpecificationError('invalid aggregation names specified ' - 'for selected objects') - results = {} for name, func in arg: obj = self diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 405589f501532..cc619a998cdd8 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1558,6 +1558,13 @@ def f(): 'ra', 'std'), ('rb', 'mean'), ('rb', 'std')]) assert_frame_equal(result, expected, check_like=True) + # same name as the original column + # GH9052 + expected = g['D'].agg({'result1': np.sum, 'result2': np.mean}) + expected = expected.rename(columns={'result1': 'D'}) + result = g['D'].agg({'D': np.sum, 'result2': np.mean}) + assert_frame_equal(result, expected, check_like=True) + def test_multi_iter(self): s = Series(np.arange(6)) k1 = np.array(['a', 'a', 'a', 'b', 'b', 'b']) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index a22f87cb90420..ba2eb3463d169 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -500,7 +500,7 @@ def _downsample(self, how, **kwargs): # do we have a regular frequency if ax.freq is not None or ax.inferred_freq is not None: - if len(self.grouper.binlabels) > len(ax): + if len(self.grouper.binlabels) > len(ax) and how is None: # let's do an asfreq return self.asfreq() diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 4e2d596681942..262526f3f4c7c 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -419,25 +419,32 @@ def test_agg_misc(self): assert_frame_equal(result, expected, check_like=True) # series like aggs - expected = pd.concat([t['A'].sum(), - t['A'].std()], - axis=1) - expected.columns = ['sum', 'std'] - for t in [r, g]: - result = r['A'].agg({'A': ['sum', 'std']}) + result = t['A'].agg({'A': ['sum', 'std']}) + expected = pd.concat([t['A'].sum(), + t['A'].std()], + axis=1) + expected.columns = ['sum', 'std'] + + assert_frame_equal(result, expected, check_like=True) + + expected = pd.concat([t['A'].agg(['sum', 'std']), + t['A'].agg(['mean', 'std'])], + axis=1) + expected.columns = pd.MultiIndex.from_tuples([('A', 'sum'), + ('A', 'std'), + ('B', 'mean'), + ('B', 'std')]) + result = t['A'].agg({'A': ['sum', 'std'], 'B': ['mean', 'std']}) assert_frame_equal(result, expected, check_like=True) # errors + # invalid names in the agg specification for t in [r, g]: - # invalid names in the agg specification def f(): - r['A'].agg({'A': ['sum', 'std'], 'B': ['mean', 'std']}) - self.assertRaises(SpecificationError, f) - - def f(): - r[['A']].agg({'A': ['sum', 'std'], 'B': ['mean', 'std']}) + r[['A']].agg({'A': ['sum', 'std'], + 'B': ['mean', 'std']}) self.assertRaises(SpecificationError, f) def test_agg_nested_dicts(self): @@ -918,6 +925,19 @@ def test_resample_ohlc(self): self.assertEqual(xs['low'], s[:5].min()) self.assertEqual(xs['close'], s[4]) + def test_resample_ohlc_result(self): + + # GH 12332 + index = pd.date_range('1-1-2000', '2-15-2000', freq='h') + index = index.union(pd.date_range('4-15-2000', '5-15-2000', freq='h')) + s = Series(range(len(index)), index=index) + + a = s.loc[:'4-15-2000'].resample('30T').ohlc() + self.assertIsInstance(a, DataFrame) + + b = s.loc[:'4-14-2000'].resample('30T').ohlc() + self.assertIsInstance(b, DataFrame) + def test_resample_ohlc_dataframe(self): df = ( pd.DataFrame({
closes #9052 closes #12332
https://api.github.com/repos/pandas-dev/pandas/pulls/12329
2016-02-15T14:50:17Z
2016-02-15T20:28:58Z
null
2016-02-15T21:49:01Z
FIX: pivot_table dropna=False drops index level names
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 75a38544fb8eb..67585d156a090 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -540,3 +540,4 @@ of columns didn't match the number of series provided (:issue:`12039`). - Bug in ``.loc`` setitem indexer preventing the use of a TZ-aware DatetimeIndex (:issue:`12050`) - Big in ``.style`` indexes and multi-indexes not appearing (:issue:`11655`) +- Bug in ``.pivot_table()`` where ``dropna`` argument drops columns and index level names (:issue:`12133`) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index 7a04847947bf2..0852d0114d697 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -126,13 +126,15 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', if not dropna: try: - m = MultiIndex.from_arrays(cartesian_product(table.index.levels)) + m = MultiIndex.from_arrays(cartesian_product(table.index.levels), + names=table.index.names) table = table.reindex_axis(m, axis=0) except AttributeError: pass # it's a single level try: - m = MultiIndex.from_arrays(cartesian_product(table.columns.levels)) + m = MultiIndex.from_arrays(cartesian_product(table.columns.levels), + names=table.columns.names) table = table.reindex_axis(m, axis=1) except AttributeError: pass # it's a single level or a series diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index d303f489d9dea..93d4802b7d622 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -96,6 +96,18 @@ def test_pivot_table_dropna(self): assert_equal(pv_col.columns.values, m.values) assert_equal(pv_ind.index.values, m.values) + #issue 12133, dropna=False arg drops index level names + idx = pd.MultiIndex.from_tuples([(1000000, 201308), (1000000, 201310)], + names=('quantity', 'month')) + tup = (("B", "c"), ("B", "d"), ("C", "c"), ("C", "d")) + clm = pd.MultiIndex.from_tuples(tup, names=('customer', 'product')) + pv = pd.DataFrame([[50000, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, 30000]], + index=idx, columns=clm) + pv_gen = df[2:].pivot_table('amount', ['quantity', 'month'], + ['customer', 'product'], dropna=False) + tm.assert_frame_equal(pv, pv_gen) + def test_pass_array(self): result = self.data.pivot_table( 'D', index=self.data.A, columns=self.data.C)
closes #12133 level names are lost on `MultiIndex.from_arrays(cartesian_product(table.index.levels))`
https://api.github.com/repos/pandas-dev/pandas/pulls/12327
2016-02-15T06:39:20Z
2016-04-14T01:27:52Z
null
2016-05-01T18:30:24Z
DOC/DEPR: remove remaining occurences of mpl_style from docs (GH12190)
diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index d67e31b576654..599724d88bf63 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -10,10 +10,7 @@ pd.options.display.max_rows = 15 import matplotlib - try: - matplotlib.style.use('ggplot') - except AttributeError: - pd.options.display.mpl_style = 'default' + matplotlib.style.use('ggplot') import matplotlib.pyplot as plt plt.close('all') diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index c3c49e62703a9..b1d400cae156b 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -10,10 +10,7 @@ np.set_printoptions(precision=4, suppress=True) pd.options.display.max_rows = 15 import matplotlib - try: - matplotlib.style.use('ggplot') - except AttributeError: - pd.options.display.mpl_style = 'default' + matplotlib.style.use('ggplot') import matplotlib.pyplot as plt plt.close('all') @@ -34,11 +31,6 @@ The plots in this document are made using matplotlib's ``ggplot`` style (new in import matplotlib matplotlib.style.use('ggplot') -If your version of matplotlib is 1.3 or lower, you can set ``display.mpl_style`` to ``'default'`` -with ``pd.options.display.mpl_style = 'default'`` -to produce more appealing plots. -When set, matplotlib's ``rcParams`` are changed (globally!) to nicer-looking settings. - We provide the basics in pandas to easily create decent looking plots. See the :ref:`ecosystem <ecosystem.visualization>` section for visualization libraries that go beyond the basics documented here. @@ -189,7 +181,7 @@ For labeled, non-time series data, you may wish to produce a bar plot: @savefig bar_plot_ex.png df.ix[5].plot.bar(); plt.axhline(0, color='k') -Calling a DataFrame's :meth:`~DataFrame.plot.bar` method produces a multiple +Calling a DataFrame's :meth:`plot.bar() <DataFrame.plot.bar>` method produces a multiple bar plot: .. ipython:: python
@TomAugspurger There were some remaining usages of `options.display.mpl_style` in the docs, so removed those + also removed the reference to it in the text.
https://api.github.com/repos/pandas-dev/pandas/pulls/12326
2016-02-14T12:27:11Z
2016-02-14T17:04:07Z
null
2016-02-14T17:04:07Z
CLN: change getargspec -> signature
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index d991b3f1fb866..cbdb69d1df8c3 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -36,6 +36,7 @@ from unicodedata import east_asian_width import struct import inspect +from collections import namedtuple PY2 = sys.version_info[0] == 2 PY3 = (sys.version_info[0] >= 3) @@ -70,8 +71,32 @@ def str_to_bytes(s, encoding=None): def bytes_to_str(b, encoding=None): return b.decode(encoding or 'utf-8') + # The signature version below is directly copied from Django, + # https://github.com/django/django/pull/4846 def signature(f): - return list(inspect.signature(f).parameters.keys()) + sig = inspect.signature(f) + args = [ + p.name for p in sig.parameters.values() + if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD + ] + varargs = [ + p.name for p in sig.parameters.values() + if p.kind == inspect.Parameter.VAR_POSITIONAL + ] + varargs = varargs[0] if varargs else None + keywords = [ + p.name for p in sig.parameters.values() + if p.kind == inspect.Parameter.VAR_KEYWORD + ] + keywords = keywords[0] if keywords else None + defaults = [ + p.default for p in sig.parameters.values() + if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD + and p.default is not p.empty + ] or None + argspec = namedtuple('Signature',['args','defaults', + 'varargs','keywords']) + return argspec(args,defaults,varargs,keywords) # have to explicitly put builtins into the namespace range = range @@ -110,7 +135,7 @@ def bytes_to_str(b, encoding='ascii'): return b def signature(f): - return inspect.getargspec(f).args + return inspect.getargspec(f) # import iterator versions of these functions range = xrange diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index a9dfe07c20856..455e5cda03307 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1,7 +1,6 @@ # coding=utf-8 # pylint: disable-msg=E1101,W0612 -from inspect import getargspec from itertools import product from distutils.version import LooseVersion @@ -465,7 +464,7 @@ def testit(): self.assertRaises(ValueError, f, self.series, axis=1) # Unimplemented numeric_only parameter. - if 'numeric_only' in getargspec(f).args: + if 'numeric_only' in compat.signature(f).args: self.assertRaisesRegexp(NotImplementedError, name, f, self.series, numeric_only=True) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 9b9f952bb5aea..dd7468723c9c7 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -201,7 +201,7 @@ def wrapper(x): self.assertRaises(Exception, f, axis=obj.ndim) # Unimplemented numeric_only parameter. - if 'numeric_only' in signature(f): + if 'numeric_only' in signature(f).args: self.assertRaisesRegexp(NotImplementedError, name, f, numeric_only=True) diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py index c2d25b30c2b22..0a59c2e8eb1c3 100644 --- a/pandas/util/decorators.py +++ b/pandas/util/decorators.py @@ -1,4 +1,4 @@ -from pandas.compat import StringIO, callable +from pandas.compat import StringIO, callable, signature from pandas.lib import cache_readonly # noqa import sys import warnings @@ -275,8 +275,7 @@ def make_signature(func): >>> print(_make_signature(f)) a,b,c=2 """ - from inspect import getargspec - spec = getargspec(func) + spec = signature(func) if spec.defaults is None: n_wo_defaults = len(spec.args) defaults = ('',) * n_wo_defaults
Change signature in compat to be able to return defaults, varargs, kwargs Closes #12171
https://api.github.com/repos/pandas-dev/pandas/pulls/12325
2016-02-14T01:08:35Z
2016-02-17T13:20:15Z
null
2016-02-17T13:20:17Z
DOC: whatsnew link v0.18.0 for float_indexers
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index a1969c75c2d1d..6a059cafc8624 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -21,7 +21,7 @@ Highlights include: - API breaking change to the ``.resample`` method to make it more ``.groupby`` like, see :ref:`here <whatsnew_0180.breaking.resample>`. - Removal of support for positional indexing with floats, which was deprecated - since 0.14.0. This will now raise a ``TypeError``, see :ref:`here <whatsnew_0180.enhancements.float_indexers>`. + since 0.14.0. This will now raise a ``TypeError``, see :ref:`here <whatsnew_0180.float_indexers>`. - The ``.to_xarray()`` function has been added for compatibility with the `xarray package <http://xarray.pydata.org/en/stable/>`__, see :ref:`here <whatsnew_0180.enhancements.xarray>`. - Addition of the :ref:`.str.extractall() method <whatsnew_0180.enhancements.extract>`, @@ -327,11 +327,8 @@ In addition, ``.round()``, ``.floor()`` and ``.ceil()`` will be available thru t Formatting of integer in FloatIndex ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Integers in ``FloatIndex``, e.g. 1., are now formatted with a decimal point -and a ``0`` digit, e.g. ``1.0`` (:issue:`11713`) - -This change affects the display in jupyter, but also the output of IO methods -like ``.to_csv`` or ``.to_html`` +Integers in ``FloatIndex``, e.g. 1., are now formatted with a decimal point and a ``0`` digit, e.g. ``1.0`` (:issue:`11713`) +This change not only affects the display in a jupyter notebook, but also the output of IO methods like ``.to_csv`` or ``.to_html`` Previous Behavior: @@ -887,7 +884,7 @@ In 0.18.0, this deprecation warning is removed and these will now raise a ``Type Previous Behavior: -.. code-block:: +.. code-block:: python In [1]: s = Series([1,2,3]) In [2]: s[1.0] @@ -904,7 +901,7 @@ Previous Behavior: New Behavior: -.. code-block:: +.. code-block:: python In [4]: s[1.0] TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> @@ -915,6 +912,14 @@ New Behavior: In [4]: s.loc[1.0] TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'> +Float indexing on a ``Float64Index`` is unchanged. + +.. ipython:: python + + s = Series([1,2,3],index=np.arange(3.)) + s[1.0] + s[1.0:2.5] + .. _whatsnew_0180.prior_deprecations: Removal of prior version deprecations/changes
https://api.github.com/repos/pandas-dev/pandas/pulls/12324
2016-02-13T20:24:33Z
2016-02-13T21:33:29Z
null
2016-02-13T21:33:29Z
DOC: some rewording of highlights
diff --git a/doc/source/release.rst b/doc/source/release.rst index 51554b54dfb83..37ca1be631c32 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -48,13 +48,20 @@ users upgrade to this version. Highlights include: -- Window functions are now methods on ``.groupby`` like objects, see :ref:`here <whatsnew_0180.enhancements.moments>`. -- ``pd.test()`` top-level nose test runner is available (:issue:`4327`) -- Adding support for a ``RangeIndex`` as a specialized form of the ``Int64Index`` for memory savings, see :ref:`here <whatsnew_0180.enhancements.rangeindex>`. -- API breaking ``.resample`` changes to make it more ``.groupby`` like, see :ref:`here <whatsnew_0180.breaking.resample>`. -- Removal of support for deprecated float indexers; these will now raise a ``TypeError``, see :ref:`here <whatsnew_0180.enhancements.float_indexers>`. -- The ``.to_xarray()`` function has been added for compatibility with the `xarray package <http://xarray.pydata.org/en/stable/>`__, see :ref:`here <whatsnew_0180.enhancements.xarray>`. -- Addition of the :ref:`.str.extractall() method <whatsnew_0180.enhancements.extract>`, and API changes to the :ref:`.str.extract() method <whatsnew_0180.enhancements.extract>` and :ref:`.str.cat() method <whatsnew_0180.enhancements.strcat>`. +- Moving and expanding window functions are now methods on Series and DataFrame, + similar to ``.groupby``, see :ref:`here <whatsnew_0180.enhancements.moments>`. +- Adding support for a ``RangeIndex`` as a specialized form of the ``Int64Index`` + for memory savings, see :ref:`here <whatsnew_0180.enhancements.rangeindex>`. +- API breaking change to the ``.resample`` method to make it more ``.groupby`` + like, see :ref:`here <whatsnew_0180.breaking.resample>`. +- Removal of support for positional indexing with floats, which was deprecated + since 0.14.0. This will now raise a ``TypeError``, see :ref:`here <whatsnew_0180.enhancements.float_indexers>`. +- The ``.to_xarray()`` function has been added for compatibility with the + `xarray package <http://xarray.pydata.org/en/stable/>`__, see :ref:`here <whatsnew_0180.enhancements.xarray>`. +- Addition of the :ref:`.str.extractall() method <whatsnew_0180.enhancements.extract>`, + and API changes to the :ref:`.str.extract() method <whatsnew_0180.enhancements.extract>` + and :ref:`.str.cat() method <whatsnew_0180.enhancements.strcat>`. +- ``pd.test()`` top-level nose test runner is available (:issue:`4327`). See the :ref:`v0.18.0 Whatsnew <whatsnew_0180>` overview for an extensive list of all enhancements and bugs that have been fixed in 0.17.1. diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index a0a6332c5dc63..781bba50c3c24 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -9,22 +9,25 @@ users upgrade to this version. .. warning:: - pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 (:issue:`7718`) - -.. warning:: - - pandas >= 0.18.0 will no longer support compatibility with Python version 3.3 (:issue:`11273`) + pandas >= 0.18.0 no longer supports compatibility with Python version 2.6 + and 3.3 (:issue:`7718`, :issue:`11273`) Highlights include: -- Window functions are now methods on ``.groupby`` like objects, see :ref:`here <whatsnew_0180.enhancements.moments>`. -- ``pd.test()`` top-level nose test runner is available (:issue:`4327`) -- Adding support for a ``RangeIndex`` as a specialized form of the ``Int64Index`` for memory savings, see :ref:`here <whatsnew_0180.enhancements.rangeindex>`. -- API breaking ``.resample`` changes to make it more ``.groupby`` like, see :ref:`here <whatsnew_0180.breaking.resample>`. -- Removal of support for deprecated float indexers; these will now raise a ``TypeError``, see :ref:`here <whatsnew_0180.enhancements.float_indexers>`. -- The ``.to_xarray()`` function has been added for compatibility with the `xarray package <http://xarray.pydata.org/en/stable/>`__, see :ref:`here <whatsnew_0180.enhancements.xarray>`. -- Addition of the :ref:`.str.extractall() method <whatsnew_0180.enhancements.extractall>`, and API changes to the :ref:`.str.extract() method <whatsnew_0180.enhancements.extract>` and :ref:`.str.cat() method <whatsnew_0180.enhancements.strcat>`. - +- Moving and expanding window functions are now methods on Series and DataFrame, + similar to ``.groupby``, see :ref:`here <whatsnew_0180.enhancements.moments>`. +- Adding support for a ``RangeIndex`` as a specialized form of the ``Int64Index`` + for memory savings, see :ref:`here <whatsnew_0180.enhancements.rangeindex>`. +- API breaking change to the ``.resample`` method to make it more ``.groupby`` + like, see :ref:`here <whatsnew_0180.breaking.resample>`. +- Removal of support for positional indexing with floats, which was deprecated + since 0.14.0. This will now raise a ``TypeError``, see :ref:`here <whatsnew_0180.enhancements.float_indexers>`. +- The ``.to_xarray()`` function has been added for compatibility with the + `xarray package <http://xarray.pydata.org/en/stable/>`__, see :ref:`here <whatsnew_0180.enhancements.xarray>`. +- Addition of the :ref:`.str.extractall() method <whatsnew_0180.enhancements.extract>`, + and API changes to the :ref:`.str.extract() method <whatsnew_0180.enhancements.extract>` + and :ref:`.str.cat() method <whatsnew_0180.enhancements.strcat>`. +- ``pd.test()`` top-level nose test runner is available (:issue:`4327`). Check the :ref:`API Changes <whatsnew_0180.api_breaking>` and :ref:`deprecations <whatsnew_0180.deprecations>` before updating.
@jreback I proofread the highlights and made some edits (but language is always a bit subjective). Comments?
https://api.github.com/repos/pandas-dev/pandas/pulls/12323
2016-02-13T19:38:38Z
2016-02-13T20:51:09Z
null
2016-02-13T20:51:09Z
WARN: fix some warnings
diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index d5d0bd32a9356..7c5ec8f35bf44 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -212,9 +212,13 @@ def test_getitem_boolean(self): assert_frame_equal(subframe_obj, subframe) # test that Series indexers reindex - indexer_obj = indexer_obj.reindex(self.tsframe.index[::-1]) - subframe_obj = self.tsframe[indexer_obj] - assert_frame_equal(subframe_obj, subframe) + # we are producing a warning that since the passed boolean + # key is not the same as the given index, we will reindex + # not sure this is really necessary + with tm.assert_produces_warning(UserWarning): + indexer_obj = indexer_obj.reindex(self.tsframe.index[::-1]) + subframe_obj = self.tsframe[indexer_obj] + assert_frame_equal(subframe_obj, subframe) # test df[df > 0] for df in [self.tsframe, self.mixed_frame, diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index 5b50778d358ad..06046accaa0d9 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -1197,8 +1197,16 @@ def tester(a, b): # TODO: Fix this exception - needs to be fixed! (see GH5035) # (previously this was a TypeError because series returned # NotImplemented + + # this is an alignment issue; these are equivalent + # https://github.com/pydata/pandas/issues/5284 + + self.assertRaises(ValueError, lambda: d.__and__(s, axis='columns')) self.assertRaises(ValueError, tester, s, d) + # this is wrong as its not a boolean result + # result = d.__and__(s,axis='index') + def test_operators_corner(self): series = self.ts diff --git a/pandas/tests/test_graphics_others.py b/pandas/tests/test_graphics_others.py index 7301edcd52c3c..983d0c310f71d 100644 --- a/pandas/tests/test_graphics_others.py +++ b/pandas/tests/test_graphics_others.py @@ -425,9 +425,12 @@ def test_scatter_matrix_axis(self): with tm.RNGContext(42): df = DataFrame(randn(100, 3)) - axes = _check_plot_works(scatter_matrix, filterwarnings='always', - frame=df, range_padding=.1) + # we are plotting multiples on a sub-plot + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(scatter_matrix, filterwarnings='always', + frame=df, range_padding=.1) axes0_labels = axes[0][0].yaxis.get_majorticklabels() + # GH 5662 expected = ['-2', '-1', '0', '1', '2'] self._check_text_labels(axes0_labels, expected) @@ -435,8 +438,11 @@ def test_scatter_matrix_axis(self): axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) df[0] = ((df[0] - 2) / 3) - axes = _check_plot_works(scatter_matrix, filterwarnings='always', - frame=df, range_padding=.1) + + # we are plotting multiples on a sub-plot + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(scatter_matrix, filterwarnings='always', + frame=df, range_padding=.1) axes0_labels = axes[0][0].yaxis.get_majorticklabels() expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0'] self._check_text_labels(axes0_labels, expected)
catch warning on reindexing a boolean indexer add some documentation on a na scalar comparison test warning remove warning for plotting multiple on a sub-plot closes #8397
https://api.github.com/repos/pandas-dev/pandas/pulls/12320
2016-02-13T14:34:57Z
2016-02-13T15:18:23Z
null
2016-02-13T15:18:23Z
BUG: Prevent abuse of kwargs in stat functions
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 095c48e54c44c..c6d02acf75477 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -824,6 +824,8 @@ Other API Changes - As part of the new API for :ref:`window functions <whatsnew_0180.enhancements.moments>` and :ref:`resampling <whatsnew_0180.breaking.resample>`, aggregation functions have been clarified, raising more informative error messages on invalid aggregations. (:issue:`9052`). A full set of examples are presented in :ref:`groupby <groupby.aggregation>`. +- Statistical functions for ``NDFrame`` objects will now raise if non-numpy-compatible arguments are passed in for ``**kwargs`` (:issue:`12301`) + .. _whatsnew_0180.deprecations: Deprecations diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 5e171e3339d8b..3f7b27eca2b55 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5207,12 +5207,29 @@ def _doc_parms(cls): %(outname)s : %(name1)s\n""" +def _validate_kwargs(fname, kwargs, *compat_args): + """ + Checks whether parameters passed to the + **kwargs argument in a 'stat' function 'fname' + are valid parameters as specified in *compat_args + + """ + list(map(kwargs.__delitem__, filter( + kwargs.__contains__, compat_args))) + if kwargs: + bad_arg = list(kwargs)[0] # first 'key' element + raise TypeError(("{fname}() got an unexpected " + "keyword argument '{arg}'". + format(fname=fname, arg=bad_arg))) + + def _make_stat_function(name, name1, name2, axis_descr, desc, f): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender(_num_doc) def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: @@ -5233,6 +5250,7 @@ def _make_stat_function_ddof(name, name1, name2, axis_descr, desc, f): @Appender(_num_ddof_doc) def stat_func(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: @@ -5254,6 +5272,7 @@ def _make_cum_function(name, name1, name2, axis_descr, desc, accum_func, @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if axis is None: axis = self._stat_axis_number else: @@ -5288,6 +5307,7 @@ def _make_logical_function(name, name1, name2, axis_descr, desc, f): @Appender(_bool_doc) def logical_func(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs): + _validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index ee83e97de76eb..7983ac7fff834 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -16,12 +16,14 @@ from pandas.compat import range, zip from pandas import compat -from pandas.util.testing import (assert_series_equal, +from pandas.util.testing import (assertRaisesRegexp, + assert_series_equal, assert_frame_equal, assert_panel_equal, assert_panel4d_equal, assert_almost_equal, assert_equal) + import pandas.util.testing as tm @@ -483,8 +485,6 @@ def test_split_compat(self): self.assertTrue(len(np.array_split(o, 2)) == 2) def test_unexpected_keyword(self): # GH8597 - from pandas.util.testing import assertRaisesRegexp - df = DataFrame(np.random.randn(5, 2), columns=['jim', 'joe']) ca = pd.Categorical([0, 0, 2, 2, 3, np.nan]) ts = df['joe'].copy() @@ -502,6 +502,20 @@ def test_unexpected_keyword(self): # GH8597 with assertRaisesRegexp(TypeError, 'unexpected keyword'): ts.fillna(0, in_place=True) + # See gh-12301 + def test_stat_unexpected_keyword(self): + obj = self._construct(5) + starwars = 'Star Wars' + + with assertRaisesRegexp(TypeError, 'unexpected keyword'): + obj.max(epic=starwars) # stat_function + with assertRaisesRegexp(TypeError, 'unexpected keyword'): + obj.var(epic=starwars) # stat_function_ddof + with assertRaisesRegexp(TypeError, 'unexpected keyword'): + obj.sum(epic=starwars) # cum_function + with assertRaisesRegexp(TypeError, 'unexpected keyword'): + obj.any(epic=starwars) # logical_function + class TestSeries(tm.TestCase, Generic): _typ = Series
Addresses issue #12301 by filtering `kwargs` argument in stat functions to prevent the passage of clearly invalid arguments while at the same time maintaining compatibility with analogous `numpy` functions.
https://api.github.com/repos/pandas-dev/pandas/pulls/12318
2016-02-13T03:32:15Z
2016-02-14T17:08:43Z
null
2016-02-14T19:16:45Z
DOC: whatsnews for style fixes
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index eec7ca1dfd95d..c8b004f2de1af 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -954,6 +954,8 @@ Bug Fixes - Bug in ``DataFrame.duplicated`` and ``drop_duplicates`` causing spurious matches when setting ``keep=False`` (:issue:`11864`) - Bug in ``.loc`` result with duplicated key may have ``Index`` with incorrect dtype (:issue:`11497`) - Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`) +- Bug in ``DataFrame.style`` with spurious zeros (:issue:`12134`) +- Bug in ``DataFrame.style`` with integer columns not starting at 0 (:issue:`12125`) - Bug in ``.style.bar`` may not rendered properly using specific browser (:issue:`11678`) - Bug in rich comparison of ``Timedelta`` with a ``numpy.array`` of ``Timedelta`` that caused an infinite recursion (:issue:`11835`) - Bug in ``DataFrame.round`` dropping column index name (:issue:`11986`)
Forgot them in https://github.com/pydata/pandas/pull/12162
https://api.github.com/repos/pandas-dev/pandas/pulls/12317
2016-02-13T02:02:00Z
2016-02-13T02:36:00Z
null
2016-11-03T12:38:53Z
TST: validation tests for concat of same timezones
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 2dcb3fbbd5a0d..b1dcd471fb79c 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -918,7 +918,7 @@ Bug Fixes - Bug in not treating ``NaT`` as a missing value in datetimelikes when factorizing & with ``Categoricals`` (:issue:`12077`) - Bug in getitem when the values of a ``Series`` were tz-aware (:issue:`12089`) - Bug in ``Series.str.get_dummies`` when one of the variables was 'name' (:issue:`12180`) -- Bug in ``pd.concat`` while concatenating tz-aware NaT series. (:issue:`11693`, :issue:`11755`) +- Bug in ``pd.concat`` while concatenating tz-aware NaT series. (:issue:`11693`, :issue:`11755`, :issue:`12217`) - Bug in ``pd.read_stata`` with version <= 108 files (:issue:`12232`) - Bug in ``Series.resample`` using a frequency of ``Nano`` when the index is a ``DatetimeIndex`` and contains non-zero nanosecond parts (:issue:`12037`) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 77ee54d84bf3d..046d2322165b5 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1088,6 +1088,49 @@ def test_concat_tz_series(self): result = concat([x, y], ignore_index=True) tm.assert_series_equal(result, expected) + # 12217 + # 12306 fixed I think + + # Concat'ing two UTC times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('UTC') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('UTC') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, UTC]') + + # Concat'ing two London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 2+1 London times + first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 1+2 London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [
closes #12217 xref #12306 which I think fixed
https://api.github.com/repos/pandas-dev/pandas/pulls/12316
2016-02-12T19:09:50Z
2016-02-13T01:15:50Z
null
2016-02-13T01:15:50Z
COMPAT: silence mpl_style warnings
diff --git a/pandas/core/config.py b/pandas/core/config.py index b6f00034429b2..7b1e5b29f1cbb 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -127,7 +127,11 @@ def _set_option(*args, **kwargs): root[k] = v if o.cb: - o.cb(key) + if silent: + with warnings.catch_warnings(record=True): + o.cb(key) + else: + o.cb(key) def _describe_option(pat='', _print_desc=True): diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index cf8a06465a7a4..f9b91db608093 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -253,7 +253,7 @@ def mpl_style_cb(key): warnings.warn(pc_mpl_style_deprecation_warning, FutureWarning, - stacklevel=4) + stacklevel=5) import sys from pandas.tools.plotting import mpl_stylesheet diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 071e280bd112a..9305cabe6bda5 100755 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -4109,7 +4109,8 @@ def test_str_accessor_api_for_categorical(self): ('decode', ("UTF-8",), {}), ('encode', ("UTF-8",), {}), ('endswith', ("a",), {}), - ('extract', ("([a-z]*) ",), {}), + ('extract', ("([a-z]*) ",), {"expand":False}), + ('extract', ("([a-z]*) ",), {"expand":True}), ('extractall', ("([a-z]*) ",), {}), ('find', ("a",), {}), ('findall', ("a",), {}),
closes #12311
https://api.github.com/repos/pandas-dev/pandas/pulls/12315
2016-02-12T18:28:39Z
2016-02-13T02:35:59Z
null
2016-02-13T02:35:59Z
TST: validation tests for resample/groupby preservation
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 2dcb3fbbd5a0d..bc96a0ea30443 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -574,7 +574,7 @@ other anchored offsets like ``MonthBegin`` and ``YearBegin``. Resample API ^^^^^^^^^^^^ -Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`). +Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`). .. ipython:: python diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 1cece8b060377..4e2d596681942 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -1220,6 +1220,24 @@ def test_resample_empty(self): # (ex: doing mean with dtype of np.object) pass + def test_resample_dtype_preservation(self): + + # GH 12202 + # validation tests for dtype preservation + + df = DataFrame({'date': pd.date_range(start='2016-01-01', + periods=4, freq='W'), + 'group': [1, 1, 2, 2], + 'val': Series([5, 6, 7, 8], + dtype='int32')} + ).set_index('date') + + result = df.resample('1D').ffill() + self.assertEqual(result.val.dtype, np.int32) + + result = df.groupby('group').resample('1D').ffill() + self.assertEqual(result.val.dtype, np.int32) + def test_weekly_resample_buglet(self): # #1327 rng = date_range('1/1/2000', freq='B', periods=20)
closes #12202
https://api.github.com/repos/pandas-dev/pandas/pulls/12314
2016-02-12T18:25:50Z
2016-02-12T20:40:12Z
null
2016-02-12T20:40:12Z
DOC: Update wrong example in df.first doc-string
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 8882a9cc48ffc..5e171e3339d8b 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3939,7 +3939,7 @@ def first(self, offset): Examples -------- - ts.last('10D') -> First 10 days + ts.first('10D') -> First 10 days Returns -------
https://api.github.com/repos/pandas-dev/pandas/pulls/12310
2016-02-12T15:08:37Z
2016-02-12T15:16:06Z
null
2016-02-12T15:16:09Z
Subclassing improvements
diff --git a/pandas/core/base.py b/pandas/core/base.py index 96732a7140f9e..e2b854702ba1c 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -943,9 +943,15 @@ def value_counts(self, normalize=False, sort=True, ascending=False, counts : Series """ from pandas.core.algorithms import value_counts + from pandas import Index + result = value_counts(self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna) - return result + + if isinstance(self, Index): + return result + else: + return self._constructor(result) def unique(self): """ diff --git a/pandas/core/common.py b/pandas/core/common.py index d26c59e62de30..34392a8d92df8 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -168,6 +168,9 @@ def _use_inf_as_null(key): def _isnull_ndarraylike(obj): + if isinstance(obj, pd.SparseSeries): + obj = pd.SparseArray(obj) + values = getattr(obj, 'values', obj) dtype = values.dtype @@ -197,8 +200,8 @@ def _isnull_ndarraylike(obj): # box if isinstance(obj, gt.ABCSeries): - from pandas import Series - result = Series(result, index=obj.index, name=obj.name, copy=False) + result = obj._constructor( + result, index=obj.index, name=obj.name, copy=False) return result @@ -226,8 +229,8 @@ def _isnull_ndarraylike_old(obj): # box if isinstance(obj, gt.ABCSeries): - from pandas import Series - result = Series(result, index=obj.index, name=obj.name, copy=False) + result = obj._constructor( + result, index=obj.index, name=obj.name, copy=False) return result diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 69def7502a6f7..efc4ad838d032 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1918,6 +1918,7 @@ def _ixs(self, i, axis=0): # if we are a copy, mark as such copy = (isinstance(new_values, np.ndarray) and new_values.base is None) + result = self._constructor_sliced(new_values, index=self.columns, name=self.index[i], @@ -4900,7 +4901,7 @@ def f(x): if axis == 0: result = com._coerce_to_dtypes(result, self.dtypes) - return Series(result, index=labels) + return self._constructor_sliced(result, index=labels) def idxmin(self, axis=0, skipna=True): """ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 9ecaaebc2b523..1a96fac4bbe2e 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1773,7 +1773,6 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): result = self._constructor_sliced(new_values, index=self.columns, name=self.index[loc], copy=copy, dtype=new_values.dtype) - else: result = self.iloc[loc] result.index = new_index @@ -4898,7 +4897,12 @@ def describe_numeric_1d(series): formatted_percentiles + ['max']) d = ([series.count(), series.mean(), series.std(), series.min()] + [series.quantile(x) for x in percentiles] + [series.max()]) - return pd.Series(d, index=stat_index, name=series.name) + if isinstance(self, ABCSeries): + return self._constructor( + d, index=stat_index, name=series.name) + else: + return self._constructor_sliced( + d, index=stat_index, name=series.name) def describe_categorical_1d(data): names = ['count', 'unique'] @@ -4918,7 +4922,12 @@ def describe_categorical_1d(data): names += ['top', 'freq'] result += [top, freq] - return pd.Series(result, index=names, name=data.name) + if isinstance(self, ABCSeries): + return self._constructor( + result, index=names, name=data.name) + else: + return self._constructor_sliced( + result, index=names, name=data.name) def describe_1d(data): if com.is_bool_dtype(data): @@ -4957,7 +4966,7 @@ def describe_1d(data): d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1) d.columns = data.columns.copy() - return d + return self._constructor(d) def _check_percentile(self, q): """Validate percentiles (used by describe and quantile).""" diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index bea62e98e4a2a..f1544b0d8fccb 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -36,8 +36,11 @@ is_categorical_dtype, _values_from_object, is_datetime_or_timedelta_dtype, is_bool, is_bool_dtype, AbstractMethodError, - _maybe_fill) + _maybe_fill, ABCSeries) from pandas.core.config import option_context, is_callable + +from pandas.sparse.frame import SparseDataFrame + import pandas.lib as lib from pandas.lib import Timestamp import pandas.tslib as tslib @@ -340,6 +343,16 @@ def __init__(self, obj, keys=None, axis=0, level=None, if axis != 0: raise ValueError('as_index=False only valid for axis=0') + self.inputconstructor = obj._constructor + if isinstance(obj, ABCSeries): + self.inputconstructor_sliced = obj._constructor + else: + self.inputconstructor_sliced = obj._constructor_sliced + if isinstance(obj, Panel): + self.inputconstructor_expanddim = obj._constructor + else: + self.inputconstructor_expanddim = obj._constructor_expanddim + self.lenshape = obj.ndim self.as_index = as_index self.keys = keys self.sort = sort @@ -393,6 +406,38 @@ def indices(self): self._assure_grouper() return self.grouper.indices + @property + def inputconstructor(self): + return self._inputconstructor + + @inputconstructor.setter + def inputconstructor(self, constructor): + self._inputconstructor = constructor + + @property + def inputconstructor_sliced(self): + return self._inputconstructor_sliced + + @inputconstructor_sliced.setter + def inputconstructor_sliced(self, constructor): + self._inputconstructor_sliced = constructor + + @property + def inputconstructor_expanddim(self): + return self._inputconstructor_expanddim + + @inputconstructor_expanddim.setter + def inputconstructor_expanddim(self, constructor): + self._inputconstructor_expanddim = constructor + + @property + def lenshape(self): + return self._lenshape + + @lenshape.setter + def lenshape(self, lenshape_in): + self._lenshape = lenshape_in + def _get_indices(self, names): """ safe get multiple indices, translate keys for @@ -1328,7 +1373,7 @@ def cumcount(self, ascending=True): index = self._selected_obj.index cumcounts = self._cumcount_array(ascending=ascending) - return Series(cumcounts, index) + return self.inputconstructor_sliced(cumcounts, index) @Substitution(name='groupby') @Appender(_doc_template) @@ -2603,7 +2648,7 @@ def aggregate(self, func_or_funcs, *args, **kwargs): result = self._aggregate_named(func_or_funcs, *args, **kwargs) index = Index(sorted(result), name=self.grouper.names[0]) - ret = Series(result, index=index) + ret = self.inputconstructor(result, index=index) if not self.as_index: # pragma: no cover print('Warning, ignoring as_index=True') @@ -2659,19 +2704,25 @@ def _aggregate_multiple_funcs(self, arg, _level): if _level: return results return list(compat.itervalues(results))[0] - return DataFrame(results, columns=columns) + + if self.lenshape == 2: + return self.inputconstructor(results, columns=columns) + elif self.lenshape == 1: + return self.inputconstructor_expanddim(results, columns=columns) def _wrap_output(self, output, index, names=None): """ common agg/transform wrapping logic """ output = output[self.name] if names is not None: - return DataFrame(output, index=index, columns=names) + return self.inputconstructor_expanddim( + output, index=index, columns=names) else: name = self.name if name is None: name = self._selected_obj.name - return Series(output, index=index, name=name) + return self.inputconstructor( + output, index=index, name=name) def _wrap_aggregated_output(self, output, names=None): return self._wrap_output(output=output, @@ -2873,6 +2924,7 @@ def nunique(self, dropna=True): inc[idx] = 1 out = np.add.reduceat(inc, idx).astype('int64', copy=False) + res = out if ids[0] != -1 else out[1:] ri = self.grouper.result_index @@ -2881,9 +2933,7 @@ def nunique(self, dropna=True): res, out = np.zeros(len(ri), dtype=out.dtype), res res[ids] = out - return Series(res, - index=ri, - name=self.name) + return self.inputconstructor_sliced(res, index=ri, name=self.name) @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) @@ -3024,10 +3074,9 @@ def count(self): ids = com._ensure_platform_int(ids) out = np.bincount(ids[mask], minlength=ngroups or None) - return Series(out, - index=self.grouper.result_index, - name=self.name, - dtype='int64') + return self.inputconstructor_sliced( + out, index=self.grouper.result_index, + name=self.name, dtype='int64') def _apply_to_column_groupbys(self, func): """ return a pass thru """ @@ -3141,7 +3190,11 @@ def aggregate(self, arg, *args, **kwargs): result.columns.levels[0], name=self._selected_obj.columns.name) except: - result = self._aggregate_generic(arg, *args, **kwargs) + if self.lenshape == 2: + result = self.inputconstructor( + self._aggregate_generic(arg, *args, **kwargs)) + else: + result = self._aggregate_generic(arg, *args, **kwargs) if not self.as_index: self._insert_inaxis_grouper_inplace(result) @@ -3719,7 +3772,12 @@ def _wrap_agged_blocks(self, items, blocks): if self.axis == 1: result = result.T - return self._reindex_output(result)._convert(datetime=True) + if isinstance(self.inputconstructor(), SparseDataFrame): + return DataFrame( + self._reindex_output(result)._convert(datetime=True)) + else: + return self.inputconstructor( + self._reindex_output(result)._convert(datetime=True)) def _reindex_output(self, result): """ diff --git a/pandas/core/ops.py b/pandas/core/ops.py index d1bb67fa0bc13..b13d7c85a6c07 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -784,7 +784,13 @@ def wrapper(self, other, axis=None): # always return a full value series here res = _values_from_object(res) - res = pd.Series(res, index=self.index, name=self.name, dtype='bool') + from pandas.sparse.api import SparseSeries + if isinstance(self, SparseSeries): + res = pd.Series( + res, index=self.index, name=self.name, dtype='bool') + else: + res = self._constructor( + res, index=self.index, name=self.name, dtype='bool') return res return wrapper diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 8d237016d1b33..860c0b3594ad3 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -63,7 +63,7 @@ class _Unstacker(object): """ def __init__(self, values, index, level=-1, value_columns=None, - fill_value=None): + fill_value=None, return_type=DataFrame): self.is_categorical = None if values.ndim == 1: @@ -74,6 +74,7 @@ def __init__(self, values, index, level=-1, value_columns=None, self.values = values self.value_columns = value_columns self.fill_value = fill_value + self.return_type = return_type if value_columns is None and values.shape[1] != 1: # pragma: no cover raise ValueError('must pass column labels for multi-column data') @@ -169,7 +170,7 @@ def get_result(self): ordered=ordered) for i in range(values.shape[-1])] - return DataFrame(values, index=index, columns=columns) + return self.return_type(values, index=index, columns=columns) def get_new_values(self): values = self.values @@ -330,8 +331,9 @@ def pivot(self, index=None, columns=None, values=None): index = self.index else: index = self[index] - indexed = Series(self[values].values, - index=MultiIndex.from_arrays([index, self[columns]])) + indexed = self._constructor_sliced( + self[values].values, + index=MultiIndex.from_arrays([index, self[columns]])) return indexed.unstack(columns) @@ -407,7 +409,8 @@ def unstack(obj, level, fill_value=None): return obj.T.stack(dropna=False) else: unstacker = _Unstacker(obj.values, obj.index, level=level, - fill_value=fill_value) + fill_value=fill_value, + return_type=obj._constructor_expanddim) return unstacker.get_result() @@ -439,8 +442,8 @@ def _unstack_frame(obj, level, fill_value=None): newb = make_block(new_values.T, placement=new_placement) new_blocks.append(newb) - result = DataFrame(BlockManager(new_blocks, new_axes)) - mask_frame = DataFrame(BlockManager(mask_blocks, new_axes)) + result = obj._constructor(BlockManager(new_blocks, new_axes)) + mask_frame = obj._constructor(BlockManager(mask_blocks, new_axes)) return result.ix[:, mask_frame.sum(0) > 0] else: unstacker = _Unstacker(obj.values, obj.index, level=level, @@ -509,7 +512,7 @@ def factorize(index): mask = notnull(new_values) new_values = new_values[mask] new_index = new_index[mask] - return Series(new_values, index=new_index) + return frame._constructor_sliced(new_values, index=new_index) def stack_multiple(frame, level, dropna=True): diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index b71235a8f6576..04b0734d7ae5a 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -340,6 +340,10 @@ def test_count(self): expected = Series(0, index=[]) tm.assert_series_equal(result, expected) + def test_count_subclassing(self): + df = tm.SubclassedDataFrame(data=[[1, 2, np.nan], [2, 3, 4]]) + tm.assertIsInstance(df.count(), tm.SubclassedSeries) + def test_sum(self): self._check_stat_op('sum', np.sum, has_numeric_only=True) @@ -349,6 +353,10 @@ def test_sum(self): has_numeric_only=True, check_dtype=False, check_less_precise=True) + def test_sum_subclassing(self): + df = tm.SubclassedDataFrame(data=[[1, 2, 3], [2, 3, 4]]) + tm.assertIsInstance(df.sum(), tm.SubclassedSeries) + def test_stat_operators_attempt_obj_array(self): data = { 'a': [-0.00049987540199591344, -0.0016467257772919831, diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 78354f32acbda..e8ae5e0434dd3 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -61,6 +61,13 @@ def test_getitem(self): res = df['@awesome_domain'] assert_numpy_array_equal(ad, res.values) + def test_getitem_subclassing(self): + df = tm.SubclassedDataFrame( + np.random.randn(6, 4), + index=list('abcdef'), columns=list('ABCD')) + tm.assertIsInstance(df['A'], tm.SubclassedSeries) + tm.assertIsInstance(df[['A', 'C']], tm.SubclassedDataFrame) + def test_getitem_dupe_cols(self): df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=['a', 'a', 'b']) try: diff --git a/pandas/tests/frame/test_missing.py b/pandas/tests/frame/test_missing.py index 8a6cbe44465c1..2f92ced0d2732 100644 --- a/pandas/tests/frame/test_missing.py +++ b/pandas/tests/frame/test_missing.py @@ -247,6 +247,13 @@ def test_fillna(self): result = df.fillna(value={'Date': df['Date2']}) assert_frame_equal(result, expected) + def test_fillna_subclassing(self): + df = tm.SubclassedDataFrame({'a': [np.nan, 1, 2, np.nan, np.nan], + 'b': ['1', '2', '3', None, None], + 'c': [None, '1', '2', '3', '4']}, + index=list('VWXYZ')) + tm.assertIsInstance(df.fillna(0), tm.SubclassedDataFrame) + def test_fillna_dtype_conversion(self): # make sure that fillna on an empty frame works df = DataFrame(index=["A", "B", "C"], columns=[1, 2, 3, 4, 5]) diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index ee7c296f563f0..5c1a960c080bb 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -909,6 +909,13 @@ def test_comp(func): test_comp(operator.ge) test_comp(operator.le) + def test_comparison_subclassing(self): + df = tm.SubclassedDataFrame({'A': np.random.randn(4), + 'B': np.random.randn(4)}) + df2 = tm.SubclassedDataFrame({'A': list('abcd'), 'B': list('dcba')}) + tm.assertIsInstance(df > 0, tm.SubclassedDataFrame) + tm.assertIsInstance(df2 == 'a', tm.SubclassedDataFrame) + def test_string_comparison(self): df = DataFrame([{"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}]) mask_a = df.a > 1 diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index b86b248ead290..07faf7812660a 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -1381,6 +1381,16 @@ def test_loc_general(self): assert_series_equal(result, expected) self.assertEqual(result.dtype, object) + def test_loc_subclassing(self): + df = tm.SubclassedDataFrame( + data=[[1, 2, 3, 4], [2, 3, 4, 5], [2, 3, 4, 5]], + index=list('abc'), columns=list('ABCD')) + tm.assertIsInstance(df.loc['a'], tm.SubclassedSeries) + tm.assertIsInstance(df.loc[:'b'], tm.SubclassedDataFrame) + tm.assertIsInstance(df.loc['a':, 'A'], tm.SubclassedSeries) + tm.assertIsInstance( + df.loc[['a', 'c'], ['A', 'D']], tm.SubclassedDataFrame) + def test_loc_setitem_consistency(self): # GH 6149 # coerce similary for setitem and loc when rows have a null-slice @@ -1621,6 +1631,16 @@ def test_iloc_getitem_frame(self): # trying to use a label self.assertRaises(ValueError, df.iloc.__getitem__, tuple(['j', 'D'])) + def test_iloc_getitem_subclassing(self): + df = tm.SubclassedDataFrame( + data=[[1, 2, 3, 4], [2, 3, 4, 5], [2, 3, 4, 5]]) + tm.assertIsInstance(df.iloc[1], tm.SubclassedSeries) + tm.assertIsInstance(df.iloc[:1], tm.SubclassedDataFrame) + tm.assertIsInstance(df.iloc[0:1, 2:3], tm.SubclassedDataFrame) + tm.assertIsInstance(df.iloc[[0, 2], [0, 3]], tm.SubclassedDataFrame) + tm.assertIsInstance(df.iloc[0:1, :], tm.SubclassedDataFrame) + tm.assertIsInstance(df.iloc[:, 0:1], tm.SubclassedDataFrame) + def test_iloc_getitem_panel(self): # GH 7189 @@ -2156,6 +2176,10 @@ def test_ix_general(self): expected = DataFrame({'amount': [222, 333, 444]}, index=index) tm.assert_frame_equal(res, expected) + def test_ix_subclassing(self): + df = tm.SubclassedDataFrame([[1, 2, 3, 4], [2, 3, 4, 5], [2, 3, 4, 5]]) + tm.assertIsInstance(df.ix[1], tm.SubclassedSeries) + def test_ix_weird_slicing(self): # http://stackoverflow.com/q/17056560/1240268 df = DataFrame({'one': [1, 2, 3, np.nan, np.nan], diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 433f0f4bc67f5..d7e842d7f3539 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1661,6 +1661,10 @@ def test_apply_categorical(self): tm.assert_series_equal(result, exp) self.assertEqual(result.dtype, np.object) + def test_apply_subclassing(self): + s1 = tm.SubclassedSeries(np.random.randn(6), index=list('abcdef')) + tm.assertIsInstance(s1.apply(lambda x: x), tm.SubclassedSeries) + def test_shift_int(self): ts = self.ts.astype(int) shifted = ts.shift(1) @@ -1884,3 +1888,9 @@ def test_value_counts_categorical_not_ordered(self): index=exp_idx, name='xxx') tm.assert_series_equal(s.value_counts(normalize=True), exp) tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_unstack_subclassing(self): + index = MultiIndex(levels=[['bar', 'foo'], ['one', 'three', 'two']], + labels=[[1, 1, 0, 0], [0, 1, 0, 2]]) + s = tm.SubclassedSeries(np.arange(4.), index=index) + tm.assertIsInstance(s.unstack(), tm.SubclassedDataFrame) diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index ed10f5b0a7af3..e67c413d2d460 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -312,6 +312,11 @@ def test_fillna(self): expected = Series([0, 1, val, val, 4], dtype='object') assert_series_equal(result, expected) + def test_fillna_subclassing(self): + df = tm.SubclassedSeries([np.nan, 1, 2, np.nan, np.nan], + index=list('VWXYZ')) + tm.assertIsInstance(df.fillna(0), tm.SubclassedSeries) + def test_fillna_bug(self): x = Series([nan, 1., nan, 3., nan], ['z', 'a', 'b', 'c', 'd']) filled = x.fillna(method='ffill') diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index 3588faa8b42f1..79a73585210e0 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -862,6 +862,12 @@ def test_object_comparisons(self): expected = -(s == 'a') assert_series_equal(result, expected) + def test_comparison_subclassing(self): + s1 = tm.SubclassedSeries(np.random.randn(6), index=list('abcdef')) + s2 = tm.SubclassedSeries(data=list('abcdef')) + tm.assertIsInstance(s1 > 0, tm.SubclassedSeries) + tm.assertIsInstance(s2 == 'a', tm.SubclassedSeries) + def test_comparison_tuples(self): # GH11339 # comparisons vs tuple diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 8af93ad0ecb2e..d0a3b1e4e2ce4 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -432,6 +432,10 @@ def test_value_counts(self): expected = Series([1, 1, 1, 1], index=expected_index) tm.assert_series_equal(result.sort_index(), expected.sort_index()) + def test_value_counts_subclassing(self): + s1 = tm.SubclassedSeries(np.random.randn(6), index=list('abcdef')) + tm.assertIsInstance(s1.value_counts(), tm.SubclassedSeries) + def test_value_counts_bins(self): s = [1, 2, 3, 4] result = algos.value_counts(s, bins=1) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index cff5bbe14f1eb..552b6e88529e2 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -3911,6 +3911,16 @@ def f(): self.assert_index_equal(df['grade'].cat.categories, dfx['grade'].cat.categories) + def test_concat_subclassing(self): + s1 = tm.SubclassedSeries(np.random.randn(6), index=list('abcdef')) + tm.assertIsInstance(pd.concat([s1, s1]), tm.SubclassedSeries) + tm.assertIsInstance( + pd.concat([s1, s1], axis=1), tm.SubclassedDataFrame) + + df1 = tm.SubclassedDataFrame( + np.random.randn(6, 4), index=list('abcdef'), columns=list('ABCD')) + tm.assertIsInstance(pd.concat([df1, df1]), tm.SubclassedDataFrame) + def test_concat_preserve(self): # GH 8641 diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 2f4c2b414cc30..909273e22e072 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -795,6 +795,12 @@ def test_describe(self): self.series.describe() self.ts.describe() + def test_describe_subclassing(self): + df1 = tm.SubclassedDataFrame([[1, 2], [2, 3]], + index=list('ab'), columns=list('AB')) + tm.assertIsInstance(df1.describe(), tm.SubclassedDataFrame) + tm.assertIsInstance(df1['A'].describe(), tm.SubclassedSeries) + def test_describe_objects(self): s = Series(['a', 'b', 'b', np.nan, np.nan, np.nan, 'c', 'd', 'a', 'a']) result = s.describe() diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 6659e6b106a67..6ff9654393383 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -743,6 +743,14 @@ def test_get_group(self): self.assertRaises(ValueError, lambda: g.get_group(('foo', 'bar', 'baz'))) + def test_get_group_subclassing(self): + df = tm.SubclassedDataFrame( + {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8)}) + result = df.groupby('A').get_group('foo') + tm.assertIsInstance(result, tm.SubclassedDataFrame) + def test_get_group_grouped_by_tuple(self): # GH 8121 df = DataFrame([[(1, ), (1, 2), (1, ), (1, 2)]], index=['ids']).T @@ -2585,6 +2593,21 @@ def test_apply_transform(self): expected = grouped.transform(lambda x: x * 2) assert_series_equal(result, expected) + def test_apply_transform_agg_subclassing(self): + df = tm.SubclassedDataFrame( + {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8)}) + + grouped = df.groupby('A') + result_apply = grouped.apply(lambda x: x * 2) + result_transform = grouped.transform(lambda x: x * 2) + result_agg = grouped.agg(lambda x: 0) + + tm.assertIsInstance(result_apply, tm.SubclassedDataFrame) + tm.assertIsInstance(result_transform, tm.SubclassedDataFrame) + tm.assertIsInstance(result_agg, tm.SubclassedDataFrame) + def test_apply_multikey_corner(self): grouped = self.tsframe.groupby([lambda x: x.year, lambda x: x.month]) @@ -2808,6 +2831,14 @@ def test_count(self): count_B = df.groupby('A')['B'].count() assert_series_equal(count_B, expected['B']) + def test_count_subclassing(self): + df = tm.SubclassedDataFrame( + {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8), 'D': np.random.randn(8)}) + result = df.groupby('A').count() + tm.assertIsInstance(result, tm.SubclassedDataFrame) + def test_count_object(self): df = pd.DataFrame({'a': ['a'] * 3 + ['b'] * 3, 'c': [2] * 3 + [3] * 3}) result = df.groupby('c').a.count() @@ -5845,8 +5876,11 @@ def test_tab_completion(self): 'cumprod', 'tail', 'resample', 'cummin', 'fillna', 'cumsum', 'cumcount', 'all', 'shift', 'skew', 'bfill', 'ffill', 'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith', 'cov', - 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin', - 'ffill', 'bfill', 'pad', 'backfill', 'rolling', 'expanding']) + 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin', 'inputconstructor', + 'inputconstructor_sliced', 'inputconstructor_expanddim', + 'lenshape', 'ffill', 'bfill', 'pad', 'backfill', 'rolling', + 'expanding']) + self.assertEqual(results, expected) def test_lexsort_indexer(self): diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 182c0637ae29c..5bd70729e5a45 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -18,7 +18,7 @@ from pandas.core.internals import (items_overlap_with_suffix, concatenate_block_managers) from pandas.util.decorators import Appender, Substitution -from pandas.core.common import ABCSeries +from pandas.core.common import ABCSeries, is_sparse import pandas.core.algorithms as algos import pandas.core.common as com @@ -949,7 +949,11 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, if (len(non_empties) and (keys is None and names is None and levels is None and join_axes is None)): objs = non_empties - sample = objs[0] + + for obj in objs: + if not is_sparse(obj): + sample = obj + break if sample is None: sample = objs[0] @@ -1008,8 +1012,38 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, self.new_axes = self._get_new_axes() + if self._is_series: + self.constructor_series = sample._constructor + self.constructor_frame = sample._constructor_expanddim + elif self._is_frame: + self.constructor_series = sample._constructor_sliced + self.constructor_frame = sample._constructor + else: + self._constructor_series = Series + self._constructor_frame = sample._constructor_sliced + + @property + def constructor_series(self): + return self._constructor_series + + @constructor_series.setter + def constructor_series(self, constructor): + self._constructor_series = constructor + + @property + def constructor_frame(self): + return self._constructor_frame + + @constructor_frame.setter + def constructor_frame(self, constructor): + self._constructor_frame = constructor + def get_result(self): + return_types = { + 'series': self._constructor_series, + 'frame': self._constructor_frame} + # series only if self._is_series: @@ -1024,7 +1058,8 @@ def get_result(self): new_data = _concat._concat_compat(values) name = com._consensus_name_attr(self.objs) - cons = _concat._get_series_result_type(new_data) + + cons = _concat._get_series_result_type(new_data, return_types) return (cons(new_data, index=self.new_axes[0], name=name, dtype=new_data.dtype) @@ -1033,7 +1068,8 @@ def get_result(self): # combine as columns in a frame else: data = dict(zip(range(len(self.objs)), self.objs)) - cons = _concat._get_series_result_type(data) + cons = _concat._get_series_result_type( + data, return_types=return_types) index, columns = self.new_axes df = cons(data, index=index) diff --git a/pandas/types/concat.py b/pandas/types/concat.py index 5cd7abb6889b7..e78b217abc994 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -44,27 +44,32 @@ def get_dtype_kinds(l): return typs -def _get_series_result_type(result): +def _get_series_result_type(result, return_types=None): """ return appropriate class of Series concat input is either dict or array-like """ + + if return_types is None: + from pandas.core.frame import DataFrame + from pandas.core.series import Series + return_types = {'series': Series, + 'frame': DataFrame} + if isinstance(result, dict): # concat Series with axis 1 if all(com.is_sparse(c) for c in compat.itervalues(result)): from pandas.sparse.api import SparseDataFrame return SparseDataFrame else: - from pandas.core.frame import DataFrame - return DataFrame + return return_types['frame'] elif com.is_sparse(result): # concat Series with axis 1 from pandas.sparse.api import SparseSeries return SparseSeries else: - from pandas.core.series import Series - return Series + return return_types['series'] def _get_frame_result_type(result, objs):
Addresses indexing issues related to subclassing (#11559). Also made some changes to base, groupby, reshape, strings. So for example, if df was a SubclassedDataFrame and s was a SubclassedSeries `df.groupby('A').apply(lambda x: x)` and `s.value_counts()` would return SubclassedDataFrame and SubclassedSeries instances, respectively.
https://api.github.com/repos/pandas-dev/pandas/pulls/12308
2016-02-12T09:30:57Z
2016-11-22T11:44:44Z
null
2016-11-22T11:44:44Z
BUG: fix NaT support for msgpack format.
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ae5842b22349a..a8bf703cffdcb 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -967,4 +967,9 @@ Bug Fixes - Bug in ``.skew`` and ``.kurt`` due to roundoff error for highly similar values (:issue:`11974`) - Bug in ``buffer_rd_bytes`` src->buffer could be freed more than once if reading failed, causing a segfault (:issue:`12098`) + - Bug in ``crosstab`` where arguments with non-overlapping indexes would return a ``KeyError`` (:issue:`10291`) + + +- Bug in ``to_msgpack`` and ``from_msgpack`` which did not correctly serialize + or deserialize ``NaT`` (:issue:`12307`). diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 33310893a4a66..372c8d80e5a1a 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -47,7 +47,8 @@ from pandas.compat import u from pandas import (Timestamp, Period, Series, DataFrame, # noqa Index, MultiIndex, Float64Index, Int64Index, - Panel, RangeIndex, PeriodIndex, DatetimeIndex) + Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT) +from pandas.tslib import NaTType from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame @@ -383,7 +384,7 @@ def encode(obj): } for b in data.blocks]} elif isinstance(obj, (datetime, date, np.datetime64, timedelta, - np.timedelta64)): + np.timedelta64, NaTType)): if isinstance(obj, Timestamp): tz = obj.tzinfo if tz is not None: @@ -395,6 +396,8 @@ def encode(obj): 'value': obj.value, 'offset': offset, 'tz': tz} + if isinstance(obj, NaTType): + return {'typ': 'nat'} elif isinstance(obj, np.timedelta64): return {'typ': 'timedelta64', 'data': obj.view('i8')} @@ -462,6 +465,8 @@ def decode(obj): return obj elif typ == 'timestamp': return Timestamp(obj['value'], tz=obj['tz'], offset=obj['offset']) + elif typ == 'nat': + return NaT elif typ == 'period': return Period(ordinal=obj['ordinal'], freq=obj['freq']) elif typ == 'index': diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 4bb34e276e81c..d1c05069b4172 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -18,7 +18,7 @@ from pandas.tests.test_panel import assert_panel_equal import pandas -from pandas import Timestamp, tslib +from pandas import Timestamp, NaT, tslib nan = np.nan @@ -225,6 +225,10 @@ def test_timestamp(self): i_rec = self.encode_decode(i) self.assertEqual(i, i_rec) + def test_nat(self): + nat_rec = self.encode_decode(NaT) + self.assertIs(NaT, nat_rec) + def test_datetimes(self): # fails under 2.6/win32 (np.datetime64 seems broken)
This currently fails with an attribute error trying to get the isoformat of `NaT`
https://api.github.com/repos/pandas-dev/pandas/pulls/12307
2016-02-12T05:12:00Z
2016-02-12T13:47:44Z
null
2016-02-12T13:47:55Z
BUG: GH12290 where tz_convert used values of uninitialized arrays
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ae5842b22349a..052e8ba0c2f2a 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -906,6 +906,7 @@ Bug Fixes - Bug in ``NaT`` subtraction from ``Timestamp`` or ``DatetimeIndex`` with timezones (:issue:`11718`) +- Bug in subtraction of ``Series`` of a single tz-aware ``Timestamp`` (:issue:`12290`) - Bug in ``Timedelta.round`` with negative values (:issue:`11690`) - Bug in ``.loc`` against ``CategoricalIndex`` may result in normal ``Index`` (:issue:`11586`) diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index d36cc0c303dfe..cf8a47cc50af4 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -660,6 +660,17 @@ def run_ops(ops, get_ser, test_ser): self.assertRaises(TypeError, lambda: td1 - dt1) self.assertRaises(TypeError, lambda: td2 - dt2) + def test_sub_single_tz(self): + # GH12290 + s1 = Series([pd.Timestamp('2016-02-10', tz='America/Sao_Paulo')]) + s2 = Series([pd.Timestamp('2016-02-08', tz='America/Sao_Paulo')]) + result = s1 - s2 + expected = Series([Timedelta('2days')]) + assert_series_equal(result, expected) + result = s2 - s1 + expected = Series([Timedelta('-2days')]) + assert_series_equal(result, expected) + def test_ops_nat(self): # GH 11349 timedelta_series = Series([NaT, Timedelta('1s')]) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index fe5d06e520cbf..112a41b72f3ea 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -3530,7 +3530,7 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): if _get_zone(tz2) == 'UTC': return utc_dates - result = np.empty(n, dtype=np.int64) + result = np.zeros(n, dtype=np.int64) if _is_tzlocal(tz2): for i in range(n): v = utc_dates[i]
closes #12290
https://api.github.com/repos/pandas-dev/pandas/pulls/12306
2016-02-12T04:06:08Z
2016-02-12T13:39:08Z
null
2016-02-12T13:39:21Z
BUG: Fix subtraction with timezone Series
diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 01df9218c1936..cfd32e9c33c07 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -487,9 +487,9 @@ def _offset(lvalues, rvalues): # with tz, convert to UTC if self.is_datetime64tz_lhs: - lvalues = lvalues.tz_localize(None) + lvalues = lvalues.tz_convert('UTC') if self.is_datetime64tz_rhs: - rvalues = rvalues.tz_localize(None) + rvalues = rvalues.tz_convert('UTC') lvalues = lvalues.view(np.int64) rvalues = rvalues.view(np.int64) diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index d36cc0c303dfe..94c7a8dd7b5d1 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -1375,3 +1375,18 @@ def test_datetime64_with_index(self): df['expected'] = df['date'] - df.index.to_series() df['result'] = df['date'] - df.index assert_series_equal(df['result'], df['expected'], check_names=False) + + # See gh-12290 + def test_datetime64_single_non_native(self): + f = Series([pd.Timestamp('2016-02-08 13:43:14.605000', + tz='America/Sao_Paulo')]) + s = Series([pd.Timestamp('2016-02-10 13:43:14.605000', + tz='America/Sao_Paulo')]) + + out = f - s + expected = Series(pd.Timedelta('-2days')) + assert_series_equal(out, expected) + + out = s - f + expected = Series(pd.Timedelta('2days')) + assert_series_equal(out, expected)
Addresses issue in #12290 with timezone `Series` subtraction in which single element `Series` objects containing tz-aware objects would return a timedelta of zero, even though it visually could not be the case. The bug was traced to the conversion of the contained timezones to UTC, in which the method call was somehow returning `NaT`, even though attempts to replicate that behaviour were unsuccessful. This new method call fixes the issue and is in some ways more intuitive given the comment above the conversions.
https://api.github.com/repos/pandas-dev/pandas/pulls/12302
2016-02-12T00:40:08Z
2016-02-12T06:37:21Z
null
2023-05-11T01:13:22Z
NaT.isoformat() returns 'NaT'.
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index a1056591abd01..f40be1236e3de 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -437,6 +437,8 @@ Backwards incompatible API changes - ``Series.head(0)`` and ``Series.tail(0)`` return empty series, rather than ``self``. (:issue:`11937`) - ``to_msgpack`` and ``read_msgpack`` encoding now defaults to ``'utf-8'``. (:issue:`12170`) - the order of keyword arguments to text file parsing functions (``.read_csv()``, ``.read_table()``, ``.read_fwf()``) changed to group related arguments. (:issue:`11555`) +- ``NaTType.isoformat`` now returns the string ``'NaT`` to allow the result to + be passed to the constructor of ``Timestamp``. (:issue:`12300`) NaT and Timedelta operations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -507,6 +509,11 @@ Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`1 pd.Timestamp('2012-01-01') - ser +``NaT.isoformat()`` now returns ``'NaT'``. This change allows allows +``pd.Timestamp`` to rehydrate any timestamp like object from its isoformat +(:issue:`12300`). + + Signature change for .rank ^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 2ad25ad738649..cb5241c205d03 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -10,6 +10,7 @@ from pandas.compat import range, zip, lrange, StringIO, PY3, lzip, u import pandas.compat as compat import itertools +from operator import methodcaller import os import sys from textwrap import dedent @@ -4083,6 +4084,10 @@ def test_tz_dateutil(self): dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45, tzinfo=utc) self.assertEqual(str(dt_datetime_us), str(Timestamp(dt_datetime_us))) + def test_nat_representations(self): + for f in (str, repr, methodcaller('isoformat')): + self.assertEqual(f(pd.NaT), 'NaT') + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 5665e502b8558..2c5620769d1b4 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -973,9 +973,9 @@ def test_NaT_methods(self): # GH 9513 raise_methods = ['astimezone', 'combine', 'ctime', 'dst', 'fromordinal', 'fromtimestamp', 'isocalendar', - 'isoformat', 'strftime', 'strptime', 'time', - 'timestamp', 'timetuple', 'timetz', 'toordinal', - 'tzname', 'utcfromtimestamp', 'utcnow', 'utcoffset', + 'strftime', 'strptime', 'time', 'timestamp', + 'timetuple', 'timetz', 'toordinal', 'tzname', + 'utcfromtimestamp', 'utcnow', 'utcoffset', 'utctimetuple'] nat_methods = ['date', 'now', 'replace', 'to_datetime', 'today'] nan_methods = ['weekday', 'isoweekday'] @@ -992,6 +992,9 @@ def test_NaT_methods(self): if hasattr(NaT, method): self.assertIs(getattr(NaT, method)(), NaT) + # GH 12300 + self.assertEqual(NaT.isoformat(), 'NaT') + def test_to_datetime_types(self): # empty string diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 112a41b72f3ea..04ee609814b5b 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -673,6 +673,10 @@ class NaTType(_NaT): def __str__(self): return 'NaT' + def isoformat(self, sep='T'): + # This allows Timestamp(ts.isoformat()) to always correctly roundtrip. + return 'NaT' + def __hash__(self): return NPY_NAT @@ -736,7 +740,7 @@ _nat_methods = ['date', 'now', 'replace', 'to_datetime', 'today'] _nan_methods = ['weekday', 'isoweekday', 'total_seconds'] -_implemented_methods = ['to_datetime64'] +_implemented_methods = ['to_datetime64', 'isoformat'] _implemented_methods.extend(_nat_methods) _implemented_methods.extend(_nan_methods)
NaT.isoformat() returning 'NaT' allows users to use Timestamp to rehydrate any _Timestamp object from its isoformat. This means that it is safe to use _Timestamp.isoformat() as a serialization format for timestamps where NaT is a valid value.
https://api.github.com/repos/pandas-dev/pandas/pulls/12300
2016-02-11T23:06:54Z
2016-02-15T22:57:14Z
null
2016-02-15T22:57:28Z
BUG: Make dict iterators real iterators, provide "next()" in Python 2
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index cbdb69d1df8c3..aade3b8411bb9 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -153,31 +153,28 @@ def signature(f): lfilter = builtins.filter -def iteritems(obj, **kwargs): - """replacement for six's iteritems for Python2/3 compat - uses 'iteritems' if available and otherwise uses 'items'. +if PY2: + def iteritems(obj, **kw): + return obj.iteritems(**kw) - Passes kwargs to method. - """ - func = getattr(obj, "iteritems", None) - if not func: - func = obj.items - return func(**kwargs) + def iterkeys(obj, **kw): + return obj.iterkeys(**kw) + def itervalues(obj, **kw): + return obj.itervalues(**kw) -def iterkeys(obj, **kwargs): - func = getattr(obj, "iterkeys", None) - if not func: - func = obj.keys - return func(**kwargs) + next = lambda it : it.next() +else: + def iteritems(obj, **kw): + return iter(obj.items(**kw)) + def iterkeys(obj, **kw): + return iter(obj.keys(**kw)) -def itervalues(obj, **kwargs): - func = getattr(obj, "itervalues", None) - if not func: - func = obj.values - return func(**kwargs) + def itervalues(obj, **kw): + return iter(obj.values(**kw)) + next = next def bind_method(cls, name, func): """Bind a method to class, python 2 and python 3 compatible. diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 14d788fdded7e..67db2e60959b4 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3083,7 +3083,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, # fill in 2d chunks result = dict([(col, s.fillna(method=method, value=value)) - for col, s in compat.iteritems(self)]) + for col, s in self.iteritems()]) return self._constructor.from_dict(result).__finalize__(self) # 2d or less diff --git a/pandas/core/panel.py b/pandas/core/panel.py index e0989574d79e1..5cf9c114c540b 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -398,7 +398,7 @@ def to_sparse(self, fill_value=None, kind='block'): y : SparseDataFrame """ from pandas.core.sparse import SparsePanel - frames = dict(compat.iteritems(self)) + frames = dict(self.iteritems()) return SparsePanel(frames, items=self.items, major_axis=self.major_axis, minor_axis=self.minor_axis, default_kind=kind, @@ -450,7 +450,7 @@ def to_excel(self, path, na_rep='', engine=None, **kwargs): writer = path kwargs['na_rep'] = na_rep - for item, df in compat.iteritems(self): + for item, df in self.iteritems(): name = str(item) df.to_excel(writer, name, **kwargs) writer.save() diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index c94b387f7554a..14881e0fb5a54 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -2726,7 +2726,7 @@ def write(self, obj, **kwargs): self.attrs.default_kind = obj.default_kind self.write_index('items', obj.items) - for name, sdf in compat.iteritems(obj): + for name, sdf in obj.iteritems(): key = 'sparse_frame_%s' % name if key not in self.group._v_children: node = self._handle.create_group(self.group, key) diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index 524508b828980..db2638b2a521d 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -392,7 +392,7 @@ def _combine(self, other, func, axis=0): return self._combinePanel(other, func) elif np.isscalar(other): new_frames = dict((k, func(v, other)) - for k, v in compat.iteritems(self)) + for k, v in self.iteritems()) return self._new_like(new_frames) def _combineFrame(self, other, func, axis=0): @@ -469,7 +469,7 @@ def major_xs(self, key): y : DataFrame index -> minor axis, columns -> items """ - slices = dict((k, v.xs(key)) for k, v in compat.iteritems(self)) + slices = dict((k, v.xs(key)) for k, v in self.iteritems()) return DataFrame(slices, index=self.minor_axis, columns=self.items) def minor_xs(self, key): @@ -486,7 +486,7 @@ def minor_xs(self, key): y : SparseDataFrame index -> major axis, columns -> items """ - slices = dict((k, v[key]) for k, v in compat.iteritems(self)) + slices = dict((k, v[key]) for k, v in self.iteritems()) return SparseDataFrame(slices, index=self.major_axis, columns=self.items, default_fill_value=self.default_fill_value, diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index 24a73b3825a70..dc66e01ac3f78 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -104,7 +104,7 @@ def assert_sp_frame_equal(left, right, exact_indices=True): def assert_sp_panel_equal(left, right, exact_indices=True): - for item, frame in compat.iteritems(left): + for item, frame in left.iteritems(): assert (item in right) # trade-off? assert_sp_frame_equal(frame, right[item], exact_indices=exact_indices) diff --git a/pandas/stats/tests/test_fama_macbeth.py b/pandas/stats/tests/test_fama_macbeth.py index deff392d6a16c..2c69eb64fd61d 100644 --- a/pandas/stats/tests/test_fama_macbeth.py +++ b/pandas/stats/tests/test_fama_macbeth.py @@ -44,7 +44,7 @@ def checkFamaMacBethExtended(self, window_type, x, y, **kwds): end = index[i + window - 1] x2 = {} - for k, v in compat.iteritems(x): + for k, v in x.iteritems(): x2[k] = v.truncate(start, end) y2 = y.truncate(start, end) diff --git a/pandas/stats/tests/test_ols.py b/pandas/stats/tests/test_ols.py index 175ad9dc33dc2..8e659d42bab25 100644 --- a/pandas/stats/tests/test_ols.py +++ b/pandas/stats/tests/test_ols.py @@ -573,7 +573,7 @@ def test_wls_panel(self): stack_y = y.stack() stack_x = DataFrame(dict((k, v.stack()) - for k, v in compat.iteritems(x))) + for k, v in x.iteritems())) weights = x.std('items') stack_weights = weights.stack() diff --git a/pandas/tests/test_compat.py b/pandas/tests/test_compat.py index 2ea95b4e0b300..68c0b81eb18ce 100644 --- a/pandas/tests/test_compat.py +++ b/pandas/tests/test_compat.py @@ -4,7 +4,8 @@ """ from pandas.compat import (range, zip, map, filter, lrange, lzip, lmap, - lfilter, builtins) + lfilter, builtins, iterkeys, itervalues, iteritems, + next) import pandas.util.testing as tm @@ -61,3 +62,8 @@ def test_zip(self): expected = list(builtins.zip(*lst)), lengths = 10, self.check_result(actual, expected, lengths) + + def test_dict_iterators(self): + self.assertEqual(next(itervalues({1: 2})), 2) + self.assertEqual(next(iterkeys({1: 2})), 1) + self.assertEqual(next(iteritems({1: 2})), (1, 2)) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index dd7468723c9c7..0a1e15921dad7 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -318,10 +318,10 @@ def test_keys(self): def test_iteritems(self): # Test panel.iteritems(), aka panel.iteritems() # just test that it works - for k, v in compat.iteritems(self.panel): + for k, v in self.panel.iteritems(): pass - self.assertEqual(len(list(compat.iteritems(self.panel))), + self.assertEqual(len(list(self.panel.iteritems())), len(self.panel.items)) @ignore_sparse_panel_future_warning @@ -1105,7 +1105,7 @@ def test_ctor_dict(self): assert_panel_equal(result, expected) def test_constructor_dict_mixed(self): - data = dict((k, v.values) for k, v in compat.iteritems(self.panel)) + data = dict((k, v.values) for k, v in self.panel.iteritems()) result = Panel(data) exp_major = Index(np.arange(len(self.panel.major_axis))) self.assertTrue(result.major_axis.equals(exp_major)) @@ -1872,7 +1872,7 @@ def test_shift(self): # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel(dict((i, f.shift(-1)[:-1]) - for i, f in compat.iteritems(self.panel))) + for i, f in self.panel.iteritems())) assert_panel_equal(result, expected) # mixed dtypes #6959 @@ -2072,7 +2072,7 @@ def test_to_excel(self): except ImportError: raise nose.SkipTest("need xlwt xlrd openpyxl") - for item, df in compat.iteritems(self.panel): + for item, df in self.panel.iteritems(): recdf = reader.parse(str(item), index_col=0) assert_frame_equal(df, recdf) @@ -2092,7 +2092,7 @@ def test_to_excel_xlsxwriter(self): except ImportError as e: raise nose.SkipTest("cannot write excel file: %s" % e) - for item, df in compat.iteritems(self.panel): + for item, df in self.panel.iteritems(): recdf = reader.parse(str(item), index_col=0) assert_frame_equal(df, recdf) diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 6238f13864552..40447fffdebbd 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -12,7 +12,6 @@ from pandas.core.panel4d import Panel4D from pandas.core.series import remove_na import pandas.core.common as com -from pandas import compat from pandas.util.testing import (assert_panel_equal, assert_panel4d_equal, @@ -232,7 +231,7 @@ def test_keys(self): def test_iteritems(self): """Test panel4d.iteritems()""" - self.assertEqual(len(list(compat.iteritems(self.panel4d))), + self.assertEqual(len(list(self.panel4d.iteritems())), len(self.panel4d.labels)) def test_combinePanel4d(self): @@ -731,7 +730,7 @@ def test_ctor_dict(self): # assert_panel_equal(result, expected) def test_constructor_dict_mixed(self): - data = dict((k, v.values) for k, v in compat.iteritems(self.panel4d)) + data = dict((k, v.values) for k, v in self.panel4d.iteritems()) result = Panel4D(data) exp_major = Index(np.arange(len(self.panel4d.major_axis))) self.assertTrue(result.major_axis.equals(exp_major)) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 046d2322165b5..57a165f467a21 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -2671,7 +2671,7 @@ def test_panel_join_many(self): data_dict = {} for p in panels: - data_dict.update(compat.iteritems(p)) + data_dict.update(p.iteritems()) joined = panels[0].join(panels[1:], how='inner') expected = Panel.from_dict(data_dict, intersect=True)
Currently, `compat.itervalues` and friends are not real iterators (under Python 3): compare ``` In [3]: next(six.itervalues({1:2})) Out[3]: 2 ``` with ``` In [4]: next(pd.compat.itervalues({1:2})) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-4-177f5d3e9571> in <module>() ----> 1 next(pd.compat.itervalues({1:2})) TypeError: 'dict_values' object is not an iterator ``` This PR fixes this (it drops support for `**kwargs`: although it is supported by `six`, I fail to see what's the utility, and it's anyway apparently not used in the `pandas` codebase - but if I'm missing something, it is trivial to reintroduce it) and provides `next()` in Python 2.
https://api.github.com/repos/pandas-dev/pandas/pulls/12299
2016-02-11T22:29:45Z
2016-03-03T21:34:52Z
null
2016-03-03T21:51:50Z
Improvements to str_cat
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 740da62d4f11c..a904e45dd47c9 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -251,6 +251,38 @@ the ``extractall`` method returns all matches. .. _whatsnew_0180.enhancements.rounding: + +Changes to str.cat +^^^^^^^^^^^^^^^^^^ + +The :ref:`.str.cat <text.cat>` concatenates the members of a Series. Before, if NaN values +were present in the Series, calling `cat()` on it would return NaN, unlike the rest of the +`Series.str.*` API. This behavior has been amended to ignore NaN values by default. +(:issue:`11435`). + +A new, friendlier ValueError was also added to protect against the mistake of supplying the +`sep` as an arg, rather than a kwarg. +(:issue:`11334`). + +.. code-block:: python + + >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ') + 'a b c' + + >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?') + 'a b ? c' + + >>> Series(['a','b',np.nan,'c']).str.cat(' ') + --------------------------------------------------------------------------- + ValueError Traceback (most recent call last) + <ipython-input-3-338c379049e9> in <module>() + ----> 1 Series(['a','b',np.nan,'c']).str.cat(' ') + + [...] + + ValueError: Did you mean to supply a `sep` keyword? + + Datetimelike rounding ^^^^^^^^^^^^^^^^^^^^^ diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 727e3fcb377bd..41fdb5e4a8e03 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -1,7 +1,7 @@ import numpy as np from pandas.compat import zip -from pandas.core.common import (isnull, _values_from_object, is_bool_dtype, +from pandas.core.common import (isnull, notnull, _values_from_object, is_bool_dtype, is_list_like, is_categorical_dtype, is_object_dtype, take_1d) import pandas.compat as compat @@ -37,7 +37,7 @@ def str_cat(arr, others=None, sep=None, na_rep=None): If None, returns str concatenating strings of the Series sep : string or None, default None na_rep : string or None, default None - If None, an NA in any array will propagate + If None, NA in the series are ignored. Returns ------- @@ -45,6 +45,15 @@ def str_cat(arr, others=None, sep=None, na_rep=None): Examples -------- + When ``na_rep`` is `None` (default behavior), NaN value(s) + in the Series are ignored. + + >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ') + 'a b c' + + >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?') + 'a b ? c' + If ``others`` is specified, corresponding values are concatenated with the separator. Result will be a Series of strings. @@ -103,18 +112,23 @@ def str_cat(arr, others=None, sep=None, na_rep=None): arr = np.asarray(arr, dtype=object) mask = isnull(arr) if na_rep is None and mask.any(): - return np.nan + if sep == '': + na_rep = '' + else: + return sep.join(arr[notnull(arr)]) return sep.join(np.where(mask, na_rep, arr)) def _length_check(others): n = None for x in others: - if n is None: - n = len(x) - elif len(x) != n: - raise ValueError('All arrays must be same length') - + try: + if n is None: + n = len(x) + elif len(x) != n: + raise ValueError('All arrays must be same length') + except TypeError: + raise ValueError("Did you mean to supply a `sep` keyword?") return n diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index f0bb002a1c96d..2e439cdd3842e 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -100,7 +100,8 @@ def test_cat(self): # single array result = strings.str_cat(one) - self.assertTrue(isnull(result)) + exp = 'aabbc' + self.assertEqual(result, exp) result = strings.str_cat(one, na_rep='NA') exp = 'aabbcNA' @@ -114,6 +115,10 @@ def test_cat(self): exp = 'a_a_b_b_c_NA' self.assertEqual(result, exp) + result = strings.str_cat(two, sep='-') + exp = 'a-b-d-foo' + self.assertEqual(result, exp) + # Multiple arrays result = strings.str_cat(one, [two], na_rep='NA') exp = ['aa', 'aNA', 'bb', 'bd', 'cfoo', 'NANA'] @@ -2453,6 +2458,16 @@ def test_cat_on_filtered_index(self): self.assertEqual(str_multiple.loc[1], '2011 2 2') + def test_str_cat_raises_intuitive_error(self): + # https://github.com/pydata/pandas/issues/11334 + s = Series(['a','b','c','d']) + message = "Did you mean to supply a `sep` keyword?" + with tm.assertRaisesRegexp(ValueError, message): + s.str.cat('|') + with tm.assertRaisesRegexp(ValueError, message): + s.str.cat(' ') + + def test_index_str_accessor_visibility(self): from pandas.core.strings import StringMethods
closes #11435 closes #11334. Modified behavior of str_cat to ignore NaN values in the Series by default. Added some doc examples to make the behavior with nan more clear, and catch an error condition when a sep is passed instead of an others (array) as an argument and print a more intuitive error message. This is a retry of PR #12000. Before, if NaN values were present in the Series, calling `cat()` on it would return NaN, unlike the rest of the `Series.str.` API. This behavior has been amended to ignore NaN values by default. (#11435) A new, friendlier ValueError was also added to protect against the mistake of supplying the `sep` as an arg, rather than a kwarg. (#11334) ``` python >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ') 'a b c' >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?') 'a b ? c' >>> Series(['a','b',np.nan,'c']).str.cat(' ') --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-3-338c379049e9> in <module>() ----> 1 Series(['a','b',np.nan,'c']).str.cat(' ') [...] ValueError: Did you mean to supply a `sep` keyword? ```
https://api.github.com/repos/pandas-dev/pandas/pulls/12297
2016-02-11T21:26:45Z
2016-02-12T15:01:29Z
null
2016-02-12T16:52:07Z
Fix #12292 : error when read one empty column from excel file
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index fa959f651d135..61046a9f2626b 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1088,6 +1088,7 @@ Bug Fixes - Bug in ``Series.plot`` failing when index has a ``CustomBusinessDay`` frequency (:issue:`7222`). - Bug in ``read_excel`` failing to read data with one column when ``squeeze=True`` (:issue:`12157`) +- Bug in ``read_excel`` failing to read one empty column (:issue:`12292`) - Bug in ``.groupby`` where a ``KeyError`` was not raised for a wrong column if there was only one row in the dataframe (:issue:`11741`) - Bug in ``.read_csv`` with dtype specified on empty data producing an error (:issue:`12048`) - Bug in ``.read_csv`` where strings like ``'2E'`` are treated as valid floats (:issue:`12237`) diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 06e99aa8ebce9..5656c360b3990 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -448,21 +448,26 @@ def _parse_cell(cell_contents, cell_typ): if com.is_list_like(header) and len(header) > 1: has_index_names = True - parser = TextParser(data, header=header, index_col=index_col, - has_index_names=has_index_names, - na_values=na_values, - thousands=thousands, - parse_dates=parse_dates, - date_parser=date_parser, - skiprows=skiprows, - skip_footer=skip_footer, - squeeze=squeeze, - **kwds) - - output[asheetname] = parser.read() - if not squeeze or isinstance(output[asheetname], DataFrame): - output[asheetname].columns = output[ - asheetname].columns.set_names(header_names) + # GH 12292 : error when read one empty column from excel file + try: + parser = TextParser(data, header=header, index_col=index_col, + has_index_names=has_index_names, + na_values=na_values, + thousands=thousands, + parse_dates=parse_dates, + date_parser=date_parser, + skiprows=skiprows, + skip_footer=skip_footer, + squeeze=squeeze, + **kwds) + + output[asheetname] = parser.read() + if not squeeze or isinstance(output[asheetname], DataFrame): + output[asheetname].columns = output[ + asheetname].columns.set_names(header_names) + except StopIteration: + # No Data, return an empty DataFrame + output[asheetname] = DataFrame() if ret_dict: return output diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 389d41327d75c..b8d61047a7b6d 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -401,6 +401,60 @@ def test_read_excel_blank_with_header(self): actual = self.get_exceldf('blank_with_header', 'Sheet1') tm.assert_frame_equal(actual, expected) + # GH 12292 : error when read one empty column from excel file + def test_read_one_empty_col_no_header(self): + df = pd.DataFrame( + [["", 1, 100], + ["", 2, 200], + ["", 3, 300], + ["", 4, 400]] + ) + with ensure_clean(self.ext) as path: + df.to_excel(path, 'no_header', index=False, header=False) + actual_header_none = read_excel( + path, + 'no_header', + parse_cols=[0], + header=None + ) + + actual_header_zero = read_excel( + path, + 'no_header', + parse_cols=[0], + header=0 + ) + expected = DataFrame() + tm.assert_frame_equal(actual_header_none, expected) + tm.assert_frame_equal(actual_header_zero, expected) + + def test_read_one_empty_col_with_header(self): + df = pd.DataFrame( + [["", 1, 100], + ["", 2, 200], + ["", 3, 300], + ["", 4, 400]] + ) + with ensure_clean(self.ext) as path: + df.to_excel(path, 'with_header', index=False, header=True) + actual_header_none = read_excel( + path, + 'with_header', + parse_cols=[0], + header=None + ) + + actual_header_zero = read_excel( + path, + 'with_header', + parse_cols=[0], + header=0 + ) + expected_header_none = DataFrame(pd.Series([0], dtype='int64')) + tm.assert_frame_equal(actual_header_none, expected_header_none) + expected_header_zero = DataFrame(columns=[0], dtype='int64') + tm.assert_frame_equal(actual_header_zero, expected_header_zero) + class XlrdTests(ReadingTestsBase): """
Fix #12292 also fix #9002
https://api.github.com/repos/pandas-dev/pandas/pulls/12296
2016-02-11T17:52:01Z
2016-02-27T15:09:05Z
null
2016-02-27T18:42:21Z
BUG: Refine validation of parameters to RangeIndex.__init__
diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index f4f5745659002..ca12a06b4888e 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -23,13 +23,14 @@ class RangeIndex(Int64Index): Parameters ---------- - start : int (default: 0) + start : int (default: 0), or other RangeIndex instance. + If int and "stop" is not given, interpreted as "stop" instead. stop : int (default: 0) step : int (default: 1) name : object, optional Name to be stored in the index copy : bool, default False - Make a copy of input if its a RangeIndex + Unused, accepted for homogeneity with other index types. """ @@ -46,20 +47,17 @@ def __new__(cls, start=None, stop=None, step=None, name=None, dtype=None, # RangeIndex if isinstance(start, RangeIndex): - if not copy: - return start if name is None: - name = getattr(start, 'name', None) - start, stop, step = start._start, start._stop, start._step + name = start.name + return cls._simple_new(name=name, + **dict(start._get_data_as_items())) # validate the arguments def _ensure_int(value, field): try: new_value = int(value) - except: - new_value = value - - if not com.is_integer(new_value) or new_value != value: + assert(new_value == value) + except (ValueError, AssertionError): raise TypeError("RangeIndex(...) must be called with integers," " {value} was passed for {field}".format( value=type(value).__name__, diff --git a/pandas/tests/indexes/test_range.py b/pandas/tests/indexes/test_range.py index cf7fe67be6401..567d7acd062c3 100644 --- a/pandas/tests/indexes/test_range.py +++ b/pandas/tests/indexes/test_range.py @@ -138,6 +138,16 @@ def test_constructor_range(self): self.assertRaises(TypeError, lambda: Index(range(1, 5, 2), dtype='float64')) + def test_constructor_name(self): + # GH12288 + orig = RangeIndex(10) + orig.name = 'original' + + copy = RangeIndex(orig) + copy.name = 'copy' + + self.assertTrue(orig.name, 'original') + def test_numeric_compat2(self): # validate that we are handling the RangeIndex overrides to numeric ops # and returning RangeIndex where possible
closes #12288
https://api.github.com/repos/pandas-dev/pandas/pulls/12295
2016-02-11T15:21:45Z
2016-02-12T14:38:36Z
null
2016-02-12T15:39:40Z
BUG: Series.plot() doesn't work with CustomBusinessDay frequency
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ae5842b22349a..1b9a6d50db161 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -950,6 +950,7 @@ Bug Fixes - Bug in ``.plot`` potentially modifying the ``colors`` input when the number of columns didn't match the number of series provided (:issue:`12039`). +- Bug in ``Series.plot`` doesn't work with CustomBusinessDay frequency (:issue:`7222`). - Bug in ``read_excel`` failing to read data with one column when ``squeeze=True`` (:issue:`12157`) - Bug in ``.groupby`` where a ``KeyError`` was not raised for a wrong column if there was only one row in the dataframe (:issue:`11741`) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index f224016be1e49..b339d25cd6c45 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1270,6 +1270,15 @@ def test_xticklabels(self): exp = ['P%02d' % i for i in [0, 3, 5, 9]] self._check_text_labels(ax.get_xticklabels(), exp) + def test_custom_business_day_freq(self): + # GH7222 + from pandas.tseries.offsets import CustomBusinessDay + s = Series(range(100, 121), index=pd.bdate_range( + start='2014-05-01', end='2014-06-01', + freq=CustomBusinessDay(holidays=['2014-05-26']))) + + _check_plot_works(s.plot) + @tm.mplskip class TestDataFramePlots(TestPlotBase): diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index a150e55b06ff3..90acd99c738cc 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -671,6 +671,7 @@ def get_standard_freq(freq): "Q": 2000, # Quarterly - December year end (default quarterly) "A": 1000, # Annual "W": 4000, # Weekly + "C": 5000, # Custom Business Day })
Closes #7222 Missing freqstr 'C' in `_period_code_map` I found `pandas/tseries/tests/test_frequencies.py:TestFrequencyCode.test_freq_code` cover this change.
https://api.github.com/repos/pandas-dev/pandas/pulls/12294
2016-02-11T15:13:40Z
2016-02-12T14:30:24Z
null
2016-02-12T14:30:34Z
ENH: add empty() methods for DataFrame and Series
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 324f30ed00bed..a73b5a9736e07 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -29,6 +29,7 @@ _maybe_box_datetimelike, is_categorical_dtype, is_object_dtype, is_internal_type, is_datetimetz, _possibly_infer_to_datetimelike, _dict_compat) +from pandas.core.dtypes import ExtensionDtype from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import (maybe_droplevels, convert_to_index_sliceable, @@ -771,6 +772,36 @@ def dot(self, other): else: # pragma: no cover raise TypeError('unsupported type: %s' % type(other)) + @classmethod + def empty(cls, shape, dtype=float): + """ + Return a new DataFrame of given shape and type, without initializing entries. + + Parameters + ---------- + shape : int or tuple of int + Shape of the empty DataFrame + dtype : data-type, optional + Desired output data-type + + Returns + ------- + out : DataFrame + DataFrame of uninitialized (arbitrary) data + with the given shape and dtype. + + See Also + -------- + numpy.empty : initializes an empty array of given shape and type + Series.empty : initializes an empty Series of given length and type + + """ + if ExtensionDtype.is_dtype(dtype): + return cls(np.empty(shape, dtype=object).tolist(), dtype=dtype) + + else: + return cls(np.empty(shape, dtype=dtype)) + # ---------------------------------------------------------------------- # IO methods (to / from other formats) diff --git a/pandas/core/series.py b/pandas/core/series.py index 286a2ba79585a..a0b08bac6a6dd 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -26,6 +26,7 @@ _coerce_to_dtype, SettingWithCopyError, _maybe_box_datetimelike, ABCDataFrame, _dict_compat) +from pandas.core.dtypes import ExtensionDtype from pandas.core.index import (Index, MultiIndex, InvalidIndexError, Float64Index, _ensure_index) from pandas.core.indexing import check_bool_indexer, maybe_convert_indices @@ -243,6 +244,36 @@ def from_array(cls, arr, index=None, name=None, dtype=None, copy=False, return cls(arr, index=index, name=name, dtype=dtype, copy=copy, fastpath=fastpath) + @classmethod + def empty(cls, length, dtype=float): + """ + Return a new Series of given length and type, without initializing entries. + + Parameters + ---------- + length : int + Length of the empty Series + dtype : data-type, optional + Desired output data-type + + Returns + ------- + out : Series + Series of uninitialized (arbitrary) data + with the given length and dtype. + + See Also + -------- + numpy.empty : initializes an empty array of given shape and type + DataFrame.empty : initializes an empty DataFrame of given shape and type + + """ + if ExtensionDtype.is_dtype(dtype): + return cls.from_array(np.empty(length, dtype=object), dtype=dtype) + + else: + return cls(np.empty(length, dtype=dtype)) + @property def _constructor(self): return Series diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 87c263e129361..1e8560aa77bf7 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -1995,3 +1995,24 @@ def test_from_records_len0_with_columns(self): self.assertTrue(np.array_equal(result.columns, ['bar'])) self.assertEqual(len(result), 0) self.assertEqual(result.index.name, 'foo') + + def test_empty(self): + from pandas.core.dtypes import DatetimeTZDtype + + df = DataFrame({'dt': pd.date_range( + "2015-01-01", periods=3, tz='Europe/Brussels')}) + dt = df.values.dtype + + params = [ + (dt, (4, 8), dt), + (None, (7, 1), float), + (np.int64, (3, 5), np.int64), + (DatetimeTZDtype, (2, 3), object), + ] + + for in_dt, shape, out_dt in params: + df = DataFrame.empty(shape, dtype=in_dt) + self.assertEqual(df.shape, shape) + + for col in df.columns: + self.assertEqual(df[col].dtype, out_dt) diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index c5783779c67c8..f7af65f97415c 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -706,3 +706,22 @@ def f(): self.assertEqual(s.dtype, 'timedelta64[ns]') s = Series([pd.NaT, np.nan, '1 Day']) self.assertEqual(s.dtype, 'timedelta64[ns]') + + def test_empty(self): + from pandas.core.dtypes import DatetimeTZDtype + + df = pd.DataFrame({'dt': pd.date_range( + "2015-01-01", periods=3, tz='Europe/Brussels')}) + dt = df.values.dtype + + params = [ + (dt, 6, dt), + (None, 3, float), + (np.int64, 10, np.int64), + (DatetimeTZDtype, 5, object), + ] + + for in_dt, length, out_dt in params: + s = Series.empty(length, dtype=in_dt) + self.assertEqual(s.size, length) + self.assertEqual(s.dtype, out_dt)
Added `empty()` methods to the `Series` and `DataFrame` classes analogous to the `empty()` function in the `numpy` library that can also accept `scipy` duck-type `dtypes` in addition to `numpy` `dtypes`.
https://api.github.com/repos/pandas-dev/pandas/pulls/12291
2016-02-11T11:18:36Z
2016-02-11T12:02:49Z
null
2023-05-11T01:13:22Z
BUG: provide chunks with progressively numbered (default) indices
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 317383e866464..5cc17216fd8f2 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -596,6 +596,40 @@ New Behavior: idx1.difference(idx2) idx1.symmetric_difference(idx2) +.. _whatsnew_0190.api.autogenerated_chunksize_index: + +:func:`read_csv` called with ``chunksize`` parameter generates correct index +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When :func:`read_csv` is called with ``chunksize='n'`` and without specifying an index, +each chunk used to have an independently generated index from `0`` to ``n-1``. +They are now given instead a progressive index, starting from ``0`` for the first chunk, +from ``n`` for the second, and so on, so that, when concatenated, they are identical to +the result of calling :func:`read_csv` without the ``chunksize=`` argument. +(:issue:`12185`) + +.. ipython :: python + + data = 'A,B\n0,1\n2,3\n4,5\n6,7' + +Previous behaviour: + +.. code-block:: ipython + + In [2]: pd.concat(pd.read_csv(StringIO(data), chunksize=2)) + Out[2]: + A B + 0 0 1 + 1 2 3 + 0 4 5 + 1 6 7 + +New behaviour: + +.. ipython :: python + + pd.concat(pd.read_csv(StringIO(data), chunksize=2)) + .. _whatsnew_0190.deprecations: Deprecations diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index f6a84ea9debaa..34c83c01dc01b 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -16,7 +16,7 @@ is_list_like, is_integer_dtype, is_float, is_scalar) -from pandas.core.index import Index, MultiIndex +from pandas.core.index import Index, MultiIndex, RangeIndex from pandas.core.frame import DataFrame from pandas.core.common import AbstractMethodError from pandas.core.config import get_option @@ -700,6 +700,7 @@ def __init__(self, f, engine=None, **kwds): # miscellanea self.engine = engine self._engine = None + self._currow = 0 options = self._get_options_with_defaults(engine) @@ -913,8 +914,20 @@ def read(self, nrows=None): # May alter columns / col_dict index, columns, col_dict = self._create_index(ret) + if index is None: + if col_dict: + # Any column is actually fine: + new_rows = len(compat.next(compat.itervalues(col_dict))) + index = RangeIndex(self._currow, self._currow + new_rows) + else: + new_rows = 0 + else: + new_rows = len(index) + df = DataFrame(col_dict, columns=columns, index=index) + self._currow += new_rows + if self.squeeze and len(df.columns) == 1: return df[df.columns[0]].copy() return df diff --git a/pandas/io/tests/parser/common.py b/pandas/io/tests/parser/common.py index 11eed79e03267..f3adb0e39982c 100644 --- a/pandas/io/tests/parser/common.py +++ b/pandas/io/tests/parser/common.py @@ -461,6 +461,18 @@ def test_get_chunk_passed_chunksize(self): piece = result.get_chunk() self.assertEqual(len(piece), 2) + def test_read_chunksize_generated_index(self): + # GH 12185 + reader = self.read_csv(StringIO(self.data1), chunksize=2) + df = self.read_csv(StringIO(self.data1)) + + tm.assert_frame_equal(pd.concat(reader), df) + + reader = self.read_csv(StringIO(self.data1), chunksize=2, index_col=0) + df = self.read_csv(StringIO(self.data1), index_col=0) + + tm.assert_frame_equal(pd.concat(reader), df) + def test_read_text_list(self): data = """A,B,C\nfoo,1,2,3\nbar,4,5,6""" as_list = [['A', 'B', 'C'], ['foo', '1', '2', '3'], ['bar', diff --git a/pandas/io/tests/parser/test_network.py b/pandas/io/tests/parser/test_network.py index d5370db4b55db..8b8a6de36fc03 100644 --- a/pandas/io/tests/parser/test_network.py +++ b/pandas/io/tests/parser/test_network.py @@ -122,8 +122,6 @@ def test_parse_public_s3_bucket_chunked(self): self.assertFalse(df.empty) true_df = local_tips.iloc[ chunksize * i_chunk: chunksize * (i_chunk + 1)] - # Chunking doesn't preserve row numbering - true_df = true_df.reset_index().drop('index', axis=1) tm.assert_frame_equal(true_df, df) @tm.network @@ -143,8 +141,6 @@ def test_parse_public_s3_bucket_chunked_python(self): self.assertFalse(df.empty) true_df = local_tips.iloc[ chunksize * i_chunk: chunksize * (i_chunk + 1)] - # Chunking doesn't preserve row numbering - true_df = true_df.reset_index().drop('index', axis=1) tm.assert_frame_equal(true_df, df) @tm.network diff --git a/pandas/io/tests/test_common.py b/pandas/io/tests/test_common.py index 0acf3244fe8fa..a443df5dac586 100644 --- a/pandas/io/tests/test_common.py +++ b/pandas/io/tests/test_common.py @@ -86,7 +86,6 @@ def test_iterator(self): it = read_csv(StringIO(self.data1), chunksize=1) first = next(it) tm.assert_frame_equal(first, expected.iloc[[0]]) - expected.index = [0 for i in range(len(expected))] tm.assert_frame_equal(concat(it), expected.iloc[1:])
closes #12185 Notice the test I fix was indeed wrong - I had written that line as a workaround waiting for this fix.
https://api.github.com/repos/pandas-dev/pandas/pulls/12289
2016-02-11T09:45:34Z
2016-07-29T00:15:15Z
null
2016-07-29T08:19:14Z
BUG: Fixes KeyError when indexes don't overlap.
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ac4d21e78b4b3..858c590720415 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -954,3 +954,4 @@ Bug Fixes - Bug in ``.skew`` and ``.kurt`` due to roundoff error for highly similar values (:issue:`11974`) - Bug in ``buffer_rd_bytes`` src->buffer could be freed more than once if reading failed, causing a segfault (:issue:`12098`) +- Bug in ``crosstab`` where arguments with non-overlapping indexes would return a KeyError (:issue:`10291`) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index 8e920e0a99a7a..b88aeb310752e 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -153,7 +153,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', margins_name=margins_name) # discard the top level - if values_passed and not values_multi: + if values_passed and not values_multi and not table.empty: table = table[values[0]] if len(index) == 0 and len(columns) > 0: @@ -396,6 +396,9 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None, Any Series passed will have their name attributes used unless row or column names for the cross-tabulation are specified + In the event that there aren't overlapping indexes an empty DataFrame will + be returned. + Examples -------- >>> a diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 2720350afb9fe..845f50aa65d70 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -892,6 +892,17 @@ def test_categorical_margins(self): table = data.pivot_table('x', 'y', 'z', margins=True) tm.assert_frame_equal(table, expected) + def test_crosstab_no_overlap(self): + # GS 10291 + + s1 = pd.Series([1, 2, 3], index=[1, 2, 3]) + s2 = pd.Series([4, 5, 6], index=[4, 5, 6]) + + actual = crosstab(s1, s2) + expected = pd.DataFrame() + + tm.assert_frame_equal(actual, expected) + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
Closes #10291
https://api.github.com/repos/pandas-dev/pandas/pulls/12287
2016-02-11T06:00:43Z
2016-02-12T03:02:26Z
null
2016-02-15T06:43:33Z
BUG: Allow 'apply' to be used with non-numpy-dtype DataFrames
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ae5842b22349a..542402b92364e 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -967,4 +967,7 @@ Bug Fixes - Bug in ``.skew`` and ``.kurt`` due to roundoff error for highly similar values (:issue:`11974`) - Bug in ``buffer_rd_bytes`` src->buffer could be freed more than once if reading failed, causing a segfault (:issue:`12098`) + - Bug in ``crosstab`` where arguments with non-overlapping indexes would return a ``KeyError`` (:issue:`10291`) + +- Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 324f30ed00bed..8a3ac4db37d2d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4072,22 +4072,24 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): # this only matters if the reduction in values is of different dtype # e.g. if we want to apply to a SparseFrame, then can't directly reduce if reduce: - values = self.values - # Create a dummy Series from an empty array - index = self._get_axis(axis) - empty_arr = np.empty(len(index), dtype=values.dtype) - dummy = Series(empty_arr, index=self._get_axis(axis), - dtype=values.dtype) + # we cannot reduce using non-numpy dtypes, + # as demonstrated in gh-12244 + if not is_internal_type(values): + # Create a dummy Series from an empty array + index = self._get_axis(axis) + empty_arr = np.empty(len(index), dtype=values.dtype) + dummy = Series(empty_arr, index=self._get_axis(axis), + dtype=values.dtype) - try: - labels = self._get_agg_axis(axis) - result = lib.reduce(values, func, axis=axis, dummy=dummy, - labels=labels) - return Series(result, index=labels) - except Exception: - pass + try: + labels = self._get_agg_axis(axis) + result = lib.reduce(values, func, axis=axis, dummy=dummy, + labels=labels) + return Series(result, index=labels) + except Exception: + pass dtype = object if self._is_mixed_type else None if axis == 0: diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py index e68b94342985d..120a51ab0b809 100644 --- a/pandas/tests/frame/test_apply.py +++ b/pandas/tests/frame/test_apply.py @@ -400,3 +400,19 @@ def test_applymap(self): result = df.applymap(str) for f in ['datetime', 'timedelta']: self.assertEqual(result.loc[0, f], str(df.loc[0, f])) + + # See gh-12244 + def test_apply_non_numpy_dtype(self): + df = DataFrame({'dt': pd.date_range( + "2015-01-01", periods=3, tz='Europe/Brussels')}) + result = df.apply(lambda x: x) + assert_frame_equal(result, df) + + result = df.apply(lambda x: x + pd.Timedelta('1day')) + expected = DataFrame({'dt': pd.date_range( + "2015-01-02", periods=3, tz='Europe/Brussels')}) + assert_frame_equal(result, expected) + + df = DataFrame({'dt': ['a', 'b', 'c', 'a']}, dtype='category') + result = df.apply(lambda x: x) + assert_frame_equal(result, df)
Addresses issue in #12244 in which a non-numpy-dtype for DataFrame.values causes a `TypeError` to be thrown in the `reduce == True` case for `DataFrame.apply`. Resolved by first passing `DataFrame.values` through `Series` initialization and taking its `values` attribute, which is an `ndarray` and hence will have a valid `dtype`. Note that the output of `apply` will still have the original `dtype`.
https://api.github.com/repos/pandas-dev/pandas/pulls/12284
2016-02-10T17:47:21Z
2016-02-12T13:42:08Z
null
2016-02-12T14:27:12Z
DOC: extract/extractall clarifications
diff --git a/doc/source/text.rst b/doc/source/text.rst index c8a878747a9b7..a0cc32ecea531 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -196,9 +196,9 @@ DataFrame with one column per group. Elements that do not match return a row filled with ``NaN``. Thus, a Series of messy strings can be "converted" into a like-indexed Series or DataFrame of cleaned-up or more useful strings, without -necessitating ``get()`` to access tuples or ``re.match`` objects. The -results dtype always is object, even if no match is found and the -result only contains ``NaN``. +necessitating ``get()`` to access tuples or ``re.match`` objects. The +dtype of the result is always object, even if no match is found and +the result only contains ``NaN``. Named groups like @@ -275,15 +275,16 @@ Extract all matches in each subject (extractall) .. _text.extractall: +.. versionadded:: 0.18.0 + Unlike ``extract`` (which returns only the first match), .. ipython:: python s = pd.Series(["a1a2", "b1", "c1"], ["A", "B", "C"]) s - s.str.extract("[ab](?P<digit>\d)", expand=False) - -.. versionadded:: 0.18.0 + two_groups = '(?P<letter>[a-z])(?P<digit>[0-9])' + s.str.extract(two_groups, expand=True) the ``extractall`` method returns every match. The result of ``extractall`` is always a ``DataFrame`` with a ``MultiIndex`` on its @@ -292,7 +293,7 @@ indicates the order in the subject. .. ipython:: python - s.str.extractall("[ab](?P<digit>\d)") + s.str.extractall(two_groups) When each subject string in the Series has exactly one match, @@ -300,14 +301,13 @@ When each subject string in the Series has exactly one match, s = pd.Series(['a3', 'b3', 'c2']) s - two_groups = '(?P<letter>[a-z])(?P<digit>[0-9])' then ``extractall(pat).xs(0, level='match')`` gives the same result as ``extract(pat)``. .. ipython:: python - extract_result = s.str.extract(two_groups, expand=False) + extract_result = s.str.extract(two_groups, expand=True) extract_result extractall_result = s.str.extractall(two_groups) extractall_result @@ -315,7 +315,7 @@ then ``extractall(pat).xs(0, level='match')`` gives the same result as Testing for Strings that Match or Contain a Pattern -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +--------------------------------------------------- You can check whether elements contain a pattern: @@ -355,7 +355,7 @@ Methods like ``match``, ``contains``, ``startswith``, and ``endswith`` take s4.str.contains('A', na=False) Creating Indicator Variables -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +---------------------------- You can extract dummy variables from string columns. For example if they are separated by a ``'|'``: diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ec002fae3b4b9..554647bb015e1 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -157,50 +157,50 @@ Currently the default is ``expand=None`` which gives a ``FutureWarning`` and use .. ipython:: python - pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=False) + pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None) -Extracting a regular expression with one group returns a ``DataFrame`` -with one column if ``expand=True``. +Extracting a regular expression with one group returns a Series if +``expand=False``. .. ipython:: python - pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=True) + pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=False) -It returns a Series if ``expand=False``. +It returns a ``DataFrame`` with one column if ``expand=True``. .. ipython:: python - pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=False) + pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=True) Calling on an ``Index`` with a regex with exactly one capture group -returns a ``DataFrame`` with one column if ``expand=True``, +returns an ``Index`` if ``expand=False``. .. ipython:: python s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"]) s - s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True) - -It returns an ``Index`` if ``expand=False``. - -.. ipython:: python - s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False) -Calling on an ``Index`` with a regex with more than one capture group -returns a ``DataFrame`` if ``expand=True``. +It returns a ``DataFrame`` with one column if ``expand=True``. .. ipython:: python - s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True) + s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True) -It raises ``ValueError`` if ``expand=False``. +Calling on an ``Index`` with a regex with more than one capture group +raises ``ValueError`` if ``expand=False``. .. code-block:: python >>> s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False) ValueError: only one regex group is supported with Index +It returns a ``DataFrame`` if ``expand=True``. + +.. ipython:: python + + s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True) + In summary, ``extract(expand=True)`` always returns a ``DataFrame`` with a row for every subject string, and a column for every capture group.
This PR clarifies the new documentation for extract and extractall. It was requested by @jreback in https://github.com/pydata/pandas/pull/11386#issuecomment-182105142
https://api.github.com/repos/pandas-dev/pandas/pulls/12281
2016-02-10T13:47:37Z
2016-02-10T16:22:36Z
null
2016-02-10T16:22:59Z
API: to_msgpack and read_msgpack encoding defaults to utf-8
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ec002fae3b4b9..2071ec2985748 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -346,6 +346,7 @@ Backwards incompatible API changes - ``DataFrame.round()`` leaves non-numeric columns unchanged in its return, rather than raises. (:issue:`11885`) - ``DataFrame.head(0)`` and ``DataFrame.tail(0)`` return empty frames, rather than ``self``. (:issue:`11937`) - ``Series.head(0)`` and ``Series.tail(0)`` return empty series, rather than ``self``. (:issue:`11937`) +- ``to_msgpack`` and ``read_msgpack`` encoding now defaults to ``'utf-8'``. (:issue:`12170`) NaT and Timedelta operations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 7ae00fb501614..a746a93c3dc16 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -939,7 +939,7 @@ def to_hdf(self, path_or_buf, key, **kwargs): from pandas.io import pytables return pytables.to_hdf(path_or_buf, key, self, **kwargs) - def to_msgpack(self, path_or_buf=None, **kwargs): + def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs): """ msgpack (serialize) object to input file path @@ -957,7 +957,8 @@ def to_msgpack(self, path_or_buf=None, **kwargs): """ from pandas.io import packers - return packers.to_msgpack(path_or_buf, self, **kwargs) + return packers.to_msgpack(path_or_buf, self, encoding=encoding, + **kwargs) def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None): diff --git a/pandas/io/packers.py b/pandas/io/packers.py index a16f3600736b8..33310893a4a66 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -75,6 +75,7 @@ def to_msgpack(path_or_buf, *args, **kwargs): path_or_buf : string File path, buffer-like, or None if None, return generated string args : an object or objects to serialize + encoding: encoding for unicode objects append : boolean whether to append to an existing msgpack (default is False) compress : type of compressor (zlib or blosc), default to None (no @@ -103,7 +104,7 @@ def writer(fh): writer(path_or_buf) -def read_msgpack(path_or_buf, iterator=False, **kwargs): +def read_msgpack(path_or_buf, encoding='utf-8', iterator=False, **kwargs): """ Load msgpack pandas object from the specified file path @@ -114,6 +115,7 @@ def read_msgpack(path_or_buf, iterator=False, **kwargs): Parameters ---------- path_or_buf : string File path, BytesIO like or string + encoding: Encoding for decoding msgpack str type iterator : boolean, if True, return an iterator to the unpacker (default is False) @@ -127,7 +129,7 @@ def read_msgpack(path_or_buf, iterator=False, **kwargs): return Iterator(path_or_buf) def read(fh): - l = list(unpack(fh, **kwargs)) + l = list(unpack(fh, encoding=encoding, **kwargs)) if len(l) == 1: return l[0] return l @@ -573,7 +575,7 @@ def create_block(b): def pack(o, default=encode, - encoding='latin1', unicode_errors='strict', use_single_float=False, + encoding='utf-8', unicode_errors='strict', use_single_float=False, autoreset=1, use_bin_type=1): """ Pack an object and return the packed bytes. @@ -587,7 +589,7 @@ def pack(o, default=encode, def unpack(packed, object_hook=decode, - list_hook=None, use_list=False, encoding='latin1', + list_hook=None, use_list=False, encoding='utf-8', unicode_errors='strict', object_pairs_hook=None, max_buffer_size=0, ext_hook=ExtType): """ @@ -607,7 +609,7 @@ def unpack(packed, object_hook=decode, class Packer(_Packer): def __init__(self, default=encode, - encoding='latin1', + encoding='utf-8', unicode_errors='strict', use_single_float=False, autoreset=1, @@ -624,7 +626,7 @@ class Unpacker(_Unpacker): def __init__(self, file_like=None, read_size=0, use_list=False, object_hook=decode, - object_pairs_hook=None, list_hook=None, encoding='latin1', + object_pairs_hook=None, list_hook=None, encoding='utf-8', unicode_errors='strict', max_buffer_size=0, ext_hook=ExtType): super(Unpacker, self).__init__(file_like=file_like, read_size=read_size, diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 6905225600ae6..4bb34e276e81c 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -299,11 +299,8 @@ def test_multi_index(self): def test_unicode(self): i = tm.makeUnicodeIndex(100) - # this currently fails - self.assertRaises(UnicodeEncodeError, self.encode_decode, i) - - # i_rec = self.encode_decode(i) - # self.assertTrue(i.equals(i_rec)) + i_rec = self.encode_decode(i) + self.assertTrue(i.equals(i_rec)) class TestSeries(TestPackers): @@ -615,6 +612,14 @@ def test_utf(self): result = self.encode_decode(frame, encoding=encoding) assert_frame_equal(result, frame) + def test_default_encoding(self): + for frame in compat.itervalues(self.frame): + result = frame.to_msgpack() + expected = frame.to_msgpack(encoding='utf8') + self.assertEqual(result, expected) + result = self.encode_decode(frame) + assert_frame_equal(result, frame) + class TestMsgpack(): """ @@ -652,7 +657,11 @@ def check_min_structure(self, data): typ], '"{0}" not found in data["{1}"]'.format(kind, typ) def compare(self, vf, version): - data = read_msgpack(vf) + # GH12277 encoding default used to be latin-1, now utf-8 + if LooseVersion(version) < '0.18.0': + data = read_msgpack(vf, encoding='latin-1') + else: + data = read_msgpack(vf) self.check_min_structure(data) for typ, dv in data.items(): assert typ in self.all_data, ('unpacked data contains '
closes #12170
https://api.github.com/repos/pandas-dev/pandas/pulls/12277
2016-02-10T10:31:03Z
2016-02-10T16:20:49Z
null
2016-02-10T16:20:58Z
Hdf warning
diff --git a/doc/source/io.rst b/doc/source/io.rst index 64967f1979807..ccd38e241a4f1 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -2895,6 +2895,26 @@ everything in the sub-store and BELOW, so be *careful*. .. _io.hdf5-types: + +.. warning:: Hierarchical keys cannot be retrieved as dotted (attribute) access as described above for items stored under root node. + + .. ipython:: python + + store.foo.bar.bah + AttributeError: 'HDFStore' object has no attribute 'foo' + + store.root.foo.bar.bah + /foo/bar/bah (Group) '' + children := ['block0_items' (Array), 'axis1' (Array), 'axis0' (Array), 'block0_values' (Array)] + + Use explicit string based keys + + .. ipython:: python + + bah = store['foo/bar/bah'] + + + Storing Types '''''''''''''
In accordance with #12268 this is now a separate PR. I think that this warning could be useful either way for no non-daily hdf user: - Telling that string method is preferred - Adding a warning
https://api.github.com/repos/pandas-dev/pandas/pulls/12276
2016-02-10T09:13:59Z
2016-04-23T13:05:57Z
null
2016-04-23T13:05:58Z
BUG: option to avoid latex in qtconsole #12182
diff --git a/doc/source/options.rst b/doc/source/options.rst index 0b2ba03a441a2..d00ba047a56a6 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -307,6 +307,9 @@ display.large_repr truncate For DataFrames exceeding max_rows/max_co or switch to the view from df.info() (the behaviour in earlier versions of pandas). allowable settings, ['truncate', 'info'] +display.latex.repr False Whether to produce a latex DataFrame + representation for jupyter frontends + that support it. display.latex.escape True Escapes special caracters in Dataframes, when using the to_latex method. display.latex.longtable False Specifies if the to_latex method of a Dataframe diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index d30c0321568bc..793565bd54581 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -312,6 +312,23 @@ New Behavior: s.index print(s.to_csv(path=None)) +Latex Represenation +^^^^^^^^^^^^^^^^^^^ + +``DataFrame`` has gained a ``_repr_latex_`` method in order to allow +for conversion to latex in a ipython/jupyter notebook using +nbconvert. (:issue:`11778`) + +Note that this must be activated by setting the option ``display.latex.repr`` +to ``True`` (issue:`12182`) + +For example, if you have a jupyter notebook you plan to convert to latex using +nbconvert, place the statement ``pd.set_option('display.latex.repr', True)`` +in the first cell to have the contained DataFrame output also stored as latex. + +Options ``display.latex.escape`` and ``display.latex.longtable`` have also been +added to the configuration and are used automatically by the ``to_latex`` +method. See the :ref:`options documentation<options>` for more info. .. _whatsnew_0180.enhancements.other: @@ -324,7 +341,6 @@ Other enhancements - add support for ``AWS_S3_HOST`` env variable when reading from s3 (:issue:`12198`) - A simple version of ``Panel.round()`` is now implemented (:issue:`11763`) - For Python 3.x, ``round(DataFrame)``, ``round(Series)``, ``round(Panel)`` will work (:issue:`11763`) -- ``DataFrame`` has gained a ``_repr_latex_`` method in order to allow for automatic conversion to latex in a ipython/jupyter notebook using nbconvert. Options ``display.latex.escape`` and ``display.latex.longtable`` have been added to the configuration and are used automatically by the ``to_latex`` method. (:issue:`11778`) - ``sys.getsizeof(obj)`` returns the memory usage of a pandas object, including the values it contains (:issue:`11597`) - ``Series`` gained an ``is_unique`` attribute (:issue:`11946`) diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 01a39583001c1..93a6968be8602 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -155,6 +155,13 @@ (default: False) """ +pc_latex_repr_doc = """ +: boolean + Whether to produce a latex DataFrame representation for jupyter + environments that support it. + (default: False) +""" + pc_line_width_deprecation_warning = """\ line_width has been deprecated, use display.width instead (currently both are identical) @@ -314,6 +321,8 @@ def mpl_style_cb(key): pc_east_asian_width_doc, validator=is_bool) cf.register_option('unicode.ambiguous_as_wide', False, pc_east_asian_width_doc, validator=is_bool) + cf.register_option('latex.repr', False, + pc_latex_repr_doc, validator=is_bool) cf.register_option('latex.escape', True, pc_latex_escape, validator=is_bool) cf.register_option('latex.longtable', False, pc_latex_longtable, diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 27e932cb54b95..324f30ed00bed 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -574,7 +574,10 @@ def _repr_latex_(self): Returns a LaTeX representation for a particular Dataframe. Mainly for use with nbconvert (jupyter notebook conversion to pdf). """ - return self.to_latex() + if get_option('display.latex.repr'): + return self.to_latex() + else: + return None @property def style(self): diff --git a/pandas/tests/frame/test_repr_info.py b/pandas/tests/frame/test_repr_info.py index 2dda4a37e6449..07446d32c55fb 100644 --- a/pandas/tests/frame/test_repr_info.py +++ b/pandas/tests/frame/test_repr_info.py @@ -186,10 +186,14 @@ def test_latex_repr(self): \bottomrule \end{tabular} """ - with option_context("display.latex.escape", False): + with option_context("display.latex.escape", False, + 'display.latex.repr', True): df = DataFrame([[r'$\alpha$', 'b', 'c'], [1, 2, 3]]) self.assertEqual(result, df._repr_latex_()) + # GH 12182 + self.assertIsNone(df._repr_latex_()) + def test_info(self): io = StringIO() self.frame.info(buf=io)
closes #12182
https://api.github.com/repos/pandas-dev/pandas/pulls/12275
2016-02-10T00:59:40Z
2016-02-10T18:51:40Z
null
2016-02-10T18:52:27Z
DEPR: remove order kw from .factorize(), xref #6930
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 8fa28fa422f64..c16947d16635a 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -787,6 +787,7 @@ Removal of prior version deprecations/changes - Removal of the ``read_frame`` and ``frame_query`` (both aliases for ``pd.read_sql``) and ``write_frame`` (alias of ``to_sql``) functions in the ``pd.io.sql`` namespace, deprecated since 0.14.0 (:issue:`6292`). +- Removal of the ``order`` keyword from ``.factorize()`` (:issue:`6930`) .. _whatsnew_0180.performance: diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index d516471ededb6..2c70da413bb8c 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -163,7 +163,6 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): Sequence sort : boolean, default False Sort by values - order : deprecated na_sentinel : int, default -1 Value to mark "not found" size_hint : hint to the hashtable sizer @@ -178,11 +177,6 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): note: an array of Periods will ignore sort as it returns an always sorted PeriodIndex """ - if order is not None: - msg = "order is deprecated. See " \ - "https://github.com/pydata/pandas/issues/6926" - warn(msg, FutureWarning, stacklevel=2) - from pandas import Index, Series, DatetimeIndex vals = np.asarray(values) diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 4b17736dd149a..ef3f6210f0c21 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -53,12 +53,6 @@ def test_strings(self): class TestFactorize(tm.TestCase): _multiprocess_can_split_ = True - def test_warn(self): - - s = Series([1, 2, 3]) - with tm.assert_produces_warning(FutureWarning): - algos.factorize(s, order='A') - def test_basic(self): labels, uniques = algos.factorize(['a', 'b', 'b', 'a', 'a', 'c', 'c',
xref #6930
https://api.github.com/repos/pandas-dev/pandas/pulls/12274
2016-02-10T00:30:58Z
2016-02-10T03:57:37Z
null
2016-02-10T03:58:21Z
Fix #12169 - Resample category data with timedelta index
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 421822380c2da..3b181bd874c19 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -796,3 +796,5 @@ Bug Fixes - Bug in ``.skew`` and ``.kurt`` due to roundoff error for highly similar values (:issue:`11974`) - Bug in ``buffer_rd_bytes`` src->buffer could be freed more than once if reading failed, causing a segfault (:issue:`12098`) + +- Bug in ``df.resample`` on categorical data with TimedeltaIndex (:issue:`12169`) diff --git a/pandas/core/series.py b/pandas/core/series.py index 68ae58737916b..a6cfacc369122 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -177,7 +177,8 @@ def __init__(self, data=None, index=None, dtype=None, name=None, default=np.nan) else: data = np.nan - elif isinstance(index, PeriodIndex): + # GH #12169 + elif isinstance(index, (PeriodIndex, TimedeltaIndex)): data = ([data.get(i, nan) for i in index] if data else np.nan) else: diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 6ae24bbccfa74..deaa0cd081fc0 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -519,6 +519,24 @@ def test_constructor_dict_multiindex(self): ser = ser.reindex(index=expected.index) check(ser, expected) + def test_constructor_dict_timedelta_index(self): + # GH #12169 : Resample category data with timedelta index + # construct Series from dict as data and TimedeltaIndex as index + # will result NaN in result Series data + expected = Series( + data=['A'] * 3, + index=pd.to_timedelta([0, 10, 20], unit='s') + ) + + result = Series( + data={pd.to_timedelta(0, unit='s'): 'A', + pd.to_timedelta(10, unit='s'): 'A', + pd.to_timedelta(20, unit='s'): 'A'}, + index=pd.to_timedelta([0, 10, 20], unit='s') + ) + # this should work + assert_series_equal(result, expected) + def test_constructor_subclass_dict(self): data = tm.TestSubDict((x, 10.0 * x) for x in range(10)) series = Series(data) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index c761a35649ba9..233d795015089 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -1147,6 +1147,18 @@ def test_resample_base_with_timedeltaindex(self): self.assertTrue(without_base.index.equals(exp_without_base)) self.assertTrue(with_base.index.equals(exp_with_base)) + def test_resample_categorical_data_with_timedeltaindex(self): + # GH #12169 + df = DataFrame({'Group_obj': 'A'}, + index=pd.to_timedelta(list(range(20)), unit='s')) + df['Group'] = df['Group_obj'].astype('category') + result = df.resample('10s').agg(lambda x: (x.value_counts().index[0])) + expected = DataFrame({'Group_obj': ['A', 'A'], + 'Group': ['A', 'A']}, + index=pd.to_timedelta([0, 10], unit='s')) + expected = expected.reindex_axis(['Group_obj', 'Group'], 1) + tm.assert_frame_equal(result, expected) + def test_resample_daily_anchored(self): rng = date_range('1/1/2000 0:00:00', periods=10000, freq='T') ts = Series(np.random.randn(len(rng)), index=rng)
closes #12169
https://api.github.com/repos/pandas-dev/pandas/pulls/12271
2016-02-09T17:42:12Z
2016-02-10T17:29:34Z
null
2016-02-10T23:06:42Z
Fix #12037 Error when Resampling using pd.tseries.offsets.Nano as period
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index ec002fae3b4b9..3c7ec83b959fe 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -888,3 +888,5 @@ Bug Fixes - Bug in ``.skew`` and ``.kurt`` due to roundoff error for highly similar values (:issue:`11974`) - Bug in ``buffer_rd_bytes`` src->buffer could be freed more than once if reading failed, causing a segfault (:issue:`12098`) + +- Bug in ``Series.resample`` using pd.tseries.offsets.Nano as period when the index is an DatetimeIndex and contains non-zero nanosecond part (:issue:`12037`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 4e7962686db59..a22f87cb90420 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -854,9 +854,14 @@ def _get_time_bins(self, ax): closed=self.closed, base=self.base) tz = ax.tz + # GH #12037 + # use first/last directly instead of call replace() on them + # because replace() will swallow the nanosecond part + # thus last bin maybe slightly before the end if the end contains + # nanosecond part and lead to `Values falls after last bin` error binner = labels = DatetimeIndex(freq=self.freq, - start=first.replace(tzinfo=None), - end=last.replace(tzinfo=None), + start=first, + end=last, tz=tz, name=ax.name) diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index c761a35649ba9..8d24ff9fa587e 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -1224,6 +1224,29 @@ def test_monthly_resample_error(self): # it works! ts.resample('M') + def test_nanosecond_resample_error(self): + # GH 12307 - Values falls after last bin when + # Resampling using pd.tseries.offsets.Nano as period + start = 1443707890427 + exp_start = 1443707890400 + indx = pd.date_range( + start=pd.to_datetime(start), + periods=10, + freq='100n' + ) + ts = pd.Series(range(len(indx)), index=indx) + r = ts.resample(pd.tseries.offsets.Nano(100)) + result = r.agg('mean') + + exp_indx = pd.date_range( + start=pd.to_datetime(exp_start), + periods=10, + freq='100n' + ) + exp = pd.Series(range(len(exp_indx)), index=exp_indx) + + assert_series_equal(result, exp) + def test_resample_anchored_intraday(self): # #1471, #1458
Closes #12037
https://api.github.com/repos/pandas-dev/pandas/pulls/12270
2016-02-09T17:02:22Z
2016-02-10T17:37:09Z
null
2016-02-10T23:06:41Z
Respect byteorder in StataReader
diff --git a/pandas/io/stata.py b/pandas/io/stata.py index a878829a60404..e5051aef9c4ea 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -1349,7 +1349,7 @@ def _read_strls(self): buf = buf[0:2] + buf[4:10] else: buf = buf[0:2] + buf[6:] - v_o = struct.unpack('Q', buf)[0] + v_o = struct.unpack(self.byteorder + 'Q', buf)[0] typ = struct.unpack('B', self.path_or_buf.read(1))[0] length = struct.unpack(self.byteorder + 'I', self.path_or_buf.read(4))[0]
Partially fix #11282 for s390x
https://api.github.com/repos/pandas-dev/pandas/pulls/12269
2016-02-09T14:08:21Z
2016-03-12T17:53:16Z
null
2016-03-12T17:53:16Z
added pd as namespace
diff --git a/doc/source/io.rst b/doc/source/io.rst index 36d4bd89261c4..15338392ad09e 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1399,7 +1399,7 @@ Writing to a file, with a date index and a date column dfj2['date'] = Timestamp('20130101') dfj2['ints'] = list(range(5)) dfj2['bools'] = True - dfj2.index = date_range('20130101', periods=5) + dfj2.index = pd.date_range('20130101', periods=5) dfj2.to_json('test.json') open('test.json').read() @@ -2553,7 +2553,7 @@ for some advanced strategies .. ipython:: python - store = HDFStore('store.h5') + store = pd.HDFStore('store.h5') print(store) Objects can be written to the file just like adding key-value pairs to a @@ -2562,7 +2562,7 @@ dict: .. ipython:: python np.random.seed(1234) - index = date_range('1/1/2000', periods=8) + index = pd.date_range('1/1/2000', periods=8) s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e']) df = DataFrame(randn(8, 3), index=index, columns=['A', 'B', 'C']) @@ -2611,7 +2611,7 @@ Closing a Store, Context Manager # Working with, and automatically closing the store with the context # manager - with HDFStore('store.h5') as store: + with pd.HDFStore('store.h5') as store: store.keys() .. ipython:: python @@ -2754,7 +2754,7 @@ enable ``put/append/to_hdf`` to by default store in the ``table`` format. .. ipython:: python - store = HDFStore('store.h5') + store = pd.HDFStore('store.h5') df1 = df[0:4] df2 = df[4:] @@ -2801,6 +2801,22 @@ everything in the sub-store and BELOW, so be *careful*. .. _io.hdf5-types: +.. warning:: Hierarchical keys cannot be retrieved as dotted (attribute) access as described above for items stored under root node. + + .. ipython:: python + + store.foo.bar.bah + AttributeError: 'HDFStore' object has no attribute 'foo' + + store.root.foo.bar.bah + /foo/bar/bah (Group) '' + children := ['block0_items' (Array), 'axis1' (Array), 'axis0' (Array), 'block0_values' (Array)] + + + + + + Storing Types ''''''''''''' @@ -3364,7 +3380,7 @@ Compression for all objects within the file .. code-block:: python - store_compressed = HDFStore('store_compressed.h5', complevel=9, complib='blosc') + store_compressed = pd.HDFStore('store_compressed.h5', complevel=9, complib='blosc') Or on-the-fly compression (this only applies to tables). You can turn off file compression for a specific table by passing ``complevel=0`` @@ -3573,7 +3589,7 @@ It is possible to write an ``HDFStore`` object that can easily be imported into index=range(100)) df_for_r.head() - store_export = HDFStore('export.h5') + store_export = pd.HDFStore('export.h5') store_export.append('df_for_r', df_for_r, data_columns=df_dc.columns) store_export @@ -3662,7 +3678,7 @@ number of options, please see the docstring. .. ipython:: python # a legacy store - legacy_store = HDFStore(legacy_file_path,'r') + legacy_store = pd.HDFStore(legacy_file_path,'r') legacy_store # copy (and return the new handle)
more consistent with code in the rest of the document
https://api.github.com/repos/pandas-dev/pandas/pulls/12268
2016-02-09T12:37:03Z
2016-03-13T16:16:59Z
null
2023-05-11T01:13:22Z
Do not raise error when reopening a read-only HDFStore.
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index cd8f7699a85d1..4ec3d7a48195b 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -546,23 +546,26 @@ def open(self, mode='a', **kwargs): raise except (ValueError) as e: + if 'is already opened, but in read-only mode' in str(e): + print('Reopening %s in read-only mode' % self._path) + self._handle = tables.open_file(self._path, 'r', **kwargs) + else: + # trap PyTables >= 3.1 FILE_OPEN_POLICY exception + # to provide an updated message + if 'FILE_OPEN_POLICY' in str(e): + e = ValueError( + "PyTables [{version}] no longer supports opening multiple " + "files\n" + "even in read-only mode on this HDF5 version " + "[{hdf_version}]. You can accept this\n" + "and not open the same file multiple times at once,\n" + "upgrade the HDF5 version, or downgrade to PyTables 3.0.0 " + "which allows\n" + "files to be opened multiple times at once\n" + .format(version=tables.__version__, + hdf_version=tables.get_hdf5_version())) - # trap PyTables >= 3.1 FILE_OPEN_POLICY exception - # to provide an updated message - if 'FILE_OPEN_POLICY' in str(e): - e = ValueError( - "PyTables [{version}] no longer supports opening multiple " - "files\n" - "even in read-only mode on this HDF5 version " - "[{hdf_version}]. You can accept this\n" - "and not open the same file multiple times at once,\n" - "upgrade the HDF5 version, or downgrade to PyTables 3.0.0 " - "which allows\n" - "files to be opened multiple times at once\n" - .format(version=tables.__version__, - hdf_version=tables.get_hdf5_version())) - - raise e + raise e except (Exception) as e: diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index b08d24747bcd3..ef8604eb42161 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -4779,6 +4779,19 @@ def test_read_hdf_open_store(self): self.assertTrue(store.is_open) store.close() + # GH12266 + with ensure_clean_path(self.path) as path: + store = HDFStore(path) + store.put('df', df) + store.close() + os.chmod(path, 0o400) + store1 = HDFStore(path) + store2 = HDFStore(path) + tm.assert_frame_equal(df, store1['df']) + tm.assert_frame_equal(df, store2['df']) + store1.close() + store2.close() + def test_read_hdf_iterator(self): df = DataFrame(np.random.rand(4, 5), index=list('abcd'),
closes #12266
https://api.github.com/repos/pandas-dev/pandas/pulls/12267
2016-02-09T10:58:43Z
2016-02-09T14:49:33Z
null
2017-03-08T17:01:59Z
PEP8: fix sql.py and test_sql.py
diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 072ca86600ea0..c29286016a34f 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -21,7 +21,6 @@ from pandas.core.base import PandasObject from pandas.core.dtypes import DatetimeTZDtype from pandas.tseries.tools import to_datetime -from pandas.util.decorators import Appender from contextlib import contextmanager diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index a5f6acc113466..c2d6f5af48388 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -2324,7 +2324,7 @@ def _check_roundtrip(self, frame): frame2['Idx'] = Index(lrange(len(frame2))) + 10 sql.to_sql(frame2, name='test_table2', con=self.conn, index=False) result = sql.read_sql("select * from test_table2", self.conn, - index_col='Idx') + index_col='Idx') expected = frame.copy() expected.index = Index(lrange(len(frame2))) + 10 expected.index.name = 'Idx' @@ -2672,7 +2672,7 @@ def _check_roundtrip(self, frame): sql.to_sql(frame2, name='test_table2', con=self.conn, flavor='mysql', index=False) result = sql.read_sql("select * from test_table2", self.conn, - index_col='Idx') + index_col='Idx') expected = frame.copy() # HACK! Change this once indexes are handled properly. @@ -2738,7 +2738,7 @@ def test_keyword_as_column_names(self): _skip_if_no_pymysql() df = DataFrame({'From': np.ones(5)}) sql.to_sql(df, con=self.conn, name='testkeywords', - if_exists='replace', flavor='mysql', index=False) + if_exists='replace', flavor='mysql', index=False) def test_if_exists(self): _skip_if_no_pymysql()
Master is failing due to my PR https://github.com/pydata/pandas/pull/12205 Strange that it did not fail on travis before merging ?
https://api.github.com/repos/pandas-dev/pandas/pulls/12263
2016-02-08T18:07:31Z
2016-02-08T21:13:52Z
null
2016-02-08T22:17:21Z
BUG: Make Styler ignore None index names to match to_html behaviour
diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb index 1881727001546..77813a03c704a 100644 --- a/doc/source/html-styling.ipynb +++ b/doc/source/html-styling.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -93,7 +93,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\">\n", + " <table id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -114,346 +114,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - 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" \n", - " 5.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -462,10 +358,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2c320>" + "<pandas.core.style.Styler at 0x10b1ba8d0>" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -497,7 +393,7 @@ " ' <style type=\"text/css\" >',\n", " ' ',\n", " ' ',\n", - " ' #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {',\n", + " ' #T_a45fea9e_c56b_11e5_893a_a45e60bd97fbrow0_col2 {',\n", " ' ',\n", " ' background-color: red;',\n", " ' ',\n", @@ -505,7 +401,7 @@ " ' ']" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -532,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -558,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -570,301 +466,301 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4 {\n", + " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -872,7 +768,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\">\n", + " <table id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -893,346 +789,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -1241,10 +1033,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c351d0>" + "<pandas.core.style.Styler at 0x111d9dc50>" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1274,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -1290,7 +1082,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -1302,31 +1094,31 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0 {\n", + " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: yellow;\n", " \n", @@ -1334,7 +1126,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\">\n", + " <table id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -1355,346 +1147,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -1703,10 +1391,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35160>" + "<pandas.core.style.Styler at 0x111dd4898>" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1724,7 +1412,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -1736,7 +1424,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", @@ -1744,7 +1432,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", @@ -1752,7 +1440,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", @@ -1760,7 +1448,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", @@ -1768,7 +1456,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", @@ -1776,7 +1464,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", @@ -1784,7 +1472,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", @@ -1792,7 +1480,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", @@ -1800,7 +1488,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", @@ -1808,7 +1496,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", @@ -1816,7 +1504,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", @@ -1824,7 +1512,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", @@ -1832,7 +1520,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", @@ -1840,7 +1528,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", @@ -1848,7 +1536,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", @@ -1856,7 +1544,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", @@ -1864,7 +1552,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", @@ -1872,7 +1560,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", @@ -1880,7 +1568,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", @@ -1888,7 +1576,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", @@ -1896,7 +1584,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", @@ -1904,7 +1592,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", @@ -1912,7 +1600,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", @@ -1920,7 +1608,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", @@ -1928,7 +1616,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", @@ -1936,7 +1624,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", @@ -1944,7 +1632,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", @@ -1952,7 +1640,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", @@ -1960,7 +1648,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -1968,7 +1656,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", @@ -1976,7 +1664,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", @@ -1984,7 +1672,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", @@ -1992,7 +1680,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", @@ -2000,7 +1688,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", @@ -2008,7 +1696,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", @@ -2016,7 +1704,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", @@ -2024,7 +1712,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", @@ -2032,7 +1720,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", @@ -2040,7 +1728,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", @@ -2048,7 +1736,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", @@ -2056,7 +1744,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", @@ -2064,7 +1752,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", @@ -2072,7 +1760,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", @@ -2080,7 +1768,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", @@ -2088,7 +1776,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", @@ -2096,7 +1784,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", @@ -2104,7 +1792,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", @@ -2112,7 +1800,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", @@ -2120,7 +1808,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", @@ -2128,7 +1816,7 @@ " \n", " }\n", " \n", - " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4 {\n", + " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -2138,7 +1826,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\">\n", + " <table id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -2159,346 +1847,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -2507,10 +2091,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2ce48>" + "<pandas.core.style.Styler at 0x111dc0dd8>" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -2537,7 +2121,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -2559,7 +2143,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -2571,7 +2155,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0 {\n", + " #T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: darkorange;\n", " \n", @@ -2579,7 +2163,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\">\n", + " <table id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -2600,346 +2184,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - 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" \n", - " 2\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -2948,10 +2428,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d278>" + "<pandas.core.style.Styler at 0x111dd4eb8>" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2999,7 +2479,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -3011,19 +2491,19 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2 {\n", + " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1 {\n", + " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3 {\n", + " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", @@ -3031,7 +2511,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\">\n", + " <table id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -3052,346 +2532,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " 0.104011\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - 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" \n", - " 1.392855\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -3400,10 +2776,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d438>" + "<pandas.core.style.Styler at 0x111dd4ac8>" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3421,7 +2797,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -3433,49 +2809,49 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3 {\n", + " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -3483,7 +2859,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\">\n", + " <table id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -3504,346 +2880,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.31628\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - 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" \n", - " 2\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - 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" \n", - " 0.104011\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -3852,10 +3124,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d4e0>" + "<pandas.core.style.Styler at 0x111daa668>" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -3882,19 +3154,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Builtin Styles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself." + "## Finer Control: Display Values\n", + "\n", + "We distinguish the *display* value from the *actual* value in `Styler`.\n", + "To control the display value, the text is printed in each cell, use `Styler.format`. Cells can be formatted according to a [format spec string](https://docs.python.org/3/library/string.html#format-specification-mini-language) or a callable that takes a single value and returns a string." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -3906,15 +3174,9 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: red;\n", - " \n", - " }\n", - " \n", " </style>\n", "\n", - " <table id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\">\n", + " <table id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -3935,346 +3197,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 100.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 132.92%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -31.63%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -99.08%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 200.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -107.08%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -143.87%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 56.44%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 29.57%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 300.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -162.64%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 21.96%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 67.88%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 188.93%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 400.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 96.15%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 10.40%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -48.12%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 85.02%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 500.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 145.34%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 105.77%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 16.56%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 51.50%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 600.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -133.69%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 56.29%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 139.29%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -6.33%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 700.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 12.17%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 120.76%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.20%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 162.78%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 800.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 35.45%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 103.75%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -38.57%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 51.98%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 900.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 168.66%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -132.60%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 142.90%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -208.94%\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 1000.00%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -12.98%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 63.15%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -58.65%\n", " \n", - " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 29.07%\n", " \n", " </tr>\n", " \n", @@ -4283,28 +3441,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35a90>" + "<pandas.core.style.Styler at 0x111dd45f8>" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.highlight_null(null_color='red')" + "df.style.format(\"{:.2%}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap." + "Use a dictionary to format specific columns." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -4316,669 +3474,265 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #e5ffe5;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1 {\n", - " \n", - " background-color: #188d18;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" None>\n", " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #e5ffe5;\n", + "\n", + " <thead>\n", " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3 {\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " background-color: #c7eec7;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4 {\n", + " </thead>\n", + " <tbody>\n", " \n", - " background-color: #a6dca6;\n", + " <tr>\n", + " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1000\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.32\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -100\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " +0.56\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " background-color: #ccf1cc;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1 {\n", - " \n", - " background-color: #c0eac0;\n", - " \n", - " }\n", - " \n", - 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" \n", - " background-color: #cff3cf;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4 {\n", - " \n", - " background-color: #4fac4f;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0 {\n", - " \n", - " background-color: #198e19;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1 {\n", - " \n", - " background-color: #008000;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2 {\n", - " \n", - " background-color: #dcfadc;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3 {\n", - " \n", - " background-color: #008000;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4 {\n", - " \n", - " background-color: #e5ffe5;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0 {\n", - " \n", - " background-color: #008000;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1 {\n", - " \n", - " background-color: #7ec67e;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2 {\n", - " \n", - " background-color: #319b31;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3 {\n", - " \n", - " background-color: #e5ffe5;\n", - " \n", - " }\n", - " \n", - " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4 {\n", - " \n", - " background-color: #5cb35c;\n", - " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\">\n", - " \n", - "\n", - " <thead>\n", - " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", - " \n", - " </thead>\n", - " <tbody>\n", + " <tr>\n", + " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -200\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " +0.68\n", + " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 1000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.48\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " +0.17\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -100\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " +1.39\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.39\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 2000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " +1.43\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -000\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.59\n", " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", - " \n", - " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -4987,33 +3741,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35828>" + "<pandas.core.style.Styler at 0x111dd4588>" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import seaborn as sns\n", - "\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "\n", - "s = df.style.background_gradient(cmap=cm)\n", - "s" + "df.style.format({'B': \"{:0<4.0f}\", 'D': '{:+.2f}'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still." + "Or pass in a callable (or dictionary of callables) for more flexible handling." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -5025,159 +3774,9 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1 {\n", - " \n", - " background-color: #e5e419;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3 {\n", - " \n", - " background-color: #46327e;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0 {\n", - " \n", - " background-color: #3b528b;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1 {\n", - " \n", - " background-color: #433e85;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3 {\n", - " \n", - " background-color: #bddf26;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4 {\n", - " \n", - " background-color: #25838e;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0 {\n", - " \n", - " background-color: #21918c;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2 {\n", - " \n", - " background-color: #35b779;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0 {\n", - " \n", - " background-color: #5ec962;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1 {\n", - " \n", - " background-color: #95d840;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2 {\n", - " \n", - " background-color: #26ad81;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4 {\n", - " \n", - " background-color: #2cb17e;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3 {\n", - " \n", - " background-color: #1f9e89;\n", - " \n", - " }\n", - " \n", - " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4 {\n", - " \n", - " background-color: #1f958b;\n", - " \n", - " }\n", - " \n", " </style>\n", "\n", - " <table id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\">\n", + " <table id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -5198,176 +3797,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " ±1.33\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " ±1.07\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " ±1.63\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " ±0.96\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " ±1.45\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " ±1.34\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " ±0.12\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " ±0.35\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " ±1.69\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " ±0.13\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", + " \n", + " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -5376,22 +4041,35 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c354a8>" + "<pandas.core.style.Styler at 0x111dd4c50>" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Uses the full color range\n", - "df.loc[:4].style.background_gradient(cmap='viridis')" + "df.style.format({\"B\": lambda x: \"±{:.2f}\".format(abs(x))})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Builtin Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -5403,212 +4081,18 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #31688e;\n", + " #T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col2 {\n", " \n", - " : ;\n", + " background-color: red;\n", " \n", " }\n", " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1 {\n", - " \n", - " background-color: #efe51c;\n", - " \n", - " : ;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" None>\n", " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " background-color: red;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3 {\n", - " \n", - " background-color: #277f8e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0 {\n", - " \n", - " background-color: #21918c;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1 {\n", - " \n", - " background-color: #25858e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3 {\n", - " \n", - " background-color: #d5e21a;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4 {\n", - " \n", - " background-color: #29af7f;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0 {\n", - " \n", - " background-color: #35b779;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2 {\n", - " \n", - " background-color: #6ccd5a;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0 {\n", - " \n", - " background-color: #90d743;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1 {\n", - " \n", - " background-color: #b8de29;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2 {\n", - " \n", - " background-color: #5ac864;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3 {\n", - " \n", - " background-color: #31688e;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4 {\n", - " \n", - " background-color: #63cb5f;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3 {\n", - " \n", - " background-color: #46c06f;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4 {\n", - " \n", - " background-color: #3bbb75;\n", - " \n", - " : ;\n", - " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\">\n", - " \n", - "\n", - " <thead>\n", + "\n", + " <thead>\n", " \n", " <tr>\n", " \n", @@ -5626,176 +4110,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -5804,32 +4354,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2c400>" + "<pandas.core.style.Styler at 0x111dd4d68>" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Compreess the color range\n", - "(df.loc[:4]\n", - " .style\n", - " .background_gradient(cmap='viridis', low=.5, high=0)\n", - " .highlight_null('red'))" + "df.style.highlight_null(null_color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can include \"bar charts\" in your DataFrame." + "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -5841,209 +4387,309 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col0 {\n", " \n", - " width: 10em;\n", + " background-color: #e5ffe5;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col1 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);\n", + " background-color: #188d18;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col2 {\n", " \n", - " width: 10em;\n", + " background-color: #e5ffe5;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col3 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);\n", + " background-color: #c7eec7;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col4 {\n", " \n", - " width: 10em;\n", + " background-color: #a6dca6;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col0 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);\n", + " background-color: #ccf1cc;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col1 {\n", " \n", - " width: 10em;\n", + " background-color: #c0eac0;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col2 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 16.77000113307442%, transparent 0%);\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col3 {\n", " \n", - " width: 10em;\n", + " background-color: #62b662;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col4 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 22.22222222222222%, transparent 0%);\n", + " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col0 {\n", " \n", - " width: 10em;\n", + " background-color: #b3e3b3;\n", " \n", - " height: 80%;\n", + " }\n", + " \n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col1 {\n", " \n", - " background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0 {\n", + " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col2 {\n", " \n", - " width: 10em;\n", + " background-color: #56af56;\n", " \n", - 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" <table id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\">\n", + " <table id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -6064,346 +4710,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -6412,28 +4954,33 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35dd8>" + "<pandas.core.style.Styler at 0x111dc0a58>" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.bar(subset=['A', 'B'], color='#d65f5f')" + "import seaborn as sns\n", + "\n", + "cm = sns.light_palette(\"green\", as_cmap=True)\n", + "\n", + "s = df.style.background_gradient(cmap=cm)\n", + "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "There's also `.highlight_min` and `.highlight_max`." + "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -6445,466 +4992,159 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: yellow;\n", + " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: yellow;\n", + " background-color: #e5e419;\n", " \n", " }\n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: yellow;\n", + " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 {\n", + " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col3 {\n", " \n", - 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" \n", - " -0.99081\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", - " \n", - " </tr>\n", - " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th class=\"col_heading level2 col0\">None\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <th class=\"blank\">\n", " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", - " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", - " \n", - " </tr>\n", + " </thead>\n", + " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", @@ -7273,28 +5299,22 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c35d30>" + "<pandas.core.style.Styler at 0x111d9dcc0>" ] }, - "execution_count": 21, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.highlight_min(axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `Styler.set_properties` when the style doesn't actually depend on the values." + "# Uses the full color range\n", + "df.loc[:4].style.background_gradient(cmap='viridis')" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -7306,509 +5326,599 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col0 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: black;\n", + " background-color: #efe51c;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col2 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #440154;\n", " \n", - " border-color: white;\n", + " background-color: red;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: black;\n", + " background-color: #277f8e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col4 {\n", " \n", - " color: lawngreen;\n", + " background-color: #31688e;\n", " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: black;\n", + " background-color: #21918c;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col1 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #25858e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col3 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #d5e21a;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: black;\n", + " background-color: #29af7f;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col0 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #35b779;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col2 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #6ccd5a;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col4 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: black;\n", + " background-color: #90d743;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col1 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #b8de29;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: black;\n", + " background-color: #5ac864;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col3 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #31688e;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: black;\n", + " background-color: #63cb5f;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col0 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col2 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #fde725;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: black;\n", + " background-color: #46c06f;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4 {\n", + " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col4 {\n", " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", + " background-color: #3bbb75;\n", " \n", - " border-color: white;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0 {\n", + " </style>\n", + "\n", + " <table id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" None>\n", + " \n", + "\n", + " <thead>\n", " \n", - " color: lawngreen;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " background-color: black;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " border-color: white;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - " color: lawngreen;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " background-color: black;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", - " border-color: white;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", + " \n", + " </tr>\n", " \n", - " }\n", + " <tr>\n", + " \n", + " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", + " \n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111daaba8>" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compreess the color range\n", + "(df.loc[:4]\n", + " .style\n", + " .background_gradient(cmap='viridis', low=.5, high=0)\n", + " .highlight_null('red'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can include \"bar charts\" in your DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2 {\n", + " \n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3 {\n", - " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4 {\n", - " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " }\n", - " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0 {\n", - " \n", - " color: lawngreen;\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", + " height: 80%;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 16.77000113307442%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 22.22222222222222%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0 {\n", - " \n", - " color: lawngreen;\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: black;\n", + " width: 10em;\n", " \n", - " border-color: white;\n", + " height: 80%;\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 33.333333333333336%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 78.1150827834652%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 44.44444444444444%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 92.96229618327422%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 55.55555555555556%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 8.737388253449494%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 66.66666666666667%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 52.764243600289866%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 77.77777777777777%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 59.79187201238315%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 88.88888888888889%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 100.00000000000001%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col0 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4 {\n", + " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col1 {\n", " \n", - " color: lawngreen;\n", + " width: 10em;\n", " \n", - " background-color: black;\n", + " height: 80%;\n", " \n", - " border-color: white;\n", + " background: linear-gradient(90deg,#d65f5f 45.17326030334935%, transparent 0%);\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\">\n", + " <table id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -7829,346 +5939,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -8177,37 +6183,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c2cf60>" + "<pandas.core.style.Styler at 0x111dc0d68>" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.set_properties(**{'background-color': 'black',\n", - " 'color': 'lawngreen',\n", - " 'border-color': 'white'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sharing Styles" + "df.style.bar(subset=['A', 'B'], color='#d65f5f')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with `df1.style.export`, and import it on the second DataFrame with `df1.style.set`" + "There's also `.highlight_min` and `.highlight_max`." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -8219,669 +6216,295 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col4 {\n", " \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1 {\n", - " \n", - " color: black;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col2 {\n", " \n", - " color: black;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col1 {\n", " \n", - " color: red;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col3 {\n", " \n", - " color: red;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0 {\n", + " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col0 {\n", " \n", - " color: black;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1 {\n", - " \n", - " color: red;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" None>\n", " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2 {\n", + "\n", + " <thead>\n", " \n", - " color: red;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3 {\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " color: black;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - 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" \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1 {\n", - " \n", - " color: red;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2 {\n", - " \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3 {\n", - " \n", - " color: red;\n", - " \n", - " }\n", - " \n", - " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4 {\n", - " \n", - " color: black;\n", - " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\">\n", - " \n", - "\n", - " <thead>\n", - " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - 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" \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", - " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", - " \n", - " </tr>\n", + " <tr>\n", + " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - 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" <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -8890,23 +6513,21 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d160>" + "<pandas.core.style.Styler at 0x111dd47f0>" ] }, - "execution_count": 23, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df2 = -df\n", - "style1 = df.style.applymap(color_negative_red)\n", - "style1" + "df.style.highlight_max(axis=0)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -8918,309 +6539,39 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col0 {\n", + " #T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow0_col0 {\n", " \n", - " color: red;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - 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" \n", - " -5.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " -1.453425\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " -1.057737\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " -0.165562\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " -0.515018\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " -6.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " 1.336936\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " -0.562861\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " -1.392855\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " 0.063328\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " -7.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " -0.121668\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " -1.207603\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " 0.00204\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " -1.627796\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " -8.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " -0.354493\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " -1.037528\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " 0.385684\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " -0.519818\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " -9.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " -1.686583\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " 1.325963\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " -1.428984\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " 2.089354\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " -10.0\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " 0.12982\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " -0.631523\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " 0.586538\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " -0.29072\n", - " \n", + " <td id=\"T_a4bf3f9e_c56b_11e5_be35_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -9589,65 +6836,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d198>" + "<pandas.core.style.Styler at 0x111dc0780>" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "style2 = df2.style\n", - "style2.use(style1.export())\n", - "style2" + "df.style.highlight_min(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that you're able share the styles even though they're data aware. The styles are re-evaluated on the new DataFrame they've been `use`d upon." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Other options\n", - "\n", - "You've seen a few methods for data-driven styling.\n", - "`Styler` also provides a few other options for styles that don't depend on the data.\n", - "\n", - "- precision\n", - "- captions\n", - "- table-wide styles\n", - "\n", - "Each of these can be specified in two ways:\n", - "\n", - "- A keyword argument to `pandas.core.Styler`\n", - "- A call to one of the `.set_` methods, e.g. `.set_caption`\n", - "\n", - "The best method to use depends on the context. Use the `Styler` constructor when building many styled DataFrames that should all share the same properties. For interactive use, the`.set_` methods are more convenient." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Precision" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can control the precision of floats using pandas' regular `display.precision` option." + "Use `Styler.set_properties` when the style doesn't actually depend on the values." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -9659,409 +6869,509 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col1 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col2 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col3 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col4 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col1 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col2 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col3 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col1 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col2 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col3 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " background-color: yellow;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - 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" #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col3 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col0 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col1 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - 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" color: black;\n", + " background-color: black;\n", " \n", - " background-color: yellow;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col1 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col2 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col3 {\n", " \n", - " color: red;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", - " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4 {\n", + " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col4 {\n", " \n", - " color: black;\n", + " background-color: black;\n", " \n", - " : ;\n", + " border-color: white;\n", + " \n", + " color: lawngreen;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\">\n", + " <table id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -10082,346 +7392,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.33\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.32\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.07\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.44\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.3\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.63\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.22\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.68\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.89\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.96\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.1\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.48\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.85\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.45\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.06\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.17\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.34\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.39\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.06\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.12\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.21\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.63\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.35\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.04\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.39\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.69\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.33\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.43\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.09\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.13\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.63\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.59\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29\n", - " \n", + " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -10430,32 +7636,37 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7dbe0>" + "<pandas.core.style.Styler at 0x111dd42b0>" ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "with pd.option_context('display.precision', 2):\n", - " html = (df.style\n", - " .applymap(color_negative_red)\n", - " .apply(highlight_max))\n", - "html" + "df.style.set_properties(**{'background-color': 'black',\n", + " 'color': 'lawngreen',\n", + " 'border-color': 'white'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or through a `set_precision` method." + "## Sharing Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with `df1.style.export`, and import it on the second DataFrame with `df1.style.set`" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -10467,409 +7678,309 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", - " background-color: yellow;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", - " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4 {\n", + " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", - " : ;\n", - " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\">\n", + " <table id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -10890,346 +8001,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.33\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.32\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.07\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.44\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.3\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.63\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.22\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.68\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.89\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.96\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.1\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.48\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.85\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.45\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.06\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.17\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.34\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.56\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.39\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.06\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.12\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.21\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.63\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.35\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.04\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.39\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.52\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.69\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.33\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.43\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.09\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.13\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.63\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.59\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", " \n", - " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29\n", - " \n", + " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", " \n", " </tr>\n", " \n", @@ -11238,45 +8245,23 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7dc18>" + "<pandas.core.style.Styler at 0x111daa828>" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style\\\n", - " .applymap(color_negative_red)\\\n", - " .apply(highlight_max)\\\n", - " .set_precision(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use `df.round(2).style` if you'd prefer to round from the start." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Captions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Regular table captions can be added in a few ways." + "df2 = -df\n", + "style1 = df.style.applymap(color_negative_red)\n", + "style1" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -11288,311 +8273,309 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #188d18;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: #c7eec7;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col4 {\n", " \n", - " background-color: #a6dca6;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #ccf1cc;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col1 {\n", " \n", - " background-color: #c0eac0;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col3 {\n", " \n", - " background-color: #62b662;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #5cb35c;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col0 {\n", " \n", - " background-color: #b3e3b3;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col2 {\n", " \n", - " background-color: #56af56;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: #56af56;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col4 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: #99d599;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col1 {\n", " \n", - " background-color: #329c32;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: #5fb55f;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col3 {\n", " \n", - " background-color: #daf9da;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: #3ba13b;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col0 {\n", " \n", - " background-color: #80c780;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: #108910;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col2 {\n", " \n", - " background-color: #0d870d;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: #90d090;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col4 {\n", " \n", - " background-color: #4fac4f;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col0 {\n", " \n", - " background-color: #66b866;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col1 {\n", " \n", - " background-color: #d2f4d2;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col2 {\n", " \n", - " background-color: #389f38;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col3 {\n", " \n", - " background-color: #048204;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col4 {\n", " \n", - " background-color: #70be70;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col0 {\n", " \n", - " background-color: #4daa4d;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col1 {\n", " \n", - " background-color: #6cbc6c;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col2 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col3 {\n", " \n", - " background-color: #a3daa3;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col4 {\n", " \n", - " background-color: #0e880e;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col0 {\n", " \n", - " background-color: #329c32;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col1 {\n", " \n", - " background-color: #5cb35c;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col2 {\n", " \n", - " background-color: #0e880e;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col3 {\n", " \n", - " background-color: #cff3cf;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col4 {\n", " \n", - " background-color: #4fac4f;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col0 {\n", " \n", - " background-color: #198e19;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col1 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col2 {\n", " \n", - " background-color: #dcfadc;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col4 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: #008000;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #7ec67e;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col2 {\n", " \n", - " background-color: #319b31;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #e5ffe5;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4 {\n", + " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: #5cb35c;\n", + " color: red;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\">\n", - " \n", - " <caption>Colormaps, with a caption.</caption>\n", + " <table id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -11613,346 +8596,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - 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" \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " 0.31628\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " 0.99081\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " -2\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - 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" <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " -1.88927\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " -4\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " -0.961538\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - 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" \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " -1.39285\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " 0.063328\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " -7\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - 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" <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " -0.519818\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " -9\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " -1.68658\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " 1.32596\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " -1.42898\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " 2.08935\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " -10\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " 0.12982\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " -0.631523\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " 0.586538\n", " \n", - " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " -0.29072\n", " \n", " </tr>\n", " \n", @@ -11961,38 +8840,65 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7d828>" + "<pandas.core.style.Styler at 0x111dc0390>" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.set_caption('Colormaps, with a caption.')\\\n", - " .background_gradient(cmap=cm)" + "style2 = df2.style\n", + "style2.use(style1.export())\n", + "style2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Table Styles" + "Notice that you're able share the styles even though they're data aware. The styles are re-evaluated on the new DataFrame they've been `use`d upon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The next option you have are \"table styles\".\n", - "These are styles that apply to the table as a whole, but don't look at the data.\n", - "Certain sytlings, including pseudo-selectors like `:hover` can only be used this way." + "## Other options\n", + "\n", + "You've seen a few methods for data-driven styling.\n", + "`Styler` also provides a few other options for styles that don't depend on the data.\n", + "\n", + "- precision\n", + "- captions\n", + "- table-wide styles\n", + "\n", + "Each of these can be specified in two ways:\n", + "\n", + "- A keyword argument to `pandas.core.Styler`\n", + "- A call to one of the `.set_` methods, e.g. `.set_caption`\n", + "\n", + "The best method to use depends on the context. Use the `Styler` constructor when building many styled DataFrames that should all share the same properties. For interactive use, the`.set_` methods are more convenient." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Precision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can control the precision of floats using pandas' regular `display.precision` option." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -12003,795 +8909,410 @@ "\n", " <style type=\"text/css\" >\n", " \n", - " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb tr:hover {\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #ffff99;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb th {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col1 {\n", " \n", - " font-size: 150%;\n", + " color: black;\n", " \n", - " text-align: center;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb caption {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col2 {\n", " \n", - " caption-side: bottom;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col3 {\n", + " \n", + " color: red;\n", + " \n", + " : ;\n", + " \n", + " }\n", " \n", - " </style>\n", - "\n", - " <table id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\">\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col4 {\n", + " \n", + " color: red;\n", + " \n", + " : ;\n", + " \n", + " }\n", " \n", - " <caption>Hover to highlight.</caption>\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col0 {\n", + " \n", + " color: black;\n", + " \n", + " : ;\n", + " \n", + " }\n", " \n", - "\n", - " <thead>\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col1 {\n", " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", + " color: red;\n", " \n", - " </thead>\n", - " <tbody>\n", + " : ;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col2 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", - " \n", - " </tr>\n", + " color: red;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", - " \n", - " </tr>\n", + " : ;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col3 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", - " \n", - " </tr>\n", + " color: black;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - 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" \n", - " 0.519818\n", - " \n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col4 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", - " \n", - " </tr>\n", + " color: black;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", - " \n", - " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", - " \n", - " </tr>\n", + " : ;\n", " \n", - " </tbody>\n", - " </table>\n", - " " - ], - "text/plain": [ - "<pandas.core.style.Styler at 0x114c42710>" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import HTML\n", - "\n", - "def hover(hover_color=\"#ffff99\"):\n", - " return dict(selector=\"tr:hover\",\n", - " props=[(\"background-color\", \"%s\" % hover_color)])\n", - "\n", - "styles = [\n", - " hover(),\n", - " dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n", - " (\"text-align\", \"center\")]),\n", - " dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])\n", - "]\n", - "html = (df.style.set_table_styles(styles)\n", - " .set_caption(\"Hover to highlight.\"))\n", - "html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`table_styles` should be a list of dictionaries.\n", - "Each dictionary should have the `selector` and `props` keys.\n", - "The value for `selector` should be a valid CSS selector.\n", - "Recall that all the styles are already attached to an `id`, unique to\n", - "each `Styler`. This selector is in addition to that `id`.\n", - "The value for `props` should be a list of tuples of `('attribute', 'value')`.\n", - "\n", - "`table_styles` are extremely flexible, but not as fun to type out by hand.\n", - "We hope to collect some useful ones either in pandas, or preferable in a new package that [builds on top](#Extensibility) the tools here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Limitations\n", - "\n", - "- DataFrame only `(use Series.to_frame().style)`\n", - "- The index and columns must be unique\n", - "- No large repr, and performance isn't great; this is intended for summary DataFrames\n", - "- You can only style the *values*, not the index or columns\n", - "- You can only apply styles, you can't insert new HTML entities\n", - "\n", - "Some of these will be addressed in the future.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Terms\n", - "\n", - "- Style function: a function that's passed into `Styler.apply` or `Styler.applymap` and returns values like `'css attribute: value'`\n", - "- Builtin style functions: style functions that are methods on `Styler`\n", - "- table style: a dictionary with the two keys `selector` and `props`. `selector` is the CSS selector that `props` will apply to. `props` is a list of `(attribute, value)` tuples. A list of table styles passed into `Styler`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fun stuff\n", - "\n", - "Here are a few interesting examples.\n", - "\n", - "`Styler` interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " <style type=\"text/css\" >\n", - " \n", + " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col0 {\n", " \n", - " background-color: #557e79;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #779894;\n", + " color: red;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col2 {\n", " \n", - " background-color: #557e79;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - 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" background-color: #557e79;\n", + " color: black;\n", + " \n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #e7eeed;\n", + " color: red;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col2 {\n", " \n", - " background-color: #9bb4b0;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #557e79;\n", + " color: red;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4 {\n", + " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: #d7e2e0;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\">\n", + " <table id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" None>\n", " \n", "\n", " <thead>\n", @@ -12812,346 +9333,242 @@ " \n", " </tr>\n", " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", - " \n", - " 0\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 1.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.329212\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " nan\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.31628\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.32\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.99081\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " \n", - " 1\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " 2.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " -1.070816\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.438713\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " 0.564417\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.56\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 0.295722\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.3\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " \n", - " 2\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " 3.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " -1.626404\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " 0.219565\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.22\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.678805\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.68\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 1.889273\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.9\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " \n", - " 3\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " \n", - " 4.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " \n", - " 0.961538\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.96\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " \n", - " 0.104011\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " \n", - " -0.481165\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.48\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " \n", - " 0.850229\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.85\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " \n", - " 4\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " 5.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 1.453425\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.5\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " 1.057737\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " 0.165562\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.17\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 0.515018\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.52\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " \n", - " 5\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " 6.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " -1.336936\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " 0.562861\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.56\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " 1.392855\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " -0.063328\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " \n", - " 6\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " 7.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 0.121668\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.12\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " 1.207603\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -0.00204\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.002\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 1.627796\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " \n", - " 7\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " 8.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 0.354493\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.35\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " 1.037528\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.385684\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.39\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 0.519818\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.52\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " \n", - " 8\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 9.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 1.686583\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.7\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -1.325963\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.3\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " 1.428984\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.4\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " -2.089354\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.1\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " \n", - " 9\n", - " \n", + " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 10.0\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " -0.12982\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.13\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " 0.631523\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.63\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.586538\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.59\n", " \n", - " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 0.29072\n", - " \n", + " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29\n", " \n", " </tr>\n", " \n", @@ -13160,47 +9577,32 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x113487dd8>" + "<pandas.core.style.Styler at 0x111e0a160>" ] }, + "execution_count": 26, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "from IPython.html import widgets\n", - "@widgets.interact\n", - "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n", - " return df.style.background_gradient(\n", - " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n", - " as_cmap=True)\n", - " )" + "with pd.option_context('display.precision', 2):\n", + " html = (df.style\n", + " .applymap(color_negative_red)\n", + " .apply(highlight_max))\n", + "html" ] }, { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "def magnify():\n", - " return [dict(selector=\"th\",\n", - " props=[(\"font-size\", \"4pt\")]),\n", - " dict(selector=\"td\",\n", - " props=[('padding', \"0em 0em\")]),\n", - " dict(selector=\"th:hover\",\n", - " props=[(\"font-size\", \"12pt\")]),\n", - " dict(selector=\"tr:hover td:hover\",\n", - " props=[('max-width', '200px'),\n", - " ('font-size', '12pt')])\n", - "]" + "Or through a `set_precision` method." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -13211,1376 +9613,2365 @@ "\n", " <style type=\"text/css\" >\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th {\n", + " \n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col0 {\n", " \n", - " font-size: 4pt;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb td {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col1 {\n", " \n", - " padding: 0em 0em;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th:hover {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col2 {\n", " \n", - " font-size: 12pt;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb tr:hover td:hover {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col3 {\n", " \n", - " max-width: 200px;\n", + " color: red;\n", " \n", - " font-size: 12pt;\n", + " : ;\n", " \n", " }\n", " \n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: #ecf2f8;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col4 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #b8cce5;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2 {\n", - " \n", - " background-color: #efb1be;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col1 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #f3c2cc;\n", - " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4 {\n", - " \n", - " background-color: #eeaab7;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col3 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #f8dce2;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6 {\n", - " \n", - " background-color: #f2c1cb;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col0 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #83a6d2;\n", - " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8 {\n", - " \n", - " background-color: #de5f79;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col2 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: #c3d4e9;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10 {\n", - " \n", - " background-color: #eeacba;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: #f7dae0;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12 {\n", - " \n", - " background-color: #6f98ca;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col1 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: #e890a1;\n", - " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14 {\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col3 {\n", " \n", - " background-color: #f2f2f2;\n", + " color: red;\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15 {\n", - " \n", - " background-color: #ea96a7;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16 {\n", - " \n", - " background-color: #adc4e1;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - 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" \n", - " background-color: #f3c2cc;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col3 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11 {\n", - " \n", - " background-color: #f8dfe4;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12 {\n", - " \n", - " background-color: #b0c6e2;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col0 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13 {\n", - " \n", - " background-color: #ec9faf;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col1 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - 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" \n", - " background-color: #efb1be;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col0 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18 {\n", - " \n", - " background-color: #f2f2f2;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col1 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19 {\n", - " \n", - " background-color: #f8dce2;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col2 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20 {\n", - " \n", - " background-color: #a0bbdc;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col3 {\n", " \n", - " max-width: 80px;\n", + " color: red;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21 {\n", - " \n", - " background-color: #d73c5b;\n", + " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col4 {\n", " \n", - " max-width: 80px;\n", + " color: black;\n", " \n", - " font-size: 1pt;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22 {\n", - " \n", - " background-color: #86a8d3;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" None>\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23 {\n", + "\n", + " <thead>\n", " \n", - " background-color: #d9e4f1;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.3\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.32\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99\n", + " \n", + " </tr>\n", " \n", - " background-color: #ecf2f8;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.56\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.3\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.22\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.68\n", + " \n", + " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.9\n", + " \n", + " </tr>\n", " \n", - 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You can always use `df.round(2).style` if you'd prefer to round from the start." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Captions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Regular table captions can be added in a few ways." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", + " \n", + " \n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col0 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2 {\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #188d18;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col2 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - 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" \n", - " background-color: #e58195;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col0 {\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7 {\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #4479bb;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #7ec67e;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8 {\n", - " \n", - " background-color: #f2c1cb;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col2 {\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #319b31;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9 {\n", - " \n", - " background-color: #b1c7e2;\n", - " \n", - " max-width: 80px;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col3 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10 {\n", - " \n", - " background-color: #d4e0ef;\n", + " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col4 {\n", " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11 {\n", - " \n", - " background-color: #c1d3e8;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" None>\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12 {\n", - " \n", - " background-color: #f2f2f2;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", + " <caption>Colormaps, with a caption.</caption>\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13 {\n", - " \n", - " background-color: #cedced;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + "\n", + " <thead>\n", " \n", - 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" }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " background-color: #eeacba;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", - 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" }\n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111e0add8>" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.style.set_caption('Colormaps, with a caption.')\\\n", + " .background_gradient(cmap=cm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Table Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next option you have are \"table styles\".\n", + "These are styles that apply to the table as a whole, but don't look at the data.\n", + "Certain sytlings, including pseudo-selectors like `:hover` can only be used this way." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19 {\n", - " \n", - " background-color: #f5ccd4;\n", + " #T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb tr:hover {\n", " \n", - 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" }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", " \n", - " background-color: #f4c9d2;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", + " \n", + " </tr>\n", " \n", - " background-color: #f5ced6;\n", + " <tr>\n", + " \n", + " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", + " \n", + " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111e12f28>" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "\n", + "def hover(hover_color=\"#ffff99\"):\n", + " return dict(selector=\"tr:hover\",\n", + " props=[(\"background-color\", \"%s\" % hover_color)])\n", + "\n", + "styles = [\n", + " hover(),\n", + " dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n", + " (\"text-align\", \"center\")]),\n", + " dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])\n", + "]\n", + "html = (df.style.set_table_styles(styles)\n", + " .set_caption(\"Hover to highlight.\"))\n", + "html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`table_styles` should be a list of dictionaries.\n", + "Each dictionary should have the `selector` and `props` keys.\n", + "The value for `selector` should be a valid CSS selector.\n", + "Recall that all the styles are already attached to an `id`, unique to\n", + "each `Styler`. This selector is in addition to that `id`.\n", + "The value for `props` should be a list of tuples of `('attribute', 'value')`.\n", + "\n", + "`table_styles` are extremely flexible, but not as fun to type out by hand.\n", + "We hope to collect some useful ones either in pandas, or preferable in a new package that [builds on top](#Extensibility) the tools here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Limitations\n", + "\n", + "- DataFrame only `(use Series.to_frame().style)`\n", + "- The index and columns must be unique\n", + "- No large repr, and performance isn't great; this is intended for summary DataFrames\n", + "- You can only style the *values*, not the index or columns\n", + "- You can only apply styles, you can't insert new HTML entities\n", + "\n", + "Some of these will be addressed in the future.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Terms\n", + "\n", + "- Style function: a function that's passed into `Styler.apply` or `Styler.applymap` and returns values like `'css attribute: value'`\n", + "- Builtin style functions: style functions that are methods on `Styler`\n", + "- table style: a dictionary with the two keys `selector` and `props`. `selector` is the CSS selector that `props` will apply to. `props` is a list of `(attribute, value)` tuples. A list of table styles passed into `Styler`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fun stuff\n", + "\n", + "Here are a few interesting examples.\n", + "\n", + "`Styler` interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", + " \n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col0 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #779894;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col2 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5 {\n", + " 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#T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #fbeaed;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col4 {\n", " \n", - " background-color: #6490c6;\n", + " background-color: #557e79;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col0 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #e7eeed;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col2 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #9bb4b0;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3 {\n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #557e79;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col4 {\n", " \n", - " font-size: 1pt;\n", + " background-color: #d7e2e0;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4 {\n", + " </style>\n", + "\n", + " <table id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" None>\n", + " \n", + "\n", + " <thead>\n", " \n", - " background-color: #4479bb;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " </thead>\n", + " <tbody>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 1\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1.32921\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " nan\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.31628\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.99081\n", + " \n", + " </tr>\n", " \n", - " background-color: #fae6ea;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " 2\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " -1.07082\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.43871\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " 0.564417\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 0.295722\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " 3\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " -1.6264\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " 0.219565\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.678805\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 1.88927\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 0.961538\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " 0.104011\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " -0.481165\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 0.850229\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6 {\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " 5\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 1.45342\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " 1.05774\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " 0.165562\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 0.515018\n", + " \n", + " </tr>\n", " \n", - " background-color: #e68598;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " 5\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " 6\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " -1.33694\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " 0.562861\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " 1.39285\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " -0.063328\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " 6\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " 7\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 0.121668\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " 1.2076\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -0.00204021\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 1.6278\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " 8\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 0.354493\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " 1.03753\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.385684\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 0.519818\n", + " \n", + " </tr>\n", " \n", - " }\n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 9\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 1.68658\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -1.32596\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " 1.42898\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " -2.08935\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 10\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " -0.12982\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " 0.631523\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.586538\n", + " \n", + " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 0.29072\n", + " \n", + " </tr>\n", + " \n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111dc08d0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.html import widgets\n", + "@widgets.interact\n", + "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n", + " return df.style.background_gradient(\n", + " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n", + " as_cmap=True)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def magnify():\n", + " return [dict(selector=\"th\",\n", + " props=[(\"font-size\", \"4pt\")]),\n", + " dict(selector=\"td\",\n", + " props=[('padding', \"0em 0em\")]),\n", + " dict(selector=\"th:hover\",\n", + " props=[(\"font-size\", \"12pt\")]),\n", + " dict(selector=\"tr:hover td:hover\",\n", + " props=[('max-width', '200px'),\n", + " ('font-size', '12pt')])\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb th {\n", " \n", - " background-color: #6d96ca;\n", + " font-size: 4pt;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb td {\n", " \n", - " font-size: 1pt;\n", + " padding: 0em 0em;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb th:hover {\n", " \n", - " background-color: #f9e3e7;\n", + " font-size: 12pt;\n", " \n", - " max-width: 80px;\n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb tr:hover td:hover {\n", " \n", - " font-size: 1pt;\n", + " max-width: 200px;\n", + " \n", + " font-size: 12pt;\n", " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9 {\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #ecf2f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -14588,9 +11979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #b8cce5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14598,9 +11989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: #e0e9f4;\n", + " background-color: #efb1be;\n", " \n", " max-width: 80px;\n", " \n", @@ -14608,9 +11999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -14618,9 +12009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col4 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #eeaab7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14628,9 +12019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col5 {\n", " \n", - " background-color: #e47a90;\n", + " background-color: #f8dce2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14638,9 +12029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col6 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14648,9 +12039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col7 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #83a6d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14658,9 +12049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col8 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #de5f79;\n", " \n", " max-width: 80px;\n", " \n", @@ -14668,9 +12059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col9 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #c3d4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -14678,9 +12069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col10 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -14688,9 +12079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col11 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -14698,9 +12089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -14708,9 +12099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col13 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #e890a1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14718,9 +12109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col14 {\n", " \n", - " background-color: #a3bddd;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14728,9 +12119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col15 {\n", " \n", - " background-color: #7199cb;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14738,9 +12129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col16 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #adc4e1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14748,9 +12139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col17 {\n", " \n", - " background-color: #4e80be;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14758,9 +12149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col18 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #b7cbe5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14768,9 +12159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -14778,9 +12169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col20 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #6590c7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14788,9 +12179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col21 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -14798,9 +12189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col22 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14808,9 +12199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col23 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #cfddee;\n", " \n", " max-width: 80px;\n", " \n", @@ -14818,9 +12209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col24 {\n", " \n", - " background-color: #f2c1cb;\n", + " background-color: #e58094;\n", " \n", " max-width: 80px;\n", " \n", @@ -14828,9 +12219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e4798e;\n", " \n", " max-width: 80px;\n", " \n", @@ -14838,9 +12229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col1 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #aec5e1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14848,9 +12239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #c5d5ea;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -14858,9 +12249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col3 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #ec9faf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14868,9 +12259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -14878,9 +12269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col5 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #f9e0e5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14888,9 +12279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col6 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #db4f6b;\n", " \n", " max-width: 80px;\n", " \n", @@ -14898,9 +12289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col7 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14908,9 +12299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col8 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #e57f93;\n", " \n", " max-width: 80px;\n", " \n", @@ -14918,9 +12309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col9 {\n", " \n", - " background-color: #d8e3f1;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14928,9 +12319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col10 {\n", " \n", - " background-color: #eb9bab;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -14938,9 +12329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col11 {\n", " \n", - " background-color: #a3bddd;\n", + " background-color: #f8dfe4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14948,9 +12339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #b0c6e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14958,9 +12349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col13 {\n", " \n", - " background-color: #81a4d1;\n", + " background-color: #ec9faf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14968,9 +12359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col14 {\n", " \n", - " background-color: #95b3d8;\n", + " background-color: #e27389;\n", " \n", " max-width: 80px;\n", " \n", @@ -14978,9 +12369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col15 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #eb9dac;\n", " \n", " max-width: 80px;\n", " \n", @@ -14988,9 +12379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col16 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f1b8c3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14998,9 +12389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col17 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #efb1be;\n", " \n", " max-width: 80px;\n", " \n", @@ -15008,9 +12399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col18 {\n", " \n", - " background-color: #f2bdc8;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15018,9 +12409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f8dce2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15028,9 +12419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col20 {\n", " \n", - " background-color: #5686c1;\n", + " background-color: #a0bbdc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15038,9 +12429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col21 {\n", " \n", - " background-color: #f0b5c1;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15048,9 +12439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col22 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #86a8d3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15058,9 +12449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col23 {\n", " \n", - " background-color: #628fc6;\n", + " background-color: #d9e4f1;\n", " \n", " max-width: 80px;\n", " \n", @@ -15068,9 +12459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col24 {\n", " \n", - " background-color: #e68396;\n", + " background-color: #ecf2f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15078,9 +12469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col0 {\n", " \n", - " background-color: #eda7b5;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15088,9 +12479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #5887c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15098,9 +12489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col2 {\n", " \n", - " background-color: #e8eff7;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15108,9 +12499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: #eaf0f7;\n", + " background-color: #c7d7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15118,9 +12509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col4 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #82a5d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -15128,9 +12519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col5 {\n", " \n", - " background-color: #de5c76;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15138,9 +12529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col6 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e78a9d;\n", " \n", " max-width: 80px;\n", " \n", @@ -15148,9 +12539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col7 {\n", " \n", - " background-color: #dd5671;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15158,9 +12549,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col8 {\n", " \n", - " background-color: #e993a4;\n", + " background-color: #f1bbc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15168,9 +12559,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col9 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #779dcd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15178,9 +12569,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col10 {\n", " \n", - " background-color: #e991a3;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -15188,9 +12579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col11 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15198,9 +12589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #e0e9f4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15208,9 +12599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col13 {\n", " \n", - " background-color: #a0bbdc;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15218,9 +12609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col14 {\n", " \n", - " background-color: #96b4d9;\n", + " background-color: #e78a9d;\n", " \n", " max-width: 80px;\n", " \n", @@ -15228,9 +12619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col15 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15238,9 +12629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col16 {\n", " \n", - " background-color: #d3dfef;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15248,9 +12639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col17 {\n", " \n", - " background-color: #487cbc;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15258,9 +12649,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col18 {\n", " \n", - " background-color: #ea9aaa;\n", + " background-color: #b9cde6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15268,9 +12659,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col19 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15278,9 +12669,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #a0bbdc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15288,9 +12679,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col21 {\n", " \n", - " background-color: #eb9ead;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15298,9 +12689,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col22 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #618ec5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15308,9 +12699,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col23 {\n", " \n", - " background-color: #4d7fbe;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15318,9 +12709,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col24 {\n", " \n", - " background-color: #e994a5;\n", + " background-color: #c3d4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -15328,7 +12719,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col0 {\n", " \n", " background-color: #f6d5db;\n", " \n", @@ -15338,9 +12729,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col1 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #5384c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -15348,9 +12739,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: #d5e1f0;\n", + " background-color: #f1bbc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15358,9 +12749,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col3 {\n", " \n", - " background-color: #f0b4c0;\n", + " background-color: #c7d7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15368,9 +12759,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15378,9 +12769,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col5 {\n", " \n", - " background-color: #da4966;\n", + " background-color: #e7eef6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15388,9 +12779,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col6 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e58195;\n", " \n", " max-width: 80px;\n", " \n", @@ -15398,9 +12789,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15408,9 +12799,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col8 {\n", " \n", - " background-color: #ea96a7;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15418,9 +12809,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col9 {\n", " \n", - " background-color: #ecf2f8;\n", + " background-color: #b1c7e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15428,9 +12819,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col10 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -15438,9 +12829,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col11 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #c1d3e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15448,9 +12839,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col12 {\n", " \n", - " background-color: #dc516d;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15458,9 +12849,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col13 {\n", " \n", - " background-color: #91b0d7;\n", + " background-color: #cedced;\n", " \n", " max-width: 80px;\n", " \n", @@ -15468,9 +12859,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col14 {\n", " \n", - " background-color: #a8c0df;\n", + " background-color: #e7899c;\n", " \n", " max-width: 80px;\n", " \n", @@ -15478,9 +12869,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col15 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -15488,9 +12879,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col16 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #e2eaf4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15498,9 +12889,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col17 {\n", " \n", - " background-color: #6590c7;\n", + " background-color: #f7d6dd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15508,9 +12899,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col18 {\n", " \n", - " background-color: #ea99a9;\n", + " background-color: #a8c0df;\n", " \n", " max-width: 80px;\n", " \n", @@ -15518,9 +12909,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15528,9 +12919,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #b7cbe5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15538,9 +12929,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col21 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15548,9 +12939,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col22 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #739acc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15558,9 +12949,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col23 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #8dadd5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15568,9 +12959,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col24 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #cddbed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15578,9 +12969,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col0 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -15588,9 +12979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #5887c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15598,9 +12989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col2 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15608,9 +12999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15618,9 +13009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15628,9 +13019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col5 {\n", " \n", - " background-color: #e27389;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -15638,9 +13029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col6 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #e78c9e;\n", " \n", " max-width: 80px;\n", " \n", @@ -15648,9 +13039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #5182bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15658,9 +13049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col8 {\n", " \n", - " background-color: #ea9aaa;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15668,9 +13059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col9 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #c1d3e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15678,9 +13069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col10 {\n", " \n", - " background-color: #e993a4;\n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15688,9 +13079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col11 {\n", " \n", - " background-color: #b9cde6;\n", + " background-color: #c9d8eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15698,9 +13089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col12 {\n", " \n", - " background-color: #de5f79;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15708,9 +13099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col13 {\n", " \n", - " background-color: #b3c9e3;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15718,9 +13109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col14 {\n", " \n", - " background-color: #9fbadc;\n", + " background-color: #e47c91;\n", " \n", " max-width: 80px;\n", " \n", @@ -15728,9 +13119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col15 {\n", " \n", - " background-color: #6f98ca;\n", + " background-color: #eda8b6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15738,9 +13129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col16 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -15748,9 +13139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col17 {\n", " \n", - " background-color: #6f98ca;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15758,9 +13149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col18 {\n", " \n", - " background-color: #ea96a7;\n", + " background-color: #b3c9e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15768,9 +13159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col19 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15778,9 +13169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15788,9 +13179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col21 {\n", " \n", - " background-color: #f0b7c2;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15798,9 +13189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col22 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #7da2cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15808,9 +13199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col23 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15818,9 +13209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col24 {\n", " \n", - " background-color: #f2bdc8;\n", + " background-color: #a2bcdd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15828,9 +13219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col0 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15838,9 +13229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col1 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #6490c6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15848,9 +13239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col2 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15858,9 +13249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col3 {\n", " \n", - " background-color: #efb1be;\n", + " background-color: #f3c6cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15868,9 +13259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col4 {\n", " \n", - " background-color: #eaf0f7;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15878,9 +13269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col5 {\n", " \n", - " background-color: #e0657d;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -15888,9 +13279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col6 {\n", " \n", - " background-color: #eca1b0;\n", + " background-color: #e68598;\n", " \n", " max-width: 80px;\n", " \n", @@ -15898,9 +13289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6d96ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15908,9 +13299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col8 {\n", " \n", - " background-color: #e27087;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15918,9 +13309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col9 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15928,9 +13319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col10 {\n", " \n", - " background-color: #e68699;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15938,9 +13329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col11 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #e0e9f4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15948,9 +13339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15958,9 +13349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col13 {\n", " \n", - " background-color: #d1deee;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15968,9 +13359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col14 {\n", " \n", - " background-color: #82a5d1;\n", + " background-color: #e47a90;\n", " \n", " max-width: 80px;\n", " \n", @@ -15978,9 +13369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col15 {\n", " \n", - " background-color: #7099cb;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15988,9 +13379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col16 {\n", " \n", - " background-color: #a9c1e0;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -15998,9 +13389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col17 {\n", " \n", - " background-color: #6892c8;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -16008,9 +13399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col18 {\n", " \n", - " background-color: #f7d6dd;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -16018,9 +13409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f6d2d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16028,9 +13419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col20 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #b5cae4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16038,9 +13429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col21 {\n", " \n", - " background-color: #e4ecf5;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16048,9 +13439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col22 {\n", " \n", - " background-color: #d8e3f1;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -16058,9 +13449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col23 {\n", " \n", - " background-color: #477bbc;\n", + " background-color: #a3bddd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16068,9 +13459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col24 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #7199cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16078,9 +13469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col0 {\n", " \n", - " background-color: #e7eef6;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -16088,9 +13479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col1 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #4e80be;\n", " \n", " max-width: 80px;\n", " \n", @@ -16098,9 +13489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col2 {\n", " \n", - " background-color: #a6bfde;\n", + " background-color: #f8dee3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16108,9 +13499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col3 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16118,9 +13509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col4 {\n", " \n", - " background-color: #a9c1e0;\n", + " background-color: #6a94c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16128,9 +13519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col5 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16138,9 +13529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col6 {\n", " \n", - " background-color: #ea99a9;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -16148,9 +13539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col7 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16158,9 +13549,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col8 {\n", " \n", - " background-color: #f0b7c2;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16168,9 +13559,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col9 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16178,9 +13569,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col10 {\n", " \n", - " background-color: #eb9ead;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -16188,9 +13579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col11 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #c5d5ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -16198,9 +13589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col12 {\n", " \n", - " background-color: #d8415f;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -16208,9 +13599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col13 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #c6d6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -16218,9 +13609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col14 {\n", " \n", - " background-color: #5182bf;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16228,9 +13619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col15 {\n", " \n", - " background-color: #457abb;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16238,9 +13629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col16 {\n", " \n", - " background-color: #92b1d7;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -16248,9 +13639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col17 {\n", " \n", - " background-color: #7ba0cf;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16258,9 +13649,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col18 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #d8e3f1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16268,9 +13659,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col19 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #eb9bab;\n", " \n", " max-width: 80px;\n", " \n", @@ -16278,9 +13669,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col20 {\n", " \n", - " background-color: #5485c1;\n", + " background-color: #a3bddd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16288,9 +13679,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col21 {\n", " \n", - " background-color: #f5cfd7;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16298,9 +13689,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col22 {\n", " \n", - " background-color: #d4e0ef;\n", + " background-color: #81a4d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16308,9 +13699,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col23 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16318,9 +13709,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col24 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16328,9 +13719,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col0 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16338,9 +13729,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col1 {\n", " \n", - " background-color: #b0c6e2;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -16348,9 +13739,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col2 {\n", " \n", - " background-color: #9fbadc;\n", + " background-color: #f2bdc8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16358,9 +13749,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col3 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16368,9 +13759,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col4 {\n", " \n", - " background-color: #cad9ec;\n", + " background-color: #5686c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16378,9 +13769,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col5 {\n", " \n", - " background-color: #e991a3;\n", + " background-color: #f0b5c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16388,9 +13779,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col6 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16398,9 +13789,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col7 {\n", " \n", - " background-color: #de5c76;\n", + " background-color: #628fc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -16408,9 +13799,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col8 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #e68396;\n", " \n", " max-width: 80px;\n", " \n", @@ -16418,9 +13809,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col9 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #eda7b5;\n", " \n", " max-width: 80px;\n", " \n", @@ -16428,9 +13819,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col10 {\n", " \n", - " background-color: #eb9dac;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16438,9 +13829,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col11 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16448,9 +13839,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16458,9 +13849,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col13 {\n", " \n", - " background-color: #acc3e1;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16468,9 +13859,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col14 {\n", " \n", - " background-color: #497dbd;\n", + " background-color: #de5c76;\n", " \n", " max-width: 80px;\n", " \n", @@ -16478,9 +13869,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col15 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16488,9 +13879,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col16 {\n", " \n", - " background-color: #bccfe7;\n", + " background-color: #dd5671;\n", " \n", " max-width: 80px;\n", " \n", @@ -16498,9 +13889,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col17 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #e993a4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16508,9 +13899,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col18 {\n", " \n", - " background-color: #eda6b4;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16518,9 +13909,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col19 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #e991a3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16528,9 +13919,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col20 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16538,9 +13929,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col21 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16548,9 +13939,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col22 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #a0bbdc;\n", " \n", " max-width: 80px;\n", " \n", @@ -16558,9 +13949,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col23 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #96b4d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16568,9 +13959,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col24 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16578,9 +13969,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col0 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #d3dfef;\n", " \n", " max-width: 80px;\n", " \n", @@ -16588,9 +13979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col1 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #487cbc;\n", " \n", " max-width: 80px;\n", " \n", @@ -16598,9 +13989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col2 {\n", " \n", - " background-color: #bbcee6;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -16608,9 +13999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16618,9 +14009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16628,9 +14019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col5 {\n", " \n", - " background-color: #e78a9d;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -16638,9 +14029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col6 {\n", " \n", - " background-color: #eda7b5;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -16648,9 +14039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col7 {\n", " \n", - " background-color: #dc546f;\n", + " background-color: #4d7fbe;\n", " \n", " max-width: 80px;\n", " \n", @@ -16658,9 +14049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col8 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #e994a5;\n", " \n", " max-width: 80px;\n", " \n", @@ -16668,9 +14059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col9 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -16678,9 +14069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col10 {\n", " \n", - " background-color: #eeaab7;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16688,9 +14079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col11 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #d5e1f0;\n", " \n", " max-width: 80px;\n", " \n", @@ -16698,9 +14089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #f0b4c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -16708,9 +14099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col13 {\n", " \n", - " background-color: #b8cce5;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16718,9 +14109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col14 {\n", " \n", - " background-color: #7099cb;\n", + " background-color: #da4966;\n", " \n", " max-width: 80px;\n", " \n", @@ -16728,9 +14119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col15 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16738,9 +14129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col16 {\n", " \n", - " background-color: #91b0d7;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16748,9 +14139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col17 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16758,9 +14149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col18 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #ecf2f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16768,9 +14159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col19 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -16778,9 +14169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col20 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #dde7f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16788,9 +14179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col21 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #dc516d;\n", " \n", " max-width: 80px;\n", " \n", @@ -16798,9 +14189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col22 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #91b0d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16808,9 +14199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col23 {\n", " \n", - " background-color: #6b95c9;\n", + " background-color: #a8c0df;\n", " \n", " max-width: 80px;\n", " \n", @@ -16818,9 +14209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col24 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16828,9 +14219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: #e8eff7;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16838,9 +14229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #6590c7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16848,9 +14239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col2 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #ea99a9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16858,9 +14249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #95b3d8;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16868,9 +14259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -16878,9 +14269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col5 {\n", " \n", - " background-color: #eeabb8;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -16888,9 +14279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col6 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16898,9 +14289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col7 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #6a94c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -16908,9 +14299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col8 {\n", " \n", - " background-color: #eca4b3;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -16918,9 +14309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col9 {\n", " \n", - " background-color: #f7d6dd;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16928,9 +14319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col10 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16938,9 +14329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col11 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #dfe8f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16948,9 +14339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -16958,9 +14349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col13 {\n", " \n", - " background-color: #9bb7da;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16968,9 +14359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col14 {\n", " \n", - " background-color: #618ec5;\n", + " background-color: #e27389;\n", " \n", " max-width: 80px;\n", " \n", @@ -16978,9 +14369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col15 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -16988,9 +14379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col16 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -16998,9 +14389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col17 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -17008,9 +14399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col18 {\n", " \n", - " background-color: #f5cfd7;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17018,9 +14409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e993a4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17028,9 +14419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col20 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #b9cde6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17038,9 +14429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col21 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #de5f79;\n", " \n", " max-width: 80px;\n", " \n", @@ -17048,9 +14439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col22 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #b3c9e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17058,9 +14449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col23 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17068,9 +14459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col24 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -17078,9 +14469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col0 {\n", " \n", - " background-color: #cedced;\n", + " background-color: #c6d6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -17088,9 +14479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col1 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -17098,9 +14489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col2 {\n", " \n", - " background-color: #9cb8db;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17108,9 +14499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col3 {\n", " \n", - " background-color: #749bcc;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17118,9 +14509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col4 {\n", " \n", - " background-color: #b2c8e3;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17128,9 +14519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col5 {\n", " \n", - " background-color: #f8dfe4;\n", + " background-color: #f0b7c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17138,9 +14529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col6 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -17148,9 +14539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col7 {\n", " \n", - " background-color: #eeabb8;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -17158,9 +14549,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col8 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #f2bdc8;\n", " \n", " max-width: 80px;\n", " \n", @@ -17168,9 +14559,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col9 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17178,9 +14569,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col10 {\n", " \n", - " background-color: #e2eaf4;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17188,9 +14579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col11 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17198,9 +14589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col12 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #efb1be;\n", " \n", " max-width: 80px;\n", " \n", @@ -17208,9 +14599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col13 {\n", " \n", - " background-color: #94b2d8;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17218,9 +14609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col14 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #e0657d;\n", " \n", " max-width: 80px;\n", " \n", @@ -17228,9 +14619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col15 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #eca1b0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17238,9 +14629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col16 {\n", " \n", - " background-color: #6993c8;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17248,9 +14639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col17 {\n", " \n", - " background-color: #6590c7;\n", + " background-color: #e27087;\n", " \n", " max-width: 80px;\n", " \n", @@ -17258,9 +14649,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col18 {\n", " \n", - " background-color: #e2eaf4;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17268,9 +14659,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e68699;\n", " \n", " max-width: 80px;\n", " \n", @@ -17278,9 +14669,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col20 {\n", " \n", - " background-color: #457abb;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -17288,9 +14679,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col21 {\n", " \n", - " background-color: #e3ebf5;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17298,9 +14689,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col22 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #d1deee;\n", " \n", " max-width: 80px;\n", " \n", @@ -17308,9 +14699,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col23 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #82a5d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17318,9 +14709,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col24 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #7099cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17328,9 +14719,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col0 {\n", " \n", - " background-color: #96b4d9;\n", + " background-color: #a9c1e0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17338,9 +14729,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col1 {\n", " \n", - " background-color: #b0c6e2;\n", + " background-color: #6892c8;\n", " \n", " max-width: 80px;\n", " \n", @@ -17348,9 +14739,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col2 {\n", " \n", - " background-color: #b2c8e3;\n", + " background-color: #f7d6dd;\n", " \n", " max-width: 80px;\n", " \n", @@ -17358,9 +14749,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col3 {\n", " \n", - " background-color: #4a7ebd;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17368,9 +14759,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col4 {\n", " \n", - " background-color: #92b1d7;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17378,9 +14769,29 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col5 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e4ecf5;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col6 {\n", + " \n", + " background-color: #d8e3f1;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col7 {\n", + " \n", + " background-color: #477bbc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17388,7 +14799,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col8 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17398,9 +14809,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col9 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #e7eef6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17408,9 +14819,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col10 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17418,9 +14829,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col11 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #a6bfde;\n", " \n", " max-width: 80px;\n", " \n", @@ -17428,9 +14839,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col12 {\n", " \n", - " background-color: #c9d8eb;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17438,9 +14849,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col13 {\n", " \n", - " background-color: #bfd1e8;\n", + " background-color: #a9c1e0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17448,9 +14859,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col14 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -17458,9 +14869,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col15 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #ea99a9;\n", " \n", " max-width: 80px;\n", " \n", @@ -17468,9 +14879,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col16 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17478,9 +14889,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col17 {\n", " \n", - " background-color: #5a88c3;\n", + " background-color: #f0b7c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17488,9 +14899,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col18 {\n", " \n", - " background-color: #628fc6;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -17498,9 +14909,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col19 {\n", " \n", - " background-color: #749bcc;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -17508,9 +14919,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col20 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17518,9 +14929,19 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col21 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #d8415f;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col22 {\n", + " \n", + " background-color: #b5cae4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17528,7 +14949,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col23 {\n", " \n", " background-color: #5182bf;\n", " \n", @@ -17538,9 +14959,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col24 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #457abb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17548,9 +14969,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col0 {\n", " \n", - " background-color: #d4e0ef;\n", + " background-color: #92b1d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17558,9 +14979,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col1 {\n", " \n", - " background-color: #5182bf;\n", + " background-color: #7ba0cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -17568,9 +14989,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col2 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -17578,9 +14999,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col3 {\n", " \n", - " background-color: #85a7d2;\n", + " background-color: #f7d7de;\n", " \n", " max-width: 80px;\n", " \n", @@ -17588,9 +15009,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col4 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #5485c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17598,9 +15019,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col5 {\n", " \n", - " background-color: #bccfe7;\n", + " background-color: #f5cfd7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17608,9 +15029,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col6 {\n", " \n", - " background-color: #5f8cc5;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -17618,9 +15039,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col7 {\n", " \n", - " background-color: #a2bcdd;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17628,9 +15049,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17638,9 +15059,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col9 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #dfe8f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17648,9 +15069,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col10 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #b0c6e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17658,9 +15079,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col11 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17668,9 +15089,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col12 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17678,9 +15099,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col13 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #cad9ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17688,9 +15109,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col14 {\n", " \n", - " background-color: #b7cbe5;\n", + " background-color: #e991a3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17698,9 +15119,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col15 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17708,9 +15129,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col16 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #de5c76;\n", " \n", " max-width: 80px;\n", " \n", @@ -17718,9 +15139,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col17 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17728,9 +15149,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col18 {\n", " \n", - " background-color: #739acc;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -17738,9 +15159,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col19 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #eb9dac;\n", " \n", " max-width: 80px;\n", " \n", @@ -17748,9 +15169,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col20 {\n", " \n", - " background-color: #6d96ca;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17758,9 +15179,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col21 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -17768,9 +15189,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col22 {\n", " \n", - " background-color: #eeaebb;\n", + " background-color: #acc3e1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17778,9 +15199,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col23 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #497dbd;\n", " \n", " max-width: 80px;\n", " \n", @@ -17788,9 +15209,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col24 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17798,9 +15219,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #bccfe7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17808,9 +15229,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col1 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17818,9 +15239,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col2 {\n", " \n", - " background-color: #ec9faf;\n", + " background-color: #eda6b4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17828,9 +15249,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col3 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17838,9 +15259,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col4 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -17848,9 +15269,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col5 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -17858,9 +15279,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col6 {\n", " \n", - " background-color: #4c7ebd;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17868,9 +15289,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col7 {\n", " \n", - " background-color: #d1deee;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -17878,9 +15299,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col8 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -17888,9 +15309,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col9 {\n", " \n", - " background-color: #f7d9df;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -17898,9 +15319,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col10 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #dde7f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -17908,9 +15329,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col11 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #bbcee6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17918,9 +15339,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col12 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17928,9 +15349,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col13 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -17938,9 +15359,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col14 {\n", " \n", - " background-color: #bccfe7;\n", + " background-color: #e78a9d;\n", " \n", " max-width: 80px;\n", " \n", @@ -17948,9 +15369,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col15 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #eda7b5;\n", " \n", " max-width: 80px;\n", " \n", @@ -17958,9 +15379,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col16 {\n", " \n", - " background-color: #9eb9db;\n", + " background-color: #dc546f;\n", " \n", " max-width: 80px;\n", " \n", @@ -17968,9 +15389,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col17 {\n", " \n", - " background-color: #5485c1;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -17978,9 +15399,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col18 {\n", " \n", - " background-color: #8babd4;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -17988,9 +15409,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col19 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #eeaab7;\n", " \n", " max-width: 80px;\n", " \n", @@ -17998,9 +15419,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col20 {\n", " \n", - " background-color: #5b89c3;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18008,9 +15429,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col21 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -18018,9 +15439,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col22 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #b8cce5;\n", " \n", " max-width: 80px;\n", " \n", @@ -18028,9 +15449,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col23 {\n", " \n", - " background-color: #497dbd;\n", + " background-color: #7099cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -18038,9 +15459,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col24 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #5e8bc4;\n", " \n", " max-width: 80px;\n", " \n", @@ -18048,9 +15469,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #91b0d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18058,9 +15479,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col1 {\n", " \n", - " background-color: #5686c1;\n", + " background-color: #86a8d3;\n", " \n", " max-width: 80px;\n", " \n", @@ -18068,9 +15489,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col2 {\n", " \n", - " background-color: #eda8b6;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -18078,9 +15499,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col3 {\n", " \n", - " background-color: #d9e4f1;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18088,9 +15509,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col4 {\n", " \n", - " background-color: #d5e1f0;\n", + " background-color: #5e8bc4;\n", " \n", " max-width: 80px;\n", " \n", @@ -18098,9 +15519,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col5 {\n", " \n", - " background-color: #bfd1e8;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -18108,9 +15529,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col6 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -18118,9 +15539,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col7 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #6b95c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -18128,9 +15549,19 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col8 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #f3c6cf;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col9 {\n", + " \n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18138,7 +15569,7 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col10 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -18148,9 +15579,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col11 {\n", " \n", - " background-color: #eeaab7;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -18158,9 +15589,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col12 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -18168,9 +15599,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col13 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -18178,9 +15609,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col14 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #eeabb8;\n", " \n", " max-width: 80px;\n", " \n", @@ -18188,9 +15619,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col15 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -18198,9 +15629,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col16 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -18208,9 +15639,9 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col17 {\n", " \n", - " background-color: #9eb9db;\n", + " background-color: #eca4b3;\n", " \n", " max-width: 80px;\n", " \n", @@ -18218,2771 +15649,3087 @@ " \n", " }\n", " \n", - " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23 {\n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col18 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f7d6dd;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " 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80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col24 {\n", + " \n", + " background-color: #89aad4;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " </style>\n", + "\n", + " <table id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" None>\n", + " \n", + " <caption>Hover to magify</caption>\n", + " \n", + "\n", + " <thead>\n", + " \n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">0\n", + " \n", + " <th class=\"col_heading level0 col1\">1\n", + " \n", + " <th class=\"col_heading level0 col2\">2\n", + " \n", + " <th class=\"col_heading level0 col3\">3\n", + " \n", + " <th class=\"col_heading level0 col4\">4\n", + " \n", + " <th class=\"col_heading level0 col5\">5\n", + " \n", + " <th class=\"col_heading level0 col6\">6\n", + " \n", + " <th class=\"col_heading level0 col7\">7\n", + " \n", + " <th class=\"col_heading level0 col8\">8\n", + " \n", + " <th class=\"col_heading level0 col9\">9\n", + " \n", + " <th class=\"col_heading level0 col10\">10\n", + " \n", + " <th class=\"col_heading level0 col11\">11\n", + " \n", + " <th class=\"col_heading level0 col12\">12\n", + " \n", + " <th class=\"col_heading level0 col13\">13\n", + " \n", + " <th class=\"col_heading level0 col14\">14\n", + " \n", + " <th class=\"col_heading level0 col15\">15\n", + " \n", + " <th class=\"col_heading level0 col16\">16\n", + " \n", + " <th class=\"col_heading level0 col17\">17\n", + " \n", + " <th class=\"col_heading level0 col18\">18\n", + " \n", + " <th class=\"col_heading level0 col19\">19\n", + " \n", + " <th class=\"col_heading level0 col20\">20\n", + " \n", + " <th class=\"col_heading level0 col21\">21\n", + " \n", + " <th class=\"col_heading level0 col22\">22\n", + " \n", + " <th class=\"col_heading level0 col23\">23\n", + " \n", + " <th class=\"col_heading level0 col24\">24\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th class=\"col_heading level2 col0\">None\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " </tr>\n", + " \n", + " </thead>\n", + " <tbody>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", + " 0\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " 0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " -0.84\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " -0.59\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " -0.96\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n", + " -0.22\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n", + " -0.62\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n", + " 0.87\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n", + " -0.92\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n", + " -0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n", + " -1.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n", + " 0.076\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n", + " 1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n", + " 1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n", + " -0.42\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n", + " 0.68\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n", + " -1.6\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row1\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " 1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n", + " 0.0037\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n", + " 1.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n", + " -0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n", + " -0.015\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n", + " -1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n", + " -0.68\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n", + " -0.81\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n", + " 0.35\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n", + " -0.055\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n", + " 0.78\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n", + " 0.44\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row2\">\n", + " 2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " -0.65\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " 3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " 0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n", + " -0.37\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n", + " -3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n", + " 3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n", + " -1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n", + " 0.21\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n", + " -0.24\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n", + " -0.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n", + " -0.78\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n", + " -3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n", + " -0.82\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n", + " -0.21\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n", + " -0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n", + " 0.86\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n", + " -0.68\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col20\" class=\"data row2 col20\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col21\" class=\"data row2 col21\">\n", + " -4.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col22\" class=\"data row2 col22\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col23\" class=\"data row2 col23\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col24\" class=\"data row2 col24\">\n", + " 0.61\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row3\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " 3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " 0.43\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col5\" class=\"data row3 col5\">\n", + " -0.43\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col6\" class=\"data row3 col6\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col7\" class=\"data row3 col7\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n", + " 1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n", + " 0.12\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n", + " 0.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n", + " -0.89\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n", + " 0.27\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n", + " -3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n", + " -2.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n", + " -0.31\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n", + " 0.91\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n", + " -5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n", + " 0.28\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row4\">\n", + " 4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " 4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " -1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " 5.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n", + " -3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n", + " 4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n", + " -0.72\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n", + " 1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n", + " -0.18\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n", + " 0.83\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n", + " -0.22\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n", + " -4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n", + " -0.97\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n", + " -6.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n", + " 3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n", + " 2.1\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row5\">\n", + " 5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " -0.84\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n", + " -0.99\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n", + " 3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n", + " -0.94\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n", + " 1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n", + " 0.087\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n", + " -0.11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n", + " -0.85\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n", + " 0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n", + " -6.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n", + " 3.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row6\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " -0.74\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " 5.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n", + " 3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n", + " 0.34\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n", + " 0.57\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n", + " 0.54\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n", + " -4.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n", + " -4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col17\" class=\"data row6 col17\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col18\" class=\"data row6 col18\">\n", + " -0.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col19\" class=\"data row6 col19\">\n", + " -4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col20\" class=\"data row6 col20\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col21\" class=\"data row6 col21\">\n", + " -8.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col22\" class=\"data row6 col22\">\n", + " 3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col23\" class=\"data row6 col23\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n", + " 5.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row7\">\n", + " 7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " -0.44\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " 4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " -0.21\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n", + " 5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n", + " 0.18\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n", + " 0.11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n", + " 0.036\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n", + " -6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n", + " -0.45\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n", + " -6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n", + " 0.71\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n", + " 0.67\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n", + " -7.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n", + " 6.7\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row8\">\n", + " 8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " 0.92\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " -0.65\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n", + " -1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n", + " -3.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n", + " -1.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n", + " 0.82\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n", + " -2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n", + " -0.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n", + " -6.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n", + " -0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n", + " -6.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n", + " -3.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n", + " -0.043\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n", + " -4.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n", + " 0.51\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n", + " -5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n", + " 3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n", + " 2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n", + " 5.1\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row9\">\n", + " 9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " 0.38\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " 5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " -0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " 7.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n", + " -0.44\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n", + " -0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n", + " -0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n", + " 0.26\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n", + " -3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n", + " -0.82\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n", + " -6.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n", + " -8.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n", + " 0.41\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n", + " -4.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n", + " -6.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n", + " 3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n", + " 5.2\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row10\">\n", + " 10\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n", + " -0.98\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n", + " 7.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n", + " -0.59\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n", + " -0.71\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n", + " -0.46\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n", + " 0.48\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n", + " -6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n", + " -3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n", + " -7.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n", + " -5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n", + " -0.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n", + " -4.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n", + " -0.52\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n", + " -7.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n", + " 1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n", + " 5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n", + " 5.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row11\">\n", + " 11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n", + " 4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n", + " -0.49\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n", + " 0.017\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n", + " -0.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n", + " 0.49\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n", + " 2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n", + " -5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n", + " -8.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n", + " -3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n", + " -4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n", + " -7.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n", + " 5.8\n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row12\">\n", + " 12\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n", + " 3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n", + " -3.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n", + " 0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n", + " 6.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n", + " -0.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n", + " 2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n", + " 0.75\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n", + " -5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n", + " -7.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n", + " -4.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n", + " -9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n", + " 6.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n", + " 5.6\n", + " \n", + " </tr>\n", " \n", - " background-color: #89aad4;\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row13\">\n", + " 13\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n", + " 4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n", + " 6.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n", + " 8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n", + " -0.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n", + " 0.71\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n", + " 2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n", + " -0.16\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n", + " -0.46\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n", + " -5.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n", + " -3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n", + " -7.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n", + " -4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n", + " 0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n", + " -3.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n", + " -8.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n", + " 2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n", + " 6.7\n", + " \n", + " </tr>\n", " \n", - " max-width: 80px;\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row14\">\n", + " 14\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n", + " 3.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n", + " 4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n", + " 6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n", + " -0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n", + " -0.97\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n", + " 1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n", + " 3.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n", + " 0.29\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n", + " -4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n", + " -6.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n", + " -4.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n", + " -2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n", + " -2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n", + " -1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n", + " -9.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n", + " 6.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n", + " 7.5\n", + " \n", + " </tr>\n", " \n", - " font-size: 1pt;\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row15\">\n", + " 15\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n", + " 5.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n", + " -4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n", + " 5.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n", + " -1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n", + " 5.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n", + " -3.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n", + " -0.33\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n", + " 2.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n", + " 4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n", + " 1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n", + " -3.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n", + " -4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n", + " -5.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n", + " -4.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n", + " -2.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n", + " -1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n", + " -11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n", + " 2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n", + " 6.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n", + " 5.9\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " </style>\n", - "\n", - " <table id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\">\n", - " \n", - " <caption>Hover to magify</caption>\n", - " \n", - "\n", - " <thead>\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row16\">\n", + " 16\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n", + " 4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n", + " 4.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n", + " -2.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n", + " -0.47\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n", + " 5.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n", + " -4.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n", + " 1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n", + " 0.23\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n", + " 0.099\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n", + " 1.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n", + " -3.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n", + " -3.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n", + " -5.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n", + " -3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n", + " -1.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n", + " -0.56\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n", + " -13\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n", + " 2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n", + " 6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n", + " 4.9\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th class=\"blank\">\n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row17\">\n", + " 17\n", " \n", - " <th class=\"col_heading level0 col0\">0\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n", + " 5.6\n", " \n", - " <th class=\"col_heading level0 col1\">1\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n", + " 4.6\n", " \n", - " <th class=\"col_heading level0 col2\">2\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n", + " -3.5\n", " \n", - " <th class=\"col_heading level0 col3\">3\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n", + " -3.8\n", " \n", - " <th class=\"col_heading level0 col4\">4\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n", + " 6.6\n", " \n", - " <th class=\"col_heading level0 col5\">5\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n", + " -2.6\n", " \n", - " <th class=\"col_heading level0 col6\">6\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n", + " -0.75\n", " \n", - " <th class=\"col_heading level0 col7\">7\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n", + " 6.6\n", " \n", - " <th class=\"col_heading level0 col8\">8\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n", + " -4.8\n", " \n", - " <th class=\"col_heading level0 col9\">9\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n", + " 3.6\n", " \n", - " <th class=\"col_heading level0 col10\">10\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n", + " -0.29\n", " \n", - " <th class=\"col_heading level0 col11\">11\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n", + " 0.56\n", " \n", - " <th class=\"col_heading level0 col12\">12\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n", + " 5.8\n", " \n", - " <th class=\"col_heading level0 col13\">13\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n", + " 2\n", " \n", - " <th class=\"col_heading level0 col14\">14\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n", + " -2.3\n", " \n", - " <th class=\"col_heading level0 col15\">15\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n", + " -2.3\n", " \n", - " <th class=\"col_heading level0 col16\">16\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n", + " -5\n", " \n", - " <th class=\"col_heading level0 col17\">17\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n", + " -3.2\n", " \n", - " <th class=\"col_heading level0 col18\">18\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n", + " -3.1\n", " \n", - " <th class=\"col_heading level0 col19\">19\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n", + " -2.4\n", " \n", - " <th class=\"col_heading level0 col20\">20\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n", + " 0.84\n", " \n", - " <th class=\"col_heading level0 col21\">21\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n", + " -13\n", " \n", - " <th class=\"col_heading level0 col22\">22\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n", + " 3.6\n", " \n", - " <th class=\"col_heading level0 col23\">23\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n", + " 7.4\n", " \n", - " <th class=\"col_heading level0 col24\">24\n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n", + " 4.7\n", " \n", " </tr>\n", " \n", - " </thead>\n", - " <tbody>\n", + " <tr>\n", + " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row18\">\n", + " 18\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n", + " 6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n", + " 5.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n", + " -2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n", + " -4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n", + " 7.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n", + " -1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n", + " 7.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n", + " -4.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n", + " 1.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n", + " -0.63\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n", + " 0.35\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n", + " 7.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n", + " 0.87\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n", + " -2.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n", + " -4.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n", + " -2.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n", + " 1.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n", + " -9.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n", + " 3.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n", + " 7.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n", + " 4.4\n", + " \n", + " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row0\">\n", - " \n", - " 0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " \n", - " 0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " \n", - " 1.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " \n", - " -0.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " \n", - " -0.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " \n", - " -0.96\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n", - " \n", - " -0.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n", - " \n", - " -0.62\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n", - " \n", - " 1.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n", - " \n", - " -2.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n", - " \n", - " 0.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n", - " \n", - " -0.92\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n", - " \n", - " -0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n", - " \n", - " 2.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n", - " \n", - " -1.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n", - " \n", - " 0.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n", - " \n", - " -1.25\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n", - " \n", - " 1.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n", - " \n", - " -1.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n", - " \n", - " 1.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n", - " \n", - " -0.42\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n", - " \n", - " 2.29\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n", - " \n", - " -2.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n", - " \n", - " 2.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n", - " \n", - " 0.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n", - " \n", - " -1.58\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row1\">\n", - " \n", - " 1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " \n", - " -1.75\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " \n", - " 1.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " \n", - " -1.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " \n", - " 1.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n", - " \n", - " 0.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n", - " \n", - " -2.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n", - " \n", - " 3.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n", - " \n", - " -1.66\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n", - " \n", - " 1.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n", - " \n", - " -0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n", - " \n", - " -0.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n", - " \n", - " 1.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n", - " \n", - " -1.09\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n", - " \n", - " -1.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n", - " \n", - " -0.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n", - " \n", - " -0.81\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n", - " \n", - " 0.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n", - " \n", - " -0.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n", - " \n", - " 1.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n", - " \n", - " -2.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n", - " \n", - " 2.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n", - " \n", - " 0.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n", - " \n", - " 0.44\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row2\">\n", - " \n", - " 2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " \n", - " -0.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " \n", - " 3.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " \n", - " -1.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " \n", - " 0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " \n", - " 2.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n", - " \n", - " -0.37\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n", - " \n", - " -3.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n", - " \n", - " 3.73\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n", - " \n", - " -1.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n", - " \n", - " 2.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n", - " \n", - " 0.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n", - " \n", - " -0.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n", - " \n", - " -0.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n", - " \n", - " -0.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n", - " \n", - " -3.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n", - " \n", - " -0.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n", - " \n", - " -0.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n", - " \n", - " -0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n", - " \n", - " 0.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n", - " \n", - " 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n", - " \n", - " -2.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n", - " \n", - " 1.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n", - " \n", - " 0.12\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n", - " \n", - " 0.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n", - " \n", - " -0.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n", - " \n", - " 0.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n", - " \n", - " -3.67\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n", - " \n", - " -2.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n", - " \n", - " -0.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n", - " \n", - " -1.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n", - " \n", - " 1.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n", - " \n", - " 0.91\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n", - " \n", - " -5.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n", - " \n", - " 2.81\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n", - " \n", - " 2.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n", - " \n", - " 0.28\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row4\">\n", - " \n", - " 4\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " \n", - " -3.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " \n", - " 4.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " \n", - " -1.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " \n", - " -1.7\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " \n", - " 5.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n", - " \n", - " -1.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n", - " \n", - " -3.81\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n", - " \n", - " 4.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n", - " \n", - " -0.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n", - " \n", - " 1.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n", - " \n", - " -0.18\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n", - " \n", - " 0.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n", - " \n", - " -0.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n", - " \n", - " -1.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n", - " \n", - " -4.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n", - " \n", - " -2.88\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n", - " \n", - " -0.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n", - " \n", - " -1.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n", - " \n", - " 1.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n", - " \n", - " -1.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n", - " \n", - " 2.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n", - " \n", - " -6.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n", - " \n", - " 3.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n", - " \n", - " 2.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n", - " \n", - " 2.09\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row5\">\n", - " \n", - " 5\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " \n", - " -0.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " \n", - " 4.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " \n", - " -1.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " \n", - " -2.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " \n", - " 5.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n", - " \n", - " -0.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n", - " \n", - " -4.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n", - " \n", - " 3.94\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n", - " \n", - " -1.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n", - " \n", - " -0.94\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n", - " \n", - " 1.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n", - " \n", - " 0.09\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n", - " \n", - " -1.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n", - " \n", - " -0.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n", - " \n", - " -4.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n", - " \n", - " -0.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n", - " \n", - " -2.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n", - " \n", - " -1.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n", - " \n", - " 0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n", - " \n", - " -1.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n", - " \n", - " 1.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n", - " \n", - " -6.51\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n", - " \n", - " 2.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n", - " \n", - " 2.14\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n", - " \n", - " 3.77\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row6\">\n", - " \n", - " 6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " \n", - " -0.74\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " \n", - " 5.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " \n", - " -2.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " \n", - " 4.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n", - " \n", - " -1.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n", - " \n", - " -3.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n", - " \n", - " 3.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n", - " \n", - " -3.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n", - " \n", - " -1.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n", - " \n", - " 0.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n", - " \n", - " 0.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n", - " \n", - " -1.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n", - " \n", - " 0.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n", - " \n", - " -4.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n", - " \n", - " 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n", - " \n", - " 5.81\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row7\">\n", - " \n", - " 7\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " \n", - " -0.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " \n", - " 4.69\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " \n", - " -2.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " \n", - " -0.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " \n", - " 5.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n", - " \n", - " -2.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n", - " \n", - " 5.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n", - " \n", - " -4.5\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n", - " \n", - " -3.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n", - " \n", - " -1.73\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n", - " \n", - " 0.18\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n", - " \n", - " 0.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n", - " \n", - " 0.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n", - " \n", - " -5.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n", - " \n", - " -0.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n", - " \n", - " -6.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n", - " \n", - " -3.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n", - " \n", - " 0.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n", - " \n", - " -3.95\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n", - " \n", - " 0.67\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n", - " \n", - " -7.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n", - " \n", - " 2.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n", - " \n", - " 3.39\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n", - " \n", - " 6.66\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row8\">\n", - " \n", - " 8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " \n", - " 0.92\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " \n", - " 5.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " \n", - " -3.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " \n", - " -0.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " \n", - " 5.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n", - " \n", - " -3.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n", - " \n", - " -1.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n", - " \n", - " 5.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n", - " \n", - " -3.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n", - " \n", - " -1.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n", - " \n", - " -1.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n", - " \n", - " 0.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n", - " \n", - " -2.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n", - " \n", - " -0.4\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n", - " \n", - " -6.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n", - " \n", - " -0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n", - " \n", - " -6.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n", - " \n", - " -3.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n", - " \n", - " -0.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n", - " \n", - " -4.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n", - " \n", - " 0.51\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n", - " \n", - " -5.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n", - " \n", - " 3.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n", - " \n", - " 2.4\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n", - " \n", - " 5.08\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row9\">\n", - " \n", - " 9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " \n", - " 0.38\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " \n", - " 5.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " \n", - " -4.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " \n", - " -0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " \n", - " 7.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n", - " \n", - " -2.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n", - " \n", - " -0.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n", - " \n", - " 5.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n", - " \n", - " -1.96\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n", - " \n", - " -0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n", - " \n", - " -0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n", - " \n", - " 0.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n", - " \n", - " -3.37\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n", - " \n", - " -0.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n", - " \n", - " -6.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n", - " \n", - " -2.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n", - " \n", - " -8.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n", - " \n", - " -4.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n", - " \n", - " 0.41\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n", - " \n", - " -4.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n", - " \n", - " 1.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n", - " \n", - " -6.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n", - " \n", - " 2.14\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n", - " \n", - " 3.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n", - " \n", - " 5.16\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row10\">\n", - " \n", - " 10\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n", - " \n", - " 2.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n", - " \n", - " 5.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n", - " \n", - " -3.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n", - " \n", - " -0.98\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n", - " \n", - " 7.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n", - " \n", - " -2.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n", - " \n", - " -0.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n", - " \n", - " 5.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n", - " \n", - " -2.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n", - " \n", - " -0.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n", - " \n", - " -0.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n", - " \n", - " 1.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n", - " \n", - " -2.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n", - " \n", - " 0.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n", - " \n", - " -5.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n", - " \n", - " -3.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n", - " \n", - " -7.77\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n", - " \n", - " -5.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n", - " \n", - " -0.7\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n", - " \n", - " -4.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n", - " \n", - " -0.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n", - " \n", - " -7.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n", - " \n", - " 1.54\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n", - " \n", - " 5.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n", - " \n", - " 5.81\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row11\">\n", - " \n", - " 11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n", - " \n", - " 1.86\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n", - " \n", - " 4.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n", - " \n", - " -2.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n", - " \n", - " -1.38\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n", - " \n", - " 5.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n", - " \n", - " -0.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n", - " \n", - " 0.02\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n", - " \n", - " 5.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n", - " \n", - " -1.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n", - " \n", - " -0.6\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n", - " \n", - " 0.49\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n", - " \n", - " 1.96\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n", - " \n", - " -1.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n", - " \n", - " 1.88\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n", - " \n", - " -5.92\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n", - " \n", - " -4.55\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n", - " \n", - " -8.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n", - " \n", - " -3.42\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n", - " \n", - " -2.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n", - " \n", - " -4.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n", - " \n", - " -1.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n", - " \n", - " -7.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n", - " \n", - " 1.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n", - " \n", - " 5.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n", - " \n", - " 5.83\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row12\">\n", - " \n", - " 12\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n", - " \n", - " 3.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n", - " \n", - " 4.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n", - " \n", - " -3.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n", - " \n", - " -2.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n", - " \n", - " 5.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n", - " \n", - " -2.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n", - " \n", - " 0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n", - " \n", - " 6.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n", - " \n", - " -2.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n", - " \n", - " -0.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n", - " \n", - " 1.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n", - " \n", - " 2.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n", - " \n", - " -1.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n", - " \n", - " 0.75\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n", - " \n", - " -5.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n", - " \n", - " -4.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n", - " \n", - " -7.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n", - " \n", - " -2.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n", - " \n", - " -2.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n", - " \n", - " -4.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n", - " \n", - " -1.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n", - " \n", - " -8.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n", - " \n", - " 2.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n", - " \n", - " 6.42\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n", - " \n", - " 5.6\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row13\">\n", - " \n", - " 13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n", - " \n", - " 2.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n", - " \n", - " 4.45\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n", - " \n", - " -3.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n", - " \n", - " -2.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n", - " \n", - " 6.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n", - " \n", - " -3.25\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n", - " \n", - " -2.17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n", - " \n", - " 7.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n", - " \n", - " -2.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n", - " \n", - " -0.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n", - " \n", - " 0.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n", - " \n", - " 2.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n", - " \n", - " -0.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n", - " \n", - " -0.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n", - " \n", - " -5.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n", - " \n", - " -3.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n", - " \n", - " -7.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n", - " \n", - " -4.0\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n", - " \n", - " 0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n", - " \n", - " -3.67\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n", - " \n", - " -1.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n", - " \n", - " -8.71\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n", - " \n", - " 2.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n", - " \n", - " 5.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n", - " \n", - " 6.71\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row14\">\n", - " \n", - " 14\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n", - " \n", - " 3.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n", - " \n", - " 4.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n", - " \n", - " -3.88\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n", - " \n", - " -1.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n", - " \n", - " 6.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n", - " \n", - " -3.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n", - " \n", - " -1.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n", - " \n", - " 5.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n", - " \n", - " -2.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n", - " \n", - " -0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n", - " \n", - " -0.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n", - " \n", - " 1.72\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n", - " \n", - " 3.61\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n", - " \n", - " 0.29\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n", - " \n", - " -4.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n", - " \n", - " -4.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n", - " \n", - " -6.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n", - " \n", - " -4.5\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n", - " \n", - " -2.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n", - " \n", - " -2.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n", - " \n", - " -1.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n", - " \n", - " -9.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n", - " \n", - " 3.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n", - " \n", - " 6.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n", - " \n", - " 7.53\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row15\">\n", - " \n", - " 15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n", - " \n", - " 5.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n", - " \n", - " 5.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n", - " \n", - " -3.98\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n", - " \n", - " -2.26\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n", - " \n", - " 5.91\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n", - " \n", - " -3.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n", - " \n", - " -1.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n", - " \n", - " 5.68\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n", - " \n", - " -3.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n", - " \n", - " -0.33\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n", - " \n", - " -1.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n", - " \n", - " 2.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n", - " \n", - " 4.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n", - " \n", - " 1.01\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n", - " \n", - " -3.22\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n", - " \n", - " -4.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n", - " \n", - " -5.74\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n", - " \n", - " -4.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n", - " \n", - " -2.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n", - " \n", - " -1.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n", - " \n", - " -1.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n", - " \n", - " -11.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n", - " \n", - " 2.59\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n", - " \n", - " 6.69\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n", - " \n", - " 5.91\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row16\">\n", - " \n", - " 16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n", - " \n", - " 4.08\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n", - " \n", - " 4.34\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n", - " \n", - " -2.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n", - " \n", - " -3.3\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n", - " \n", - " 6.04\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n", - " \n", - " -2.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n", - " \n", - " -0.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n", - " \n", - " 5.28\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n", - " \n", - " -4.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n", - " \n", - " 1.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n", - " \n", - " 0.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n", - " \n", - " 0.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n", - " \n", - " 5.79\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n", - " \n", - " 1.8\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n", - " \n", - " -3.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n", - " \n", - " -3.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n", - " \n", - " -5.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n", - " \n", - " -2.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n", - " \n", - " -2.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n", - " \n", - " -1.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n", - " \n", - " -0.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n", - " \n", - " -13.13\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n", - " \n", - " 2.07\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n", - " \n", - " 6.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n", - " \n", - " 4.94\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row17\">\n", - " \n", - " 17\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n", - " \n", - " 5.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n", - " \n", - " 4.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n", - " \n", - " -3.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n", - " \n", - " -3.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n", - " \n", - " 6.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n", - " \n", - " -2.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n", - " \n", - " -0.75\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n", - " \n", - " 6.58\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n", - " \n", - " -4.78\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n", - " \n", - " 3.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n", - " \n", - " -0.29\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n", - " \n", - " 0.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n", - " \n", - " 5.76\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n", - " \n", - " 2.05\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n", - " \n", - " -2.27\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n", - " \n", - " -2.31\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n", - " \n", - " -4.95\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n", - " \n", - " -3.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n", - " \n", - " -3.06\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n", - " \n", - " -2.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n", - " \n", - " 0.84\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n", - " \n", - " -12.57\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n", - " \n", - " 3.56\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n", - " \n", - " 7.36\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n", - " \n", - " 4.7\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row18\">\n", - " \n", - " 18\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n", - " \n", - " 5.99\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n", - " \n", - " 5.82\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n", - " \n", - " -2.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n", - " \n", - " -4.15\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n", - " \n", - " 7.12\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n", - " \n", - " -3.32\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n", - " \n", - " -1.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n", - " \n", - " 7.93\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n", - " \n", - " -4.85\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n", - " \n", - " 1.44\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n", - " \n", - " -0.63\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n", - " \n", - " 0.35\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n", - " \n", - " 7.47\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n", - " \n", - " 0.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n", - " \n", - " -1.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n", - " \n", - " -2.09\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n", - " \n", - " -4.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n", - " \n", - " -2.55\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n", - " \n", - " -2.46\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n", - " \n", - " -2.89\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n", - " \n", - " 1.9\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n", - " \n", - " -9.74\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n", - " \n", - " 3.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n", - " \n", - " 7.07\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n", - " \n", - " 4.39\n", - " \n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row19\">\n", - " \n", - " 19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n", - " \n", - " 4.03\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n", - " \n", - " 6.23\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n", - " \n", - " -4.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n", - " \n", - " -4.11\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n", - " \n", - " 7.19\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n", - " \n", - " -4.1\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n", - " \n", - " -1.52\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n", - " \n", - " 6.53\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n", - " \n", - " -5.21\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n", - " \n", - " -0.24\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n", - " \n", - " 0.01\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n", - " \n", - " 1.16\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n", - " \n", - " 6.43\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n", - " \n", - " -1.97\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n", - " \n", - " -2.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n", - " \n", - " -1.66\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n", - " \n", - " -5.2\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n", - " \n", - " -3.25\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n", - " \n", - " -2.87\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n", - " \n", - " -1.65\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n", - " \n", - " 1.64\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n", - " \n", - " -10.66\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n", - " \n", - " 2.83\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n", - " \n", - " 7.48\n", - " \n", - " \n", - " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n", - " \n", - " 3.94\n", - " \n", + " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row19\">\n", + " 19\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n", + " 4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n", + " 6.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n", + " 7.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n", + " -4.1\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n", + " -1.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n", + " 6.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n", + " -5.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n", + " -0.24\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n", + " 0.0072\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n", + " 1.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n", + " 6.4\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n", + " -2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n", + " -2.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n", + " -5.2\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n", + " -3.3\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n", + " -2.9\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n", + " -1.7\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n", + " 1.6\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n", + " -11\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n", + " 2.8\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n", + " 7.5\n", + " \n", + " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n", + " 3.9\n", " \n", " </tr>\n", " \n", @@ -20991,10 +18738,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x114b9b1d0>" + "<pandas.core.style.Styler at 0x111c7ea58>" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } diff --git a/pandas/core/style.py b/pandas/core/style.py index 15fcec118e7d4..d6d2cc818693d 100644 --- a/pandas/core/style.py +++ b/pandas/core/style.py @@ -7,7 +7,8 @@ from contextlib import contextmanager from uuid import uuid1 import copy -from collections import defaultdict, MutableMapping +from collections import defaultdict +from collections.abc import MutableMapping try: from jinja2 import Template @@ -215,7 +216,7 @@ def _translate(self): "class": " ".join(cs)}) head.append(row_es) - if self.data.index.names: + if self.data.index.names and self.data.index.names != [None]: index_header_row = [] for c, name in enumerate(self.data.index.names): @@ -272,7 +273,7 @@ def _translate(self): caption=caption, table_attributes=self.table_attributes) def format(self, formatter, subset=None): - """ + ''' Format the text display value of cells. .. versionadded:: 0.18.0 @@ -281,8 +282,6 @@ def format(self, formatter, subset=None): ---------- formatter: str, callable, or dict subset: IndexSlice - A argument to DataFrame.loc that restricts which elements - ``formatter`` is applied to. Returns ------- @@ -293,9 +292,8 @@ def format(self, formatter, subset=None): ``formatter`` is either an ``a`` or a dict ``{column name: a}`` where ``a`` is one of - - - str: this will be wrapped in: ``a.format(x)`` - - callable: called with the value of an individual cell + - str: this will be wrapped in: ``a.format(x)`` + - callable: called with the value of an individual cell The default display value for numeric values is the "general" (``g``) format with ``pd.options.display.precision`` precision. @@ -307,7 +305,7 @@ def format(self, formatter, subset=None): >>> df.style.format("{:.2%}") >>> df['c'] = ['a', 'b', 'c', 'd'] >>> df.style.format({'C': str.upper}) - """ + ''' if subset is None: row_locs = range(len(self.data)) col_locs = range(len(self.data.columns)) @@ -855,11 +853,11 @@ def _highlight_extrema(data, color='yellow', max_=True): def _maybe_wrap_formatter(formatter): - if com.is_string_like(formatter): - return lambda x: formatter.format(x) - elif callable(formatter): - return formatter - else: + if not (callable(formatter) or com.is_string_like(formatter)): msg = "Expected a template string or callable, got {} instead".format( formatter) raise TypeError(msg) + if not callable(formatter): + return lambda x: formatter.format(x) + else: + return formatter diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py index ef5a966d65545..fb008c2d9a6f5 100644 --- a/pandas/tests/test_style.py +++ b/pandas/tests/test_style.py @@ -129,6 +129,27 @@ def test_set_properties_subset(self): expected = {(0, 0): ['color: white']} self.assertEqual(result, expected) + def test_empty_index_name_doesnt_display(self): + # https://github.com/pydata/pandas/pull/12090#issuecomment-180695902 + df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}) + result = df.style._translate() + + expected = [[{'class': 'blank', 'type': 'th', 'value': ''}, + {'class': 'col_heading level0 col0', + 'display_value': 'A', + 'type': 'th', + 'value': 'A'}, + {'class': 'col_heading level0 col1', + 'display_value': 'B', + 'type': 'th', + 'value': 'B'}, + {'class': 'col_heading level0 col2', + 'display_value': 'C', + 'type': 'th', + 'value': 'C'}]] + + self.assertEqual(result['head'], expected) + def test_index_name(self): # https://github.com/pydata/pandas/issues/11655 df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
Addresses https://github.com/pydata/pandas/pull/12090#issuecomment-180695902 by making the `Styler` behaviour match regular `to_html` behaviour. This PR is based from #12162. ##### New behaviour <img width="596" alt="screen shot 2016-02-08 at 10 35 23 am" src="https://cloud.githubusercontent.com/assets/3064019/12890011/b183e81e-ce4f-11e5-9b9f-c021fcb33c5a.png"> cc @TomAugspurger
https://api.github.com/repos/pandas-dev/pandas/pulls/12260
2016-02-08T15:37:34Z
2016-03-03T19:06:28Z
null
2016-03-04T13:35:00Z