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DOC: Add context to pd.to_timedelta examples | diff --git a/doc/source/timedeltas.rst b/doc/source/timedeltas.rst
index 620270c8307b8..39b73b307be4e 100644
--- a/doc/source/timedeltas.rst
+++ b/doc/source/timedeltas.rst
@@ -47,14 +47,14 @@ You can construct a ``Timedelta`` scalar through various arguments:
# like datetime.timedelta
# note: these MUST be specified as keyword arguments
- Timedelta(days=1,seconds=1)
+ Timedelta(days=1, seconds=1)
# integers with a unit
- Timedelta(1,unit='d')
+ Timedelta(1, unit='d')
# from a timedelta/np.timedelta64
- Timedelta(timedelta(days=1,seconds=1))
- Timedelta(np.timedelta64(1,'ms'))
+ Timedelta(timedelta(days=1, seconds=1))
+ Timedelta(np.timedelta64(1, 'ms'))
# negative Timedeltas have this string repr
# to be more consistent with datetime.timedelta conventions
@@ -70,7 +70,7 @@ You can construct a ``Timedelta`` scalar through various arguments:
Timedelta(Second(2))
-Further, operations among the scalars yield another scalar ``Timedelta``
+Further, operations among the scalars yield another scalar ``Timedelta``.
.. ipython:: python
@@ -84,18 +84,30 @@ to_timedelta
Prior to 0.15.0 ``pd.to_timedelta`` would return a ``Series`` for list-like/Series input, and a ``np.timedelta64`` for scalar input.
It will now return a ``TimedeltaIndex`` for list-like input, ``Series`` for Series input, and ``Timedelta`` for scalar input.
- The arguments to ``pd.to_timedelta`` are now ``(arg,unit='ns',box=True)``, previously were ``(arg,box=True,unit='ns')`` as these are more logical.
+ The arguments to ``pd.to_timedelta`` are now ``(arg, unit='ns', box=True)``, previously were ``(arg, box=True, unit='ns')`` as these are more logical.
Using the top-level ``pd.to_timedelta``, you can convert a scalar, array, list, or Series from a recognized timedelta format / value into a ``Timedelta`` type.
-It will construct Series if the input is a Series, a scalar if the input is scalar-like, otherwise will output a ``TimedeltaIndex``
+It will construct Series if the input is a Series, a scalar if the input is scalar-like, otherwise will output a ``TimedeltaIndex``.
+
+You can parse a single string to a Timedelta:
.. ipython:: python
to_timedelta('1 days 06:05:01.00003')
to_timedelta('15.5us')
- to_timedelta(['1 days 06:05:01.00003','15.5us','nan'])
- to_timedelta(np.arange(5),unit='s')
- to_timedelta(np.arange(5),unit='d')
+
+or a list/array of strings:
+
+.. ipython:: python
+
+ to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
+
+The ``unit`` keyword argument specifies the unit of the Timedelta:
+
+.. ipython:: python
+
+ to_timedelta(np.arange(5), unit='s')
+ to_timedelta(np.arange(5), unit='d')
.. _timedeltas.operations:
@@ -116,41 +128,41 @@ subtraction operations on ``datetime64[ns]`` Series, or ``Timestamps``.
df.dtypes
s - s.max()
- s - datetime(2011,1,1,3,5)
+ s - datetime(2011, 1, 1, 3, 5)
s + timedelta(minutes=5)
s + Minute(5)
s + Minute(5) + Milli(5)
-Operations with scalars from a ``timedelta64[ns]`` series
+Operations with scalars from a ``timedelta64[ns]`` series:
.. ipython:: python
y = s - s[0]
y
-Series of timedeltas with ``NaT`` values are supported
+Series of timedeltas with ``NaT`` values are supported:
.. ipython:: python
y = s - s.shift()
y
-Elements can be set to ``NaT`` using ``np.nan`` analogously to datetimes
+Elements can be set to ``NaT`` using ``np.nan`` analogously to datetimes:
.. ipython:: python
y[1] = np.nan
y
-Operands can also appear in a reversed order (a singular object operated with a Series)
+Operands can also appear in a reversed order (a singular object operated with a Series):
.. ipython:: python
s.max() - s
- datetime(2011,1,1,3,5) - s
+ datetime(2011, 1, 1, 3, 5) - s
timedelta(minutes=5) + s
-``min, max`` and the corresponding ``idxmin, idxmax`` operations are supported on frames
+``min, max`` and the corresponding ``idxmin, idxmax`` operations are supported on frames:
.. ipython:: python
@@ -185,7 +197,7 @@ pass a timedelta to get a particular value.
y.fillna(10)
y.fillna(Timedelta('-1 days, 00:00:05'))
-You can also negate, multiply and use ``abs`` on ``Timedeltas``
+You can also negate, multiply and use ``abs`` on ``Timedeltas``:
.. ipython:: python
@@ -205,7 +217,7 @@ Numeric reduction operation for ``timedelta64[ns]`` will return ``Timedelta`` ob
.. ipython:: python
- y2 = Series(to_timedelta(['-1 days +00:00:05','nat','-1 days +00:00:05','1 days']))
+ y2 = Series(to_timedelta(['-1 days +00:00:05', 'nat', '-1 days +00:00:05', '1 days']))
y2
y2.mean()
y2.median()
@@ -225,22 +237,22 @@ Note that division by the numpy scalar is true division, while astyping is equiv
.. ipython:: python
- td = Series(date_range('20130101',periods=4)) - \
- Series(date_range('20121201',periods=4))
- td[2] += timedelta(minutes=5,seconds=3)
+ td = Series(date_range('20130101', periods=4)) - \
+ Series(date_range('20121201', periods=4))
+ td[2] += timedelta(minutes=5, seconds=3)
td[3] = np.nan
td
# to days
- td / np.timedelta64(1,'D')
+ td / np.timedelta64(1, 'D')
td.astype('timedelta64[D]')
# to seconds
- td / np.timedelta64(1,'s')
+ td / np.timedelta64(1, 's')
td.astype('timedelta64[s]')
# to months (these are constant months)
- td / np.timedelta64(1,'M')
+ td / np.timedelta64(1, 'M')
Dividing or multiplying a ``timedelta64[ns]`` Series by an integer or integer Series
yields another ``timedelta64[ns]`` dtypes Series.
@@ -248,7 +260,7 @@ yields another ``timedelta64[ns]`` dtypes Series.
.. ipython:: python
td * -1
- td * Series([1,2,3,4])
+ td * Series([1, 2, 3, 4])
Attributes
----------
@@ -261,7 +273,7 @@ These operations can also be directly accessed via the ``.dt`` property of the `
Note that the attributes are NOT the displayed values of the ``Timedelta``. Use ``.components`` to retrieve the displayed values.
-For a ``Series``
+For a ``Series``:
.. ipython:: python
@@ -300,15 +312,15 @@ or ``np.timedelta64`` objects. Passing ``np.nan/pd.NaT/nat`` will represent miss
.. ipython:: python
- TimedeltaIndex(['1 days','1 days, 00:00:05',
- np.timedelta64(2,'D'),timedelta(days=2,seconds=2)])
+ TimedeltaIndex(['1 days', '1 days, 00:00:05',
+ np.timedelta64(2,'D'), timedelta(days=2,seconds=2)])
Similarly to ``date_range``, you can construct regular ranges of a ``TimedeltaIndex``:
.. ipython:: python
- timedelta_range(start='1 days',periods=5,freq='D')
- timedelta_range(start='1 days',end='2 days',freq='30T')
+ timedelta_range(start='1 days', periods=5, freq='D')
+ timedelta_range(start='1 days', end='2 days', freq='30T')
Using the TimedeltaIndex
~~~~~~~~~~~~~~~~~~~~~~~~
@@ -319,7 +331,7 @@ Similarly to other of the datetime-like indices, ``DatetimeIndex`` and ``PeriodI
.. ipython:: python
s = Series(np.arange(100),
- index=timedelta_range('1 days',periods=100,freq='h'))
+ index=timedelta_range('1 days', periods=100, freq='h'))
s
Selections work similary, with coercion on string-likes and slices:
@@ -343,9 +355,9 @@ Finally, the combination of ``TimedeltaIndex`` with ``DatetimeIndex`` allow cert
.. ipython:: python
- tdi = TimedeltaIndex(['1 days',pd.NaT,'2 days'])
+ tdi = TimedeltaIndex(['1 days', pd.NaT, '2 days'])
tdi.tolist()
- dti = date_range('20130101',periods=3)
+ dti = date_range('20130101', periods=3)
dti.tolist()
(dti + tdi).tolist()
(dti - tdi).tolist()
| Update the reStructuredText tutorial on timedeltas to include
context, similar to edits suggested for #11785.
Also, add a space after various commas (PEP8), and periods/colons
at the end of sentences for consistency.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11789 | 2015-12-07T18:15:20Z | 2015-12-09T15:20:41Z | 2015-12-09T15:20:41Z | 2015-12-15T19:46:03Z |
ENH: allow saving of meta-data via CArrays to support wide tables | diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index fb57a7f8bd838..d33aa614f8067 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -41,6 +41,8 @@
import pandas.algos as algos
import pandas.tslib as tslib
+from tables.exceptions import NoSuchNodeError, NodeError
+
from contextlib import contextmanager
from distutils.version import LooseVersion
@@ -1455,6 +1457,7 @@ def infer(self, handler):
"""infer this column from the table: create and return a new object"""
table = handler.table
new_self = self.copy()
+ new_self._handle = handler._handle
new_self.set_table(table)
new_self.get_attr()
new_self.read_metadata(handler)
@@ -1511,6 +1514,10 @@ def cvalues(self):
""" return my cython values """
return self.values
+ @property
+ def handle(self):
+ return self._handle
+
def __iter__(self):
return iter(self.values)
@@ -1534,6 +1541,7 @@ def validate_names(self):
pass
def validate_and_set(self, handler, append, **kwargs):
+ self._handle = handler._handle
self.set_table(handler.table)
self.validate_col()
self.validate_attr(append)
@@ -2043,13 +2051,35 @@ def convert(self, values, nan_rep, encoding):
def get_attr(self):
""" get the data for this colummn """
self.values = getattr(self.attrs, self.kind_attr, None)
+ if self.values is None:
+ try:
+ data = self.handle.get_node(self.attrs._v_node._v_parent, self.kind_attr)[:]
+ if len(data.shape) > 1 and data.shape[1] > 1: # multiIndex
+ self.values = map(tuple, data.tolist())
+ else:
+ self.values = data.tolist()
+
+ except NoSuchNodeError:
+ pass
self.dtype = getattr(self.attrs, self.dtype_attr, None)
self.meta = getattr(self.attrs, self.meta_attr, None)
self.set_kind()
def set_attr(self):
""" set the data for this colummn """
- setattr(self.attrs, self.kind_attr, self.values)
+ #setattr(self.attrs, self.kind_attr, self.values)
+ try:
+ self.handle.create_carray(self.attrs._v_node._v_parent,
+ self.kind_attr,
+ obj=np.array(self.values))
+ except NodeError as e:
+ self.handle.remove_node(self.attrs._v_node._v_parent,
+ self.kind_attr)
+ self.handle.create_carray(self.attrs._v_node._v_parent,
+ self.kind_attr,
+ obj=np.array(self.values))
+ except Exception as e: # for debugging
+ raise
setattr(self.attrs, self.meta_attr, self.meta)
if self.dtype is not None:
setattr(self.attrs, self.dtype_attr, self.dtype)
@@ -3020,12 +3050,50 @@ def set_info(self):
""" update our table index info """
self.attrs.info = self.info
+ def set_non_index_axes(self):
+ """ Write the axes to carrays """
+ def f(dim, flds):
+ name = "non_index_axes_%d" % dim
+ try:
+ self._handle.create_carray(self.attrs._v_node, name, obj=np.array(flds))
+ except ValueError as e:
+ # Should probably make this check:
+ #if e.message == "unknown type: 'object'":
+ # raise ValueError("axis {} has dtype 'object' which cannot be saved to carray".format(dim))
+ raise
+ except NodeError as e:
+ self._handle.remove_node(self.attrs._v_node, name)
+ self._handle.create_carray(self.attrs._v_node, name, obj=np.array(flds))
+ return dim, flds
+
+ replacement = [f(dim, flds) for dim, flds in self.non_index_axes]
+ self.attrs.non_index_axes = replacement
+
+ def get_non_index_axes(self):
+ """Load the non-index axes from their carrays. This is a pass-through
+ for tables stored prior to v0.17"""
+ def f(dim, flds):
+ if isinstance(flds, string_types):
+ flds = self._handle.get_node(self.attrs._v_node, flds)[:]
+ if len(flds.shape) > 1 and flds.shape[1] > 1:
+ flds = map(tuple, flds.tolist())
+ else:
+ flds = flds.tolist()
+ return dim, flds
+ else:
+ return dim, flds #if not a string presumably pre v17 list
+ non_index_axes = getattr(self.attrs, 'non_index_axes', [])
+ new = [f(dim, flds) for dim, flds in non_index_axes]
+ return new
+
def set_attrs(self):
""" set our table type & indexables """
self.attrs.table_type = str(self.table_type)
self.attrs.index_cols = self.index_cols()
self.attrs.values_cols = self.values_cols()
- self.attrs.non_index_axes = self.non_index_axes
+
+ #self.attrs.non_index_axes = self.non_index_axes
+ self.set_non_index_axes()
self.attrs.data_columns = self.data_columns
self.attrs.nan_rep = self.nan_rep
self.attrs.encoding = self.encoding
@@ -3035,8 +3103,7 @@ def set_attrs(self):
def get_attrs(self):
""" retrieve our attributes """
- self.non_index_axes = getattr(
- self.attrs, 'non_index_axes', None) or []
+ self.non_index_axes = self.get_non_index_axes()
self.data_columns = getattr(
self.attrs, 'data_columns', None) or []
self.info = getattr(
diff --git a/pandas/io/tests/data/legacy_hdf/legacy_non_index_axes_0.17.1.h5 b/pandas/io/tests/data/legacy_hdf/legacy_non_index_axes_0.17.1.h5
new file mode 100644
index 0000000000000..bfbb9171f1bae
Binary files /dev/null and b/pandas/io/tests/data/legacy_hdf/legacy_non_index_axes_0.17.1.h5 differ
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index fd8c28bfe0f85..a4b0ea413c08c 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -4606,6 +4606,22 @@ def test_read_nokey(self):
df.to_hdf(path, 'df2', mode='a')
self.assertRaises(ValueError, read_hdf, path)
+ def test_legacy_non_index_axes(self):
+ filename = tm.get_data_path('legacy_hdf/legacy_non_index_axes_0.17.1.h5')
+ with HDFStore(filename, 'r') as store:
+ df_legacy = store.get("df")
+
+ index = pd.date_range(start = Timestamp("2015-11-01 0:00"), freq = "H", periods = 3, tz = None)
+ columns = MultiIndex(levels=[['A', 'B', 'C', 'D'],
+ [1, 2, 3]],
+ labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
+ [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+ names=['alpha', 'num'])
+ data = np.array([index.asi8+i for i in range(10)])
+ df_new = DataFrame(data.T, columns=columns, index=index)
+
+ tm.assert_frame_equal(df_legacy, df_new)
+ #df_new.to_hdf(filename, "df", format = "table")
class TestHDFComplexValues(Base):
# GH10447
@@ -5025,5 +5041,7 @@ def _test_sort(obj):
if __name__ == '__main__':
import nose
+ #nose.runmodule(argv=[__file__, '-vvs'],
+ # exit=False)
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| supersedes #10243
closes #6245
Here is the code as it stands now. Appending MultiIndex axes now works. The one test that still fails is test_append_frame_column_oriented: writing the DateTimeIndex as columns fails because the index is converted to an array of timestamps so np.array creates an object array. If it could be passed down unmolested np.array() would do the right thing. I don't yet have a good idea how to fix that.
I added a test for reading the old format, let me know if you think it's sufficient coverage. I've been testing the reading more broadly against my own data.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11788 | 2015-12-07T17:24:11Z | 2016-01-30T15:45:05Z | null | 2019-01-11T13:25:30Z |
DOC: Add examples for pandas.tseries.timedeltas | diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py
index 11200bb2540cd..a880f29989f57 100644
--- a/pandas/tseries/timedeltas.py
+++ b/pandas/tseries/timedeltas.py
@@ -32,6 +32,28 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise', coerce=None):
Returns
-------
ret : timedelta64/arrays of timedelta64 if parsing succeeded
+
+ Examples
+ --------
+
+ Parsing a single string to a Timedelta:
+
+ >>> pd.to_timedelta('1 days 06:05:01.00003')
+ Timedelta('1 days 06:05:01.000030')
+ >>> pd.to_timedelta('15.5us')
+ Timedelta('0 days 00:00:00.000015')
+
+ Parsing a list or array of strings:
+
+ >>> pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
+ TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015', NaT], dtype='timedelta64[ns]', freq=None)
+
+ Converting numbers by specifying the `unit` keyword argument:
+
+ >>> pd.to_timedelta(np.arange(5), unit='s')
+ TimedeltaIndex(['00:00:00', '00:00:01', '00:00:02', '00:00:03', '00:00:04'], dtype='timedelta64[ns]', freq=None)
+ >>> pd.to_timedelta(np.arange(5), unit='d')
+ TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
"""
unit = _validate_timedelta_unit(unit)
| Copied examples from the reStructuredText [tutorial](http://pandas.pydata.org/pandas-docs/stable/timedeltas.html) on timedeltas.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11785 | 2015-12-07T14:30:38Z | 2015-12-16T14:02:19Z | 2015-12-16T14:02:19Z | 2015-12-16T14:05:40Z |
DOC: Add examples for pandas.Series.append | diff --git a/pandas/core/series.py b/pandas/core/series.py
index f3e059b3d6e98..5a468f419f4ab 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1481,6 +1481,36 @@ def append(self, to_append, verify_integrity=False):
Returns
-------
appended : Series
+
+ Examples
+ --------
+ >>> s1 = pd.Series([1, 2, 3])
+ >>> s2 = pd.Series([4, 5, 6])
+ >>> s3 = pd.Series([4, 5, 6], index=[3,4,5])
+ >>> s1.append(s2)
+ 0 1
+ 1 2
+ 2 3
+ 0 4
+ 1 5
+ 2 6
+ dtype: int64
+
+ >>> s1.append(s3)
+ 0 1
+ 1 2
+ 2 3
+ 3 4
+ 4 5
+ 5 6
+ dtype: int64
+
+ With `verify_integrity` set to True:
+
+ >>> s1.append(s2, verify_integrity=True)
+ ValueError: Indexes have overlapping values: [0, 1, 2]
+
+
"""
from pandas.tools.merge import concat
| https://api.github.com/repos/pandas-dev/pandas/pulls/11781 | 2015-12-07T06:30:24Z | 2015-12-07T09:11:52Z | 2015-12-07T09:11:52Z | 2015-12-07T14:27:50Z | |
BUG: to_numeric should raise if input is more than one dimension #11776 | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 8fe7a5753f1d1..cc2bb47dea3bd 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -196,3 +196,4 @@ Bug Fixes
- Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`)
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
+- Bug in ``to_numeric`` where it does not raise if input is more than one dimension (:issue:`11776`)
diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py
index 72ce7d8659157..a00b27c81e668 100644
--- a/pandas/tools/tests/test_util.py
+++ b/pandas/tools/tests/test_util.py
@@ -113,7 +113,6 @@ def test_error(self):
expected = pd.Series([1, -3.14, np.nan])
tm.assert_series_equal(res, expected)
-
def test_list(self):
s = ['1', '-3.14', '7']
res = to_numeric(s)
@@ -136,6 +135,14 @@ def test_all_nan(self):
expected = pd.Series([np.nan, np.nan, np.nan])
tm.assert_series_equal(res, expected)
+ def test_type_check(self):
+ # GH 11776
+ df = pd.DataFrame({'a': [1, -3.14, 7], 'b': ['4', '5', '6']})
+ with tm.assertRaisesRegexp(TypeError, "1-d array"):
+ to_numeric(df)
+ for errors in ['ignore', 'raise', 'coerce']:
+ with tm.assertRaisesRegexp(TypeError, "1-d array"):
+ to_numeric(df, errors=errors)
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/tools/util.py b/pandas/tools/util.py
index 925c23255b5f5..c3ebadfdb9e0b 100644
--- a/pandas/tools/util.py
+++ b/pandas/tools/util.py
@@ -56,7 +56,7 @@ def to_numeric(arg, errors='raise'):
Parameters
----------
- arg : list, tuple or array of objects, or Series
+ arg : 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 NaN
@@ -84,6 +84,8 @@ def to_numeric(arg, errors='raise'):
index, name = arg.index, arg.name
elif isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
+ elif getattr(arg, 'ndim', 1) > 1:
+ raise TypeError('arg must be a list, tuple, 1-d array, or Series')
conv = arg
arg = com._ensure_object(arg)
diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py
index c6fb54ceec644..db0ee4696d289 100644
--- a/pandas/tseries/tests/test_period.py
+++ b/pandas/tseries/tests/test_period.py
@@ -2984,6 +2984,16 @@ def test_to_datetime_1703(self):
result = index.to_datetime()
self.assertEqual(result[0], Timestamp('1/1/2012'))
+ def test_to_datetime_dimensions(self):
+ # GH 11776
+ df = DataFrame({'a': ['1/1/2012', '1/2/2012'],
+ 'b': ['12/30/2012', '12/31/2012']})
+ with tm.assertRaisesRegexp(TypeError, "1-d array"):
+ to_datetime(df)
+ for errors in ['ignore', 'raise', 'coerce']:
+ with tm.assertRaisesRegexp(TypeError, "1-d array"):
+ to_datetime(df, errors=errors)
+
def test_get_loc_msg(self):
idx = period_range('2000-1-1', freq='A', periods=10)
bad_period = Period('2012', 'A')
diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py
index 4bff2e3c5c2cd..67f1b12ec8ead 100644
--- a/pandas/tseries/tests/test_timedeltas.py
+++ b/pandas/tseries/tests/test_timedeltas.py
@@ -444,26 +444,36 @@ def check(value):
def test_timedelta_range(self):
- expected = to_timedelta(np.arange(5),unit='D')
- result = timedelta_range('0 days',periods=5,freq='D')
+ expected = to_timedelta(np.arange(5), unit='D')
+ result = timedelta_range('0 days', periods=5, freq='D')
tm.assert_index_equal(result, expected)
- expected = to_timedelta(np.arange(11),unit='D')
- result = timedelta_range('0 days','10 days',freq='D')
+ expected = to_timedelta(np.arange(11), unit='D')
+ result = timedelta_range('0 days', '10 days', freq='D')
tm.assert_index_equal(result, expected)
- expected = to_timedelta(np.arange(5),unit='D') + Second(2) + Day()
- result = timedelta_range('1 days, 00:00:02','5 days, 00:00:02',freq='D')
+ expected = to_timedelta(np.arange(5), unit='D') + Second(2) + Day()
+ result = timedelta_range('1 days, 00:00:02', '5 days, 00:00:02', freq='D')
tm.assert_index_equal(result, expected)
- expected = to_timedelta([1,3,5,7,9],unit='D') + Second(2)
- result = timedelta_range('1 days, 00:00:02',periods=5,freq='2D')
+ expected = to_timedelta([1,3,5,7,9], unit='D') + Second(2)
+ result = timedelta_range('1 days, 00:00:02', periods=5, freq='2D')
tm.assert_index_equal(result, expected)
- expected = to_timedelta(np.arange(50),unit='T')*30
- result = timedelta_range('0 days',freq='30T',periods=50)
+ expected = to_timedelta(np.arange(50), unit='T') * 30
+ result = timedelta_range('0 days', freq='30T', periods=50)
tm.assert_index_equal(result, expected)
+ # GH 11776
+ arr = np.arange(10).reshape(2, 5)
+ df = pd.DataFrame(np.arange(10).reshape(2, 5))
+ for arg in (arr, df):
+ with tm.assertRaisesRegexp(TypeError, "1-d array"):
+ to_timedelta(arg)
+ for errors in ['ignore', 'raise', 'coerce']:
+ with tm.assertRaisesRegexp(TypeError, "1-d array"):
+ to_timedelta(arg, errors=errors)
+
# issue10583
df = pd.DataFrame(np.random.normal(size=(10,4)))
df.index = pd.timedelta_range(start='0s', periods=10, freq='s')
diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py
index 11200bb2540cd..9a21d426c3aab 100644
--- a/pandas/tseries/timedeltas.py
+++ b/pandas/tseries/timedeltas.py
@@ -19,7 +19,7 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise', coerce=None):
Parameters
----------
- arg : string, timedelta, array of strings (with possible NAs)
+ arg : string, timedelta, list, tuple, 1-d array, or Series
unit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, which is an integer/float number
box : boolean, default True
- If True returns a Timedelta/TimedeltaIndex of the results
@@ -37,7 +37,7 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise', coerce=None):
def _convert_listlike(arg, box, unit, name=None):
- if isinstance(arg, (list,tuple)) or ((hasattr(arg,'__iter__') and not hasattr(arg,'dtype'))):
+ if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
arg = np.array(list(arg), dtype='O')
# these are shortcutable
@@ -62,8 +62,10 @@ def _convert_listlike(arg, box, unit, name=None):
return Series(values, index=arg.index, name=arg.name, dtype='m8[ns]')
elif isinstance(arg, ABCIndexClass):
return _convert_listlike(arg, box=box, unit=unit, name=arg.name)
- elif is_list_like(arg):
+ elif is_list_like(arg) and getattr(arg, 'ndim', 1) == 1:
return _convert_listlike(arg, box=box, unit=unit)
+ elif getattr(arg, 'ndim', 1) > 1:
+ raise TypeError('arg must be a string, timedelta, list, tuple, 1-d array, or Series')
# ...so it must be a scalar value. Return scalar.
return _coerce_scalar_to_timedelta_type(arg, unit=unit, box=box, errors=errors)
diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py
index c38878fe398e4..995c920358b9f 100644
--- a/pandas/tseries/tools.py
+++ b/pandas/tseries/tools.py
@@ -188,7 +188,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
Parameters
----------
- arg : string, datetime, array of strings (with possible NAs)
+ 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
@@ -288,7 +288,7 @@ def _to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
def _convert_listlike(arg, box, format, name=None):
- if isinstance(arg, (list,tuple)):
+ if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
@@ -312,8 +312,9 @@ def _convert_listlike(arg, box, format, name=None):
result = arg.astype('datetime64[ns]')
if box:
return DatetimeIndex(result, tz='utc' if utc else None, name=name)
-
return result
+ elif getattr(arg, 'ndim', 1) > 1:
+ raise TypeError('arg must be a string, datetime, list, tuple, 1-d array, or Series')
arg = com._ensure_object(arg)
require_iso8601 = False
| closes #11776
| https://api.github.com/repos/pandas-dev/pandas/pulls/11780 | 2015-12-07T05:22:33Z | 2015-12-10T12:10:45Z | 2015-12-10T12:10:45Z | 2015-12-10T12:10:49Z |
BUG: [GH 11738] Fix for end_time when multiple of a frequency is requested | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 8fe7a5753f1d1..37a121992dadd 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -187,7 +187,7 @@ Bug Fixes
-
+- Bug in parsing timezone offset strings with non-zero minutes (:issue:`11708`)
@@ -196,3 +196,5 @@ Bug Fixes
- Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`)
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
+
+- Bug in ``end_time`` computation in ``Period`` when a multiple of time period is requested (:issue: `11738`)
diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx
index 43adbbb66b80e..b325ccbb581d1 100644
--- a/pandas/src/period.pyx
+++ b/pandas/src/period.pyx
@@ -889,7 +889,8 @@ cdef class Period(object):
ordinal = self.ordinal
else:
# freq.n can't be negative or 0
- ordinal = (self + self.freq.n).start_time.value - 1
+ # ordinal = (self + self.freq.n).start_time.value - 1
+ ordinal = (self + 1).start_time.value - 1
return Timestamp(ordinal)
def to_timestamp(self, freq=None, how='start', tz=None):
diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py
index c6fb54ceec644..68bdacd0c4f46 100644
--- a/pandas/tseries/tests/test_period.py
+++ b/pandas/tseries/tests/test_period.py
@@ -566,19 +566,26 @@ def _ex(*args):
xp = _ex(2012, 2, 1)
self.assertEqual(xp, p.end_time)
- xp = _ex(2012, 1, 2)
p = Period('2012', freq='D')
- self.assertEqual(p.end_time, xp)
+ xp = _ex(2012, 1, 2)
+ self.assertEqual(xp, p.end_time)
- xp = _ex(2012, 1, 1, 1)
p = Period('2012', freq='H')
- self.assertEqual(p.end_time, xp)
+ xp = _ex(2012, 1, 1, 1)
+ self.assertEqual(xp, p.end_time)
+ p = Period('2012', freq='B')
xp = _ex(2012, 1, 3)
- self.assertEqual(Period('2012', freq='B').end_time, xp)
+ self.assertEqual(xp, p.end_time)
+ p = Period('2012', freq='W')
xp = _ex(2012, 1, 2)
- self.assertEqual(Period('2012', freq='W').end_time, xp)
+ self.assertEqual(xp, p.end_time)
+
+ # Test for GH 11738
+ p = Period('2012', freq='15D')
+ xp = _ex(2012, 1, 16)
+ self.assertEqual(xp, p.end_time)
p = Period('NaT', freq='W')
self.assertTrue(p.end_time is tslib.NaT)
| closes #11738
1. Added a new test with the multiple of a frequency.
2. Rearranged some tests to improve readability of the code.
3. Added bug fix to whatsnew
4. Added a comment in the test
Didn't follow the contributing guidelines carefully in PR #11771 and hence this new PR.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11779 | 2015-12-07T03:36:26Z | 2015-12-07T11:11:04Z | null | 2015-12-07T18:26:48Z |
Missing _repr_latex_ method for latex/pdf conversion in jupyter notebooks | diff --git a/doc/source/options.rst b/doc/source/options.rst
index 1b125fa76f68b..0b2ba03a441a2 100644
--- a/doc/source/options.rst
+++ b/doc/source/options.rst
@@ -307,6 +307,10 @@ 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.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
+ uses the longtable format.
display.line_width 80 Deprecated. Use `display.width` instead.
display.max_columns 20 max_rows and max_columns are used
in __repr__() methods to decide if
@@ -498,3 +502,5 @@ 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/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 21e3e86e07f37..cc7d357eb3dbd 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -26,7 +26,6 @@ Check the :ref:`API Changes <whatsnew_0180.api>` and :ref:`deprecations <whatsne
New features
~~~~~~~~~~~~
-
.. _whatsnew_0180.enhancements.moments:
Window functions are now methods
@@ -108,6 +107,7 @@ Other enhancements
- ``read_excel`` now supports s3 urls of the format ``s3://bucketname/filename`` (:issue:`11447`)
- 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`)
.. _whatsnew_0180.enhancements.rounding:
diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py
index 35689030d9c09..df24ef6f3743e 100644
--- a/pandas/core/config_init.py
+++ b/pandas/core/config_init.py
@@ -220,6 +220,19 @@
df.info() is called. Valid values True,False,'deep'
"""
+pc_latex_escape = """
+: bool
+ This specifies if the to_latex method of a Dataframe uses escapes special
+ characters.
+ method. Valid values: False,True
+"""
+
+pc_latex_longtable = """
+:bool
+ This specifies if the to_latex method of a Dataframe uses the longtable format.
+ method. Valid values: False,True
+"""
+
style_backup = dict()
@@ -297,6 +310,10 @@ 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.escape',True, pc_latex_escape,
+ validator=is_bool)
+ cf.register_option('latex.longtable',False,pc_latex_longtable,
+ validator=is_bool)
cf.deprecate_option('display.line_width',
msg=pc_line_width_deprecation_warning,
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 2fc0786aa1e09..fe2f2242e0d21 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -576,6 +576,13 @@ def _repr_html_(self):
else:
return None
+ 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()
+
@property
def style(self):
"""
@@ -1540,7 +1547,7 @@ 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=False, escape=True):
+ longtable=None, escape=None):
"""
Render a DataFrame to a tabular environment table. You can splice
this into a LaTeX document. Requires \\usepackage{booktabs}.
@@ -1552,10 +1559,12 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None,
column_format : str, default None
The columns format as specified in `LaTeX table format
<https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g 'rcl' for 3 columns
- longtable : boolean, default False
+ longtable : boolean, default will be read from the pandas config module
+ default: False
Use a longtable environment instead of tabular. Requires adding
a \\usepackage{longtable} to your LaTeX preamble.
- escape : boolean, default True
+ escape : boolean, default will be read from the pandas config module
+ default: True
When set to False prevents from escaping latex special
characters in column names.
@@ -1565,7 +1574,12 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None,
warnings.warn("colSpace is deprecated, use col_space",
FutureWarning, stacklevel=2)
col_space = colSpace
-
+ # Get defaults from the pandas config
+ if longtable is None:
+ longtable = get_option("display.latex.longtable")
+ if escape is None:
+ escape = get_option("display.latex.escape")
+
formatter = fmt.DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
header=header, index=index,
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 70548b7c9f42f..45f528ceec02b 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -4752,6 +4752,21 @@ def test_to_dict(self):
for k2, v2 in compat.iteritems(v):
self.assertEqual(v2, recons_data[k2][k])
+ def test_latex_repr(self):
+ result=r"""\begin{tabular}{llll}
+\toprule
+{} & 0 & 1 & 2 \\
+\midrule
+0 & $\alpha$ & b & c \\
+1 & 1 & 2 & 3 \\
+\bottomrule
+\end{tabular}
+"""
+ with option_context("display.latex.escape",False):
+ df=DataFrame([[r'$\alpha$','b','c'],[1,2,3]])
+ self.assertEqual(result,df._repr_latex_())
+
+
def test_to_dict_timestamp(self):
# GH11247
| Hello, this is my very first pull request on github. I added latex/pdf conversion capabilities to the dataframe when used in a Jupyter/Ipython notebook. The method simply uses the existing to_latex method of the Dataframe class. I believe teh following Issues or PR have already attempted this, but they seem closed: #11399 and #9821
The _repr_latex_ method looks for newly created options:
- display.latex_escape
- display.latex_longtable
which are passed as arguments to the to_latex method.
a simple test has been added to check if the conversion works. However, if the
implementation of the to_latex method changes, the test function would also have to be updated.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11778 | 2015-12-06T21:16:17Z | 2015-12-19T17:14:51Z | 2015-12-19T17:14:51Z | 2018-06-19T09:41:27Z |
BUG/API: QuarterBegin n=0 #11406 | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index aa43ebd70320f..a2c6212c58e01 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -89,12 +89,40 @@ In addition, ``.round()`` will be available thru the ``.dt`` accessor of ``Serie
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Bug in QuarterBegin with n=0
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+In previous versions, the behavior of the QuarterBegin offset was inconsistent
+depending on the date when the ``n`` parameter was 0.
+The general semantics of anchored offsets for ``n=0`` is to not move the date
+when it is an anchor point (e.g., a quarter start date), and otherwise roll
+forward to the next anchor point.
+.. ipython:: python
+
+ d = pd.Timestamp('2014-02-01')
+ d
+ d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
+ d + pd.offsets.QuarterBegin(n=0, startingMonth=1)
+
+For the ``QuarterBegin`` offset in previous versions, the date would be rolled
+*backwards* if date was in the same month as the quarter start date.
+
+.. code-block:: python
+ In [3]: d = pd.Timestamp('2014-02-15')
+ In [4]: d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
+ Out[4]: Timestamp('2014-02-01 00:00:00')
+
+This behavior has been corrected in version 0.18.0, which is consistent with
+other anchored offsets like ``MonthBegin`` and ``YearBegin``.
+
+.. ipython:: python
+ d = pd.Timestamp('2014-02-15')
+ d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
Other API Changes
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index 69c1c60c354dc..caad86dfdb728 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -1767,7 +1767,7 @@ def apply(self, other):
# make sure you roll forward, so negate
monthsSince = monthsSince - 3
- if n < 0 and (monthsSince == 0 and other.day > 1):
+ if n <= 0 and (monthsSince == 0 and other.day > 1):
# after start, so come back an extra period as if rolled forward
n = n + 1
diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py
index fada4a966c10b..30fadfac2a3ae 100644
--- a/pandas/tseries/tests/test_offsets.py
+++ b/pandas/tseries/tests/test_offsets.py
@@ -2918,8 +2918,8 @@ def test_offset(self):
datetime(2008, 2, 29): datetime(2008, 4, 1),
datetime(2008, 3, 15): datetime(2008, 4, 1),
datetime(2008, 3, 31): datetime(2008, 4, 1),
- datetime(2008, 4, 15): datetime(2008, 4, 1),
- datetime(2008, 4, 30): datetime(2008, 4, 1), }))
+ datetime(2008, 4, 15): datetime(2008, 7, 1),
+ datetime(2008, 4, 30): datetime(2008, 7, 1), }))
tests.append((QuarterBegin(startingMonth=1, n=-1),
{datetime(2008, 1, 1): datetime(2007, 10, 1),
| closes #11406 - makes `QuarterBegin` for `n=0` match Month/Year.
xref #11427 - once this is merged the failures there should pass - there is also a little more documentation about general offset semantics in that PR.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11777 | 2015-12-06T20:57:55Z | 2015-12-10T12:26:09Z | null | 2015-12-10T12:26:09Z |
BUG: Parsing offset strings with non-zero minutes was incorrect | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 8fe7a5753f1d1..5d26896ba1e07 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -187,7 +187,7 @@ Bug Fixes
-
+- Bug in parsing timezone offset strings with non-zero minutes (:issue:`11708`)
diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c
index f7835971ed0b7..bc4ef1b3c8184 100644
--- a/pandas/src/datetime/np_datetime_strings.c
+++ b/pandas/src/datetime/np_datetime_strings.c
@@ -869,7 +869,7 @@ parse_iso_8601_datetime(char *str, int len,
if (out_local != NULL) {
*out_local = 1;
// Unlike NumPy, do not change internal value to local time
- *out_tzoffset = 60 * offset_hour - offset_minute;
+ *out_tzoffset = 60 * offset_hour + offset_minute;
}
}
diff --git a/pandas/tests/test_tseries.py b/pandas/tests/test_tseries.py
index b94c91f72802a..72318f8073595 100644
--- a/pandas/tests/test_tseries.py
+++ b/pandas/tests/test_tseries.py
@@ -10,7 +10,6 @@
import pandas._period as period
import pandas.algos as algos
from pandas.core import common as com
-from pandas.tseries.holiday import Holiday, SA, next_monday,USMartinLutherKingJr,USMemorialDay,AbstractHolidayCalendar
import datetime
from pandas import DateOffset
@@ -694,40 +693,6 @@ def test_get_period_field_raises_on_out_of_range(self):
def test_get_period_field_array_raises_on_out_of_range(self):
self.assertRaises(ValueError, period.get_period_field_arr, -1, np.empty(1), 0)
-class TestFederalHolidayCalendar(tm.TestCase):
-
- # Test for issue 10278
- def test_no_mlk_before_1984(self):
- class MLKCalendar(AbstractHolidayCalendar):
- rules=[USMartinLutherKingJr]
- holidays = MLKCalendar().holidays(start='1984', end='1988').to_pydatetime().tolist()
- # Testing to make sure holiday is not incorrectly observed before 1986
- self.assertEqual(holidays, [datetime.datetime(1986, 1, 20, 0, 0), datetime.datetime(1987, 1, 19, 0, 0)])
-
- def test_memorial_day(self):
- class MemorialDay(AbstractHolidayCalendar):
- rules=[USMemorialDay]
- holidays = MemorialDay().holidays(start='1971', end='1980').to_pydatetime().tolist()
- # Fixes 5/31 error and checked manually against wikipedia
- self.assertEqual(holidays, [datetime.datetime(1971, 5, 31, 0, 0), datetime.datetime(1972, 5, 29, 0, 0),
- datetime.datetime(1973, 5, 28, 0, 0), datetime.datetime(1974, 5, 27, 0, 0),
- datetime.datetime(1975, 5, 26, 0, 0), datetime.datetime(1976, 5, 31, 0, 0),
- datetime.datetime(1977, 5, 30, 0, 0), datetime.datetime(1978, 5, 29, 0, 0),
- datetime.datetime(1979, 5, 28, 0, 0)])
-
-
-
-
-class TestHolidayConflictingArguments(tm.TestCase):
-
- # GH 10217
-
- def test_both_offset_observance_raises(self):
-
- with self.assertRaises(NotImplementedError) as cm:
- h = Holiday("Cyber Monday", month=11, day=1,
- offset=[DateOffset(weekday=SA(4))], observance=next_monday)
-
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/tseries/tests/test_holiday.py b/pandas/tseries/tests/test_holiday.py
index 1da397e768a86..dc07a6d455f86 100644
--- a/pandas/tseries/tests/test_holiday.py
+++ b/pandas/tseries/tests/test_holiday.py
@@ -7,7 +7,7 @@
USFederalHolidayCalendar, USMemorialDay, USThanksgivingDay,
nearest_workday, next_monday_or_tuesday, next_monday,
previous_friday, sunday_to_monday, Holiday, DateOffset,
- MO, Timestamp, AbstractHolidayCalendar, get_calendar,
+ MO, SA, Timestamp, AbstractHolidayCalendar, get_calendar,
HolidayCalendarFactory, next_workday, previous_workday,
before_nearest_workday, EasterMonday, GoodFriday,
after_nearest_workday, weekend_to_monday, USLaborDay,
@@ -358,7 +358,37 @@ def test_after_nearest_workday(self):
self.assertEqual(after_nearest_workday(self.sa), self.mo)
self.assertEqual(after_nearest_workday(self.su), self.tu)
self.assertEqual(after_nearest_workday(self.fr), self.mo)
-
+
+class TestFederalHolidayCalendar(tm.TestCase):
+
+ # Test for issue 10278
+ def test_no_mlk_before_1984(self):
+ class MLKCalendar(AbstractHolidayCalendar):
+ rules=[USMartinLutherKingJr]
+ holidays = MLKCalendar().holidays(start='1984', end='1988').to_pydatetime().tolist()
+ # Testing to make sure holiday is not incorrectly observed before 1986
+ self.assertEqual(holidays, [datetime(1986, 1, 20, 0, 0), datetime(1987, 1, 19, 0, 0)])
+
+ def test_memorial_day(self):
+ class MemorialDay(AbstractHolidayCalendar):
+ rules=[USMemorialDay]
+ holidays = MemorialDay().holidays(start='1971', end='1980').to_pydatetime().tolist()
+ # Fixes 5/31 error and checked manually against wikipedia
+ self.assertEqual(holidays, [datetime(1971, 5, 31, 0, 0), datetime(1972, 5, 29, 0, 0),
+ datetime(1973, 5, 28, 0, 0), datetime(1974, 5, 27, 0, 0),
+ datetime(1975, 5, 26, 0, 0), datetime(1976, 5, 31, 0, 0),
+ datetime(1977, 5, 30, 0, 0), datetime(1978, 5, 29, 0, 0),
+ datetime(1979, 5, 28, 0, 0)])
+
+class TestHolidayConflictingArguments(tm.TestCase):
+
+ # GH 10217
+
+ def test_both_offset_observance_raises(self):
+
+ with self.assertRaises(NotImplementedError) as cm:
+ h = Holiday("Cyber Monday", month=11, day=1,
+ offset=[DateOffset(weekday=SA(4))], observance=next_monday)
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index f618b2593597e..4fe136fceb671 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -141,6 +141,21 @@ def test_constructor_with_stringoffset(self):
expected_repr = "Timestamp('2013-11-01 14:00:00+0900', tz='Asia/Tokyo')"
self.assertEqual(repr(result), expected_repr)
self.assertEqual(result, eval(repr(result)))
+
+ # GH11708
+ # This should be 2015-11-18 10:00 in UTC -> converted to Asia/Katmandu tz
+ result = Timestamp("2015-11-18 15:45:00+05:45", tz="Asia/Katmandu")
+ self.assertEqual(result.value, Timestamp("2015-11-18 10:00").value)
+ expected_repr = "Timestamp('2015-11-18 15:45:00+0545', tz='Asia/Katmandu')"
+ self.assertEqual(repr(result), expected_repr)
+ self.assertEqual(result, eval(repr(result)))
+
+ # This should be 2015-11-18 10:00 in UTC -> converted to Asia/Kolkata tz
+ result = Timestamp("2015-11-18 15:30:00+05:30", tz="Asia/Kolkata")
+ self.assertEqual(result.value, Timestamp("2015-11-18 10:00").value)
+ expected_repr = "Timestamp('2015-11-18 15:30:00+0530', tz='Asia/Kolkata')"
+ self.assertEqual(repr(result), expected_repr)
+ self.assertEqual(result, eval(repr(result)))
def test_constructor_invalid(self):
with tm.assertRaisesRegexp(TypeError, 'Cannot convert input'):
@@ -620,6 +635,21 @@ def test_parsers_quarterly_with_freq(self):
result, _, _ = tools.parse_time_string(date_str, freq=freq)
self.assertEqual(result, exp)
+ def test_parsers_timezone_minute_offsets_roundtrip(self):
+ #GH11708
+ base = to_datetime("2013-01-01 00:00:00")
+ dt_strings = [('2013-01-01 05:45+0545',
+ "Asia/Katmandu",
+ "Timestamp('2013-01-01 05:45:00+0545', tz='Asia/Katmandu')"),
+ ('2013-01-01 05:30+0530',
+ "Asia/Kolkata",
+ "Timestamp('2013-01-01 05:30:00+0530', tz='Asia/Kolkata')")]
+
+ for dt_string, tz, dt_string_repr in dt_strings:
+ dt_time = to_datetime(dt_string)
+ self.assertEqual(base, dt_time)
+ converted_time = dt_time.tz_localize('UTC').tz_convert(tz)
+ self.assertEqual(dt_string_repr, repr(converted_time))
class TestArrayToDatetime(tm.TestCase):
def test_parsing_valid_dates(self):
@@ -721,7 +751,7 @@ def test_parsing_timezone_offsets(self):
'01-01-2013 08:00:00+08:00',
'2013-01-01T08:00:00.000000000+0800',
'2012-12-31T16:00:00.000000000-0800',
- '12-31-2012 23:00:00-01:00',
+ '12-31-2012 23:00:00-01:00'
]
expected_output = tslib.array_to_datetime(
| Closes #11708
Minutes needed to be added to the hour offset rather than subtracted.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11774 | 2015-12-06T12:49:21Z | 2015-12-07T00:33:58Z | 2015-12-07T00:33:58Z | 2015-12-07T00:34:07Z |
BUG: Fixed DataFrame info() for duplicated columns, GH11761 | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 8fe7a5753f1d1..f35a4f7970bee 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -196,3 +196,9 @@ Bug Fixes
- Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`)
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
+
+
+
+
+
+- Bug in ``DataFrame.info`` when duplicated column names exist (:issue:`11761`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 81bbbcc873107..bc60e1dff9ab5 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1655,7 +1655,7 @@ def _verbose_repr():
dtypes = self.dtypes
for i, col in enumerate(self.columns):
- dtype = dtypes[col]
+ dtype = dtypes.iloc[i]
col = com.pprint_thing(col)
count = ""
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index d60c5a4bd1e78..6ab4c8fb7251d 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -7559,6 +7559,18 @@ def test_info_duplicate_columns(self):
columns=['a', 'a', 'b', 'b'])
frame.info(buf=io)
+ def test_info_duplicate_columns_shows_correct_dtypes(self):
+ # GH11761
+ io = StringIO()
+
+ frame = DataFrame([[1, 2.0]],
+ columns=['a', 'a'])
+ frame.info(buf=io)
+ io.seek(0)
+ lines = io.readlines()
+ self.assertEqual('a 1 non-null int64\n', lines[3])
+ self.assertEqual('a 1 non-null float64\n', lines[4])
+
def test_info_shows_column_dtypes(self):
dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
'complex128', 'object', 'bool']
| See #11761.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11772 | 2015-12-06T12:21:23Z | 2015-12-07T11:33:41Z | null | 2015-12-07T11:46:05Z |
BUG: [GH 11738] Fix for end_time when multiple of a frequency is requested | diff --git a/.gitignore b/.gitignore
index d33df2df6e548..12594beff1d26 100644
--- a/.gitignore
+++ b/.gitignore
@@ -82,6 +82,7 @@ scikits
# Performance Testing #
#######################
asv_bench/
+mytests/
# Documentation generated files #
#################################
diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx
index 43adbbb66b80e..b325ccbb581d1 100644
--- a/pandas/src/period.pyx
+++ b/pandas/src/period.pyx
@@ -889,7 +889,8 @@ cdef class Period(object):
ordinal = self.ordinal
else:
# freq.n can't be negative or 0
- ordinal = (self + self.freq.n).start_time.value - 1
+ # ordinal = (self + self.freq.n).start_time.value - 1
+ ordinal = (self + 1).start_time.value - 1
return Timestamp(ordinal)
def to_timestamp(self, freq=None, how='start', tz=None):
diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py
index c6fb54ceec644..5234c8fd78562 100644
--- a/pandas/tseries/tests/test_period.py
+++ b/pandas/tseries/tests/test_period.py
@@ -566,19 +566,25 @@ def _ex(*args):
xp = _ex(2012, 2, 1)
self.assertEqual(xp, p.end_time)
- xp = _ex(2012, 1, 2)
p = Period('2012', freq='D')
- self.assertEqual(p.end_time, xp)
+ xp = _ex(2012, 1, 2)
+ self.assertEqual(xp, p.end_time)
- xp = _ex(2012, 1, 1, 1)
p = Period('2012', freq='H')
- self.assertEqual(p.end_time, xp)
+ xp = _ex(2012, 1, 1, 1)
+ self.assertEqual(xp, p.end_time)
+ p = Period('2012', freq='B')
xp = _ex(2012, 1, 3)
- self.assertEqual(Period('2012', freq='B').end_time, xp)
+ self.assertEqual(xp, p.end_time)
+ p = Period('2012', freq='W')
xp = _ex(2012, 1, 2)
- self.assertEqual(Period('2012', freq='W').end_time, xp)
+ self.assertEqual(xp, p.end_time)
+
+ p = Period('2012', freq='15D')
+ xp = _ex(2012, 1, 16)
+ self.assertEqual(xp, p.end_time)
p = Period('NaT', freq='W')
self.assertTrue(p.end_time is tslib.NaT)
| closes #11738
1. Added a new test with the multiple of a frequency.
2. Rearranged some tests to improve readability of the code.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11771 | 2015-12-06T06:13:56Z | 2015-12-07T03:06:22Z | null | 2015-12-07T11:05:39Z |
MNT: simplify AutoDateFormatter | diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py
index a9fee1d5c3ee6..9bcb6348f01cc 100644
--- a/pandas/tseries/converter.py
+++ b/pandas/tseries/converter.py
@@ -23,6 +23,9 @@
from pandas.tseries.frequencies import FreqGroup
from pandas.tseries.period import Period, PeriodIndex
+from matplotlib.dates import (HOURS_PER_DAY, MINUTES_PER_DAY,
+ SEC_PER_DAY, MUSECONDS_PER_DAY)
+
def register():
units.registry[lib.Timestamp] = DatetimeConverter()
@@ -136,11 +139,6 @@ def get_datevalue(date, freq):
return None
raise ValueError("Unrecognizable date '%s'" % date)
-HOURS_PER_DAY = 24.
-MINUTES_PER_DAY = 60. * HOURS_PER_DAY
-SECONDS_PER_DAY = 60. * MINUTES_PER_DAY
-MUSECONDS_PER_DAY = 1e6 * SECONDS_PER_DAY
-
def _dt_to_float_ordinal(dt):
"""
@@ -222,31 +220,6 @@ def __init__(self, locator, tz=None, defaultfmt='%Y-%m-%d'):
# matplotlib.dates._UTC has no _utcoffset called by pandas
if self._tz is dates.UTC:
self._tz._utcoffset = self._tz.utcoffset(None)
- self.scaled = {
- 365.0: '%Y',
- 30.: '%b %Y',
- 1.0: '%b %d %Y',
- 1. / 24.: '%H:%M:%S',
- 1. / 24. / 3600. / 1000.: '%H:%M:%S.%f'
- }
-
- def _get_fmt(self, x):
-
- scale = float(self._locator._get_unit())
-
- fmt = self.defaultfmt
-
- for k in sorted(self.scaled):
- if k >= scale:
- fmt = self.scaled[k]
- break
-
- return fmt
-
- def __call__(self, x, pos=0):
- fmt = self._get_fmt(x)
- self._formatter = dates.DateFormatter(fmt, self._tz)
- return self._formatter(x, pos)
class PandasAutoDateLocator(dates.AutoDateLocator):
| - import constants from matplolib.dates
- do not re-implement AutoDateFormatter functionality that
is already upstream
| https://api.github.com/repos/pandas-dev/pandas/pulls/11770 | 2015-12-06T04:18:03Z | 2015-12-08T15:20:46Z | 2015-12-08T15:20:46Z | 2016-05-10T21:03:01Z |
WIP: display_format for style | diff --git a/pandas/core/style.py b/pandas/core/style.py
index 59bf6118a79c6..3b7ef77172124 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -19,6 +19,7 @@
import numpy as np
import pandas as pd
from pandas.compat import lzip
+import pandas.core.common as com
from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice
try:
import matplotlib.pyplot as plt
@@ -117,11 +118,7 @@ class Styler(object):
<tr>
{% for c in r %}
<{{c.type}} id="T_{{uuid}}{{c.id}}" class="{{c.class}}">
- {% if c.value is number %}
- {{c.value|round(precision)}}
- {% else %}
- {{c.value}}
- {% endif %}
+ {{c.display_value}}
{% endfor %}
</tr>
{% endfor %}
@@ -152,6 +149,13 @@ def __init__(self, data, precision=None, table_styles=None, uuid=None,
precision = pd.options.display.precision
self.precision = precision
self.table_attributes = table_attributes
+ # display_funcs maps (row, col) -> formatting function
+ def default_display_func(x):
+ if com.is_float(x):
+ return '{:>.{precision}g}'.format(x, precision=self.precision)
+ else:
+ return x
+ self._display_funcs = defaultdict(lambda: default_display_func)
def _repr_html_(self):
'''
@@ -202,7 +206,10 @@ def _translate(self):
cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c]
cs.extend(cell_context.get(
"col_headings", {}).get(r, {}).get(c, []))
- row_es.append({"type": "th", "value": clabels[r][c],
+ value = clabels[r][c]
+ row_es.append({"type": "th",
+ "value": value,
+ "display_value": value,
"class": " ".join(cs)})
head.append(row_es)
@@ -213,14 +220,21 @@ def _translate(self):
"row_headings", {}).get(r, {}).get(c, []))
row_es = [{"type": "th",
"value": rlabels[r][c],
- "class": " ".join(cs)}
+ "class": " ".join(cs),
+ "display_value": rlabels[r][c]}
for c in range(len(rlabels[r]))]
for c, col in enumerate(self.data.columns):
cs = [DATA_CLASS, "row%s" % r, "col%s" % c]
cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
- row_es.append({"type": "td", "value": self.data.iloc[r][c],
- "class": " ".join(cs), "id": "_".join(cs[1:])})
+ formatter = self._display_funcs[(r, c)]
+ value = self.data.iloc[r, c]
+ row_es.append({"type": "td",
+ "value": value,
+ "class": " ".join(cs),
+ "id": "_".join(cs[1:]),
+ "display_value": formatter(value)
+ })
props = []
for x in ctx[r, c]:
# have to handle empty styles like ['']
@@ -238,6 +252,48 @@ def _translate(self):
precision=precision, table_styles=table_styles,
caption=caption, table_attributes=self.table_attributes)
+ def format(self, formatter, subset=None):
+ '''
+ formatter is either an `a` or a dict `{column_name: a}` where
+ a is one of
+ - str: wrapped in: ``a.format(x)``
+ - callable: called with x.
+
+ now `.set_precision(2)` becomes
+
+ ```
+ .format('{:2g}')
+ ```
+ '''
+ from itertools import product
+ from collections.abc import MutableMapping
+
+ just_row_subset = (com.is_list_like(subset) and
+ not isinstance(subset, tuple))
+
+ if issubclass(type(formatter), MutableMapping):
+ # formatter is a dict
+ for col, col_formatter in formatter.items():
+ # get the row index locations out of subset
+ j = self.data.columns.get_indexer_for([col])[0]
+ if not callable(col_formatter):
+ formatter_func = lambda x: col_formatter.format(x)
+
+ if subset is not None and just_row_subset:
+ locs = self.data.index.get_indexer_for(subset)
+ elif subset is not None:
+ locs = self.data.index.get_indexer_for(subset)
+ else:
+ locs = range(len(self.data))
+ for i in locs:
+ self._display_funcs[(i, j)] = formatter_func
+ # single scalar to format all cells with
+ else:
+ locs = product(*(range(x) for x in self.data.shape))
+ for i, j in locs:
+ self._display_funcs[(i, j)] = lambda x: formatter.format(x)
+ return self
+
def render(self):
"""
Render the built up styles to HTML
| Unfinished at the moment. Just wanted to push something up since there are other PRs that could build on this.
This moves the logic of "what value should I display for this cell?" from the Template to the `Styler` class itself. The two biggest benefits are
1. User extensibility: Users provide formatting functions (via an API that's TBD) which are called upon the data values when we `translate`.
2. Maintainability: Instead of complex logic in the template, we have (slightly) less complex logic in the class itself.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11768 | 2015-12-05T17:53:05Z | 2015-12-08T01:44:52Z | null | 2015-12-08T01:44:52Z |
CLN: remove if conditions that are almost never True | diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 713cf08bfc3e2..849a34ba4ca4f 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -75,12 +75,11 @@ PyDateTime_IMPORT
# numpy_pydatetime_import
cdef int64_t NPY_NAT = util.get_nat()
+iNaT = NPY_NAT
# < numpy 1.7 compat for NaT
compat_NaT = np.array([NPY_NAT]).astype('m8[ns]').item()
-# numpy actual nat object
-np_NaT = np.datetime64('NaT')
try:
basestring
@@ -127,7 +126,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False):
if _is_utc(tz):
for i in range(n):
value = arr[i]
- if value == iNaT:
+ if value == NPY_NAT:
result[i] = NaT
else:
pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts)
@@ -135,7 +134,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False):
elif _is_tzlocal(tz) or _is_fixed_offset(tz):
for i in range(n):
value = arr[i]
- if value == iNaT:
+ if value == NPY_NAT:
result[i] = NaT
else:
pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts)
@@ -150,7 +149,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False):
for i in range(n):
value = arr[i]
- if value == iNaT:
+ if value == NPY_NAT:
result[i] = NaT
else:
@@ -169,7 +168,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False):
for i in range(n):
value = arr[i]
- if value == iNaT:
+ if value == NPY_NAT:
result[i] = NaT
else:
pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts)
@@ -188,7 +187,7 @@ def ints_to_pytimedelta(ndarray[int64_t] arr, box=False):
for i in range(n):
value = arr[i]
- if value == iNaT:
+ if value == NPY_NAT:
result[i] = NaT
else:
if box:
@@ -633,7 +632,7 @@ class NaTType(_NaT):
return 'NaT'
def __hash__(self):
- return iNaT
+ return NPY_NAT
def __int__(self):
return NPY_NAT
@@ -706,8 +705,6 @@ def __nat_unpickle(*args):
NaT = NaTType()
-iNaT = util.get_nat()
-
cdef inline bint _checknull_with_nat(object val):
""" utility to check if a value is a nat or not """
return val is None or (
@@ -1162,10 +1159,10 @@ cdef convert_to_tsobject(object ts, object tz, object unit):
if util.is_string_object(ts):
return convert_str_to_tsobject(ts, tz, unit)
- if ts is None or ts is NaT or ts is np_NaT:
+ if ts is None or ts is NaT:
obj.value = NPY_NAT
elif is_datetime64_object(ts):
- if ts.view('i8') == iNaT:
+ if ts.view('i8') == NPY_NAT:
obj.value = NPY_NAT
else:
obj.value = _get_datetime64_nanos(ts)
@@ -1453,7 +1450,7 @@ def datetime_to_datetime64(ndarray[object] values):
for i in range(n):
val = values[i]
if _checknull_with_nat(val):
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
elif PyDateTime_Check(val):
if val.tzinfo is not None:
if inferred_tz is not None:
@@ -1526,7 +1523,7 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None,
# a format based on precision
basic_format = format is None and tz is None
if basic_format:
- consider_values = values[values != iNaT]
+ consider_values = values[values != NPY_NAT]
show_ns = (consider_values%1000).any()
if not show_ns:
@@ -1540,7 +1537,7 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None,
for i in range(N):
val = values[i]
- if val == iNaT:
+ if val == NPY_NAT:
result[i] = na_rep
elif basic_format:
@@ -1869,7 +1866,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
for i in range(n):
val = values[i]
if _checknull_with_nat(val):
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
elif PyDateTime_Check(val):
seen_datetime=1
if val.tzinfo is not None:
@@ -1880,7 +1877,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
_check_dts_bounds(&_ts.dts)
except ValueError:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
else:
@@ -1895,7 +1892,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
_check_dts_bounds(&dts)
except ValueError:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
elif PyDate_Check(val):
@@ -1905,43 +1902,43 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
seen_datetime=1
except ValueError:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
elif util.is_datetime64_object(val):
- if val is np_NaT or val.view('i8') == iNaT:
- iresult[i] = iNaT
+ if get_datetime64_value(val) == NPY_NAT:
+ iresult[i] = NPY_NAT
else:
try:
iresult[i] = _get_datetime64_nanos(val)
seen_datetime=1
except ValueError:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
# if we are coercing, dont' allow integers
elif is_integer_object(val) and not is_coerce:
- if val == iNaT:
- iresult[i] = iNaT
+ if val == NPY_NAT:
+ iresult[i] = NPY_NAT
else:
iresult[i] = val*m
seen_integer=1
elif is_float_object(val) and not is_coerce:
- if val != val or val == iNaT:
- iresult[i] = iNaT
+ if val != val or val == NPY_NAT:
+ iresult[i] = NPY_NAT
else:
iresult[i] = cast_from_unit(val,unit)
seen_integer=1
else:
try:
if len(val) == 0:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
elif val in _nat_strings:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
_string_to_dts(val, &dts, &out_local, &out_tzoffset)
value = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts)
@@ -1954,7 +1951,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
# if requiring iso8601 strings, skip trying other formats
if require_iso8601:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
elif is_raise:
raise ValueError("time data %r does match format specified" %
@@ -1967,7 +1964,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
yearfirst=yearfirst, freq=freq)
except Exception:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise TypeError("invalid string coercion to datetime")
@@ -1976,12 +1973,12 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
iresult[i] = _ts.value
except ValueError:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
except:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
@@ -2002,12 +1999,12 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise',
# set as nan except if its a NaT
if _checknull_with_nat(val):
- if val is np_NaT or val.view('i8') == iNaT:
+ if val.view('i8') == NPY_NAT:
oresult[i] = NaT
else:
oresult[i] = np.nan
elif util.is_datetime64_object(val):
- if val is np_NaT or val.view('i8') == iNaT:
+ if get_datetime64_value(val) == NPY_NAT:
oresult[i] = NaT
else:
oresult[i] = val.item()
@@ -2723,7 +2720,7 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
# have_hhmmss : tracks if we have a regular format hh:mm:ss
if ts in _nat_strings or not len(ts):
- return iNaT
+ return NPY_NAT
for c in ts:
@@ -2764,7 +2761,7 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
r = timedelta_from_spec(number, frac, unit)
except ValueError:
if coerce:
- return iNaT
+ return NPY_NAT
raise
unit, number, frac = [], [c], []
@@ -2793,7 +2790,7 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
have_hhmmss = 1
else:
if coerce:
- return iNaT
+ return NPY_NAT
raise ValueError("expecting hh:mm:ss format, received: {0}".format(ts))
unit, number = [], []
@@ -2829,7 +2826,7 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
result += timedelta_as_neg(r, neg)
except ValueError:
if coerce:
- return iNaT
+ return NPY_NAT
raise
# we have a dot as part of a regular format
@@ -2838,7 +2835,7 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
if (len(number) or len(frac)) and not len(unit) and current_unit is None:
if coerce:
- return iNaT
+ return NPY_NAT
raise ValueError("no units specified")
if len(frac) > 0 and len(frac) <= 3:
@@ -2869,11 +2866,11 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
result += timedelta_as_neg(r, neg)
except ValueError:
if coerce:
- return iNaT
+ return NPY_NAT
raise
else:
if coerce:
- return iNaT
+ return NPY_NAT
raise ValueError("unit abbreviation w/o a number")
# treat as nanoseconds
@@ -2888,7 +2885,7 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
result += timedelta_as_neg(r, neg)
except ValueError:
if coerce:
- return iNaT
+ return NPY_NAT
raise
return result
@@ -2912,19 +2909,19 @@ cdef inline convert_to_timedelta64(object ts, object unit, object coerce):
# handle the numpy < 1.7 case
"""
if _checknull_with_nat(ts):
- return np.timedelta64(iNaT)
+ return np.timedelta64(NPY_NAT)
elif isinstance(ts, Timedelta):
# already in the proper format
ts = np.timedelta64(ts.value)
elif util.is_datetime64_object(ts):
# only accept a NaT here
- if ts.astype('int64') == iNaT:
- return np.timedelta64(iNaT)
+ if ts.astype('int64') == NPY_NAT:
+ return np.timedelta64(NPY_NAT)
elif isinstance(ts, np.timedelta64):
ts = ts.astype("m8[{0}]".format(unit.lower()))
elif is_integer_object(ts):
- if ts == iNaT:
- return np.timedelta64(iNaT)
+ if ts == NPY_NAT:
+ return np.timedelta64(NPY_NAT)
else:
if util.is_array(ts):
ts = ts.astype('int64').item()
@@ -2950,7 +2947,7 @@ cdef inline convert_to_timedelta64(object ts, object unit, object coerce):
ts = np.timedelta64(ts)
elif not isinstance(ts, np.timedelta64):
if coerce:
- return np.timedelta64(iNaT)
+ return np.timedelta64(NPY_NAT)
raise ValueError("Invalid type for timedelta scalar: %s" % type(ts))
return ts.astype('timedelta64[ns]')
@@ -3035,11 +3032,11 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, errors='
val = values[i]
if util.is_string_object(val):
if val in _nat_strings:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
else:
if _checknull_with_nat(val):
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
else:
val = str(val)
@@ -3049,13 +3046,13 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, errors='
found = format_regex.match(val)
if not found:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise ValueError("time data %r does not match format %r (match)" %
(values[i], fmt))
if len(val) != found.end():
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise ValueError("unconverted data remains: %s" %
values[i][found.end():])
@@ -3065,7 +3062,7 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, errors='
found = format_regex.search(val)
if not found:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise ValueError("time data %r does not match format %r (search)" %
(values[i], fmt))
@@ -3200,7 +3197,7 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, errors='
day = datetime_result.day
except ValueError:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
if weekday == -1:
@@ -3220,7 +3217,7 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, errors='
_check_dts_bounds(&dts)
except ValueError:
if is_coerce:
- iresult[i] = iNaT
+ iresult[i] = NPY_NAT
continue
raise
@@ -3367,8 +3364,8 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
if _is_tzlocal(tz1):
for i in range(n):
v = vals[i]
- if v == iNaT:
- utc_dates[i] = iNaT
+ if v == NPY_NAT:
+ utc_dates[i] = NPY_NAT
else:
pandas_datetime_to_datetimestruct(v, PANDAS_FR_ns, &dts)
dt = datetime(dts.year, dts.month, dts.day, dts.hour,
@@ -3380,7 +3377,7 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
trans, deltas, typ = _get_dst_info(tz1)
# all-NaT
- tt = vals[vals!=iNaT]
+ tt = vals[vals!=NPY_NAT]
if not len(tt):
return vals
@@ -3392,8 +3389,8 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
offset = deltas[pos]
for i in range(n):
v = vals[i]
- if v == iNaT:
- utc_dates[i] = iNaT
+ if v == NPY_NAT:
+ utc_dates[i] = NPY_NAT
else:
while pos + 1 < trans_len and v >= trans[pos + 1]:
pos += 1
@@ -3409,8 +3406,8 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
if _is_tzlocal(tz2):
for i in range(n):
v = utc_dates[i]
- if v == iNaT:
- result[i] = iNaT
+ if v == NPY_NAT:
+ result[i] = NPY_NAT
else:
pandas_datetime_to_datetimestruct(v, PANDAS_FR_ns, &dts)
dt = datetime(dts.year, dts.month, dts.day, dts.hour,
@@ -3425,10 +3422,10 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
# use first non-NaT element
# if all-NaT, return all-NaT
- if (result==iNaT).all():
+ if (result==NPY_NAT).all():
return result
- pos = trans.searchsorted(utc_dates[utc_dates!=iNaT][0]) - 1
+ pos = trans.searchsorted(utc_dates[utc_dates!=NPY_NAT][0]) - 1
if pos < 0:
raise ValueError('First time before start of DST info')
@@ -3436,7 +3433,7 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
offset = deltas[pos]
for i in range(n):
v = utc_dates[i]
- if vals[i] == iNaT:
+ if vals[i] == NPY_NAT:
result[i] = vals[i]
else:
while pos + 1 < trans_len and v >= trans[pos + 1]:
| use typed `iNaT` in Cython code when possible, remove checking that is almost always `False`
| https://api.github.com/repos/pandas-dev/pandas/pulls/11767 | 2015-12-05T16:12:27Z | 2015-12-07T11:24:53Z | 2015-12-07T11:24:53Z | 2015-12-07T11:24:56Z |
DOC: Explain `Styler.set_table_styles` | diff --git a/pandas/core/style.py b/pandas/core/style.py
index 5974e0e660a0b..59bf6118a79c6 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -520,17 +520,31 @@ def set_caption(self, caption):
def set_table_styles(self, table_styles):
"""
- Set the table styles on a Styler
+ Set the table styles on a Styler. These are placed in a
+ ``<style>`` tag before the generated HTML table.
.. versionadded:: 0.17.1
Parameters
----------
table_styles: list
+ Each individual table_style should be a dictionary with
+ ``selector`` and ``props`` keys. ``selector`` should be a CSS
+ selector that the style will be applied to (automatically
+ prefixed by the table's UUID) and ``props`` should be a list of
+ tuples with ``(attribute, value)``.
Returns
-------
self
+
+ Examples
+ --------
+ >>> df = pd.DataFrame(np.random.randn(10, 4))
+ >>> df.style.set_table_styles(
+ ... [{'selector': 'tr:hover',
+ ... 'props': [('background-color', 'yellow')]}]
+ ... )
"""
self.table_styles = table_styles
return self
| Also included an example.
Side-note, the API could probably be a lot cleaner here. Accept `*args` instead of a list e.g.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11766 | 2015-12-05T14:07:43Z | 2015-12-05T16:37:39Z | 2015-12-05T16:37:39Z | 2016-11-03T12:38:48Z |
DOC: Remove infer_types from the documentation of read_html GH 11764 | diff --git a/doc/source/io.rst b/doc/source/io.rst
index a9ec5c24b5d46..8001cd3723601 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -1790,12 +1790,6 @@ as well)
dfs = read_html(url, skiprows=range(2))
-Don't infer numeric and date types
-
-.. code-block:: python
-
- dfs = read_html(url, infer_types=False)
-
Specify an HTML attribute
.. code-block:: python
| Remove infer_types from read_html.
Closes #11764
| https://api.github.com/repos/pandas-dev/pandas/pulls/11765 | 2015-12-05T06:07:49Z | 2015-12-05T15:13:23Z | 2015-12-05T15:13:23Z | 2015-12-07T06:42:49Z |
DOC: pd.read_csv doc-string clarification #11555 | diff --git a/doc/source/io.rst b/doc/source/io.rst
index 36d4bd89261c4..0bf6d02ee3f3a 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -75,120 +75,199 @@ The two workhorse functions for reading text files (a.k.a. flat files) are
:func:`~pandas.io.parsers.read_csv` and :func:`~pandas.io.parsers.read_table`.
They both use the same parsing code to intelligently convert tabular
data into a DataFrame object. See the :ref:`cookbook<cookbook.csv>`
-for some advanced strategies
+for some advanced strategies.
+
+Parsing options
+'''''''''''''''
+
+:func:`~pandas.io.parsers.read_csv` and :func:`~pandas.io.parsers.read_table`
+accept the following arguments:
+
+Basic
++++++
+
+filepath_or_buffer : various
+ Either a path to a file (a :class:`python:str`, :class:`python:pathlib.Path`,
+ or :class:`py:py._path.local.LocalPath`), URL (including http, ftp, and S3
+ locations), or any object with a ``read()`` method (such as an open file or
+ :class:`~python:io.StringIO`).
+sep : str, defaults to ``','`` for :func:`~pandas.io.parsers.read_csv`, ``\t`` for :func:`~pandas.io.parsers.read_table`.
+ Delimiter to use. If sep is ``None``, will try to automatically determine this.
+ Regular expressions are accepted, use of a regular expression will force use
+ of the python parsing engine and will ignore quotes in the data.
+delimiter : str, default ``None``
+ Alternative argument name for sep.
+
+Column and Index Locations and Names
+++++++++++++++++++++++++++++++++++++
+
+header : int or list of ints, default ``'infer'``
+ Row number(s) to use as the column names, and the start of the data. Default
+ behavior is as if ``header=0`` if no ``names`` passed, otherwise as if
+ ``header=None``. Explicitly pass ``header=0`` to be able to replace existing
+ names. The header can be a list of ints that specify row locations for a
+ multi-index on the columns e.g. ``[0,1,3]``. Intervening rows that are not
+ specified will be skipped (e.g. 2 in this example is skipped). Note that
+ this parameter ignores commented lines and empty lines if
+ ``skip_blank_lines=True``, so header=0 denotes the first line of data
+ rather than the first line of the file.
+names : array-like, default ``None``
+ List of column names to use. If file contains no header row, then you should
+ explicitly pass ``header=None``.
+index_col : int or sequence or ``False``, default ``None``
+ Column to use as the row labels of the DataFrame. If a sequence is given, a
+ MultiIndex is used. If you have a malformed file with delimiters at the end of
+ each line, you might consider ``index_col=False`` to force pandas to *not* use
+ the first column as the index (row names).
+usecols : array-like, default ``None``
+ Return a subset of the columns. Results in much faster parsing time and lower
+ memory usage
+squeeze : boolean, default ``False``
+ If the parsed data only contains one column then return a Series.
+prefix : str, default ``None``
+ Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
+mangle_dupe_cols : boolean, default ``True``
+ Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'.
+
+General Parsing Configuration
++++++++++++++++++++++++++++++
+
+dtype : Type name or dict of column -> type, default ``None``
+ Data type for data or columns. E.g. ``{'a': np.float64, 'b': np.int32}``
+ (unsupported with ``engine='python'``). Use `str` or `object` to preserve and
+ not interpret dtype.
+engine : {``'c'``, ``'python'``}
+ Parser engine to use. The C engine is faster while the python engine is
+ currently more feature-complete.
+converters : dict, default ``None``
+ Dict of functions for converting values in certain columns. Keys can either be
+ integers or column labels.
+true_values : list, default ``None``
+ Values to consider as ``True``.
+false_values : list, default ``None``
+ Values to consider as ``False``.
+skipinitialspace : boolean, default ``False``
+ Skip spaces after delimiter.
+skiprows : list-like or integer, default ``None``
+ Line numbers to skip (0-indexed) or number of lines to skip (int) at the start
+ of the file.
+skipfooter : int, default ``0``
+ Number of lines at bottom of file to skip (unsupported with engine='c').
+nrows : int, default ``None``
+ Number of rows of file to read. Useful for reading pieces of large files.
+
+NA and Missing Data Handling
+++++++++++++++++++++++++++++
+
+na_values : str, list-like or dict, default ``None``
+ Additional strings to recognize as NA/NaN. If dict passed, specific per-column
+ NA values. By default the following values are interpreted as NaN:
+ ``'-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', '#N/A', 'N/A', 'NA',
+ '#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan', ''``.
+keep_default_na : boolean, default ``True``
+ If na_values are specified and keep_default_na is ``False`` the default NaN
+ values are overridden, otherwise they're appended to.
+na_filter : boolean, default ``True``
+ Detect missing value markers (empty strings and the value of na_values). In
+ data without any NAs, passing ``na_filter=False`` can improve the performance
+ of reading a large file.
+verbose : boolean, default ``False``
+ Indicate number of NA values placed in non-numeric columns.
+skip_blank_lines : boolean, default ``True``
+ If ``True``, skip over blank lines rather than interpreting as NaN values.
+
+Datetime Handling
++++++++++++++++++
+
+parse_dates : boolean or list of ints or names or list of lists or dict, default ``False``
+ - If ``True`` -> try parsing the index.
+ - If ``[1, 2, 3]`` -> try parsing columns 1, 2, 3 each as a separate date
+ column.
+ - If ``[[1, 3]]`` -> combine columns 1 and 3 and parse as a single date
+ column.
+ - If ``{'foo' : [1, 3]}`` -> parse columns 1, 3 as date and call result 'foo'.
+ 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 the
+ datetime format to speed up the processing.
+keep_date_col : boolean, default ``False``
+ If ``True`` and parse_dates specifies combining multiple columns then keep the
+ original columns.
+date_parser : function, default ``None``
+ Function to use for converting a sequence of string columns to an array of
+ datetime instances. The default uses ``dateutil.parser.parser`` to do the
+ conversion. Pandas will try to call date_parser in three different ways,
+ advancing to the next if an exception occurs: 1) Pass one or more arrays (as
+ defined by parse_dates) as arguments; 2) concatenate (row-wise) the string
+ values from the columns defined by parse_dates into a single array and pass
+ that; and 3) call date_parser once for each row using one or more strings
+ (corresponding to the columns defined by parse_dates) as arguments.
+dayfirst : boolean, default ``False``
+ DD/MM format dates, international and European format.
+
+Iteration
++++++++++
+
+iterator : boolean, default ``False``
+ Return `TextFileReader` object for iteration or getting chunks with
+ ``get_chunk()``.
+chunksize : int, default ``None``
+ Return `TextFileReader` object for iteration. See :ref:`iterating and chunking
+ <io.chunking>` below.
+
+Quoting, Compression, and File Format
++++++++++++++++++++++++++++++++++++++
+
+compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``None``}, default ``'infer'``
+ For on-the-fly decompression of on-disk data. If 'infer', then use gzip or bz2
+ if filepath_or_buffer is a string ending in '.gz' or '.bz2', respectively, and
+ no decompression otherwise. Set to ``None`` for no decompression.
+thousands : str, default ``None``
+ Thousands separator.
+decimal : str, default ``'.'``
+ Character to recognize as decimal point. E.g. use ``','`` for European data.
+lineterminator : str (length 1), default ``None``
+ Character to break file into lines. Only valid with C parser.
+quotechar : str (length 1)
+ The character used to denote the start and end of a quoted item. Quoted items
+ can include the delimiter and it will be ignored.
+quoting : int or ``csv.QUOTE_*`` instance, default ``None``
+ Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
+ ``QUOTE_MINIMAL`` (0), ``QUOTE_ALL`` (1), ``QUOTE_NONNUMERIC`` (2) or
+ ``QUOTE_NONE`` (3). Default (``None``) results in ``QUOTE_MINIMAL``
+ behavior.
+escapechar : str (length 1), default ``None``
+ One-character string used to escape delimiter when quoting is ``QUOTE_NONE``.
+comment : str, default ``None``
+ Indicates remainder of line should not be parsed. If found at the beginning of
+ a line, the line will be ignored altogether. This parameter must be a single
+ character. Like empty lines (as long as ``skip_blank_lines=True``), fully
+ commented lines are ignored by the parameter `header` but not by `skiprows`.
+ For example, if ``comment='#'``, parsing '#empty\\na,b,c\\n1,2,3' with
+ `header=0` will result in 'a,b,c' being treated as the header.
+encoding : str, default ``None``
+ Encoding to use for UTF when reading/writing (e.g. ``'utf-8'``). `List of
+ Python standard encodings
+ <https://docs.python.org/3/library/codecs.html#standard-encodings>`_.
+dialect : str or :class:`python:csv.Dialect` instance, default ``None``
+ If ``None`` defaults to Excel dialect. Ignored if sep longer than 1 char. See
+ :class:`python:csv.Dialect` documentation for more details.
+tupleize_cols : boolean, default ``False``
+ Leave a list of tuples on columns as is (default is to convert to a MultiIndex
+ on the columns).
+
+Error Handling
+++++++++++++++
-They can take a number of arguments:
-
- - ``filepath_or_buffer``: Either a path to a file (a :class:`python:str`,
- :class:`python:pathlib.Path`, or :class:`py:py._path.local.LocalPath`), URL
- (including http, ftp, and S3 locations), or any object with a ``read``
- method (such as an open file or :class:`~python:io.StringIO`).
- - ``sep`` or ``delimiter``: A delimiter / separator to split fields
- on. With ``sep=None``, ``read_csv`` will try to infer the delimiter
- automatically in some cases by "sniffing".
- The separator may be specified as a regular expression; for instance
- you may use '\|\\s*' to indicate a pipe plus arbitrary whitespace, but ignores quotes in the data when a regex is used in separator.
- - ``delim_whitespace``: Parse whitespace-delimited (spaces or tabs) file
- (much faster than using a regular expression)
- - ``compression``: decompress ``'gzip'`` and ``'bz2'`` formats on the fly.
- Set to ``'infer'`` (the default) to guess a format based on the file
- extension.
- - ``dialect``: string or :class:`python:csv.Dialect` instance to expose more
- ways to specify the file format
- - ``dtype``: A data type name or a dict of column name to data type. If not
- specified, data types will be inferred. (Unsupported with
- ``engine='python'``)
- - ``header``: row number(s) to use as the column names, and the start of the
- data. Defaults to 0 if no ``names`` passed, otherwise ``None``. Explicitly
- pass ``header=0`` to be able to replace existing names. The header can be
- a list of integers that specify row locations for a multi-index on the columns
- E.g. [0,1,3]. Intervening rows that are not specified will be
- skipped (e.g. 2 in this example are skipped). Note that this parameter
- ignores commented lines and empty lines if ``skip_blank_lines=True`` (the default),
- so header=0 denotes the first line of data rather than the first line of the file.
- - ``skip_blank_lines``: whether to skip over blank lines rather than interpreting
- them as NaN values
- - ``skiprows``: A collection of numbers for rows in the file to skip. Can
- also be an integer to skip the first ``n`` rows
- - ``index_col``: column number, column name, or list of column numbers/names,
- to use as the ``index`` (row labels) of the resulting DataFrame. By default,
- it will number the rows without using any column, unless there is one more
- data column than there are headers, in which case the first column is taken
- as the index.
- - ``names``: List of column names to use as column names. To replace header
- existing in file, explicitly pass ``header=0``.
- - ``na_values``: optional string or list of strings to recognize as NaN (missing
- values), either in addition to or in lieu of the default set.
- - ``true_values``: list of strings to recognize as ``True``
- - ``false_values``: list of strings to recognize as ``False``
- - ``keep_default_na``: whether to include the default set of missing values
- in addition to the ones specified in ``na_values``
- - ``parse_dates``: if True then index will be parsed as dates
- (False by default). You can specify more complicated options to parse
- a subset of columns or a combination of columns into a single date column
- (list of ints or names, list of lists, or dict)
- [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column
- [[1, 3]] -> combine columns 1 and 3 and parse as a single date column
- {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo'
- - ``keep_date_col``: if True, then date component columns passed into
- ``parse_dates`` will be retained in the output (False by default).
- - ``date_parser``: function to use to parse strings into datetime
- objects. If ``parse_dates`` is True, it defaults to the very robust
- ``dateutil.parser``. Specifying this implicitly sets ``parse_dates`` as True.
- You can also use functions from community supported date converters from
- date_converters.py
- - ``dayfirst``: if True then uses the DD/MM international/European date format
- (This is False by default)
- - ``thousands``: specifies the thousands separator. If not None, this character will
- be stripped from numeric dtypes. However, if it is the first character in a field,
- that column will be imported as a string. In the PythonParser, if not None,
- then parser will try to look for it in the output and parse relevant data to numeric
- dtypes. Because it has to essentially scan through the data again, this causes a
- significant performance hit so only use if necessary.
- - ``lineterminator`` : string (length 1), default ``None``, Character to break file into lines. Only valid with C parser
- - ``quotechar`` : string, The character to used to denote the start and end of a quoted item.
- Quoted items can include the delimiter and it will be ignored.
- - ``quoting`` : int,
- Controls whether quotes should be recognized. Values are taken from `csv.QUOTE_*` values.
- Acceptable values are 0, 1, 2, and 3 for QUOTE_MINIMAL, QUOTE_ALL,
- QUOTE_NONNUMERIC and QUOTE_NONE, respectively.
- - ``skipinitialspace`` : boolean, default ``False``, Skip spaces after delimiter
- - ``escapechar`` : string, to specify how to escape quoted data
- - ``comment``: Indicates remainder of line should not be parsed. If found at the
- beginning of a line, the line will be ignored altogether. This parameter
- must be a single character. Like empty lines, fully commented lines
- are ignored by the parameter `header` but not by `skiprows`. For example,
- if comment='#', parsing '#empty\n1,2,3\na,b,c' with `header=0` will
- result in '1,2,3' being treated as the header.
- - ``nrows``: Number of rows to read out of the file. Useful to only read a
- small portion of a large file
- - ``iterator``: If True, return a ``TextFileReader`` to enable reading a file
- into memory piece by piece
- - ``chunksize``: An number of rows to be used to "chunk" a file into
- pieces. Will cause an ``TextFileReader`` object to be returned. More on this
- below in the section on :ref:`iterating and chunking <io.chunking>`
- - ``skip_footer``: number of lines to skip at bottom of file (default 0)
- (Unsupported with ``engine='c'``)
- - ``converters``: a dictionary of functions for converting values in certain
- columns, where keys are either integers or column labels
- - ``encoding``: a string representing the encoding to use for decoding
- unicode data, e.g. ``'utf-8``` or ``'latin-1'``. `Full list of Python
- standard encodings
- <https://docs.python.org/3/library/codecs.html#standard-encodings>`_
- - ``verbose``: show number of NA values inserted in non-numeric columns
- - ``squeeze``: if True then output with only one column is turned into Series
- - ``error_bad_lines``: if False then any lines causing an error will be skipped :ref:`bad lines <io.bad_lines>`
- - ``usecols``: a subset of columns to return, results in much faster parsing
- time and lower memory usage.
- - ``mangle_dupe_cols``: boolean, default True, then duplicate columns will be specified
- as 'X.0'...'X.N', rather than 'X'...'X'
- - ``tupleize_cols``: boolean, default False, if False, convert a list of tuples
- to a multi-index of columns, otherwise, leave the column index as a list of
- tuples
- - ``float_precision`` : string, default None. Specifies which converter the C
- engine should use for floating-point values. The options are None for the
- ordinary converter, 'high' for the high-precision converter, and
- 'round_trip' for the round-trip converter.
+error_bad_lines : boolean, default ``True``
+ Lines with too many fields (e.g. a csv line with too many commas) will by
+ default cause an exception to be raised, and no DataFrame will be returned. If
+ ``False``, then these "bad lines" will dropped from the DataFrame that is
+ returned (only valid with C parser). See :ref:`bad lines <io.bad_lines>`
+ below.
+warn_bad_lines : boolean, default ``True``
+ If error_bad_lines is ``False``, and warn_bad_lines is ``True``, a warning for
+ each "bad line" will be output (only valid with C parser).
.. ipython:: python
:suppress:
@@ -4093,7 +4172,6 @@ The key functions are:
.. _io.bigquery_reader:
-.. _io.bigquery_authentication:
Authentication
''''''''''''''
diff --git a/doc/source/merging.rst b/doc/source/merging.rst
index 074b15bbbcb66..7908428135308 100644
--- a/doc/source/merging.rst
+++ b/doc/source/merging.rst
@@ -80,29 +80,33 @@ some configurable handling of "what to do with the other axes":
pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False)
-- ``objs``: list or dict of Series, DataFrame, or Panel objects. If a dict is
- passed, the sorted keys will be used as the `keys` argument, unless it is
- passed, in which case the values will be selected (see below)
-- ``axis``: {0, 1, ...}, default 0. The axis to concatenate along
+- ``objs``: a sequence or mapping of Series, DataFrame, or Panel objects. If a
+ dict is passed, the sorted keys will be used as the `keys` argument, unless
+ it is passed, in which case the values will be selected (see below). Any None
+ objects will be dropped silently unless they are all None in which case a
+ ValueError will be raised.
+- ``axis``: {0, 1, ...}, default 0. The axis to concatenate along.
- ``join``: {'inner', 'outer'}, default 'outer'. How to handle indexes on
- other axis(es). Outer for union and inner for intersection
+ other axis(es). Outer for union and inner for intersection.
- ``join_axes``: list of Index objects. Specific indexes to use for the other
- n - 1 axes instead of performing inner/outer set logic
+ n - 1 axes instead of performing inner/outer set logic.
- ``keys``: sequence, default None. Construct hierarchical index using the
- passed keys as the outermost level If multiple levels passed, should
+ passed keys as the outermost level. If multiple levels passed, should
contain tuples.
-- ``levels`` : list of sequences, default None. If keys passed, specific
- levels to use for the resulting MultiIndex. Otherwise they will be inferred
- from the keys
+- ``levels`` : list of sequences, default None. Specific levels (unique values)
+ to use for constructing a MultiIndex. Otherwise they will be inferred from the
+ keys.
- ``names``: list, default None. Names for the levels in the resulting
- hierarchical index
+ hierarchical index.
- ``verify_integrity``: boolean, default False. Check whether the new
concatenated axis contains duplicates. This can be very expensive relative
- to the actual data concatenation
+ to the actual data concatenation.
- ``ignore_index`` : boolean, default False. If True, do not use the index
values on the concatenation axis. The resulting axis will be labeled 0, ...,
n - 1. This is useful if you are concatenating objects where the
- concatenation axis does not have meaningful indexing information.
+ concatenation axis does not have meaningful indexing information. Note
+ the index values on the other axes are still respected in the join.
+- ``copy`` : boolean, default True. If False, do not copy data unnecessarily.
Without a little bit of context and example many of these arguments don't make
much sense. Let's take the above example. Suppose we wanted to associate
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 4265113076b23..1114df5e08154 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -247,6 +247,8 @@ 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>`
+- the order of keyword arguments to text file parsing functions (``.read_csv()``, ``.read_table()``, ``.read_fwf()``) changed to group related arguments. (:issue:`#11555`)
+
.. _whatsnew_0180.api_breaking:
Backwards incompatible API changes
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 1593716097985..24bc71517c2a9 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -28,6 +28,14 @@
import pandas.lib as lib
import pandas.parser as _parser
+# common NA values
+# no longer excluding inf representations
+# '1.#INF','-1.#INF', '1.#INF000000',
+_NA_VALUES = set([
+ '-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', '#N/A',
+ 'N/A', 'NA', '#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan', ''
+])
+
class ParserWarning(Warning):
pass
@@ -40,70 +48,79 @@ class ParserWarning(Warning):
Parameters
----------
-filepath_or_buffer : string or file handle / StringIO
- The string could be a URL. Valid URL schemes include
- http, ftp, s3, and file. For file URLs, a
- host is expected. For instance, a local file could be
+filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object \
+with a read() method (such as a file handle or StringIO)
+ The string could be a URL. Valid URL schemes include http, ftp, s3, and
+ file. For file URLs, a host is expected. For instance, a local file could be
file ://localhost/path/to/table.csv
%s
-lineterminator : string (length 1), default None
- Character to break file into lines. Only valid with C parser
-quotechar : string (length 1)
- The character used to denote the start and end of a quoted item. Quoted
- items can include the delimiter and it will be ignored.
-quoting : int or csv.QUOTE_* instance, default None
- Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
- QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
- Default (None) results in QUOTE_MINIMAL behavior.
-skipinitialspace : boolean, default False
- Skip spaces after delimiter
-escapechar : string (length 1), default None
- One-character string used to escape delimiter when quoting is QUOTE_NONE.
-dtype : Type name or dict of column -> type, default None
- Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
- (Unsupported with engine='python')
-compression : {'gzip', 'bz2', 'infer', None}, default 'infer'
- For on-the-fly decompression of on-disk data. If 'infer', then use gzip or
- bz2 if filepath_or_buffer is a string ending in '.gz' or '.bz2',
- respectively, and no decompression otherwise. Set to None for no
- decompression.
-dialect : string or csv.Dialect instance, default None
- If None defaults to Excel dialect. Ignored if sep longer than 1 char
- See csv.Dialect documentation for more details
-header : int, list of ints, default 'infer'
+delimiter : str, default None
+ Alternative argument name for sep.
+header : int or list of ints, default 'infer'
Row number(s) to use as the column names, and the start of the data.
- Defaults to 0 if no ``names`` passed, otherwise ``None``. Explicitly pass
- ``header=0`` to be able to replace existing names. The header can be a list
- of integers that specify row locations for a multi-index on the columns
- E.g. [0,1,3]. Intervening rows that are not specified will be skipped
- (e.g. 2 in this example are skipped). Note that this parameter ignores
- commented lines and empty lines if ``skip_blank_lines=True``, so header=0
- denotes the first line of data rather than the first line of the file.
-skiprows : list-like or integer, default None
- Line numbers to skip (0-indexed) or number of lines to skip (int)
- at the start of the file
+ Default behavior is as if set to 0 if no ``names`` passed, otherwise
+ ``None``. Explicitly pass ``header=0`` to be able to replace existing names.
+ The header can be a list of integers that specify row locations for a
+ multi-index on the columns e.g. [0,1,3]. Intervening rows that are not
+ specified will be skipped (e.g. 2 in this example is skipped). Note that
+ this parameter ignores commented lines and empty lines if
+ ``skip_blank_lines=True``, so header=0 denotes the first line of data rather
+ than the first line of the file.
+names : array-like, default None
+ List of column names to use. If file contains no header row, then you
+ should explicitly pass header=None
index_col : int or sequence or False, default None
Column to use as the row labels of the DataFrame. If a sequence is given, a
MultiIndex is used. If you have a malformed file with delimiters at the end
of each line, you might consider index_col=False to force pandas to _not_
use the first column as the index (row names)
-names : array-like, default None
- List of column names to use. If file contains no header row, then you
- should explicitly pass header=None
-prefix : string, default None
- Prefix to add to column numbers when no header, e.g 'X' for X0, X1, ...
-na_values : str, list-like or dict, default None
- Additional strings to recognize as NA/NaN. If dict passed, specific
- per-column NA values
+usecols : array-like, default None
+ Return a subset of the columns.
+ Results in much faster parsing time and lower memory usage.
+squeeze : boolean, default False
+ If the parsed data only contains one column then return a Series
+prefix : str, default None
+ Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
+mangle_dupe_cols : boolean, default True
+ Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'
+dtype : Type name or dict of column -> type, default None
+ Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
+ (Unsupported with engine='python'). Use `str` or `object` to preserve and
+ not interpret dtype.
+%s
+converters : dict, default None
+ Dict of functions for converting values in certain columns. Keys can either
+ be integers or column labels
true_values : list, default None
Values to consider as True
false_values : list, default None
Values to consider as False
+skipinitialspace : boolean, default False
+ Skip spaces after delimiter.
+skiprows : list-like or integer, default None
+ Line numbers to skip (0-indexed) or number of lines to skip (int)
+ at the start of the file
+skipfooter : int, default 0
+ Number of lines at bottom of file to skip (Unsupported with engine='c')
+nrows : int, default None
+ Number of rows of file to read. Useful for reading pieces of large files
+na_values : str or list-like or dict, default None
+ Additional strings to recognize as NA/NaN. If dict passed, specific
+ per-column NA values. By default the following values are interpreted as
+ NaN: `'""" + "'`, `'".join(sorted(_NA_VALUES)) + """'`.
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN
- values are overridden, otherwise they're appended to
-parse_dates : various, default False
-
+ values are overridden, otherwise they're appended to.
+na_filter : boolean, default True
+ Detect missing value markers (empty strings and the value of na_values). In
+ data without any NAs, passing na_filter=False can improve the performance
+ of reading a large file
+verbose : boolean, default False
+ Indicate number of NA values placed in non-numeric columns
+skip_blank_lines : boolean, default True
+ 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.
@@ -112,12 +129,15 @@ class ParserWarning(Warning):
* 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
+ the datetime format to speed up the processing
keep_date_col : boolean, default False
If True and parse_dates specifies combining multiple columns then
keep the original columns.
date_parser : function, default None
Function to use for converting a sequence of string columns to an array of
- datetime instances. The default uses dateutil.parser.parser to do the
+ datetime instances. The default uses ``dateutil.parser.parser`` to do the
conversion. Pandas will try to call date_parser in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by parse_dates) as arguments; 2) concatenate (row-wise) the
@@ -126,8 +146,34 @@ class ParserWarning(Warning):
strings (corresponding to the columns defined by parse_dates) as arguments.
dayfirst : boolean, default False
DD/MM format dates, international and European format
+iterator : boolean, default False
+ Return TextFileReader object for iteration or getting chunks with
+ ``get_chunk()``.
+chunksize : int, default None
+ Return TextFileReader object for iteration. `See IO Tools docs for more
+ information
+ <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ on
+ ``iterator`` and ``chunksize``.
+compression : {'infer', 'gzip', 'bz2', None}, default 'infer'
+ For on-the-fly decompression of on-disk data. If 'infer', then use gzip or
+ bz2 if filepath_or_buffer is a string ending in '.gz' or '.bz2',
+ respectively, and no decompression otherwise. Set to None for no
+ decompression.
thousands : str, default None
Thousands separator
+decimal : str, default '.'
+ Character to recognize as decimal point (e.g. use ',' for European data).
+lineterminator : str (length 1), default None
+ Character to break file into lines. Only valid with C parser.
+quotechar : str (length 1), optional
+ The character used to denote the start and end of a quoted item. Quoted
+ items can include the delimiter and it will be ignored.
+quoting : int or csv.QUOTE_* instance, default None
+ Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
+ QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
+ Default (None) results in QUOTE_MINIMAL behavior.
+escapechar : str (length 1), default None
+ One-character string used to escape delimiter when quoting is QUOTE_NONE.
comment : str, default None
Indicates remainder of line should not be parsed. If found at the beginning
of a line, the line will be ignored altogether. This parameter must be a
@@ -136,42 +182,13 @@ class ParserWarning(Warning):
`skiprows`. For example, if comment='#', parsing '#empty\\na,b,c\\n1,2,3'
with `header=0` will result in 'a,b,c' being
treated as the header.
-decimal : str, default '.'
- Character to recognize as decimal point. E.g. use ',' for European data
-nrows : int, default None
- Number of rows of file to read. Useful for reading pieces of large files
-iterator : boolean, default False
- Return TextFileReader object for iteration or getting chunks with
- ``get_chunk()``.
-chunksize : int, default None
- Return TextFileReader object for iteration. `See IO Tools docs for more
- information
- <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ on
- ``iterator`` and ``chunksize``.
-skipfooter : int, default 0
- Number of lines at bottom of file to skip (Unsupported with engine='c')
-converters : dict, default None
- Dict of functions for converting values in certain columns. Keys can either
- be integers or column labels
-verbose : boolean, default False
- Indicate number of NA values placed in non-numeric columns
-delimiter : string, default None
- Alternative argument name for sep. Regular expressions are accepted.
-encoding : string, default None
+encoding : str, default None
Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
standard encodings
<https://docs.python.org/3/library/codecs.html#standard-encodings>`_
-squeeze : boolean, default False
- If the parsed data only contains one column then return a Series
-na_filter : boolean, default True
- Detect missing value markers (empty strings and the value of na_values). In
- data without any NAs, passing na_filter=False can improve the performance
- of reading a large file
-usecols : array-like, default None
- Return a subset of the columns.
- Results in much faster parsing time and lower memory usage.
-mangle_dupe_cols : boolean, default True
- Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'
+dialect : str or csv.Dialect instance, default None
+ If None defaults to Excel dialect. Ignored if sep longer than 1 char
+ See csv.Dialect documentation for more details
tupleize_cols : boolean, default False
Leave a list of tuples on columns as is (default is to convert to
a Multi Index on the columns)
@@ -183,41 +200,34 @@ class ParserWarning(Warning):
warn_bad_lines : boolean, default True
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
"bad line" will be output. (Only valid with C parser).
-infer_datetime_format : boolean, default False
- If True and parse_dates is enabled for a column, attempt to infer
- the datetime format to speed up the processing
-skip_blank_lines : boolean, default True
- If True, skip over blank lines rather than interpreting as NaN values
Returns
-------
result : DataFrame or TextParser
"""
-_csv_params = """sep : string, default ','
- Delimiter to use. If sep is None, will try to automatically determine
- this. Regular expressions are accepted.
-engine : {'c', 'python'}
+# engine is not used in read_fwf() so is factored out of the shared docstring
+_engine_doc = """engine : {'c', 'python'}, optional
Parser engine to use. The C engine is faster while the python engine is
currently more feature-complete."""
-_table_params = """sep : string, default \\t (tab-stop)
- Delimiter to use. Regular expressions are accepted.
-engine : {'c', 'python'}
- Parser engine to use. The C engine is faster while the python engine is
- currently more feature-complete."""
+_sep_doc = """sep : str, default {default}
+ Delimiter to use. If sep is None, will try to automatically determine
+ this. Regular expressions are accepted and will force use of the python
+ parsing engine and will ignore quotes in the data."""
_read_csv_doc = """
Read CSV (comma-separated) file into DataFrame
%s
-""" % (_parser_params % _csv_params)
+""" % (_parser_params % (_sep_doc.format(default="','"), _engine_doc))
_read_table_doc = """
Read general delimited file into DataFrame
%s
-""" % (_parser_params % _table_params)
+""" % (_parser_params % (_sep_doc.format(default="\\t (tab-stop)"),
+ _engine_doc))
_fwf_widths = """\
colspecs : list of pairs (int, int) or 'infer'. optional
@@ -238,7 +248,7 @@ class ParserWarning(Warning):
Also, 'delimiter' is used to specify the filler character of the
fields if it is not spaces (e.g., '~').
-""" % (_parser_params % _fwf_widths)
+""" % (_parser_params % (_fwf_widths, ''))
def _read(filepath_or_buffer, kwds):
@@ -370,65 +380,76 @@ def _make_parser_function(name, sep=','):
def parser_f(filepath_or_buffer,
sep=sep,
- dialect=None,
- compression='infer',
-
- doublequote=True,
- escapechar=None,
- quotechar='"',
- quoting=csv.QUOTE_MINIMAL,
- skipinitialspace=False,
- lineterminator=None,
+ delimiter=None,
+ # Column and Index Locations and Names
header='infer',
- index_col=None,
names=None,
- prefix=None,
- skiprows=None,
- skipfooter=None,
- skip_footer=0,
- na_values=None,
- true_values=None,
- false_values=None,
- delimiter=None,
- converters=None,
- dtype=None,
+ index_col=None,
usecols=None,
+ squeeze=False,
+ prefix=None,
+ mangle_dupe_cols=True,
+ # General Parsing Configuration
+ dtype=None,
engine=None,
- delim_whitespace=False,
- as_recarray=False,
- na_filter=True,
- compact_ints=False,
- use_unsigned=False,
- low_memory=_c_parser_defaults['low_memory'],
- buffer_lines=None,
- warn_bad_lines=True,
- error_bad_lines=True,
+ converters=None,
+ true_values=None,
+ false_values=None,
+ skipinitialspace=False,
+ skiprows=None,
+ skipfooter=None,
+ nrows=None,
+ # NA and Missing Data Handling
+ na_values=None,
keep_default_na=True,
- thousands=None,
- comment=None,
- decimal=b'.',
+ na_filter=True,
+ verbose=False,
+ skip_blank_lines=True,
+ # Datetime Handling
parse_dates=False,
+ infer_datetime_format=False,
keep_date_col=False,
- dayfirst=False,
date_parser=None,
+ dayfirst=False,
- memory_map=False,
- float_precision=None,
- nrows=None,
+ # Iteration
iterator=False,
chunksize=None,
- verbose=False,
+ # Quoting, Compression, and File Format
+ compression='infer',
+ thousands=None,
+ decimal=b'.',
+ lineterminator=None,
+ quotechar='"',
+ quoting=csv.QUOTE_MINIMAL,
+ escapechar=None,
+ comment=None,
encoding=None,
- squeeze=False,
- mangle_dupe_cols=True,
+ dialect=None,
tupleize_cols=False,
- infer_datetime_format=False,
- skip_blank_lines=True):
+
+ # Error Handling
+ error_bad_lines=True,
+ warn_bad_lines=True,
+
+ # Deprecated
+ skip_footer=0,
+
+ # Internal
+ doublequote=True,
+ delim_whitespace=False,
+ as_recarray=False,
+ compact_ints=False,
+ use_unsigned=False,
+ low_memory=_c_parser_defaults['low_memory'],
+ buffer_lines=None,
+ memory_map=False,
+ float_precision=None):
# Alias sep -> delimiter.
if delimiter is None:
@@ -537,15 +558,6 @@ def read_fwf(filepath_or_buffer, colspecs='infer', widths=None, **kwds):
return _read(filepath_or_buffer, kwds)
-# common NA values
-# no longer excluding inf representations
-# '1.#INF','-1.#INF', '1.#INF000000',
-_NA_VALUES = set([
- '-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', '#N/A',
- 'N/A', 'NA', '#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan', ''
-])
-
-
class TextFileReader(BaseIterator):
"""
| closes #11555
Updated IO Tools documentation for read_csv() and read_table() to be consistent with the doc-string.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11756 | 2015-12-03T21:36:37Z | 2016-02-08T06:11:21Z | null | 2016-02-08T06:37:41Z |
Consolidate list-like checking to is_list_like | diff --git a/pandas/core/common.py b/pandas/core/common.py
index b3a42335e14da..61b689d05b9ea 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -2016,14 +2016,15 @@ def _asarray_tuplesafe(values, dtype=None):
def _index_labels_to_array(labels):
- if isinstance(labels, (compat.string_types, tuple)):
+ from .index import Index # ABCIndex doesn't work here
+ if isinstance(labels, tuple): #multiindex
+ labels = [labels]
+ elif isinstance(labels, Index):
+ # convert to list , or _asarray_tuplesafe will pull out the values, which will
+ # be incorrect for Indexes whose values need to be boxed
+ labels = list(labels)
+ elif not is_list_like(labels):
labels = [labels]
-
- if not isinstance(labels, (list, np.ndarray)):
- try:
- labels = list(labels)
- except TypeError: # non-iterable
- labels = [labels]
labels = _asarray_tuplesafe(labels)
@@ -2327,7 +2328,7 @@ def is_re_compilable(obj):
def is_list_like(arg):
- return (hasattr(arg, '__iter__') and
+ return (hasattr(arg, '__iter__') and
not isinstance(arg, compat.string_and_binary_types))
def is_named_tuple(arg):
| Closes https://github.com/pydata/pandas/issues/11740
Let me know what tests would be helpful for these types of 'cleaning' PRs, and whether a "what's new" is needed.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11751 | 2015-12-03T05:52:31Z | 2016-01-06T17:21:07Z | null | 2016-12-22T05:57:03Z |
ENH : GH11729 added index parameter in series to_string | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index aa43ebd70320f..b1b9c46293a91 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -32,6 +32,7 @@ Other enhancements
^^^^^^^^^^^^^^^^^^
- Handle truncated floats in SAS xport files (:issue:`11713`)
+- Added option to hide index in ``Series.to_string`` (:issue:`11729`)
- ``read_excel`` now supports s3 urls of the format ``s3://bucketname/filename`` (:issue:`11447`)
.. _whatsnew_0180.enhancements.rounding:
diff --git a/pandas/core/format.py b/pandas/core/format.py
index 009e3877f0139..07f16a5ef480a 100644
--- a/pandas/core/format.py
+++ b/pandas/core/format.py
@@ -126,14 +126,15 @@ def to_string(self):
class SeriesFormatter(object):
def __init__(self, series, buf=None, length=True, header=True,
- na_rep='NaN', name=False, float_format=None, dtype=True,
- max_rows=None):
+ index=True, na_rep='NaN', name=False, float_format=None,
+ dtype=True, max_rows=None):
self.series = series
self.buf = buf if buf is not None else StringIO()
self.name = name
self.na_rep = na_rep
self.header = header
self.length = length
+ self.index = index
self.max_rows = max_rows
if float_format is None:
@@ -241,7 +242,10 @@ def to_string(self):
fmt_values.insert(row_num + n_header_rows, dot_str)
fmt_index.insert(row_num + 1, '')
- result = self.adj.adjoin(3, *[fmt_index[1:], fmt_values])
+ if self.index:
+ result = self.adj.adjoin(3, *[fmt_index[1:], fmt_values])
+ else:
+ result = self.adj.adjoin(3, fmt_values)
if self.header and have_header:
result = fmt_index[0] + '\n' + result
diff --git a/pandas/core/series.py b/pandas/core/series.py
index f3e059b3d6e98..8f8286aecfca3 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -959,7 +959,7 @@ def __unicode__(self):
return result
def to_string(self, buf=None, na_rep='NaN', float_format=None, header=True,
- length=False, dtype=False, name=False, max_rows=None):
+ index=True, length=False, dtype=False, name=False, max_rows=None):
"""
Render a string representation of the Series
@@ -974,6 +974,8 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None, header=True,
default None
header: boolean, default True
Add the Series header (index name)
+ index : bool, optional
+ Add index (row) labels, default True
length : boolean, default False
Add the Series length
dtype : boolean, default False
@@ -990,8 +992,8 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None, header=True,
"""
the_repr = self._get_repr(float_format=float_format, na_rep=na_rep,
- header=header, length=length, dtype=dtype,
- name=name, max_rows=max_rows)
+ header=header, index=index, length=length,
+ dtype=dtype, name=name, max_rows=max_rows)
# catch contract violations
if not isinstance(the_repr, compat.text_type):
@@ -1009,15 +1011,15 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None, header=True,
f.write(the_repr)
def _get_repr(
- self, name=False, header=True, length=True, dtype=True, na_rep='NaN',
- float_format=None, max_rows=None):
+ self, name=False, header=True, index=True, length=True, dtype=True,
+ na_rep='NaN', float_format=None, max_rows=None):
"""
Internal function, should always return unicode string
"""
formatter = fmt.SeriesFormatter(self, name=name,
length=length, header=header,
- dtype=dtype,
+ index=index, dtype=dtype,
na_rep=na_rep,
float_format=float_format,
max_rows=max_rows)
diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py
index c076ef0470d79..935c85ca3e29d 100644
--- a/pandas/tests/test_format.py
+++ b/pandas/tests/test_format.py
@@ -3181,6 +3181,16 @@ def test_to_string_float_na_spacing(self):
'4 NaN')
self.assertEqual(result, expected)
+ def test_to_string_without_index(self):
+ #GH 11729 Test index=False option
+ s= Series([1, 2, 3, 4])
+ result = s.to_string(index=False)
+ expected = (u(' 1\n') +
+ ' 2\n' +
+ ' 3\n' +
+ ' 4')
+ self.assertEqual(result, expected)
+
def test_unicode_name_in_footer(self):
s = Series([1, 2], name=u('\u05e2\u05d1\u05e8\u05d9\u05ea'))
sf = fmt.SeriesFormatter(s, name=u('\u05e2\u05d1\u05e8\u05d9\u05ea'))
| Fixes #11729
Please Review .
| https://api.github.com/repos/pandas-dev/pandas/pulls/11750 | 2015-12-03T04:46:43Z | 2015-12-12T14:04:00Z | 2015-12-12T14:04:00Z | 2015-12-13T03:09:33Z |
Improve(?) explanation of SettingWithCopy warning | diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index 80dc1be8ee2ea..af5087689ca4d 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -1522,23 +1522,58 @@ Contrast this to ``df.loc[:,('one','second')]`` which passes a nested tuple of `
``__getitem__``. This allows pandas to deal with this as a single entity. Furthermore this order of operations *can* be significantly
faster, and allows one to index *both* axes if so desired.
-Why does the assignment when using chained indexing fail!
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Why does assignment fail when using chained indexing?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-So, why does this show the ``SettingWithCopy`` warning / and possibly not work when you do chained indexing and assignment:
+The problem in the previous section is just a performance issue. What's up with
+the ``SettingWithCopy`` warning? We don't **usually** throw warnings around when
+you do something that might cost a few extra milliseconds!
+
+But it turns out that assigning to the product of chained indexing has
+inherently unpredictable results. To see this, think about how the Python
+interpreter executes this code:
.. code-block:: python
- dfmi['one']['second'] = value
+ dfmi.loc[:,('one','second')] = value
+ # becomes
+ dfmi.loc.__setitem__((slice(None), ('one', 'second')), value)
-Since the chained indexing is 2 calls, it is possible that either call may return a **copy** of the data because of the way it is sliced.
-Thus when setting, you are actually setting a **copy**, and not the original frame data. It is impossible for pandas to figure this out because their are 2 separate python operations that are not connected.
+But this code is handled differently:
+
+.. code-block:: python
-The ``SettingWithCopy`` warning is a 'heuristic' to detect this (meaning it tends to catch most cases but is simply a lightweight check). Figuring this out for real is way complicated.
+ dfmi['one']['second'] = value
+ # becomes
+ dfmi.__getitem__('one').__setitem__('second', value)
+
+See that ``__getitem__`` in there? Outside of simple cases, it's very hard to
+predict whether it will return a view or a copy (it depends on the memory layout
+of the array, about which *pandas* makes no guarantees), and therefore whether
+the ``__setitem__`` will modify ``dfmi`` or a temporary object that gets thrown
+out immediately afterward. **That's** what ``SettingWithCopy`` is warning you
+about!
+
+.. note:: You may be wondering whether we should be concerned about the ``loc``
+ property in the first example. But ``dfmi.loc`` is guaranteed to be ``dfmi``
+ itself with modified indexing behavior, so ``dfmi.loc.__getitem__`` /
+ ``dfmi.loc.__setitem__`` operate on ``dfmi`` directly. Of course,
+ ``dfmi.loc.__getitem__(idx)`` may be a view or a copy of ``dfmi``.
+
+Sometimes a ``SettingWithCopy`` warning will arise at times when there's no
+obvious chained indexing going on. **These** are the bugs that
+``SettingWithCopy`` is designed to catch! Pandas is probably trying to warn you
+that you've done this:
+
+.. code-block:: python
-The ``.loc`` operation is a single python operation, and thus can select a slice (which still may be a copy), but allows pandas to assign that slice back into the frame after it is modified, thus setting the values as you would think.
+ def do_something(df):
+ foo = df[['bar', 'baz']] # Is foo a view? A copy? Nobody knows!
+ # ... many lines here ...
+ foo['quux'] = value # We don't know whether this will modify df or not!
+ return foo
-The reason for having the ``SettingWithCopy`` warning is this. Sometimes when you slice an array you will simply get a view back, which means you can set it no problem. However, even a single dtyped array can generate a copy if it is sliced in a particular way. A multi-dtyped DataFrame (meaning it has say ``float`` and ``object`` data), will almost always yield a copy. Whether a view is created is dependent on the memory layout of the array.
+Yikes!
Evaluation order matters
~~~~~~~~~~~~~~~~~~~~~~~~
| After playing with R a bunch, I started feeling like the explanation of `SettingWithCopy` wasn't getting to the core of the matter, which is actually an essential consequence of python slice assignment semantics. Here's how python handles chained assignment:
``` python
df['foo']['bar'] = quux
# becomes
df.__getitem__('foo').__setitem__('bar', quux)
```
whereas in R, it's this:
``` R
df["foo"]["bar"] <- quux
# becomes
df["foo"] <- `[<-`(df["foo"], "bar", quux)
# becomes
df <- `[<-`(df, "foo", `[<-`(`[`(df, "foo"), "bar", quux))
```
That last is a lot of line noise, though the R method names ``[`` and ``[<-`` are more concise than `__getitem__` and `__setitem__`! But imagine that you could call `__setitem__` with a kwarg `inplace=False` that would cause it to return a modified copy instead of modifying the original object. Then the R version would translate to this in python:
``` python
df = df.__setitem__('foo',
df.__getitem__('foo')
.__setitem__('bar', quux, inplace=False),
inplace=False)
```
This is incredibly awkward, but it has the advantage of making `SettingWithCopy` unnecessary— _everything_ is a copy, and yet things get set nonetheless.
So this commit is an attempt to explain this without requiring the reader to know R.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11746 | 2015-12-02T23:32:45Z | 2015-12-04T23:02:59Z | 2015-12-04T23:02:59Z | 2015-12-04T23:03:27Z |
TST in .drop and .groupby for dataframes with multi-indexed columns | diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py
index 1b7f057de9677..a9a40f584ea95 100644
--- a/pandas/indexes/multi.py
+++ b/pandas/indexes/multi.py
@@ -1092,7 +1092,7 @@ def drop(self, labels, level=None, errors='raise'):
elif is_bool_indexer(loc):
if self.lexsort_depth == 0:
warnings.warn('dropping on a non-lexsorted multi-index'
- 'without a level parameter may impact '
+ ' without a level parameter may impact '
'performance.',
PerformanceWarning,
stacklevel=2)
diff --git a/pandas/tests/frame/test_axis_select_reindex.py b/pandas/tests/frame/test_axis_select_reindex.py
index 32df9fac42550..9bc7edf11a55e 100644
--- a/pandas/tests/frame/test_axis_select_reindex.py
+++ b/pandas/tests/frame/test_axis_select_reindex.py
@@ -114,6 +114,29 @@ def test_drop(self):
df.drop(labels=df[df.b > 0].index, inplace=True)
assert_frame_equal(df, expected)
+ def test_drop_multiindex_not_lexsorted(self):
+ # GH 11640
+
+ # define the lexsorted version
+ lexsorted_mi = MultiIndex.from_tuples(
+ [('a', ''), ('b1', 'c1'), ('b2', 'c2')], names=['b', 'c'])
+ lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
+ self.assertTrue(lexsorted_df.columns.is_lexsorted())
+
+ # define the non-lexsorted version
+ not_lexsorted_df = DataFrame(columns=['a', 'b', 'c', 'd'],
+ data=[[1, 'b1', 'c1', 3],
+ [1, 'b2', 'c2', 4]])
+ not_lexsorted_df = not_lexsorted_df.pivot_table(
+ index='a', columns=['b', 'c'], values='d')
+ not_lexsorted_df = not_lexsorted_df.reset_index()
+ self.assertFalse(not_lexsorted_df.columns.is_lexsorted())
+
+ # compare the results
+ tm.assert_frame_equal(lexsorted_df, not_lexsorted_df)
+ tm.assert_frame_equal(lexsorted_df.drop('a', axis=1),
+ not_lexsorted_df.drop('a', axis=1))
+
def test_reindex(self):
newFrame = self.frame.reindex(self.ts1.index)
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 7e40885fdacb5..753bbccf850e4 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -4198,6 +4198,29 @@ def test_groupby_multiindex_missing_pair(self):
tm.assert_frame_equal(res, exp)
+ def test_groupby_multiindex_not_lexsorted(self):
+ # GH 11640
+
+ # define the lexsorted version
+ lexsorted_mi = MultiIndex.from_tuples(
+ [('a', ''), ('b1', 'c1'), ('b2', 'c2')], names=['b', 'c'])
+ lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
+ self.assertTrue(lexsorted_df.columns.is_lexsorted())
+
+ # define the non-lexsorted version
+ not_lexsorted_df = DataFrame(columns=['a', 'b', 'c', 'd'],
+ data=[[1, 'b1', 'c1', 3],
+ [1, 'b2', 'c2', 4]])
+ not_lexsorted_df = not_lexsorted_df.pivot_table(
+ index='a', columns=['b', 'c'], values='d')
+ not_lexsorted_df = not_lexsorted_df.reset_index()
+ self.assertFalse(not_lexsorted_df.columns.is_lexsorted())
+
+ # compare the results
+ tm.assert_frame_equal(lexsorted_df, not_lexsorted_df)
+ tm.assert_frame_equal(lexsorted_df.groupby('a').mean(),
+ not_lexsorted_df.groupby('a').mean())
+
def test_groupby_levels_and_columns(self):
# GH9344, GH9049
idx_names = ['x', 'y']
| ## Bug 1 [edit: issues #11640 and #12078, fixed by PR #12158 but not entirely tested]
There was a bug deeply hidden in `pandas/core/index.py`, where it was assumed that the `.get_loc` method of an index would only return an integer or a slice, while it can also return a mask.
Simply correcting that bug has two consequences:
- now for a dataframe `df` with multi-indexed columns, `df.drop('a', axis=1)` works if `df['a']` returns something, e.g. if the column `('a', '')` exists
- likewise, `df.groupby('a')` also works in that case
Example:
```
In [2]: df = pd.DataFrame(columns=['a','b','c','d'], data=[[1,'b1','c1',3], [1,'b2','c2',4]])
In [3]: dg = df.pivot_table(index='a', columns=['b','c'], values='d').reset_index()
In [4]: dg
Out[4]:
b a b1 b2
c c1 c2
0 1 3 4
In [5]: dg['a']
Out[5]:
0 1
Name: a, dtype: int64
In [8]: dg.drop('a', axis=1)
Out[8]:
b b1 b2
c c1 c2
0 3 4
In [9]: dg.groupby('a').mean()
Out[9]:
b b1 b2
c c1 c2
a
1 3 4
```
## Bug 2 [edit: #11741, solved by PR #12063]
I also correct a little bug I mentioned in the talk on issue #11640: `.groupby` failed to raise a `KeyError` for a non-existing column when there was only one row:
```
In [6]: dg.groupby('z').mean()
Out[6]:
b a b1 b2
c c1 c2
z 1 3 4
```
Now we do have:
```
In [4]: dg.groupby('z').mean()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
...
KeyError: 'z'
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/11717 | 2015-11-28T17:46:30Z | 2016-01-29T14:40:50Z | null | 2016-01-29T14:49:25Z |
Fix to BUG GH11698. added default value of mask | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index d4ed68b9f4343..a20ca31dfbdc0 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -108,7 +108,12 @@ Bug Fixes
-
+- Bug in ``Timedelta.round`` with negative values (:issue:`11690`)
- Bug in ``.loc`` against ``CategoricalIndex`` may result in normal ``Index`` (:issue:`11586`)
- Bug groupby on tz-aware data where selection not returning ``Timestamp`` (:issue:`11616`)
- Bug in timezone info lost when broadcasting scalar datetime to ``DataFrame`` (:issue:`11682`)
+
+- Bug in ``.loc`` result with duplicated key may have ``Index`` with incorrect dtype (:issue:`11497`)
+
+- Bug in ``df.replace`` while replacing value in mixed datatype Dataframe (:issue:`11698`)
+
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 1b08140ebec09..28c845f63f9d4 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -588,6 +588,7 @@ def replace(self, to_replace, value, inplace=False, filter=None,
compatibility."""
original_to_replace = to_replace
+ mask = isnull(self.values)
# try to replace, if we raise an error, convert to ObjectBlock and retry
try:
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 5c4fa5a2e9c56..d60c5a4bd1e78 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -9666,6 +9666,13 @@ def test_replace(self):
df = DataFrame(index=['a', 'b'])
assert_frame_equal(df, df.replace(5, 7))
+ # GH 11698
+ # test for mixed data types.
+ df = pd.DataFrame([('-', pd.to_datetime('20150101')), ('a', pd.to_datetime('20150102'))])
+ df1 = df.replace('-', np.nan)
+ expected_df = pd.DataFrame([(np.nan, pd.to_datetime('20150101')), ('a', pd.to_datetime('20150102'))])
+ assert_frame_equal(df1, expected_df)
+
def test_replace_list(self):
obj = {'a': list('ab..'), 'b': list('efgh'), 'c': list('helo')}
dfobj = DataFrame(obj)
| closes #11698
Please review.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11715 | 2015-11-27T21:14:17Z | 2015-12-02T15:05:09Z | null | 2015-12-03T04:47:39Z |
ENH: Add support for s3 in read_excel #11447 | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index c517f89855601..6da7947d681c6 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -32,6 +32,7 @@ Other enhancements
^^^^^^^^^^^^^^^^^^
- Handle truncated floats in SAS xport files (:issue:`11713`)
+- ``read_excel`` now supports s3 urls of the format ``s3://bucketname/filename`` (:issue:`11447`)
.. _whatsnew_0180.enhancements.rounding:
diff --git a/pandas/io/excel.py b/pandas/io/excel.py
index ffd2768c78824..304cc3d346d1f 100644
--- a/pandas/io/excel.py
+++ b/pandas/io/excel.py
@@ -11,7 +11,7 @@
from pandas.core.frame import DataFrame
from pandas.io.parsers import TextParser
-from pandas.io.common import _is_url, _urlopen, _validate_header_arg
+from pandas.io.common import _is_url, _urlopen, _validate_header_arg, get_filepath_or_buffer, _is_s3_url
from pandas.tseries.period import Period
from pandas import json
from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass,
@@ -199,7 +199,10 @@ def __init__(self, io, **kwds):
raise ValueError("Unknown engine: %s" % engine)
if isinstance(io, compat.string_types):
- if _is_url(io):
+ if _is_s3_url(io):
+ buffer, _, _ = get_filepath_or_buffer(io)
+ self.book = xlrd.open_workbook(file_contents=buffer.read())
+ elif _is_url(io):
data = _urlopen(io).read()
self.book = xlrd.open_workbook(file_contents=data)
else:
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py
index 27e607870cebc..35aa847492d69 100644
--- a/pandas/io/tests/test_excel.py
+++ b/pandas/io/tests/test_excel.py
@@ -65,6 +65,13 @@ def _skip_if_no_excelsuite():
_skip_if_no_openpyxl()
+def _skip_if_no_boto():
+ try:
+ import boto # NOQA
+ except ImportError:
+ raise nose.SkipTest('boto not installed, skipping')
+
+
_seriesd = tm.getSeriesData()
_tsd = tm.getTimeSeriesData()
_frame = DataFrame(_seriesd)[:10]
@@ -429,6 +436,15 @@ def test_read_from_http_url(self):
local_table = self.get_exceldf('test1')
tm.assert_frame_equal(url_table, local_table)
+ @tm.network(check_before_test=True)
+ def test_read_from_s3_url(self):
+ _skip_if_no_boto()
+
+ url = ('s3://pandas-test/test1' + self.ext)
+ url_table = read_excel(url)
+ local_table = self.get_exceldf('test1')
+ tm.assert_frame_equal(url_table, local_table)
+
@slow
def test_read_from_file_url(self):
| closes #11447
Updated excel.py to use pandas.io.common.get_filepath_or_buffer (handles s3 urls) instead of urlopen, only when _is_s3_url returns true. This was the minimally invasive change.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11714 | 2015-11-27T20:09:28Z | 2015-12-01T20:38:19Z | 2015-12-01T20:38:19Z | 2015-12-01T20:38:28Z |
Truncated float support for SAS Xport files | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 7e01303477f8f..e906d9e461541 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -31,6 +31,10 @@ New features
Other enhancements
^^^^^^^^^^^^^^^^^^
+- Handle truncated floats in SAS xport files (see
+ :class:`~pandas.io.sas.XPortReader`` and ``pd.read_sas``).
+
+
.. _whatsnew_0180.enhancements.rounding:
Datetimelike rounding
diff --git a/pandas/io/sas.py b/pandas/io/sas.py
index 5f55f861afb72..006c2aaf55ca8 100644
--- a/pandas/io/sas.py
+++ b/pandas/io/sas.py
@@ -1,5 +1,5 @@
"""
-Tools for reading SAS XPort files into Pandas objects.
+Read a SAS XPort format file into a Pandas DataFrame.
Based on code from Jack Cushman (github.com/jcushman/xport).
@@ -25,10 +25,6 @@
'nifl', 'nifd', 'npos', '_']
-# TODO: Support for 4 byte floats, see https://github.com/jcushman/xport/pull/3
-# Need a test file
-
-
_base_params_doc = """\
Parameters
----------
@@ -161,15 +157,33 @@ def _split_line(s, parts):
return out
+def _handle_truncated_float_vec(vec, nbytes):
+ # This feature is not well documented, but some SAS XPORT files
+ # have 2-7 byte "truncated" floats. To read these truncated
+ # floats, pad them with zeros on the right to make 8 byte floats.
+ #
+ # References:
+ # https://github.com/jcushman/xport/pull/3
+ # The R "foreign" library
+
+ if nbytes != 8:
+ vec1 = np.zeros(len(vec), np.dtype('S8'))
+ dtype = np.dtype('S%d,S%d' % (nbytes, 8 - nbytes))
+ vec2 = vec1.view(dtype=dtype)
+ vec2['f0'] = vec
+ return vec2
+
+ return vec
+
+
def _parse_float_vec(vec):
"""
- Parse a vector of 8-byte values representing IBM 8 byte floats
- into native 8 byte floats.
+ Parse a vector of float values representing IBM 8 byte floats into
+ native 8 byte floats.
"""
dtype = np.dtype('>u4,>u4')
vec1 = vec.view(dtype=dtype)
-
xport1 = vec1['f0']
xport2 = vec1['f1']
@@ -266,7 +280,8 @@ def _read_header(self):
raise ValueError("Header record is not an XPORT file.")
line2 = self._get_row()
- file_info = _split_line(line2, [ ['prefix',24], ['version',8], ['OS',8], ['_',24], ['created',16]])
+ file_info = _split_line(line2, [['prefix', 24], ['version', 8], ['OS', 8],
+ ['_', 24], ['created', 16]])
if file_info['prefix'] != "SAS SAS SASLIB":
raise ValueError("Header record has invalid prefix.")
file_info['created'] = _parse_date(file_info['created'])
@@ -283,11 +298,11 @@ def _read_header(self):
fieldnamelength = int(header1[-5:-2]) # usually 140, could be 135
# member info
- member_info = _split_line(self._get_row(), [['prefix',8], ['set_name',8],
- ['sasdata',8],['version',8],
- ['OS',8],['_',24],['created',16]])
- member_info.update( _split_line(self._get_row(), [['modified',16], ['_',16],
- ['label',40],['type',8]]))
+ member_info = _split_line(self._get_row(), [['prefix', 8], ['set_name', 8],
+ ['sasdata', 8],['version', 8],
+ ['OS', 8],['_', 24],['created', 16]])
+ member_info.update( _split_line(self._get_row(), [['modified', 16], ['_', 16],
+ ['label', 40],['type', 8]]))
member_info['modified'] = _parse_date(member_info['modified'])
member_info['created'] = _parse_date(member_info['created'])
self.member_info = member_info
@@ -313,8 +328,9 @@ def _read_header(self):
field = dict(zip(_fieldkeys, fieldstruct))
del field['_']
field['ntype'] = types[field['ntype']]
- if field['ntype'] == 'numeric' and field['field_length'] != 8:
- raise TypeError("Only 8-byte floats are currently implemented. Can't read field %s." % field)
+ fl = field['field_length']
+ if field['ntype'] == 'numeric' and ((fl < 2) or (fl > 8)):
+ raise TypeError("Floating point field width %d is not between 2 and 8." % fw)
for k, v in field.items():
try:
@@ -339,11 +355,7 @@ def _read_header(self):
# Setup the dtype.
dtypel = []
for i,field in enumerate(self.fields):
- ntype = field['ntype']
- if ntype == "numeric":
- dtypel.append(('s' + str(i), ">u8"))
- elif ntype == "char":
- dtypel.append(('s' + str(i), "S" + str(field['field_length'])))
+ dtypel.append(('s' + str(i), "S" + str(field['field_length'])))
dtype = np.dtype(dtypel)
self._dtype = dtype
@@ -416,8 +428,8 @@ def get_chunk(self, size=None):
def _missing_double(self, vec):
v = vec.view(dtype='u1,u1,u2,u4')
miss = (v['f1'] == 0) & (v['f2'] == 0) & (v['f3'] == 0)
- miss1 = ((v['f0'] >= 0x41) & (v['f0'] <= 0x5a)) |\
- (v['f0'] == 0x5f) | (v['f0'] == 0x2e)
+ miss1 = (((v['f0'] >= 0x41) & (v['f0'] <= 0x5a)) |
+ (v['f0'] == 0x5f) | (v['f0'] == 0x2e))
miss &= miss1
return miss
@@ -440,6 +452,7 @@ def read(self, nrows=None):
vec = data['s%d' % j]
ntype = self.fields[j]['ntype']
if ntype == "numeric":
+ vec = _handle_truncated_float_vec(vec, self.fields[j]['field_length'])
miss = self._missing_double(vec)
v = _parse_float_vec(vec)
v[miss] = np.nan
diff --git a/pandas/io/tests/data/paxraw_d_short.csv b/pandas/io/tests/data/paxraw_d_short.csv
new file mode 100644
index 0000000000000..776799df5d8a2
--- /dev/null
+++ b/pandas/io/tests/data/paxraw_d_short.csv
@@ -0,0 +1,101 @@
+SEQN,PAXSTAT,PAXCAL,PAXDAY,PAXN,PAXHOUR,PAXMINUT,PAXINTEN,PAXSTEP
+31128,1,1,1,1,0,0,166,4
+31128,1,1,1,2,0,1,27,0
+31128,1,1,1,3,0,2,0,0
+31128,1,1,1,4,0,3,276,4
+31128,1,1,1,5,0,4,0,0
+31128,1,1,1,6,0,5,0,0
+31128,1,1,1,7,0,6,0,0
+31128,1,1,1,8,0,7,0,0
+31128,1,1,1,9,0,8,0,0
+31128,1,1,1,10,0,9,0,0
+31128,1,1,1,11,0,10,0,0
+31128,1,1,1,12,0,11,0,0
+31128,1,1,1,13,0,12,0,0
+31128,1,1,1,14,0,13,0,0
+31128,1,1,1,15,0,14,0,0
+31128,1,1,1,16,0,15,0,0
+31128,1,1,1,17,0,16,0,0
+31128,1,1,1,18,0,17,0,0
+31128,1,1,1,19,0,18,0,0
+31128,1,1,1,20,0,19,0,0
+31128,1,1,1,21,0,20,260,3
+31128,1,1,1,22,0,21,0,0
+31128,1,1,1,23,0,22,0,0
+31128,1,1,1,24,0,23,19,0
+31128,1,1,1,25,0,24,34,1
+31128,1,1,1,26,0,25,47,4
+31128,1,1,1,27,0,26,4,0
+31128,1,1,1,28,0,27,11,0
+31128,1,1,1,29,0,28,48,1
+31128,1,1,1,30,0,29,58,3
+31128,1,1,1,31,0,30,32,2
+31128,1,1,1,32,0,31,15,1
+31128,1,1,1,33,0,32,117,3
+31128,1,1,1,34,0,33,24,0
+31128,1,1,1,35,0,34,61,7
+31128,1,1,1,36,0,35,115,12
+31128,1,1,1,37,0,36,183,11
+31128,1,1,1,38,0,37,68,5
+31128,1,1,1,39,0,38,73,3
+31128,1,1,1,40,0,39,93,7
+31128,1,1,1,41,0,40,201,14
+31128,1,1,1,42,0,41,126,6
+31128,1,1,1,43,0,42,61,4
+31128,1,1,1,44,0,43,97,7
+31128,1,1,1,45,0,44,62,3
+31128,1,1,1,46,0,45,77,10
+31128,1,1,1,47,0,46,105,8
+31128,1,1,1,48,0,47,209,12
+31128,1,1,1,49,0,48,72,4
+31128,1,1,1,50,0,49,50,1
+31128,1,1,1,51,0,50,324,7
+31128,1,1,1,52,0,51,582,16
+31128,1,1,1,53,0,52,387,31
+31128,1,1,1,54,0,53,780,54
+31128,1,1,1,55,0,54,618,10
+31128,1,1,1,56,0,55,0,0
+31128,1,1,1,57,0,56,0,0
+31128,1,1,1,58,0,57,0,0
+31128,1,1,1,59,0,58,123,1
+31128,1,1,1,60,0,59,0,0
+31128,1,1,1,61,1,0,0,0
+31128,1,1,1,62,1,1,0,0
+31128,1,1,1,63,1,2,0,0
+31128,1,1,1,64,1,3,0,0
+31128,1,1,1,65,1,4,0,0
+31128,1,1,1,66,1,5,0,0
+31128,1,1,1,67,1,6,0,0
+31128,1,1,1,68,1,7,0,0
+31128,1,1,1,69,1,8,0,0
+31128,1,1,1,70,1,9,0,0
+31128,1,1,1,71,1,10,0,0
+31128,1,1,1,72,1,11,0,0
+31128,1,1,1,73,1,12,0,0
+31128,1,1,1,74,1,13,0,0
+31128,1,1,1,75,1,14,0,0
+31128,1,1,1,76,1,15,0,0
+31128,1,1,1,77,1,16,0,0
+31128,1,1,1,78,1,17,0,0
+31128,1,1,1,79,1,18,0,0
+31128,1,1,1,80,1,19,0,0
+31128,1,1,1,81,1,20,0,0
+31128,1,1,1,82,1,21,0,0
+31128,1,1,1,83,1,22,0,0
+31128,1,1,1,84,1,23,0,0
+31128,1,1,1,85,1,24,0,0
+31128,1,1,1,86,1,25,0,0
+31128,1,1,1,87,1,26,0,0
+31128,1,1,1,88,1,27,0,0
+31128,1,1,1,89,1,28,0,0
+31128,1,1,1,90,1,29,0,0
+31128,1,1,1,91,1,30,0,0
+31128,1,1,1,92,1,31,0,0
+31128,1,1,1,93,1,32,0,0
+31128,1,1,1,94,1,33,0,0
+31128,1,1,1,95,1,34,2,0
+31128,1,1,1,96,1,35,0,0
+31128,1,1,1,97,1,36,0,0
+31128,1,1,1,98,1,37,0,0
+31128,1,1,1,99,1,38,0,0
+31128,1,1,1,100,1,39,0,0
diff --git a/pandas/io/tests/data/paxraw_d_short.xpt b/pandas/io/tests/data/paxraw_d_short.xpt
new file mode 100644
index 0000000000000..da5bf98244342
Binary files /dev/null and b/pandas/io/tests/data/paxraw_d_short.xpt differ
diff --git a/pandas/io/tests/test_sas.py b/pandas/io/tests/test_sas.py
index 8d1041229bf3c..2691b3f8b9c5f 100644
--- a/pandas/io/tests/test_sas.py
+++ b/pandas/io/tests/test_sas.py
@@ -22,6 +22,7 @@ def setUp(self):
self.file01 = os.path.join(self.dirpath, "DEMO_G.XPT")
self.file02 = os.path.join(self.dirpath, "SSHSV1_A.XPT")
self.file03 = os.path.join(self.dirpath, "DRXFCD_G.XPT")
+ self.file04 = os.path.join(self.dirpath, "paxraw_d_short.xpt")
def test1(self):
@@ -110,3 +111,21 @@ def test3(self):
data = read_sas(self.file03)
tm.assert_frame_equal(data, data_csv)
+
+
+ def test4(self):
+ # Test with paxraw_d_short.xpt, a shortened version of:
+ # http://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/PAXRAW_D.ZIP
+ # This file has truncated floats (5 bytes in this case).
+
+ data_csv = pd.read_csv(self.file04.replace(".xpt", ".csv"))
+
+ data = XportReader(self.file04).read()
+ for x in data:
+ data[x] = data[x].astype(np.int64)
+ tm.assert_frame_equal(data, data_csv)
+
+ data = read_sas(self.file04)
+ for x in data:
+ data[x] = data[x].astype(np.int64)
+ tm.assert_frame_equal(data, data_csv)
| SAS xport files allow floats to be stored with fewer than 8 bytes, with implicit zeros in the high order bytes. Currently we only handle full width (8 byte) floats. This PR adds support for truncated floats.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11713 | 2015-11-27T15:54:03Z | 2015-12-01T15:15:06Z | null | 2015-12-01T15:15:06Z |
BUG: work around for np.bincount with minlength=0 | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 926e191c96754..497ee2a8dbb4d 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -106,6 +106,7 @@ Performance Improvements
Bug Fixes
~~~~~~~~~
+- Bug in ``GroupBy.size`` when data-frame is empty. (:issue:`11699`)
- Bug in ``.loc`` against ``CategoricalIndex`` may result in normal ``Index`` (:issue:`11586`)
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py
index 456fedb272e18..462ead70c9f93 100644
--- a/pandas/core/categorical.py
+++ b/pandas/core/categorical.py
@@ -1095,7 +1095,8 @@ def value_counts(self, dropna=True):
ix, clean = np.arange(ncat), mask.all()
if dropna or clean:
- count = bincount(code if clean else code[mask], minlength=ncat)
+ obs = code if clean else code[mask]
+ count = bincount(obs, minlength=ncat or None)
else:
count = bincount(np.where(mask, code, ncat))
ix = np.append(ix, -1)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index e9aa9066b75a5..584b946d47618 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -1439,7 +1439,7 @@ def size(self):
"""
ids, _, ngroup = self.group_info
ids = com._ensure_platform_int(ids)
- out = np.bincount(ids[ids != -1], minlength=ngroup)
+ out = np.bincount(ids[ids != -1], minlength=ngroup or None)
return Series(out, index=self.result_index, dtype='int64')
@cache_readonly
@@ -2822,7 +2822,7 @@ def count(self):
mask = (ids != -1) & ~isnull(val)
ids = com._ensure_platform_int(ids)
- out = np.bincount(ids[mask], minlength=ngroups) if ngroups != 0 else []
+ out = np.bincount(ids[mask], minlength=ngroups or None)
return Series(out, index=self.grouper.result_index, name=self.name, dtype='int64')
diff --git a/pandas/core/series.py b/pandas/core/series.py
index e603c6aa75d6f..f3e059b3d6e98 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1142,7 +1142,8 @@ def count(self, level=None):
lab[mask] = cnt = len(lev)
lev = lev.insert(cnt, _get_na_value(lev.dtype.type))
- out = np.bincount(lab[notnull(self.values)], minlength=len(lev))
+ obs = lab[notnull(self.values)]
+ out = np.bincount(obs, minlength=len(lev) or None)
return self._constructor(out, index=lev, dtype='int64').__finalize__(self)
def mode(self):
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 025ed17194979..c3b9aee57c0de 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -2525,6 +2525,11 @@ def test_size(self):
right = df.groupby(key, sort=sort)['c'].apply(lambda a: a.shape[0])
assert_series_equal(left, right, check_names=False)
+ # GH11699
+ df = DataFrame([], columns=['A', 'B'])
+ out = Series([], dtype='int64', index=Index([], name='A'))
+ assert_series_equal(df.groupby('A').size(), out)
+
def test_count(self):
from string import ascii_lowercase
n = 1 << 15
| closes https://github.com/pydata/pandas/issues/11699
xref https://github.com/pydata/pandas/issues/11189#issuecomment-152613636
| https://api.github.com/repos/pandas-dev/pandas/pulls/11709 | 2015-11-26T18:28:51Z | 2015-11-29T17:32:18Z | 2015-11-29T17:32:18Z | 2015-11-29T19:51:39Z |
BUG: GH11697 where memory allocation failed in rolling_median | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 7e01303477f8f..82249a86c36ad 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -190,3 +190,4 @@ Bug Fixes
- 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`)
diff --git a/pandas/src/skiplist.h b/pandas/src/skiplist.h
index c7117f16c9496..2d70090302c94 100644
--- a/pandas/src/skiplist.h
+++ b/pandas/src/skiplist.h
@@ -60,7 +60,7 @@ typedef struct {
} skiplist_t;
static PANDAS_INLINE double urand(void) {
- return rand() / ((double) RAND_MAX + 1);
+ return ((double) rand() + 1) / ((double) RAND_MAX + 2);
}
static PANDAS_INLINE int int_min(int a, int b) {
diff --git a/pandas/stats/tests/test_moments.py b/pandas/stats/tests/test_moments.py
index e2ed27156d2b5..b9efa875735d2 100644
--- a/pandas/stats/tests/test_moments.py
+++ b/pandas/stats/tests/test_moments.py
@@ -1919,6 +1919,12 @@ def test_rolling_median_how_resample(self):
x = mom.rolling_median(series, window=1, freq='D')
assert_series_equal(expected, x)
+ def test_rolling_median_memory_error(self):
+ # GH11722
+ n = 20000
+ mom.rolling_median(Series(np.random.randn(n)), window=2, center=False)
+ mom.rolling_median(Series(np.random.randn(n)), window=2, center=False)
+
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
| closes #11697
thanks @ajspeck
| https://api.github.com/repos/pandas-dev/pandas/pulls/11706 | 2015-11-26T04:20:15Z | 2015-12-01T12:10:28Z | 2015-12-01T12:10:28Z | 2015-12-01T12:11:08Z |
BUG GH11693 Support NaT series concatenation | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 733e86e38e47a..384174f1fc5a0 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -345,3 +345,6 @@ Bug Fixes
- Bug in ``read_sql`` with pymysql connections failing to return chunked data (:issue:`11522`)
+
+- Bug in ``pd.concat`` while concatenating tz-aware NaT series. (:issue:`11693`)
+- Bug in ``pd.concat`` while concatenating tz-aware series with time series. (:issue:`11755`)
\ No newline at end of file
diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py
index 9399f537191e7..a95d4abbc6a42 100644
--- a/pandas/tools/merge.py
+++ b/pandas/tools/merge.py
@@ -955,8 +955,12 @@ def get_result(self):
# stack blocks
if self.axis == 0:
- new_data = com._concat_compat([x._values for x in self.objs])
+ to_concat = [x._values for x in self.objs]
+ typs = com.get_dtype_kinds(to_concat)
+ new_data = com._concat_compat(to_concat)
name = com._consensus_name_attr(self.objs)
+ if 'datetimetz' in typs and ('datetime' in typs or 'object' in typs):
+ return Series(new_data, index=self.new_axes[0], name=name, dtype='object').__finalize__(self, method='concat')
return Series(new_data, index=self.new_axes[0], name=name).__finalize__(self, method='concat')
# combine as columns in a frame
diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py
index 6db2d2e15f699..17691a3d28ea4 100644
--- a/pandas/tools/tests/test_merge.py
+++ b/pandas/tools/tests/test_merge.py
@@ -994,6 +994,47 @@ def test_merge_on_datetime64tz(self):
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.
+ x = pd.Series( pd.date_range('20151124 08:00', '20151124 09:00', freq='1h', tz = "US/Eastern"))
+ y = pd.Series( pd.date_range('20151124 10:00', '20151124 11:00', freq='1h', tz = "US/Eastern"))
+ y[:] = pd.NaT
+ expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT], index=[0, 1, 0, 1])
+ tm.assert_series_equal(pd.concat([x,y]), expected)
+
+ # all NaT with tz
+ x[:] = pd.NaT
+ expected = pd.Series([pd.NaT for i in range(4)], index=[0, 1, 0, 1], dtype ='datetime64[ns, US/Eastern]')
+ tm.assert_series_equal(pd.concat([x,y]), expected)
+
+ #without tz
+ x = pd.Series( pd.date_range('20151124 08:00', '20151124 09:00', freq='1h'))
+ y = pd.Series( pd.date_range('20151124 10:00', '20151124 11:00', freq='1h'))
+ y[:] = pd.NaT
+ expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT], index=[0, 1, 0, 1])
+ tm.assert_series_equal(pd.concat([x, y]), expected)
+
+ #all NaT without tz
+ x[:] = pd.NaT
+ expected = pd.Series([pd.NaT for i in range(4)], index=[0, 1, 0, 1], dtype ='datetime64[ns]')
+ tm.assert_series_equal(pd.concat([x,y]), expected)
+
+ def test_concat_tz_series(self):
+ #tz and no tz
+ #GH 11755
+ x = pd.Series(pd.date_range('20151124 08:00', '20151124 09:00', freq = '1h', tz = "UTC") )
+ y = pd.Series(pd.date_range('2012-01-01', '2012-01-02'))
+ expected = pd.Series([x[0], x[1], y[0], y[1]], index=[0, 1, 0, 1], dtype='object')
+ tm.assert_series_equal(pd.concat([x,y]), expected)
+
+ #tz and object
+ #GH 11887
+ x = pd.Series(pd.date_range('20151124 08:00', '20151124 09:00', freq = '1h', tz = "UTC") )
+ y = pd.Series(['a', 'b'])
+ expected = pd.Series([x[0], x[1], y[0], y[1]], index=[0, 1, 0, 1], dtype='object')
+ tm.assert_series_equal(pd.concat([x,y]), expected)
+
def test_indicator(self):
# PR #10054. xref #7412 and closes #8790.
df1 = DataFrame({'col1':[0,1], 'col_left':['a','b'], 'col_conflict':[1,2]})
diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py
index c033706a4d82f..2e8615b581cd7 100644
--- a/pandas/tseries/common.py
+++ b/pandas/tseries/common.py
@@ -231,14 +231,15 @@ def _concat_compat(to_concat, axis=0):
def convert_to_pydatetime(x, axis):
# coerce to an object dtype
- if x.dtype == _NS_DTYPE:
-
- if hasattr(x, 'tz'):
- x = x.asobject
+ # if dtype is of datetimetz or timezone
+ if x.dtype.kind == _NS_DTYPE.kind:
shape = x.shape
x = tslib.ints_to_pydatetime(x.view(np.int64).ravel())
x = x.reshape(shape)
+ if hasattr(x, 'tz'):
+ x = x.asobject
+
elif x.dtype == _TD_DTYPE:
shape = x.shape
x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel())
@@ -251,6 +252,11 @@ def convert_to_pydatetime(x, axis):
# datetimetz
if 'datetimetz' in typs:
+ # if to_concat have 'datetime' or 'object', then we need to coerce to object
+ if 'datetime' in typs or 'object' in typs:
+ to_concat = [convert_to_pydatetime(x, axis) for x in to_concat]
+ return np.concatenate(to_concat,axis=axis)
+
# we require ALL of the same tz for datetimetz
tzs = set([ getattr(x,'tz',None) for x in to_concat ])-set([None])
if len(tzs) == 1:
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index a6908a0c36ad4..56342067c85f8 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -3427,6 +3427,10 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2):
trans, deltas, typ = _get_dst_info(tz2)
trans_len = len(trans)
+ #if all NaT, return all NaT
+ if (utc_dates==iNaT).all():
+ return utc_dates
+
# use first non-NaT element
# if all-NaT, return all-NaT
if (result==NPY_NAT).all():
| Please review .
Closes #11693
| https://api.github.com/repos/pandas-dev/pandas/pulls/11705 | 2015-11-26T02:30:13Z | 2016-02-01T18:59:22Z | null | 2016-02-01T18:59:22Z |
Doc: fix typo in style docs (collied->collide) | diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb
index 2bead2e18e0e0..1881727001546 100644
--- a/doc/source/html-styling.ipynb
+++ b/doc/source/html-styling.ipynb
@@ -518,7 +518,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The `row0_col2` is the identifier for that particular cell. We've also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn't collied with the styling from another within the same notebook or page (you can set the `uuid` if you'd like to tie together the styling of two DataFrames).\n",
+ "The `row0_col2` is the identifier for that particular cell. We've also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn't collide with the styling from another within the same notebook or page (you can set the `uuid` if you'd like to tie together the styling of two DataFrames).\n",
"\n",
"When writing style functions, you take care of producing the CSS attribute / value pairs you want. Pandas matches those up with the CSS classes that identify each cell."
]
| https://api.github.com/repos/pandas-dev/pandas/pulls/11702 | 2015-11-25T18:21:28Z | 2015-11-25T18:24:04Z | 2015-11-25T18:24:04Z | 2015-11-25T18:24:11Z | |
BUG/API: Index creation with different tz coerces DatetimeIndex | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index c1b7ff82f4c76..3e864918f06a5 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -187,7 +187,6 @@ Bug Fixes
- Bug in timezone info lost when broadcasting scalar datetime to ``DataFrame`` (:issue:`11682`)
-
- Bug in parsing timezone offset strings with non-zero minutes (:issue:`11708`)
@@ -197,3 +196,5 @@ Bug Fixes
- Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`)
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
+- Bug in ``Index`` creation from ``Timestamp`` with mixed tz coerces to UTC (:issue:`11488`)
+
diff --git a/pandas/core/index.py b/pandas/core/index.py
index fa23f2e1efe3f..1433d755d294d 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -179,8 +179,13 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False,
elif inferred != 'string':
if (inferred.startswith('datetime') or
tslib.is_timestamp_array(subarr)):
- from pandas.tseries.index import DatetimeIndex
- return DatetimeIndex(subarr, copy=copy, name=name, **kwargs)
+
+ if (lib.is_datetime_with_singletz_array(subarr) or
+ 'tz' in kwargs):
+ # only when subarr has the same tz
+ from pandas.tseries.index import DatetimeIndex
+ return DatetimeIndex(subarr, copy=copy, name=name, **kwargs)
+
elif (inferred.startswith('timedelta') or
lib.is_timedelta_array(subarr)):
from pandas.tseries.tdi import TimedeltaIndex
diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx
index 74bd437373c19..1a5703eb91053 100644
--- a/pandas/src/inference.pyx
+++ b/pandas/src/inference.pyx
@@ -1,6 +1,6 @@
import sys
cimport util
-from tslib import NaT
+from tslib import NaT, get_timezone
from datetime import datetime, timedelta
iNaT = util.get_nat()
@@ -431,6 +431,35 @@ def is_datetime64_array(ndarray values):
return False
return null_count != n
+
+cpdef is_datetime_with_singletz_array(ndarray[object] values):
+ """
+ Check values have the same tzinfo attribute.
+ Doesn't check values are datetime-like types.
+ """
+
+ cdef Py_ssize_t i, j, n = len(values)
+ cdef object base_val, base_tz, val, tz
+
+ if n == 0:
+ return False
+
+ for i in range(n):
+ base_val = values[i]
+ if base_val is not NaT:
+ base_tz = get_timezone(getattr(base_val, 'tzinfo', None))
+
+ for j in range(i, n):
+ val = values[j]
+ if val is not NaT:
+ tz = getattr(val, 'tzinfo', None)
+ if base_tz != tz and base_tz != get_timezone(tz):
+ return False
+ break
+
+ return True
+
+
def is_timedelta_array(ndarray values):
cdef Py_ssize_t i, null_count = 0, n = len(values)
cdef object v
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index bc9d303dc3b1a..e2fa6a90429dc 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -3493,6 +3493,189 @@ def test_construction_with_alt(self):
def test_pickle_compat_construction(self):
pass
+ def test_construction_index_with_mixed_timezones(self):
+ # GH 11488
+ # no tz results in DatetimeIndex
+ result = Index([Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNone(result.tz)
+
+ # same tz results in DatetimeIndex
+ result = Index([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ Timestamp('2011-01-02 10:00', tz='Asia/Tokyo')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00')], tz='Asia/Tokyo', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNotNone(result.tz)
+ self.assertEqual(result.tz, exp.tz)
+
+ # same tz results in DatetimeIndex (DST)
+ result = Index([Timestamp('2011-01-01 10:00', tz='US/Eastern'),
+ Timestamp('2011-08-01 10:00', tz='US/Eastern')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 10:00'), Timestamp('2011-08-01 10:00')],
+ tz='US/Eastern', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNotNone(result.tz)
+ self.assertEqual(result.tz, exp.tz)
+
+ # different tz results in Index(dtype=object)
+ result = Index([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx')
+ exp = Index([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00', tz='US/Eastern')],
+ dtype='object', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertFalse(isinstance(result, DatetimeIndex))
+
+ result = Index([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx')
+ exp = Index([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ Timestamp('2011-01-02 10:00', tz='US/Eastern')],
+ dtype='object', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertFalse(isinstance(result, DatetimeIndex))
+
+ # passing tz results in DatetimeIndex
+ result = Index([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00', tz='US/Eastern')],
+ tz='Asia/Tokyo', name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 19:00'), Timestamp('2011-01-03 00:00')],
+ tz='Asia/Tokyo', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+
+ # length = 1
+ result = Index([Timestamp('2011-01-01')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01')], name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNone(result.tz)
+
+ # length = 1 with tz
+ result = Index([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 10:00')], tz='Asia/Tokyo', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNotNone(result.tz)
+ self.assertEqual(result.tz, exp.tz)
+
+ def test_construction_index_with_mixed_timezones_with_NaT(self):
+ # GH 11488
+ result = Index([pd.NaT, Timestamp('2011-01-01'),
+ pd.NaT, Timestamp('2011-01-02')], name='idx')
+ exp = DatetimeIndex([pd.NaT, Timestamp('2011-01-01'),
+ pd.NaT, Timestamp('2011-01-02')], name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNone(result.tz)
+
+ # same tz results in DatetimeIndex
+ result = Index([pd.NaT, Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ pd.NaT, Timestamp('2011-01-02 10:00', tz='Asia/Tokyo')], name='idx')
+ exp = DatetimeIndex([pd.NaT, Timestamp('2011-01-01 10:00'),
+ pd.NaT, Timestamp('2011-01-02 10:00')], tz='Asia/Tokyo', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNotNone(result.tz)
+ self.assertEqual(result.tz, exp.tz)
+
+ # same tz results in DatetimeIndex (DST)
+ result = Index([Timestamp('2011-01-01 10:00', tz='US/Eastern'),
+ pd.NaT, Timestamp('2011-08-01 10:00', tz='US/Eastern')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 10:00'), pd.NaT, Timestamp('2011-08-01 10:00')],
+ tz='US/Eastern', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNotNone(result.tz)
+ self.assertEqual(result.tz, exp.tz)
+
+ # different tz results in Index(dtype=object)
+ result = Index([pd.NaT, Timestamp('2011-01-01 10:00'),
+ pd.NaT, Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx')
+ exp = Index([pd.NaT, Timestamp('2011-01-01 10:00'),
+ pd.NaT, Timestamp('2011-01-02 10:00', tz='US/Eastern')],
+ dtype='object', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertFalse(isinstance(result, DatetimeIndex))
+
+ result = Index([pd.NaT, Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ pd.NaT, Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx')
+ exp = Index([pd.NaT, Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ pd.NaT, Timestamp('2011-01-02 10:00', tz='US/Eastern')],
+ dtype='object', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertFalse(isinstance(result, DatetimeIndex))
+
+ # passing tz results in DatetimeIndex
+ result = Index([pd.NaT, Timestamp('2011-01-01 10:00'),
+ pd.NaT, Timestamp('2011-01-02 10:00', tz='US/Eastern')],
+ tz='Asia/Tokyo', name='idx')
+ exp = DatetimeIndex([pd.NaT, Timestamp('2011-01-01 19:00'),
+ pd.NaT, Timestamp('2011-01-03 00:00')],
+ tz='Asia/Tokyo', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+
+ # all NaT
+ result = Index([pd.NaT, pd.NaT], name='idx')
+ exp = DatetimeIndex([pd.NaT, pd.NaT], name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNone(result.tz)
+
+ # all NaT with tz
+ result = Index([pd.NaT, pd.NaT], tz='Asia/Tokyo', name='idx')
+ exp = DatetimeIndex([pd.NaT, pd.NaT], tz='Asia/Tokyo', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+ self.assertIsNotNone(result.tz)
+ self.assertEqual(result.tz, exp.tz)
+
+ def test_construction_dti_with_mixed_timezones(self):
+ # GH 11488 (not changed, added explicit tests)
+
+ # no tz results in DatetimeIndex
+ result = DatetimeIndex([Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+
+ # same tz results in DatetimeIndex
+ result = DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ Timestamp('2011-01-02 10:00', tz='Asia/Tokyo')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00')], tz='Asia/Tokyo', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+
+ # same tz results in DatetimeIndex (DST)
+ result = DatetimeIndex([Timestamp('2011-01-01 10:00', tz='US/Eastern'),
+ Timestamp('2011-08-01 10:00', tz='US/Eastern')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 10:00'), Timestamp('2011-08-01 10:00')],
+ tz='US/Eastern', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+
+ # different tz coerces tz-naive to tz-awareIndex(dtype=object)
+ result = DatetimeIndex([Timestamp('2011-01-01 10:00'),
+ Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx')
+ exp = DatetimeIndex([Timestamp('2011-01-01 05:00'), Timestamp('2011-01-02 10:00')],
+ tz='US/Eastern', name='idx')
+ self.assert_index_equal(result, exp, exact=True)
+ self.assertTrue(isinstance(result, DatetimeIndex))
+
+ # tz mismatch affecting to tz-aware raises TypeError/ValueError
+ with tm.assertRaises(ValueError):
+ DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx')
+
+ with tm.assertRaises(TypeError):
+ DatetimeIndex([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00', tz='US/Eastern')],
+ tz='Asia/Tokyo', name='idx')
+
+ with tm.assertRaises(ValueError):
+ DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
+ Timestamp('2011-01-02 10:00', tz='US/Eastern')], tz='US/Eastern', name='idx')
+
def test_get_loc(self):
idx = pd.date_range('2000-01-01', periods=3)
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index dd3f300bcf266..858a0e1570240 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -718,7 +718,11 @@ def assert_attr_equal(attr, left, right, obj='Attributes'):
# np.nan
return True
- result = left_attr == right_attr
+ try:
+ result = left_attr == right_attr
+ except TypeError:
+ # datetimetz on rhs may raise TypeError
+ result = False
if not isinstance(result, bool):
result = result.all()
| Closes #11488.
Based on the type check added in #11588, the change affects to fewer functions than I originally thought.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11696 | 2015-11-24T20:45:14Z | 2015-12-10T12:17:20Z | null | 2015-12-10T12:47:58Z |
BUG: Index does not inherit existing Index or DatatetimeIndex object … | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 5b8c282d3e4ed..ea5c3b6e4c7d0 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -238,4 +238,6 @@ Bug Fixes
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
+- Bug in ``Index`` prevent copying name of passed ``Index`` or ``DatetimeIndex`` objects, when a new name is not provided (:issue:`11193`)
+
- Bug in ``read_excel`` failing to read any non-empty sheets when empty sheets exist and ``sheetname=None`` (:issue:`11711`)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 9b75153d9b84b..6da30ffefb41f 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -120,6 +120,9 @@ class Index(IndexOpsMixin, StringAccessorMixin, PandasObject):
def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False,
tupleize_cols=True, **kwargs):
+ if name is None and hasattr(data, 'name'):
+ name = data.name
+
# no class inference!
if fastpath:
return cls._simple_new(data, name)
@@ -128,6 +131,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False,
return CategoricalIndex(data, copy=copy, name=name, **kwargs)
if isinstance(data, (np.ndarray, Index, ABCSeries)):
+
if issubclass(data.dtype.type, np.datetime64) or is_datetimetz(data):
from pandas.tseries.index import DatetimeIndex
result = DatetimeIndex(data, copy=copy, name=name, **kwargs)
@@ -4052,6 +4056,8 @@ def __new__(cls, levels=None, labels=None, sortorder=None, names=None,
# compat with Index
if name is not None:
names = name
+# elif cls.names is not None:
+# names = cls.names
if levels is None or labels is None:
raise TypeError("Must pass both levels and labels")
if len(levels) != len(labels):
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index 5d959892791fe..db391dca7114c 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -415,6 +415,7 @@ def test_value_counts_unique_nunique(self):
# resets name from Index
expected_index = pd.Index(o[::-1])
+ expected_index.name = None
# attach name to klass
o = o.repeat(range(1, len(o) + 1))
@@ -424,16 +425,18 @@ def test_value_counts_unique_nunique(self):
# resets name from Index
expected_index = pd.Index(o[::-1])
+ expected_index.name = None
# attach name to klass
o = o.repeat(range(1, len(o) + 1))
o.name = 'a'
# don't test boolean
- elif isinstance(o,Index) and o.is_boolean():
+ elif isinstance(o, Index) and o.is_boolean():
continue
elif isinstance(o, Index):
expected_index = pd.Index(values[::-1])
+ expected_index.name = None
o = o.repeat(range(1, len(o) + 1))
o.name = 'a'
else:
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index e2fa6a90429dc..a240b8c1c1e78 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -60,6 +60,31 @@ def test_shift(self):
self.assertRaises(NotImplementedError, idx.shift, 1)
self.assertRaises(NotImplementedError, idx.shift, 1, 2)
+ def test_create_index_existing_name(self):
+
+ # GH11193, when an existing index is passed, and a new name is not specified, the new index should inherit the
+ # previous object name
+ expected = self.create_index()
+ if not isinstance(expected, MultiIndex):
+ expected.name = 'foo'
+ result = pd.Index(expected)
+ tm.assert_index_equal(result, expected)
+
+ result = pd.Index(expected, name='bar')
+ expected.name = 'bar'
+ tm.assert_index_equal(result, expected)
+ else:
+ expected.names = ['foo', 'bar']
+ result = pd.Index(expected)
+ tm.assert_index_equal(result, Index(Index([('foo', 'one'), ('foo', 'two'), ('bar', 'one'), ('baz', 'two'),
+ ('qux', 'one'), ('qux', 'two')], dtype='object'),
+ names=['foo', 'bar']))
+
+ result = pd.Index(expected, names=['A', 'B'])
+ tm.assert_index_equal(result, Index(Index([('foo', 'one'), ('foo', 'two'), ('bar', 'one'), ('baz', 'two'),
+ ('qux', 'one'), ('qux', 'two')], dtype='object'),
+ names=['A', 'B']))
+
def test_numeric_compat(self):
idx = self.create_index()
@@ -3043,7 +3068,7 @@ def test_identical(self):
i = self.index.copy(dtype=object)
i = i.rename('foo')
same_values = Index(i, dtype=object)
- self.assertTrue(same_values.identical(self.index.copy(dtype=object)))
+ self.assertTrue(same_values.identical(i))
self.assertFalse(i.identical(self.index))
self.assertTrue(Index(same_values, name='foo', dtype=object
| This PR is meant to fix issue #11193
The issue prevent, when creating a new `Index` object based on a passed `Index` or `DatetimeIndex` object, to inherit the existing `Index` name if a new name is not provided.
I've also created two unit test which test the new behaviour on both cases (passing an `Index` or `DatetimeIndex` object) and added a comment in the whatsnew documentation.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11695 | 2015-11-24T20:18:45Z | 2015-12-18T18:26:10Z | null | 2015-12-18T18:26:10Z |
ENH: rounding for datetimelike Indexes/Scalars | diff --git a/doc/source/api.rst b/doc/source/api.rst
index ac79528f31e04..c7f815914358b 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -526,6 +526,7 @@ These can be accessed like ``Series.dt.<property>``.
Series.dt.tz_convert
Series.dt.normalize
Series.dt.strftime
+ Series.dt.round
**Timedelta Properties**
@@ -1507,7 +1508,7 @@ Time-specific operations
DatetimeIndex.snap
DatetimeIndex.tz_convert
DatetimeIndex.tz_localize
-
+ DatetimeIndex.round
Conversion
~~~~~~~~~~
@@ -1548,6 +1549,7 @@ Conversion
TimedeltaIndex.to_pytimedelta
TimedeltaIndex.to_series
+ TimedeltaIndex.round
GroupBy
-------
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 926e191c96754..d233edd9c88f3 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -31,13 +31,53 @@ New features
Other enhancements
^^^^^^^^^^^^^^^^^^
+.. _whatsnew_0180.enhancements.rounding:
+Datetimelike rounding
+^^^^^^^^^^^^^^^^^^^^^
+``DatetimeIndex``, ``Timestamp``, ``TimedeltaIndex``, ``Timedelta`` have gained the ``.round()`` method for datetimelike rounding. (:issue:`4314`)
+Naive datetimes
+.. ipython:: python
+ dr = pd.date_range('20130101 09:12:56.1234', periods=3)
+ dr
+ dr.round('s')
+ # Timestamp scalar
+ dr[0]
+ dr[0].round('10s')
+Tz-aware are rounded in local times
+
+.. ipython:: python
+
+ dr = dr.tz_localize('US/Eastern')
+ dr
+ dr.round('s')
+
+Timedeltas
+
+.. ipython:: python
+
+ t = timedelta_range('1 days 2 hr 13 min 45 us',periods=3,freq='d')
+ t
+ t.round('10min')
+
+ # Timedelta scalar
+ t[0]
+ t[0].round('2h')
+
+
+In addition, ``.round()`` will be available thru the ``.dt`` accessor of ``Series``.
+
+.. ipython:: python
+
+ s = Series(dr)
+ s
+ s.dt.round('D')
.. _whatsnew_0180.api:
@@ -65,6 +105,9 @@ Other API Changes
+
+
+
.. _whatsnew_0180.deprecations:
Deprecations
@@ -107,5 +150,5 @@ Bug Fixes
~~~~~~~~~
-
+- 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/test_categorical.py b/pandas/tests/test_categorical.py
index e98c98fdec8b3..64908f96bfdd8 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -3753,6 +3753,7 @@ def test_dt_accessor_api_for_categorical(self):
special_func_defs = [
('strftime', ("%Y-%m-%d",), {}),
('tz_convert', ("EST",), {}),
+ ('round', ("D",), {}),
#('tz_localize', ("UTC",), {}),
]
_special_func_names = [f[0] for f in special_func_defs]
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 4b0f9a9f633b4..0fb66ee2dfa7c 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -88,9 +88,9 @@ def test_dt_namespace_accessor(self):
ok_for_period_methods = ['strftime']
ok_for_dt = ok_for_base + ['date','time','microsecond','nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start',
'is_quarter_end', 'is_year_start', 'is_year_end', 'tz']
- ok_for_dt_methods = ['to_period','to_pydatetime','tz_localize','tz_convert', 'normalize', 'strftime']
+ ok_for_dt_methods = ['to_period','to_pydatetime','tz_localize','tz_convert', 'normalize', 'strftime', 'round']
ok_for_td = ['days','seconds','microseconds','nanoseconds']
- ok_for_td_methods = ['components','to_pytimedelta','total_seconds']
+ ok_for_td_methods = ['components','to_pytimedelta','total_seconds','round']
def get_expected(s, name):
result = getattr(Index(s._values),prop)
@@ -139,6 +139,17 @@ def compare(s, name):
expected = Series(DatetimeIndex(s.values).tz_localize('UTC').tz_convert('US/Eastern'),index=s.index)
tm.assert_series_equal(result, expected)
+ # round
+ s = Series(date_range('20130101 09:10:11',periods=5))
+ result = s.dt.round('D')
+ expected = Series(date_range('20130101',periods=5))
+ 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(date_range('20130101',periods=5)).dt.tz_localize('US/Eastern')
+ tm.assert_series_equal(result, expected)
+
# datetimeindex with tz
s = Series(date_range('20130101',periods=5,tz='US/Eastern'))
for prop in ok_for_dt:
diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py
index b063360b91280..4f0780ef2d660 100644
--- a/pandas/tseries/base.py
+++ b/pandas/tseries/base.py
@@ -41,6 +41,46 @@ def strftime(self, date_format):
"""
return np.asarray(self.format(date_format=date_format))
+class TimelikeOps(object):
+ """ common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex """
+
+ def round(self, freq):
+ """
+ Round the index to the specified freq; this is a floor type of operation
+
+ Paramaters
+ ----------
+ freq : freq string/object
+
+ Returns
+ -------
+ index of same type
+
+ Raises
+ ------
+ ValueError if the freq cannot be converted
+ """
+
+ from pandas.tseries.frequencies import to_offset
+ unit = to_offset(freq).nanos
+
+ # round the local times
+ if getattr(self,'tz',None) is not None:
+ values = self.tz_localize(None).asi8
+ else:
+ values = self.asi8
+ result = (unit*np.floor(values/unit)).astype('i8')
+ attribs = self._get_attributes_dict()
+ if 'freq' in attribs:
+ attribs['freq'] = None
+ if 'tz' in attribs:
+ attribs['tz'] = None
+ result = self._shallow_copy(result, **attribs)
+
+ # reconvert to local tz
+ if getattr(self,'tz',None) is not None:
+ result = result.tz_localize(self.tz)
+ return result
class DatetimeIndexOpsMixin(object):
""" common ops mixin to support a unified inteface datetimelike Index """
diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py
index 31b5281aa86a6..c033706a4d82f 100644
--- a/pandas/tseries/common.py
+++ b/pandas/tseries/common.py
@@ -145,7 +145,8 @@ def to_pydatetime(self):
accessors=DatetimeIndex._datetimelike_ops,
typ='property')
DatetimeProperties._add_delegate_accessors(delegate=DatetimeIndex,
- accessors=["to_period","tz_localize","tz_convert","normalize","strftime"],
+ accessors=["to_period","tz_localize","tz_convert",
+ "normalize","strftime","round"],
typ='method')
class TimedeltaProperties(Properties):
@@ -181,7 +182,7 @@ def components(self):
accessors=TimedeltaIndex._datetimelike_ops,
typ='property')
TimedeltaProperties._add_delegate_accessors(delegate=TimedeltaIndex,
- accessors=["to_pytimedelta", "total_seconds"],
+ accessors=["to_pytimedelta", "total_seconds", "round"],
typ='method')
class PeriodProperties(Properties):
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index 4fd61e28233a6..007e4381c47d4 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -19,7 +19,7 @@
from pandas.tseries.frequencies import (
to_offset, get_period_alias,
Resolution)
-from pandas.tseries.base import DatelikeOps, DatetimeIndexOpsMixin
+from pandas.tseries.base import DatelikeOps, TimelikeOps, DatetimeIndexOpsMixin
from pandas.tseries.offsets import DateOffset, generate_range, Tick, CDay
from pandas.tseries.tools import parse_time_string, normalize_date
from pandas.tseries.timedeltas import to_timedelta
@@ -126,7 +126,7 @@ def _new_DatetimeIndex(cls, d):
result = result.tz_localize('UTC').tz_convert(tz)
return result
-class DatetimeIndex(DatelikeOps, DatetimeIndexOpsMixin, Int64Index):
+class DatetimeIndex(DatelikeOps, TimelikeOps, DatetimeIndexOpsMixin, Int64Index):
"""
Immutable ndarray of datetime64 data, represented internally as int64, and
which can be boxed to Timestamp objects that are subclasses of datetime and
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index 0dac09a243d36..69c1c60c354dc 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -496,6 +496,10 @@ def freqstr(self):
return fstr
+ @property
+ def nanos(self):
+ raise ValueError("{0} is a non-fixed frequency".format(self))
+
class SingleConstructorOffset(DateOffset):
@classmethod
diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py
index 89229fc48bcb2..d2f53b165f557 100644
--- a/pandas/tseries/tdi.py
+++ b/pandas/tseries/tdi.py
@@ -12,7 +12,7 @@
from pandas.tseries.frequencies import to_offset
import pandas.core.common as com
from pandas.tseries import timedeltas
-from pandas.tseries.base import DatetimeIndexOpsMixin
+from pandas.tseries.base import TimelikeOps, DatetimeIndexOpsMixin
from pandas.tseries.timedeltas import to_timedelta, _coerce_scalar_to_timedelta_type
import pandas.tseries.offsets as offsets
from pandas.tseries.offsets import Tick, DateOffset
@@ -24,16 +24,6 @@
Timedelta = tslib.Timedelta
-_resolution_map = {
- 'ns' : offsets.Nano,
- 'us' : offsets.Micro,
- 'ms' : offsets.Milli,
- 's' : offsets.Second,
- 'm' : offsets.Minute,
- 'h' : offsets.Hour,
- 'D' : offsets.Day,
- }
-
def _td_index_cmp(opname, nat_result=False):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
@@ -73,7 +63,7 @@ def wrapper(self, other):
return wrapper
-class TimedeltaIndex(DatetimeIndexOpsMixin, Int64Index):
+class TimedeltaIndex(DatetimeIndexOpsMixin, TimelikeOps, Int64Index):
"""
Immutable ndarray of timedelta64 data, represented internally as int64, and
which can be boxed to timedelta objects
@@ -706,7 +696,7 @@ def _maybe_cast_slice_bound(self, label, side, kind):
if side == 'left':
return lbound
else:
- return (lbound + _resolution_map[parsed.resolution]() -
+ return (lbound + to_offset(parsed.resolution) -
Timedelta(1, 'ns'))
elif is_integer(label) or is_float(label):
self._invalid_indexer('slice',label)
@@ -734,9 +724,8 @@ def _partial_td_slice(self, key, freq, use_lhs=True, use_rhs=True):
# figure out the resolution of the passed td
# and round to it
- reso = parsed.resolution
t1 = parsed.round(reso)
- t2 = t1 + _resolution_map[reso]() - Timedelta(1,'ns')
+ t2 = t1 + to_offset(parsed.resolution) - Timedelta(1,'ns')
stamps = self.asi8
diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py
index 45b98b0f85b1c..4bff2e3c5c2cd 100644
--- a/pandas/tseries/tests/test_timedeltas.py
+++ b/pandas/tseries/tests/test_timedeltas.py
@@ -74,10 +74,11 @@ def test_construction(self):
self.assertEqual(Timedelta('-1:00:00'), -timedelta(hours=1))
self.assertEqual(Timedelta('-01:00:00'), -timedelta(hours=1))
- # more strings
+ # more strings & abbrevs
# GH 8190
self.assertEqual(Timedelta('1 h'), timedelta(hours=1))
self.assertEqual(Timedelta('1 hour'), timedelta(hours=1))
+ self.assertEqual(Timedelta('1 hr'), timedelta(hours=1))
self.assertEqual(Timedelta('1 hours'), timedelta(hours=1))
self.assertEqual(Timedelta('-1 hours'), -timedelta(hours=1))
self.assertEqual(Timedelta('1 m'), timedelta(minutes=1))
@@ -164,6 +165,64 @@ def test_construction(self):
self.assertEqual(Timedelta(pd.offsets.Hour(2)),Timedelta('0 days, 02:00:00'))
self.assertEqual(Timedelta(pd.offsets.Second(2)),Timedelta('0 days, 00:00:02'))
+ def test_round(self):
+
+ t1 = Timedelta('1 days 02:34:56.789123456')
+ t2 = Timedelta('-1 days 02:34:56.789123456')
+
+ for (freq, s1, s2) in [('N', t1, t2),
+ ('U', Timedelta('1 days 02:34:56.789123000'),Timedelta('-1 days 02:34:56.789123000')),
+ ('L', Timedelta('1 days 02:34:56.789000000'),Timedelta('-1 days 02:34:56.789000000')),
+ ('S', Timedelta('1 days 02:34:56'),Timedelta('-1 days 02:34:56')),
+ ('2S', Timedelta('1 days 02:34:56'),Timedelta('-1 days 02:34:56')),
+ ('5S', Timedelta('1 days 02:34:55'),Timedelta('-1 days 02:34:55')),
+ ('T', Timedelta('1 days 02:34:00'),Timedelta('-1 days 02:34:00')),
+ ('12T', Timedelta('1 days 02:24:00'),Timedelta('-1 days 02:24:00')),
+ ('H', Timedelta('1 days 02:00:00'),Timedelta('-1 days 02:00:00')),
+ ('d', Timedelta('1 days'),Timedelta('-1 days'))]:
+ r1 = t1.round(freq)
+ self.assertEqual(r1, s1)
+ r2 = t2.round(freq)
+ self.assertEqual(r2, s2)
+
+ # invalid
+ for freq in ['Y','M','foobar']:
+ self.assertRaises(ValueError, lambda : t1.round(freq))
+
+ t1 = timedelta_range('1 days',periods=3,freq='1 min 2 s 3 us')
+ t2 = -1*t1
+ t1a = timedelta_range('1 days',periods=3,freq='1 min 2 s')
+ t1b = timedelta_range('1 days',periods=3,freq='1 min')
+ t1c = pd.TimedeltaIndex([1,1,1],unit='D')
+
+ # note that negative times round DOWN! so don't give whole numbers
+ for (freq, s1, s2) in [('N', t1, t2),
+ ('U', t1, t2),
+ ('L', t1a, TimedeltaIndex(['-1 days +00:00:00', '-2 days +23:58:57.999000',
+ '-2 days +23:57:55.999000'],
+ dtype='timedelta64[ns]', freq=None)),
+ ('S', t1a, TimedeltaIndex(['-1 days +00:00:00', '-2 days +23:58:57', '-2 days +23:57:55'],
+ dtype='timedelta64[ns]', freq=None)),
+ ('2S', t1a, TimedeltaIndex(['-1 days +00:00:00', '-2 days +23:58:56', '-2 days +23:57:54'],
+ dtype='timedelta64[ns]', freq=None)),
+ ('5S', t1b, TimedeltaIndex(['-1 days +00:00:00', '-2 days +23:58:55', '-2 days +23:57:55'],
+ dtype='timedelta64[ns]', freq=None)),
+ ('T', t1b, TimedeltaIndex(['-1 days +00:00:00', '-2 days +23:58:00', '-2 days +23:57:00'],
+ dtype='timedelta64[ns]', freq=None)),
+ ('12T', t1c, TimedeltaIndex(['-1 days +00:00:00', '-2 days +23:48:00', '-2 days +23:48:00'],
+ dtype='timedelta64[ns]', freq=None)),
+ ('H', t1c, TimedeltaIndex(['-1 days +00:00:00', '-2 days +23:00:00', '-2 days +23:00:00'],
+ dtype='timedelta64[ns]', freq=None)),
+ ('d', t1c, pd.TimedeltaIndex([-1,-2,-2],unit='D'))]:
+ r1 = t1.round(freq)
+ tm.assert_index_equal(r1, s1)
+ r2 = t2.round(freq)
+ tm.assert_index_equal(r2, s2)
+
+ # invalid
+ for freq in ['Y','M','foobar']:
+ self.assertRaises(ValueError, lambda : t1.round(freq))
+
def test_repr(self):
self.assertEqual(repr(Timedelta(10,unit='d')),"Timedelta('10 days 00:00:00')")
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index b5556804b3548..de264f5559fd0 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -2702,6 +2702,41 @@ def test_sort_values(self):
self.assertTrue(ordered[::-1].is_monotonic)
self.assert_numpy_array_equal(dexer, [0, 2, 1])
+ def test_round(self):
+
+ # round
+ dt = Timestamp('20130101 09:10:11')
+ result = dt.round('D')
+ expected = Timestamp('20130101')
+ self.assertEqual(result, expected)
+
+ dti = date_range('20130101 09:10:11',periods=5)
+ result = dti.round('D')
+ expected = date_range('20130101',periods=5)
+ tm.assert_index_equal(result, expected)
+
+ # round with tz
+ dt = Timestamp('20130101 09:10:11',tz='US/Eastern')
+ result = dt.round('D')
+ expected = Timestamp('20130101',tz='US/Eastern')
+ self.assertEqual(result, expected)
+
+ dt = Timestamp('20130101 09:10:11',tz='US/Eastern')
+ result = dt.round('s')
+ self.assertEqual(result, dt)
+
+ dti = date_range('20130101 09:10:11',periods=5).tz_localize('UTC').tz_convert('US/Eastern')
+ result = dti.round('D')
+ expected = date_range('20130101',periods=5).tz_localize('US/Eastern')
+ tm.assert_index_equal(result, expected)
+
+ result = dti.round('s')
+ tm.assert_index_equal(result, dti)
+
+ # invalid
+ for freq in ['Y','M','foobar']:
+ self.assertRaises(ValueError, lambda : dti.round(freq))
+
def test_insert(self):
idx = DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-02'], name='idx')
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 713cf08bfc3e2..fa263b458c1e9 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -362,6 +362,28 @@ class Timestamp(_Timestamp):
def _repr_base(self):
return '%s %s' % (self._date_repr, self._time_repr)
+ def round(self, freq):
+ """
+ return a new Timestamp rounded to this resolution
+
+ Parameters
+ ----------
+ freq : a freq string indicating the rouding resolution
+ """
+ cdef int64_t unit
+ cdef object result, value
+
+ from pandas.tseries.frequencies import to_offset
+ unit = to_offset(freq).nanos
+ if self.tz is not None:
+ value = self.tz_localize(None).value
+ else:
+ value = self.value
+ result = Timestamp(unit*np.floor(value/unit),unit='ns')
+ if self.tz is not None:
+ result = result.tz_localize(self.tz)
+ return result
+
@property
def tz(self):
"""
@@ -2301,52 +2323,34 @@ class Timedelta(_Timedelta):
self._ensure_components()
if self._ns:
- return "ns"
+ return "N"
elif self._us:
- return "us"
+ return "U"
elif self._ms:
- return "ms"
+ return "L"
elif self._s:
- return "s"
+ return "S"
elif self._m:
- return "m"
+ return "T"
elif self._h:
- return "h"
+ return "H"
else:
return "D"
- def round(self, reso):
+ def round(self, freq):
"""
return a new Timedelta rounded to this resolution
Parameters
----------
- reso : a string indicating the rouding resolution, accepting values
- d,h,m,s,ms,us
-
+ freq : a freq string indicating the rouding resolution
"""
- cdef int64_t frac, value = np.abs(self.value)
-
- self._ensure_components()
- frac = int(self._ms*1e6 + self._us*1e3+ self._ns)
- if reso == 'us':
- value -= self._ns
- elif reso == 'ms':
- value -= self._us*1000 + self._ns
- elif reso == 's':
- value -= frac
- elif reso == 'm':
- value -= int(self._s*1e9) + frac
- elif reso == 'h':
- value -= int((60*self._m + self._s)*1e9) + frac
- elif reso == 'd' or reso == 'D':
- value -= int((3600*self._h + 60*self._m + self._s)*1e9) + frac
- else:
- raise ValueError("invalid resolution")
+ cdef int64_t result, unit
- if self._sign < 0:
- value *= -1
- return Timedelta(value,unit='ns')
+ from pandas.tseries.frequencies import to_offset
+ unit = to_offset(freq).nanos
+ result = unit*np.floor(self.value/unit)
+ return Timedelta(result,unit='ns')
def _repr_base(self, format=None):
"""
@@ -2636,11 +2640,13 @@ def convert_to_timedelta(object ts, object unit='ns', errors='raise'):
assert is_raise or is_ignore or is_coerce
return convert_to_timedelta64(ts, unit, is_coerce)
-cdef dict timedelta_abbrevs = { 'd' : 'd',
+cdef dict timedelta_abbrevs = { 'D' : 'd',
+ 'd' : 'd',
'days' : 'd',
'day' : 'd',
'hours' : 'h',
'hour' : 'h',
+ 'hr' : 'h',
'h' : 'h',
'm' : 'm',
'minute' : 'm',
@@ -2666,6 +2672,7 @@ cdef dict timedelta_abbrevs = { 'd' : 'd',
'nanos' : 'ns',
'nanosecond' : 'ns',
}
+timedelta_abbrevs_map = timedelta_abbrevs
cdef inline int64_t timedelta_as_neg(int64_t value, bint neg):
"""
| closes #4314
| https://api.github.com/repos/pandas-dev/pandas/pulls/11690 | 2015-11-24T13:07:34Z | 2015-11-29T18:03:36Z | 2015-11-29T18:03:36Z | 2020-05-31T03:23:24Z |
Use lower case to load jinja2 | diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py
index 7f9840f73a583..0bdcbedee0900 100644
--- a/pandas/util/print_versions.py
+++ b/pandas/util/print_versions.py
@@ -89,7 +89,7 @@ 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__)
]
deps_blob = list()
| `import imp; imp.load_module("Jinja2", *imp.find_module("Jinja2"))`
raises `ImportError`, where as the lowercase version works fine.
Signed-off-by: Justin Lecher jlec@gentoo.org
| https://api.github.com/repos/pandas-dev/pandas/pulls/11689 | 2015-11-24T12:04:44Z | 2015-11-24T13:09:22Z | 2015-11-24T13:09:22Z | 2015-11-24T13:09:25Z |
Use tz.gettz() instead of zoneinfo.gettz() | diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py
index 7f9840f73a583..0bdcbedee0900 100644
--- a/pandas/util/print_versions.py
+++ b/pandas/util/print_versions.py
@@ -89,7 +89,7 @@ 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__)
]
deps_blob = list()
| zoneinfo.gettz() seems to have problems (1 & 2) on system which do not install
the zoninfo tarball (e.g. Debian, Gentoo and Fedora) but rely on the system
zoneinfo files. This results in test failures (3 & 4)
tz.gettz() doesn't suffer from this problem.
1 https://github.com/dateutil/dateutil/issues/8
2 https://github.com/dateutil/dateutil/issues/11
3 https://github.com/pydata/pandas/issues/9059
4 https://github.com/pydata/pandas/issues/8639
Signed-off-by: Justin Lecher jlec@gentoo.org
| https://api.github.com/repos/pandas-dev/pandas/pulls/11688 | 2015-11-24T11:58:23Z | 2015-11-24T12:03:06Z | null | 2015-11-24T12:03:14Z |
API: DataFrame.to_csv formatting parameters for float indexes | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index b443bb74e98ea..4614ce9acf3d5 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -325,6 +325,7 @@ Bug Fixes
- Bug in timezone info lost when broadcasting scalar datetime to ``DataFrame`` (:issue:`11682`)
+
- Bug in ``Index`` creation from ``Timestamp`` with mixed tz coerces to UTC (:issue:`11488`)
- Bug in ``to_numeric`` where it does not raise if input is more than one dimension (:issue:`11776`)
@@ -348,4 +349,6 @@ Bug Fixes
- Bug in ``read_sql`` with pymysql connections failing to return chunked data (:issue:`11522`)
+- Bug in ``.to_csv`` ignoring formatting parameters ``decimal``, ``na_rep``, ``float_format`` for float indexes (:issue:`11553`)
+
- Bug in ``DataFrame`` when masking an empty ``DataFrame`` (:issue:`11859`)
diff --git a/pandas/core/format.py b/pandas/core/format.py
index 07f16a5ef480a..91ac6f11f4ae9 100644
--- a/pandas/core/format.py
+++ b/pandas/core/format.py
@@ -6,7 +6,7 @@
import sys
from pandas.core.base import PandasObject
-from pandas.core.common import adjoin, notnull
+from pandas.core.common import adjoin, isnull, notnull
from pandas.core.index import Index, MultiIndex, _ensure_index
from pandas import compat
from pandas.compat import(StringIO, lzip, range, map, zip, reduce, u,
@@ -1631,6 +1631,7 @@ def _save_chunk(self, start_i, end_i):
ix = data_index.to_native_types(slicer=slicer,
na_rep=self.na_rep,
float_format=self.float_format,
+ decimal=self.decimal,
date_format=self.date_format,
quoting=self.quoting)
@@ -1983,7 +1984,8 @@ def format_array(values, formatter, float_format=None, na_rep='NaN',
class GenericArrayFormatter(object):
def __init__(self, values, digits=7, formatter=None, na_rep='NaN',
- space=12, float_format=None, justify='right'):
+ space=12, float_format=None, justify='right',
+ decimal='.', quoting=None):
self.values = values
self.digits = digits
self.na_rep = na_rep
@@ -1991,6 +1993,8 @@ def __init__(self, values, digits=7, formatter=None, na_rep='NaN',
self.formatter = formatter
self.float_format = float_format
self.justify = justify
+ self.decimal = decimal
+ self.quoting = quoting
def get_result(self):
fmt_values = self._format_strings()
@@ -2101,6 +2105,42 @@ def _format_strings(self):
return fmt_values
+ def get_formatted_data(self):
+ """Returns the array with its float values converted into strings using
+ the parameters given at initalisation.
+
+ Note: the method `.get_result()` does something similar, but with a
+ fixed-width output suitable for screen printing. The output here is not
+ fixed-width.
+ """
+ values = self.values
+ mask = isnull(values)
+
+ # the following variable is to be applied on each value to format it
+ # according to the string containing the float format, self.float_format
+ # and the character to use as decimal separator, self.decimal
+ formatter = None
+ if self.float_format and self.decimal != '.':
+ formatter = lambda v: (
+ (self.float_format % v).replace('.', self.decimal, 1))
+ elif self.decimal != '.': # no float format
+ formatter = lambda v: str(v).replace('.', self.decimal, 1)
+ elif self.float_format: # no special decimal separator
+ formatter = lambda v: self.float_format % v
+
+ if formatter is None and not self.quoting:
+ values = values.astype(str)
+ else:
+ values = np.array(values, dtype='object')
+
+ values[mask] = self.na_rep
+ if formatter:
+ imask = (~mask).ravel()
+ values.flat[imask] = np.array(
+ [formatter(val) for val in values.ravel()[imask]])
+
+ return values
+
class IntArrayFormatter(GenericArrayFormatter):
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 552eb7fb81180..8325e16515b90 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -3926,6 +3926,14 @@ def _convert_slice_indexer(self, key, kind=None):
# translate to locations
return self.slice_indexer(key.start, key.stop, key.step)
+ def _format_native_types(self, na_rep='', float_format=None,
+ decimal='.', quoting=None, **kwargs):
+ from pandas.core.format import FloatArrayFormatter
+ formatter = FloatArrayFormatter(self.values, na_rep=na_rep,
+ float_format=float_format,
+ decimal=decimal, quoting=quoting)
+ return formatter.get_formatted_data()
+
def get_value(self, series, key):
""" we always want to get an index value, never a value """
if not np.isscalar(key):
@@ -4448,12 +4456,27 @@ def _reference_duplicate_name(self, name):
# count the times name equals an element in self.names.
return sum(name == n for n in self.names) > 1
- def _format_native_types(self, **kwargs):
- # we go through the levels and format them
- levels = [level._format_native_types(**kwargs)
- for level in self.levels]
- mi = MultiIndex(levels=levels, labels=self.labels, names=self.names,
+ def _format_native_types(self, na_rep='nan', **kwargs):
+ new_levels = []
+ new_labels = []
+
+ # go through the levels and format them
+ for level, label in zip(self.levels, self.labels):
+ level = level._format_native_types(na_rep=na_rep, **kwargs)
+ # add nan values, if there are any
+ mask = (label == -1)
+ if mask.any():
+ nan_index = len(level)
+ level = np.append(level, na_rep)
+ label = label.values()
+ label[mask] = nan_index
+ new_levels.append(level)
+ new_labels.append(label)
+
+ # reconstruct the multi-index
+ mi = MultiIndex(levels=new_levels, labels=new_labels, names=self.names,
sortorder=self.sortorder, verify_integrity=False)
+
return mi.values
@property
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 123dca9f3ee5c..961f5f437baf9 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -1390,28 +1390,12 @@ def to_native_types(self, slicer=None, na_rep='', float_format=None, decimal='.'
values = self.values
if slicer is not None:
values = values[:, slicer]
- mask = isnull(values)
-
- formatter = None
- if float_format and decimal != '.':
- formatter = lambda v : (float_format % v).replace('.',decimal,1)
- elif decimal != '.':
- formatter = lambda v : ('%g' % v).replace('.',decimal,1)
- elif float_format:
- formatter = lambda v : float_format % v
- if formatter is None and not quoting:
- values = values.astype(str)
- else:
- values = np.array(values, dtype='object')
-
- values[mask] = na_rep
- if formatter:
- imask = (~mask).ravel()
- values.flat[imask] = np.array(
- [formatter(val) for val in values.ravel()[imask]])
-
- return values
+ from pandas.core.format import FloatArrayFormatter
+ formatter = FloatArrayFormatter(values, na_rep=na_rep,
+ float_format=float_format,
+ decimal=decimal, quoting=quoting)
+ return formatter.get_formatted_data()
def should_store(self, value):
# when inserting a column should not coerce integers to floats
diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py
index f2290877676fa..6c4e4dd844fc9 100644
--- a/pandas/tests/test_format.py
+++ b/pandas/tests/test_format.py
@@ -2932,6 +2932,52 @@ def test_to_csv_decimal(self):
expected_float_format = ';col1;col2;col3\n0;1;a;10,10\n'
self.assertEqual(df.to_csv(decimal=',',sep=';', float_format = '%.2f'), expected_float_format)
+ # GH 11553: testing if decimal is taken into account for '0.0'
+ df = pd.DataFrame({'a': [0, 1.1], 'b': [2.2, 3.3], 'c': 1})
+ expected = 'a,b,c\n0^0,2^2,1\n1^1,3^3,1\n'
+ self.assertEqual(
+ df.to_csv(index=False, decimal='^'), expected)
+
+ # same but for an index
+ self.assertEqual(
+ df.set_index('a').to_csv(decimal='^'), expected)
+
+ # same for a multi-index
+ self.assertEqual(
+ df.set_index(['a', 'b']).to_csv(decimal="^"), expected)
+
+ def test_to_csv_float_format(self):
+ # testing if float_format is taken into account for the index
+ # GH 11553
+ df = pd.DataFrame({'a': [0, 1], 'b': [2.2, 3.3], 'c': 1})
+ expected = 'a,b,c\n0,2.20,1\n1,3.30,1\n'
+ self.assertEqual(
+ df.set_index('a').to_csv(float_format='%.2f'), expected)
+
+ # same for a multi-index
+ self.assertEqual(
+ df.set_index(['a', 'b']).to_csv(float_format='%.2f'), expected)
+
+ def test_to_csv_na_rep(self):
+ # testing if NaN values are correctly represented in the index
+ # GH 11553
+ df = DataFrame({'a': [0, np.NaN], 'b': [0, 1], 'c': [2, 3]})
+ expected = "a,b,c\n0.0,0,2\n_,1,3\n"
+ self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected)
+ self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected)
+
+ # now with an index containing only NaNs
+ df = DataFrame({'a': np.NaN, 'b': [0, 1], 'c': [2, 3]})
+ expected = "a,b,c\n_,0,2\n_,1,3\n"
+ self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected)
+ self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected)
+
+ # check if na_rep parameter does not break anything when no NaN
+ df = DataFrame({'a': 0, 'b': [0, 1], 'c': [2, 3]})
+ expected = "a,b,c\n0,0,2\n0,1,3\n"
+ self.assertEqual(df.set_index('a').to_csv(na_rep='_'), expected)
+ self.assertEqual(df.set_index(['a', 'b']).to_csv(na_rep='_'), expected)
+
def test_to_csv_date_format(self):
# GH 10209
df_sec = DataFrame({'A': pd.date_range('20130101',periods=5,freq='s')})
| Fix issue #11553
Two things:
1) I've created a `Float64Index._format_native_types` method which is a copy-paste of `FloatBlock.to_native_types`. I would have preferred to call the latter directly, but I'm not sure what the `placement` parameter of the `FloatBlock` constructor means. I guess I doesn't really matters, since I could put whatever value and it should work (I think), and my hesitation a bit unfounded, but I don't know if it would be really a clean solution. Maybe someone can think of a more elegant way?
2) Since a `Float64Index` containing only NaNs collapses when part of a multi-index, its NaNs values would not be converted using `na_rep`, so I had to hack a solution. I put a comment in the relevant part. I'm not quite convinced myself of the elegance of the solution, though.
What do you think?
| https://api.github.com/repos/pandas-dev/pandas/pulls/11681 | 2015-11-23T17:38:21Z | 2015-12-27T17:24:32Z | 2015-12-27T17:24:32Z | 2015-12-27T17:25:29Z |
BUG: style.bar results in incorrect gradient using specific browser | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 3d000aa59c6d0..0710f65dc9a74 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -226,4 +226,7 @@ Bug Fixes
- 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 ``.style.bar`` may not rendered properly using specific browser (:issue:`11678`)
+
+
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
diff --git a/pandas/core/style.py b/pandas/core/style.py
index 59bf6118a79c6..d8cb53e04ea03 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -656,10 +656,10 @@ def set_properties(self, subset=None, **kwargs):
@staticmethod
def _bar(s, color, width):
normed = width * (s - s.min()) / (s.max() - s.min())
- attrs = 'width: 10em; height: 80%;'\
- 'background: linear-gradient(90deg,'\
- '{c} {w}%, transparent 0%)'
- return [attrs.format(c=color, w=x) for x in normed]
+
+ base = 'width: 10em; height: 80%;'
+ attrs = base + 'background: linear-gradient(90deg,{c} {w}%, transparent 0%)'
+ return [attrs.format(c=color, w=x) if x != 0 else base for x in normed]
def bar(self, subset=None, axis=0, color='#d65f5f', width=100):
"""
diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py
index ba989b474954d..486f997f9a7c8 100644
--- a/pandas/tests/test_style.py
+++ b/pandas/tests/test_style.py
@@ -198,9 +198,7 @@ def test_bar(self):
df = pd.DataFrame({'A': [0, 1, 2]})
result = df.style.bar()._compute().ctx
expected = {
- (0, 0): ['width: 10em', ' height: 80%',
- 'background: linear-gradient('
- '90deg,#d65f5f 0.0%, transparent 0%)'],
+ (0, 0): ['width: 10em', ' height: 80%'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient('
'90deg,#d65f5f 50.0%, transparent 0%)'],
@@ -212,9 +210,7 @@ def test_bar(self):
result = df.style.bar(color='red', width=50)._compute().ctx
expected = {
- (0, 0): ['width: 10em', ' height: 80%',
- 'background: linear-gradient('
- '90deg,red 0.0%, transparent 0%)'],
+ (0, 0): ['width: 10em', ' height: 80%'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient('
'90deg,red 25.0%, transparent 0%)'],
@@ -231,6 +227,44 @@ def test_bar(self):
result = df.style.bar(color='red', width=50)._compute().ctx
self.assertEqual(result, expected)
+ def test_bar_0points(self):
+ df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+ result = df.style.bar()._compute().ctx
+ expected = {(0, 0): ['width: 10em', ' height: 80%'],
+ (0, 1): ['width: 10em', ' height: 80%'],
+ (0, 2): ['width: 10em', ' height: 80%'],
+ (1, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 50.0%, transparent 0%)'],
+ (1, 1): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 50.0%, transparent 0%)'],
+ (1, 2): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 50.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%)'],
+ (2, 1): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%)'],
+ (2, 2): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%)']}
+ self.assertEqual(result, expected)
+
+ result = df.style.bar(axis=1)._compute().ctx
+ expected = {(0, 0): ['width: 10em', ' height: 80%'],
+ (0, 1): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 50.0%, transparent 0%)'],
+ (0, 2): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%)'],
+ (1, 0): ['width: 10em', ' height: 80%'],
+ (1, 1): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 50.0%, transparent 0%)'],
+ (1, 2): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%'],
+ (2, 1): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 50.0%, transparent 0%)'],
+ (2, 2): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%)']}
+ self.assertEqual(result, expected)
+
def test_highlight_null(self, null_color='red'):
df = pd.DataFrame({'A': [0, np.nan]})
result = df.style.highlight_null()._compute().ctx
| It's not a bug on pandas side, but CSS `linear-gradient` with `0%` width seems not be rendered properly using specific browsers.
Below is the output using Mac OSX 10.10.5 / Firefox.
The fix simply exclude `linear-gradient` if its width is 0.
After the fix (same environment)
| https://api.github.com/repos/pandas-dev/pandas/pulls/11678 | 2015-11-23T03:35:51Z | 2015-12-10T20:59:56Z | 2015-12-10T20:59:56Z | 2015-12-12T00:26:48Z |
ENH: Read from compressed data sources | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 5ccf829fd5a42..bd9707381190a 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -31,7 +31,7 @@ New features
Other enhancements
^^^^^^^^^^^^^^^^^^
-
+- `read_pickle` can now unpickle from compressed files (:issue:`11666`).
diff --git a/pandas/io/common.py b/pandas/io/common.py
index e46f609077810..bf5c76950fe22 100644
--- a/pandas/io/common.py
+++ b/pandas/io/common.py
@@ -237,6 +237,33 @@ def _stringify_path(filepath_or_buffer):
return filepath_or_buffer
+def get_compression_type(filepath_or_buffer, compression_kwd):
+ """
+ Determine the compression type of a file or buffer.
+
+ Parameters
+ ----------
+ filepath_or_buffer : string
+ File path
+ compression_kwd: {'gzip', 'bz2', 'infer', None}
+ Compression type ('infer' looks for the file extensions .gz and .bz2, using gzip and bz2 to decompress
+ respectively).
+
+ Returns
+ -------
+ compression : {'gzip', 'bz2', None} depending on result
+ """
+ # If the input could be a filename, check for a recognizable compression extension.
+ # If we're reading from a URL, the `get_filepath_or_buffer` will use header info
+ # to determine compression, so use what it finds in that case.
+ inferred_compression = None
+ if compression_kwd == 'infer' and isinstance(filepath_or_buffer, compat.string_types):
+ if filepath_or_buffer.endswith('.gz'):
+ inferred_compression = 'gzip'
+ elif filepath_or_buffer.endswith('.bz2'):
+ inferred_compression = 'bz2'
+ return inferred_compression
+
def get_filepath_or_buffer(filepath_or_buffer, encoding=None,
compression=None):
"""
@@ -467,4 +494,4 @@ def _check_as_is(x):
# write to the target stream
self.stream.write(data)
# empty queue
- self.queue.truncate(0)
\ No newline at end of file
+ self.queue.truncate(0)
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 9d25eaecc6620..354b6d59dc719 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -17,7 +17,7 @@
from pandas.core.common import AbstractMethodError
from pandas.core.config import get_option
from pandas.io.date_converters import generic_parser
-from pandas.io.common import (get_filepath_or_buffer, _validate_header_arg,
+from pandas.io.common import (get_filepath_or_buffer, get_compression_type, _validate_header_arg,
_get_handle, UnicodeReader, UTF8Recoder)
from pandas.tseries import tools
@@ -242,25 +242,7 @@ def _read(filepath_or_buffer, kwds):
if skipfooter is not None:
kwds['skip_footer'] = skipfooter
- # If the input could be a filename, check for a recognizable compression extension.
- # If we're reading from a URL, the `get_filepath_or_buffer` will use header info
- # to determine compression, so use what it finds in that case.
- inferred_compression = kwds.get('compression')
- if inferred_compression == 'infer':
- if isinstance(filepath_or_buffer, compat.string_types):
- if filepath_or_buffer.endswith('.gz'):
- inferred_compression = 'gzip'
- elif filepath_or_buffer.endswith('.bz2'):
- inferred_compression = 'bz2'
- else:
- inferred_compression = None
- else:
- inferred_compression = None
-
- filepath_or_buffer, _, compression = get_filepath_or_buffer(filepath_or_buffer,
- encoding,
- compression=kwds.get('compression', None))
- kwds['compression'] = inferred_compression if compression == 'infer' else compression
+ kwds['compression'] = get_compression_type(filepath_or_buffer, encoding, kwds['compression'])
if kwds.get('date_parser', None) is not None:
if isinstance(kwds['parse_dates'], bool):
diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py
index 52a9ef0370e9e..e444ed30ecd81 100644
--- a/pandas/io/pickle.py
+++ b/pandas/io/pickle.py
@@ -1,4 +1,5 @@
from pandas.compat import cPickle as pkl, pickle_compat as pc, PY3
+from pandas.io.common import _get_handle, get_compression_type
def to_pickle(obj, path):
"""
@@ -14,7 +15,7 @@ def to_pickle(obj, path):
pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)
-def read_pickle(path):
+def read_pickle(path, compression='infer'):
"""
Load pickled pandas object (or any other pickled object) from the specified
file path
@@ -26,6 +27,9 @@ def read_pickle(path):
----------
path : string
File path
+ compression: {'gzip', 'bz2', 'infer', None}, default 'infer'
+ Compression type, ('infer' looks for the file extensions .gz and .bz2, using gzip and bz2 to decompress
+ respectively).
Returns
-------
@@ -41,19 +45,20 @@ def try_read(path, encoding=None):
# cpickle
# GH 6899
+ _compression = get_compression_type(path, compression)
try:
- with open(path, 'rb') as fh:
+ with _get_handle(path, 'rb', encoding, _compression) as fh:
return pkl.load(fh)
except (Exception) as e:
# reg/patched pickle
try:
- with open(path, 'rb') as fh:
+ with _get_handle(path, 'rb', encoding, _compression) as fh:
return pc.load(fh, encoding=encoding, compat=False)
# compat pickle
except:
- with open(path, 'rb') as fh:
+ with _get_handle(path, 'rb', encoding, _compression) as fh:
return pc.load(fh, encoding=encoding, compat=True)
try:
| This is the start of an effort to address compression in `read_*` methods (at the moment, just `read_pickle`, as per #11666. So far, I've factored out the compression handling in the `_read` function of `pandas.io.parsers` and used that in the pickling read routine.
Tests on the way, along with actual testing that it works, docs and (if we want), I can also put something similar in the other `read_*` methods too.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11677 | 2015-11-22T23:40:42Z | 2016-01-20T14:11:40Z | null | 2016-01-20T14:11:40Z |
DOC: minor fixes | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 15a840ff3c7ba..81bbbcc873107 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -584,7 +584,7 @@ def style(self):
See Also
--------
- pandas.core.Styler
+ pandas.core.style.Styler
"""
from pandas.core.style import Styler
return Styler(self)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 3df81481f1e84..6aeb4d83649ef 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1862,7 +1862,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
- index=date_index)
+ ... index=date_index)
>>> df2
prices
2010-01-01 100
diff --git a/pandas/core/style.py b/pandas/core/style.py
index 550b0f16eabb7..5974e0e660a0b 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -63,7 +63,7 @@ class Styler(object):
Attributes
----------
- tempate: Jinja Template
+ template: Jinja Template
Notes
-----
@@ -744,6 +744,3 @@ def _highlight_extrema(data, color='yellow', max_=True):
extrema = data == data.min().min()
return pd.DataFrame(np.where(extrema, attr, ''),
index=data.index, columns=data.columns)
-
-
-
| Some minor fixes that went into the 0.17.1 docs
| https://api.github.com/repos/pandas-dev/pandas/pulls/11673 | 2015-11-22T10:07:03Z | 2015-11-22T10:09:15Z | 2015-11-22T10:09:15Z | 2015-11-22T10:09:15Z |
BUG: GH11616 fixes timezone selection error | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 5ccf829fd5a42..fa16a0513f4bb 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -105,3 +105,6 @@ Performance Improvements
Bug Fixes
~~~~~~~~~
+
+- Bug aggregating on UTC timestamps with selection returns int64 object (:issue:`11616`)
+- Bug timezone info lost when broadcasting scalar datetime to DataFrame (:issue:`11682`)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 43d018edb872c..b3a42335e14da 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -1025,7 +1025,7 @@ def _infer_dtype_from_scalar(val):
dtype = np.object_
- elif isinstance(val, (np.datetime64, datetime)) and getattr(val,'tz',None) is None:
+ elif isinstance(val, (np.datetime64, datetime)) and getattr(val,'tzinfo',None) is None:
val = lib.Timestamp(val).value
dtype = np.dtype('M8[ns]')
@@ -1321,7 +1321,9 @@ def _possibly_downcast_to_dtype(result, dtype):
try:
result = result.astype(dtype)
except:
- pass
+ if dtype.tz:
+ # convert to datetime and change timezone
+ result = pd.to_datetime(result).tz_localize(dtype.tz)
except:
pass
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 025ed17194979..0120320692f9a 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -32,6 +32,7 @@
import pandas.core.nanops as nanops
import pandas.util.testing as tm
+import pytz
import pandas as pd
from numpy.testing import assert_equal
@@ -4096,6 +4097,27 @@ def test_groupby_with_empty(self):
grouped = series.groupby(grouper)
assert next(iter(grouped), None) is None
+ def test_groupby_with_timezone_selection(self):
+ # GH 11616
+ # Test that column selection returns output in correct timezone.
+ np.random.seed(42)
+ df = pd.DataFrame({
+ 'factor': np.random.randint(0, 3, size=60),
+ 'time': pd.date_range('01/01/2000 00:00', periods=60, freq='s', tz='UTC')
+ })
+ df1 = df.groupby('factor').max()['time']
+ df2 = df.groupby('factor')['time'].max()
+ tm.assert_series_equal(df1, df2)
+
+ def test_timezone_info(self):
+ #GH 11682
+ # Timezone info lost when broadcasting scalar datetime to DataFrame
+ df = pd.DataFrame({'a': [1], 'b': [datetime.now(pytz.utc)]})
+ tm.assert_equal(df['b'][0].tzinfo, pytz.utc)
+ df = pd.DataFrame({'a': [1,2,3]})
+ df['b'] = datetime.now(pytz.utc)
+ tm.assert_equal(df['b'][0].tzinfo, pytz.utc)
+
def test_groupby_with_timegrouper(self):
# GH 4161
# TimeGrouper requires a sorted index
| closes #11616
closes #11682
| https://api.github.com/repos/pandas-dev/pandas/pulls/11672 | 2015-11-22T03:18:43Z | 2015-11-27T13:40:00Z | null | 2015-11-27T14:43:04Z |
BLD: Change pytables to tables in the asv dependencies. | diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json
index 6a739873a032f..0c20fb3cddc1a 100644
--- a/asv_bench/asv.conf.json
+++ b/asv_bench/asv.conf.json
@@ -41,7 +41,7 @@
"sqlalchemy": [],
"scipy": [],
"numexpr": [],
- "pytables": [],
+ "tables": [],
"openpyxl": [],
"xlsxwriter": [],
"xlrd": [],
| The asv benchmarks fails to build due to [PyTables PyPI package name being `tables` and not `pytables`](https://pypi.python.org/pypi/tables). This PR fixes the issue and the asv benchmarks now builds correctly.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11671 | 2015-11-22T00:37:58Z | 2015-11-22T02:31:36Z | null | 2015-11-22T18:35:09Z |
WIP: Better formatting for .set_precision | diff --git a/pandas/core/style.py b/pandas/core/style.py
index 6ee5befd4ec02..57348fb3c8955 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -6,6 +6,7 @@
from contextlib import contextmanager
from uuid import uuid1
import copy
+import numbers
from collections import defaultdict
try:
@@ -19,6 +20,7 @@
import numpy as np
import pandas as pd
from pandas.compat import lzip
+import pandas.core.common as com
from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice
try:
import matplotlib.pyplot as plt
@@ -112,11 +114,7 @@ class Styler(object):
<tr>
{% for c in r %}
<{{c.type}} id="T_{{uuid}}{{c.id}}" class="{{c.class}}">
- {% if c.value is number %}
- {{c.value|round(precision)}}
- {% else %}
- {{c.value}}
- {% endif %}
+ {{c.display_value}}
{% endfor %}
</tr>
{% endfor %}
@@ -144,9 +142,27 @@ def __init__(self, data, precision=None, table_styles=None, uuid=None,
self.table_styles = table_styles
self.caption = caption
if precision is None:
- precision = pd.options.display.precision
+ precision = {'__default__': pd.options.display.precision}
+ elif isinstance(precision, numbers.Integral):
+ precision = {'__default__': pd.options.display.precision}
+ elif isinstance(precision, dict):
+ precision = {k: precision[k] for k in precision} # prevent tampering
+ if '__default__' not in precision:
+ precision['__default__'] = pd.options.display.precision
+ else:
+ raise TypeError('Precision is of an unknown type, it has to be None,\
+ an integer or a dict containing formats for specific\
+ columns.')
+
self.precision = precision
self.table_attributes = table_attributes
+ # display_funcs maps (row, col) -> formatting function
+ def default_display_func(x):
+ if com.is_float(x):
+ return '{:>.{precision}g}'.format(x, precision=self.precision)
+ else:
+ return x
+ self._display_funcs = defaultdict(lambda: default_display_func)
def _repr_html_(self):
'''
@@ -197,7 +213,10 @@ def _translate(self):
cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c]
cs.extend(cell_context.get(
"col_headings", {}).get(r, {}).get(c, []))
- row_es.append({"type": "th", "value": clabels[r][c],
+ value = clabels[r][c]
+ row_es.append({"type": "th",
+ "value": value,
+ "display_value": value,
"class": " ".join(cs)})
head.append(row_es)
@@ -208,14 +227,21 @@ def _translate(self):
"row_headings", {}).get(r, {}).get(c, []))
row_es = [{"type": "th",
"value": rlabels[r][c],
- "class": " ".join(cs)}
+ "class": " ".join(cs),
+ "display_value": rlabels[r][c]}
for c in range(len(rlabels[r]))]
for c, col in enumerate(self.data.columns):
cs = [DATA_CLASS, "row%s" % r, "col%s" % c]
cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
- row_es.append({"type": "td", "value": self.data.iloc[r][c],
- "class": " ".join(cs), "id": "_".join(cs[1:])})
+ formatter = self._display_funcs[(r, c)]
+ value = self.data.iloc[r, c]
+ row_es.append({"type": "td",
+ "value": value,
+ "class": " ".join(cs),
+ "id": "_".join(cs[1:]),
+ "display_value": formatter(value)
+ })
props = []
for x in ctx[r, c]:
# have to handle empty styles like ['']
@@ -233,6 +259,43 @@ def _translate(self):
precision=precision, table_styles=table_styles,
caption=caption, table_attributes=self.table_attributes)
+ def format(self, formatter, subset=None):
+ '''
+ formatter is either an `a` or a dict `{column_name: a}` where
+ a is one of
+ - str: wrapped in: `a`.format(x)`
+ - callable: called with x and the column and row index of x.
+
+ now `.set_precision(2)` becomes
+
+ ```
+ .format('{:2g}')
+ ```
+ '''
+ from itertools import product
+ from collections.abc import MutableMapping
+
+ if issubclass(type(formatter), MutableMapping):
+ # formatter is a dict
+ for col, col_formatter in formatter.items():
+ # get the row index locations out of subset
+ j = self.data.columns.get_indexer_for([col])[0]
+ if not callable(col_formatter):
+ formatter_func = lambda x: col_formatter.format(x)
+
+ if subset is not None:
+ locs = self.data.index.get_indexer_for(subset)
+ else:
+ locs = range(len(self.data))
+ for i in locs:
+ self._display_funcs[(i, j)] = formatter_func
+ # single scalar to format all cells with
+ else:
+ locs = product(*(range(x) for x in self.data.shape))
+ for i, j in locs:
+ self._display_funcs[(i, j)] = lambda x: formatter.format(x)
+ return self
+
def render(self):
"""
Render the built up styles to HTML
@@ -399,7 +462,7 @@ def applymap(self, func, subset=None, **kwargs):
kwargs))
return self
- def set_precision(self, precision):
+ def set_precision(self, precision=None, subsets={}):
"""
Set the precision used to render.
@@ -413,7 +476,20 @@ def set_precision(self, precision):
-------
self
"""
- self.precision = precision
+
+ if not subsets and precision is None:
+ # reset everything
+ self.precision = {'__default__': pd.options.display.precision}
+ return self
+
+ if precision is None:
+ self.precision['__default__'] = pd.options.display.precision
+ elif isinstance(precision, numbers.Integral):
+ self.precision['__default__'] = precision
+
+ for k in subsets:
+ self.precision[k] = subsets[k]
+
return self
def set_table_attributes(self, attributes):
| closes #11656
I found some extra time behind the sofa and started working on this. I made quite a few additions but by default everything should work as it did.
- just calling `.set_precision` with an `int` will truncate all columns to that many decimal places as before
- precision can be set per column as an extra `dict` to `.set_precision`
- the precision values can be either `int`s or valid python `str.format` strings
- I wanted to add this to allow formats like `{:.2e}` and possibly some string/date manipulations
- the `.set_precision` should perhaps be called `set_format` (or `format_cells`) as a result
- all formatting can be reset by calling `.set_precision()` (no args)
_what's missing?_
- the documentation needs to be updated to reflect these changes (if you're happy with what's here I'll move on to updating the docs)
- should leave the index alone and just format the data columns
- tests?
- support for `pd.IndexSlice` for subsets?
---
```
import itertools
import pandas as pd
import numpy as np
jobs = itertools.product(['a', 'b', 'c', 'd'], np.arange(1e-4, 1e-3, .0003), range(10))
rows = []
for v, v2, itr in jobs:
rows.append({'param_1': v, 'param_2': v2, 'iter': itr,
'score_1': np.random.randint(0, 100, size=(1,))[0],
'score_2': np.random.rand(1, )[0]})
df_multi = pd.DataFrame(rows)
agg = df_multi.groupby(by=['param_1', 'param_2'])[['score_1', 'score_2']].agg(['mean', 'sem'])
```
---
printing with just the defaults
crazy awesome new formatting (forget that the `kwarg` is called `column_formats` that's been changed to `subsets`)
| https://api.github.com/repos/pandas-dev/pandas/pulls/11667 | 2015-11-20T18:05:27Z | 2016-03-12T17:47:24Z | null | 2016-03-12T17:47:24Z |
DOC: add nbviewer link | diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb
index 6f4569ab32f89..2bead2e18e0e0 100644
--- a/doc/source/html-styling.ipynb
+++ b/doc/source/html-styling.ipynb
@@ -8,6 +8,8 @@
"\n",
"<p style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your [feedback](https://github.com/pydata/pandas/issues).*<p style=\"color: red\">\n",
"\n",
+ "This document is written as a Jupyter Notebook, and can be viewed or downloaded [here](http://nbviewer.ipython.org/github/pydata/pandas/blob/master/doc/source/html-styling.ipynb).\n",
+ "\n",
"You can apply **conditional formatting**, the visual styling of a DataFrame\n",
"depending on the data within, by using the ``DataFrame.style`` property.\n",
"This is a property that returns a ``pandas.Styler`` object, which has\n",
| points to master.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11664 | 2015-11-20T15:58:22Z | 2015-11-20T16:38:49Z | 2015-11-20T16:38:49Z | 2016-11-03T12:38:42Z |
DOC: a few touchups | diff --git a/doc/source/api.rst b/doc/source/api.rst
index 668e297dc8f18..ac79528f31e04 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -1710,7 +1710,7 @@ Style Export and Import
Styler.render
Styler.export
- Styler.set
+ Styler.use
.. currentmodule:: pandas
diff --git a/pandas/core/style.py b/pandas/core/style.py
index 2559b9a3a5dc2..550b0f16eabb7 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -68,11 +68,11 @@ class Styler(object):
Notes
-----
Most styling will be done by passing style functions into
- Styler.apply or Styler.applymap. Style functions should
- return values with strings containing CSS 'attr: value' that will
+ ``Styler.apply`` or ``Styler.applymap``. Style functions should
+ return values with strings containing CSS ``'attr: value'`` that will
be applied to the indicated cells.
- If using in the Jupyter notebook, Styler has defined a _repr_html_
+ If using in the Jupyter notebook, Styler has defined a ``_repr_html_``
to automatically render itself. Otherwise call Styler.render to get
the genterated HTML.
@@ -306,6 +306,10 @@ def __deepcopy__(self, memo):
return self._copy(deepcopy=True)
def clear(self):
+ '''
+ "Reset" the styler, removing any previously applied styles.
+ Returns None.
+ '''
self.ctx.clear()
self._todo = []
@@ -360,9 +364,9 @@ def apply(self, func, axis=0, subset=None, **kwargs):
Notes
-----
- This is similar to DataFrame.apply, except that axis=None applies
- the function to the entire DataFrame at once, rather tha column
- or rowwise.
+ This is similar to ``DataFrame.apply``, except that ``axis=None``
+ applies the function to the entire DataFrame at once,
+ rather than column-wise or row-wise.
"""
self._todo.append((lambda instance: getattr(instance, '_apply'),
(func, axis, subset),
@@ -424,7 +428,7 @@ def set_precision(self, precision):
def set_table_attributes(self, attributes):
"""
Set the table attributes. These are the items
- that show up in the opening <table> tag in addition
+ that show up in the opening ``<table>`` tag in addition
to to automatic (by default) id.
.. versionadded:: 0.17.1
@@ -443,7 +447,7 @@ def set_table_attributes(self, attributes):
def export(self):
"""
Export the styles to applied to the current Styler.
- Can be applied to a second style with `.use`.
+ Can be applied to a second style with ``Styler.use``.
.. versionadded:: 0.17.1
@@ -460,7 +464,7 @@ def export(self):
def use(self, styles):
"""
Set the styles on the current Styler, possibly using styles
- from `Styler.export`
+ from ``Styler.export``.
.. versionadded:: 0.17.1
| Comments in https://github.com/pydata/pandas/pull/11660#issuecomment-158427446
@jorisvandenbossche
@jreback
I think we're ok to merge.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11663 | 2015-11-20T15:33:55Z | 2015-11-20T16:48:23Z | 2015-11-20T16:48:23Z | 2016-11-03T12:38:40Z |
COMPAT: compat of scalars on all platforms, xref #11638 | diff --git a/pandas/core/common.py b/pandas/core/common.py
index d8e21e8a7bc0c..43d018edb872c 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -1037,16 +1037,16 @@ def _infer_dtype_from_scalar(val):
dtype = np.bool_
elif is_integer(val):
- if isinstance(val, int):
- dtype = np.int64
- else:
+ if isinstance(val, np.integer):
dtype = type(val)
+ else:
+ dtype = np.int64
elif is_float(val):
- if isinstance(val, float):
- dtype = np.float64
- else:
+ if isinstance(val, np.floating):
dtype = type(val)
+ else:
+ dtype = np.float64
elif is_complex(val):
dtype = np.complex_
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index a268842878edb..dfc034b3eda8c 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -109,7 +109,7 @@ def test_infer_dtype_from_scalar(self):
np.uint64, np.int64 ]:
data = dtypec(12)
dtype, val = com._infer_dtype_from_scalar(data)
- self.assertEqual(dtype, dtypec)
+ self.assertEqual(dtype, type(data))
data = 12
dtype, val = com._infer_dtype_from_scalar(data)
| https://api.github.com/repos/pandas-dev/pandas/pulls/11662 | 2015-11-20T14:45:04Z | 2015-11-20T15:16:14Z | 2015-11-20T15:16:14Z | 2015-11-20T15:16:15Z | |
DOC: whatsnew edits | diff --git a/doc/source/basics.rst b/doc/source/basics.rst
index 757cff43f87e7..b80ac18a2ac51 100644
--- a/doc/source/basics.rst
+++ b/doc/source/basics.rst
@@ -1725,6 +1725,7 @@ pass ``convert_numeric=True``. This will force strings and numbers alike to be n
they will be set to ``np.nan``.
.. ipython:: python
+ :okwarning:
df3['D'] = '1.'
df3['E'] = '1'
diff --git a/doc/source/computation.rst b/doc/source/computation.rst
index 144fd22301ee5..b2fa7f6749379 100644
--- a/doc/source/computation.rst
+++ b/doc/source/computation.rst
@@ -267,6 +267,7 @@ sugar for applying the moving window operator to all of the DataFrame's columns:
.. ipython:: python
:suppress:
+
plt.close('all')
.. ipython:: python
@@ -541,7 +542,7 @@ which gives
.. math::
- y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ...
+ y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ...
+ (1 - \alpha)^t x_{0}}{1 + (1 - \alpha) + (1 - \alpha)^2 + ...
+ (1 - \alpha)^t}
@@ -620,7 +621,6 @@ Here is an example for a univariate time series:
.. ipython:: python
- plt.close('all')
ts.plot(style='k--')
@savefig ewma_ex.png
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index f5f45b742d7ca..eac0042d3a4ca 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -17,7 +17,7 @@ Highlights include:
- Support for Conditional HTML Formatting, see :ref:`here <whatsnew_0171.style>`
- Releasing the GIL on the csv reader & other ops, see :ref:`here <whatsnew_0171.performance>`
-- Regression in ``DataFrame.drop_duplicates`` from 0.16.2, causing incorrect results on integer values (:issue:`11376`)
+- Fixed regression in ``DataFrame.drop_duplicates`` from 0.16.2, causing incorrect results on integer values (:issue:`11376`)
.. contents:: What's new in v0.17.1
:local:
@@ -59,11 +59,11 @@ Enhancements
- ``DatetimeIndex`` now supports conversion to strings with ``astype(str)`` (:issue:`10442`)
- Support for ``compression`` (gzip/bz2) in :meth:`pandas.DataFrame.to_csv` (:issue:`7615`)
-- Improve the error message in :func:`pandas.io.gbq.to_gbq` when a streaming insert fails (:issue:`11285`)
- ``pd.read_*`` functions can now also accept :class:`python:pathlib.Path`, or :class:`py:py._path.local.LocalPath`
objects for the ``filepath_or_buffer`` argument. (:issue:`11033`)
+ - The ``DataFrame`` and ``Series`` functions ``.to_csv()``, ``.to_html()`` and ``.to_latex()`` can now handle paths beginning with tildes (e.g. ``~/Documents/``) (:issue:`11438`)
- ``DataFrame`` now uses the fields of a ``namedtuple`` as columns, if columns are not supplied (:issue:`11181`)
-- Improve the error message displayed in :func:`pandas.io.gbq.to_gbq` when the DataFrame does not match the schema of the destination table (:issue:`11359`)
+- ``DataFrame.itertuples()`` now returns ``namedtuple`` objects, when possible. (:issue:`11269`, :issue:`11625`)
- Added ``axvlines_kwds`` to parallel coordinates plot (:issue:`10709`)
- Option to ``.info()`` and ``.memory_usage()`` to provide for deep introspection of memory consumption. Note that this can be expensive to compute and therefore is an optional parameter. (:issue:`11595`)
@@ -98,23 +98,20 @@ Enhancements
- ``pivot_table`` now has a ``margins_name`` argument so you can use something other than the default of 'All' (:issue:`3335`)
- Implement export of ``datetime64[ns, tz]`` dtypes with a fixed HDF5 store (:issue:`11411`)
-- The ``DataFrame`` and ``Series`` functions ``.to_csv()``, ``.to_html()`` and ``.to_latex()`` can now handle paths beginning with tildes (e.g. ``~/Documents/``). (:issue:`11438`)
+- Pretty printing sets (e.g. in DataFrame cells) now uses set literal syntax (``{x, y}``) instead of
+ Legacy Python syntax (``set([x, y])``) (:issue:`11215`)
+- Improve the error message in :func:`pandas.io.gbq.to_gbq` when a streaming insert fails (:issue:`11285`)
+ and when the DataFrame does not match the schema of the destination table (:issue:`11359`)
.. _whatsnew_0171.api:
API changes
~~~~~~~~~~~
-- raise ``NotImplementedError`` in ``Index.shift`` for non-supported index types (:issue:`8083`)
-- min and max reductions on ``datetime64`` and ``timedelta64`` dtyped series now
+- raise ``NotImplementedError`` in ``Index.shift`` for non-supported index types (:issue:`8038`)
+- ``min`` and ``max`` reductions on ``datetime64`` and ``timedelta64`` dtyped series now
result in ``NaT`` and not ``nan`` (:issue:`11245`).
-- Regression from 0.16.2 for output formatting of long floats/nan, restored in (:issue:`11302`)
-- Prettyprinting sets (e.g. in DataFrame cells) now uses set literal syntax (``{x, y}``) instead of
- Legacy Python syntax (``set([x, y])``) (:issue:`11215`)
- Indexing with a null key will raise a ``TypeError``, instead of a ``ValueError`` (:issue:`11356`)
-- ``Series.sort_index()`` now correctly handles the ``inplace`` option (:issue:`11402`)
-- ``SparseArray.__iter__()`` now does not cause ``PendingDeprecationWarning`` in Python 3.5 (:issue:`11622`)
-- ``DataFrame.itertuples()`` now returns ``namedtuple`` objects, when possible. (:issue:`11269`, :issue:`11625`)
- ``Series.ptp`` will now ignore missing values by default (:issue:`11163`)
.. _whatsnew_0171.deprecations:
@@ -147,6 +144,9 @@ Performance Improvements
Bug Fixes
~~~~~~~~~
+- ``SparseArray.__iter__()`` now does not cause ``PendingDeprecationWarning`` in Python 3.5 (:issue:`11622`)
+- Regression from 0.16.2 for output formatting of long floats/nan, restored in (:issue:`11302`)
+- ``Series.sort_index()`` now correctly handles the ``inplace`` option (:issue:`11402`)
- Incorrectly distributed .c file in the build on ``PyPi`` when reading a csv of floats and passing ``na_values=<a scalar>`` would show an exception (:issue:`11374`)
- Bug in ``.to_latex()`` output broken when the index has a name (:issue:`10660`)
- Bug in ``HDFStore.append`` with strings whose encoded length exceded the max unencoded length (:issue:`11234`)
| https://api.github.com/repos/pandas-dev/pandas/pulls/11661 | 2015-11-20T14:37:11Z | 2015-11-20T15:39:53Z | 2015-11-20T15:39:53Z | 2015-11-20T15:39:53Z | |
DOC: warnings and remove HTML | diff --git a/doc/source/html-styling.html b/doc/source/html-styling.html
deleted file mode 100644
index f751d9575ddb5..0000000000000
--- a/doc/source/html-styling.html
+++ /dev/null
@@ -1,21211 +0,0 @@
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><em>New in version 0.17.1</em></p>
-<p><p style="color: red"><em>Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your <a href="https://github.com/pydata/pandas/issues">feedback</a>.</em><p style="color: red"></p>
-<p>You can apply <strong>conditional formatting</strong>, the visual styling of a DataFrame
-depending on the data within, by using the <code>DataFrame.style</code> property.
-This is a property that returns a <code>pandas.Styler</code> object, which has
-useful methods for formatting and displaying DataFrames.</p>
-<p>The styling is accomplished using CSS.
-You write "style functions" that take scalars, <code>DataFrame</code>s or <code>Series</code>, and return <em>like-indexed</em> DataFrames or Series with CSS <code>"attribute: value"</code> pairs for the values.
-These functions can be incrementally passed to the <code>Styler</code> which collects the styles before rendering.</p>
-<h3 id="Contents">Contents<a class="anchor-link" href="#Contents">¶</a></h3><ul>
-<li><a href="#Building-Styles">Building Styles</a></li>
-<li><a href="#Finer-Control:-Slicing">Finer Control: Slicing</a></li>
-<li><a href="#Builtin-Styles">Builtin Styles</a></li>
-<li><a href="#Other-options">Other options</a></li>
-<li><a href="#Sharing-Styles">Sharing Styles</a></li>
-<li><a href="#Limitations">Limitations</a></li>
-<li><a href="#Terms">Terms</a></li>
-<li><a href="#Extensibility">Extensibility</a></li>
-</ul>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-Styles">¶</a></h1><p>Pass your style functions into one of the following methods:</p>
-<ul>
-<li><code>Styler.applymap</code>: elementwise</li>
-<li><code>Styler.apply</code>: column-/row-/table-wise</li>
-</ul>
-<p>Both of those methods take a function (and some other keyword arguments) and applies your function to the DataFrame in a certain way.
-<code>Styler.applymap</code> works through the DataFrame elementwise.
-<code>Styler.apply</code> passes each column or row into your DataFrame one-at-a-time or the entire table at once, depending on the <code>axis</code> keyword argument.
-For columnwise use <code>axis=0</code>, rowwise use <code>axis=1</code>, and for the entire table at once use <code>axis=None</code>.</p>
-<p>The result of the function application, a CSS attribute-value pair, is stored in an internal dictionary on your <code>Styler</code> object.</p>
-<p>Let's see some examples.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [3]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">24</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s">'A'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)})</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s">'BCDE'</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="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Here's a boring example of rendering a DataFrame, without any (visible) styles:</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [4]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[4]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- </style>
-
- <table id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><em>Note</em>: The <code>DataFrame.style</code> attribute is a propetry that returns a <code>Styler</code> object. <code>Styler</code> has a <code>_repr_html_</code> method defined on it so they are rendered automatically. If you want the actual HTML back for further processing or for writing to file call the <code>.render()</code> method which returns a string.</p>
-<p>The above output looks very similar to the standard DataFrame HTML representation. But we've done some work behind the scenes to attach CSS classes to each cell. We can view these by calling the <code>.render</code> method.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [5]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_null</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">'</span><span class="se">\n</span><span class="s">'</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[5]:</div>
-
-
-<div class="output_text output_subarea output_execute_result">
-<pre>['',
- ' <style type="text/css" >',
- ' ',
- ' ',
- ' #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {',
- ' ',
- ' background-color: red;',
- ' ',
- ' }',
- ' ']</pre>
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>The <code>row0_col2</code> is the identifier for that particular cell. We've also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn't collied with the styling from another within the same notebook or page (you can set the <code>uuid</code> if you'd like to tie together the styling of two DataFrames).</p>
-<p>When writing style functions, you take care of producing the CSS attribute / value pairs you want. Pandas matches those up with the CSS classes that identify each cell.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Let's write a simple style function that will color negative numbers red and positive numbers black.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [6]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">color_negative_red</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
- <span class="sd">"""</span>
-<span class="sd"> Takes a scalar and returns a string with</span>
-<span class="sd"> the css property `'color: red'` for negative</span>
-<span class="sd"> strings, black otherwise.</span>
-<span class="sd"> """</span>
- <span class="n">color</span> <span class="o">=</span> <span class="s">'red'</span> <span class="k">if</span> <span class="n">val</span> <span class="o"><</span> <span class="mi">0</span> <span class="k">else</span> <span class="s">'black'</span>
- <span class="k">return</span> <span class="s">'color: %s'</span> <span class="o">%</span> <span class="n">color</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>In this case, the cell's style depends only on it's own value.
-That means we should use the <code>Styler.applymap</code> method which works elementwise.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [7]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>
-<span class="n">s</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[7]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2 {
-
- color: black;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3 {
-
- color: red;
-
- }
-
- #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4 {
-
- color: black;
-
- }
-
- </style>
-
- <table id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Notice the similarity with the standard <code>df.applymap</code>, which operates on DataFrames elementwise. We want you to be able to resuse your existing knowledge of how to interact with DataFrames.</p>
-<p>Notice also that our function returned a string containing the CSS attribute and value, separated by a colon just like in a <code><style></code> tag. This will be a common theme.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Now suppose you wanted to highlight the maximum value in each column.
-We can't use <code>.applymap</code> anymore since that operated elementwise.
-Instead, we'll turn to <code>.apply</code> which operates columnwise (or rowwise using the <code>axis</code> keyword). Later on we'll see that something like <code>highlight_max</code> is already defined on <code>Styler</code> so you wouldn't need to write this yourself.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [8]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">highlight_max</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
- <span class="sd">'''</span>
-<span class="sd"> highlight the maximum in a Series yellow.</span>
-<span class="sd"> '''</span>
- <span class="n">is_max</span> <span class="o">=</span> <span class="n">s</span> <span class="o">==</span> <span class="n">s</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
- <span class="k">return</span> <span class="p">[</span><span class="s">'background-color: yellow'</span> <span class="k">if</span> <span class="n">v</span> <span class="k">else</span> <span class="s">''</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">is_max</span><span class="p">]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [9]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[9]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4 {
-
- background-color: yellow;
-
- }
-
- #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2 {
-
- background-color: yellow;
-
- }
-
- #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1 {
-
- background-color: yellow;
-
- }
-
- #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3 {
-
- background-color: yellow;
-
- }
-
- #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0 {
-
- background-color: yellow;
-
- }
-
- </style>
-
- <table id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>We encourage you to use method chains to build up a style piecewise, before finally rending at the end of the chain.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [10]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span>\
- <span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span><span class="o">.</span>\
- <span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[10]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0 {
-
- color: black;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1 {
-
- color: black;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2 {
-
- color: black;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3 {
-
- color: red;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4 {
-
- color: red;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0 {
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- color: black;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4 {
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- color: black;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0 {
-
- color: black;
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- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1 {
-
- color: red;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2 {
-
- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3 {
-
- color: black;
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- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4 {
-
- color: black;
-
- background-color: yellow;
-
- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0 {
-
- color: black;
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- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1 {
-
- color: black;
-
- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2 {
-
- color: black;
-
- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3 {
-
- color: red;
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- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4 {
-
- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0 {
-
- color: black;
-
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1 {
-
- color: black;
-
- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2 {
-
- color: black;
-
- : ;
-
- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3 {
-
- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4 {
-
- color: black;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0 {
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1 {
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- color: red;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2 {
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- color: black;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3 {
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- color: black;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4 {
-
- color: red;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0 {
-
- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1 {
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- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2 {
-
- color: black;
-
- background-color: yellow;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3 {
-
- color: red;
-
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4 {
-
- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0 {
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- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1 {
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- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2 {
-
- color: black;
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- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3 {
-
- color: red;
-
- : ;
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4 {
-
- color: black;
-
- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0 {
-
- color: black;
-
- : ;
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- }
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- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1 {
-
- color: black;
-
- background-color: yellow;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2 {
-
- color: red;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3 {
-
- color: black;
-
- background-color: yellow;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4 {
-
- color: red;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0 {
-
- color: black;
-
- background-color: yellow;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1 {
-
- color: red;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2 {
-
- color: black;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3 {
-
- color: red;
-
- : ;
-
- }
-
- #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4 {
-
- color: black;
-
- : ;
-
- }
-
- </style>
-
- <table id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Above we used <code>Styler.apply</code> to pass in each column one at a time.</p>
-<p style="background-color: #DEDEBE">*Debugging Tip*: If you're having trouble writing your style function, try just passing it into <code style="background-color: #DEDEBE">df.apply</code>. <code style="background-color: #DEDEBE">Styler.apply</code> uses that internally, so the result should be the same.</p><p>What if you wanted to highlight just the maximum value in the entire table?
-Use <code>.apply(function, axis=None)</code> to indicate that your function wants the entire table, not one column or row at a time. Let's try that next.</p>
-<p>We'll rewrite our <code>highlight-max</code> to handle either Series (from <code>.apply(axis=0 or 1)</code>) or DataFrames (from <code>.apply(axis=None)</code>). We'll also allow the color to be adjustable, to demonstrate that <code>.apply</code>, and <code>.applymap</code> pass along keyword arguments.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [11]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">highlight_max</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'yellow'</span><span class="p">):</span>
- <span class="sd">'''</span>
-<span class="sd"> highlight the maximum in a Series or DataFrame</span>
-<span class="sd"> '''</span>
- <span class="n">attr</span> <span class="o">=</span> <span class="s">'background-color: {}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">color</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="c"># Series from .apply(axis=0) or axis=1</span>
- <span class="n">is_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
- <span class="k">return</span> <span class="p">[</span><span class="n">attr</span> <span class="k">if</span> <span class="n">v</span> <span class="k">else</span> <span class="s">''</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">is_max</span><span class="p">]</span>
- <span class="k">else</span><span class="p">:</span> <span class="c"># from .apply(axis=None)</span>
- <span class="n">is_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
- <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">is_max</span><span class="p">,</span> <span class="n">attr</span><span class="p">,</span> <span class="s">''</span><span class="p">),</span>
- <span class="n">index</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [12]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'darkorange'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="k">None</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0 {
-
- background-color: darkorange;
-
- }
-
- </style>
-
- <table id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Building-Styles-Summary">Building Styles Summary<a class="anchor-link" href="#Building-Styles-Summary">¶</a></h3><p>Style functions should return strings with one or more CSS <code>attribute: value</code> delimited by semicolons. Use</p>
-<ul>
-<li><code>Styler.applymap(func)</code> for elementwise styles</li>
-<li><code>Styler.apply(func, axis=0)</code> for columnwise styles</li>
-<li><code>Styler.apply(func, axis=1)</code> for rowwise styles</li>
-<li><code>Styler.apply(func, axis=None)</code> for tablewise styles</li>
-</ul>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" href="#Finer-Control:-Slicing">¶</a></h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Both <code>Styler.apply</code>, and <code>Styler.applymap</code> accept a <code>subset</code> keyword.
-This allows you to apply styles to specific rows or columns, without having to code that logic into your <code>style</code> function.</p>
-<p>The value passed to <code>subset</code> behaves simlar to slicing a DataFrame.</p>
-<ul>
-<li>A scalar is treated as a column label</li>
-<li>A list (or series or numpy array)</li>
-<li>A tuple is treated as <code>(row_indexer, column_indexer)</code></li>
-</ul>
-<p>Consider using <code>pd.IndexSlice</code> to construct the tuple for the last one.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [13]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s">'B'</span><span class="p">,</span> <span class="s">'C'</span><span class="p">,</span> <span class="s">'D'</span><span class="p">])</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[13]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2 {
-
- background-color: yellow;
-
- }
-
- #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1 {
-
- background-color: yellow;
-
- }
-
- #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3 {
-
- background-color: yellow;
-
- }
-
- </style>
-
- <table id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>For row and column slicing, any valid indexer to <code>.loc</code> will work.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [14]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">,</span>
- <span class="n">subset</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">IndexSlice</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">[</span><span class="s">'B'</span><span class="p">,</span> <span class="s">'D'</span><span class="p">]])</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[14]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1 {
-
- color: red;
-
- }
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3 {
-
- color: black;
-
- }
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1 {
-
- color: black;
-
- }
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3 {
-
- color: red;
-
- }
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1 {
-
- color: black;
-
- }
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3 {
-
- color: black;
-
- }
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1 {
-
- color: red;
-
- }
-
- #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3 {
-
- color: black;
-
- }
-
- </style>
-
- <table id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Only label-based slicing is supported right now, not positional.</p>
-<p>If your style function uses a <code>subset</code> or <code>axis</code> keyword argument, consider wrapping your function in a <code>functools.partial</code>, partialing out that keyword.</p>
-<div class="highlight"><pre><span class="n">my_func2</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span><span class="n">my_func</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styles">¶</a></h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Finally, we expect certain styling functions to be common enough that we've included a few "built-in" to the <code>Styler</code>, so you don't have to write them yourself.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [15]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_null</span><span class="p">(</span><span class="n">null_color</span><span class="o">=</span><span class="s">'red'</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[15]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2 {
-
- background-color: red;
-
- }
-
- </style>
-
- <table id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>You can create "heatmaps" with the <code>background_gradient</code> method. These require matplotlib, and we'll use <a href="http://stanford.edu/~mwaskom/software/seaborn/">Seaborn</a> to get a nice colormap.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [16]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
-
-<span class="n">cm</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">light_palette</span><span class="p">(</span><span class="s">"green"</span><span class="p">,</span> <span class="n">as_cmap</span><span class="o">=</span><span class="k">True</span><span class="p">)</span>
-
-<span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">)</span>
-<span class="n">s</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[16]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1 {
-
- background-color: #188d18;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3 {
-
- background-color: #c7eec7;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4 {
-
- background-color: #a6dca6;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0 {
-
- background-color: #ccf1cc;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1 {
-
- background-color: #c0eac0;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3 {
-
- background-color: #62b662;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4 {
-
- background-color: #5cb35c;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0 {
-
- background-color: #b3e3b3;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2 {
-
- background-color: #56af56;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3 {
-
- background-color: #56af56;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4 {
-
- background-color: #008000;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0 {
-
- background-color: #99d599;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1 {
-
- background-color: #329c32;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2 {
-
- background-color: #5fb55f;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3 {
-
- background-color: #daf9da;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4 {
-
- background-color: #3ba13b;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0 {
-
- background-color: #80c780;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1 {
-
- background-color: #108910;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2 {
-
- background-color: #0d870d;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3 {
-
- background-color: #90d090;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4 {
-
- background-color: #4fac4f;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0 {
-
- background-color: #66b866;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1 {
-
- background-color: #d2f4d2;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2 {
-
- background-color: #389f38;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3 {
-
- background-color: #048204;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4 {
-
- background-color: #70be70;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0 {
-
- background-color: #4daa4d;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1 {
-
- background-color: #6cbc6c;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2 {
-
- background-color: #008000;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3 {
-
- background-color: #a3daa3;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4 {
-
- background-color: #0e880e;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0 {
-
- background-color: #329c32;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1 {
-
- background-color: #5cb35c;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2 {
-
- background-color: #0e880e;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3 {
-
- background-color: #cff3cf;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4 {
-
- background-color: #4fac4f;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0 {
-
- background-color: #198e19;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1 {
-
- background-color: #008000;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2 {
-
- background-color: #dcfadc;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3 {
-
- background-color: #008000;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0 {
-
- background-color: #008000;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1 {
-
- background-color: #7ec67e;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2 {
-
- background-color: #319b31;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4 {
-
- background-color: #5cb35c;
-
- }
-
- </style>
-
- <table id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><code>Styler.background_gradient</code> takes the keyword arguments <code>low</code> and <code>high</code>. Roughly speaking these extend the range of your data by <code>low</code> and <code>high</code> 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.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [17]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="c"># Uses the full color range</span>
-<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s">'viridis'</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[17]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0 {
-
- background-color: #440154;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1 {
-
- background-color: #e5e419;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2 {
-
- background-color: #440154;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3 {
-
- background-color: #46327e;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4 {
-
- background-color: #440154;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0 {
-
- background-color: #3b528b;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1 {
-
- background-color: #433e85;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2 {
-
- background-color: #440154;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3 {
-
- background-color: #bddf26;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4 {
-
- background-color: #25838e;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0 {
-
- background-color: #21918c;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1 {
-
- background-color: #440154;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2 {
-
- background-color: #35b779;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3 {
-
- background-color: #fde725;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4 {
-
- background-color: #fde725;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0 {
-
- background-color: #5ec962;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1 {
-
- background-color: #95d840;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2 {
-
- background-color: #26ad81;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3 {
-
- background-color: #440154;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4 {
-
- background-color: #2cb17e;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0 {
-
- background-color: #fde725;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1 {
-
- background-color: #fde725;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2 {
-
- background-color: #fde725;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3 {
-
- background-color: #1f9e89;
-
- }
-
- #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4 {
-
- background-color: #1f958b;
-
- }
-
- </style>
-
- <table id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [18]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="c"># Compreess the color range</span>
-<span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span>
- <span class="o">.</span><span class="n">style</span>
- <span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s">'viridis'</span><span class="p">,</span> <span class="n">low</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="o">.</span><span class="n">highlight_null</span><span class="p">(</span><span class="s">'red'</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[18]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0 {
-
- background-color: #31688e;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1 {
-
- background-color: #efe51c;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2 {
-
- background-color: #440154;
-
- background-color: red;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3 {
-
- background-color: #277f8e;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4 {
-
- background-color: #31688e;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0 {
-
- background-color: #21918c;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1 {
-
- background-color: #25858e;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2 {
-
- background-color: #31688e;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3 {
-
- background-color: #d5e21a;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4 {
-
- background-color: #29af7f;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0 {
-
- background-color: #35b779;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1 {
-
- background-color: #31688e;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2 {
-
- background-color: #6ccd5a;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3 {
-
- background-color: #fde725;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4 {
-
- background-color: #fde725;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0 {
-
- background-color: #90d743;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1 {
-
- background-color: #b8de29;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2 {
-
- background-color: #5ac864;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3 {
-
- background-color: #31688e;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4 {
-
- background-color: #63cb5f;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0 {
-
- background-color: #fde725;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1 {
-
- background-color: #fde725;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2 {
-
- background-color: #fde725;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3 {
-
- background-color: #46c06f;
-
- : ;
-
- }
-
- #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4 {
-
- background-color: #3bbb75;
-
- : ;
-
- }
-
- </style>
-
- <table id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>You can include "bar charts" in your DataFrame.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [19]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s">'A'</span><span class="p">,</span> <span class="s">'B'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s">'#d65f5f'</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[19]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 16.77000113307442%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 22.22222222222222%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 33.333333333333336%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 78.1150827834652%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 44.44444444444444%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 92.96229618327422%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 55.55555555555556%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 8.737388253449494%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 66.66666666666667%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 52.764243600289866%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 77.77777777777777%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 59.79187201238315%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 88.88888888888889%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 100.00000000000001%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);
-
- }
-
- #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1 {
-
- width: 10em;
-
- height: 80%;
-
- background: linear-gradient(90deg,#d65f5f 45.17326030334935%, transparent 0%);
-
- }
-
- </style>
-
- <table id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row0">
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- 0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col2" class="data row0 col2">
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- nan
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
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- <tr>
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- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row1">
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- 1
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row4">
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- 4
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>There's also <code>.highlight_min</code> and <code>.highlight_max</code>.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [20]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[20]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4 {
-
- background-color: yellow;
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- }
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- #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2 {
-
- background-color: yellow;
-
- }
-
- #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1 {
-
- background-color: yellow;
-
- }
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- #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 {
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- background-color: yellow;
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- #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0 {
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- background-color: yellow;
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- }
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-
- <table id="T_35620402_8d9b_11e5_8913_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
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-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
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- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
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-
- nan
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- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
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- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
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- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row1">
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- 1
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-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col0" class="data row1 col0">
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-
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-
- -1.070816
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-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col2" class="data row1 col2">
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- -1.438713
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-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col3" class="data row1 col3">
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- 0.564417
-
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- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col4" class="data row1 col4">
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- 0.295722
-
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- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row2">
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- 2
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row3">
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- 3
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
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- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row4">
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- 4
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
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- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row5">
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- 5
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
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-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col2" class="data row5 col2">
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- 0.562861
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
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-
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- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row6">
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- 6
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-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [21]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[21]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col0 {
-
- background-color: yellow;
-
- }
-
- #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2 {
-
- background-color: yellow;
-
- }
-
- #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1 {
-
- background-color: yellow;
-
- }
-
- #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4 {
-
- background-color: yellow;
-
- }
-
- #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3 {
-
- background-color: yellow;
-
- }
-
- </style>
-
- <table id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Use <code>Styler.set_properties</code> when the style doesn't actually depend on the values.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [22]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_properties</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s">'background-color'</span><span class="p">:</span> <span class="s">'black'</span><span class="p">,</span>
- <span class="s">'color'</span><span class="p">:</span> <span class="s">'lawngreen'</span><span class="p">,</span>
- <span class="s">'border-color'</span><span class="p">:</span> <span class="s">'white'</span><span class="p">})</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[22]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4 {
-
- color: lawngreen;
-
- background-color: black;
-
- border-color: white;
-
- }
-
- </style>
-
- <table id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styles">¶</a></h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>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 <code>df1.style.export</code>, and import it on the second DataFrame with <code>df1.style.set</code></p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [23]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df2</span> <span class="o">=</span> <span class="o">-</span><span class="n">df</span>
-<span class="n">style1</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>
-<span class="n">style1</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[23]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3 {
-
- color: red;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4 {
-
- color: red;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1 {
-
- color: red;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2 {
-
- color: red;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1 {
-
- color: red;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0 {
-
- color: black;
-
- }
-
- #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1 {
-
- color: black;
-
- }
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-
- <th class="col_heading level0 col0">A
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- <th class="col_heading level0 col1">B
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- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
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- </thead>
- <tbody>
-
- <tr>
-
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- 0
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- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1" class="data row0 col1">
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- 1.329212
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
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-
- </tr>
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- <tr>
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- 1
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2" class="data row1 col2">
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- -1.438713
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row2">
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- 2
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
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-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
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-
- </tr>
-
- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row4">
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- 4
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
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-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
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-
- </tr>
-
- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
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-
- </tr>
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- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row6">
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- 6
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-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
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-
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-
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-
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-<div class="cell border-box-sizing code_cell rendered">
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-<div class="prompt input_prompt">In [24]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">style2</span> <span class="o">=</span> <span class="n">df2</span><span class="o">.</span><span class="n">style</span>
-<span class="n">style2</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">style1</span><span class="o">.</span><span class="n">export</span><span class="p">())</span>
-<span class="n">style2</span>
-</pre></div>
-
-</div>
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-</div>
-
-<div class="output_wrapper">
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-
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-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
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-
- -1.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- -1.329212
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- 0.31628
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- 0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- -2.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- 1.070816
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- 1.438713
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- -0.564417
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- -0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- -3.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- 1.626404
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- -0.219565
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- -0.678805
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- -1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- -4.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- -0.961538
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- -0.104011
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- 0.481165
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- -0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- -5.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- -1.453425
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- -1.057737
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- -0.165562
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- -0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- -6.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- 1.336936
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- -0.562861
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- -1.392855
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- 0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- -7.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- -0.121668
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- -1.207603
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- 0.00204
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- -1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- -8.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- -0.354493
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- -1.037528
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- 0.385684
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- -0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- -9.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- -1.686583
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- 1.325963
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- -1.428984
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- 2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- -10.0
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- 0.12982
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- -0.631523
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- 0.586538
-
-
- <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- -0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>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 <code>use</code>d upon.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Other-options">Other options<a class="anchor-link" href="#Other-options">¶</a></h2><p>You've seen a few methods for data-driven styling.
-<code>Styler</code> also provides a few other options for styles that don't depend on the data.</p>
-<ul>
-<li>precision</li>
-<li>captions</li>
-<li>table-wide styles</li>
-</ul>
-<p>Each of these can be specified in two ways:</p>
-<ul>
-<li>A keyword argument to <code>pandas.core.Styler</code></li>
-<li>A call to one of the <code>.set_</code> methods, e.g. <code>.set_caption</code></li>
-</ul>
-<p>The best method to use depends on the context. Use the <code>Styler</code> constructor when building many styled DataFrames that should all share the same properties. For interactive use, the<code>.set_</code> methods are more convenient.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a></h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>You can control the precision of floats using pandas' regular <code>display.precision</code> option.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [25]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="k">with</span> <span class="n">pd</span><span class="o">.</span><span class="n">option_context</span><span class="p">(</span><span class="s">'display.precision'</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
- <span class="n">html</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">style</span>
- <span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>
- <span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">))</span>
-<span class="n">html</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[25]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
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-
- color: black;
-
- : ;
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- #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2 {
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- background-color: yellow;
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- }
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- background-color: yellow;
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- }
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-
- color: black;
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- : ;
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- }
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- #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3 {
-
- color: red;
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- : ;
-
- }
-
- #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4 {
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- color: black;
-
- : ;
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- }
-
- </style>
-
- <table id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.33
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.32
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.07
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.44
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.56
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.3
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.63
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.22
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.68
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.89
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.96
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.1
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.48
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.85
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.45
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.06
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.17
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.52
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.34
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.56
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.39
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.06
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.12
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.21
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.63
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.35
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.04
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.39
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.52
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.69
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.33
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.43
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.09
-
-
- </tr>
-
- <tr>
-
- <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.13
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.63
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.59
-
-
- <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Or through a <code>set_precision</code> method.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [26]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span>\
- <span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>\
- <span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">)</span>\
- <span class="o">.</span><span class="n">set_precision</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[26]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
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- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1 {
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- color: red;
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- : ;
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- : ;
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- color: black;
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- : ;
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- color: black;
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- : ;
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- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1 {
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- color: black;
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- : ;
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- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2 {
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- color: black;
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- background-color: yellow;
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- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3 {
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- color: red;
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- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4 {
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- color: black;
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- color: black;
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- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 {
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- color: black;
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- : ;
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- }
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- color: black;
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- : ;
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- }
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- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1 {
-
- color: black;
-
- background-color: yellow;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2 {
-
- color: red;
-
- : ;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3 {
-
- color: black;
-
- background-color: yellow;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4 {
-
- color: red;
-
- : ;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0 {
-
- color: black;
-
- background-color: yellow;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1 {
-
- color: red;
-
- : ;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2 {
-
- color: black;
-
- : ;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3 {
-
- color: red;
-
- : ;
-
- }
-
- #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4 {
-
- color: black;
-
- : ;
-
- }
-
- </style>
-
- <table id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb">
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.33
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.32
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.07
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.44
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.56
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.3
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.63
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.22
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.68
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.89
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.96
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.1
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.48
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.85
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.45
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.06
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.17
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.52
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.34
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.56
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.39
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.06
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.12
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.21
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.63
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.35
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.04
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.39
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.52
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.69
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.33
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.43
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.09
-
-
- </tr>
-
- <tr>
-
- <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.13
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.63
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.59
-
-
- <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use <code>df.round(2).style</code> if you'd prefer to round from the start.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Regular table captions can be added in a few ways.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [27]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">'Colormaps, with a caption.'</span><span class="p">)</span>\
- <span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[27]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0 {
-
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-
- background-color: #188d18;
-
- }
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2 {
-
- background-color: #e5ffe5;
-
- }
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3 {
-
- background-color: #c7eec7;
-
- }
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4 {
-
- background-color: #a6dca6;
-
- }
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0 {
-
- background-color: #ccf1cc;
-
- }
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-
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- background-color: #e5ffe5;
-
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- background-color: #62b662;
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-
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0 {
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-
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2 {
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0 {
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1 {
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- background-color: #108910;
-
- }
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2 {
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- background-color: #0d870d;
-
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3 {
-
- background-color: #90d090;
-
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4 {
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- background-color: #4fac4f;
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0 {
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- background-color: #66b866;
-
- }
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1 {
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- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2 {
-
- background-color: #389f38;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3 {
-
- background-color: #048204;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4 {
-
- background-color: #70be70;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0 {
-
- background-color: #4daa4d;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1 {
-
- background-color: #6cbc6c;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2 {
-
- background-color: #008000;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3 {
-
- background-color: #a3daa3;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4 {
-
- background-color: #0e880e;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0 {
-
- background-color: #329c32;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1 {
-
- background-color: #5cb35c;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2 {
-
- background-color: #0e880e;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3 {
-
- background-color: #cff3cf;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4 {
-
- background-color: #4fac4f;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0 {
-
- background-color: #198e19;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1 {
-
- background-color: #008000;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2 {
-
- background-color: #dcfadc;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3 {
-
- background-color: #008000;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0 {
-
- background-color: #008000;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1 {
-
- background-color: #7ec67e;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2 {
-
- background-color: #319b31;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3 {
-
- background-color: #e5ffe5;
-
- }
-
- #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4 {
-
- background-color: #5cb35c;
-
- }
-
- </style>
-
- <table id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb">
-
- <caption>Colormaps, with a caption.</caption>
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">¶</a></h3>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>The next option you have are "table styles".
-These are styles that apply to the table as a whole, but don't look at the data.
-Certain sytlings, including pseudo-selectors like <code>:hover</code> can only be used this way.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [28]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">HTML</span>
-
-<span class="k">def</span> <span class="nf">hover</span><span class="p">(</span><span class="n">hover_color</span><span class="o">=</span><span class="s">"#ffff99"</span><span class="p">):</span>
- <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"tr:hover"</span><span class="p">,</span>
- <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"background-color"</span><span class="p">,</span> <span class="s">"%s"</span> <span class="o">%</span> <span class="n">hover_color</span><span class="p">)])</span>
-
-<span class="n">styles</span> <span class="o">=</span> <span class="p">[</span>
- <span class="n">hover</span><span class="p">(),</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th"</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"150%"</span><span class="p">),</span>
- <span class="p">(</span><span class="s">"text-align"</span><span class="p">,</span> <span class="s">"center"</span><span class="p">)]),</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"caption"</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"caption-side"</span><span class="p">,</span> <span class="s">"bottom"</span><span class="p">)])</span>
-<span class="p">]</span>
-<span class="n">html</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_table_styles</span><span class="p">(</span><span class="n">styles</span><span class="p">)</span>
- <span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">"Hover to highlight."</span><span class="p">))</span>
-<span class="n">html</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[28]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
- #T_359bea52_8d9b_11e5_be48_a45e60bd97fb tr:hover {
-
- background-color: #ffff99;
-
- }
-
- #T_359bea52_8d9b_11e5_be48_a45e60bd97fb th {
-
- font-size: 150%;
-
- text-align: center;
-
- }
-
- #T_359bea52_8d9b_11e5_be48_a45e60bd97fb caption {
-
- caption-side: bottom;
-
- }
-
-
- </style>
-
- <table id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb">
-
- <caption>Hover to highlight.</caption>
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row1">
-
- 1
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- 2.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- -1.070816
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.438713
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><code>table_styles</code> should be a list of dictionaries.
-Each dictionary should have the <code>selector</code> and <code>props</code> keys.
-The value for <code>selector</code> should be a valid CSS selector.
-Recall that all the styles are already attached to an <code>id</code>, unique to
-each <code>Styler</code>. This selector is in addition to that <code>id</code>.
-The value for <code>props</code> should be a list of tuples of <code>('attribute', 'value')</code>.</p>
-<p><code>table_styles</code> are extremely flexible, but not as fun to type out by hand.
-We hope to collect some useful ones either in pandas, or preferable in a new package that <a href="#Extensibility">builds on top</a> the tools here.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Limitations">Limitations<a class="anchor-link" href="#Limitations">¶</a></h1><ul>
-<li>DataFrame only <code>(use Series.to_frame().style)</code></li>
-<li>The index and columns must be unique</li>
-<li>No large repr, and performance isn't great; this is intended for summary DataFrames</li>
-<li>You can only style the <em>values</em>, not the index or columns</li>
-<li>You can only apply styles, you can't insert new HTML entities</li>
-</ul>
-<p>Some of these will be addressed in the future.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Terms">Terms<a class="anchor-link" href="#Terms">¶</a></h1><ul>
-<li>Style function: a function that's passed into <code>Styler.apply</code> or <code>Styler.applymap</code> and returns values like <code>'css attribute: value'</code></li>
-<li>Builtin style functions: style functions that are methods on <code>Styler</code></li>
-<li>table style: a dictionary with the two keys <code>selector</code> and <code>props</code>. <code>selector</code> is the CSS selector that <code>props</code> will apply to. <code>props</code> is a list of <code>(attribute, value)</code> tuples. A list of table styles passed into <code>Styler</code>.</li>
-</ul>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a></h2><p>Here are a few interesting examples.</p>
-<p><code>Styler</code> 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.</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [29]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">IPython.html</span> <span class="k">import</span> <span class="n">widgets</span>
-<span class="nd">@widgets</span><span class="o">.</span><span class="n">interact</span>
-<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">h_neg</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">359</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">h_pos</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">359</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">99.9</span><span class="p">),</span> <span class="n">l</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">99.9</span><span class="p">)):</span>
- <span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span>
- <span class="n">cmap</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">palettes</span><span class="o">.</span><span class="n">diverging_palette</span><span class="p">(</span><span class="n">h_neg</span><span class="o">=</span><span class="n">h_neg</span><span class="p">,</span> <span class="n">h_pos</span><span class="o">=</span><span class="n">h_pos</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span>
- <span class="n">as_cmap</span><span class="o">=</span><span class="k">True</span><span class="p">)</span>
- <span class="p">)</span>
-</pre></div>
-
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- #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2 {
-
- background-color: #9bb4b0;
-
- }
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- #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3 {
-
- background-color: #557e79;
-
- }
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- #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4 {
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- background-color: #d7e2e0;
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- }
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-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">A
-
- <th class="col_heading level0 col1">B
-
- <th class="col_heading level0 col2">C
-
- <th class="col_heading level0 col3">D
-
- <th class="col_heading level0 col4">E
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row0">
-
- 0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 1.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.329212
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- nan
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.31628
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.99081
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row1">
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- 1
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col0" class="data row1 col0">
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- 2.0
-
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- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col1" class="data row1 col1">
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- -1.070816
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- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col2" class="data row1 col2">
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- -1.438713
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- 0.564417
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 0.295722
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row2">
-
- 2
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- 3.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- -1.626404
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- 0.219565
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.678805
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 1.889273
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row3">
-
- 3
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- 4.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 0.961538
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- 0.104011
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- -0.481165
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 0.850229
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row4">
-
- 4
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- 5.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 1.453425
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- 1.057737
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- 0.165562
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 0.515018
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row5">
-
- 5
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- 6.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- -1.336936
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- 0.562861
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- 1.392855
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- -0.063328
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row6">
-
- 6
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- 7.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 0.121668
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- 1.207603
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -0.00204
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 1.627796
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row7">
-
- 7
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- 8.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 0.354493
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- 1.037528
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.385684
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 0.519818
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row8">
-
- 8
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 9.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 1.686583
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -1.325963
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- 1.428984
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- -2.089354
-
-
- </tr>
-
- <tr>
-
- <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row9">
-
- 9
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 10.0
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- -0.12982
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- 0.631523
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.586538
-
-
- <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 0.29072
-
-
- </tr>
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-
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-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [30]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">magnify</span><span class="p">():</span>
- <span class="k">return</span> <span class="p">[</span><span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th"</span><span class="p">,</span>
- <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"4pt"</span><span class="p">)]),</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"td"</span><span class="p">,</span>
- <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">'padding'</span><span class="p">,</span> <span class="s">"0em 0em"</span><span class="p">)]),</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th:hover"</span><span class="p">,</span>
- <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"12pt"</span><span class="p">)]),</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"tr:hover td:hover"</span><span class="p">,</span>
- <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">'max-width'</span><span class="p">,</span> <span class="s">'200px'</span><span class="p">),</span>
- <span class="p">(</span><span class="s">'font-size'</span><span class="p">,</span> <span class="s">'12pt'</span><span class="p">)])</span>
-<span class="p">]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [31]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">25</span><span class="p">)</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">diverging_palette</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="n">as_cmap</span><span class="o">=</span><span class="k">True</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">25</span><span class="p">))</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
-
-<span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</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="o">.</span><span class="n">set_properties</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s">'max-width'</span><span class="p">:</span> <span class="s">'80px'</span><span class="p">,</span> <span class="s">'font-size'</span><span class="p">:</span> <span class="s">'1pt'</span><span class="p">})</span>\
- <span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">"Hover to magify"</span><span class="p">)</span>\
- <span class="o">.</span><span class="n">set_precision</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>\
- <span class="o">.</span><span class="n">set_table_styles</span><span class="p">(</span><span class="n">magnify</span><span class="p">())</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt output_prompt">Out[31]:</div>
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-
- <style type="text/css" >
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th {
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- font-size: 4pt;
-
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-
- padding: 0em 0em;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th:hover {
-
- font-size: 12pt;
-
- }
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb tr:hover td:hover {
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- max-width: 200px;
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5 {
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9 {
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-
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- }
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12 {
-
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- max-width: 80px;
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- font-size: 1pt;
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- }
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13 {
-
- background-color: #e890a1;
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- max-width: 80px;
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14 {
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- max-width: 80px;
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- font-size: 1pt;
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- }
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15 {
-
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- max-width: 80px;
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- }
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16 {
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- background-color: #adc4e1;
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- max-width: 80px;
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- font-size: 1pt;
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- }
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17 {
-
- background-color: #eca3b1;
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- max-width: 80px;
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- font-size: 1pt;
-
- }
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18 {
-
- background-color: #b7cbe5;
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- max-width: 80px;
-
- font-size: 1pt;
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- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24 {
-
- background-color: #8babd4;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0 {
-
- background-color: #86a8d3;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1 {
-
- background-color: #5b89c3;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2 {
-
- background-color: #f2bfca;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3 {
-
- background-color: #f2bfca;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4 {
-
- background-color: #497dbd;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5 {
-
- background-color: #f2bfca;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6 {
-
- background-color: #f2f2f2;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7 {
-
- background-color: #5686c1;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8 {
-
- background-color: #eda8b6;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9 {
-
- background-color: #d9e4f1;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10 {
-
- background-color: #d5e1f0;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11 {
-
- background-color: #bfd1e8;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12 {
-
- background-color: #5787c2;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13 {
-
- background-color: #fbeaed;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14 {
-
- background-color: #f8dee3;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15 {
-
- background-color: #f2f2f2;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16 {
-
- background-color: #eeaab7;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17 {
-
- background-color: #f6d1d8;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18 {
-
- background-color: #f7d7de;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19 {
-
- background-color: #f2f2f2;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20 {
-
- background-color: #b5cae4;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21 {
-
- background-color: #d73c5b;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22 {
-
- background-color: #9eb9db;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23 {
-
- background-color: #4479bb;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24 {
-
- background-color: #89aad4;
-
- max-width: 80px;
-
- font-size: 1pt;
-
- }
-
- </style>
-
- <table id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb">
-
- <caption>Hover to magify</caption>
-
-
- <thead>
-
- <tr>
-
- <th class="blank">
-
- <th class="col_heading level0 col0">0
-
- <th class="col_heading level0 col1">1
-
- <th class="col_heading level0 col2">2
-
- <th class="col_heading level0 col3">3
-
- <th class="col_heading level0 col4">4
-
- <th class="col_heading level0 col5">5
-
- <th class="col_heading level0 col6">6
-
- <th class="col_heading level0 col7">7
-
- <th class="col_heading level0 col8">8
-
- <th class="col_heading level0 col9">9
-
- <th class="col_heading level0 col10">10
-
- <th class="col_heading level0 col11">11
-
- <th class="col_heading level0 col12">12
-
- <th class="col_heading level0 col13">13
-
- <th class="col_heading level0 col14">14
-
- <th class="col_heading level0 col15">15
-
- <th class="col_heading level0 col16">16
-
- <th class="col_heading level0 col17">17
-
- <th class="col_heading level0 col18">18
-
- <th class="col_heading level0 col19">19
-
- <th class="col_heading level0 col20">20
-
- <th class="col_heading level0 col21">21
-
- <th class="col_heading level0 col22">22
-
- <th class="col_heading level0 col23">23
-
- <th class="col_heading level0 col24">24
-
- </tr>
-
- </thead>
- <tbody>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row0">
-
- 0
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0" class="data row0 col0">
-
- 0.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1" class="data row0 col1">
-
- 1.03
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2" class="data row0 col2">
-
- -0.84
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3" class="data row0 col3">
-
- -0.59
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4" class="data row0 col4">
-
- -0.96
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5" class="data row0 col5">
-
- -0.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6" class="data row0 col6">
-
- -0.62
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7" class="data row0 col7">
-
- 1.84
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8" class="data row0 col8">
-
- -2.05
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9" class="data row0 col9">
-
- 0.87
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10" class="data row0 col10">
-
- -0.92
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11" class="data row0 col11">
-
- -0.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12" class="data row0 col12">
-
- 2.15
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13" class="data row0 col13">
-
- -1.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14" class="data row0 col14">
-
- 0.08
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15" class="data row0 col15">
-
- -1.25
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16" class="data row0 col16">
-
- 1.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17" class="data row0 col17">
-
- -1.05
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18" class="data row0 col18">
-
- 1.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19" class="data row0 col19">
-
- -0.42
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20" class="data row0 col20">
-
- 2.29
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21" class="data row0 col21">
-
- -2.59
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22" class="data row0 col22">
-
- 2.82
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23" class="data row0 col23">
-
- 0.68
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24" class="data row0 col24">
-
- -1.58
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row1">
-
- 1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0" class="data row1 col0">
-
- -1.75
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1" class="data row1 col1">
-
- 1.56
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2" class="data row1 col2">
-
- -1.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3" class="data row1 col3">
-
- -1.1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4" class="data row1 col4">
-
- 1.03
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5" class="data row1 col5">
-
- 0.0
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6" class="data row1 col6">
-
- -2.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7" class="data row1 col7">
-
- 3.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8" class="data row1 col8">
-
- -1.66
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9" class="data row1 col9">
-
- 1.27
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10" class="data row1 col10">
-
- -0.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11" class="data row1 col11">
-
- -0.02
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12" class="data row1 col12">
-
- 1.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13" class="data row1 col13">
-
- -1.09
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14" class="data row1 col14">
-
- -1.86
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15" class="data row1 col15">
-
- -1.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16" class="data row1 col16">
-
- -0.68
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17" class="data row1 col17">
-
- -0.81
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18" class="data row1 col18">
-
- 0.35
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19" class="data row1 col19">
-
- -0.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20" class="data row1 col20">
-
- 1.79
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21" class="data row1 col21">
-
- -2.82
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22" class="data row1 col22">
-
- 2.26
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23" class="data row1 col23">
-
- 0.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24" class="data row1 col24">
-
- 0.44
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row2">
-
- 2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0" class="data row2 col0">
-
- -0.65
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1" class="data row2 col1">
-
- 3.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2" class="data row2 col2">
-
- -1.76
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3" class="data row2 col3">
-
- 0.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4" class="data row2 col4">
-
- 2.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5" class="data row2 col5">
-
- -0.37
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6" class="data row2 col6">
-
- -3.0
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7" class="data row2 col7">
-
- 3.73
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8" class="data row2 col8">
-
- -1.87
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9" class="data row2 col9">
-
- 2.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10" class="data row2 col10">
-
- 0.21
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11" class="data row2 col11">
-
- -0.24
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12" class="data row2 col12">
-
- -0.1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13" class="data row2 col13">
-
- -0.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14" class="data row2 col14">
-
- -3.02
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15" class="data row2 col15">
-
- -0.82
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16" class="data row2 col16">
-
- -0.21
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17" class="data row2 col17">
-
- -0.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18" class="data row2 col18">
-
- 0.86
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19" class="data row2 col19">
-
- -0.68
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col20" class="data row2 col20">
-
- 1.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col21" class="data row2 col21">
-
- -4.89
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col22" class="data row2 col22">
-
- 3.03
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col23" class="data row2 col23">
-
- 1.91
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col24" class="data row2 col24">
-
- 0.61
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row3">
-
- 3
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col0" class="data row3 col0">
-
- -1.62
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col1" class="data row3 col1">
-
- 3.71
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col2" class="data row3 col2">
-
- -2.31
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col3" class="data row3 col3">
-
- 0.43
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col4" class="data row3 col4">
-
- 4.17
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col5" class="data row3 col5">
-
- -0.43
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col6" class="data row3 col6">
-
- -3.86
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7" class="data row3 col7">
-
- 4.16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8" class="data row3 col8">
-
- -2.15
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9" class="data row3 col9">
-
- 1.08
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10" class="data row3 col10">
-
- 0.12
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11" class="data row3 col11">
-
- 0.6
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12" class="data row3 col12">
-
- -0.89
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13" class="data row3 col13">
-
- 0.27
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14" class="data row3 col14">
-
- -3.67
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15" class="data row3 col15">
-
- -2.71
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16" class="data row3 col16">
-
- -0.31
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17" class="data row3 col17">
-
- -1.59
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18" class="data row3 col18">
-
- 1.35
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19" class="data row3 col19">
-
- -1.83
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20" class="data row3 col20">
-
- 0.91
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21" class="data row3 col21">
-
- -5.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22" class="data row3 col22">
-
- 2.81
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23" class="data row3 col23">
-
- 2.11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24" class="data row3 col24">
-
- 0.28
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row4">
-
- 4
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0" class="data row4 col0">
-
- -3.35
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1" class="data row4 col1">
-
- 4.48
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2" class="data row4 col2">
-
- -1.86
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3" class="data row4 col3">
-
- -1.7
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4" class="data row4 col4">
-
- 5.19
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5" class="data row4 col5">
-
- -1.02
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6" class="data row4 col6">
-
- -3.81
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7" class="data row4 col7">
-
- 4.72
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8" class="data row4 col8">
-
- -0.72
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9" class="data row4 col9">
-
- 1.08
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10" class="data row4 col10">
-
- -0.18
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11" class="data row4 col11">
-
- 0.83
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12" class="data row4 col12">
-
- -0.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13" class="data row4 col13">
-
- -1.08
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14" class="data row4 col14">
-
- -4.27
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15" class="data row4 col15">
-
- -2.88
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16" class="data row4 col16">
-
- -0.97
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17" class="data row4 col17">
-
- -1.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18" class="data row4 col18">
-
- 1.53
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19" class="data row4 col19">
-
- -1.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20" class="data row4 col20">
-
- 2.21
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21" class="data row4 col21">
-
- -6.34
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22" class="data row4 col22">
-
- 3.34
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23" class="data row4 col23">
-
- 2.49
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24" class="data row4 col24">
-
- 2.09
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row5">
-
- 5
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0" class="data row5 col0">
-
- -0.84
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1" class="data row5 col1">
-
- 4.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2" class="data row5 col2">
-
- -1.65
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3" class="data row5 col3">
-
- -2.0
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4" class="data row5 col4">
-
- 5.34
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5" class="data row5 col5">
-
- -0.99
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6" class="data row5 col6">
-
- -4.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7" class="data row5 col7">
-
- 3.94
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8" class="data row5 col8">
-
- -1.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9" class="data row5 col9">
-
- -0.94
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10" class="data row5 col10">
-
- 1.24
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11" class="data row5 col11">
-
- 0.09
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12" class="data row5 col12">
-
- -1.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13" class="data row5 col13">
-
- -0.11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14" class="data row5 col14">
-
- -4.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15" class="data row5 col15">
-
- -0.85
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16" class="data row5 col16">
-
- -2.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17" class="data row5 col17">
-
- -1.35
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18" class="data row5 col18">
-
- 0.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19" class="data row5 col19">
-
- -1.63
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20" class="data row5 col20">
-
- 1.54
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21" class="data row5 col21">
-
- -6.51
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22" class="data row5 col22">
-
- 2.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23" class="data row5 col23">
-
- 2.14
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24" class="data row5 col24">
-
- 3.77
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row6">
-
- 6
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0" class="data row6 col0">
-
- -0.74
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1" class="data row6 col1">
-
- 5.35
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2" class="data row6 col2">
-
- -2.11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3" class="data row6 col3">
-
- -1.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4" class="data row6 col4">
-
- 4.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5" class="data row6 col5">
-
- -1.85
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6" class="data row6 col6">
-
- -3.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7" class="data row6 col7">
-
- 3.76
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8" class="data row6 col8">
-
- -3.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9" class="data row6 col9">
-
- -1.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10" class="data row6 col10">
-
- 0.34
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11" class="data row6 col11">
-
- 0.57
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12" class="data row6 col12">
-
- -1.82
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13" class="data row6 col13">
-
- 0.54
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14" class="data row6 col14">
-
- -4.43
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15" class="data row6 col15">
-
- -1.83
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16" class="data row6 col16">
-
- -4.03
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17" class="data row6 col17">
-
- -2.62
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18" class="data row6 col18">
-
- -0.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19" class="data row6 col19">
-
- -4.68
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20" class="data row6 col20">
-
- 1.93
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21" class="data row6 col21">
-
- -8.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22" class="data row6 col22">
-
- 3.34
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23" class="data row6 col23">
-
- 2.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24" class="data row6 col24">
-
- 5.81
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row7">
-
- 7
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0" class="data row7 col0">
-
- -0.44
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1" class="data row7 col1">
-
- 4.69
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2" class="data row7 col2">
-
- -2.3
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3" class="data row7 col3">
-
- -0.21
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4" class="data row7 col4">
-
- 5.93
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5" class="data row7 col5">
-
- -2.63
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6" class="data row7 col6">
-
- -1.83
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7" class="data row7 col7">
-
- 5.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8" class="data row7 col8">
-
- -4.5
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9" class="data row7 col9">
-
- -3.16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10" class="data row7 col10">
-
- -1.73
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11" class="data row7 col11">
-
- 0.18
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12" class="data row7 col12">
-
- 0.11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13" class="data row7 col13">
-
- 0.04
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14" class="data row7 col14">
-
- -5.99
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15" class="data row7 col15">
-
- -0.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16" class="data row7 col16">
-
- -6.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17" class="data row7 col17">
-
- -3.89
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18" class="data row7 col18">
-
- 0.71
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19" class="data row7 col19">
-
- -3.95
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20" class="data row7 col20">
-
- 0.67
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21" class="data row7 col21">
-
- -7.26
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22" class="data row7 col22">
-
- 2.97
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23" class="data row7 col23">
-
- 3.39
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24" class="data row7 col24">
-
- 6.66
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row8">
-
- 8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0" class="data row8 col0">
-
- 0.92
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1" class="data row8 col1">
-
- 5.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2" class="data row8 col2">
-
- -3.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3" class="data row8 col3">
-
- -0.65
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4" class="data row8 col4">
-
- 5.99
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5" class="data row8 col5">
-
- -3.19
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6" class="data row8 col6">
-
- -1.83
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7" class="data row8 col7">
-
- 5.63
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8" class="data row8 col8">
-
- -3.53
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9" class="data row8 col9">
-
- -1.3
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10" class="data row8 col10">
-
- -1.61
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11" class="data row8 col11">
-
- 0.82
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12" class="data row8 col12">
-
- -2.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13" class="data row8 col13">
-
- -0.4
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14" class="data row8 col14">
-
- -6.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15" class="data row8 col15">
-
- -0.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16" class="data row8 col16">
-
- -6.6
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17" class="data row8 col17">
-
- -3.48
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18" class="data row8 col18">
-
- -0.04
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19" class="data row8 col19">
-
- -4.6
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20" class="data row8 col20">
-
- 0.51
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21" class="data row8 col21">
-
- -5.85
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22" class="data row8 col22">
-
- 3.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23" class="data row8 col23">
-
- 2.4
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24" class="data row8 col24">
-
- 5.08
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row9">
-
- 9
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0" class="data row9 col0">
-
- 0.38
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1" class="data row9 col1">
-
- 5.54
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2" class="data row9 col2">
-
- -4.49
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3" class="data row9 col3">
-
- -0.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4" class="data row9 col4">
-
- 7.05
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5" class="data row9 col5">
-
- -2.64
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6" class="data row9 col6">
-
- -0.44
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7" class="data row9 col7">
-
- 5.35
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8" class="data row9 col8">
-
- -1.96
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9" class="data row9 col9">
-
- -0.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10" class="data row9 col10">
-
- -0.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11" class="data row9 col11">
-
- 0.26
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12" class="data row9 col12">
-
- -3.37
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13" class="data row9 col13">
-
- -0.82
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14" class="data row9 col14">
-
- -6.05
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15" class="data row9 col15">
-
- -2.61
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16" class="data row9 col16">
-
- -8.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17" class="data row9 col17">
-
- -4.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18" class="data row9 col18">
-
- 0.41
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19" class="data row9 col19">
-
- -4.71
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20" class="data row9 col20">
-
- 1.89
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21" class="data row9 col21">
-
- -6.93
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22" class="data row9 col22">
-
- 2.14
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23" class="data row9 col23">
-
- 3.0
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24" class="data row9 col24">
-
- 5.16
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row10">
-
- 10
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0" class="data row10 col0">
-
- 2.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1" class="data row10 col1">
-
- 5.84
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2" class="data row10 col2">
-
- -3.9
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3" class="data row10 col3">
-
- -0.98
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4" class="data row10 col4">
-
- 7.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5" class="data row10 col5">
-
- -2.49
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6" class="data row10 col6">
-
- -0.59
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7" class="data row10 col7">
-
- 5.59
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8" class="data row10 col8">
-
- -2.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9" class="data row10 col9">
-
- -0.71
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10" class="data row10 col10">
-
- -0.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11" class="data row10 col11">
-
- 1.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12" class="data row10 col12">
-
- -2.79
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13" class="data row10 col13">
-
- 0.48
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14" class="data row10 col14">
-
- -5.97
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15" class="data row10 col15">
-
- -3.44
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16" class="data row10 col16">
-
- -7.77
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17" class="data row10 col17">
-
- -5.49
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18" class="data row10 col18">
-
- -0.7
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19" class="data row10 col19">
-
- -4.61
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20" class="data row10 col20">
-
- -0.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21" class="data row10 col21">
-
- -7.72
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22" class="data row10 col22">
-
- 1.54
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23" class="data row10 col23">
-
- 5.02
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24" class="data row10 col24">
-
- 5.81
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row11">
-
- 11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0" class="data row11 col0">
-
- 1.86
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1" class="data row11 col1">
-
- 4.47
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2" class="data row11 col2">
-
- -2.17
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3" class="data row11 col3">
-
- -1.38
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4" class="data row11 col4">
-
- 5.9
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5" class="data row11 col5">
-
- -0.49
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6" class="data row11 col6">
-
- 0.02
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7" class="data row11 col7">
-
- 5.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8" class="data row11 col8">
-
- -1.04
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9" class="data row11 col9">
-
- -0.6
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10" class="data row11 col10">
-
- 0.49
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11" class="data row11 col11">
-
- 1.96
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12" class="data row11 col12">
-
- -1.47
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13" class="data row11 col13">
-
- 1.88
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14" class="data row11 col14">
-
- -5.92
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15" class="data row11 col15">
-
- -4.55
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16" class="data row11 col16">
-
- -8.15
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17" class="data row11 col17">
-
- -3.42
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18" class="data row11 col18">
-
- -2.24
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19" class="data row11 col19">
-
- -4.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20" class="data row11 col20">
-
- -1.17
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21" class="data row11 col21">
-
- -7.9
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22" class="data row11 col22">
-
- 1.36
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23" class="data row11 col23">
-
- 5.31
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24" class="data row11 col24">
-
- 5.83
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row12">
-
- 12
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0" class="data row12 col0">
-
- 3.19
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1" class="data row12 col1">
-
- 4.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2" class="data row12 col2">
-
- -3.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3" class="data row12 col3">
-
- -2.27
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4" class="data row12 col4">
-
- 5.93
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5" class="data row12 col5">
-
- -2.64
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6" class="data row12 col6">
-
- 0.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7" class="data row12 col7">
-
- 6.72
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8" class="data row12 col8">
-
- -2.84
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9" class="data row12 col9">
-
- -0.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10" class="data row12 col10">
-
- 1.89
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11" class="data row12 col11">
-
- 2.63
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12" class="data row12 col12">
-
- -1.53
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13" class="data row12 col13">
-
- 0.75
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14" class="data row12 col14">
-
- -5.27
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15" class="data row12 col15">
-
- -4.53
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16" class="data row12 col16">
-
- -7.57
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17" class="data row12 col17">
-
- -2.85
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18" class="data row12 col18">
-
- -2.17
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19" class="data row12 col19">
-
- -4.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20" class="data row12 col20">
-
- -1.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21" class="data row12 col21">
-
- -8.99
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22" class="data row12 col22">
-
- 2.11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23" class="data row12 col23">
-
- 6.42
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24" class="data row12 col24">
-
- 5.6
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row13">
-
- 13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0" class="data row13 col0">
-
- 2.31
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1" class="data row13 col1">
-
- 4.45
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2" class="data row13 col2">
-
- -3.87
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3" class="data row13 col3">
-
- -2.05
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4" class="data row13 col4">
-
- 6.76
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5" class="data row13 col5">
-
- -3.25
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6" class="data row13 col6">
-
- -2.17
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7" class="data row13 col7">
-
- 7.99
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8" class="data row13 col8">
-
- -2.56
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9" class="data row13 col9">
-
- -0.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10" class="data row13 col10">
-
- 0.71
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11" class="data row13 col11">
-
- 2.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12" class="data row13 col12">
-
- -0.16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13" class="data row13 col13">
-
- -0.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14" class="data row13 col14">
-
- -5.1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15" class="data row13 col15">
-
- -3.79
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16" class="data row13 col16">
-
- -7.58
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17" class="data row13 col17">
-
- -4.0
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18" class="data row13 col18">
-
- 0.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19" class="data row13 col19">
-
- -3.67
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20" class="data row13 col20">
-
- -1.05
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21" class="data row13 col21">
-
- -8.71
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22" class="data row13 col22">
-
- 2.47
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23" class="data row13 col23">
-
- 5.87
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24" class="data row13 col24">
-
- 6.71
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row14">
-
- 14
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0" class="data row14 col0">
-
- 3.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1" class="data row14 col1">
-
- 4.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2" class="data row14 col2">
-
- -3.88
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3" class="data row14 col3">
-
- -1.58
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4" class="data row14 col4">
-
- 6.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5" class="data row14 col5">
-
- -3.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6" class="data row14 col6">
-
- -1.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7" class="data row14 col7">
-
- 5.57
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8" class="data row14 col8">
-
- -2.93
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9" class="data row14 col9">
-
- -0.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10" class="data row14 col10">
-
- -0.97
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11" class="data row14 col11">
-
- 1.72
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12" class="data row14 col12">
-
- 3.61
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13" class="data row14 col13">
-
- 0.29
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14" class="data row14 col14">
-
- -4.21
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15" class="data row14 col15">
-
- -4.1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16" class="data row14 col16">
-
- -6.68
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17" class="data row14 col17">
-
- -4.5
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18" class="data row14 col18">
-
- -2.19
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19" class="data row14 col19">
-
- -2.43
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20" class="data row14 col20">
-
- -1.64
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21" class="data row14 col21">
-
- -9.36
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22" class="data row14 col22">
-
- 3.36
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23" class="data row14 col23">
-
- 6.11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24" class="data row14 col24">
-
- 7.53
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row15">
-
- 15
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0" class="data row15 col0">
-
- 5.64
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1" class="data row15 col1">
-
- 5.31
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2" class="data row15 col2">
-
- -3.98
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3" class="data row15 col3">
-
- -2.26
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4" class="data row15 col4">
-
- 5.91
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5" class="data row15 col5">
-
- -3.3
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6" class="data row15 col6">
-
- -1.03
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7" class="data row15 col7">
-
- 5.68
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8" class="data row15 col8">
-
- -3.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9" class="data row15 col9">
-
- -0.33
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10" class="data row15 col10">
-
- -1.16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11" class="data row15 col11">
-
- 2.19
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12" class="data row15 col12">
-
- 4.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13" class="data row15 col13">
-
- 1.01
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14" class="data row15 col14">
-
- -3.22
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15" class="data row15 col15">
-
- -4.31
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16" class="data row15 col16">
-
- -5.74
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17" class="data row15 col17">
-
- -4.44
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18" class="data row15 col18">
-
- -2.3
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19" class="data row15 col19">
-
- -1.36
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20" class="data row15 col20">
-
- -1.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21" class="data row15 col21">
-
- -11.27
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22" class="data row15 col22">
-
- 2.59
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23" class="data row15 col23">
-
- 6.69
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24" class="data row15 col24">
-
- 5.91
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row16">
-
- 16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0" class="data row16 col0">
-
- 4.08
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1" class="data row16 col1">
-
- 4.34
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2" class="data row16 col2">
-
- -2.44
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3" class="data row16 col3">
-
- -3.3
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4" class="data row16 col4">
-
- 6.04
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5" class="data row16 col5">
-
- -2.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6" class="data row16 col6">
-
- -0.47
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7" class="data row16 col7">
-
- 5.28
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8" class="data row16 col8">
-
- -4.84
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9" class="data row16 col9">
-
- 1.58
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10" class="data row16 col10">
-
- 0.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11" class="data row16 col11">
-
- 0.1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12" class="data row16 col12">
-
- 5.79
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13" class="data row16 col13">
-
- 1.8
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14" class="data row16 col14">
-
- -3.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15" class="data row16 col15">
-
- -3.85
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16" class="data row16 col16">
-
- -5.53
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17" class="data row16 col17">
-
- -2.97
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18" class="data row16 col18">
-
- -2.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19" class="data row16 col19">
-
- -1.15
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20" class="data row16 col20">
-
- -0.56
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21" class="data row16 col21">
-
- -13.13
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22" class="data row16 col22">
-
- 2.07
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23" class="data row16 col23">
-
- 6.16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24" class="data row16 col24">
-
- 4.94
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row17">
-
- 17
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0" class="data row17 col0">
-
- 5.64
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1" class="data row17 col1">
-
- 4.57
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2" class="data row17 col2">
-
- -3.53
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3" class="data row17 col3">
-
- -3.76
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4" class="data row17 col4">
-
- 6.58
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5" class="data row17 col5">
-
- -2.58
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6" class="data row17 col6">
-
- -0.75
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7" class="data row17 col7">
-
- 6.58
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8" class="data row17 col8">
-
- -4.78
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9" class="data row17 col9">
-
- 3.63
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10" class="data row17 col10">
-
- -0.29
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11" class="data row17 col11">
-
- 0.56
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12" class="data row17 col12">
-
- 5.76
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13" class="data row17 col13">
-
- 2.05
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14" class="data row17 col14">
-
- -2.27
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15" class="data row17 col15">
-
- -2.31
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16" class="data row17 col16">
-
- -4.95
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17" class="data row17 col17">
-
- -3.16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18" class="data row17 col18">
-
- -3.06
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19" class="data row17 col19">
-
- -2.43
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20" class="data row17 col20">
-
- 0.84
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21" class="data row17 col21">
-
- -12.57
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22" class="data row17 col22">
-
- 3.56
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23" class="data row17 col23">
-
- 7.36
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24" class="data row17 col24">
-
- 4.7
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row18">
-
- 18
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0" class="data row18 col0">
-
- 5.99
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1" class="data row18 col1">
-
- 5.82
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2" class="data row18 col2">
-
- -2.85
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3" class="data row18 col3">
-
- -4.15
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4" class="data row18 col4">
-
- 7.12
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5" class="data row18 col5">
-
- -3.32
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6" class="data row18 col6">
-
- -1.21
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7" class="data row18 col7">
-
- 7.93
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8" class="data row18 col8">
-
- -4.85
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9" class="data row18 col9">
-
- 1.44
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10" class="data row18 col10">
-
- -0.63
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11" class="data row18 col11">
-
- 0.35
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12" class="data row18 col12">
-
- 7.47
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13" class="data row18 col13">
-
- 0.87
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14" class="data row18 col14">
-
- -1.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15" class="data row18 col15">
-
- -2.09
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16" class="data row18 col16">
-
- -4.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17" class="data row18 col17">
-
- -2.55
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18" class="data row18 col18">
-
- -2.46
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19" class="data row18 col19">
-
- -2.89
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20" class="data row18 col20">
-
- 1.9
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21" class="data row18 col21">
-
- -9.74
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22" class="data row18 col22">
-
- 3.43
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23" class="data row18 col23">
-
- 7.07
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24" class="data row18 col24">
-
- 4.39
-
-
- </tr>
-
- <tr>
-
- <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row19">
-
- 19
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0" class="data row19 col0">
-
- 4.03
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1" class="data row19 col1">
-
- 6.23
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2" class="data row19 col2">
-
- -4.1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3" class="data row19 col3">
-
- -4.11
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4" class="data row19 col4">
-
- 7.19
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5" class="data row19 col5">
-
- -4.1
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6" class="data row19 col6">
-
- -1.52
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7" class="data row19 col7">
-
- 6.53
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8" class="data row19 col8">
-
- -5.21
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9" class="data row19 col9">
-
- -0.24
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10" class="data row19 col10">
-
- 0.01
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11" class="data row19 col11">
-
- 1.16
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12" class="data row19 col12">
-
- 6.43
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13" class="data row19 col13">
-
- -1.97
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14" class="data row19 col14">
-
- -2.64
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15" class="data row19 col15">
-
- -1.66
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16" class="data row19 col16">
-
- -5.2
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17" class="data row19 col17">
-
- -3.25
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18" class="data row19 col18">
-
- -2.87
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19" class="data row19 col19">
-
- -1.65
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20" class="data row19 col20">
-
- 1.64
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21" class="data row19 col21">
-
- -10.66
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22" class="data row19 col22">
-
- 2.83
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23" class="data row19 col23">
-
- 7.48
-
-
- <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24" class="data row19 col24">
-
- 3.94
-
-
- </tr>
-
- </tbody>
- </table>
-
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Extensibility">Extensibility<a class="anchor-link" href="#Extensibility">¶</a></h1><p>The core of pandas is, and will remain, its "high-performance, easy-to-use data structures".
-With that in mind, we hope that <code>DataFrame.style</code> accomplishes two goals</p>
-<ul>
-<li>Provide an API that is pleasing to use interactively and is "good enough" for many tasks</li>
-<li>Provide the foundations for dedicated libraries to build on</li>
-</ul>
-<p>If you build a great library on top of this, let us know and we'll <a href="http://pandas.pydata.org/pandas-docs/stable/ecosystem.html">link</a> to it.</p>
-<h2 id="Subclassing">Subclassing<a class="anchor-link" href="#Subclassing">¶</a></h2><p>This section contains a bit of information about the implementation of <code>Styler</code>.
-Since the feature is so new all of this is subject to change, even more so than the end-use API.</p>
-<p>As users apply styles (via <code>.apply</code>, <code>.applymap</code> or one of the builtins), we don't actually calculate anything.
-Instead, we append functions and arguments to a list <code>self._todo</code>.
-When asked (typically in <code>.render</code> we'll walk through the list and execute each function (this is in <code>self._compute()</code>.
-These functions update an internal <code>defaultdict(list)</code>, <code>self.ctx</code> which maps DataFrame row / column positions to CSS attribute, value pairs.</p>
-<p>We take the extra step through <code>self._todo</code> so that we can export styles and set them on other <code>Styler</code>s.</p>
-<p>Rendering uses <a href="http://jinja.pocoo.org/">Jinja</a> templates.
-The <code>.translate</code> method takes <code>self.ctx</code> and builds another dictionary ready to be passed into <code>Styler.template.render</code>, the Jinja template.</p>
-<h2 id="Alternate-templates">Alternate templates<a class="anchor-link" href="#Alternate-templates">¶</a></h2><p>We've used <a href="http://jinja.pocoo.org/">Jinja</a> templates to build up the HTML.
-The template is stored as a class variable <code>Styler.template.</code>. Subclasses can override that.</p>
-<div class="highlight"><pre><span class="k">class</span> <span class="nc">CustomStyle</span><span class="p">(</span><span class="n">Styler</span><span class="p">):</span>
- <span class="n">template</span> <span class="o">=</span> <span class="n">Template</span><span class="p">(</span><span class="s">"""..."""</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
\ No newline at end of file
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index f5f45b742d7ca..d1cefea7042ae 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -31,10 +31,17 @@ New features
Conditional HTML Formatting
^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. warning::
+ This is a new feature and is under active development.
+ We'll be adding features an possibly making breaking changes in future
+ releases. Feedback is welcome_.
+
+.. _welcome: https://github.com/pydata/pandas/issues/11610
+
We've added *experimental* support for conditional HTML formatting:
the visual styling of a DataFrame based on the data.
The styling is accomplished with HTML and CSS.
-Acesses the styler class with :attr:`pandas.DataFrame.style`, attribute,
+Acesses the styler class with the :attr:`pandas.DataFrame.style`, attribute,
an instance of :class:`~pandas.core.style.Styler` with your data attached.
Here's a quick example:
@@ -50,6 +57,7 @@ We can render the HTML to get the following table.
.. raw:: html
:file: whatsnew_0171_html_table.html
+:class:`~pandas.core.style.Styler` interacts nicely with the Jupyter Notebook.
See the :ref:`documentation <style>` for more.
.. _whatsnew_0171.enhancements:
diff --git a/pandas/core/style.py b/pandas/core/style.py
index 6ee5befd4ec02..2559b9a3a5dc2 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -44,6 +44,11 @@ class Styler(object):
.. versionadded:: 0.17.1
+ .. warning::
+ This is a new feature and is under active development.
+ We'll be adding features and possibly making breaking changes in future
+ releases.
+
Parameters
----------
data: Series or DataFrame
| Couple comments in https://github.com/pydata/pandas/issues/11610#issuecomment-158411503
- warning in the whatsnew
- warning in the Styler docstring
- removes generated HTML version of the notebook, that's built as part of the doc build.
Building the docs now to double check, I'll merge when those are good.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11660 | 2015-11-20T14:24:12Z | 2015-11-20T14:44:28Z | 2015-11-20T14:44:28Z | 2016-11-03T12:38:39Z |
DOC: Clarify foramtting | diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb
index fc59c3ca88100..6f4569ab32f89 100644
--- a/doc/source/html-styling.ipynb
+++ b/doc/source/html-styling.ipynb
@@ -2525,7 +2525,7 @@
"source": [
"Above we used `Styler.apply` to pass in each column one at a time.\n",
"\n",
- "<p style=\"background-color: #DEDEBE\">*Debugging Tip*: If you're having trouble writing your style function, try just passing it into <code style=\"background-color: #DEDEBE\">df.apply</code>. <code style=\"background-color: #DEDEBE\">Styler.apply</code> uses that internally, so the result should be the same.</p>\n",
+ "<p style=\"background-color: #DEDEBE\">*Debugging Tip*: If you're having trouble writing your style function, try just passing it into <code style=\"background-color: #DEDEBE\">DataFrame.apply</code>. Internally, <code style=\"background-color: #DEDEBE\">Styler.apply</code> uses <code style=\"background-color: #DEDEBE\">DataFrame.apply</code> so the result should be the same.</p>\n",
"\n",
"What if you wanted to highlight just the maximum value in the entire table?\n",
"Use `.apply(function, axis=None)` to indicate that your function wants the entire table, not one column or row at a time. Let's try that next.\n",
| Addresses [this comment](https://github.com/pydata/pandas/issues/11610#issuecomment-158326899) about the formatting being unclear.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11659 | 2015-11-20T13:45:15Z | 2015-11-20T13:56:04Z | 2015-11-20T13:56:04Z | 2016-11-03T12:38:38Z |
BUG: indexing with a range , #11652 | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index de6c20a292eac..c72334c5910a9 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -169,7 +169,7 @@ Bug Fixes
-
+- Bug in indexing with a ``range``, (:issue:`11652`)
- Bug in ``to_sql`` using unicode column names giving UnicodeEncodeError with (:issue:`11431`).
diff --git a/pandas/core/index.py b/pandas/core/index.py
index b0cd72e572c09..cdd0de4e1196d 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -1755,7 +1755,8 @@ def get_loc(self, key, method=None, tolerance=None):
if tolerance is not None:
raise ValueError('tolerance argument only valid if using pad, '
'backfill or nearest lookups')
- return self._engine.get_loc(_values_from_object(key))
+ key = _values_from_object(key)
+ return self._engine.get_loc(key)
indexer = self.get_indexer([key], method=method,
tolerance=tolerance)
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 25a110ee43039..2b1cb0a1e1b31 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -104,6 +104,8 @@ def _get_setitem_indexer(self, key):
if isinstance(key, tuple) and not self.ndim < len(key):
return self._convert_tuple(key, is_setter=True)
+ if isinstance(key, range):
+ return self._convert_range(key, is_setter=True)
try:
return self._convert_to_indexer(key, is_setter=True)
@@ -156,6 +158,10 @@ def _convert_tuple(self, key, is_setter=False):
keyidx.append(idx)
return tuple(keyidx)
+ def _convert_range(self, key, is_setter=False):
+ """ convert a range argument """
+ return list(key)
+
def _convert_scalar_indexer(self, key, axis):
# if we are accessing via lowered dim, use the last dim
ax = self.obj._get_axis(min(axis, self.ndim - 1))
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index e24edbeae68ef..cb06b714d4700 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -4726,6 +4726,17 @@ def test_indexing_dtypes_on_empty(self):
assert_series_equal(df2.loc[:,'a'], df2.iloc[:,0])
assert_series_equal(df2.loc[:,'a'], df2.ix[:,0])
+ def test_range_in_series_indexing(self):
+ # range can cause an indexing error
+ # GH 11652
+ for x in [5, 999999, 1000000]:
+ s = pd.Series(index=range(x))
+ s.loc[range(1)] = 42
+ assert_series_equal(s.loc[range(1)],Series(42.0,index=[0]))
+
+ s.loc[range(2)] = 43
+ assert_series_equal(s.loc[range(2)],Series(43.0,index=[0,1]))
+
@slow
def test_large_dataframe_indexing(self):
#GH10692
| closes #11652
| https://api.github.com/repos/pandas-dev/pandas/pulls/11653 | 2015-11-19T22:47:48Z | 2015-11-20T00:38:08Z | 2015-11-20T00:38:08Z | 2015-11-20T00:38:08Z |
Fix link to numexpr | diff --git a/doc/source/install.rst b/doc/source/install.rst
index 9accc188d567f..fcf497c0146c2 100644
--- a/doc/source/install.rst
+++ b/doc/source/install.rst
@@ -224,7 +224,7 @@ Dependencies
Recommended Dependencies
~~~~~~~~~~~~~~~~~~~~~~~~
-* `numexpr <http://code.google.com/p/numexpr/>`__: for accelerating certain numerical operations.
+* `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.
| Numexpr was hosted on Google code, but moved to Github.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11651 | 2015-11-19T21:34:09Z | 2015-11-19T21:40:15Z | 2015-11-19T21:40:15Z | 2015-11-19T21:40:19Z |
DOC: sponsor notice | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index de6c20a292eac..5b2b316158c40 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -3,6 +3,12 @@
v0.17.1 (November 21, 2015)
---------------------------
+.. note::
+
+ We are proud to announce that *pandas* has become a sponsored project of the not-for-profit `NUMFocus organization`_. This will help ensure the success of development of *pandas* as a world-class open-source project.
+
+:: _numfocus organization: http://numfocus.org/news/2015/10/09/numfocus-announces-new-fiscally-sponsored-project-pandas/
+
This is a minor bug-fix release from 0.17.0 and includes a large number of
bug fixes along several new features, enhancements, and performance improvements.
We recommend that all users upgrade to this version.
| cc @jorisvandenbossche
cc @cpcloud
cc @shoyer
cc @wesm
| https://api.github.com/repos/pandas-dev/pandas/pulls/11650 | 2015-11-19T20:39:41Z | 2015-11-20T12:43:54Z | 2015-11-20T12:43:54Z | 2015-11-20T15:19:21Z |
Bug#11637 fix to_csv | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index c4e8ae44011ec..6061fd42ebac2 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -164,6 +164,7 @@ Bug Fixes
- Bug in ``squeeze()`` with zero length arrays (:issue:`11230`, :issue:`8999`)
- Bug in ``describe()`` dropping column names for hierarchical indexes (:issue:`11517`)
- Bug in ``DataFrame.pct_change()`` not propagating ``axis`` keyword on ``.fillna`` method (:issue:`11150`)
+- Bug in ``.to_csv()`` incorrect output when a mix of integer and string column names passed as columns parameter (:issue:`11637`)
diff --git a/pandas/core/format.py b/pandas/core/format.py
index efa4b182f1133..8cc30a3444e9f 100644
--- a/pandas/core/format.py
+++ b/pandas/core/format.py
@@ -1327,7 +1327,7 @@ def __init__(self, obj, path_or_buf=None, sep=",", na_rep='', float_format=None,
date_format=date_format,
quoting=self.quoting)
else:
- cols = np.asarray(list(cols))
+ cols = list(cols)
self.obj = self.obj.loc[:, cols]
# update columns to include possible multiplicity of dupes
@@ -1339,7 +1339,7 @@ def __init__(self, obj, path_or_buf=None, sep=",", na_rep='', float_format=None,
date_format=date_format,
quoting=self.quoting)
else:
- cols = np.asarray(list(cols))
+ cols = list(cols)
# save it
self.cols = cols
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 79001cf4be9bc..cb3c779c3167e 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -39,6 +39,7 @@
from pandas.util.misc import is_little_endian
from pandas.util.testing import (assert_almost_equal,
+ assert_equal,
assert_numpy_array_equal,
assert_series_equal,
assert_frame_equal,
@@ -6987,6 +6988,15 @@ def test_to_csv_no_index(self):
result = read_csv(path)
assert_frame_equal(df,result)
+ def test_to_csv_with_mix_columns(self):
+ #GH11637, incorrect output when a mix of integer and string column
+ # names passed as columns parameter in to_csv
+
+ df = DataFrame({0: ['a', 'b', 'c'],
+ 1: ['aa', 'bb', 'cc']})
+ df['test'] = 'txt'
+ assert_equal(df.to_csv(), df.to_csv(columns=[0, 1, 'test']))
+
def test_to_csv_headers(self):
# GH6186, the presence or absence of `index` incorrectly
# causes to_csv to have different header semantics.
| This is a fix to Bug #11637.
Error Description : incorrect output when a mix of integer and string column names passed as columns parameter in to_csv().
[format.py](https://github.com/pydata/pandas/blob/master/pandas/core/format.py#L1330) is the cause of this discrepancy.
Please review .
| https://api.github.com/repos/pandas-dev/pandas/pulls/11649 | 2015-11-19T20:02:39Z | 2015-11-20T00:40:45Z | null | 2015-11-20T01:41:16Z |
DOC: fix up doc-string creations in generic.py | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 072a9f0da5d11..3df81481f1e84 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -4589,162 +4589,23 @@ def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
def _add_numeric_operations(cls):
""" add the operations to the cls; evaluate the doc strings again """
- 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')
-
- _num_doc = """
-
-%(desc)s
-
-Parameters
-----------
-axis : """ + axis_descr + """
-skipna : boolean, default True
- Exclude NA/null values. If an entire row/column is NA, the result
- will be NA
-level : int or level name, default None
- If the axis is a MultiIndex (hierarchical), count along a
- particular level, collapsing into a """ + name + """
-numeric_only : boolean, default None
- Include only float, int, boolean data. If None, will attempt to use
- everything, then use only numeric data
-
-Returns
--------
-%(outname)s : """ + name + " or " + cls.__name__ + " (if level specified)\n"
-
- _bool_doc = """
-
-%(desc)s
-
-Parameters
-----------
-axis : """ + axis_descr + """
-skipna : boolean, default True
- Exclude NA/null values. If an entire row/column is NA, the result
- will be NA
-level : int or level name, default None
- If the axis is a MultiIndex (hierarchical), count along a
- particular level, collapsing into a """ + name + """
-bool_only : boolean, default None
- Include only boolean data. If None, will attempt to use everything,
- then use only boolean data
-
-Returns
--------
-%(outname)s : """ + name + " or " + cls.__name__ + " (if level specified)\n"
-
- _cnum_doc = """
-
-Parameters
-----------
-axis : """ + axis_descr + """
-skipna : boolean, default True
- Exclude NA/null values. If an entire row/column is NA, the result
- will be NA
-
-Returns
--------
-%(outname)s : """ + name + "\n"
-
- def _make_stat_function(name, desc, f):
-
- @Substitution(outname=name, desc=desc)
- @Appender(_num_doc)
- def stat_func(self, axis=None, skipna=None, level=None,
- numeric_only=None, **kwargs):
- if skipna is None:
- skipna = True
- if axis is None:
- axis = self._stat_axis_number
- 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)
- stat_func.__name__ = name
- return stat_func
-
- cls.sum = _make_stat_function(
- 'sum', 'Return the sum of the values for the requested axis',
- nanops.nansum)
- cls.mean = _make_stat_function(
- 'mean', 'Return the mean of the values for the requested axis',
- nanops.nanmean)
- cls.skew = _make_stat_function(
- 'skew',
- 'Return unbiased skew over requested axis\nNormalized by N-1',
- nanops.nanskew)
- cls.kurt = _make_stat_function(
- 'kurt',
- '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', 'Return the product of the values for the requested axis',
- nanops.nanprod)
- cls.product = cls.prod
- cls.median = _make_stat_function(
- 'median', 'Return the median of the values for the requested axis',
- nanops.nanmedian)
- cls.max = _make_stat_function('max', """
-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', """
-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)
-
- if cls.__name__ == 'Series':
- def nanptp(values, axis=0, skipna=True):
- nmax = nanops.nanmax(values, axis, skipna)
- nmin = nanops.nanmin(values, axis, skipna)
- return nmax - nmin
-
- cls.ptp = _make_stat_function('ptp', """
-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)
-
- def _make_logical_function(name, desc, f):
-
- @Substitution(outname=name, desc=desc)
- @Appender(_bool_doc)
- def logical_func(self, axis=None, bool_only=None, skipna=None,
- level=None, **kwargs):
- 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.")
- return self._agg_by_level(name, axis=axis, level=level,
- 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
+ axis_descr, name, name2 = _doc_parms(cls)
cls.any = _make_logical_function(
- 'any', 'Return whether any element is True over requested axis',
+ 'any', name, name2, axis_descr,
+ 'Return whether any element is True over requested axis',
nanops.nanany)
cls.all = _make_logical_function(
- 'all', 'Return whether all elements are True over requested axis',
+ 'all', name, name2, axis_descr,
+ 'Return whether all elements are True over requested axis',
nanops.nanall)
@Substitution(outname='mad',
desc="Return the mean absolute deviation of the values "
- "for the requested axis")
+ "for the requested axis",
+ name1=name,
+ name2=name2,
+ axis_descr=axis_descr)
@Appender(_num_doc)
def mad(self, axis=None, skipna=None, level=None):
if skipna is None:
@@ -4763,39 +4624,20 @@ def mad(self, axis=None, skipna=None, level=None):
return np.abs(demeaned).mean(axis=axis, skipna=skipna)
cls.mad = mad
- def _make_stat_function_ddof(name, desc, f):
-
- @Substitution(outname=name, desc=desc)
- @Appender(_num_doc)
- def stat_func(self, axis=None, skipna=None, level=None, ddof=1,
- numeric_only=None, **kwargs):
- if skipna is None:
- skipna = True
- if axis is None:
- axis = self._stat_axis_number
- 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,
- skipna=skipna, ddof=ddof)
- stat_func.__name__ = name
- return stat_func
-
cls.sem = _make_stat_function_ddof(
- 'sem',
+ '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',
+ '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',
+ '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",
@@ -4803,7 +4645,10 @@ def stat_func(self, axis=None, skipna=None, level=None, ddof=1,
@Substitution(outname='compounded',
desc="Return the compound percentage of the values for "
- "the requested axis")
+ "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:
@@ -4811,51 +4656,264 @@ def compound(self, axis=None, skipna=None, level=None):
return (1 + self).prod(axis=axis, skipna=skipna, level=level) - 1
cls.compound = compound
- def _make_cum_function(name, accum_func, mask_a, mask_b):
-
- @Substitution(outname=name)
- @Appender("Return cumulative {0} over requested axis.".format(name)
- + _cnum_doc)
- def func(self, axis=None, dtype=None, out=None, skipna=True,
- **kwargs):
- if axis is None:
- axis = self._stat_axis_number
- else:
- axis = self._get_axis_number(axis)
-
- y = _values_from_object(self).copy()
-
- 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)
- elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)):
- mask = isnull(self)
- np.putmask(y, mask, mask_a)
- result = accum_func(y, axis)
- np.putmask(result, mask, mask_b)
- else:
- result = accum_func(y, axis)
-
- d = self._construct_axes_dict()
- d['copy'] = False
- return self._constructor(result, **d).__finalize__(self)
-
- func.__name__ = name
- return func
-
cls.cummin = _make_cum_function(
- 'min', lambda y, axis: np.minimum.accumulate(y, axis),
+ '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', lambda y, axis: y.cumsum(axis), 0., np.nan)
+ 'sum', name, name2, axis_descr,
+ "cumulative sum",
+ lambda y, axis: y.cumsum(axis), 0., np.nan)
cls.cumprod = _make_cum_function(
- 'prod', lambda y, axis: y.cumprod(axis), 1., np.nan)
+ 'prod', name, name2, axis_descr,
+ "cumulative product",
+ lambda y, axis: y.cumprod(axis), 1., np.nan)
cls.cummax = _make_cum_function(
- 'max', lambda y, axis: np.maximum.accumulate(y, axis),
+ '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,
+ 'Return the sum of the values for the requested axis',
+ nanops.nansum)
+ cls.mean = _make_stat_function(
+ '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,
+ 'Return unbiased skew over requested axis\nNormalized by N-1',
+ 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',
+ nanops.nankurt)
+ cls.kurtosis = cls.kurt
+ cls.prod = _make_stat_function(
+ '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,
+ '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)
+
+ @classmethod
+ def _add_series_only_operations(cls):
+ """ add the series only operations to the cls; evaluate the doc strings again """
+
+ axis_descr, name, name2 = _doc_parms(cls)
+
+ def nanptp(values, axis=0, skipna=True):
+ nmax = nanops.nanmax(values, axis, skipna)
+ 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)
+
+
+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)
+ ])
+ name = (cls._constructor_sliced.__name__
+ if cls._AXIS_LEN > 1 else 'scalar')
+ name2 = cls.__name__
+ return axis_descr, name, name2
+
+_num_doc = """
+
+%(desc)s
+
+Parameters
+----------
+axis : %(axis_descr)s
+skipna : boolean, default True
+ Exclude NA/null values. If an entire row/column is NA, the result
+ will be NA
+level : int or level name, default None
+ If the axis is a MultiIndex (hierarchical), count along a
+ particular level, collapsing into a %(name1)s
+numeric_only : boolean, default None
+ Include only float, int, boolean data. If None, will attempt to use
+ everything, then use only numeric data
+
+Returns
+-------
+%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
+
+_num_ddof_doc = """
+
+%(desc)s
+
+Parameters
+----------
+axis : %(axis_descr)s
+skipna : boolean, default True
+ Exclude NA/null values. If an entire row/column is NA, the result
+ will be NA
+level : int or level name, default None
+ If the axis is a MultiIndex (hierarchical), count along a
+ particular level, collapsing into a %(name1)s
+ddof : int, default 1
+ degrees of freedom
+numeric_only : boolean, default None
+ Include only float, int, boolean data. If None, will attempt to use
+ everything, then use only numeric data
+
+Returns
+-------
+%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
+
+_bool_doc = """
+
+%(desc)s
+
+Parameters
+----------
+axis : %(axis_descr)s
+skipna : boolean, default True
+ Exclude NA/null values. If an entire row/column is NA, the result
+ will be NA
+level : int or level name, default None
+ If the axis is a MultiIndex (hierarchical), count along a
+ particular level, collapsing into a %(name1)s
+bool_only : boolean, default None
+ Include only boolean data. If None, will attempt to use everything,
+ then use only boolean data
+
+Returns
+-------
+%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
+
+_cnum_doc = """
+
+Parameters
+----------
+axis : %(axis_descr)s
+skipna : boolean, default True
+ Exclude NA/null values. If an entire row/column is NA, the result
+ will be NA
+
+Returns
+-------
+%(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)
+ @Appender(_num_doc)
+ def stat_func(self, axis=None, skipna=None, level=None,
+ numeric_only=None, **kwargs):
+ if skipna is None:
+ skipna = True
+ if axis is None:
+ axis = self._stat_axis_number
+ 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)
+ 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)
+ @Appender(_num_ddof_doc)
+ def stat_func(self, axis=None, skipna=None, level=None, ddof=1,
+ numeric_only=None, **kwargs):
+ if skipna is None:
+ skipna = True
+ if axis is None:
+ axis = self._stat_axis_number
+ 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,
+ 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):
+ if axis is None:
+ axis = self._stat_axis_number
+ else:
+ axis = self._get_axis_number(axis)
+
+ y = _values_from_object(self).copy()
+
+ 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)
+ elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)):
+ mask = isnull(self)
+ np.putmask(y, mask, mask_a)
+ result = accum_func(y, axis)
+ np.putmask(result, mask, mask_b)
+ else:
+ result = accum_func(y, axis)
+
+ d = self._construct_axes_dict()
+ d['copy'] = False
+ return self._constructor(result, **d).__finalize__(self)
+
+ 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)
+ @Appender(_bool_doc)
+ def logical_func(self, axis=None, bool_only=None, skipna=None,
+ level=None, **kwargs):
+ 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.")
+ return self._agg_by_level(name, axis=axis, level=level,
+ 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
+
# install the indexerse
for _name, _indexer in indexing.get_indexers_list():
NDFrame._create_indexer(_name, _indexer)
diff --git a/pandas/core/panel.py b/pandas/core/panel.py
index f05e5a8357877..d2fcd6ed19378 100644
--- a/pandas/core/panel.py
+++ b/pandas/core/panel.py
@@ -1530,7 +1530,6 @@ def f(self, other, axis=0):
Panel._add_aggregate_operations()
Panel._add_numeric_operations()
-
# legacy
class WidePanel(Panel):
def __init__(self, *args, **kwargs):
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 15b3f9b7cdcb2..e603c6aa75d6f 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2717,6 +2717,7 @@ def _dir_additions(self):
Series._setup_axes(['index'], info_axis=0, stat_axis=0,
aliases={'rows': 0})
Series._add_numeric_operations()
+Series._add_series_only_operations()
_INDEX_TYPES = ndarray, Index, list, tuple
#------------------------------------------------------------------------------
| change in function locations, very minor corrections for functions like `DataFrame.sum`
| https://api.github.com/repos/pandas-dev/pandas/pulls/11646 | 2015-11-19T13:18:00Z | 2015-11-19T13:38:45Z | 2015-11-19T13:38:45Z | 2015-11-19T13:38:45Z |
BUG #11638 return correct dtype for int and float | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index c4e8ae44011ec..695c1078f06e1 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -164,6 +164,7 @@ Bug Fixes
- Bug in ``squeeze()`` with zero length arrays (:issue:`11230`, :issue:`8999`)
- Bug in ``describe()`` dropping column names for hierarchical indexes (:issue:`11517`)
- Bug in ``DataFrame.pct_change()`` not propagating ``axis`` keyword on ``.fillna`` method (:issue:`11150`)
+- Bug in ``core.common._infer_dtype_from_scalar()`` do not upcast int and float (:issue:`11638`)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 4490aaf58a002..d8e21e8a7bc0c 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -1002,8 +1002,7 @@ def _infer_fill_value(val):
def _infer_dtype_from_scalar(val):
- """ interpret the dtype from a scalar, upcast floats and ints
- return the new value and the dtype """
+ """ interpret the dtype from a scalar """
dtype = np.object_
@@ -1037,12 +1036,17 @@ def _infer_dtype_from_scalar(val):
elif is_bool(val):
dtype = np.bool_
- # provide implicity upcast on scalars
elif is_integer(val):
- dtype = np.int64
+ if isinstance(val, int):
+ dtype = np.int64
+ else:
+ dtype = type(val)
elif is_float(val):
- dtype = np.float64
+ if isinstance(val, float):
+ dtype = np.float64
+ else:
+ dtype = type(val)
elif is_complex(val):
dtype = np.complex_
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index 89826209fa46d..f49854cdee2f9 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -98,6 +98,37 @@ def test_abc_types(self):
self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod)
+class TestInferDtype(tm.TestCase):
+ df = pd.DataFrame({'one': np.arange(6, dtype=np.int8)})
+
+ def test_preserves_correct_dtype(self):
+ # Test that data type is preserved . #5782
+
+ self.df.loc[1, 'one'] = 6
+ self.assertEqual(self.df.dtypes.one, np.dtype(np.int8))
+ self.df.one = np.int8(7)
+ self.assertEqual(self.df.dtypes.one, np.dtype(np.int8))
+
+ def test_infer_dtype_from_scalar(self):
+ # Test that _infer_dtype_from_scalar is returning correct dtype for int and float.
+
+ data = np.int8(12)
+ dtype, val = com._infer_dtype_from_scalar(data)
+ self.assertEqual(dtype, np.int8)
+
+ data = 12
+ dtype, val = com._infer_dtype_from_scalar(data)
+ self.assertEqual(dtype, np.int64)
+
+ data = np.float16(2.0)
+ dtype, val = com._infer_dtype_from_scalar(data)
+ self.assertEqual(dtype, np.float16)
+
+ data = np.float(12)
+ dtype, val = com._infer_dtype_from_scalar(data)
+ self.assertEqual(dtype, np.float64)
+
+
def test_notnull():
assert notnull(1.)
assert not notnull(None)
| closes #11638
Please review.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11644 | 2015-11-19T03:27:19Z | 2015-11-20T00:59:20Z | null | 2015-11-20T01:41:08Z |
PERF: improves performance in remove_unused_categories | diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py
index be4b3f59bead4..456fedb272e18 100644
--- a/pandas/core/categorical.py
+++ b/pandas/core/categorical.py
@@ -816,16 +816,14 @@ def remove_unused_categories(self, inplace=False):
set_categories
"""
cat = self if inplace else self.copy()
- _used = sorted(np.unique(cat._codes))
- if _used[0] == -1:
- _used = _used[1:]
+ idx, inv = np.unique(cat._codes, return_inverse=True)
- new_categories = cat.categories.take(_ensure_platform_int(_used))
+ if idx.size != 0 and idx[0] == -1: # na sentinel
+ idx, inv = idx[1:], inv - 1
+
+ cat._codes = inv
+ cat._categories = cat.categories.take(idx)
- from pandas.core.index import _ensure_index
- new_categories = _ensure_index(new_categories)
- cat._codes = _get_codes_for_values(cat.__array__(), new_categories)
- cat._categories = new_categories
if not inplace:
return cat
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 3da4ad62b45af..e98c98fdec8b3 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -850,6 +850,21 @@ def test_remove_unused_categories(self):
self.assert_numpy_array_equal(res.categories, np.array(["a","b","c"]))
self.assert_numpy_array_equal(c.categories, exp_categories_all)
+ val = ['F', np.nan, 'D', 'B', 'D', 'F', np.nan]
+ cat = pd.Categorical(values=val, categories=list('ABCDEFG'))
+ out = cat.remove_unused_categories()
+ self.assert_numpy_array_equal(out.categories, ['B', 'D', 'F'])
+ self.assert_numpy_array_equal(out.codes, [ 2, -1, 1, 0, 1, 2, -1])
+ self.assertEqual(out.get_values().tolist(), val)
+
+ alpha = list('abcdefghijklmnopqrstuvwxyz')
+ val = np.random.choice(alpha[::2], 10000).astype('object')
+ val[np.random.choice(len(val), 100)] = np.nan
+
+ cat = pd.Categorical(values=val, categories=alpha)
+ out = cat.remove_unused_categories()
+ self.assertEqual(out.get_values().tolist(), val.tolist())
+
def test_nan_handling(self):
# Nans are represented as -1 in codes
| on master:
``` python
In [1]: np.random.seed(2718281)
In [2]: n = 1 << 20
In [3]: ix = tm.makeCategoricalIndex(n, n // 10)
In [4]: %timeit ix.remove_unused_categories()
1 loops, best of 3: 527 ms per loop
```
on branch:
``` python
In [4]: %timeit ix.remove_unused_categories()
1 loops, best of 3: 216 ms per loop
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/11643 | 2015-11-19T02:02:07Z | 2015-11-19T03:13:22Z | null | 2015-11-21T21:41:14Z |
TST: tests for outputing ambiguous times, #11619, fixed already in #11301 | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index fe712d8f0710f..c4e8ae44011ec 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -153,6 +153,7 @@ Bug Fixes
- Prevent adding new attributes to the accessors ``.str``, ``.dt`` and ``.cat``. Retrieving such
a value was not possible, so error out on setting it. (:issue:`10673`)
- Bug in tz-conversions with an ambiguous time and ``.dt`` accessors (:issue:`11295`)
+- Bug in output formatting when using an index of ambiguous times (:issue:`11619`)
- Bug in comparisons of Series vs list-likes (:issue:`11339`)
- Bug in ``DataFrame.replace`` with a ``datetime64[ns, tz]`` and a non-compat to_replace (:issue:`11326`, :issue:`11153`)
- Bug in list-like indexing with a mixed-integer Index (:issue:`11320`)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 0cae8b356b517..79001cf4be9bc 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -15449,8 +15449,8 @@ def test_to_csv_date_format(self):
assert_frame_equal(test, nat_frame)
def test_to_csv_with_dst_transitions(self):
- pname = '__tmp_to_csv_date_format_with_dst__'
- with ensure_clean(pname) as path:
+
+ with ensure_clean('csv_date_format_with_dst') as path:
# make sure we are not failing on transitions
times = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
tz="Europe/London",
@@ -15468,6 +15468,25 @@ def test_to_csv_with_dst_transitions(self):
result.index = pd.to_datetime(result.index).tz_localize('UTC').tz_convert('Europe/London')
assert_frame_equal(result,df)
+ # GH11619
+ idx = pd.date_range('2015-01-01', '2015-12-31', freq = 'H', tz='Europe/Paris')
+ df = DataFrame({'values' : 1, 'idx' : idx},
+ index=idx)
+ with ensure_clean('csv_date_format_with_dst') as path:
+ df.to_csv(path,index=True)
+ result = read_csv(path,index_col=0)
+ result.index = pd.to_datetime(result.index).tz_localize('UTC').tz_convert('Europe/Paris')
+ result['idx'] = pd.to_datetime(result['idx']).astype('datetime64[ns, Europe/Paris]')
+ assert_frame_equal(result,df)
+
+ # assert working
+ df.astype(str)
+
+ with ensure_clean('csv_date_format_with_dst') as path:
+ df.to_pickle(path)
+ result = pd.read_pickle(path)
+ assert_frame_equal(result,df)
+
def test_concat_empty_dataframe_dtypes(self):
df = DataFrame(columns=list("abc"))
| closes #11619
| https://api.github.com/repos/pandas-dev/pandas/pulls/11641 | 2015-11-18T16:17:40Z | 2015-11-18T16:49:58Z | 2015-11-18T16:49:58Z | 2015-11-18T16:49:58Z |
BUG: remove_unused_categories with NaN values (GH11599) | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index fe712d8f0710f..eec400facffae 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -187,3 +187,4 @@ Bug Fixes
- Bug in ``DataFrame.join()`` with ``how='right'`` producing a ``TypeError`` (:issue:`11519`)
- Bug in ``Series.quantile`` with empty list results has ``Index`` with ``object`` dtype (:issue:`11588`)
- Bug in ``pd.merge`` results in empty ``Int64Index`` rather than ``Index(dtype=object)`` when the merge result is empty (:issue:`11588`)
+- Bug in ``remove_unused_categories`` when having ``NaN`` values (:issue:`11599`).
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py
index cd4038d2b142b..be4b3f59bead4 100644
--- a/pandas/core/categorical.py
+++ b/pandas/core/categorical.py
@@ -817,6 +817,9 @@ def remove_unused_categories(self, inplace=False):
"""
cat = self if inplace else self.copy()
_used = sorted(np.unique(cat._codes))
+ if _used[0] == -1:
+ _used = _used[1:]
+
new_categories = cat.categories.take(_ensure_platform_int(_used))
from pandas.core.index import _ensure_index
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 0ed84763aa9b6..3da4ad62b45af 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -844,6 +844,11 @@ def test_remove_unused_categories(self):
self.assert_numpy_array_equal(c.categories, exp_categories_dropped)
self.assertIsNone(res)
+ # with NaN values (GH11599)
+ c = Categorical(["a","b","c",np.nan], categories=["a","b","c","d","e"])
+ res = c.remove_unused_categories()
+ self.assert_numpy_array_equal(res.categories, np.array(["a","b","c"]))
+ self.assert_numpy_array_equal(c.categories, exp_categories_all)
def test_nan_handling(self):
| Closes #11599
| https://api.github.com/repos/pandas-dev/pandas/pulls/11639 | 2015-11-18T13:19:31Z | 2015-11-18T21:34:56Z | 2015-11-18T21:34:56Z | 2015-11-18T21:34:56Z |
CLN: Followup to HTML Formatting | diff --git a/ci/requirements_all.txt b/ci/requirements_all.txt
index 6a0b695c5de87..6a05f2db8901f 100644
--- a/ci/requirements_all.txt
+++ b/ci/requirements_all.txt
@@ -20,3 +20,4 @@ lxml
sqlalchemy
bottleneck
pymysql
+Jinja2
diff --git a/doc/make.py b/doc/make.py
index 6b424ce2814d5..c09514d758833 100755
--- a/doc/make.py
+++ b/doc/make.py
@@ -104,11 +104,14 @@ def clean():
def html():
check_build()
+ os.system('jupyter nbconvert --to=html --template=basic '
+ '--output=source/html-styling.html source/html-styling.ipynb')
if os.system('sphinx-build -P -b html -d build/doctrees '
'source build/html'):
raise SystemExit("Building HTML failed.")
try:
# remove stale file
+ os.system('rm source/html-styling.html')
os.system('cd build; rm -f html/pandas.zip;')
except:
pass
diff --git a/doc/source/html-styling.html b/doc/source/html-styling.html
index e595cffc751a8..f751d9575ddb5 100644
--- a/doc/source/html-styling.html
+++ b/doc/source/html-styling.html
@@ -1,305 +1,10 @@
-<!DOCTYPE html>
-<html>
-<head><meta charset="utf-8" />
-<title>html-styling</title>
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
-
-<style type="text/css">
- /*!
-*
-* Twitter Bootstrap
-*
-*//*! normalize.css v3.0.2 | MIT License | git.io/normalize */html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%;font-size:10px;-webkit-tap-highlight-color:transparent}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,optgroup,strong{font-weight:700}dfn{font-style:italic}h1{font-size:2em;margin:.67em 0}mark{background:#ff0;color:#000}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0;vertical-align:middle}svg:not(:root){overflow:hidden}hr{-moz-box-sizing:content-box;box-sizing:content-box;height:0}pre,textarea{overflow:auto}code,kbd,pre,samp{font-size:1em}button,input,optgroup,select,textarea{color:inherit;font:inherit;margin:0}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}input{line-height:normal}input[type=checkbox],input[type=radio]{box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */@media print{*,:after,:before{background:0 0!important;color:#000!important;box-shadow:none!important;text-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href)")"}abbr[title]:after{content:" (" attr(title)")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}select{background:#fff!important}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons 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- </style>
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- /* Experimental typographically tinkered IJulia stylesheet
- * Copyright © 2013--2015 Jiahao Chen <jiahao@mit.edu>
- * MIT License
- *
- * To use, place in ~/.ipython/profile_notebook/static/custom/custom.css
- * and refresh the page. (The IPython/Jupyter server does not need to be restarted.)
- *
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- overflow: visible;
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-
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- },
- // Center justify equations in code and markdown cells. Elsewhere
- // we use CSS to left justify single line equations in code cells.
- displayAlign: 'center',
- "HTML-CSS": {
- styles: {'.MathJax_Display': {"margin": 0}},
- linebreaks: { automatic: true }
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-<body>
- <div tabindex="-1" id="notebook" class="border-box-sizing">
- <div class="container" id="notebook-container">
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Conditional-Formatting">Conditional Formatting<a class="anchor-link" href="#Conditional-Formatting">¶</a></h1><p><em>New in version 0.17.1</em></p>
+<p><em>New in version 0.17.1</em></p>
<p><p style="color: red"><em>Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your <a href="https://github.com/pydata/pandas/issues">feedback</a>.</em><p style="color: red"></p>
<p>You can apply <strong>conditional formatting</strong>, the visual styling of a DataFrame
depending on the data within, by using the <code>DataFrame.style</code> property.
@@ -344,7 +49,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [1]:</div>
+<div class="prompt input_prompt">In [3]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -374,7 +79,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [2]:</div>
+<div class="prompt input_prompt">In [4]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span>
@@ -388,7 +93,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[2]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[4]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
@@ -397,7 +102,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</style>
- <table id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb">
+ <table id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb">
<thead>
@@ -423,32 +128,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -457,32 +162,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -491,32 +196,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -525,32 +230,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -559,32 +264,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -593,32 +298,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -627,32 +332,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -661,32 +366,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -695,32 +400,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -729,32 +434,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -785,7 +490,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [3]:</div>
+<div class="prompt input_prompt">In [5]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_null</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">'</span><span class="se">\n</span><span class="s">'</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]</span>
@@ -799,7 +504,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[3]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[5]:</div>
<div class="output_text output_subarea output_execute_result">
@@ -807,7 +512,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
' <style type="text/css" >',
' ',
' ',
- ' #T_e7c1f51a_8bd5_11e5_803e_a45e60bd97fbrow0_col2 {',
+ ' #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {',
' ',
' background-color: red;',
' ',
@@ -844,7 +549,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [4]:</div>
+<div class="prompt input_prompt">In [6]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">color_negative_red</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
@@ -875,7 +580,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [5]:</div>
+<div class="prompt input_prompt">In [7]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>
@@ -890,308 +595,308 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[5]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[7]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2 {
color: black;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3 {
color: red;
}
- #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4 {
+ #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4 {
color: black;
@@ -1199,7 +904,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</style>
- <table id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb">
+ <table id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb">
<thead>
@@ -1225,32 +930,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -1259,32 +964,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -1293,32 +998,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -1327,32 +1032,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -1361,32 +1066,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -1395,32 +1100,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -1429,32 +1134,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -1463,32 +1168,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -1497,32 +1202,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -1531,32 +1236,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -1599,7 +1304,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [6]:</div>
+<div class="prompt input_prompt">In [8]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">highlight_max</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
@@ -1617,7 +1322,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [7]:</div>
+<div class="prompt input_prompt">In [9]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">)</span>
@@ -1631,38 +1336,38 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[7]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[9]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col4 {
+ #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4 {
background-color: yellow;
}
- #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col2 {
+ #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2 {
background-color: yellow;
}
- #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col1 {
+ #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1 {
background-color: yellow;
}
- #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col3 {
+ #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3 {
background-color: yellow;
}
- #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col0 {
+ #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0 {
background-color: yellow;
@@ -1670,7 +1375,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</style>
- <table id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb">
+ <table id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb">
<thead>
@@ -1696,32 +1401,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -1730,32 +1435,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -1764,32 +1469,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -1798,32 +1503,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -1832,32 +1537,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -1866,32 +1571,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -1900,32 +1605,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -1934,32 +1639,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -1968,32 +1673,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -2002,32 +1707,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -2057,7 +1762,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [8]:</div>
+<div class="prompt input_prompt">In [10]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span>\
@@ -2073,14 +1778,14 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[8]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[10]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0 {
color: black;
@@ -2088,7 +1793,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1 {
color: black;
@@ -2096,7 +1801,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2 {
color: black;
@@ -2104,7 +1809,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3 {
color: red;
@@ -2112,7 +1817,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4 {
color: red;
@@ -2120,7 +1825,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0 {
color: black;
@@ -2128,7 +1833,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1 {
color: red;
@@ -2136,7 +1841,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2 {
color: red;
@@ -2144,7 +1849,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3 {
color: black;
@@ -2152,7 +1857,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4 {
color: black;
@@ -2160,7 +1865,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0 {
color: black;
@@ -2168,7 +1873,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1 {
color: red;
@@ -2176,7 +1881,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2 {
color: black;
@@ -2184,7 +1889,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3 {
color: black;
@@ -2192,7 +1897,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4 {
color: black;
@@ -2200,7 +1905,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0 {
color: black;
@@ -2208,7 +1913,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1 {
color: black;
@@ -2216,7 +1921,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2 {
color: black;
@@ -2224,7 +1929,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3 {
color: red;
@@ -2232,7 +1937,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4 {
color: black;
@@ -2240,7 +1945,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0 {
color: black;
@@ -2248,7 +1953,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1 {
color: black;
@@ -2256,7 +1961,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2 {
color: black;
@@ -2264,7 +1969,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3 {
color: black;
@@ -2272,7 +1977,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4 {
color: black;
@@ -2280,7 +1985,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0 {
color: black;
@@ -2288,7 +1993,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1 {
color: red;
@@ -2296,7 +2001,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2 {
color: black;
@@ -2304,7 +2009,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3 {
color: black;
@@ -2312,7 +2017,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4 {
color: red;
@@ -2320,7 +2025,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0 {
color: black;
@@ -2328,7 +2033,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1 {
color: black;
@@ -2336,7 +2041,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2 {
color: black;
@@ -2344,7 +2049,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3 {
color: red;
@@ -2352,7 +2057,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4 {
color: black;
@@ -2360,7 +2065,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0 {
color: black;
@@ -2368,7 +2073,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1 {
color: black;
@@ -2376,7 +2081,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2 {
color: black;
@@ -2384,7 +2089,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3 {
color: red;
@@ -2392,7 +2097,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4 {
color: black;
@@ -2400,7 +2105,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0 {
color: black;
@@ -2408,7 +2113,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1 {
color: black;
@@ -2416,7 +2121,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2 {
color: red;
@@ -2424,7 +2129,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3 {
color: black;
@@ -2432,7 +2137,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4 {
color: red;
@@ -2440,7 +2145,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0 {
color: black;
@@ -2448,7 +2153,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1 {
color: red;
@@ -2456,7 +2161,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2 {
color: black;
@@ -2464,7 +2169,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3 {
color: red;
@@ -2472,7 +2177,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
}
- #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4 {
+ #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4 {
color: black;
@@ -2482,7 +2187,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</style>
- <table id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb">
+ <table id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb">
<thead>
@@ -2508,32 +2213,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col0" class="data row0 col0">
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1.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3" class="data row0 col3">
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- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -2542,32 +2247,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -2576,32 +2281,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -2610,32 +2315,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -2644,32 +2349,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -2678,32 +2383,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -2712,32 +2417,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -2746,32 +2451,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -2780,32 +2485,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -2814,32 +2519,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -2872,7 +2577,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [9]:</div>
+<div class="prompt input_prompt">In [11]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">highlight_max</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'yellow'</span><span class="p">):</span>
@@ -2896,7 +2601,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [10]:</div>
+<div class="prompt input_prompt">In [12]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'darkorange'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="k">None</span><span class="p">)</span>
@@ -2910,14 +2615,14 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[10]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0 {
+ #T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0 {
background-color: darkorange;
@@ -2925,7 +2630,7 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
</style>
- <table id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb">
+ <table id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb">
<thead>
@@ -2951,32 +2656,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -2985,32 +2690,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -3019,32 +2724,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -3053,32 +2758,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -3087,32 +2792,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -3121,32 +2826,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -3155,32 +2860,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -3189,32 +2894,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -3223,32 +2928,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -3257,32 +2962,32 @@ <h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-S
<tr>
- <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -3345,7 +3050,7 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [11]:</div>
+<div class="prompt input_prompt">In [13]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s">'B'</span><span class="p">,</span> <span class="s">'C'</span><span class="p">,</span> <span class="s">'D'</span><span class="p">])</span>
@@ -3359,26 +3064,26 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[11]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[13]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col2 {
+ #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2 {
background-color: yellow;
}
- #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col1 {
+ #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1 {
background-color: yellow;
}
- #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col3 {
+ #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3 {
background-color: yellow;
@@ -3386,7 +3091,7 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
</style>
- <table id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb">
+ <table id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb">
<thead>
@@ -3412,32 +3117,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -3446,32 +3151,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -3480,32 +3185,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -3514,32 +3219,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -3548,32 +3253,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -3582,32 +3287,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -3616,32 +3321,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col3" class="data row6 col3">
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- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -3650,32 +3355,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -3684,32 +3389,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -3718,32 +3423,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -3773,7 +3478,7 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [12]:</div>
+<div class="prompt input_prompt">In [14]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">,</span>
@@ -3788,56 +3493,56 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[14]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1 {
color: red;
}
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col3 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3 {
color: black;
}
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1 {
color: black;
}
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col3 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3 {
color: red;
}
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col1 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1 {
color: black;
}
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col3 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3 {
color: black;
}
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col1 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1 {
color: red;
}
- #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col3 {
+ #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3 {
color: black;
@@ -3845,7 +3550,7 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
</style>
- <table id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb">
+ <table id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb">
<thead>
@@ -3871,32 +3576,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
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+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -3905,32 +3610,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -3939,32 +3644,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -3973,32 +3678,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -4007,32 +3712,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -4041,32 +3746,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -4075,32 +3780,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -4109,32 +3814,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -4143,32 +3848,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -4177,32 +3882,32 @@ <h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" hre
<tr>
- <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -4254,7 +3959,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [13]:</div>
+<div class="prompt input_prompt">In [15]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_null</span><span class="p">(</span><span class="n">null_color</span><span class="o">=</span><span class="s">'red'</span><span class="p">)</span>
@@ -4268,14 +3973,14 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[13]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[15]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2 {
+ #T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2 {
background-color: red;
@@ -4283,7 +3988,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb">
+ <table id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb">
<thead>
@@ -4309,32 +4014,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
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+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -4343,32 +4048,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -4377,32 +4082,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -4411,32 +4116,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -4445,32 +4150,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -4479,32 +4184,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -4513,32 +4218,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -4547,32 +4252,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -4581,32 +4286,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row8">
8
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+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
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+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
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+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
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+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -4615,32 +4320,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb" class="row_heading level4 row9">
9
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+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -4670,7 +4375,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [14]:</div>
+<div class="prompt input_prompt">In [16]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
@@ -4689,308 +4394,308 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[14]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[16]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
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background-color: #e5ffe5;
}
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background-color: #188d18;
}
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background-color: #e5ffe5;
}
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background-color: #c7eec7;
}
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background-color: #a6dca6;
}
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background-color: #ccf1cc;
}
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background-color: #c0eac0;
}
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background-color: #e5ffe5;
}
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background-color: #62b662;
}
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background-color: #5cb35c;
}
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background-color: #b3e3b3;
}
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background-color: #e5ffe5;
}
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background-color: #56af56;
}
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background-color: #56af56;
}
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background-color: #008000;
}
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background-color: #99d599;
}
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background-color: #329c32;
}
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background-color: #5fb55f;
}
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background-color: #daf9da;
}
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background-color: #3ba13b;
}
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background-color: #80c780;
}
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background-color: #108910;
}
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background-color: #0d870d;
}
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background-color: #90d090;
}
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background-color: #4fac4f;
}
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background-color: #66b866;
}
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background-color: #d2f4d2;
}
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background-color: #389f38;
}
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background-color: #048204;
}
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background-color: #70be70;
}
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background-color: #4daa4d;
}
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background-color: #6cbc6c;
}
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background-color: #008000;
}
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background-color: #a3daa3;
}
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background-color: #0e880e;
}
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background-color: #329c32;
}
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background-color: #5cb35c;
}
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background-color: #0e880e;
}
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background-color: #cff3cf;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col4 {
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background-color: #4fac4f;
}
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background-color: #198e19;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col1 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1 {
background-color: #008000;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col2 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2 {
background-color: #dcfadc;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col3 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3 {
background-color: #008000;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col4 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4 {
background-color: #e5ffe5;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col0 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0 {
background-color: #008000;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col1 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1 {
background-color: #7ec67e;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2 {
background-color: #319b31;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3 {
background-color: #e5ffe5;
}
- #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col4 {
+ #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4 {
background-color: #5cb35c;
@@ -4998,7 +4703,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb">
+ <table id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb">
<thead>
@@ -5024,32 +4729,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
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0
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3" class="data row0 col3">
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4" class="data row0 col4">
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<tr>
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1
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1" class="data row1 col1">
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2" class="data row1 col2">
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0.564417
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -5092,32 +4797,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
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2
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1" class="data row2 col1">
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
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+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -5126,32 +4831,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -5160,32 +4865,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -5194,32 +4899,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -5228,32 +4933,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -5262,32 +4967,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -5296,32 +5001,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -5330,32 +5035,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -5385,7 +5090,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [15]:</div>
+<div class="prompt input_prompt">In [17]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="c"># Uses the full color range</span>
@@ -5400,158 +5105,158 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[15]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[17]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0 {
background-color: #440154;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1 {
background-color: #e5e419;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2 {
background-color: #440154;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3 {
background-color: #46327e;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4 {
background-color: #440154;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0 {
background-color: #3b528b;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1 {
background-color: #433e85;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2 {
background-color: #440154;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3 {
background-color: #bddf26;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4 {
background-color: #25838e;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0 {
background-color: #21918c;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1 {
background-color: #440154;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2 {
background-color: #35b779;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3 {
background-color: #fde725;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4 {
background-color: #fde725;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0 {
background-color: #5ec962;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1 {
background-color: #95d840;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2 {
background-color: #26ad81;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3 {
background-color: #440154;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4 {
background-color: #2cb17e;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0 {
background-color: #fde725;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1 {
background-color: #fde725;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2 {
background-color: #fde725;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3 {
background-color: #1f9e89;
}
- #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4 {
+ #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4 {
background-color: #1f958b;
@@ -5559,7 +5264,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb">
+ <table id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb">
<thead>
@@ -5585,32 +5290,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -5619,32 +5324,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row1">
1
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+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
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+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
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+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -5653,32 +5358,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -5687,32 +5392,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -5721,32 +5426,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -5766,7 +5471,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [16]:</div>
+<div class="prompt input_prompt">In [18]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="c"># Compreess the color range</span>
@@ -5784,14 +5489,14 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[16]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[18]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0 {
background-color: #31688e;
@@ -5799,7 +5504,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1 {
background-color: #efe51c;
@@ -5807,7 +5512,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2 {
background-color: #440154;
@@ -5815,7 +5520,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3 {
background-color: #277f8e;
@@ -5823,7 +5528,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4 {
background-color: #31688e;
@@ -5831,7 +5536,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0 {
background-color: #21918c;
@@ -5839,7 +5544,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1 {
background-color: #25858e;
@@ -5847,7 +5552,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2 {
background-color: #31688e;
@@ -5855,7 +5560,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3 {
background-color: #d5e21a;
@@ -5863,7 +5568,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4 {
background-color: #29af7f;
@@ -5871,7 +5576,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0 {
background-color: #35b779;
@@ -5879,7 +5584,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1 {
background-color: #31688e;
@@ -5887,7 +5592,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2 {
background-color: #6ccd5a;
@@ -5895,7 +5600,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3 {
background-color: #fde725;
@@ -5903,7 +5608,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4 {
background-color: #fde725;
@@ -5911,7 +5616,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0 {
background-color: #90d743;
@@ -5919,7 +5624,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1 {
background-color: #b8de29;
@@ -5927,7 +5632,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2 {
background-color: #5ac864;
@@ -5935,7 +5640,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3 {
background-color: #31688e;
@@ -5943,7 +5648,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4 {
background-color: #63cb5f;
@@ -5951,7 +5656,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0 {
background-color: #fde725;
@@ -5959,7 +5664,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1 {
background-color: #fde725;
@@ -5967,7 +5672,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2 {
background-color: #fde725;
@@ -5975,7 +5680,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3 {
background-color: #46c06f;
@@ -5983,7 +5688,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4 {
+ #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4 {
background-color: #3bbb75;
@@ -5993,7 +5698,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb">
+ <table id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb">
<thead>
@@ -6019,32 +5724,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -6053,32 +5758,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row1">
1
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+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -6087,32 +5792,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1" class="data row2 col1">
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-1.626404
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0.219565
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0.678805
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1.889273
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<tr>
- <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row3">
3
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+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
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0.961538
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0.104011
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0.850229
@@ -6155,32 +5860,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row4">
4
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+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
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+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
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+ <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
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0.165562
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0.515018
@@ -6210,7 +5915,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [17]:</div>
+<div class="prompt input_prompt">In [19]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s">'A'</span><span class="p">,</span> <span class="s">'B'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s">'#d65f5f'</span><span class="p">)</span>
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<div class="output">
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+<div class="output_area"><div class="prompt output_prompt">Out[19]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
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+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0 {
width: 10em;
@@ -6241,7 +5946,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1 {
width: 10em;
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}
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width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1 {
width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col0 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0 {
width: 10em;
@@ -6281,7 +5986,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1 {
width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0 {
width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1 {
width: 10em;
@@ -6311,7 +6016,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0 {
width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1 {
width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0 {
width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1 {
width: 10em;
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}
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+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0 {
width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1 {
width: 10em;
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}
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width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1 {
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width: 10em;
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}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0 {
width: 10em;
@@ -6401,7 +6106,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1 {
width: 10em;
@@ -6411,7 +6116,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col0 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0 {
width: 10em;
@@ -6421,7 +6126,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col1 {
+ #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1 {
width: 10em;
@@ -6433,7 +6138,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb">
+ <table id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb">
<thead>
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<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row0">
0
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
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1.329212
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
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<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row1">
1
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
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<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row2">
2
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
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0.678805
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
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<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row3">
3
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
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0.104011
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col3" class="data row3 col3">
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
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<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row4">
4
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
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0.165562
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+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -6629,32 +6334,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -6663,32 +6368,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -6697,32 +6402,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -6731,32 +6436,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -6765,32 +6470,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -6820,7 +6525,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [18]:</div>
+<div class="prompt input_prompt">In [20]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -6834,38 +6539,38 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[18]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[20]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col4 {
+ #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4 {
background-color: yellow;
}
- #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col2 {
+ #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2 {
background-color: yellow;
}
- #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col1 {
+ #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1 {
background-color: yellow;
}
- #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col3 {
+ #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 {
background-color: yellow;
}
- #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col0 {
+ #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0 {
background-color: yellow;
@@ -6873,7 +6578,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb">
+ <table id="T_35620402_8d9b_11e5_8913_a45e60bd97fb">
<thead>
@@ -6899,32 +6604,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col4" class="data row0 col4">
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@@ -6933,32 +6638,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -6967,32 +6672,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -7001,32 +6706,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -7035,32 +6740,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -7069,32 +6774,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -7103,32 +6808,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
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0.121668
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1.207603
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+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col3" class="data row6 col3">
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- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -7137,32 +6842,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -7171,32 +6876,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -7205,32 +6910,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -7250,7 +6955,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [19]:</div>
+<div class="prompt input_prompt">In [21]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -7264,38 +6969,38 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[19]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[21]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col0 {
+ #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col0 {
background-color: yellow;
}
- #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col2 {
+ #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2 {
background-color: yellow;
}
- #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col1 {
+ #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1 {
background-color: yellow;
}
- #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col4 {
+ #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4 {
background-color: yellow;
}
- #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col3 {
+ #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3 {
background-color: yellow;
@@ -7303,7 +7008,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb">
+ <table id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb">
<thead>
@@ -7329,32 +7034,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row0">
0
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1.0
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1.329212
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col3" class="data row0 col3">
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- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col4" class="data row0 col4">
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<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -7397,32 +7102,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -7431,32 +7136,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -7465,32 +7170,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -7499,32 +7204,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -7533,32 +7238,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col3" class="data row6 col3">
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -7567,32 +7272,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row7">
7
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
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0.354493
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1.037528
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
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0.519818
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<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row8">
8
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
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1.686583
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col2" class="data row8 col2">
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- <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4" class="data row8 col4">
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@@ -7635,32 +7340,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row9">
9
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+ <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
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0.631523
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0.29072
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [20]:</div>
+<div class="prompt input_prompt">In [22]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_properties</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s">'background-color'</span><span class="p">:</span> <span class="s">'black'</span><span class="p">,</span>
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<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[20]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[22]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col0 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0 {
color: lawngreen;
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}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1 {
color: lawngreen;
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}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2 {
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color: lawngreen;
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}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3 {
color: lawngreen;
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}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4 {
color: lawngreen;
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}
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color: lawngreen;
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}
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color: lawngreen;
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}
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color: lawngreen;
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}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3 {
color: lawngreen;
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}
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color: lawngreen;
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}
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color: lawngreen;
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}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1 {
color: lawngreen;
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}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2 {
color: lawngreen;
@@ -7843,7 +7548,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3 {
color: lawngreen;
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}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col4 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4 {
color: lawngreen;
@@ -7863,7 +7568,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0 {
color: lawngreen;
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}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col1 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1 {
color: lawngreen;
@@ -7883,7 +7588,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2 {
color: lawngreen;
@@ -7893,7 +7598,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3 {
color: lawngreen;
@@ -7903,7 +7608,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col4 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4 {
color: lawngreen;
@@ -7913,7 +7618,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col0 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0 {
color: lawngreen;
@@ -7923,7 +7628,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col1 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1 {
color: lawngreen;
@@ -7933,7 +7638,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2 {
color: lawngreen;
@@ -7943,7 +7648,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3 {
color: lawngreen;
@@ -7953,7 +7658,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col4 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4 {
color: lawngreen;
@@ -7963,7 +7668,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0 {
color: lawngreen;
@@ -7973,7 +7678,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1 {
color: lawngreen;
@@ -7983,7 +7688,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2 {
color: lawngreen;
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}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3 {
color: lawngreen;
@@ -8003,7 +7708,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4 {
color: lawngreen;
@@ -8013,7 +7718,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0 {
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}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1 {
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}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2 {
color: lawngreen;
@@ -8043,7 +7748,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3 {
color: lawngreen;
@@ -8053,7 +7758,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4 {
color: lawngreen;
@@ -8063,7 +7768,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0 {
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@@ -8073,7 +7778,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1 {
color: lawngreen;
@@ -8083,7 +7788,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2 {
color: lawngreen;
@@ -8093,7 +7798,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3 {
color: lawngreen;
@@ -8103,7 +7808,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4 {
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@@ -8113,7 +7818,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0 {
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@@ -8123,7 +7828,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col1 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1 {
color: lawngreen;
@@ -8133,7 +7838,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2 {
color: lawngreen;
@@ -8143,7 +7848,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3 {
color: lawngreen;
@@ -8153,7 +7858,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4 {
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@@ -8163,7 +7868,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
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+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0 {
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@@ -8173,7 +7878,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col1 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1 {
color: lawngreen;
@@ -8183,7 +7888,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2 {
color: lawngreen;
@@ -8193,7 +7898,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3 {
color: lawngreen;
@@ -8203,7 +7908,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
}
- #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4 {
+ #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4 {
color: lawngreen;
@@ -8215,7 +7920,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
</style>
- <table id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb">
+ <table id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb">
<thead>
@@ -8241,32 +7946,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -8275,32 +7980,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -8309,32 +8014,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -8343,32 +8048,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -8377,32 +8082,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -8411,32 +8116,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -8445,32 +8150,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -8479,32 +8184,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -8513,32 +8218,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -8547,32 +8252,32 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl
<tr>
- <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -8611,7 +8316,7 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [21]:</div>
+<div class="prompt input_prompt">In [23]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df2</span> <span class="o">=</span> <span class="o">-</span><span class="n">df</span>
@@ -8627,308 +8332,308 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[21]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[23]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col0 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col1 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col2 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col3 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col4 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col0 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col1 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col2 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col3 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col4 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col0 {
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color: black;
}
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color: red;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: red;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: black;
}
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+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col0 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col1 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col1 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col2 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col2 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col3 {
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color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col4 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col4 {
color: red;
}
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color: black;
}
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+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col1 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col2 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col2 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col3 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col3 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col4 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col4 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col0 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col0 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col1 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col1 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col2 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col2 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col3 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col3 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col4 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col4 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col0 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col1 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col1 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col2 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col3 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col3 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col4 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col0 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col1 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col2 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2 {
color: black;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col3 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3 {
color: red;
}
- #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col4 {
+ #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4 {
color: black;
@@ -8936,7 +8641,7 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
</style>
- <table id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb">
+ <table id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb">
<thead>
@@ -8962,32 +8667,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
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+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3" class="data row0 col3">
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- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -8996,32 +8701,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1" class="data row1 col1">
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- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -9030,32 +8735,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1" class="data row2 col1">
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- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -9064,32 +8769,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col3" class="data row3 col3">
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- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -9098,32 +8803,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -9132,32 +8837,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -9166,32 +8871,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col3" class="data row6 col3">
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- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -9200,32 +8905,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row7">
7
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+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
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+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
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+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
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+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -9234,32 +8939,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row8">
8
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+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
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+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -9268,32 +8973,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -9313,7 +9018,7 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [22]:</div>
+<div class="prompt input_prompt">In [24]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">style2</span> <span class="o">=</span> <span class="n">df2</span><span class="o">.</span><span class="n">style</span>
@@ -9329,308 +9034,308 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[22]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[24]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
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color: red;
}
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color: red;
}
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color: black;
}
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color: black;
}
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color: black;
}
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color: red;
}
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color: black;
}
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color: black;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: black;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: red;
}
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+ #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col2 {
color: red;
}
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color: black;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: black;
}
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color: red;
}
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color: red;
}
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color: black;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: black;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: red;
}
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color: black;
}
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color: red;
}
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color: red;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col1 {
+ #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col1 {
color: red;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col2 {
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color: black;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col3 {
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color: red;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col4 {
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color: black;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col0 {
+ #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0 {
color: red;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col1 {
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color: black;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col2 {
+ #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2 {
color: red;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col3 {
+ #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3 {
color: black;
}
- #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col4 {
+ #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4 {
color: red;
@@ -9638,7 +9343,7 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
</style>
- <table id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb">
+ <table id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb">
<thead>
@@ -9664,32 +9369,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row0">
0
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col0" class="data row0 col0">
-1.0
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col1" class="data row0 col1">
-1.329212
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col3" class="data row0 col3">
0.31628
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col4" class="data row0 col4">
0.99081
@@ -9698,32 +9403,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row1">
1
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col0" class="data row1 col0">
-2.0
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col1" class="data row1 col1">
1.070816
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col2" class="data row1 col2">
1.438713
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col3" class="data row1 col3">
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col4" class="data row1 col4">
-0.295722
@@ -9732,32 +9437,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col1" class="data row2 col1">
1.626404
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+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col2" class="data row2 col2">
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- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col3" class="data row2 col3">
-0.678805
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col4" class="data row2 col4">
-1.889273
@@ -9766,32 +9471,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col0" class="data row3 col0">
-4.0
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col1" class="data row3 col1">
-0.961538
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col2" class="data row3 col2">
-0.104011
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col3" class="data row3 col3">
0.481165
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col4" class="data row3 col4">
-0.850229
@@ -9800,32 +9505,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col0" class="data row4 col0">
-5.0
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col1" class="data row4 col1">
-1.453425
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col2" class="data row4 col2">
-1.057737
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col3" class="data row4 col3">
-0.165562
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col4" class="data row4 col4">
-0.515018
@@ -9834,32 +9539,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col0" class="data row5 col0">
-6.0
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col1" class="data row5 col1">
1.336936
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col2" class="data row5 col2">
-0.562861
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col3" class="data row5 col3">
-1.392855
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col4" class="data row5 col4">
0.063328
@@ -9868,32 +9573,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col0" class="data row6 col0">
-7.0
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col1" class="data row6 col1">
-0.121668
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col2" class="data row6 col2">
-1.207603
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col3" class="data row6 col3">
0.00204
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col4" class="data row6 col4">
-1.627796
@@ -9902,32 +9607,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col0" class="data row7 col0">
-8.0
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col1" class="data row7 col1">
-0.354493
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col2" class="data row7 col2">
-1.037528
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col3" class="data row7 col3">
0.385684
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col4" class="data row7 col4">
-0.519818
@@ -9936,32 +9641,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col0" class="data row8 col0">
-9.0
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col1" class="data row8 col1">
-1.686583
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2" class="data row8 col2">
1.325963
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3" class="data row8 col3">
-1.428984
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4" class="data row8 col4">
2.089354
@@ -9970,32 +9675,32 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
<tr>
- <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_35724a12_8d9b_11e5_a933_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0" class="data row9 col0">
-10.0
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1" class="data row9 col1">
0.12982
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2" class="data row9 col2">
-0.631523
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3" class="data row9 col3">
0.586538
- <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4" class="data row9 col4">
-0.29072
@@ -10012,6 +9717,16 @@ <h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styl
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>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 <code>use</code>d upon.</p>
+
+</div>
+</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
@@ -10056,7 +9771,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [23]:</div>
+<div class="prompt input_prompt">In [25]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="k">with</span> <span class="n">pd</span><span class="o">.</span><span class="n">option_context</span><span class="p">(</span><span class="s">'display.precision'</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
@@ -10074,14 +9789,14 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[23]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[25]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0 {
color: black;
@@ -10089,7 +9804,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1 {
color: black;
@@ -10097,7 +9812,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2 {
color: black;
@@ -10105,7 +9820,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3 {
color: red;
@@ -10113,7 +9828,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4 {
color: red;
@@ -10121,7 +9836,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0 {
color: black;
@@ -10129,7 +9844,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1 {
color: red;
@@ -10137,7 +9852,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2 {
color: red;
@@ -10145,7 +9860,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3 {
color: black;
@@ -10153,7 +9868,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4 {
color: black;
@@ -10161,7 +9876,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0 {
color: black;
@@ -10169,7 +9884,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1 {
color: red;
@@ -10177,7 +9892,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2 {
color: black;
@@ -10185,7 +9900,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3 {
color: black;
@@ -10193,7 +9908,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4 {
color: black;
@@ -10201,7 +9916,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0 {
color: black;
@@ -10209,7 +9924,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1 {
color: black;
@@ -10217,7 +9932,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2 {
color: black;
@@ -10225,7 +9940,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3 {
color: red;
@@ -10233,7 +9948,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4 {
color: black;
@@ -10241,7 +9956,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0 {
color: black;
@@ -10249,7 +9964,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1 {
color: black;
@@ -10257,7 +9972,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2 {
color: black;
@@ -10265,7 +9980,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3 {
color: black;
@@ -10273,7 +9988,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4 {
color: black;
@@ -10281,7 +9996,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0 {
color: black;
@@ -10289,7 +10004,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1 {
color: red;
@@ -10297,7 +10012,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2 {
color: black;
@@ -10305,7 +10020,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3 {
color: black;
@@ -10313,7 +10028,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4 {
color: red;
@@ -10321,7 +10036,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0 {
color: black;
@@ -10329,7 +10044,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1 {
color: black;
@@ -10337,7 +10052,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2 {
color: black;
@@ -10345,7 +10060,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3 {
color: red;
@@ -10353,7 +10068,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4 {
color: black;
@@ -10361,7 +10076,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0 {
color: black;
@@ -10369,7 +10084,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1 {
color: black;
@@ -10377,7 +10092,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2 {
color: black;
@@ -10385,7 +10100,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3 {
color: red;
@@ -10393,7 +10108,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4 {
color: black;
@@ -10401,7 +10116,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0 {
color: black;
@@ -10409,7 +10124,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1 {
color: black;
@@ -10417,7 +10132,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2 {
color: red;
@@ -10425,7 +10140,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3 {
color: black;
@@ -10433,7 +10148,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4 {
color: red;
@@ -10441,7 +10156,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0 {
color: black;
@@ -10449,7 +10164,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1 {
color: red;
@@ -10457,7 +10172,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2 {
color: black;
@@ -10465,7 +10180,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3 {
color: red;
@@ -10473,7 +10188,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4 {
+ #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4 {
color: black;
@@ -10483,7 +10198,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
</style>
- <table id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb">
+ <table id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb">
<thead>
@@ -10509,32 +10224,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1" class="data row0 col1">
1.33
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.32
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99
@@ -10543,32 +10258,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.07
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.44
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3" class="data row1 col3">
0.56
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4" class="data row1 col4">
0.3
@@ -10577,32 +10292,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.63
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2" class="data row2 col2">
0.22
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3" class="data row2 col3">
0.68
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4" class="data row2 col4">
1.89
@@ -10611,32 +10326,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1" class="data row3 col1">
0.96
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2" class="data row3 col2">
0.1
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.48
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4" class="data row3 col4">
0.85
@@ -10645,32 +10360,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1" class="data row4 col1">
1.45
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2" class="data row4 col2">
1.06
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3" class="data row4 col3">
0.17
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4" class="data row4 col4">
0.52
@@ -10679,32 +10394,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.34
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2" class="data row5 col2">
0.56
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3" class="data row5 col3">
1.39
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.06
@@ -10713,32 +10428,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1" class="data row6 col1">
0.12
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2" class="data row6 col2">
1.21
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4" class="data row6 col4">
1.63
@@ -10747,32 +10462,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1" class="data row7 col1">
0.35
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2" class="data row7 col2">
1.04
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.39
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4" class="data row7 col4">
0.52
@@ -10781,32 +10496,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1" class="data row8 col1">
1.69
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.33
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3" class="data row8 col3">
1.43
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.09
@@ -10815,32 +10530,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.13
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2" class="data row9 col2">
0.63
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.59
- <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29
@@ -10870,7 +10585,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [24]:</div>
+<div class="prompt input_prompt">In [26]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span>\
@@ -10887,14 +10602,14 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<div class="output">
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<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
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}
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+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1 {
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}
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}
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}
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+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4 {
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@@ -10934,7 +10649,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0 {
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@@ -10942,7 +10657,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1 {
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@@ -10950,7 +10665,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2 {
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@@ -10958,7 +10673,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3 {
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@@ -10966,7 +10681,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4 {
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@@ -10974,7 +10689,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0 {
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@@ -10982,7 +10697,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1 {
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@@ -10990,7 +10705,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2 {
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@@ -10998,7 +10713,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3 {
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@@ -11006,7 +10721,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4 {
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@@ -11014,7 +10729,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0 {
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@@ -11022,7 +10737,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1 {
color: black;
@@ -11030,7 +10745,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2 {
color: black;
@@ -11038,7 +10753,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3 {
color: red;
@@ -11046,7 +10761,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4 {
color: black;
@@ -11054,7 +10769,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0 {
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@@ -11062,7 +10777,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1 {
color: black;
@@ -11070,7 +10785,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2 {
color: black;
@@ -11078,7 +10793,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col3 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3 {
color: black;
@@ -11086,7 +10801,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4 {
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@@ -11094,7 +10809,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0 {
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@@ -11102,7 +10817,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1 {
color: red;
@@ -11110,7 +10825,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2 {
color: black;
@@ -11118,7 +10833,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3 {
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@@ -11126,7 +10841,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4 {
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@@ -11134,7 +10849,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0 {
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@@ -11142,7 +10857,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col1 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1 {
color: black;
@@ -11150,7 +10865,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2 {
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@@ -11158,7 +10873,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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@@ -11166,7 +10881,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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@@ -11174,7 +10889,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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@@ -11182,7 +10897,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 {
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@@ -11190,7 +10905,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2 {
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@@ -11198,7 +10913,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3 {
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@@ -11206,7 +10921,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4 {
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@@ -11214,7 +10929,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0 {
color: black;
@@ -11222,7 +10937,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1 {
color: black;
@@ -11230,7 +10945,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2 {
color: red;
@@ -11238,7 +10953,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3 {
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@@ -11246,7 +10961,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4 {
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@@ -11254,7 +10969,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0 {
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@@ -11262,7 +10977,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1 {
color: red;
@@ -11270,7 +10985,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2 {
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@@ -11278,7 +10993,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
- #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3 {
+ #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3 {
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@@ -11286,7 +11001,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
}
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@@ -11296,7 +11011,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
</style>
- <table id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb">
+ <table id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb">
<thead>
@@ -11322,32 +11037,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1" class="data row0 col1">
1.33
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.32
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99
@@ -11356,32 +11071,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.07
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.44
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3" class="data row1 col3">
0.56
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4" class="data row1 col4">
0.3
@@ -11390,32 +11105,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.63
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2" class="data row2 col2">
0.22
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3" class="data row2 col3">
0.68
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4" class="data row2 col4">
1.89
@@ -11424,32 +11139,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1" class="data row3 col1">
0.96
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2" class="data row3 col2">
0.1
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.48
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4" class="data row3 col4">
0.85
@@ -11458,32 +11173,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1" class="data row4 col1">
1.45
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2" class="data row4 col2">
1.06
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3" class="data row4 col3">
0.17
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4" class="data row4 col4">
0.52
@@ -11492,32 +11207,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.34
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2" class="data row5 col2">
0.56
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3" class="data row5 col3">
1.39
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.06
@@ -11526,32 +11241,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1" class="data row6 col1">
0.12
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2" class="data row6 col2">
1.21
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4" class="data row6 col4">
1.63
@@ -11560,32 +11275,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1" class="data row7 col1">
0.35
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2" class="data row7 col2">
1.04
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.39
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4" class="data row7 col4">
0.52
@@ -11594,32 +11309,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1" class="data row8 col1">
1.69
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.33
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3" class="data row8 col3">
1.43
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.09
@@ -11628,32 +11343,32 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a><
<tr>
- <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_358c8026_8d9b_11e5_a659_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.13
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2" class="data row9 col2">
0.63
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.59
- <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29
@@ -11702,7 +11417,7 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [25]:</div>
+<div class="prompt input_prompt">In [27]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">'Colormaps, with a caption.'</span><span class="p">)</span>\
@@ -11717,308 +11432,308 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[25]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[27]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0 {
background-color: #e5ffe5;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1 {
background-color: #188d18;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2 {
background-color: #e5ffe5;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3 {
background-color: #c7eec7;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4 {
background-color: #a6dca6;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0 {
background-color: #ccf1cc;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1 {
background-color: #c0eac0;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2 {
background-color: #e5ffe5;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3 {
background-color: #62b662;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4 {
background-color: #5cb35c;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0 {
background-color: #b3e3b3;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1 {
background-color: #e5ffe5;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2 {
background-color: #56af56;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3 {
background-color: #56af56;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4 {
background-color: #008000;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0 {
background-color: #99d599;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1 {
background-color: #329c32;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2 {
background-color: #5fb55f;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3 {
background-color: #daf9da;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4 {
background-color: #3ba13b;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0 {
background-color: #80c780;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1 {
background-color: #108910;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2 {
background-color: #0d870d;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3 {
background-color: #90d090;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4 {
background-color: #4fac4f;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0 {
background-color: #66b866;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1 {
background-color: #d2f4d2;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2 {
background-color: #389f38;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3 {
background-color: #048204;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4 {
background-color: #70be70;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0 {
background-color: #4daa4d;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1 {
background-color: #6cbc6c;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2 {
background-color: #008000;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3 {
background-color: #a3daa3;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4 {
background-color: #0e880e;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0 {
background-color: #329c32;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1 {
background-color: #5cb35c;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2 {
background-color: #0e880e;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3 {
background-color: #cff3cf;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4 {
background-color: #4fac4f;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0 {
background-color: #198e19;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1 {
background-color: #008000;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2 {
background-color: #dcfadc;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3 {
background-color: #008000;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4 {
background-color: #e5ffe5;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0 {
background-color: #008000;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1 {
background-color: #7ec67e;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2 {
background-color: #319b31;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3 {
background-color: #e5ffe5;
}
- #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4 {
+ #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4 {
background-color: #5cb35c;
@@ -12026,7 +11741,7 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
</style>
- <table id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb">
+ <table id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb">
<caption>Colormaps, with a caption.</caption>
@@ -12054,32 +11769,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -12088,32 +11803,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -12122,32 +11837,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -12156,32 +11871,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -12190,32 +11905,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -12224,32 +11939,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -12258,32 +11973,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -12292,32 +12007,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3" class="data row7 col3">
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-0.385684
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -12326,32 +12041,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -12360,32 +12075,32 @@ <h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3
<tr>
- <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_3595c942_8d9b_11e5_8058_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3" class="data row9 col3">
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-0.586538
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+ <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -12426,7 +12141,7 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [26]:</div>
+<div class="prompt input_prompt">In [28]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">HTML</span>
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<div class="output">
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+<div class="output_area"><div class="prompt output_prompt">Out[28]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb tr:hover {
+ #T_359bea52_8d9b_11e5_be48_a45e60bd97fb tr:hover {
background-color: #ffff99;
}
- #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb th {
+ #T_359bea52_8d9b_11e5_be48_a45e60bd97fb th {
font-size: 150%;
@@ -12474,7 +12189,7 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
}
- #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb caption {
+ #T_359bea52_8d9b_11e5_be48_a45e60bd97fb caption {
caption-side: bottom;
@@ -12483,7 +12198,7 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
</style>
- <table id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb">
+ <table id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb">
<caption>Hover to highlight.</caption>
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<tr>
- <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col0" class="data row0 col0">
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1.0
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1.329212
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nan
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col4" class="data row0 col4">
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-0.99081
@@ -12545,32 +12260,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
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1
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2.0
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col1" class="data row1 col1">
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-1.070816
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-1.438713
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0.564417
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0.295722
@@ -12579,32 +12294,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
- <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col0" class="data row2 col0">
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3.0
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+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
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0.678805
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1.889273
@@ -12613,32 +12328,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
- <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col2" class="data row3 col2">
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0.104011
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col3" class="data row3 col3">
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-0.481165
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+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -12647,32 +12362,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
- <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
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0.165562
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+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -12681,32 +12396,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
- <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col3" class="data row5 col3">
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1.392855
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -12715,32 +12430,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
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+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
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0.121668
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1.207603
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col3" class="data row6 col3">
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-0.00204
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1.627796
@@ -12749,32 +12464,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
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+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col0" class="data row7 col0">
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8.0
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0.354493
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+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
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-0.385684
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0.519818
@@ -12783,32 +12498,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
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+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row8">
8
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9.0
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1.686583
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-1.325963
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1.428984
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-2.089354
@@ -12817,32 +12532,32 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&#
<tr>
- <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -12921,7 +12636,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [27]:</div>
+<div class="prompt input_prompt">In [29]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">IPython.html</span> <span class="k">import</span> <span class="n">widgets</span>
@@ -12948,301 +12663,301 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<style type="text/css" >
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1 {
background-color: #779894;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3 {
background-color: #809f9b;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4 {
background-color: #aec2bf;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col0 {
background-color: #799995;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col1 {
background-color: #8ba7a3;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col2 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3 {
background-color: #dfe8e7;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4 {
background-color: #d7e2e0;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0 {
background-color: #9cb5b1;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2 {
background-color: #cedbd9;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3 {
background-color: #cedbd9;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0 {
background-color: #c1d1cf;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1 {
background-color: #9cb5b1;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2 {
background-color: #dce5e4;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3 {
background-color: #668b86;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4 {
background-color: #a9bebb;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0 {
background-color: #e5eceb;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1 {
background-color: #6c908b;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2 {
background-color: #678c87;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3 {
background-color: #cedbd9;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4 {
background-color: #c5d4d2;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0 {
background-color: #e5eceb;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1 {
background-color: #71948f;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2 {
background-color: #a4bbb8;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3 {
background-color: #5a827d;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4 {
background-color: #f2f2f2;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0 {
background-color: #c1d1cf;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1 {
background-color: #edf3f2;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3 {
background-color: #b3c6c4;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4 {
background-color: #698e89;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0 {
background-color: #9cb5b1;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1 {
background-color: #d7e2e0;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2 {
background-color: #698e89;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3 {
background-color: #759792;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4 {
background-color: #c5d4d2;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0 {
background-color: #799995;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2 {
background-color: #628882;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1 {
background-color: #e7eeed;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2 {
background-color: #9bb4b0;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3 {
background-color: #557e79;
}
- #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4 {
+ #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4 {
background-color: #d7e2e0;
@@ -13250,7 +12965,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
</style>
- <table id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb">
+ <table id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb">
<thead>
@@ -13276,32 +12991,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row0">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row0">
0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0" class="data row0 col0">
1.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1" class="data row0 col1">
1.329212
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2" class="data row0 col2">
nan
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.31628
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.99081
@@ -13310,32 +13025,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row1">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row1">
1
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col0" class="data row1 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col0" class="data row1 col0">
2.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col1" class="data row1 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col1" class="data row1 col1">
-1.070816
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col2" class="data row1 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col2" class="data row1 col2">
-1.438713
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col3" class="data row1 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3" class="data row1 col3">
0.564417
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4" class="data row1 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4" class="data row1 col4">
0.295722
@@ -13344,32 +13059,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row2">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row2">
2
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col0" class="data row2 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0" class="data row2 col0">
3.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1" class="data row2 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1" class="data row2 col1">
-1.626404
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col2" class="data row2 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2" class="data row2 col2">
0.219565
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col3" class="data row2 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3" class="data row2 col3">
0.678805
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col4" class="data row2 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4" class="data row2 col4">
1.889273
@@ -13378,32 +13093,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row3">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row3">
3
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col0" class="data row3 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0" class="data row3 col0">
4.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col1" class="data row3 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1" class="data row3 col1">
0.961538
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col2" class="data row3 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2" class="data row3 col2">
0.104011
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col3" class="data row3 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3" class="data row3 col3">
-0.481165
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col4" class="data row3 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4" class="data row3 col4">
0.850229
@@ -13412,32 +13127,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row4">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row4">
4
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col0" class="data row4 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0" class="data row4 col0">
5.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1" class="data row4 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1" class="data row4 col1">
1.453425
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col2" class="data row4 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2" class="data row4 col2">
1.057737
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col3" class="data row4 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3" class="data row4 col3">
0.165562
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4" class="data row4 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4" class="data row4 col4">
0.515018
@@ -13446,32 +13161,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row5">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row5">
5
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0" class="data row5 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0" class="data row5 col0">
6.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1" class="data row5 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1" class="data row5 col1">
-1.336936
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col2" class="data row5 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2" class="data row5 col2">
0.562861
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col3" class="data row5 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3" class="data row5 col3">
1.392855
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4" class="data row5 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4" class="data row5 col4">
-0.063328
@@ -13480,32 +13195,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row6">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row6">
6
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0" class="data row6 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0" class="data row6 col0">
7.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1" class="data row6 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1" class="data row6 col1">
0.121668
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col2" class="data row6 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2" class="data row6 col2">
1.207603
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col3" class="data row6 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3" class="data row6 col3">
-0.00204
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4" class="data row6 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4" class="data row6 col4">
1.627796
@@ -13514,32 +13229,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row7">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row7">
7
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col0" class="data row7 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0" class="data row7 col0">
8.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1" class="data row7 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1" class="data row7 col1">
0.354493
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col2" class="data row7 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2" class="data row7 col2">
1.037528
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3" class="data row7 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3" class="data row7 col3">
-0.385684
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4" class="data row7 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4" class="data row7 col4">
0.519818
@@ -13548,32 +13263,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row8">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row8">
8
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0" class="data row8 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0" class="data row8 col0">
9.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1" class="data row8 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1" class="data row8 col1">
1.686583
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2" class="data row8 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2" class="data row8 col2">
-1.325963
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3" class="data row8 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3" class="data row8 col3">
1.428984
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4" class="data row8 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4" class="data row8 col4">
-2.089354
@@ -13582,32 +13297,32 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row9">
+ <th id="T_35adc664_8d9b_11e5_afed_a45e60bd97fb" class="row_heading level4 row9">
9
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0" class="data row9 col0">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0" class="data row9 col0">
10.0
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1" class="data row9 col1">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1" class="data row9 col1">
-0.12982
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2" class="data row9 col2">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2" class="data row9 col2">
0.631523
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3" class="data row9 col3">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3" class="data row9 col3">
-0.586538
- <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4" class="data row9 col4">
+ <td id="T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4" class="data row9 col4">
0.29072
@@ -13627,15 +13342,17 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [28]:</div>
+<div class="prompt input_prompt">In [30]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">magnify</span><span class="p">():</span>
<span class="k">return</span> <span class="p">[</span><span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th"</span><span class="p">,</span>
<span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"4pt"</span><span class="p">)]),</span>
+ <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"td"</span><span class="p">,</span>
+ <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">'padding'</span><span class="p">,</span> <span class="s">"0em 0em"</span><span class="p">)]),</span>
<span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th:hover"</span><span class="p">,</span>
<span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"12pt"</span><span class="p">)]),</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"tr:hover td:hover"</span><span class="p">,</span>
+ <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"tr:hover td:hover"</span><span class="p">,</span>
<span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">'max-width'</span><span class="p">,</span> <span class="s">'200px'</span><span class="p">),</span>
<span class="p">(</span><span class="s">'font-size'</span><span class="p">,</span> <span class="s">'12pt'</span><span class="p">)])</span>
<span class="p">]</span>
@@ -13648,7 +13365,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [29]:</div>
+<div class="prompt input_prompt">In [31]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">25</span><span class="p">)</span>
@@ -13670,25 +13387,31 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<div class="output">
-<div class="output_area"><div class="prompt output_prompt">Out[29]:</div>
+<div class="output_area"><div class="prompt output_prompt">Out[31]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<style type="text/css" >
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th {
font-size: 4pt;
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th:hover {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb td {
+
+ padding: 0em 0em;
+
+ }
+
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th:hover {
font-size: 12pt;
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb tr:hover td:hover {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb tr:hover td:hover {
max-width: 200px;
@@ -13697,7 +13420,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0 {
background-color: #ecf2f8;
@@ -13707,7 +13430,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1 {
background-color: #b8cce5;
@@ -13717,7 +13440,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2 {
background-color: #efb1be;
@@ -13727,7 +13450,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3 {
background-color: #f3c2cc;
@@ -13737,7 +13460,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4 {
background-color: #eeaab7;
@@ -13747,7 +13470,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5 {
background-color: #f8dce2;
@@ -13757,7 +13480,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6 {
background-color: #f2c1cb;
@@ -13767,7 +13490,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7 {
background-color: #83a6d2;
@@ -13777,7 +13500,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8 {
background-color: #de5f79;
@@ -13787,7 +13510,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9 {
background-color: #c3d4e9;
@@ -13797,7 +13520,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10 {
background-color: #eeacba;
@@ -13807,7 +13530,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11 {
background-color: #f7dae0;
@@ -13817,7 +13540,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12 {
background-color: #6f98ca;
@@ -13827,7 +13550,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13 {
background-color: #e890a1;
@@ -13837,7 +13560,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14 {
background-color: #f2f2f2;
@@ -13847,7 +13570,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15 {
background-color: #ea96a7;
@@ -13857,7 +13580,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16 {
background-color: #adc4e1;
@@ -13867,7 +13590,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17 {
background-color: #eca3b1;
@@ -13877,7 +13600,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18 {
background-color: #b7cbe5;
@@ -13887,7 +13610,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19 {
background-color: #f5ced6;
@@ -13897,7 +13620,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20 {
background-color: #6590c7;
@@ -13907,7 +13630,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21 {
background-color: #d73c5b;
@@ -13917,7 +13640,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22 {
background-color: #4479bb;
@@ -13927,7 +13650,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23 {
background-color: #cfddee;
@@ -13937,7 +13660,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24 {
background-color: #e58094;
@@ -13947,7 +13670,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0 {
background-color: #e4798e;
@@ -13957,7 +13680,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1 {
background-color: #aec5e1;
@@ -13967,7 +13690,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2 {
background-color: #eb9ead;
@@ -13977,7 +13700,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3 {
background-color: #ec9faf;
@@ -13987,7 +13710,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4 {
background-color: #cbdaec;
@@ -13997,7 +13720,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5 {
background-color: #f9e0e5;
@@ -14007,7 +13730,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6 {
background-color: #db4f6b;
@@ -14017,7 +13740,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7 {
background-color: #4479bb;
@@ -14027,7 +13750,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8 {
background-color: #e57f93;
@@ -14037,7 +13760,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9 {
background-color: #bdd0e7;
@@ -14047,7 +13770,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10 {
background-color: #f3c2cc;
@@ -14057,7 +13780,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11 {
background-color: #f8dfe4;
@@ -14067,7 +13790,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12 {
background-color: #b0c6e2;
@@ -14077,7 +13800,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13 {
background-color: #ec9faf;
@@ -14087,7 +13810,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14 {
background-color: #e27389;
@@ -14097,7 +13820,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15 {
background-color: #eb9dac;
@@ -14107,7 +13830,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16 {
background-color: #f1b8c3;
@@ -14117,7 +13840,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17 {
background-color: #efb1be;
@@ -14127,7 +13850,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18 {
background-color: #f2f2f2;
@@ -14137,7 +13860,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19 {
background-color: #f8dce2;
@@ -14147,7 +13870,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20 {
background-color: #a0bbdc;
@@ -14157,7 +13880,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21 {
background-color: #d73c5b;
@@ -14167,7 +13890,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22 {
background-color: #86a8d3;
@@ -14177,7 +13900,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23 {
background-color: #d9e4f1;
@@ -14187,7 +13910,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24 {
background-color: #ecf2f8;
@@ -14197,7 +13920,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0 {
background-color: #f2f2f2;
@@ -14207,7 +13930,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1 {
background-color: #5887c2;
@@ -14217,7 +13940,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2 {
background-color: #f2bfca;
@@ -14227,7 +13950,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3 {
background-color: #c7d7eb;
@@ -14237,7 +13960,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4 {
background-color: #82a5d1;
@@ -14247,7 +13970,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5 {
background-color: #ebf1f8;
@@ -14257,7 +13980,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6 {
background-color: #e78a9d;
@@ -14267,7 +13990,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7 {
background-color: #4479bb;
@@ -14277,7 +14000,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8 {
background-color: #f1bbc6;
@@ -14287,7 +14010,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9 {
background-color: #779dcd;
@@ -14297,7 +14020,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10 {
background-color: #d4e0ef;
@@ -14307,7 +14030,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11 {
background-color: #e6edf6;
@@ -14317,7 +14040,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12 {
background-color: #e0e9f4;
@@ -14327,7 +14050,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13 {
background-color: #fbeaed;
@@ -14337,7 +14060,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14 {
background-color: #e78a9d;
@@ -14347,7 +14070,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15 {
background-color: #fae7eb;
@@ -14357,7 +14080,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16 {
background-color: #e6edf6;
@@ -14367,7 +14090,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17 {
background-color: #e6edf6;
@@ -14377,7 +14100,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18 {
background-color: #b9cde6;
@@ -14387,7 +14110,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19 {
background-color: #f2f2f2;
@@ -14397,7 +14120,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col20 {
background-color: #a0bbdc;
@@ -14407,7 +14130,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col21 {
background-color: #d73c5b;
@@ -14417,7 +14140,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col22 {
background-color: #618ec5;
@@ -14427,7 +14150,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col23 {
background-color: #8faed6;
@@ -14437,7 +14160,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col24 {
background-color: #c3d4e9;
@@ -14447,7 +14170,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col0 {
background-color: #f6d5db;
@@ -14457,7 +14180,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col1 {
background-color: #5384c0;
@@ -14467,7 +14190,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col2 {
background-color: #f1bbc6;
@@ -14477,7 +14200,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col3 {
background-color: #c7d7eb;
@@ -14487,7 +14210,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col4 {
background-color: #4479bb;
@@ -14497,7 +14220,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col5 {
background-color: #e7eef6;
@@ -14507,7 +14230,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col6 {
background-color: #e58195;
@@ -14517,7 +14240,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7 {
background-color: #4479bb;
@@ -14527,7 +14250,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8 {
background-color: #f2c1cb;
@@ -14537,7 +14260,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9 {
background-color: #b1c7e2;
@@ -14547,7 +14270,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10 {
background-color: #d4e0ef;
@@ -14557,7 +14280,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11 {
background-color: #c1d3e8;
@@ -14567,7 +14290,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12 {
background-color: #f2f2f2;
@@ -14577,7 +14300,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13 {
background-color: #cedced;
@@ -14587,7 +14310,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14 {
background-color: #e7899c;
@@ -14597,7 +14320,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15 {
background-color: #eeacba;
@@ -14607,7 +14330,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16 {
background-color: #e2eaf4;
@@ -14617,7 +14340,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17 {
background-color: #f7d6dd;
@@ -14627,7 +14350,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18 {
background-color: #a8c0df;
@@ -14637,7 +14360,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19 {
background-color: #f5ccd4;
@@ -14647,7 +14370,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20 {
background-color: #b7cbe5;
@@ -14657,7 +14380,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21 {
background-color: #d73c5b;
@@ -14667,7 +14390,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22 {
background-color: #739acc;
@@ -14677,7 +14400,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23 {
background-color: #8dadd5;
@@ -14687,7 +14410,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24 {
background-color: #cddbed;
@@ -14697,7 +14420,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0 {
background-color: #ea9aaa;
@@ -14707,7 +14430,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1 {
background-color: #5887c2;
@@ -14717,7 +14440,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2 {
background-color: #f4c9d2;
@@ -14727,7 +14450,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3 {
background-color: #f5ced6;
@@ -14737,7 +14460,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4 {
background-color: #4479bb;
@@ -14747,7 +14470,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5 {
background-color: #fae4e9;
@@ -14757,7 +14480,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6 {
background-color: #e78c9e;
@@ -14767,7 +14490,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7 {
background-color: #5182bf;
@@ -14777,7 +14500,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8 {
background-color: #f2f2f2;
@@ -14787,7 +14510,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9 {
background-color: #c1d3e8;
@@ -14797,7 +14520,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10 {
background-color: #e8eff7;
@@ -14807,7 +14530,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11 {
background-color: #c9d8eb;
@@ -14817,7 +14540,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12 {
background-color: #eaf0f7;
@@ -14827,7 +14550,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13 {
background-color: #f9e2e6;
@@ -14837,7 +14560,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14 {
background-color: #e47c91;
@@ -14847,7 +14570,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15 {
background-color: #eda8b6;
@@ -14857,7 +14580,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16 {
background-color: #fae6ea;
@@ -14867,7 +14590,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17 {
background-color: #f5ccd4;
@@ -14877,7 +14600,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18 {
background-color: #b3c9e3;
@@ -14887,7 +14610,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19 {
background-color: #f4cad3;
@@ -14897,7 +14620,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20 {
background-color: #9fbadc;
@@ -14907,7 +14630,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21 {
background-color: #d73c5b;
@@ -14917,7 +14640,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22 {
background-color: #7da2cf;
@@ -14927,7 +14650,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23 {
background-color: #95b3d8;
@@ -14937,7 +14660,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24 {
background-color: #a2bcdd;
@@ -14947,7 +14670,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0 {
background-color: #fbeaed;
@@ -14957,7 +14680,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1 {
background-color: #6490c6;
@@ -14967,7 +14690,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2 {
background-color: #f6d1d8;
@@ -14977,7 +14700,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3 {
background-color: #f3c6cf;
@@ -14987,7 +14710,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4 {
background-color: #4479bb;
@@ -14997,7 +14720,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5 {
background-color: #fae6ea;
@@ -15007,7 +14730,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6 {
background-color: #e68598;
@@ -15017,7 +14740,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7 {
background-color: #6d96ca;
@@ -15027,7 +14750,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8 {
background-color: #f9e3e7;
@@ -15037,7 +14760,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9 {
background-color: #fae7eb;
@@ -15047,7 +14770,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10 {
background-color: #bdd0e7;
@@ -15057,7 +14780,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11 {
background-color: #e0e9f4;
@@ -15067,7 +14790,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12 {
background-color: #f5ced6;
@@ -15077,7 +14800,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13 {
background-color: #e6edf6;
@@ -15087,7 +14810,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14 {
background-color: #e47a90;
@@ -15097,7 +14820,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15 {
background-color: #fbeaed;
@@ -15107,7 +14830,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16 {
background-color: #f3c5ce;
@@ -15117,7 +14840,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17 {
background-color: #f7dae0;
@@ -15127,7 +14850,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18 {
background-color: #cbdaec;
@@ -15137,7 +14860,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19 {
background-color: #f6d2d9;
@@ -15147,7 +14870,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20 {
background-color: #b5cae4;
@@ -15157,7 +14880,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21 {
background-color: #d73c5b;
@@ -15167,7 +14890,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22 {
background-color: #8faed6;
@@ -15177,7 +14900,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23 {
background-color: #a3bddd;
@@ -15187,7 +14910,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24 {
background-color: #7199cb;
@@ -15197,7 +14920,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0 {
background-color: #e6edf6;
@@ -15207,7 +14930,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1 {
background-color: #4e80be;
@@ -15217,7 +14940,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2 {
background-color: #f8dee3;
@@ -15227,7 +14950,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3 {
background-color: #f2f2f2;
@@ -15237,7 +14960,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4 {
background-color: #6a94c9;
@@ -15247,7 +14970,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5 {
background-color: #fae4e9;
@@ -15257,7 +14980,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6 {
background-color: #f3c2cc;
@@ -15267,7 +14990,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7 {
background-color: #759ccd;
@@ -15277,7 +15000,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8 {
background-color: #f2c1cb;
@@ -15287,7 +15010,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9 {
background-color: #f2f2f2;
@@ -15297,7 +15020,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10 {
background-color: #cbdaec;
@@ -15307,7 +15030,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11 {
background-color: #c5d5ea;
@@ -15317,7 +15040,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12 {
background-color: #fae6ea;
@@ -15327,7 +15050,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13 {
background-color: #c6d6ea;
@@ -15337,7 +15060,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14 {
background-color: #eca3b1;
@@ -15347,7 +15070,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15 {
background-color: #fae4e9;
@@ -15357,7 +15080,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16 {
background-color: #eeacba;
@@ -15367,7 +15090,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17 {
background-color: #f6d1d8;
@@ -15377,7 +15100,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18 {
background-color: #d8e3f1;
@@ -15387,7 +15110,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19 {
background-color: #eb9bab;
@@ -15397,7 +15120,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20 {
background-color: #a3bddd;
@@ -15407,7 +15130,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21 {
background-color: #d73c5b;
@@ -15417,7 +15140,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22 {
background-color: #81a4d1;
@@ -15427,7 +15150,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23 {
background-color: #95b3d8;
@@ -15437,7 +15160,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24 {
background-color: #4479bb;
@@ -15447,7 +15170,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0 {
background-color: #f2f2f2;
@@ -15457,7 +15180,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1 {
background-color: #759ccd;
@@ -15467,7 +15190,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2 {
background-color: #f2bdc8;
@@ -15477,7 +15200,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3 {
background-color: #f2f2f2;
@@ -15487,7 +15210,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4 {
background-color: #5686c1;
@@ -15497,7 +15220,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5 {
background-color: #f0b5c1;
@@ -15507,7 +15230,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6 {
background-color: #f4c9d2;
@@ -15517,7 +15240,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7 {
background-color: #628fc6;
@@ -15527,7 +15250,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8 {
background-color: #e68396;
@@ -15537,7 +15260,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9 {
background-color: #eda7b5;
@@ -15547,7 +15270,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10 {
background-color: #f5ccd4;
@@ -15557,7 +15280,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11 {
background-color: #e8eff7;
@@ -15567,7 +15290,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12 {
background-color: #eaf0f7;
@@ -15577,7 +15300,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13 {
background-color: #ebf1f8;
@@ -15587,7 +15310,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14 {
background-color: #de5c76;
@@ -15597,7 +15320,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15 {
background-color: #f2f2f2;
@@ -15607,7 +15330,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16 {
background-color: #dd5671;
@@ -15617,7 +15340,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17 {
background-color: #e993a4;
@@ -15627,7 +15350,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18 {
background-color: #dae5f2;
@@ -15637,7 +15360,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19 {
background-color: #e991a3;
@@ -15647,7 +15370,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20 {
background-color: #dce6f2;
@@ -15657,7 +15380,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21 {
background-color: #d73c5b;
@@ -15667,7 +15390,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22 {
background-color: #a0bbdc;
@@ -15677,7 +15400,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23 {
background-color: #96b4d9;
@@ -15687,7 +15410,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24 {
background-color: #4479bb;
@@ -15697,7 +15420,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0 {
background-color: #d3dfef;
@@ -15707,7 +15430,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1 {
background-color: #487cbc;
@@ -15717,7 +15440,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2 {
background-color: #ea9aaa;
@@ -15727,7 +15450,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3 {
background-color: #fae7eb;
@@ -15737,7 +15460,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4 {
background-color: #4479bb;
@@ -15747,7 +15470,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5 {
background-color: #eb9ead;
@@ -15757,7 +15480,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6 {
background-color: #f3c5ce;
@@ -15767,7 +15490,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7 {
background-color: #4d7fbe;
@@ -15777,7 +15500,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8 {
background-color: #e994a5;
@@ -15787,7 +15510,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9 {
background-color: #f6d5db;
@@ -15797,7 +15520,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10 {
background-color: #f5ccd4;
@@ -15807,7 +15530,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11 {
background-color: #d5e1f0;
@@ -15817,7 +15540,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12 {
background-color: #f0b4c0;
@@ -15827,7 +15550,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13 {
background-color: #f2f2f2;
@@ -15837,7 +15560,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14 {
background-color: #da4966;
@@ -15847,7 +15570,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15 {
background-color: #f2f2f2;
@@ -15857,7 +15580,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16 {
background-color: #d73c5b;
@@ -15867,7 +15590,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17 {
background-color: #ea96a7;
@@ -15877,7 +15600,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18 {
background-color: #ecf2f8;
@@ -15887,7 +15610,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19 {
background-color: #e3748a;
@@ -15897,7 +15620,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20 {
background-color: #dde7f3;
@@ -15907,7 +15630,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21 {
background-color: #dc516d;
@@ -15917,7 +15640,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22 {
background-color: #91b0d7;
@@ -15927,7 +15650,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23 {
background-color: #a8c0df;
@@ -15937,7 +15660,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24 {
background-color: #5c8ac4;
@@ -15947,7 +15670,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0 {
background-color: #dce6f2;
@@ -15957,7 +15680,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1 {
background-color: #6590c7;
@@ -15967,7 +15690,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2 {
background-color: #ea99a9;
@@ -15977,7 +15700,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3 {
background-color: #f2f2f2;
@@ -15987,7 +15710,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4 {
background-color: #4479bb;
@@ -15997,7 +15720,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5 {
background-color: #f3c5ce;
@@ -16007,7 +15730,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6 {
background-color: #f2f2f2;
@@ -16017,7 +15740,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7 {
background-color: #6a94c9;
@@ -16027,7 +15750,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8 {
background-color: #f6d5db;
@@ -16037,7 +15760,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9 {
background-color: #ebf1f8;
@@ -16047,7 +15770,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10 {
background-color: #f2f2f2;
@@ -16057,7 +15780,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11 {
background-color: #dfe8f3;
@@ -16067,7 +15790,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12 {
background-color: #efb2bf;
@@ -16077,7 +15800,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13 {
background-color: #f2f2f2;
@@ -16087,7 +15810,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14 {
background-color: #e27389;
@@ -16097,7 +15820,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15 {
background-color: #f3c5ce;
@@ -16107,7 +15830,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16 {
background-color: #d73c5b;
@@ -16117,7 +15840,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17 {
background-color: #ea9aaa;
@@ -16127,7 +15850,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18 {
background-color: #dae5f2;
@@ -16137,7 +15860,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19 {
background-color: #e993a4;
@@ -16147,7 +15870,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20 {
background-color: #b9cde6;
@@ -16157,7 +15880,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21 {
background-color: #de5f79;
@@ -16167,7 +15890,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22 {
background-color: #b3c9e3;
@@ -16177,7 +15900,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23 {
background-color: #9fbadc;
@@ -16187,7 +15910,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24 {
background-color: #6f98ca;
@@ -16197,7 +15920,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0 {
background-color: #c6d6ea;
@@ -16207,7 +15930,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1 {
background-color: #6f98ca;
@@ -16217,7 +15940,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2 {
background-color: #ea96a7;
@@ -16227,7 +15950,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3 {
background-color: #f7dae0;
@@ -16237,7 +15960,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4 {
background-color: #4479bb;
@@ -16247,7 +15970,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5 {
background-color: #f0b7c2;
@@ -16257,7 +15980,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6 {
background-color: #fae4e9;
@@ -16267,7 +15990,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7 {
background-color: #759ccd;
@@ -16277,7 +16000,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8 {
background-color: #f2bdc8;
@@ -16287,7 +16010,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9 {
background-color: #f9e2e6;
@@ -16297,7 +16020,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10 {
background-color: #fae7eb;
@@ -16307,7 +16030,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11 {
background-color: #cbdaec;
@@ -16317,7 +16040,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12 {
background-color: #efb1be;
@@ -16327,7 +16050,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13 {
background-color: #eaf0f7;
@@ -16337,7 +16060,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14 {
background-color: #e0657d;
@@ -16347,7 +16070,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15 {
background-color: #eca1b0;
@@ -16357,7 +16080,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16 {
background-color: #d73c5b;
@@ -16367,7 +16090,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17 {
background-color: #e27087;
@@ -16377,7 +16100,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18 {
background-color: #f9e2e6;
@@ -16387,7 +16110,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19 {
background-color: #e68699;
@@ -16397,7 +16120,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20 {
background-color: #fae6ea;
@@ -16407,7 +16130,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21 {
background-color: #d73c5b;
@@ -16417,7 +16140,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22 {
background-color: #d1deee;
@@ -16427,7 +16150,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23 {
background-color: #82a5d1;
@@ -16437,7 +16160,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24 {
background-color: #7099cb;
@@ -16447,7 +16170,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0 {
background-color: #a9c1e0;
@@ -16457,7 +16180,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1 {
background-color: #6892c8;
@@ -16467,7 +16190,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2 {
background-color: #f7d6dd;
@@ -16477,7 +16200,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3 {
background-color: #f2f2f2;
@@ -16487,7 +16210,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4 {
background-color: #4479bb;
@@ -16497,7 +16220,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5 {
background-color: #e4ecf5;
@@ -16507,7 +16230,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6 {
background-color: #d8e3f1;
@@ -16517,7 +16240,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7 {
background-color: #477bbc;
@@ -16527,7 +16250,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8 {
background-color: #f2f2f2;
@@ -16537,7 +16260,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9 {
background-color: #e7eef6;
@@ -16547,7 +16270,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10 {
background-color: #cbdaec;
@@ -16557,7 +16280,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11 {
background-color: #a6bfde;
@@ -16567,7 +16290,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12 {
background-color: #fae8ec;
@@ -16577,7 +16300,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13 {
background-color: #a9c1e0;
@@ -16587,7 +16310,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14 {
background-color: #e3748a;
@@ -16597,7 +16320,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15 {
background-color: #ea99a9;
@@ -16607,7 +16330,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16 {
background-color: #d73c5b;
@@ -16617,7 +16340,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17 {
background-color: #f0b7c2;
@@ -16627,7 +16350,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18 {
background-color: #f6d5db;
@@ -16637,7 +16360,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19 {
background-color: #eb9ead;
@@ -16647,7 +16370,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20 {
background-color: #f2f2f2;
@@ -16657,7 +16380,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21 {
background-color: #d8415f;
@@ -16667,7 +16390,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22 {
background-color: #b5cae4;
@@ -16677,7 +16400,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23 {
background-color: #5182bf;
@@ -16687,7 +16410,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24 {
background-color: #457abb;
@@ -16697,7 +16420,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0 {
background-color: #92b1d7;
@@ -16707,7 +16430,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1 {
background-color: #7ba0cf;
@@ -16717,7 +16440,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2 {
background-color: #f3c5ce;
@@ -16727,7 +16450,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3 {
background-color: #f7d7de;
@@ -16737,7 +16460,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4 {
background-color: #5485c1;
@@ -16747,7 +16470,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5 {
background-color: #f5cfd7;
@@ -16757,7 +16480,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6 {
background-color: #d4e0ef;
@@ -16767,7 +16490,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7 {
background-color: #4479bb;
@@ -16777,7 +16500,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8 {
background-color: #f4cad3;
@@ -16787,7 +16510,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9 {
background-color: #dfe8f3;
@@ -16797,7 +16520,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10 {
background-color: #b0c6e2;
@@ -16807,7 +16530,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11 {
background-color: #9fbadc;
@@ -16817,7 +16540,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12 {
background-color: #fae8ec;
@@ -16827,7 +16550,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13 {
background-color: #cad9ec;
@@ -16837,7 +16560,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14 {
background-color: #e991a3;
@@ -16847,7 +16570,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15 {
background-color: #eca3b1;
@@ -16857,7 +16580,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16 {
background-color: #de5c76;
@@ -16867,7 +16590,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17 {
background-color: #f4cad3;
@@ -16877,7 +16600,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18 {
background-color: #f7dae0;
@@ -16887,7 +16610,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19 {
background-color: #eb9dac;
@@ -16897,7 +16620,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20 {
background-color: #f2f2f2;
@@ -16907,7 +16630,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21 {
background-color: #d73c5b;
@@ -16917,7 +16640,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22 {
background-color: #acc3e1;
@@ -16927,7 +16650,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23 {
background-color: #497dbd;
@@ -16937,7 +16660,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24 {
background-color: #5c8ac4;
@@ -16947,7 +16670,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0 {
background-color: #bccfe7;
@@ -16957,7 +16680,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1 {
background-color: #8faed6;
@@ -16967,7 +16690,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2 {
background-color: #eda6b4;
@@ -16977,7 +16700,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3 {
background-color: #f5ced6;
@@ -16987,7 +16710,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4 {
background-color: #5c8ac4;
@@ -16997,7 +16720,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5 {
background-color: #efb2bf;
@@ -17007,7 +16730,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6 {
background-color: #f4cad3;
@@ -17017,7 +16740,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7 {
background-color: #4479bb;
@@ -17027,7 +16750,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8 {
background-color: #f3c2cc;
@@ -17037,7 +16760,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9 {
background-color: #fae8ec;
@@ -17047,7 +16770,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10 {
background-color: #dde7f3;
@@ -17057,7 +16780,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11 {
background-color: #bbcee6;
@@ -17067,7 +16790,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12 {
background-color: #f2f2f2;
@@ -17077,7 +16800,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13 {
background-color: #f2f2f2;
@@ -17087,7 +16810,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14 {
background-color: #e78a9d;
@@ -17097,7 +16820,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15 {
background-color: #eda7b5;
@@ -17107,7 +16830,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16 {
background-color: #dc546f;
@@ -17117,7 +16840,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17 {
background-color: #eca3b1;
@@ -17127,7 +16850,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18 {
background-color: #e6edf6;
@@ -17137,7 +16860,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19 {
background-color: #eeaab7;
@@ -17147,7 +16870,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20 {
background-color: #f9e3e7;
@@ -17157,7 +16880,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21 {
background-color: #d73c5b;
@@ -17167,7 +16890,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22 {
background-color: #b8cce5;
@@ -17177,7 +16900,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23 {
background-color: #7099cb;
@@ -17187,7 +16910,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24 {
background-color: #5e8bc4;
@@ -17197,7 +16920,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0 {
background-color: #91b0d7;
@@ -17207,7 +16930,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1 {
background-color: #86a8d3;
@@ -17217,7 +16940,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2 {
background-color: #efb2bf;
@@ -17227,7 +16950,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3 {
background-color: #f9e3e7;
@@ -17237,7 +16960,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4 {
background-color: #5e8bc4;
@@ -17247,7 +16970,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5 {
background-color: #f2bfca;
@@ -17257,7 +16980,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6 {
background-color: #fae6ea;
@@ -17267,7 +16990,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7 {
background-color: #6b95c9;
@@ -17277,7 +17000,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8 {
background-color: #f3c6cf;
@@ -17287,7 +17010,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9 {
background-color: #e8eff7;
@@ -17297,7 +17020,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10 {
background-color: #f2f2f2;
@@ -17307,7 +17030,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11 {
background-color: #bdd0e7;
@@ -17317,7 +17040,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12 {
background-color: #95b3d8;
@@ -17327,7 +17050,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13 {
background-color: #dae5f2;
@@ -17337,7 +17060,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14 {
background-color: #eeabb8;
@@ -17347,7 +17070,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15 {
background-color: #eeacba;
@@ -17357,7 +17080,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16 {
background-color: #e3748a;
@@ -17367,7 +17090,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17 {
background-color: #eca4b3;
@@ -17377,7 +17100,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18 {
background-color: #f7d6dd;
@@ -17387,7 +17110,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19 {
background-color: #f6d2d9;
@@ -17397,7 +17120,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20 {
background-color: #f9e3e7;
@@ -17407,7 +17130,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21 {
background-color: #d73c5b;
@@ -17417,7 +17140,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22 {
background-color: #9bb7da;
@@ -17427,7 +17150,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23 {
background-color: #618ec5;
@@ -17437,7 +17160,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24 {
background-color: #4479bb;
@@ -17447,7 +17170,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0 {
background-color: #5787c2;
@@ -17457,7 +17180,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1 {
background-color: #5e8bc4;
@@ -17467,7 +17190,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2 {
background-color: #f5cfd7;
@@ -17477,7 +17200,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3 {
background-color: #f2f2f2;
@@ -17487,7 +17210,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4 {
background-color: #5384c0;
@@ -17497,7 +17220,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5 {
background-color: #f8dee3;
@@ -17507,7 +17230,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6 {
background-color: #dce6f2;
@@ -17517,7 +17240,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7 {
background-color: #5787c2;
@@ -17527,7 +17250,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8 {
background-color: #f9e3e7;
@@ -17537,7 +17260,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9 {
background-color: #cedced;
@@ -17547,7 +17270,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10 {
background-color: #dde7f3;
@@ -17557,7 +17280,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11 {
background-color: #9cb8db;
@@ -17567,7 +17290,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12 {
background-color: #749bcc;
@@ -17577,7 +17300,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13 {
background-color: #b2c8e3;
@@ -17587,7 +17310,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14 {
background-color: #f8dfe4;
@@ -17597,7 +17320,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15 {
background-color: #f4c9d2;
@@ -17607,7 +17330,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16 {
background-color: #eeabb8;
@@ -17617,7 +17340,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17 {
background-color: #f3c6cf;
@@ -17627,7 +17350,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18 {
background-color: #f2f2f2;
@@ -17637,7 +17360,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19 {
background-color: #e2eaf4;
@@ -17647,7 +17370,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20 {
background-color: #dfe8f3;
@@ -17657,7 +17380,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21 {
background-color: #d73c5b;
@@ -17667,7 +17390,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22 {
background-color: #94b2d8;
@@ -17677,7 +17400,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23 {
background-color: #4479bb;
@@ -17687,7 +17410,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24 {
background-color: #5384c0;
@@ -17697,7 +17420,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0 {
background-color: #6993c8;
@@ -17707,7 +17430,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1 {
background-color: #6590c7;
@@ -17717,7 +17440,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2 {
background-color: #e2eaf4;
@@ -17727,7 +17450,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3 {
background-color: #f2f2f2;
@@ -17737,7 +17460,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4 {
background-color: #457abb;
@@ -17747,7 +17470,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5 {
background-color: #e3ebf5;
@@ -17757,7 +17480,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6 {
background-color: #bdd0e7;
@@ -17767,7 +17490,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7 {
background-color: #5384c0;
@@ -17777,7 +17500,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8 {
background-color: #f7d7de;
@@ -17787,7 +17510,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9 {
background-color: #96b4d9;
@@ -17797,7 +17520,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10 {
background-color: #b0c6e2;
@@ -17807,7 +17530,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11 {
background-color: #b2c8e3;
@@ -17817,7 +17540,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12 {
background-color: #4a7ebd;
@@ -17827,7 +17550,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13 {
background-color: #92b1d7;
@@ -17837,7 +17560,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14 {
background-color: #f2f2f2;
@@ -17847,7 +17570,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15 {
background-color: #f2f2f2;
@@ -17857,7 +17580,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16 {
background-color: #f4cad3;
@@ -17867,7 +17590,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17 {
background-color: #ebf1f8;
@@ -17877,7 +17600,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18 {
background-color: #dce6f2;
@@ -17887,7 +17610,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19 {
background-color: #c9d8eb;
@@ -17897,7 +17620,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20 {
background-color: #bfd1e8;
@@ -17907,7 +17630,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21 {
background-color: #d73c5b;
@@ -17917,7 +17640,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22 {
background-color: #8faed6;
@@ -17927,7 +17650,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23 {
background-color: #4479bb;
@@ -17937,7 +17660,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24 {
background-color: #5a88c3;
@@ -17947,7 +17670,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0 {
background-color: #628fc6;
@@ -17957,7 +17680,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1 {
background-color: #749bcc;
@@ -17967,7 +17690,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2 {
background-color: #f9e2e6;
@@ -17977,7 +17700,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3 {
background-color: #f8dee3;
@@ -17987,7 +17710,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4 {
background-color: #5182bf;
@@ -17997,7 +17720,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5 {
background-color: #f2f2f2;
@@ -18007,7 +17730,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6 {
background-color: #d4e0ef;
@@ -18017,7 +17740,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7 {
background-color: #5182bf;
@@ -18027,7 +17750,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8 {
background-color: #f4cad3;
@@ -18037,7 +17760,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9 {
background-color: #85a7d2;
@@ -18047,7 +17770,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10 {
background-color: #cbdaec;
@@ -18057,7 +17780,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11 {
background-color: #bccfe7;
@@ -18067,7 +17790,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12 {
background-color: #5f8cc5;
@@ -18077,7 +17800,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13 {
background-color: #a2bcdd;
@@ -18087,7 +17810,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14 {
background-color: #f2f2f2;
@@ -18097,7 +17820,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15 {
background-color: #f2f2f2;
@@ -18107,7 +17830,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16 {
background-color: #f3c6cf;
@@ -18117,7 +17840,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17 {
background-color: #fae7eb;
@@ -18127,7 +17850,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18 {
background-color: #fbeaed;
@@ -18137,7 +17860,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19 {
background-color: #f2f2f2;
@@ -18147,7 +17870,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20 {
background-color: #b7cbe5;
@@ -18157,7 +17880,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21 {
background-color: #d73c5b;
@@ -18167,7 +17890,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22 {
background-color: #86a8d3;
@@ -18177,7 +17900,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23 {
background-color: #4479bb;
@@ -18187,7 +17910,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24 {
background-color: #739acc;
@@ -18197,7 +17920,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0 {
background-color: #6a94c9;
@@ -18207,7 +17930,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1 {
background-color: #6d96ca;
@@ -18217,7 +17940,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2 {
background-color: #f4c9d2;
@@ -18227,7 +17950,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3 {
background-color: #eeaebb;
@@ -18237,7 +17960,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4 {
background-color: #5384c0;
@@ -18247,7 +17970,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5 {
background-color: #f2bfca;
@@ -18257,7 +17980,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6 {
background-color: #f2f2f2;
@@ -18267,7 +17990,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7 {
background-color: #4479bb;
@@ -18277,7 +18000,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8 {
background-color: #ec9faf;
@@ -18287,7 +18010,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9 {
background-color: #c6d6ea;
@@ -18297,7 +18020,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10 {
background-color: #f2f2f2;
@@ -18307,7 +18030,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11 {
background-color: #dae5f2;
@@ -18317,7 +18040,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12 {
background-color: #4c7ebd;
@@ -18327,7 +18050,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13 {
background-color: #d1deee;
@@ -18337,7 +18060,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14 {
background-color: #fae6ea;
@@ -18347,7 +18070,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15 {
background-color: #f7d9df;
@@ -18357,7 +18080,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16 {
background-color: #eeacba;
@@ -18367,7 +18090,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17 {
background-color: #f6d1d8;
@@ -18377,7 +18100,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18 {
background-color: #f6d2d9;
@@ -18387,7 +18110,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19 {
background-color: #f4c9d2;
@@ -18397,7 +18120,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20 {
background-color: #bccfe7;
@@ -18407,7 +18130,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21 {
background-color: #d73c5b;
@@ -18417,7 +18140,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22 {
background-color: #9eb9db;
@@ -18427,7 +18150,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23 {
background-color: #5485c1;
@@ -18437,7 +18160,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24 {
background-color: #8babd4;
@@ -18447,7 +18170,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col0 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0 {
background-color: #86a8d3;
@@ -18457,7 +18180,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col1 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1 {
background-color: #5b89c3;
@@ -18467,7 +18190,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col2 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2 {
background-color: #f2bfca;
@@ -18477,7 +18200,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col3 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3 {
background-color: #f2bfca;
@@ -18487,7 +18210,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col4 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4 {
background-color: #497dbd;
@@ -18497,7 +18220,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col5 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5 {
background-color: #f2bfca;
@@ -18507,7 +18230,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col6 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6 {
background-color: #f2f2f2;
@@ -18517,7 +18240,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col7 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7 {
background-color: #5686c1;
@@ -18527,7 +18250,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col8 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8 {
background-color: #eda8b6;
@@ -18537,7 +18260,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col9 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9 {
background-color: #d9e4f1;
@@ -18547,7 +18270,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col10 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10 {
background-color: #d5e1f0;
@@ -18557,7 +18280,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col11 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11 {
background-color: #bfd1e8;
@@ -18567,7 +18290,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col12 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12 {
background-color: #5787c2;
@@ -18577,7 +18300,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13 {
background-color: #fbeaed;
@@ -18587,7 +18310,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14 {
background-color: #f8dee3;
@@ -18597,7 +18320,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15 {
background-color: #f2f2f2;
@@ -18607,7 +18330,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16 {
background-color: #eeaab7;
@@ -18617,7 +18340,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17 {
background-color: #f6d1d8;
@@ -18627,7 +18350,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18 {
background-color: #f7d7de;
@@ -18637,7 +18360,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19 {
background-color: #f2f2f2;
@@ -18647,7 +18370,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20 {
background-color: #b5cae4;
@@ -18657,7 +18380,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21 {
background-color: #d73c5b;
@@ -18667,7 +18390,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22 {
background-color: #9eb9db;
@@ -18677,7 +18400,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23 {
background-color: #4479bb;
@@ -18687,7 +18410,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
}
- #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24 {
+ #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24 {
background-color: #89aad4;
@@ -18699,7 +18422,7 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
</style>
- <table id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb">
+ <table id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb">
<caption>Hover to magify</caption>
@@ -18767,132 +18490,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row0">
+ <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row0">
0
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0" class="data row0 col0">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0" class="data row0 col0">
0.23
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1" class="data row0 col1">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1" class="data row0 col1">
1.03
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col2" class="data row0 col2">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2" class="data row0 col2">
-0.84
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col3" class="data row0 col3">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3" class="data row0 col3">
-0.59
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col4" class="data row0 col4">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4" class="data row0 col4">
-0.96
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col5" class="data row0 col5">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5" class="data row0 col5">
-0.22
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col6" class="data row0 col6">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6" class="data row0 col6">
-0.62
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col7" class="data row0 col7">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7" class="data row0 col7">
1.84
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col8" class="data row0 col8">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8" class="data row0 col8">
-2.05
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col9" class="data row0 col9">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9" class="data row0 col9">
0.87
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col10" class="data row0 col10">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10" class="data row0 col10">
-0.92
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col11" class="data row0 col11">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11" class="data row0 col11">
-0.23
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col12" class="data row0 col12">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12" class="data row0 col12">
2.15
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col13" class="data row0 col13">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13" class="data row0 col13">
-1.33
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col14" class="data row0 col14">
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0.08
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@@ -18901,132 +18624,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
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@@ -19035,132 +18758,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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2
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2.09
@@ -19437,132 +19160,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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5
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3.77
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<tr>
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5.81
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<tr>
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4.69
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6.66
@@ -19839,132 +19562,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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8
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0.92
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5.08
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<tr>
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0.38
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1.89
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5.16
@@ -20107,132 +19830,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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10
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2.06
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5.84
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7.78
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5.59
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1.8
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0.48
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1.54
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5.02
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5.81
@@ -20241,132 +19964,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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11
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1.86
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4.47
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5.9
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5.78
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1.88
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5.83
@@ -20375,132 +20098,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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12
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3.19
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5.93
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5.6
@@ -20509,132 +20232,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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13
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2.31
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4.45
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6.76
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7.99
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0.71
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2.33
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2.47
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5.87
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6.71
@@ -20643,132 +20366,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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14
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3.78
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4.33
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6.22
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5.57
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7.53
@@ -20777,132 +20500,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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15
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5.64
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5.91
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2.59
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6.69
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5.91
@@ -20911,132 +20634,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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16
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4.08
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4.34
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6.04
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5.28
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1.58
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0.23
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0.1
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5.79
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1.8
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2.07
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6.16
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4.94
@@ -21045,132 +20768,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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17
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5.64
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4.57
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6.58
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6.58
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3.63
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0.84
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3.56
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7.36
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4.7
@@ -21179,132 +20902,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
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18
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5.99
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5.82
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7.12
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7.93
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0.35
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-2.55
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col18" class="data row18 col18">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18" class="data row18 col18">
-2.46
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col19" class="data row18 col19">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19" class="data row18 col19">
-2.89
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col20" class="data row18 col20">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20" class="data row18 col20">
1.9
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col21" class="data row18 col21">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21" class="data row18 col21">
-9.74
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col22" class="data row18 col22">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22" class="data row18 col22">
3.43
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col23" class="data row18 col23">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23" class="data row18 col23">
7.07
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col24" class="data row18 col24">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24" class="data row18 col24">
4.39
@@ -21313,132 +21036,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a><
<tr>
- <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row19">
+ <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row19">
19
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col0" class="data row19 col0">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0" class="data row19 col0">
4.03
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col1" class="data row19 col1">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1" class="data row19 col1">
6.23
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col2" class="data row19 col2">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2" class="data row19 col2">
-4.1
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col3" class="data row19 col3">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3" class="data row19 col3">
-4.11
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col4" class="data row19 col4">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4" class="data row19 col4">
7.19
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col5" class="data row19 col5">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5" class="data row19 col5">
-4.1
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col6" class="data row19 col6">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6" class="data row19 col6">
-1.52
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col7" class="data row19 col7">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7" class="data row19 col7">
6.53
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col8" class="data row19 col8">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8" class="data row19 col8">
-5.21
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col9" class="data row19 col9">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9" class="data row19 col9">
-0.24
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col10" class="data row19 col10">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10" class="data row19 col10">
0.01
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col11" class="data row19 col11">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11" class="data row19 col11">
1.16
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col12" class="data row19 col12">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12" class="data row19 col12">
6.43
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13" class="data row19 col13">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13" class="data row19 col13">
-1.97
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14" class="data row19 col14">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14" class="data row19 col14">
-2.64
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15" class="data row19 col15">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15" class="data row19 col15">
-1.66
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16" class="data row19 col16">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16" class="data row19 col16">
-5.2
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17" class="data row19 col17">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17" class="data row19 col17">
-3.25
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18" class="data row19 col18">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18" class="data row19 col18">
-2.87
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19" class="data row19 col19">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19" class="data row19 col19">
-1.65
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20" class="data row19 col20">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20" class="data row19 col20">
1.64
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21" class="data row19 col21">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21" class="data row19 col21">
-10.66
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22" class="data row19 col22">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22" class="data row19 col22">
2.83
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23" class="data row19 col23">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23" class="data row19 col23">
7.48
- <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24" class="data row19 col24">
+ <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24" class="data row19 col24">
3.94
@@ -21485,8 +21208,4 @@ <h2 id="Alternate-templates">Alternate templates<a class="anchor-link" href="#Al
</div>
</div>
-</div>
- </div>
- </div>
-</body>
-</html>
+</div>
\ No newline at end of file
diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb
index 28eb1cd09bacd..fc59c3ca88100 100644
--- a/doc/source/html-styling.ipynb
+++ b/doc/source/html-styling.ipynb
@@ -4,8 +4,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Conditional Formatting\n",
- "\n",
"*New in version 0.17.1*\n",
"\n",
"<p style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your [feedback](https://github.com/pydata/pandas/issues).*<p style=\"color: red\">\n",
@@ -54,7 +52,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 3,
"metadata": {
"collapsed": false
},
@@ -79,7 +77,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 4,
"metadata": {
"collapsed": false
},
@@ -93,7 +91,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\">\n",
+ " <table id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\">\n",
" \n",
"\n",
" <thead>\n",
@@ -119,32 +117,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
" \n",
" 0\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
" \n",
" 1.0\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
" \n",
" 1.329212\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
" \n",
" nan\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
" \n",
" -0.31628\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
" \n",
" -0.99081\n",
" \n",
@@ -153,32 +151,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
" \n",
" 1\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
" \n",
" 2.0\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
" \n",
" -1.070816\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
" \n",
" -1.438713\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
" \n",
" 0.564417\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
" \n",
" 0.295722\n",
" \n",
@@ -187,32 +185,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
" \n",
" 2\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
" \n",
" 3.0\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
" \n",
" -1.626404\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
" \n",
" 0.219565\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
" \n",
" 0.678805\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
" \n",
" 1.889273\n",
" \n",
@@ -221,32 +219,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
" \n",
" 3\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
" \n",
" 4.0\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
" \n",
" 0.961538\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
" \n",
" 0.104011\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
" \n",
" -0.481165\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
" \n",
" 0.850229\n",
" \n",
@@ -255,32 +253,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
" \n",
" 4\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
" \n",
" 5.0\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
" \n",
" 1.453425\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
" \n",
" 1.057737\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
" \n",
" 0.165562\n",
" \n",
" \n",
- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
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@@ -357,32 +355,32 @@
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- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
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- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -462,10 +460,10 @@
" "
],
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},
- "execution_count": 2,
+ "execution_count": 4,
"metadata": {},
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}
@@ -485,7 +483,7 @@
},
{
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@@ -532,7 +530,7 @@
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4 {\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0 {\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1 {\n",
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" color: black;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2 {\n",
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" color: black;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3 {\n",
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" color: red;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4 {\n",
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" color: black;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0 {\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3 {\n",
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" color: black;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0 {\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1 {\n",
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" color: black;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2 {\n",
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" color: red;\n",
" \n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3 {\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4 {\n",
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" color: red;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0 {\n",
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" color: black;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1 {\n",
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" color: red;\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2 {\n",
" \n",
" color: black;\n",
" \n",
" }\n",
" \n",
- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3 {\n",
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- " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4 {\n",
+ " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4 {\n",
" \n",
" color: black;\n",
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@@ -872,7 +870,7 @@
" \n",
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"\n",
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"\n",
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@@ -898,32 +896,32 @@
" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
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@@ -932,32 +930,32 @@
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+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
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" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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@@ -966,32 +964,32 @@
" \n",
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" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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@@ -1000,32 +998,32 @@
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" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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@@ -1034,32 +1032,32 @@
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
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@@ -1102,32 +1100,32 @@
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" 6\n",
" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
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" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
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@@ -1136,32 +1134,32 @@
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+ " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
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" 7\n",
" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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@@ -1170,32 +1168,32 @@
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+ " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
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" 8\n",
" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
" \n",
" 9.0\n",
" \n",
" \n",
- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
" \n",
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@@ -1204,32 +1202,32 @@
" \n",
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- " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
" \n",
" 9\n",
" \n",
" \n",
- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
" \n",
" 10.0\n",
" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
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" \n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
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- " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
" \n",
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@@ -1241,10 +1239,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335eac8>"
+ "<pandas.core.style.Styler at 0x111c351d0>"
]
},
- "execution_count": 5,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -1274,7 +1272,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {
"collapsed": true
},
@@ -1290,7 +1288,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 9,
"metadata": {
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},
@@ -1302,31 +1300,31 @@
" <style type=\"text/css\" >\n",
" \n",
" \n",
- " #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col4 {\n",
+ " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4 {\n",
" \n",
" background-color: yellow;\n",
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" }\n",
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- " #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col2 {\n",
+ " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2 {\n",
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" background-color: yellow;\n",
" \n",
" }\n",
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- " #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col1 {\n",
+ " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1 {\n",
" \n",
" background-color: yellow;\n",
" \n",
" }\n",
" \n",
- " #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col3 {\n",
+ " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3 {\n",
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- " #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col0 {\n",
+ " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0 {\n",
" \n",
" background-color: yellow;\n",
" \n",
@@ -1334,7 +1332,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\">\n",
+ " <table id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\">\n",
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"\n",
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@@ -1360,32 +1358,32 @@
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
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- " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
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" \n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
" \n",
" 2.0\n",
" \n",
" \n",
- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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" \n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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@@ -1428,32 +1426,32 @@
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" \n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
" \n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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@@ -1462,32 +1460,32 @@
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- " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
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" \n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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@@ -1496,32 +1494,32 @@
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" \n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
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" \n",
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- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
" \n",
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" \n",
- " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
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],
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},
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{
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+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -1992,7 +1990,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col2 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2000,7 +1998,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col3 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -2008,7 +2006,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col4 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2016,7 +2014,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2024,7 +2022,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col1 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2032,7 +2030,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col2 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2040,7 +2038,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col3 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -2048,7 +2046,7 @@
" \n",
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- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col4 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2056,7 +2054,7 @@
" \n",
" }\n",
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- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col0 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2064,7 +2062,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2072,7 +2070,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2 {\n",
" \n",
" color: red;\n",
" \n",
@@ -2080,7 +2078,7 @@
" \n",
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" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2088,7 +2086,7 @@
" \n",
" }\n",
" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4 {\n",
" \n",
" color: red;\n",
" \n",
@@ -2096,7 +2094,7 @@
" \n",
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- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2104,7 +2102,7 @@
" \n",
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- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1 {\n",
" \n",
" color: red;\n",
" \n",
@@ -2112,7 +2110,7 @@
" \n",
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- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2120,7 +2118,7 @@
" \n",
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" \n",
- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -2128,7 +2126,7 @@
" \n",
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- " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4 {\n",
+ " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -2138,7 +2136,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\">\n",
+ " <table id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\">\n",
" \n",
"\n",
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@@ -2164,32 +2162,32 @@
" \n",
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- " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
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+ " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
" \n",
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" \n",
" \n",
- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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+ " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
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" 2\n",
" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
" \n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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@@ -2266,32 +2264,32 @@
" \n",
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- " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
" \n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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" 4\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
" \n",
" 5.0\n",
" \n",
" \n",
- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
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@@ -2334,32 +2332,32 @@
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" 5\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
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" \n",
" \n",
- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
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@@ -2368,32 +2366,32 @@
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- " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
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" 6\n",
" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
" \n",
" 7.0\n",
" \n",
" \n",
- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
" \n",
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@@ -2402,32 +2400,32 @@
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
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@@ -2436,32 +2434,32 @@
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" 8\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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" 9.0\n",
" \n",
" \n",
- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -2470,32 +2468,32 @@
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" 9\n",
" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
" \n",
" 10.0\n",
" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
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" \n",
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- " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
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@@ -2507,10 +2505,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335e630>"
+ "<pandas.core.style.Styler at 0x111c2ce48>"
]
},
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -2537,7 +2535,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {
"collapsed": true
},
@@ -2559,7 +2557,7 @@
},
{
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- "execution_count": 10,
+ "execution_count": 12,
"metadata": {
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},
@@ -2571,7 +2569,7 @@
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" \n",
" \n",
- " #T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0 {\n",
+ " #T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0 {\n",
" \n",
" background-color: darkorange;\n",
" \n",
@@ -2579,7 +2577,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\">\n",
+ " <table id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\">\n",
" \n",
"\n",
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@@ -2605,32 +2603,32 @@
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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- " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
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@@ -2843,32 +2841,32 @@
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@@ -2948,10 +2946,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x113367eb8>"
+ "<pandas.core.style.Styler at 0x111c7d278>"
]
},
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -2999,7 +2997,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {
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},
@@ -3011,19 +3009,19 @@
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" \n",
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"\n",
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@@ -3295,32 +3293,32 @@
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@@ -3400,10 +3398,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335e978>"
+ "<pandas.core.style.Styler at 0x111c7d438>"
]
},
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -3421,7 +3419,7 @@
},
{
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- "execution_count": 12,
+ "execution_count": 14,
"metadata": {
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},
@@ -3433,49 +3431,49 @@
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- " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1 {\n",
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" \n",
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- " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1 {\n",
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+ " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3 {\n",
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"\n",
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@@ -3645,32 +3643,32 @@
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@@ -3747,32 +3745,32 @@
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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" 8\n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -3815,32 +3813,32 @@
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" \n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
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" \n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
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- " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
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@@ -3852,10 +3850,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335e940>"
+ "<pandas.core.style.Styler at 0x111c7d4e0>"
]
},
- "execution_count": 12,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -3894,7 +3892,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 15,
"metadata": {
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},
@@ -3906,7 +3904,7 @@
" <style type=\"text/css\" >\n",
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- " #T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2 {\n",
+ " #T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2 {\n",
" \n",
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@@ -3914,7 +3912,7 @@
" \n",
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"\n",
- " <table id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\">\n",
+ " <table id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\">\n",
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@@ -3940,32 +3938,32 @@
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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@@ -4008,32 +4006,32 @@
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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@@ -4042,32 +4040,32 @@
" \n",
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" \n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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@@ -4076,32 +4074,32 @@
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" \n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
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@@ -4110,32 +4108,32 @@
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
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@@ -4178,32 +4176,32 @@
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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@@ -4212,32 +4210,32 @@
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- " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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" \n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -4246,32 +4244,32 @@
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
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+ " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
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- " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
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@@ -4283,10 +4281,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335e2b0>"
+ "<pandas.core.style.Styler at 0x111c35a90>"
]
},
- "execution_count": 13,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -4304,7 +4302,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 16,
"metadata": {
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},
@@ -4316,301 +4314,301 @@
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- " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2 {\n",
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- " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3 {\n",
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@@ -4618,7 +4616,7 @@
" \n",
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@@ -4644,32 +4642,32 @@
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@@ -4712,32 +4710,32 @@
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@@ -4780,32 +4778,32 @@
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],
"text/plain": [
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+ "<pandas.core.style.Styler at 0x111c35828>"
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},
- "execution_count": 14,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
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},
{
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+ " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4 {\n",
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@@ -5177,7 +5175,7 @@
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" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335e9b0>"
+ "<pandas.core.style.Styler at 0x111c354a8>"
]
},
- "execution_count": 15,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -5391,7 +5389,7 @@
},
{
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+ "execution_count": 18,
"metadata": {
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},
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@@ -5605,7 +5603,7 @@
" \n",
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" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x113367ba8>"
+ "<pandas.core.style.Styler at 0x111c2c400>"
]
},
- "execution_count": 16,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -5829,7 +5827,7 @@
},
{
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- "execution_count": 17,
+ "execution_count": 19,
"metadata": {
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},
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@@ -6307,32 +6305,32 @@
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- " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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- " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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+ " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -6412,10 +6410,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335ecf8>"
+ "<pandas.core.style.Styler at 0x111c35dd8>"
]
},
- "execution_count": 17,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -6433,7 +6431,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 20,
"metadata": {
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},
@@ -6445,31 +6443,31 @@
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- " #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col1 {\n",
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+ " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 {\n",
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- " #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col0 {\n",
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@@ -6477,7 +6475,7 @@
" \n",
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+ " <table id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\">\n",
" \n",
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@@ -6503,32 +6501,32 @@
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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@@ -6571,32 +6569,32 @@
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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@@ -6605,32 +6603,32 @@
" \n",
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" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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@@ -6639,32 +6637,32 @@
" \n",
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" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
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@@ -6673,32 +6671,32 @@
" \n",
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" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
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@@ -6707,32 +6705,32 @@
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" 6\n",
" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
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@@ -6741,32 +6739,32 @@
" \n",
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- " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
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" 7\n",
" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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@@ -6775,32 +6773,32 @@
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" 8\n",
" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
" \n",
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" \n",
" \n",
- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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" \n",
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- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -6809,32 +6807,32 @@
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- " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
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" \n",
" \n",
- " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
" \n",
" 0.29072\n",
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@@ -6846,10 +6844,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335e5f8>"
+ "<pandas.core.style.Styler at 0x111c35240>"
]
},
- "execution_count": 18,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -6860,7 +6858,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 21,
"metadata": {
"collapsed": false
},
@@ -6872,31 +6870,31 @@
" <style type=\"text/css\" >\n",
" \n",
" \n",
- " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col0 {\n",
+ " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col0 {\n",
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- " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col2 {\n",
+ " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2 {\n",
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- " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col1 {\n",
+ " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1 {\n",
" \n",
" background-color: yellow;\n",
" \n",
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- " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col4 {\n",
+ " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4 {\n",
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" background-color: yellow;\n",
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- " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col3 {\n",
+ " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3 {\n",
" \n",
" background-color: yellow;\n",
" \n",
@@ -6904,7 +6902,7 @@
" \n",
" </style>\n",
"\n",
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@@ -6930,32 +6928,32 @@
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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@@ -6998,32 +6996,32 @@
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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" \n",
" \n",
- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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@@ -7066,32 +7064,32 @@
" \n",
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" 4\n",
" \n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
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@@ -7134,32 +7132,32 @@
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" \n",
" \n",
- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
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" \n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
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@@ -7168,32 +7166,32 @@
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" 7\n",
" \n",
" \n",
- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
" \n",
" 8.0\n",
" \n",
" \n",
- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
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" \n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
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" \n",
" \n",
- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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" \n",
@@ -7202,32 +7200,32 @@
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- " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
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" 8\n",
" \n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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" \n",
" \n",
- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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- " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
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+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
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+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
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- " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
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" "
],
"text/plain": [
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+ "<pandas.core.style.Styler at 0x111c35d30>"
]
},
- "execution_count": 19,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -7294,7 +7292,7 @@
},
{
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+ "execution_count": 22,
"metadata": {
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},
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+ " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3 {\n",
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- " <table id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\">\n",
+ " <table id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\">\n",
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@@ -7834,32 +7832,32 @@
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- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
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" 0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
" \n",
" 1.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
" \n",
" 1.329212\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
" \n",
" nan\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
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" \n",
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- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
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@@ -7868,32 +7866,32 @@
" \n",
" <tr>\n",
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- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
" \n",
" 1\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
" \n",
" 2.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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" \n",
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- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
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" \n",
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- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
" \n",
" 0.564417\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
" \n",
" 0.295722\n",
" \n",
@@ -7902,32 +7900,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
" \n",
" 2\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
" \n",
" 3.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
" \n",
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" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
" \n",
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" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
" \n",
" 0.678805\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
" \n",
" 1.889273\n",
" \n",
@@ -7936,32 +7934,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
" \n",
" 3\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
" \n",
" 4.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
" \n",
" 0.961538\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
" \n",
" 0.104011\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
" \n",
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" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
" \n",
" 0.850229\n",
" \n",
@@ -7970,32 +7968,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
" \n",
" 4\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
" \n",
" 5.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
" \n",
" 1.453425\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
" \n",
" 1.057737\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
" \n",
" 0.165562\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
" \n",
" 0.515018\n",
" \n",
@@ -8004,32 +8002,32 @@
" \n",
" <tr>\n",
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- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
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" \n",
" 5\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
" \n",
" 6.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
" \n",
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" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
" \n",
" 0.562861\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
" \n",
" 1.392855\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
" \n",
" -0.063328\n",
" \n",
@@ -8038,32 +8036,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
" \n",
" 6\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
" \n",
" 7.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
" \n",
" 0.121668\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
" \n",
" 1.207603\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
" \n",
" -0.00204\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
" \n",
" 1.627796\n",
" \n",
@@ -8072,32 +8070,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
" \n",
" 7\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
" \n",
" 8.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
" \n",
" 0.354493\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
" \n",
" 1.037528\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
" \n",
" -0.385684\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
" \n",
" 0.519818\n",
" \n",
@@ -8106,32 +8104,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
" \n",
" 8\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
" \n",
" 9.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
" \n",
" 1.686583\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
" \n",
" -1.325963\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
" \n",
" 1.428984\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
" \n",
" -2.089354\n",
" \n",
@@ -8140,32 +8138,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
" \n",
" 9\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
" \n",
" 10.0\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
" \n",
" -0.12982\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
" \n",
" 0.631523\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
" \n",
" -0.586538\n",
" \n",
" \n",
- " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
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@@ -8177,10 +8175,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x113367ef0>"
+ "<pandas.core.style.Styler at 0x111c2cf60>"
]
},
- "execution_count": 20,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -8207,7 +8205,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 23,
"metadata": {
"collapsed": false
},
@@ -8219,301 +8217,301 @@
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- " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col4 {\n",
+ " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4 {\n",
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- " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col0 {\n",
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+ " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1 {\n",
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- " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col2 {\n",
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- " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col4 {\n",
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@@ -8521,7 +8519,7 @@
" \n",
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"\n",
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+ " <table id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\">\n",
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"\n",
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@@ -8547,32 +8545,32 @@
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- " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
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@@ -8581,32 +8579,32 @@
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- " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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@@ -8615,32 +8613,32 @@
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+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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+ " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -8890,10 +8888,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x1133670f0>"
+ "<pandas.core.style.Styler at 0x111c7d160>"
]
},
- "execution_count": 21,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -8906,7 +8904,7 @@
},
{
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- "execution_count": 22,
+ "execution_count": 24,
"metadata": {
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@@ -8918,301 +8916,301 @@
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col1 {\n",
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col2 {\n",
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" \n",
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col3 {\n",
+ " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3 {\n",
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col4 {\n",
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col0 {\n",
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col1 {\n",
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col2 {\n",
+ " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2 {\n",
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" }\n",
" \n",
- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col3 {\n",
+ " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3 {\n",
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- " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col4 {\n",
+ " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4 {\n",
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@@ -9220,7 +9218,7 @@
" \n",
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"\n",
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"\n",
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@@ -9246,32 +9244,32 @@
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@@ -9280,32 +9278,32 @@
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@@ -9314,32 +9312,32 @@
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@@ -9382,32 +9380,32 @@
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@@ -9416,32 +9414,32 @@
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" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x113367470>"
+ "<pandas.core.style.Styler at 0x111c7d198>"
]
},
- "execution_count": 22,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -9603,6 +9601,13 @@
"style2"
]
},
+ {
+ "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": {},
@@ -9640,7 +9645,7 @@
},
{
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- "execution_count": 23,
+ "execution_count": 25,
"metadata": {
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},
@@ -9652,7 +9657,7 @@
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- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col3 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9884,7 +9889,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col4 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4 {\n",
" \n",
" color: red;\n",
" \n",
@@ -9892,7 +9897,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col0 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9900,7 +9905,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col1 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9908,7 +9913,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col2 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9916,7 +9921,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col3 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -9924,7 +9929,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col4 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9932,7 +9937,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col0 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9940,7 +9945,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col1 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9948,7 +9953,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col2 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9956,7 +9961,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col3 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -9964,7 +9969,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col4 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9972,7 +9977,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9980,7 +9985,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -9988,7 +9993,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2 {\n",
" \n",
" color: red;\n",
" \n",
@@ -9996,7 +10001,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10004,7 +10009,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10012,7 +10017,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10020,7 +10025,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10028,7 +10033,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10036,7 +10041,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10044,7 +10049,7 @@
" \n",
" }\n",
" \n",
- " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4 {\n",
+ " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10054,7 +10059,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\">\n",
+ " <table id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\">\n",
" \n",
"\n",
" <thead>\n",
@@ -10080,32 +10085,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
" \n",
" 0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
" \n",
" 1.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
" \n",
" 1.33\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
" \n",
" nan\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
" \n",
" -0.32\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
" \n",
" -0.99\n",
" \n",
@@ -10114,32 +10119,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
" \n",
" 1\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
" \n",
" 2.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
" \n",
" -1.07\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
" \n",
" -1.44\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
" \n",
" 0.56\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
" \n",
" 0.3\n",
" \n",
@@ -10148,32 +10153,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
" \n",
" 2\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
" \n",
" 3.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
" \n",
" -1.63\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
" \n",
" 0.22\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
" \n",
" 0.68\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
" \n",
" 1.89\n",
" \n",
@@ -10182,32 +10187,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
" \n",
" 3\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
" \n",
" 4.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
" \n",
" 0.96\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
" \n",
" 0.1\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
" \n",
" -0.48\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
" \n",
" 0.85\n",
" \n",
@@ -10216,32 +10221,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
" \n",
" 4\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
" \n",
" 5.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
" \n",
" 1.45\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
" \n",
" 1.06\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
" \n",
" 0.17\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
" \n",
" 0.52\n",
" \n",
@@ -10250,32 +10255,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
" \n",
" 5\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
" \n",
" 6.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
" \n",
" -1.34\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
" \n",
" 0.56\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
" \n",
" 1.39\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
" \n",
" -0.06\n",
" \n",
@@ -10284,32 +10289,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
" \n",
" 6\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
" \n",
" 7.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
" \n",
" 0.12\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
" \n",
" 1.21\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
" \n",
" -0.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
" \n",
" 1.63\n",
" \n",
@@ -10318,32 +10323,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
" \n",
" 7\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
" \n",
" 8.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
" \n",
" 0.35\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
" \n",
" 1.04\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
" \n",
" -0.39\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
" \n",
" 0.52\n",
" \n",
@@ -10352,32 +10357,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
" \n",
" 8\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
" \n",
" 9.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
" \n",
" 1.69\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
" \n",
" -1.33\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
" \n",
" 1.43\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
" \n",
" -2.09\n",
" \n",
@@ -10386,32 +10391,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
" \n",
" 9\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
" \n",
" 10.0\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
" \n",
" -0.13\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
" \n",
" 0.63\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
" \n",
" -0.59\n",
" \n",
" \n",
- " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
" \n",
" 0.29\n",
" \n",
@@ -10423,10 +10428,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x1133679b0>"
+ "<pandas.core.style.Styler at 0x111c7dbe0>"
]
},
- "execution_count": 23,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -10448,7 +10453,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 26,
"metadata": {
"collapsed": false
},
@@ -10460,7 +10465,7 @@
" <style type=\"text/css\" >\n",
" \n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10468,7 +10473,7 @@
" \n",
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1 {\n",
" \n",
" color: black;\n",
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@@ -10476,7 +10481,7 @@
" \n",
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2 {\n",
" \n",
" color: black;\n",
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@@ -10484,7 +10489,7 @@
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" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col3 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10492,7 +10497,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col4 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10500,7 +10505,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0 {\n",
" \n",
" color: black;\n",
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@@ -10508,7 +10513,7 @@
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" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10516,7 +10521,7 @@
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" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10524,7 +10529,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10532,7 +10537,7 @@
" \n",
" }\n",
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10540,7 +10545,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0 {\n",
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" color: black;\n",
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@@ -10548,7 +10553,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10556,7 +10561,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10564,7 +10569,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10572,7 +10577,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10580,7 +10585,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10588,7 +10593,7 @@
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" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10596,7 +10601,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10604,7 +10609,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10612,7 +10617,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4 {\n",
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" color: black;\n",
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@@ -10620,7 +10625,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0 {\n",
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" color: black;\n",
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@@ -10628,7 +10633,7 @@
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4 {\n",
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0 {\n",
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1 {\n",
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- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0 {\n",
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+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 {\n",
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" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10772,7 +10777,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10780,7 +10785,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10788,7 +10793,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10796,7 +10801,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10804,7 +10809,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10812,7 +10817,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10820,7 +10825,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10828,7 +10833,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10836,7 +10841,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10844,7 +10849,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3 {\n",
" \n",
" color: red;\n",
" \n",
@@ -10852,7 +10857,7 @@
" \n",
" }\n",
" \n",
- " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4 {\n",
+ " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4 {\n",
" \n",
" color: black;\n",
" \n",
@@ -10862,7 +10867,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\">\n",
+ " <table id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\">\n",
" \n",
"\n",
" <thead>\n",
@@ -10888,32 +10893,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
" \n",
" 0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
" \n",
" 1.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
" \n",
" 1.33\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
" \n",
" nan\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
" \n",
" -0.32\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
" \n",
" -0.99\n",
" \n",
@@ -10922,32 +10927,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
" \n",
" 1\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
" \n",
" 2.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
" \n",
" -1.07\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
" \n",
" -1.44\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
" \n",
" 0.56\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
" \n",
" 0.3\n",
" \n",
@@ -10956,32 +10961,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
" \n",
" 2\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
" \n",
" 3.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
" \n",
" -1.63\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
" \n",
" 0.22\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
" \n",
" 0.68\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
" \n",
" 1.89\n",
" \n",
@@ -10990,32 +10995,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
" \n",
" 3\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
" \n",
" 4.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
" \n",
" 0.96\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
" \n",
" 0.1\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
" \n",
" -0.48\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
" \n",
" 0.85\n",
" \n",
@@ -11024,32 +11029,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
" \n",
" 4\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
" \n",
" 5.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
" \n",
" 1.45\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
" \n",
" 1.06\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
" \n",
" 0.17\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
" \n",
" 0.52\n",
" \n",
@@ -11058,32 +11063,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
" \n",
" 5\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
" \n",
" 6.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
" \n",
" -1.34\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
" \n",
" 0.56\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
" \n",
" 1.39\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
" \n",
" -0.06\n",
" \n",
@@ -11092,32 +11097,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
" \n",
" 6\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
" \n",
" 7.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
" \n",
" 0.12\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
" \n",
" 1.21\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
" \n",
" -0.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
" \n",
" 1.63\n",
" \n",
@@ -11126,32 +11131,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
" \n",
" 7\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
" \n",
" 8.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
" \n",
" 0.35\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
" \n",
" 1.04\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
" \n",
" -0.39\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
" \n",
" 0.52\n",
" \n",
@@ -11160,32 +11165,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
" \n",
" 8\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
" \n",
" 9.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
" \n",
" 1.69\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
" \n",
" -1.33\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
" \n",
" 1.43\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
" \n",
" -2.09\n",
" \n",
@@ -11194,32 +11199,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
" \n",
" 9\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
" \n",
" 10.0\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
" \n",
" -0.13\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
" \n",
" 0.63\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
" \n",
" -0.59\n",
" \n",
" \n",
- " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
" \n",
" 0.29\n",
" \n",
@@ -11231,10 +11236,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11338e3c8>"
+ "<pandas.core.style.Styler at 0x111c7dc18>"
]
},
- "execution_count": 24,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -11269,7 +11274,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 27,
"metadata": {
"collapsed": false
},
@@ -11281,301 +11286,301 @@
" <style type=\"text/css\" >\n",
" \n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col0 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0 {\n",
" \n",
" background-color: #e5ffe5;\n",
" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col1 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1 {\n",
" \n",
" background-color: #188d18;\n",
" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col2 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2 {\n",
" \n",
" background-color: #e5ffe5;\n",
" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col3 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3 {\n",
" \n",
" background-color: #c7eec7;\n",
" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col4 {\n",
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" \n",
" background-color: #a6dca6;\n",
" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col0 {\n",
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" \n",
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" \n",
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" \n",
" background-color: #c0eac0;\n",
" \n",
" }\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col2 {\n",
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" \n",
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" }\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col3 {\n",
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" \n",
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" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col0 {\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col1 {\n",
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" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col2 {\n",
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+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4 {\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col0 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0 {\n",
" \n",
" background-color: #99d599;\n",
" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1 {\n",
" \n",
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" \n",
" }\n",
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+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2 {\n",
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" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4 {\n",
" \n",
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" \n",
" }\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col0 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0 {\n",
" \n",
" background-color: #80c780;\n",
" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col1 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1 {\n",
" \n",
" background-color: #108910;\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col2 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2 {\n",
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" background-color: #0d870d;\n",
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" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col3 {\n",
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" \n",
" background-color: #4fac4f;\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col0 {\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col1 {\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col2 {\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3 {\n",
" \n",
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" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col4 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4 {\n",
" \n",
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" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col0 {\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1 {\n",
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" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2 {\n",
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" \n",
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" \n",
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" }\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2 {\n",
" \n",
" background-color: #dcfadc;\n",
" \n",
" }\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3 {\n",
" \n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4 {\n",
" \n",
" background-color: #e5ffe5;\n",
" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0 {\n",
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- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1 {\n",
" \n",
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" \n",
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" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2 {\n",
" \n",
" background-color: #319b31;\n",
" \n",
" }\n",
" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3 {\n",
" \n",
" background-color: #e5ffe5;\n",
" \n",
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" \n",
- " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4 {\n",
+ " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4 {\n",
" \n",
" background-color: #5cb35c;\n",
" \n",
@@ -11583,7 +11588,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\">\n",
+ " <table id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\">\n",
" \n",
" <caption>Colormaps, with a caption.</caption>\n",
" \n",
@@ -11611,32 +11616,32 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
" \n",
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" \n",
" \n",
- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
" \n",
" 1.0\n",
" \n",
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" \n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
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" \n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
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@@ -11849,32 +11854,32 @@
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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@@ -11883,32 +11888,32 @@
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" 8\n",
" \n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
" \n",
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" \n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -11917,32 +11922,32 @@
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+ " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
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- " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
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@@ -11954,10 +11959,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x113367978>"
+ "<pandas.core.style.Styler at 0x111c7d828>"
]
},
- "execution_count": 25,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -11985,7 +11990,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 28,
"metadata": {
"collapsed": false
},
@@ -11996,13 +12001,13 @@
"\n",
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- " #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb tr:hover {\n",
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- " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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- " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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" "
],
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+ "<pandas.core.style.Styler at 0x114c42710>"
]
},
- "execution_count": 26,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
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},
{
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+ "execution_count": 29,
"metadata": {
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@@ -12784,7 +12789,7 @@
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+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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@@ -12878,32 +12883,32 @@
" \n",
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+ " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
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" 2\n",
" \n",
" \n",
- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
" \n",
" 1.889273\n",
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@@ -12912,32 +12917,32 @@
" \n",
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- " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
" \n",
" 3\n",
" \n",
" \n",
- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
" \n",
" 4.0\n",
" \n",
" \n",
- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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@@ -12946,32 +12951,32 @@
" \n",
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" \n",
- " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
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@@ -12980,32 +12985,32 @@
" \n",
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- " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
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@@ -13082,32 +13087,32 @@
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+ " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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" \n",
" \n",
- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
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@@ -13116,32 +13121,32 @@
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- " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
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" 9\n",
" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
" \n",
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" \n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
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- " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
" \n",
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@@ -13153,7 +13158,7 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11338e0f0>"
+ "<pandas.core.style.Styler at 0x113487dd8>"
]
},
"metadata": {},
@@ -13172,7 +13177,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 30,
"metadata": {
"collapsed": false
},
@@ -13181,9 +13186,11 @@
"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",
+ " dict(selector=\"tr:hover td:hover\",\n",
" props=[('max-width', '200px'),\n",
" ('font-size', '12pt')])\n",
"]"
@@ -13191,7 +13198,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 31,
"metadata": {
"collapsed": false
},
@@ -13202,19 +13209,25 @@
"\n",
" <style type=\"text/css\" >\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th {\n",
" \n",
" font-size: 4pt;\n",
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th:hover {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb td {\n",
+ " \n",
+ " padding: 0em 0em;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th:hover {\n",
" \n",
" font-size: 12pt;\n",
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb tr:hover td:hover {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb tr:hover td:hover {\n",
" \n",
" max-width: 200px;\n",
" \n",
@@ -13223,7 +13236,7 @@
" }\n",
" \n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0 {\n",
" \n",
" background-color: #ecf2f8;\n",
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@@ -13233,7 +13246,7 @@
" \n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1 {\n",
" \n",
" background-color: #b8cce5;\n",
" \n",
@@ -13243,7 +13256,7 @@
" \n",
" }\n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col2 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2 {\n",
" \n",
" background-color: #efb1be;\n",
" \n",
@@ -13253,7 +13266,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col3 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3 {\n",
" \n",
" background-color: #f3c2cc;\n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col12 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12 {\n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col20 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3 {\n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col12 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1 {\n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col20 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20 {\n",
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" background-color: #f2f2f2;\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3 {\n",
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+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3 {\n",
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- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col12 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12 {\n",
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" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col20 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20 {\n",
" \n",
" background-color: #bccfe7;\n",
" \n",
@@ -17933,7 +17946,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col21 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21 {\n",
" \n",
" background-color: #d73c5b;\n",
" \n",
@@ -17943,7 +17956,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col22 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22 {\n",
" \n",
" background-color: #9eb9db;\n",
" \n",
@@ -17953,7 +17966,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col23 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23 {\n",
" \n",
" background-color: #5485c1;\n",
" \n",
@@ -17963,7 +17976,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col24 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24 {\n",
" \n",
" background-color: #8babd4;\n",
" \n",
@@ -17973,7 +17986,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col0 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0 {\n",
" \n",
" background-color: #86a8d3;\n",
" \n",
@@ -17983,7 +17996,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col1 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1 {\n",
" \n",
" background-color: #5b89c3;\n",
" \n",
@@ -17993,7 +18006,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col2 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2 {\n",
" \n",
" background-color: #f2bfca;\n",
" \n",
@@ -18003,7 +18016,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col3 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3 {\n",
" \n",
" background-color: #f2bfca;\n",
" \n",
@@ -18013,7 +18026,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col4 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4 {\n",
" \n",
" background-color: #497dbd;\n",
" \n",
@@ -18023,7 +18036,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col5 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5 {\n",
" \n",
" background-color: #f2bfca;\n",
" \n",
@@ -18033,7 +18046,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col6 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6 {\n",
" \n",
" background-color: #f2f2f2;\n",
" \n",
@@ -18043,7 +18056,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col7 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7 {\n",
" \n",
" background-color: #5686c1;\n",
" \n",
@@ -18053,7 +18066,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col8 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8 {\n",
" \n",
" background-color: #eda8b6;\n",
" \n",
@@ -18063,7 +18076,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col9 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9 {\n",
" \n",
" background-color: #d9e4f1;\n",
" \n",
@@ -18073,7 +18086,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col10 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10 {\n",
" \n",
" background-color: #d5e1f0;\n",
" \n",
@@ -18083,7 +18096,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col11 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11 {\n",
" \n",
" background-color: #bfd1e8;\n",
" \n",
@@ -18093,7 +18106,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col12 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12 {\n",
" \n",
" background-color: #5787c2;\n",
" \n",
@@ -18103,7 +18116,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13 {\n",
" \n",
" background-color: #fbeaed;\n",
" \n",
@@ -18113,7 +18126,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14 {\n",
" \n",
" background-color: #f8dee3;\n",
" \n",
@@ -18123,7 +18136,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15 {\n",
" \n",
" background-color: #f2f2f2;\n",
" \n",
@@ -18133,7 +18146,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16 {\n",
" \n",
" background-color: #eeaab7;\n",
" \n",
@@ -18143,7 +18156,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17 {\n",
" \n",
" background-color: #f6d1d8;\n",
" \n",
@@ -18153,7 +18166,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18 {\n",
" \n",
" background-color: #f7d7de;\n",
" \n",
@@ -18163,7 +18176,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19 {\n",
" \n",
" background-color: #f2f2f2;\n",
" \n",
@@ -18173,7 +18186,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20 {\n",
" \n",
" background-color: #b5cae4;\n",
" \n",
@@ -18183,7 +18196,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21 {\n",
" \n",
" background-color: #d73c5b;\n",
" \n",
@@ -18193,7 +18206,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22 {\n",
" \n",
" background-color: #9eb9db;\n",
" \n",
@@ -18203,7 +18216,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23 {\n",
" \n",
" background-color: #4479bb;\n",
" \n",
@@ -18213,7 +18226,7 @@
" \n",
" }\n",
" \n",
- " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24 {\n",
+ " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24 {\n",
" \n",
" background-color: #89aad4;\n",
" \n",
@@ -18225,7 +18238,7 @@
" \n",
" </style>\n",
"\n",
- " <table id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\">\n",
+ " <table id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\">\n",
" \n",
" <caption>Hover to magify</caption>\n",
" \n",
@@ -18293,132 +18306,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row0\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row0\">\n",
" \n",
" 0\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
" \n",
" 0.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
" \n",
" 1.03\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
" \n",
" -0.84\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
" \n",
" -0.59\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
" \n",
" -0.96\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n",
" \n",
" -0.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n",
" \n",
" -0.62\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n",
" \n",
" 1.84\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n",
" \n",
" -2.05\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n",
" \n",
" 0.87\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n",
" \n",
" -0.92\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n",
" \n",
" -0.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n",
" \n",
" 2.15\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n",
" \n",
" -1.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n",
" \n",
" 0.08\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n",
" \n",
" -1.25\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n",
" \n",
" 1.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n",
" \n",
" -1.05\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n",
" \n",
" 1.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n",
" \n",
" -0.42\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n",
" \n",
" 2.29\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n",
" \n",
" -2.59\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n",
" \n",
" 2.82\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n",
" \n",
" 0.68\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n",
" \n",
" -1.58\n",
" \n",
@@ -18427,132 +18440,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row1\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row1\">\n",
" \n",
" 1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
" \n",
" -1.75\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
" \n",
" 1.56\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
" \n",
" -1.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
" \n",
" -1.1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
" \n",
" 1.03\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n",
" \n",
" 0.0\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n",
" \n",
" -2.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n",
" \n",
" 3.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n",
" \n",
" -1.66\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n",
" \n",
" 1.27\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n",
" \n",
" -0.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n",
" \n",
" -0.02\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n",
" \n",
" 1.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n",
" \n",
" -1.09\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n",
" \n",
" -1.86\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n",
" \n",
" -1.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n",
" \n",
" -0.68\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n",
" \n",
" -0.81\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n",
" \n",
" 0.35\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n",
" \n",
" -0.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n",
" \n",
" 1.79\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n",
" \n",
" -2.82\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n",
" \n",
" 2.26\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n",
" \n",
" 0.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n",
" \n",
" 0.44\n",
" \n",
@@ -18561,132 +18574,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row2\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row2\">\n",
" \n",
" 2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
" \n",
" -0.65\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
" \n",
" 3.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
" \n",
" -1.76\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
" \n",
" 0.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
" \n",
" 2.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n",
" \n",
" -0.37\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n",
" \n",
" -3.0\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n",
" \n",
" 3.73\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n",
" \n",
" -1.87\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n",
" \n",
" 2.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n",
" \n",
" 0.21\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n",
" \n",
" -0.24\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n",
" \n",
" -0.1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n",
" \n",
" -0.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n",
" \n",
" -3.02\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n",
" \n",
" -0.82\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n",
" \n",
" -0.21\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n",
" \n",
" -0.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n",
" \n",
" 0.86\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n",
" \n",
" -0.68\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col20\" class=\"data row2 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col20\" class=\"data row2 col20\">\n",
" \n",
" 1.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col21\" class=\"data row2 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col21\" class=\"data row2 col21\">\n",
" \n",
" -4.89\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col22\" class=\"data row2 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col22\" class=\"data row2 col22\">\n",
" \n",
" 3.03\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col23\" class=\"data row2 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col23\" class=\"data row2 col23\">\n",
" \n",
" 1.91\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col24\" class=\"data row2 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col24\" class=\"data row2 col24\">\n",
" \n",
" 0.61\n",
" \n",
@@ -18695,132 +18708,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row3\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row3\">\n",
" \n",
" 3\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
" \n",
" -1.62\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
" \n",
" 3.71\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
" \n",
" -2.31\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
" \n",
" 0.43\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
" \n",
" 4.17\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col5\" class=\"data row3 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col5\" class=\"data row3 col5\">\n",
" \n",
" -0.43\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col6\" class=\"data row3 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col6\" class=\"data row3 col6\">\n",
" \n",
" -3.86\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col7\" class=\"data row3 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7\" class=\"data row3 col7\">\n",
" \n",
" 4.16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n",
" \n",
" -2.15\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n",
" \n",
" 1.08\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n",
" \n",
" 0.12\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n",
" \n",
" 0.6\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n",
" \n",
" -0.89\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n",
" \n",
" 0.27\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n",
" \n",
" -3.67\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n",
" \n",
" -2.71\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n",
" \n",
" -0.31\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n",
" \n",
" -1.59\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n",
" \n",
" 1.35\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n",
" \n",
" -1.83\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n",
" \n",
" 0.91\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n",
" \n",
" -5.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n",
" \n",
" 2.81\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n",
" \n",
" 2.11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n",
" \n",
" 0.28\n",
" \n",
@@ -18829,132 +18842,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row4\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row4\">\n",
" \n",
" 4\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
" \n",
" -3.35\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
" \n",
" 4.48\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
" \n",
" -1.86\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
" \n",
" -1.7\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
" \n",
" 5.19\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n",
" \n",
" -1.02\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n",
" \n",
" -3.81\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n",
" \n",
" 4.72\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n",
" \n",
" -0.72\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n",
" \n",
" 1.08\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n",
" \n",
" -0.18\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n",
" \n",
" 0.83\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n",
" \n",
" -0.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n",
" \n",
" -1.08\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n",
" \n",
" -4.27\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n",
" \n",
" -2.88\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n",
" \n",
" -0.97\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n",
" \n",
" -1.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n",
" \n",
" 1.53\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n",
" \n",
" -1.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n",
" \n",
" 2.21\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n",
" \n",
" -6.34\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n",
" \n",
" 3.34\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n",
" \n",
" 2.49\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n",
" \n",
" 2.09\n",
" \n",
@@ -18963,132 +18976,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row5\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row5\">\n",
" \n",
" 5\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
" \n",
" -0.84\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
" \n",
" 4.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
" \n",
" -1.65\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
" \n",
" -2.0\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
" \n",
" 5.34\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n",
" \n",
" -0.99\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n",
" \n",
" -4.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n",
" \n",
" 3.94\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n",
" \n",
" -1.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n",
" \n",
" -0.94\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n",
" \n",
" 1.24\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n",
" \n",
" 0.09\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n",
" \n",
" -1.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n",
" \n",
" -0.11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n",
" \n",
" -4.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n",
" \n",
" -0.85\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n",
" \n",
" -2.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n",
" \n",
" -1.35\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n",
" \n",
" 0.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n",
" \n",
" -1.63\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n",
" \n",
" 1.54\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n",
" \n",
" -6.51\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n",
" \n",
" 2.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n",
" \n",
" 2.14\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n",
" \n",
" 3.77\n",
" \n",
@@ -19097,132 +19110,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row6\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row6\">\n",
" \n",
" 6\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
" \n",
" -0.74\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
" \n",
" 5.35\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
" \n",
" -2.11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
" \n",
" -1.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
" \n",
" 4.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n",
" \n",
" -1.85\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n",
" \n",
" -3.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n",
" \n",
" 3.76\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n",
" \n",
" -3.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n",
" \n",
" -1.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n",
" \n",
" 0.34\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n",
" \n",
" 0.57\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n",
" \n",
" -1.82\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n",
" \n",
" 0.54\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n",
" \n",
" -4.43\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n",
" \n",
" -1.83\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n",
" \n",
" -4.03\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col17\" class=\"data row6 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17\" class=\"data row6 col17\">\n",
" \n",
" -2.62\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col18\" class=\"data row6 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18\" class=\"data row6 col18\">\n",
" \n",
" -0.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col19\" class=\"data row6 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19\" class=\"data row6 col19\">\n",
" \n",
" -4.68\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col20\" class=\"data row6 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20\" class=\"data row6 col20\">\n",
" \n",
" 1.93\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col21\" class=\"data row6 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21\" class=\"data row6 col21\">\n",
" \n",
" -8.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col22\" class=\"data row6 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22\" class=\"data row6 col22\">\n",
" \n",
" 3.34\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col23\" class=\"data row6 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23\" class=\"data row6 col23\">\n",
" \n",
" 2.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n",
" \n",
" 5.81\n",
" \n",
@@ -19231,132 +19244,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row7\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row7\">\n",
" \n",
" 7\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
" \n",
" -0.44\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
" \n",
" 4.69\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
" \n",
" -2.3\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
" \n",
" -0.21\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
" \n",
" 5.93\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n",
" \n",
" -2.63\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n",
" \n",
" -1.83\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n",
" \n",
" 5.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n",
" \n",
" -4.5\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n",
" \n",
" -3.16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n",
" \n",
" -1.73\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n",
" \n",
" 0.18\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n",
" \n",
" 0.11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n",
" \n",
" 0.04\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n",
" \n",
" -5.99\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n",
" \n",
" -0.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n",
" \n",
" -6.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n",
" \n",
" -3.89\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n",
" \n",
" 0.71\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n",
" \n",
" -3.95\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n",
" \n",
" 0.67\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n",
" \n",
" -7.26\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n",
" \n",
" 2.97\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n",
" \n",
" 3.39\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n",
" \n",
" 6.66\n",
" \n",
@@ -19365,132 +19378,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row8\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row8\">\n",
" \n",
" 8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
" \n",
" 0.92\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
" \n",
" 5.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
" \n",
" -3.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
" \n",
" -0.65\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
" \n",
" 5.99\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n",
" \n",
" -3.19\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n",
" \n",
" -1.83\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n",
" \n",
" 5.63\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n",
" \n",
" -3.53\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n",
" \n",
" -1.3\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n",
" \n",
" -1.61\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n",
" \n",
" 0.82\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n",
" \n",
" -2.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n",
" \n",
" -0.4\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n",
" \n",
" -6.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n",
" \n",
" -0.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n",
" \n",
" -6.6\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n",
" \n",
" -3.48\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n",
" \n",
" -0.04\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n",
" \n",
" -4.6\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n",
" \n",
" 0.51\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n",
" \n",
" -5.85\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n",
" \n",
" 3.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n",
" \n",
" 2.4\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n",
" \n",
" 5.08\n",
" \n",
@@ -19499,132 +19512,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row9\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row9\">\n",
" \n",
" 9\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
" \n",
" 0.38\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
" \n",
" 5.54\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
" \n",
" -4.49\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
" \n",
" -0.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
" \n",
" 7.05\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n",
" \n",
" -2.64\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n",
" \n",
" -0.44\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n",
" \n",
" 5.35\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n",
" \n",
" -1.96\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n",
" \n",
" -0.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n",
" \n",
" -0.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n",
" \n",
" 0.26\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n",
" \n",
" -3.37\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n",
" \n",
" -0.82\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n",
" \n",
" -6.05\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n",
" \n",
" -2.61\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n",
" \n",
" -8.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n",
" \n",
" -4.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n",
" \n",
" 0.41\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n",
" \n",
" -4.71\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n",
" \n",
" 1.89\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n",
" \n",
" -6.93\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n",
" \n",
" 2.14\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n",
" \n",
" 3.0\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n",
" \n",
" 5.16\n",
" \n",
@@ -19633,132 +19646,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row10\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row10\">\n",
" \n",
" 10\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n",
" \n",
" 2.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n",
" \n",
" 5.84\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n",
" \n",
" -3.9\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n",
" \n",
" -0.98\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n",
" \n",
" 7.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n",
" \n",
" -2.49\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n",
" \n",
" -0.59\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n",
" \n",
" 5.59\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n",
" \n",
" -2.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n",
" \n",
" -0.71\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n",
" \n",
" -0.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n",
" \n",
" 1.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n",
" \n",
" -2.79\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n",
" \n",
" 0.48\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n",
" \n",
" -5.97\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n",
" \n",
" -3.44\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n",
" \n",
" -7.77\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n",
" \n",
" -5.49\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n",
" \n",
" -0.7\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n",
" \n",
" -4.61\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n",
" \n",
" -0.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n",
" \n",
" -7.72\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n",
" \n",
" 1.54\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n",
" \n",
" 5.02\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n",
" \n",
" 5.81\n",
" \n",
@@ -19767,132 +19780,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row11\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row11\">\n",
" \n",
" 11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n",
" \n",
" 1.86\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n",
" \n",
" 4.47\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n",
" \n",
" -2.17\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n",
" \n",
" -1.38\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n",
" \n",
" 5.9\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n",
" \n",
" -0.49\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n",
" \n",
" 0.02\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n",
" \n",
" 5.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n",
" \n",
" -1.04\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n",
" \n",
" -0.6\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n",
" \n",
" 0.49\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n",
" \n",
" 1.96\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n",
" \n",
" -1.47\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n",
" \n",
" 1.88\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n",
" \n",
" -5.92\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n",
" \n",
" -4.55\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n",
" \n",
" -8.15\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n",
" \n",
" -3.42\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n",
" \n",
" -2.24\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n",
" \n",
" -4.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n",
" \n",
" -1.17\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n",
" \n",
" -7.9\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n",
" \n",
" 1.36\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n",
" \n",
" 5.31\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n",
" \n",
" 5.83\n",
" \n",
@@ -19901,132 +19914,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row12\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row12\">\n",
" \n",
" 12\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n",
" \n",
" 3.19\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n",
" \n",
" 4.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n",
" \n",
" -3.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n",
" \n",
" -2.27\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n",
" \n",
" 5.93\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n",
" \n",
" -2.64\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n",
" \n",
" 0.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n",
" \n",
" 6.72\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n",
" \n",
" -2.84\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n",
" \n",
" -0.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n",
" \n",
" 1.89\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n",
" \n",
" 2.63\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n",
" \n",
" -1.53\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n",
" \n",
" 0.75\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n",
" \n",
" -5.27\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n",
" \n",
" -4.53\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n",
" \n",
" -7.57\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n",
" \n",
" -2.85\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n",
" \n",
" -2.17\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n",
" \n",
" -4.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n",
" \n",
" -1.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n",
" \n",
" -8.99\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n",
" \n",
" 2.11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n",
" \n",
" 6.42\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n",
" \n",
" 5.6\n",
" \n",
@@ -20035,132 +20048,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row13\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row13\">\n",
" \n",
" 13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n",
" \n",
" 2.31\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n",
" \n",
" 4.45\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n",
" \n",
" -3.87\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n",
" \n",
" -2.05\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n",
" \n",
" 6.76\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n",
" \n",
" -3.25\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n",
" \n",
" -2.17\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n",
" \n",
" 7.99\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n",
" \n",
" -2.56\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n",
" \n",
" -0.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n",
" \n",
" 0.71\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n",
" \n",
" 2.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n",
" \n",
" -0.16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n",
" \n",
" -0.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n",
" \n",
" -5.1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n",
" \n",
" -3.79\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n",
" \n",
" -7.58\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n",
" \n",
" -4.0\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n",
" \n",
" 0.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n",
" \n",
" -3.67\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n",
" \n",
" -1.05\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n",
" \n",
" -8.71\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n",
" \n",
" 2.47\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n",
" \n",
" 5.87\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n",
" \n",
" 6.71\n",
" \n",
@@ -20169,132 +20182,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row14\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row14\">\n",
" \n",
" 14\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n",
" \n",
" 3.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n",
" \n",
" 4.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n",
" \n",
" -3.88\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n",
" \n",
" -1.58\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n",
" \n",
" 6.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n",
" \n",
" -3.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n",
" \n",
" -1.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n",
" \n",
" 5.57\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n",
" \n",
" -2.93\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n",
" \n",
" -0.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n",
" \n",
" -0.97\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n",
" \n",
" 1.72\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n",
" \n",
" 3.61\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n",
" \n",
" 0.29\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n",
" \n",
" -4.21\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n",
" \n",
" -4.1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n",
" \n",
" -6.68\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n",
" \n",
" -4.5\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n",
" \n",
" -2.19\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n",
" \n",
" -2.43\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n",
" \n",
" -1.64\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n",
" \n",
" -9.36\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n",
" \n",
" 3.36\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n",
" \n",
" 6.11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n",
" \n",
" 7.53\n",
" \n",
@@ -20303,132 +20316,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row15\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row15\">\n",
" \n",
" 15\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n",
" \n",
" 5.64\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n",
" \n",
" 5.31\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n",
" \n",
" -3.98\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n",
" \n",
" -2.26\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n",
" \n",
" 5.91\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n",
" \n",
" -3.3\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n",
" \n",
" -1.03\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n",
" \n",
" 5.68\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n",
" \n",
" -3.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n",
" \n",
" -0.33\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n",
" \n",
" -1.16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n",
" \n",
" 2.19\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n",
" \n",
" 4.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n",
" \n",
" 1.01\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n",
" \n",
" -3.22\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n",
" \n",
" -4.31\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n",
" \n",
" -5.74\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n",
" \n",
" -4.44\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n",
" \n",
" -2.3\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n",
" \n",
" -1.36\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n",
" \n",
" -1.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n",
" \n",
" -11.27\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n",
" \n",
" 2.59\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n",
" \n",
" 6.69\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n",
" \n",
" 5.91\n",
" \n",
@@ -20437,132 +20450,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row16\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row16\">\n",
" \n",
" 16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n",
" \n",
" 4.08\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n",
" \n",
" 4.34\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n",
" \n",
" -2.44\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n",
" \n",
" -3.3\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n",
" \n",
" 6.04\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n",
" \n",
" -2.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n",
" \n",
" -0.47\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n",
" \n",
" 5.28\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n",
" \n",
" -4.84\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n",
" \n",
" 1.58\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n",
" \n",
" 0.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n",
" \n",
" 0.1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n",
" \n",
" 5.79\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n",
" \n",
" 1.8\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n",
" \n",
" -3.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n",
" \n",
" -3.85\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n",
" \n",
" -5.53\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n",
" \n",
" -2.97\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n",
" \n",
" -2.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n",
" \n",
" -1.15\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n",
" \n",
" -0.56\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n",
" \n",
" -13.13\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n",
" \n",
" 2.07\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n",
" \n",
" 6.16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n",
" \n",
" 4.94\n",
" \n",
@@ -20571,132 +20584,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row17\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row17\">\n",
" \n",
" 17\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n",
" \n",
" 5.64\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n",
" \n",
" 4.57\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n",
" \n",
" -3.53\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n",
" \n",
" -3.76\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n",
" \n",
" 6.58\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n",
" \n",
" -2.58\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n",
" \n",
" -0.75\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n",
" \n",
" 6.58\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n",
" \n",
" -4.78\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n",
" \n",
" 3.63\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n",
" \n",
" -0.29\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n",
" \n",
" 0.56\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n",
" \n",
" 5.76\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n",
" \n",
" 2.05\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n",
" \n",
" -2.27\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n",
" \n",
" -2.31\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n",
" \n",
" -4.95\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n",
" \n",
" -3.16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n",
" \n",
" -3.06\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n",
" \n",
" -2.43\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n",
" \n",
" 0.84\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n",
" \n",
" -12.57\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n",
" \n",
" 3.56\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n",
" \n",
" 7.36\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n",
" \n",
" 4.7\n",
" \n",
@@ -20705,132 +20718,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row18\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row18\">\n",
" \n",
" 18\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n",
" \n",
" 5.99\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n",
" \n",
" 5.82\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n",
" \n",
" -2.85\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n",
" \n",
" -4.15\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n",
" \n",
" 7.12\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n",
" \n",
" -3.32\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n",
" \n",
" -1.21\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n",
" \n",
" 7.93\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n",
" \n",
" -4.85\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n",
" \n",
" 1.44\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n",
" \n",
" -0.63\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n",
" \n",
" 0.35\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n",
" \n",
" 7.47\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n",
" \n",
" 0.87\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n",
" \n",
" -1.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n",
" \n",
" -2.09\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n",
" \n",
" -4.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n",
" \n",
" -2.55\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n",
" \n",
" -2.46\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n",
" \n",
" -2.89\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n",
" \n",
" 1.9\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n",
" \n",
" -9.74\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n",
" \n",
" 3.43\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n",
" \n",
" 7.07\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n",
" \n",
" 4.39\n",
" \n",
@@ -20839,132 +20852,132 @@
" \n",
" <tr>\n",
" \n",
- " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row19\">\n",
+ " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row19\">\n",
" \n",
" 19\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n",
" \n",
" 4.03\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n",
" \n",
" 6.23\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n",
" \n",
" -4.1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n",
" \n",
" -4.11\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n",
" \n",
" 7.19\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n",
" \n",
" -4.1\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n",
" \n",
" -1.52\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n",
" \n",
" 6.53\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n",
" \n",
" -5.21\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n",
" \n",
" -0.24\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n",
" \n",
" 0.01\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n",
" \n",
" 1.16\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n",
" \n",
" 6.43\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n",
" \n",
" -1.97\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n",
" \n",
" -2.64\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n",
" \n",
" -1.66\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n",
" \n",
" -5.2\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n",
" \n",
" -3.25\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n",
" \n",
" -2.87\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n",
" \n",
" -1.65\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n",
" \n",
" 1.64\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n",
" \n",
" -10.66\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n",
" \n",
" 2.83\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n",
" \n",
" 7.48\n",
" \n",
" \n",
- " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n",
+ " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n",
" \n",
" 3.94\n",
" \n",
@@ -20976,10 +20989,10 @@
" "
],
"text/plain": [
- "<pandas.core.style.Styler at 0x11335e6a0>"
+ "<pandas.core.style.Styler at 0x114b9b1d0>"
]
},
- "execution_count": 29,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/doc/source/themes/nature_with_gtoc/static/nature.css_t b/doc/source/themes/nature_with_gtoc/static/nature.css_t
index 33644101eb425..2948f0d68b402 100644
--- a/doc/source/themes/nature_with_gtoc/static/nature.css_t
+++ b/doc/source/themes/nature_with_gtoc/static/nature.css_t
@@ -299,6 +299,19 @@ td.field-body blockquote {
padding-left: 30px;
}
+.rendered_html table {
+ margin-left: auto;
+ margin-right: auto;
+ border-right: 1px solid #cbcbcb;
+ border-bottom: 1px solid #cbcbcb;
+}
+
+.rendered_html td, th {
+ border-left: 1px solid #cbcbcb;
+ border-top: 1px solid #cbcbcb;
+ margin: 0;
+ padding: 0.5em .75em;
+}
/**
* See also
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index d32725449c49b..15e262182a862 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -25,11 +25,25 @@ New features
Conditional HTML Formatting
^^^^^^^^^^^^^^^^^^^^^^^^^^^
-We've added *experimental* support for conditional HTML formatting,
+We've added *experimental* support for conditional HTML formatting:
the visual styling of a DataFrame based on the data.
The styling is accomplished with HTML and CSS.
Acesses the styler class with :attr:`pandas.DataFrame.style`, attribute,
an instance of :class:`~pandas.core.style.Styler` with your data attached.
+
+Here's a quick example:
+
+ .. ipython:: python
+
+ np.random.seed(123)
+ df = DataFrame(np.random.randn(10, 5), columns=list('abcde'))
+ html = df.style.background_gradient(cmap='viridis', low=.5)
+
+We can render the HTML to get the following table.
+
+.. raw:: html
+ :file: whatsnew_0171_html_table.html
+
See the :ref:`example notebook <style>` for more.
.. _whatsnew_0171.enhancements:
diff --git a/doc/source/whatsnew/whatsnew_0171_html_table.html b/doc/source/whatsnew/whatsnew_0171_html_table.html
new file mode 100644
index 0000000000000..12965a045e41f
--- /dev/null
+++ b/doc/source/whatsnew/whatsnew_0171_html_table.html
@@ -0,0 +1,873 @@
+
+ <style type="text/css" >
+
+
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+
+
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+
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+
+ </tr>
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+ </thead>
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+ <tr>
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+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ -0.638902
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ -0.443982
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ -0.434351
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 2.20593
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 2.186786
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ 1.004054
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.386186
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 0.737369
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.490732
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ -0.935834
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 1.175829
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ -1.253881
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ -0.637752
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ 0.907105
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ -1.428681
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ -0.140069
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.861755
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ -0.255619
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ -2.798589
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ -1.771533
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.699877
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 0.927462
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ -0.173636
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.002846
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 0.688223
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.879536
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.283627
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ -0.805367
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ -1.727669
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -0.3909
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 0.573806
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ 0.338589
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ -0.01183
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ 2.392365
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.412912
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ 0.978736
+
+
+ <td id="T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 2.238143
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
\ No newline at end of file
diff --git a/pandas/core/style.py b/pandas/core/style.py
index 11170a6fdbe33..6ee5befd4ec02 100644
--- a/pandas/core/style.py
+++ b/pandas/core/style.py
@@ -93,7 +93,7 @@ class Styler(object):
{% endfor %}
</style>
- <table id="T_{{uuid}}">
+ <table id="T_{{uuid}}" {{ table_attributes }}>
{% if caption %}
<caption>{{caption}}</caption>
{% endif %}
@@ -125,7 +125,7 @@ class Styler(object):
""")
def __init__(self, data, precision=None, table_styles=None, uuid=None,
- caption=None):
+ caption=None, table_attributes=None):
self.ctx = defaultdict(list)
self._todo = []
@@ -146,6 +146,7 @@ def __init__(self, data, precision=None, table_styles=None, uuid=None,
if precision is None:
precision = pd.options.display.precision
self.precision = precision
+ self.table_attributes = table_attributes
def _repr_html_(self):
'''
@@ -230,7 +231,7 @@ def _translate(self):
return dict(head=head, cellstyle=cellstyle, body=body, uuid=uuid,
precision=precision, table_styles=table_styles,
- caption=caption)
+ caption=caption, table_attributes=self.table_attributes)
def render(self):
"""
@@ -415,6 +416,25 @@ def set_precision(self, precision):
self.precision = precision
return self
+ def set_table_attributes(self, attributes):
+ """
+ Set the table attributes. These are the items
+ that show up in the opening <table> tag in addition
+ to to automatic (by default) id.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ precision: int
+
+ Returns
+ -------
+ self
+ """
+ self.table_attributes = attributes
+ return self
+
def export(self):
"""
Export the styles to applied to the current Styler.
diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py
index 4c875cebe149a..ba989b474954d 100644
--- a/pandas/tests/test_style.py
+++ b/pandas/tests/test_style.py
@@ -277,6 +277,15 @@ def test_table_styles(self):
self.assertTrue(styler is result)
self.assertEqual(styler.table_styles, style)
+ def test_table_attributes(self):
+ attributes = 'class="foo" data-bar'
+ styler = Styler(self.df, table_attributes=attributes)
+ result = styler.render()
+ self.assertTrue('class="foo" data-bar' in result)
+
+ result = self.df.style.set_table_attributes(attributes).render()
+ self.assertTrue('class="foo" data-bar' in result)
+
def test_precision(self):
with pd.option_context('display.precision', 10):
s = Styler(self.df)
diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py
index f0545e9949e24..7f9840f73a583 100644
--- a/pandas/util/print_versions.py
+++ b/pandas/util/print_versions.py
@@ -89,6 +89,7 @@ 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__)
]
deps_blob = list()
| Partially closes https://github.com/pydata/pandas/issues/11610#issuecomment-157197629
- CSS update
- whatsnew example
- print_versions
- requirements_all
| https://api.github.com/repos/pandas-dev/pandas/pulls/11634 | 2015-11-18T03:15:27Z | 2015-11-18T11:52:12Z | 2015-11-18T11:52:12Z | 2016-11-03T12:38:36Z |
TST: Fix two failing tests on Windows #10631 | diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 96f72c0fbb2d1..0cae8b356b517 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -11629,7 +11629,7 @@ def test_apply_mixed_dtype_corner(self):
result = df[:0].apply(np.mean, axis=1)
# the result here is actually kind of ambiguous, should it be a Series
# or a DataFrame?
- expected = Series(np.nan, index=pd.Index([], dtype=int))
+ expected = Series(np.nan, index=pd.Index([], dtype='int64'))
assert_series_equal(result, expected)
df = DataFrame({'A': ['foo'],
@@ -15458,7 +15458,8 @@ def test_to_csv_with_dst_transitions(self):
ambiguous='infer')
for i in [times, times+pd.Timedelta('10s')]:
- df = DataFrame({'A' : range(len(i))}, index=i)
+ time_range = np.array(range(len(i)), dtype='int64')
+ df = DataFrame({'A' : time_range}, index=i)
df.to_csv(path,index=True)
# we have to reconvert the index as we
| I spotted two failing tests with errors similar to what described @chris-b1 in #10631:
```
Attribute "dtype" are different
[left]: int32
[right]: int64
```
This PR fixes this on windows.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11632 | 2015-11-17T23:21:52Z | 2015-11-18T11:48:36Z | 2015-11-18T11:48:36Z | 2018-09-11T02:58:11Z |
ENH: Implement export of datetime64[ns, tz] dtypes with a fixed HDF5 #11411 | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 96f936c58c5d5..d32725449c49b 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -66,6 +66,7 @@ Enhancements
pd.Index([1, np.nan, 3]).fillna(2)
- ``pivot_table`` now has a ``margins_name`` argument so you can use something other than the default of 'All' (:issue:`3335`)
+- Implement export of ``datetime64[ns, tz]`` dtypes with a fixed HDF5 store (:issue:`11411`)
.. _whatsnew_0171.api:
@@ -159,4 +160,3 @@ Bug Fixes
- Bug in ``DataFrame.join()`` with ``how='right'`` producing a ``TypeError`` (:issue:`11519`)
- Bug in ``Series.quantile`` with empty list results has ``Index`` with ``object`` dtype (:issue:`11588`)
- Bug in ``pd.merge`` results in empty ``Int64Index`` rather than ``Index(dtype=object)`` when the merge result is empty (:issue:`11588`)
-
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 4e25b546bddf2..fb57a7f8bd838 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -47,6 +47,7 @@
# versioning attribute
_version = '0.15.2'
+### encoding ###
# PY3 encoding if we don't specify
_default_encoding = 'UTF-8'
@@ -64,22 +65,8 @@ def _ensure_encoding(encoding):
encoding = _default_encoding
return encoding
-def _set_tz(values, tz, preserve_UTC=False):
- """ set the timezone if values are an Index """
- if tz is not None and isinstance(values, Index):
- tz = _ensure_decoded(tz)
- if values.tz is None:
- values = values.tz_localize('UTC').tz_convert(tz)
- if preserve_UTC:
- if tslib.get_timezone(tz) == 'UTC':
- values = list(values)
-
- return values
-
-
Term = Expr
-
def _ensure_term(where, scope_level):
"""
ensure that the where is a Term or a list of Term
@@ -1947,14 +1934,11 @@ def set_atom_datetime64tz(self, block, info, values=None):
if values is None:
values = block.values
- # convert this column to datetime64[ns] utc, and save the tz
- values = values.tz_convert('UTC').values.view('i8').reshape(block.shape)
+ # convert this column to i8 in UTC, and save the tz
+ values = values.asi8.reshape(block.shape)
# store a converted timezone
- zone = tslib.get_timezone(block.values.tz)
- if zone is None:
- zone = tslib.tot_seconds(block.values.tz.utcoffset())
- self.tz = zone
+ self.tz = _get_tz(block.values.tz)
self.update_info(info)
self.kind = 'datetime64'
@@ -2015,18 +1999,9 @@ def convert(self, values, nan_rep, encoding):
# reverse converts
if dtype == u('datetime64'):
- # recreate the timezone
- if self.tz is not None:
-
- # data should be 2-dim here
- # we stored as utc, so just set the tz
- index = DatetimeIndex(
- self.data.ravel(), tz='UTC').tz_convert(tslib.maybe_get_tz(self.tz))
- self.data = index
-
- else:
- self.data = np.asarray(self.data, dtype='M8[ns]')
+ # recreate with tz if indicated
+ self.data = _set_tz(self.data, self.tz, coerce=True)
elif dtype == u('timedelta64'):
self.data = np.asarray(self.data, dtype='m8[ns]')
@@ -2347,7 +2322,10 @@ def read_array(self, key):
ret = data
if dtype == u('datetime64'):
- ret = np.asarray(ret, dtype='M8[ns]')
+
+ # reconstruct a timezone if indicated
+ ret = _set_tz(ret, getattr(attrs, 'tz', None), coerce=True)
+
elif dtype == u('timedelta64'):
ret = np.asarray(ret, dtype='m8[ns]')
@@ -2397,10 +2375,7 @@ def write_index(self, key, index):
node._v_attrs.freq = index.freq
if hasattr(index, 'tz') and index.tz is not None:
- zone = tslib.get_timezone(index.tz)
- if zone is None:
- zone = tslib.tot_seconds(index.tz.utcoffset())
- node._v_attrs.tz = zone
+ node._v_attrs.tz = _get_tz(index.tz)
def write_block_index(self, key, index):
self.write_array('%s_blocs' % key, index.blocs)
@@ -2574,11 +2549,20 @@ def write_array(self, key, value, items=None):
if empty_array:
self.write_array_empty(key, value)
else:
- if value.dtype.type == np.datetime64:
+ if com.is_datetime64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view('i8'))
getattr(
self.group, key)._v_attrs.value_type = 'datetime64'
- elif value.dtype.type == np.timedelta64:
+ elif com.is_datetime64tz_dtype(value.dtype):
+ # store as UTC
+ # with a zone
+ self._handle.create_array(self.group, key,
+ value.asi8)
+
+ node = getattr(self.group, key)
+ node._v_attrs.tz = _get_tz(value.tz)
+ node._v_attrs.value_type = 'datetime64'
+ elif com.is_timedelta64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view('i8'))
getattr(
self.group, key)._v_attrs.value_type = 'timedelta64'
@@ -4248,6 +4232,40 @@ def _get_info(info, name):
idx = info[name] = dict()
return idx
+### tz to/from coercion ###
+def _get_tz(tz):
+ """ for a tz-aware type, return an encoded zone """
+ zone = tslib.get_timezone(tz)
+ if zone is None:
+ zone = tslib.tot_seconds(tz.utcoffset())
+ return zone
+
+def _set_tz(values, tz, preserve_UTC=False, coerce=False):
+ """
+ coerce the values to a DatetimeIndex if tz is set
+ preserve the input shape if possible
+
+ Parameters
+ ----------
+ values : ndarray
+ tz : string/pickled tz object
+ preserve_UTC : boolean,
+ preserve the UTC of the result
+ coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
+ """
+ if tz is not None:
+ values = values.ravel()
+ tz = tslib.get_timezone(_ensure_decoded(tz))
+ values = DatetimeIndex(values)
+ if values.tz is None:
+ values = values.tz_localize('UTC').tz_convert(tz)
+ if preserve_UTC:
+ if tz == 'UTC':
+ values = list(values)
+ elif coerce:
+ values = np.asarray(values, dtype='M8[ns]')
+
+ return values
def _convert_index(index, encoding=None, format_type=None):
index_name = getattr(index, 'name', None)
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index 9ffb0bfe79b8d..fd8c28bfe0f85 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -4909,15 +4909,26 @@ def test_tseries_select_index_column(self):
result = store.select_column('frame', 'index')
self.assertEqual(rng.tz, result.dt.tz)
- def test_timezones(self):
- rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
- frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
-
+ def test_timezones_fixed(self):
with ensure_clean_store(self.path) as store:
- store['frame'] = frame
- recons = store['frame']
- self.assertTrue(recons.index.equals(rng))
- self.assertEqual(rng.tz, recons.index.tz)
+
+ # index
+ rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
+ df = DataFrame(np.random.randn(len(rng), 4), index=rng)
+ store['df'] = df
+ result = store['df']
+ assert_frame_equal(result, df)
+
+ # as data
+ # GH11411
+ _maybe_remove(store, 'df')
+ df = DataFrame({'A' : rng,
+ 'B' : rng.tz_convert('UTC').tz_localize(None),
+ 'C' : rng.tz_convert('CET'),
+ 'D' : range(len(rng))}, index=rng)
+ store['df'] = df
+ result = store['df']
+ assert_frame_equal(result, df)
def test_fixed_offset_tz(self):
rng = date_range('1/1/2000 00:00:00-07:00', '1/30/2000 00:00:00-07:00')
| closes #11411
| https://api.github.com/repos/pandas-dev/pandas/pulls/11628 | 2015-11-17T12:44:02Z | 2015-11-17T14:57:15Z | 2015-11-17T14:57:15Z | 2015-11-17T23:18:26Z |
BUG: date_range creation with an ambiguous endpoint, #11619 | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 046791d4287c9..02830e63ae81b 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -120,7 +120,8 @@ Bug Fixes
- Bug in ``HDFStore.append`` with strings whose encoded length exceded the max unencoded length (:issue:`11234`)
- Bug in merging ``datetime64[ns, tz]`` dtypes (:issue:`11405`)
- Bug in ``HDFStore.select`` when comparing with a numpy scalar in a where clause (:issue:`11283`)
-- Bug in using ``DataFrame.ix`` with a multi-index indexer(:issue:`11372`)
+- Bug in using ``DataFrame.ix`` with a multi-index indexer (:issue:`11372`)
+- Bug in ``date_range`` with ambigous endpoints (:issue:`11626`)
- Prevent adding new attributes to the accessors ``.str``, ``.dt`` and ``.cat``. Retrieving such
a value was not possible, so error out on setting it. (:issue:`10673`)
- Bug in tz-conversions with an ambiguous time and ``.dt`` accessors (:issue:`11295`)
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index 6c78f9cf3937c..9de7732d3b289 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -3049,7 +3049,7 @@ def test_select_dtypes(self):
result = store.select(
'df4', where='values>2.0')
tm.assert_frame_equal(expected, result)
-
+
# test selection with comparison against numpy scalar
# GH 11283
with ensure_clean_store(self.path) as store:
@@ -4988,6 +4988,21 @@ def test_legacy_datetimetz_object(self):
result = store['df']
assert_frame_equal(result, expected)
+ def test_dst_transitions(self):
+ # make sure we are not failing on transaitions
+ with ensure_clean_store(self.path) as store:
+ times = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
+ tz="Europe/London",
+ freq="H",
+ ambiguous='infer')
+
+ for i in [times, times+pd.Timedelta('10min')]:
+ _maybe_remove(store, 'df')
+ df = DataFrame({'A' : range(len(i)), 'B' : i }, index=i)
+ store.append('df',df)
+ result = store.select('df')
+ assert_frame_equal(result, df)
+
def _test_sort(obj):
if isinstance(obj, DataFrame):
return obj.reindex(sorted(obj.index))
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index a743ce4ffef61..e4f3032cabf88 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -15439,6 +15439,26 @@ def test_to_csv_date_format(self):
assert_frame_equal(test, nat_frame)
+ def test_to_csv_with_dst_transitions(self):
+ pname = '__tmp_to_csv_date_format_with_dst__'
+ with ensure_clean(pname) as path:
+ # make sure we are not failing on transitions
+ times = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
+ tz="Europe/London",
+ freq="H",
+ ambiguous='infer')
+
+ for i in [times, times+pd.Timedelta('10s')]:
+ df = DataFrame({'A' : range(len(i))}, index=i)
+ df.to_csv(path,index=True)
+
+ # we have to reconvert the index as we
+ # don't parse the tz's
+ result = read_csv(path,index_col=0)
+ result.index = pd.to_datetime(result.index).tz_localize('UTC').tz_convert('Europe/London')
+ assert_frame_equal(result,df)
+
+
def test_concat_empty_dataframe_dtypes(self):
df = DataFrame(columns=list("abc"))
df['a'] = df['a'].astype(np.bool_)
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index fd26e9834bd5f..4fd61e28233a6 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -355,7 +355,7 @@ def __new__(cls, data=None,
if freq is not None and not freq_infer:
inferred = subarr.inferred_freq
if inferred != freq.freqstr:
- on_freq = cls._generate(subarr[0], None, len(subarr), None, freq, tz=tz)
+ on_freq = cls._generate(subarr[0], None, len(subarr), None, freq, tz=tz, ambiguous=ambiguous)
if not np.array_equal(subarr.asi8, on_freq.asi8):
raise ValueError('Inferred frequency {0} from passed dates does not '
'conform to passed frequency {1}'.format(inferred, freq.freqstr))
@@ -440,17 +440,17 @@ def _generate(cls, start, end, periods, name, offset,
if inferred_tz is None and tz is not None:
# naive dates
if start is not None and start.tz is None:
- start = start.tz_localize(tz)
+ start = start.tz_localize(tz, ambiguous=False)
if end is not None and end.tz is None:
- end = end.tz_localize(tz)
+ end = end.tz_localize(tz, ambiguous=False)
if start and end:
if start.tz is None and end.tz is not None:
- start = start.tz_localize(end.tz)
+ start = start.tz_localize(end.tz, ambiguous=False)
if end.tz is None and start.tz is not None:
- end = end.tz_localize(start.tz)
+ end = end.tz_localize(start.tz, ambiguous=False)
if _use_cached_range(offset, _normalized, start, end):
index = cls._cached_range(start, end, periods=periods,
@@ -1884,7 +1884,7 @@ def _generate_regular_range(start, end, periods, offset):
def date_range(start=None, end=None, periods=None, freq='D', tz=None,
- normalize=False, name=None, closed=None):
+ normalize=False, name=None, closed=None, **kwargs):
"""
Return a fixed frequency datetime index, with day (calendar) as the default
frequency
@@ -1920,11 +1920,11 @@ def date_range(start=None, end=None, periods=None, freq='D', tz=None,
"""
return DatetimeIndex(start=start, end=end, periods=periods,
freq=freq, tz=tz, normalize=normalize, name=name,
- closed=closed)
+ closed=closed, **kwargs)
def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
- normalize=True, name=None, closed=None):
+ normalize=True, name=None, closed=None, **kwargs):
"""
Return a fixed frequency datetime index, with business day as the default
frequency
@@ -1961,7 +1961,7 @@ def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
return DatetimeIndex(start=start, end=end, periods=periods,
freq=freq, tz=tz, normalize=normalize, name=name,
- closed=closed)
+ closed=closed, **kwargs)
def cdate_range(start=None, end=None, periods=None, freq='C', tz=None,
diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py
index a6e5812158474..37c40dd48cf6a 100644
--- a/pandas/tseries/tests/test_timezones.py
+++ b/pandas/tseries/tests/test_timezones.py
@@ -502,6 +502,22 @@ def test_ambiguous_flags(self):
localized_is_dst = dr.tz_localize(tz, ambiguous=is_dst)
self.assert_numpy_array_equal(localized, localized_is_dst)
+ # construction with an ambiguous end-point
+ # GH 11626
+ tz=self.tzstr("Europe/London")
+
+ def f():
+ date_range("2013-10-26 23:00", "2013-10-27 01:00",
+ tz="Europe/London",
+ freq="H")
+ self.assertRaises(pytz.AmbiguousTimeError, f)
+ times = date_range("2013-10-26 23:00", "2013-10-27 01:00",
+ freq="H",
+ tz=tz,
+ ambiguous='infer')
+ self.assertEqual(times[0],Timestamp('2013-10-26 23:00',tz=tz))
+ self.assertEqual(times[-1],Timestamp('2013-10-27 01:00',tz=tz))
+
def test_ambiguous_nat(self):
tz = self.tz('US/Eastern')
times = ['11/06/2011 00:00', '11/06/2011 01:00',
| TST: some tests for datetime tz aware serialized to/from csv/hdf
closes #11626
| https://api.github.com/repos/pandas-dev/pandas/pulls/11627 | 2015-11-17T11:38:13Z | 2015-11-17T12:18:23Z | 2015-11-17T12:18:23Z | 2015-11-17T12:18:23Z |
PERF: speed-up DateFrame.itertuples() with namedtuples | diff --git a/asv_bench/benchmarks/frame_methods.py b/asv_bench/benchmarks/frame_methods.py
index a04a9d0814a30..2c07c28066faf 100644
--- a/asv_bench/benchmarks/frame_methods.py
+++ b/asv_bench/benchmarks/frame_methods.py
@@ -653,6 +653,16 @@ def j(self):
self.df3[0]
+class frame_itertuples(object):
+
+ def setup(self):
+ self.df = DataFrame(np.random.randn(50000, 10))
+
+ def time_frame_itertuples(self):
+ for row in self.df.itertuples():
+ pass
+
+
class frame_mask_bools(object):
goal_time = 0.2
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 23095b7f4d24b..304348917c36c 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -309,6 +309,8 @@
# extlinks alias
extlinks = {'issue': ('https://github.com/pydata/pandas/issues/%s',
'GH'),
+ 'pr': ('https://github.com/pydata/pandas/pull/%s',
+ 'GH-PR'),
'wiki': ('https://github.com/pydata/pandas/wiki/%s',
'wiki ')}
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 046791d4287c9..be68e9e8de2ce 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -80,7 +80,7 @@ API changes
- Indexing with a null key will raise a ``TypeError``, instead of a ``ValueError`` (:issue:`11356`)
- ``Series.sort_index()`` now correctly handles the ``inplace`` option (:issue:`11402`)
-- ``DataFrame.itertuples()`` now returns ``namedtuple`` objects, when possible. (:issue:`11269`)
+- ``DataFrame.itertuples()`` now returns ``namedtuple`` objects, when possible. (:issue:`11269`, :pr:`11625`)
- ``Series.ptp`` will now ignore missing values by default (:issue:`11163`)
.. _whatsnew_0171.deprecations:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 5447574c9ea4e..81da1223f50d5 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -43,7 +43,7 @@
import pandas.computation.expressions as expressions
from pandas.computation.eval import eval as _eval
from numpy import percentile as _quantile
-from pandas.compat import(range, zip, lrange, lmap, lzip, StringIO, u,
+from pandas.compat import(range, map, zip, lrange, lmap, lzip, StringIO, u,
OrderedDict, raise_with_traceback)
from pandas import compat
from pandas.sparse.array import SparseArray
@@ -664,7 +664,7 @@ def itertuples(self, index=True, name="Pandas"):
index : boolean, default True
If True, return the index as the first element of the tuple.
name : string, default "Pandas"
- The name of the returned namedtuple.
+ The name of the returned namedtuples or None to return regular tuples.
Notes
-----
@@ -703,13 +703,13 @@ def itertuples(self, index=True, name="Pandas"):
# Python 3 supports at most 255 arguments to constructor, and
# things get slow with this many fields in Python 2
- if len(self.columns) + index < 256:
+ if name is not None and len(self.columns) + index < 256:
# `rename` is unsupported in Python 2.6
try:
itertuple = collections.namedtuple(
name, fields+list(self.columns), rename=True)
- return (itertuple(*row) for row in zip(*arrays))
- except:
+ return map(itertuple._make, zip(*arrays))
+ except Exception:
pass
# fallback to regular tuples
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index a743ce4ffef61..ae0424c8fcbd1 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -5545,6 +5545,8 @@ def test_itertuples(self):
dfaa = df[['a', 'a']]
self.assertEqual(list(dfaa.itertuples()), [(0, 1, 1), (1, 2, 2), (2, 3, 3)])
+ self.assertEqual(repr(list(df.itertuples(name=None))), '[(0, 1, 4), (1, 2, 5), (2, 3, 6)]')
+
tup = next(df.itertuples(name='TestName'))
# no support for field renaming in Python 2.6, regular tuples are returned
| - use [namedtuple._make()](https://docs.python.org/2/library/collections.html#collections.somenamedtuple._make)
Also:
- replace bare `except:` to avoid catching `SystemExit` and `KeyboardInterrupt`
- remove the generator `return` from the `try`-clause to an `else`
- more explicit fallback to regular tuples when `name=None` (docs?)
| https://api.github.com/repos/pandas-dev/pandas/pulls/11625 | 2015-11-17T11:12:13Z | 2015-11-19T01:10:44Z | null | 2015-11-19T09:11:35Z |
CLN/COMPAT: Remove raise StopIteration syntax in SparseArray.__iter__ | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 62cec66cc22b0..d9bcbbf3d6c51 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -58,6 +58,7 @@ API changes
Legacy Python syntax (``set([x, y])``) (:issue:`11215`)
- Indexing with a null key will raise a ``TypeError``, instead of a ``ValueError`` (:issue:`11356`)
- ``Series.sort_index()`` now correctly handles the ``inplace`` option (:issue:`11402`)
+- ``SparseArray.__iter__()`` now does not cause ``PendingDeprecationWarning`` in Python 3.5 (:issue:`11622`)
- ``DataFrame.itertuples()`` now returns ``namedtuple`` objects, when possible. (:issue:`11269`)
- ``Series.ptp`` will now ignore missing values by default (:issue:`11163`)
diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py
index f275a34ca90db..b40a23fb4556a 100644
--- a/pandas/sparse/array.py
+++ b/pandas/sparse/array.py
@@ -274,7 +274,6 @@ def to_dense(self, fill=None):
def __iter__(self):
for i in range(len(self)):
yield self._get_val_at(i)
- raise StopIteration
def __getitem__(self, key):
"""
diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py
index 4ffc0b98ebc71..add680489548d 100644
--- a/pandas/sparse/tests/test_array.py
+++ b/pandas/sparse/tests/test_array.py
@@ -4,6 +4,7 @@
import numpy as np
import operator
+import warnings
from pandas.core.series import Series
from pandas.core.common import notnull
@@ -174,6 +175,18 @@ def _check_roundtrip(obj):
_check_roundtrip(self.arr)
_check_roundtrip(self.zarr)
+ def test_generator_warnings(self):
+ sp_arr = SparseArray([1, 2, 3])
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings(action='always',
+ category=DeprecationWarning)
+ warnings.filterwarnings(action='always',
+ category=PendingDeprecationWarning)
+ for _ in sp_arr:
+ pass
+ assert len(w)==0
+
+
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
| closes #11108
| https://api.github.com/repos/pandas-dev/pandas/pulls/11622 | 2015-11-17T03:09:55Z | 2015-11-18T11:36:54Z | 2015-11-18T11:36:54Z | 2015-11-18T11:36:57Z |
Roll apply nonfloat dtypes | diff --git a/pandas/algos.pyx b/pandas/algos.pyx
index 8569209f2e946..45dc64d81c338 100644
--- a/pandas/algos.pyx
+++ b/pandas/algos.pyx
@@ -1820,9 +1820,11 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int win,
return output
+
def roll_generic(ndarray[float64_t, cast=True] input,
int win, int minp, int offset,
- object func, object args, object kwargs):
+ object func, object args, object kwargs,
+ object array_to_roll=None):
cdef ndarray[double_t] output, counts, bufarr
cdef Py_ssize_t i, n
cdef float64_t *buf
@@ -1837,32 +1839,41 @@ def roll_generic(ndarray[float64_t, cast=True] input,
minp = _check_minp(win, minp, n, floor=0)
output = np.empty(n, dtype=float)
- counts = roll_sum(np.concatenate((np.isfinite(input).astype(float), np.array([0.] * offset))), win, minp)[offset:]
+ counts = roll_sum(np.concatenate((np.isfinite(input).astype(float),
+ np.array([0.] * offset))),
+ win, minp)[offset:]
+
+ # default behavior is to roll over input array
+ if array_to_roll is None:
+ array_to_roll = input
# truncated windows at the beginning, through first full-length window
for i from 0 <= i < (int_min(win, n) - offset):
if counts[i] >= minp:
- output[i] = func(input[0 : (i + offset + 1)], *args, **kwargs)
+ output[i] = func(array_to_roll[0:(i + offset + 1)],
+ *args,
+ **kwargs)
else:
output[i] = NaN
# remaining full-length windows
- buf = <float64_t*> input.data
- bufarr = np.empty(win, dtype=float)
- oldbuf = <float64_t*> bufarr.data
+ # array_to_roll is a numpy array and doing a slice of contiguous data does
+ # not make a copy
for i from (win - offset) <= i < (n - offset):
- buf = buf + 1
- bufarr.data = <char*> buf
if counts[i] >= minp:
- output[i] = func(bufarr, *args, **kwargs)
+ # full length windows will start at index 1 and be of length win
+ output[i] = \
+ func(array_to_roll[i - (win - offset) + 1:i + offset + 1],
+ *args, **kwargs)
else:
output[i] = NaN
- bufarr.data = <char*> oldbuf
# truncated windows at the end
for i from int_max(n - offset, 0) <= i < n:
if counts[i] >= minp:
- output[i] = func(input[int_max(i + offset - win + 1, 0) : n], *args, **kwargs)
+ output[i] = func(array_to_roll[int_max(i + offset - win + 1, 0):n],
+ *args,
+ **kwargs)
else:
output[i] = NaN
diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py
index 3cddae45e7516..c4183d748fc9c 100644
--- a/pandas/stats/moments.py
+++ b/pandas/stats/moments.py
@@ -355,7 +355,7 @@ def rolling_corr_pairwise(df1, df2=None, window=None, min_periods=None,
def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False,
- how=None, args=(), kwargs={}, **kwds):
+ how=None, coercion=True, args=(), kwargs={}, **kwds):
"""
Rolling statistical measure using supplied function. Designed to be
used with passed-in Cython array-based functions.
@@ -374,6 +374,10 @@ def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False,
Whether the label should correspond with center of window
how : string, default 'mean'
Method for down- or re-sampling
+ coercion: bool flag with default True. It tries to coerce args to a float
+ to optimize for speed. If rolling_apply() is invoked on objects that
+ cannot be coerced into a float, it raises a ValueError. Be sure
+ to set coercion=False in this case.
args : tuple
Passed on to func
kwargs : dict
@@ -385,7 +389,7 @@ def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False,
"""
arg = _conv_timerule(arg, freq, how)
- return_hook, values = _process_data_structure(arg)
+ return_hook, values = _process_data_structure(arg, coercion=coercion)
if values.size == 0:
result = values.copy()
@@ -393,9 +397,18 @@ def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False,
# actually calculate the moment. Faster way to do this?
offset = int((window - 1) / 2.) if center else 0
additional_nans = np.array([np.NaN] * offset)
- calc = lambda x: func(np.concatenate((x, additional_nans)) if center else x,
- window, minp=minp, args=args, kwargs=kwargs,
- **kwds)
+
+ if coercion:
+ calc = lambda x: func(np.concatenate((x, additional_nans)) if
+ center else x, window, minp=minp, args=args,
+ kwargs=kwargs, **kwds)
+ else:
+ p0 = np.arange(0, len(values), dtype=float)
+ calc = lambda x: func(np.concatenate((p0, additional_nans))
+ if center else p0, window, minp=minp,
+ args=args, kwargs=kwargs,
+ array_to_roll=x, **kwds)
+
if values.ndim > 1:
result = np.apply_along_axis(calc, axis, values)
else:
@@ -423,7 +436,7 @@ def _center_window(rs, window, axis):
return rs
-def _process_data_structure(arg, kill_inf=True):
+def _process_data_structure(arg, kill_inf=True, coercion=True):
if isinstance(arg, DataFrame):
return_hook = lambda v: type(arg)(v, index=arg.index,
columns=arg.columns)
@@ -435,12 +448,13 @@ def _process_data_structure(arg, kill_inf=True):
return_hook = lambda v: v
values = arg
- if not issubclass(values.dtype.type, float):
- values = values.astype(float)
+ if coercion:
+ if not issubclass(values.dtype.type, float):
+ values = values.astype(float)
- if kill_inf:
- values = values.copy()
- values[np.isinf(values)] = np.NaN
+ if kill_inf:
+ values = values.copy()
+ values[np.isinf(values)] = np.NaN
return return_hook, values
@@ -712,7 +726,7 @@ def call_cython(arg, window, minp, args=(), kwargs={}):
def rolling_apply(arg, window, func, min_periods=None, freq=None,
- center=False, args=(), kwargs={}):
+ center=False, coercion=True, args=(), kwargs={}):
"""Generic moving function application.
Parameters
@@ -731,6 +745,10 @@ def rolling_apply(arg, window, func, min_periods=None, freq=None,
as a frequency string or DateOffset object.
center : boolean, default False
Whether the label should correspond with center of window
+ coercion: bool flag with default True. It tries to coerce args to a float
+ to optimize for speed. If rolling_apply() is invoked on objects that
+ cannot be coerced into a float, it raises a ValueError. Be sure
+ to set coercion=False in this case.
args : tuple
Passed on to func
kwargs : dict
@@ -750,11 +768,15 @@ def rolling_apply(arg, window, func, min_periods=None, freq=None,
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
offset = int((window - 1) / 2.) if center else 0
- def call_cython(arg, window, minp, args, kwargs):
+
+ def call_cython(arg, window, minp, args, kwargs, array_to_roll=None):
minp = _use_window(minp, window)
- return algos.roll_generic(arg, window, minp, offset, func, args, kwargs)
- return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
- center=False, args=args, kwargs=kwargs)
+ return algos.roll_generic(arg, window, minp, offset, func, args,
+ kwargs, array_to_roll)
+
+ return _rolling_moment(arg, window, call_cython, min_periods,
+ freq=freq, center=False, coercion=coercion,
+ args=args, kwargs=kwargs)
def rolling_window(arg, window=None, win_type=None, min_periods=None,
diff --git a/pandas/stats/tests/test_moments.py b/pandas/stats/tests/test_moments.py
index e2ed27156d2b5..69a436798e354 100644
--- a/pandas/stats/tests/test_moments.py
+++ b/pandas/stats/tests/test_moments.py
@@ -363,6 +363,27 @@ def roll_mean(x, window, min_periods=None, freq=None, center=False):
expected = Series([1., 2., 2.])
assert_series_equal(result, expected)
+ def test_rolling_apply_nonfloat(self):
+ '''
+ test rolling_apply now also works for non-float data types if coercion
+ is set to False. The return type is still float but the 'roll'
+ is applied to arg which no longer has to be a float
+ '''
+ # check rolling_apply with coercion set to False
+ orig = Series([ord('a'), ord('b'), ord('c')], dtype=float)
+ s = Series(['a', 'b', 'c'])
+
+ for min_p in (None, 0):
+ s_res = mom.rolling_apply(s, 2, lambda x: ord(x[-1]),
+ coercion=False, min_periods=min_p)
+ o_res = mom.rolling_apply(orig, 2, lambda x: x[-1],
+ coercion=False, min_periods=min_p)
+
+ # assert that NaN values appear at same place since min_periods
+ # defines the NaN values. Also assert that valid answers match
+ assert all(np.isfinite(s_res) == np.isfinite(o_res))
+ assert all(s_res[np.isfinite(s_res)] == o_res[np.isfinite(o_res)])
+
def test_rolling_apply_out_of_bounds(self):
# #1850
arr = np.arange(4)
| Fixed issue [#4964](https://github.com/pydata/pandas/issues/4964) so rolling_apply works on non-numeric columns as well.
I followed instructions [here](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md) but I'm not sure where to add documentation in the [release notes](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md#documenting-your-code).
Also would like to get feedback on the code and complete any code changes before adding to documentation.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11620 | 2015-11-16T23:27:04Z | 2015-11-18T22:25:16Z | null | 2015-11-18T22:25:16Z |
BUG: fix col iteration in DataFrame.round, #11611 | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index e4b4317c793f7..f1959e0fbb83e 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -177,3 +177,5 @@ Bug Fixes
- Bug in ``DataFrame.join()`` with ``how='right'`` producing a ``TypeError`` (:issue:`11519`)
- Bug in ``Series.quantile`` with empty list results has ``Index`` with ``object`` dtype (:issue:`11588`)
- Bug in ``pd.merge`` results in empty ``Int64Index`` rather than ``Index(dtype=object)`` when the merge result is empty (:issue:`11588`)
+- Bug in ``DataFrame.round()`` with non-unique column index producing a Fatal Python error (:issue:`11611`)
+- Bug in ``DataFrame.round()`` with ``decimals`` being a non-unique indexed Series producing extra columns (:issue:`11618`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 5447574c9ea4e..08c8ed1e3e058 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4382,17 +4382,20 @@ def round(self, decimals=0, out=None):
from pandas.tools.merge import concat
def _dict_round(df, decimals):
- for col in df:
+ for col, vals in df.iteritems():
try:
- yield np.round(df[col], decimals[col])
+ yield np.round(vals, decimals[col])
except KeyError:
- yield df[col]
+ yield vals
if isinstance(decimals, (dict, Series)):
+ if isinstance(decimals, Series):
+ if not decimals.index.is_unique:
+ raise ValueError("Index of decimals must be unique")
new_cols = [col for col in _dict_round(self, decimals)]
elif com.is_integer(decimals):
# Dispatch to numpy.round
- new_cols = [np.round(self[col], decimals) for col in self]
+ new_cols = [np.round(v, decimals) for _, v in self.iteritems()]
else:
raise TypeError("decimals must be an integer, a dict-like or a Series")
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 0cae8b356b517..e374c221892ca 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -15821,6 +15821,19 @@ class SubclassedPanel(Panel):
dtype='int64')
tm.assert_panel_equal(result, expected)
+ def test_round(self):
+ # GH11611
+
+ df = pd.DataFrame(np.random.random([3, 3]), columns=['A', 'B', 'C'],
+ index=['first', 'second', 'third'])
+
+ dfs = pd.concat((df, df), axis=1)
+ rounded = dfs.round()
+ self.assertTrue(rounded.index.equals(dfs.index))
+
+ decimals = pd.Series([1, 0, 2], index=['A', 'B', 'A'])
+ self.assertRaises(ValueError, df.round, decimals)
+
def skip_if_no_ne(engine='numexpr'):
if engine == 'numexpr':
| Fixes #11611. The iterator now uses DataFrame.iteritems instead of direct indexing.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11618 | 2015-11-16T16:43:55Z | 2015-11-20T13:55:24Z | null | 2015-11-20T16:26:22Z |
Fix typo in the docstring of dropna | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 1e40d77839d3c..5447574c9ea4e 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2933,7 +2933,7 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None,
subset : array-like
Labels along other axis to consider, e.g. if you are dropping rows
these would be a list of columns to include
- inplace : boolean, defalt False
+ inplace : boolean, default False
If True, do operation inplace and return None.
Returns
| It should be 'default' instead of 'defalt'.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11613 | 2015-11-16T15:10:23Z | 2015-11-16T16:18:59Z | 2015-11-16T16:18:59Z | 2015-11-16T16:19:04Z |
WARN: fix performance warning failure on numpy master | diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py
index 90f6bff498e62..813354b2d0f86 100644
--- a/pandas/tseries/holiday.py
+++ b/pandas/tseries/holiday.py
@@ -1,3 +1,5 @@
+import warnings
+
from pandas import DateOffset, DatetimeIndex, Series, Timestamp
from pandas.compat import add_metaclass
from datetime import datetime, timedelta
@@ -192,10 +194,10 @@ def dates(self, start_date, end_date, return_name=False):
"""
start_date = Timestamp(start_date)
end_date = Timestamp(end_date)
-
- filter_start_date = start_date
+
+ filter_start_date = start_date
filter_end_date = end_date
-
+
if self.year is not None:
dt = Timestamp(datetime(self.year, self.month, self.day))
if return_name:
@@ -208,22 +210,22 @@ def dates(self, start_date, end_date, return_name=False):
if self.days_of_week is not None:
holiday_dates = holiday_dates[np.in1d(holiday_dates.dayofweek,
self.days_of_week)]
-
+
if self.start_date is not None:
filter_start_date = max(self.start_date.tz_localize(filter_start_date.tz), filter_start_date)
if self.end_date is not None:
filter_end_date = min(self.end_date.tz_localize(filter_end_date.tz), filter_end_date)
- holiday_dates = holiday_dates[(holiday_dates >= filter_start_date) &
+ holiday_dates = holiday_dates[(holiday_dates >= filter_start_date) &
(holiday_dates <= filter_end_date)]
if return_name:
return Series(self.name, index=holiday_dates)
return holiday_dates
-
-
+
+
def _reference_dates(self, start_date, end_date):
"""
Get reference dates for the holiday.
-
+
Return reference dates for the holiday also returning the year
prior to the start_date and year following the end_date. This ensures
that any offsets to be applied will yield the holidays within
@@ -238,13 +240,13 @@ def _reference_dates(self, start_date, end_date):
year_offset = DateOffset(years=1)
reference_start_date = Timestamp(
datetime(start_date.year-1, self.month, self.day))
-
+
reference_end_date = Timestamp(
datetime(end_date.year+1, self.month, self.day))
# Don't process unnecessary holidays
- dates = DatetimeIndex(start=reference_start_date, end=reference_end_date,
+ dates = DatetimeIndex(start=reference_start_date, end=reference_end_date,
freq=year_offset, tz=start_date.tz)
-
+
return dates
def _apply_rule(self, dates):
@@ -269,7 +271,11 @@ def _apply_rule(self, dates):
else:
offsets = self.offset
for offset in offsets:
- dates += offset
+
+ # if we are adding a non-vectorized value
+ # ignore the PerformanceWarnings:
+ with warnings.catch_warnings(record=True):
+ dates += offset
return dates
holiday_calendars = {}
@@ -327,12 +333,12 @@ def __init__(self, name=None, rules=None):
if rules is not None:
self.rules = rules
-
+
def rule_from_name(self, name):
for rule in self.rules:
if rule.name == name:
return rule
-
+
return None
def holidays(self, start=None, end=None, return_name=False):
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index 230016f00374f..a652268dd8e89 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -2637,35 +2637,32 @@ def test_datetime64_with_DateOffset(self):
assert_func(klass([x - op for x in s]), s - op)
- # split by fast/slow path to test perf warning
- off = {False:
- ['YearBegin', ('YearBegin', {'month': 5}),
- 'YearEnd', ('YearEnd', {'month': 5}),
- 'MonthBegin', 'MonthEnd', 'Week', ('Week', {'weekday': 3}),
- 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin'],
- PerformanceWarning:
- ['CustomBusinessDay', 'CDay', 'CBMonthEnd','CBMonthBegin',
- 'BMonthBegin', 'BMonthEnd', 'BusinessHour', 'BYearBegin',
- 'BYearEnd','BQuarterBegin', ('LastWeekOfMonth', {'weekday':2}),
- ('FY5253Quarter', {'qtr_with_extra_week': 1, 'startingMonth': 1,
- 'weekday': 2, 'variation': 'nearest'}),
- ('FY5253',{'weekday': 0, 'startingMonth': 2, 'variation': 'nearest'}),
- ('WeekOfMonth', {'weekday': 2, 'week': 2}), 'Easter',
- ('DateOffset', {'day': 4}), ('DateOffset', {'month': 5})]}
+ # assert these are equal on a piecewise basis
+ offsets = ['YearBegin', ('YearBegin', {'month': 5}),
+ 'YearEnd', ('YearEnd', {'month': 5}),
+ 'MonthBegin', 'MonthEnd', 'Week', ('Week', {'weekday': 3}),
+ 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin',
+ 'CustomBusinessDay', 'CDay', 'CBMonthEnd','CBMonthBegin',
+ 'BMonthBegin', 'BMonthEnd', 'BusinessHour', 'BYearBegin',
+ 'BYearEnd','BQuarterBegin', ('LastWeekOfMonth', {'weekday':2}),
+ ('FY5253Quarter', {'qtr_with_extra_week': 1, 'startingMonth': 1,
+ 'weekday': 2, 'variation': 'nearest'}),
+ ('FY5253',{'weekday': 0, 'startingMonth': 2, 'variation': 'nearest'}),
+ ('WeekOfMonth', {'weekday': 2, 'week': 2}), 'Easter',
+ ('DateOffset', {'day': 4}), ('DateOffset', {'month': 5})]
for normalize in (True, False):
- for warning, offsets in off.items():
- for do in offsets:
- if isinstance(do, tuple):
- do, kwargs = do
- else:
- do = do
- kwargs = {}
- op = getattr(pd.offsets,do)(5, normalize=normalize, **kwargs)
- with tm.assert_produces_warning(warning):
- assert_func(klass([x + op for x in s]), s + op)
- assert_func(klass([x - op for x in s]), s - op)
- assert_func(klass([op + x for x in s]), op + s)
+ for do in offsets:
+ if isinstance(do, tuple):
+ do, kwargs = do
+ else:
+ do = do
+ kwargs = {}
+ op = getattr(pd.offsets,do)(5, normalize=normalize, **kwargs)
+ assert_func(klass([x + op for x in s]), s + op)
+ assert_func(klass([x - op for x in s]), s - op)
+ assert_func(klass([op + x for x in s]), op + s)
+
# def test_add_timedelta64(self):
# rng = date_range('1/1/2000', periods=5)
# delta = rng.values[3] - rng.values[1]
| https://api.github.com/repos/pandas-dev/pandas/pulls/11609 | 2015-11-15T18:36:20Z | 2015-11-16T17:48:49Z | 2015-11-16T17:48:49Z | 2015-11-16T17:48:49Z | |
BUG: loc against CategoricalIndex may results in normal Index | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 5ccf829fd5a42..ee0cd0bf3c5ff 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -105,3 +105,7 @@ Performance Improvements
Bug Fixes
~~~~~~~~~
+
+
+
+- Bug in ``.loc`` against ``CategoricalIndex`` may result in normal ``Index`` (:issue:`11586`)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index cdd0de4e1196d..2099c1996b66b 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -3362,8 +3362,8 @@ def reindex(self, target, method=None, level=None, limit=None,
# filling in missing if needed
if len(missing):
cats = self.categories.get_indexer(target)
- if (cats==-1).any():
+ if (cats==-1).any():
# coerce to a regular index here!
result = Index(np.array(self),name=self.name)
new_target, indexer, _ = result._reindex_non_unique(np.array(target))
@@ -3397,6 +3397,12 @@ def _reindex_non_unique(self, target):
new_indexer = np.arange(len(self.take(indexer)))
new_indexer[check] = -1
+ cats = self.categories.get_indexer(target)
+ if not (cats == -1).any():
+ # .reindex returns normal Index. Revert to CategoricalIndex if
+ # all targets are included in my categories
+ new_target = self._shallow_copy(new_target)
+
return new_target, indexer, new_indexer
def get_indexer(self, target, method=None, limit=None, tolerance=None):
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 2b1cb0a1e1b31..9df72053fb0af 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -984,6 +984,9 @@ def _getitem_iterable(self, key, axis=0):
# asarray can be unsafe, NumPy strings are weird
keyarr = _asarray_tuplesafe(key)
+ if com.is_categorical_dtype(labels):
+ keyarr = labels._shallow_copy(keyarr)
+
# have the index handle the indexer and possibly return
# an indexer or raising
indexer = labels._convert_list_indexer(keyarr, kind=self.name)
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index 43a8b801fa813..bc9d303dc3b1a 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -2314,6 +2314,23 @@ def test_reindexing(self):
actual = ci.get_indexer(finder)
tm.assert_numpy_array_equal(expected, actual)
+ def test_reindex_dtype(self):
+ res, indexer = CategoricalIndex(['a', 'b', 'c', 'a']).reindex(['a', 'c'])
+ tm.assert_index_equal(res, Index(['a', 'a', 'c']), exact=True)
+ tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2]))
+
+ res, indexer = CategoricalIndex(['a', 'b', 'c', 'a']).reindex(Categorical(['a', 'c']))
+ tm.assert_index_equal(res, CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c']), exact=True)
+ tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2]))
+
+ res, indexer = CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c', 'd']).reindex(['a', 'c'])
+ tm.assert_index_equal(res, Index(['a', 'a', 'c'], dtype='object'), exact=True)
+ tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2]))
+
+ res, indexer = CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c', 'd']).reindex(Categorical(['a', 'c']))
+ tm.assert_index_equal(res, CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c']), exact=True)
+ tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2]))
+
def test_duplicates(self):
idx = CategoricalIndex([0, 0, 0], name='foo')
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index cb06b714d4700..66850ab29af39 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -4842,14 +4842,12 @@ 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)
-
- # ToDo: check_index_type can be True after GH XXX
+ 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=False)
+ 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'])
@@ -4859,19 +4857,78 @@ def test_loc_listlike(self):
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)
+ 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']]
- expected = DataFrame({'A' : [0,1,5,2,3,np.nan],
- 'B' : Series(list('aaabbe')).astype('category',categories=list('cabe')) }).set_index('B')
- assert_frame_equal(result, expected, check_index_type=False)
-
+ 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)
@@ -4898,22 +4955,22 @@ def test_reindexing(self):
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)
+ 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)
+ 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)
+ 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)
+ assert_frame_equal(result, expected, check_index_type=True)
# since we are actually reindexing with a Categorical
# then return a Categorical
@@ -4922,38 +4979,38 @@ def test_reindexing(self):
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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ assert_frame_equal(result, expected, check_index_type=True)
# passed duplicate indexers are not allowed
self.assertRaises(ValueError, lambda : self.df2.reindex(['a','a']))
| Closes #11586.
After the PR:
```
import pandas as pd
index = pd.CategoricalIndex(list('aabbca'), categories=list('cabe'))
df = pd.DataFrame({'A' : np.arange(6,dtype='int64')}, index=index)
# OK (not changed)
df.loc[['a', 'b']].index
# CategoricalIndex([u'a', u'a', u'a', u'b', u'b'], categories=[u'c', u'a', u'b', u'e'], ordered=False, dtype='category')
# Fixed to return Categorical Index if value exists in categories
df.loc[['a', 'b', 'e']].index
# CategoricalIndex([u'a', u'a', u'a', u'b', u'b', u'e'], categories=[u'a', u'b', u'e'], ordered=False, dtype='category')
# raise KeyError otherwise (not changed)
df.loc[['a', 'b', 'x']].index
# KeyError: 'a list-indexer must only include values that are in the categories'
```
There are separate paths when `CategoricalIndex` values (codes) are unique and not-unique and both fixed.
Also, `.reindex` intentionally returns normal `Index` when passed values are not `Categorical`, I kept the behavior as it is. If `.reindex` can always return `CategoricalIndex`, above 2 separate fixes are not required (fixing `.reindex` to return `CategoricalIndex` should work both paths).
```
# return normal Index(not changed)
df.index.reindex(['a', 'b'])
# (Index([u'a', u'a', u'a', u'b', u'b'], dtype='object'), array([0, 1, 5, 2, 3]))
# return CategoricalIndex(not changed)
df.index.reindex(pd.Categorical(['a', 'b']))
# (CategoricalIndex([u'a', u'a', u'a', u'b', u'b'], categories=[u'a', u'b'], ordered=False, dtype='category'), array([0, 1, 5, 2, 3]))
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/11607 | 2015-11-14T22:12:57Z | 2015-11-23T01:20:25Z | 2015-11-23T01:20:25Z | 2015-11-23T01:37:17Z |
BUG GH11600 - MultiIndex column level names lost when to_sparse() called | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 046791d4287c9..8f8c142db67d3 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -156,3 +156,4 @@ Bug Fixes
- Bug in the link-time error caused by C ``inline`` functions on FreeBSD 10+ (with ``clang``) (:issue:`10510`)
- Bug in ``DataFrame.to_csv`` in passing through arguments for formatting ``MultiIndexes``, including ``date_format`` (:issue:`7791`)
- Bug in ``DataFrame.join()`` with ``how='right'`` producing a ``TypeError`` (:issue:`11519`)
+- Bug in ``DataFrame.to_sparse()`` loses column names for MultIndexes (:issue:`11600`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 5447574c9ea4e..2eebc2b2c8e7a 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1191,7 +1191,7 @@ def to_sparse(self, fill_value=None, kind='block'):
y : SparseDataFrame
"""
from pandas.core.sparse import SparseDataFrame
- return SparseDataFrame(self._series, index=self.index,
+ return SparseDataFrame(self._series, index=self.index, columns=self.columns,
default_kind=kind,
default_fill_value=fill_value)
diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py
index f1799eb99f720..5e3f59a24e5a1 100644
--- a/pandas/sparse/frame.py
+++ b/pandas/sparse/frame.py
@@ -246,7 +246,7 @@ def to_dense(self):
df : DataFrame
"""
data = dict((k, v.to_dense()) for k, v in compat.iteritems(self))
- return DataFrame(data, index=self.index)
+ return DataFrame(data, index=self.index,columns=self.columns)
def astype(self, dtype):
raise NotImplementedError
diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py
index 9ce08c550dd0d..2f148c89ccbe9 100644
--- a/pandas/sparse/tests/test_sparse.py
+++ b/pandas/sparse/tests/test_sparse.py
@@ -11,7 +11,7 @@
import pandas as pd
dec = np.testing.dec
-from pandas.util.testing import (assert_almost_equal, assert_series_equal,
+from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_index_equal,
assert_frame_equal, assert_panel_equal, assertRaisesRegexp,
assert_numpy_array_equal, assert_attr_equal)
from numpy.testing import assert_equal
@@ -770,6 +770,24 @@ def test_combine_first(self):
assert_sp_series_equal(result, result2)
assert_sp_series_equal(result, expected)
+class TestSparseHandlingMultiIndexes(tm.TestCase):
+
+ def setUp(self):
+ miindex = pd.MultiIndex.from_product([["x","y"], ["10","20"]],names=['row-foo', 'row-bar'])
+ micol = pd.MultiIndex.from_product([['a','b','c'], ["1","2"]],names=['col-foo', 'col-bar'])
+ dense_multiindex_frame = pd.DataFrame(index=miindex, columns=micol).sortlevel().sortlevel(axis=1)
+ self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14)
+
+ def test_to_sparse_preserve_multiindex_names_columns(self):
+ sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse().copy()
+ assert_index_equal(sparse_multiindex_frame.columns,self.dense_multiindex_frame.columns)
+
+ def test_round_trip_preserve_multiindex_names(self):
+ sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse()
+ round_trip_multiindex_frame = sparse_multiindex_frame.to_dense()
+ assert_frame_equal(self.dense_multiindex_frame,round_trip_multiindex_frame,
+ check_column_type=True,check_names=True)
+
class TestSparseSeriesScipyInteraction(tm.TestCase):
# Issue 8048: add SparseSeries coo methods
diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py
index 90f6bff498e62..813354b2d0f86 100644
--- a/pandas/tseries/holiday.py
+++ b/pandas/tseries/holiday.py
@@ -1,3 +1,5 @@
+import warnings
+
from pandas import DateOffset, DatetimeIndex, Series, Timestamp
from pandas.compat import add_metaclass
from datetime import datetime, timedelta
@@ -192,10 +194,10 @@ def dates(self, start_date, end_date, return_name=False):
"""
start_date = Timestamp(start_date)
end_date = Timestamp(end_date)
-
- filter_start_date = start_date
+
+ filter_start_date = start_date
filter_end_date = end_date
-
+
if self.year is not None:
dt = Timestamp(datetime(self.year, self.month, self.day))
if return_name:
@@ -208,22 +210,22 @@ def dates(self, start_date, end_date, return_name=False):
if self.days_of_week is not None:
holiday_dates = holiday_dates[np.in1d(holiday_dates.dayofweek,
self.days_of_week)]
-
+
if self.start_date is not None:
filter_start_date = max(self.start_date.tz_localize(filter_start_date.tz), filter_start_date)
if self.end_date is not None:
filter_end_date = min(self.end_date.tz_localize(filter_end_date.tz), filter_end_date)
- holiday_dates = holiday_dates[(holiday_dates >= filter_start_date) &
+ holiday_dates = holiday_dates[(holiday_dates >= filter_start_date) &
(holiday_dates <= filter_end_date)]
if return_name:
return Series(self.name, index=holiday_dates)
return holiday_dates
-
-
+
+
def _reference_dates(self, start_date, end_date):
"""
Get reference dates for the holiday.
-
+
Return reference dates for the holiday also returning the year
prior to the start_date and year following the end_date. This ensures
that any offsets to be applied will yield the holidays within
@@ -238,13 +240,13 @@ def _reference_dates(self, start_date, end_date):
year_offset = DateOffset(years=1)
reference_start_date = Timestamp(
datetime(start_date.year-1, self.month, self.day))
-
+
reference_end_date = Timestamp(
datetime(end_date.year+1, self.month, self.day))
# Don't process unnecessary holidays
- dates = DatetimeIndex(start=reference_start_date, end=reference_end_date,
+ dates = DatetimeIndex(start=reference_start_date, end=reference_end_date,
freq=year_offset, tz=start_date.tz)
-
+
return dates
def _apply_rule(self, dates):
@@ -269,7 +271,11 @@ def _apply_rule(self, dates):
else:
offsets = self.offset
for offset in offsets:
- dates += offset
+
+ # if we are adding a non-vectorized value
+ # ignore the PerformanceWarnings:
+ with warnings.catch_warnings(record=True):
+ dates += offset
return dates
holiday_calendars = {}
@@ -327,12 +333,12 @@ def __init__(self, name=None, rules=None):
if rules is not None:
self.rules = rules
-
+
def rule_from_name(self, name):
for rule in self.rules:
if rule.name == name:
return rule
-
+
return None
def holidays(self, start=None, end=None, return_name=False):
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index 230016f00374f..a652268dd8e89 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -2637,35 +2637,32 @@ def test_datetime64_with_DateOffset(self):
assert_func(klass([x - op for x in s]), s - op)
- # split by fast/slow path to test perf warning
- off = {False:
- ['YearBegin', ('YearBegin', {'month': 5}),
- 'YearEnd', ('YearEnd', {'month': 5}),
- 'MonthBegin', 'MonthEnd', 'Week', ('Week', {'weekday': 3}),
- 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin'],
- PerformanceWarning:
- ['CustomBusinessDay', 'CDay', 'CBMonthEnd','CBMonthBegin',
- 'BMonthBegin', 'BMonthEnd', 'BusinessHour', 'BYearBegin',
- 'BYearEnd','BQuarterBegin', ('LastWeekOfMonth', {'weekday':2}),
- ('FY5253Quarter', {'qtr_with_extra_week': 1, 'startingMonth': 1,
- 'weekday': 2, 'variation': 'nearest'}),
- ('FY5253',{'weekday': 0, 'startingMonth': 2, 'variation': 'nearest'}),
- ('WeekOfMonth', {'weekday': 2, 'week': 2}), 'Easter',
- ('DateOffset', {'day': 4}), ('DateOffset', {'month': 5})]}
+ # assert these are equal on a piecewise basis
+ offsets = ['YearBegin', ('YearBegin', {'month': 5}),
+ 'YearEnd', ('YearEnd', {'month': 5}),
+ 'MonthBegin', 'MonthEnd', 'Week', ('Week', {'weekday': 3}),
+ 'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin',
+ 'CustomBusinessDay', 'CDay', 'CBMonthEnd','CBMonthBegin',
+ 'BMonthBegin', 'BMonthEnd', 'BusinessHour', 'BYearBegin',
+ 'BYearEnd','BQuarterBegin', ('LastWeekOfMonth', {'weekday':2}),
+ ('FY5253Quarter', {'qtr_with_extra_week': 1, 'startingMonth': 1,
+ 'weekday': 2, 'variation': 'nearest'}),
+ ('FY5253',{'weekday': 0, 'startingMonth': 2, 'variation': 'nearest'}),
+ ('WeekOfMonth', {'weekday': 2, 'week': 2}), 'Easter',
+ ('DateOffset', {'day': 4}), ('DateOffset', {'month': 5})]
for normalize in (True, False):
- for warning, offsets in off.items():
- for do in offsets:
- if isinstance(do, tuple):
- do, kwargs = do
- else:
- do = do
- kwargs = {}
- op = getattr(pd.offsets,do)(5, normalize=normalize, **kwargs)
- with tm.assert_produces_warning(warning):
- assert_func(klass([x + op for x in s]), s + op)
- assert_func(klass([x - op for x in s]), s - op)
- assert_func(klass([op + x for x in s]), op + s)
+ for do in offsets:
+ if isinstance(do, tuple):
+ do, kwargs = do
+ else:
+ do = do
+ kwargs = {}
+ op = getattr(pd.offsets,do)(5, normalize=normalize, **kwargs)
+ assert_func(klass([x + op for x in s]), s + op)
+ assert_func(klass([x - op for x in s]), s - op)
+ assert_func(klass([op + x for x in s]), op + s)
+
# def test_add_timedelta64(self):
# rng = date_range('1/1/2000', periods=5)
# delta = rng.values[3] - rng.values[1]
| closes #11600
Fixed problem with multi-index column level names not propagating into sparse frames or back out to dense on a round trip through sparse. Includes 4 new tests to cover some relevant scenarios. Problem fixed for the conventions to_sparse path, I'm not sure about other paths where something else is passed to SparseDataSeries directly, those would be outside scope of bug.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11606 | 2015-11-14T20:56:22Z | 2015-11-19T02:28:06Z | null | 2015-11-19T02:28:06Z |
CI: testing newer matplotlib with jinja | diff --git a/ci/requirements-2.6.run b/ci/requirements-2.6.run
index 5f8a2fde1409f..32d71beb24388 100644
--- a/ci/requirements-2.6.run
+++ b/ci/requirements-2.6.run
@@ -14,3 +14,4 @@ psycopg2=2.5.1
pymysql=0.6.0
sqlalchemy=0.7.8
xlsxwriter=0.4.6
+jinja2=2.8
diff --git a/ci/requirements-2.7.run b/ci/requirements-2.7.run
index 10049179912da..8fc074b96e0e4 100644
--- a/ci/requirements-2.7.run
+++ b/ci/requirements-2.7.run
@@ -19,3 +19,4 @@ pymysql=0.6.3
html5lib=1.0b2
beautiful-soup=4.2.1
statsmodels
+jinja2=2.8
diff --git a/ci/requirements-2.7_LOCALE.run b/ci/requirements-2.7_LOCALE.run
index 9bb37ee10f8db..95d619ec5c21e 100644
--- a/ci/requirements-2.7_LOCALE.run
+++ b/ci/requirements-2.7_LOCALE.run
@@ -6,7 +6,7 @@ openpyxl=1.6.2
xlsxwriter=0.4.6
xlrd=0.9.2
bottleneck=0.8.0
-matplotlib=1.2.1
+matplotlib=1.4.2
patsy=0.1.0
sqlalchemy=0.8.1
html5lib=1.0b2
@@ -15,3 +15,4 @@ scipy=0.11.0
beautiful-soup=4.2.1
statsmodels=0.4.3
bigquery=2.0.17
+jinja2=2.8
diff --git a/ci/requirements-3.3.run b/ci/requirements-3.3.run
index 0256802a69eba..2379ab42391db 100644
--- a/ci/requirements-3.3.run
+++ b/ci/requirements-3.3.run
@@ -14,3 +14,4 @@ lxml=3.2.1
scipy
beautiful-soup=4.2.1
statsmodels
+jinja2=2.8
diff --git a/ci/requirements-3.4.run b/ci/requirements-3.4.run
index 45d082022713e..902a2984d4b3d 100644
--- a/ci/requirements-3.4.run
+++ b/ci/requirements-3.4.run
@@ -16,3 +16,4 @@ sqlalchemy
bottleneck
pymysql=0.6.3
psycopg2
+jinja2=2.8
diff --git a/ci/requirements-3.5.run b/ci/requirements-3.5.run
index 8de8f7d8f0630..c5caf841c01a8 100644
--- a/ci/requirements-3.5.run
+++ b/ci/requirements-3.5.run
@@ -12,7 +12,7 @@ pytables
html5lib
lxml
matplotlib
-
+jinja2
# currently causing some warnings
#sqlalchemy
#pymysql
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 22d0026f27742..2004fe1df4593 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -577,6 +577,12 @@ def _repr_html_(self):
else:
return None
+ @property
+ def style(self):
+ """ return our styler """
+ from pandas.core.style import Styler
+ return Styler(self)
+
def iteritems(self):
"""
Iterator over (column name, Series) pairs.
@@ -1518,6 +1524,7 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions)
+ # TODO: a generic formatter wld b in DataFrameFormatter
formatter.to_html(classes=classes, notebook=notebook)
if buf is None:
diff --git a/pandas/core/style.py b/pandas/core/style.py
new file mode 100644
index 0000000000000..83e6261cb3b54
--- /dev/null
+++ b/pandas/core/style.py
@@ -0,0 +1,689 @@
+"""
+Module for applying conditional formatting to
+DataFrames and Series.
+"""
+from functools import partial
+from contextlib import contextmanager
+from uuid import uuid1
+import copy
+from collections import defaultdict
+
+try:
+ import warnings
+ warnings.warn("Importing jinja")
+ from jinja2 import Template
+ has_jinja = True
+except ImportError:
+ has_jinja = False
+
+import numpy as np
+import pandas as pd
+import pandas.core.common as com
+from pandas.compat import lzip
+try:
+ import matplotlib.pyplot as plt
+ from matplotlib import colors
+ has_mpl = True
+except ImportError:
+ has_mpl = False
+ no_mpl_message = "{0} requires matplotlib."
+
+
+@contextmanager
+def _mpl(func):
+ if has_mpl:
+ yield plt, colors
+ else:
+ raise ImportError(no_mpl_message.format(func.__name__))
+
+
+class Styler(object):
+ """
+ Helps style a DataFrame or Series according to the
+ data.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ data: Series or DataFrame
+ precision: int
+ precision to round floats to, defaults to pd.options.display.precision
+ table_styles: list-like, default None
+ list of {selector: (attr, value)} dicts; see Notes
+ uuid: str, default None
+ a unique identifier to avoid CSS collisons; generated automatically
+ caption: str, default None
+ caption to attach to the table
+
+ Attributes
+ ----------
+ tempate: Jinja Template
+ ctx : defaultdict
+ maps (row, column) -> [styles] where each style is a
+ TODO: <(attribute, value)> paire | <string with 'attribute: value'>
+
+ Notes
+ -----
+ Most styling will be done by passing style functions into
+ Styler.tee, Styler.apply, or Styler.applymap. Style functions should
+ return values with strings containing 'attr: value' that will be applied
+ to the indicated cells.
+
+ If using in the Jupyter notebook, Styler has defined a _repr_html_
+ to automatically render itself. Otherwise call Styler.render to get
+ the genterated HTML.
+ """
+ if has_jinja:
+ template = Template("""
+ <style type="text/css" >
+ {% for s in table_styles %}
+ #T_{{uuid}} {{s.selector}} {
+ {% for p,val in s.props %}
+ {{p}}: {{val}};
+ {% endfor %}
+ }
+ {% endfor %}
+ {% for s in cellstyle %}
+ #T_{{uuid}}{{s.selector}} {
+ {% for p,val in s.props %}
+ {{p}}: {{val}};
+ {% endfor %}
+ }
+ {% endfor %}
+ </style>
+
+ <table id="T_{{uuid}}">
+ {% if caption %}
+ <caption>{{caption}}</caption>
+ {% endif %}
+
+ <thead>
+ {% for r in head %}
+ <tr>
+ {% for c in r %}
+ <{{c.type}} class="{{c.class}}">{{c.value}}
+ {% endfor %}
+ </tr>
+ {% endfor %}
+ </thead>
+ <tbody>
+ {% for r in body %}
+ <tr>
+ {% for c in r %}
+ <{{c.type}} id="T_{{uuid}}{{c.id}}" class="{{c.class}}">
+ {% if c.value is number %}
+ {{c.value|round(precision)}}
+ {% else %}
+ {{c.value}}
+ {% endif %}
+ {% endfor %}
+ </tr>
+ {% endfor %}
+ </tbody>
+ </table>
+ """)
+
+ def __init__(self, data, precision=None, table_styles=None, uuid=None,
+ caption=None):
+ if not has_jinja:
+ msg = "pandas.Styler requires jinja2. "\
+ "Please install with `conda install Jinja2`\n"\
+ "or `pip install Jinja2`"
+ raise ImportError(msg)
+ self.ctx = defaultdict(list)
+ self._todo = []
+
+ if not isinstance(data, (pd.Series, pd.DataFrame)):
+ raise TypeError
+ if data.ndim == 1:
+ data = data.to_frame()
+ if not data.index.is_unique or not data.columns.is_unique:
+ raise ValueError("style is not supported for non-unique indicies.")
+
+ self.data = data
+ self.index = data.index
+ self.columns = data.columns
+
+ self.uuid = uuid
+ self.table_styles = table_styles
+ self.caption = caption
+ if precision is None:
+ precision = pd.options.display.precision
+ self.precision = precision
+
+ def _repr_html_(self):
+ '''
+ Hooks into Jupyter notebook rich display system.
+ '''
+ return self.render()
+
+ def _translate(self, table_styles=None, caption=None, uuid=None,
+ precision=None):
+ """
+ Convert the DataFrame in `self.data` and the attrs from `_build_styles`
+ into a dictionary of {head, body, uuid, cellstyle}
+ """
+ table_styles = table_styles or self.table_styles
+ if table_styles is None:
+ table_styles = []
+ caption = caption or self.caption
+ precision = precision or self.precision
+ ctx = self.ctx
+ uuid = uuid or str(uuid1()).replace("-", "_")
+ ROW_HEADING_CLASS = "row_heading"
+ COL_HEADING_CLASS = "col_heading"
+ DATA_CLASS = "data"
+ BLANK_CLASS = "blank"
+ BLANK_VALUE = ""
+
+ cell_context = dict()
+
+ n_rlvls = self.data.index.nlevels
+ n_clvls = self.data.columns.nlevels
+ rlabels = self.data.index.tolist()
+ clabels = self.data.columns.tolist()
+
+ idx_values = self.data.index.format(sparsify=False, adjoin=False,
+ names=False)
+ idx_values = lzip(*idx_values)
+
+ if n_rlvls == 1:
+ rlabels = [[x] for x in rlabels]
+ if n_clvls == 1:
+ clabels = [[x] for x in clabels]
+ clabels = list(zip(*clabels))
+
+ cellstyle = []
+ head = []
+
+ for r in range(n_clvls):
+ row_es = [{"type": "th", "value": BLANK_VALUE,
+ "class": " ".join([BLANK_CLASS])}] * n_rlvls
+ for c in range(len(clabels[0])):
+ cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c]
+ cs.extend(cell_context.get(
+ "col_headings", {}).get(r, {}).get(c, []))
+ row_es.append({"type": "th", "value": clabels[r][c],
+ "class": " ".join(cs)})
+ head.append(row_es)
+
+ body = []
+ for r, idx in enumerate(self.data.index):
+ cs = [ROW_HEADING_CLASS, "level%s" % c, "row%s" % r]
+ cs.extend(cell_context.get(
+ "row_headings", {}).get(r, {}).get(c, []))
+ row_es = [{"type": "th",
+ "value": rlabels[r][c],
+ "class": " ".join(cs)}
+ for c in range(len(rlabels[r]))]
+
+ for c, col in enumerate(self.data.columns):
+ cs = [DATA_CLASS, "row%s" % r, "col%s" % c]
+ cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
+ row_es.append({"type": "td", "value": self.data.iloc[r][c],
+ "class": " ".join(cs), "id": "_".join(cs[1:])})
+ props = []
+ for x in ctx[r, c]:
+ # have to handle empty styles like ['']
+ if x.count(":"):
+ props.append(x.split(":"))
+ else:
+ props.append(['', ''])
+ cellstyle.append(
+ {'props': props,
+ 'selector': "row%s_col%s" % (r, c)}
+ )
+ body.append(row_es)
+
+ # uuid required to isolate table styling from others
+ # in same notebook in ipnb
+ return dict(head=head, cellstyle=cellstyle, body=body, uuid=uuid,
+ precision=self.precision, table_styles=table_styles,
+ caption=caption)
+
+ def render(self, table_styles=None, caption=None, uuid=None,
+ precision=None, trim=True):
+ """
+ Render the built up styles to HTML
+
+ Parameters
+ ----------
+ table_styles: list or None
+ defaults to ``self.table_styles``
+ caption: str or None
+ defaults to ``self.caption``
+ uuid: str or None
+ defaults to random uuid
+ precision: int or None
+ defaults to self.precision
+ trim: bool
+ Small optimization to not create classes for cells that
+ have no styles applied to them.
+
+ Returns
+ -------
+ rendered: str
+ the rendered HTML
+
+ Notes
+ -----
+ ``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
+ the rendered HTML in the notebook.
+ """
+ self._compute()
+ table_styles = table_styles or self.table_styles
+ caption = caption or self.caption
+ uuid = uuid or self.uuid
+ d = self._translate(table_styles=table_styles, caption=caption,
+ uuid=uuid)
+ if trim:
+ # filter out empty styles, every cell will have a class
+ # but the list of props may just be [['', '']].
+ # so we have the neested anys below
+ trimmed = [x for x in d['cellstyle'] if
+ any(any(y) for y in x['props'])]
+ d['cellstyle'] = trimmed
+ return self.template.render(**d)
+
+ def _update_ctx(self, attrs):
+ """
+ update the state of the Styler. Collects a mapping
+ of {index_label: ['<property>: <value>']}
+
+ attrs: Series or DataFrame
+ should contain strings of '<property>: <value>;<prop2>: <val2>'
+ Whitespace shouldn't matter and the final trailing ';' shouldn't
+ matter.
+ """
+ for row_label, v in attrs.iterrows():
+ for col_label, col in v.iteritems():
+ i = self.index.get_indexer([row_label])[0]
+ j = self.columns.get_indexer([col_label])[0]
+ for pair in col.rstrip(";").split(";"):
+ self.ctx[(i, j)].append(pair)
+
+ def _copy(self, deepcopy=False):
+ styler = Styler(self.data, precision=self.precision,
+ caption=self.caption, uuid=self.uuid,
+ table_styles=self.table_styles)
+ if deepcopy:
+ styler.ctx = copy.deepcopy(self.ctx)
+ else:
+ styler.ctx = self.ctx
+ return styler
+
+ def __copy__(self):
+ """
+ Deep copy by default.
+ """
+ return self._copy(deepcopy=False)
+
+ def __deepcopy__(self, memo):
+ return self._copy(deepcopy=True)
+
+ def clear(self):
+ self.ctx.clear()
+
+ def _compute(self):
+ '''
+ Execute the style functions built up in `self._todo`.
+
+ Relies on the conventions that all style functions go through
+ .apply or .applymap. The append styles to apply as tuples of
+
+ (application method, *args, **kwargs)
+ '''
+ r = self
+ for func, args, kwargs in self._todo:
+ r = func(self)(*args, **kwargs)
+ return r
+
+ def _apply(self, func, axis=0, subset=None, **kwargs):
+ subset = slice(None) if subset is None else subset
+ subset = _non_reducing_slice(subset)
+ if axis is not None:
+ result = self.data.loc[subset].apply(func, axis=axis, **kwargs)
+ else:
+ # like tee
+ result = func(self.data.loc[subset], **kwargs)
+ self._update_ctx(result)
+ return self
+
+ def apply(self, func, axis=0, subset=None, **kwargs):
+ """
+ Apply a function, updating the HTML representation with the result.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ func: function
+ axis: int, str or None
+ apply to each column (``axis=0`` or ``'index'``)
+ or to each row (``axis=1`` or ``'columns'``) or
+ to the entire DataFrame at once with ``axis=None``.
+ subset: IndexSlice
+ a valid indexer to limit ``data`` to *before* applying the
+ function. Consider using a pandas.IndexSlice
+ kwargs: dict
+ pass along to ``func``
+
+ Returns
+ -------
+ self
+
+ Notes
+ -----
+ This is similar to DataFrame.apply, except that axis=None applies
+ the function to the entire DataFrame at once, rather tha column
+ or rowwise.
+
+ Examples
+ --------
+
+ """
+ self._todo.append((lambda instance: getattr(instance, '_apply'),
+ (func, axis, subset),
+ kwargs))
+ return self
+
+ def _applymap(self, func, subset=None, **kwargs):
+ func = partial(func, **kwargs) # applymap doesn't take kwargs?
+ if subset is None:
+ subset = pd.IndexSlice[:]
+ subset = _non_reducing_slice(subset)
+ result = self.data.loc[subset].applymap(func)
+ self._update_ctx(result)
+ return self
+
+ def applymap(self, func, subset=None, **kwargs):
+ """
+ Apply a function elementwise, updating the HTML
+ representation with the result.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ func : function
+ subset : IndexSlice
+ a valid indexer to limit ``data`` to *before* applying the
+ function. Consider using a pandas.IndexSlice
+ kwargs : dict
+ pass along to ``func``
+
+ Returns
+ -------
+ self
+
+ Notes
+ -----
+ Examples
+ --------
+ """
+ self._todo.append((lambda instance: getattr(instance, '_applymap'),
+ (func, subset),
+ kwargs))
+ return self
+
+ def set_precision(self, precision):
+ self.precision = precision
+ return self
+
+ def export(self):
+ return self._todo
+
+ def set(self, styles):
+ self._todo.extend(styles)
+ return self
+
+ def set_uuid(self, uuid):
+ self.uuid = uuid
+ return self
+
+ def set_caption(self, caption):
+ self.caption = caption
+ return self
+
+ def set_table_styles(self, table_styles):
+ self.table_styles = table_styles
+ return self
+
+ # -----------------------------------------------------------------------
+ # A collection of "builtin" styles
+ # -----------------------------------------------------------------------
+
+ @staticmethod
+ def _highlight_null(v, null_color):
+ return 'background-color: %s' % null_color if pd.isnull(v) else ''
+
+ def highlight_null(self, null_color='red'):
+ """
+ Shade the background ``null_color`` for missing values.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ null_color: str
+
+ """
+ self.applymap(self._highlight_null, null_color=null_color)
+ return self
+
+ def background_gradient(self, cmap='PuBu', low=0, high=0,
+ axis=0, subset=None):
+ """
+ Color the background in a gradient according to
+ the data in each column (optionally row).
+ Requires matplotlib.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ cmap: str or colormap
+ matplotlib colormap
+ low, high: float
+ compress the range by these values.
+ axis: int or str
+ 1 or 'columns' for colunwise, 0 or 'index' for rowwise
+ subset: IndexSlice
+
+ Notes
+ -----
+ Tune ``low`` and ``high`` to keep the text legible by
+ not using the entire range of the color map. These extend
+ the range of the data by ``low * (x.max() - x.min())``
+ and ``high * (x.max() - x.min())`` before normalizing.
+ """
+ subset = _maybe_numeric_slice(self.data, subset)
+ subset = _non_reducing_slice(subset)
+ self.apply(self._background_gradient, cmap=cmap, subset=subset,
+ axis=axis, low=low, high=high)
+ return self
+
+ @staticmethod
+ def _background_gradient(s, cmap='PuBu', low=0, high=0):
+ """Color background in a range according to the data."""
+ with _mpl(Styler.background_gradient) as (plt, colors):
+ rng = s.max() - s.min()
+ # extend lower / upper bounds, compresses color range
+ norm = colors.Normalize(s.min() - (rng * low),
+ s.max() + (rng * high))
+ # matplotlib modifies inplace?
+ # https://github.com/matplotlib/matplotlib/issues/5427
+ normed = norm(s.values)
+ c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
+ return ['background-color: %s' % color for color in c]
+
+ def text_shadow(self, color='black'):
+ self.set_properties(**{"text-shadow": "-1px 0 {color}, 0 1px {color}, "
+ "1px 0 {color}, 0 -1px {color};".format(
+ color=color)})
+ return self
+
+ def set_properties(self, subset=None, **kwargs):
+ """
+ Convience method for setting a non-data dependent properties
+ on each element.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice
+ a valid slice for ``data``
+ kwargs: dict
+ property: value pairs to be set for each cell
+
+ Returns
+ -------
+ self : Styler
+
+ Examples
+ --------
+ df.stle.set_properties(color="white", align="right")
+ """
+ values = ';'.join('{p}: {v}'.format(p=p, v=v) for p, v in
+ kwargs.items())
+ f = lambda x: values
+ return self.applymap(f, subset=subset)
+
+ @staticmethod
+ def _bar(s, color, width):
+ normed = width * (s - s.min()) / (s.max() - s.min())
+ attrs = 'width: 10em; height: 80%;'\
+ 'background: linear-gradient(90deg,'\
+ '{c} {w}%, transparent 0%)'
+ return [attrs.format(c=color, w=x) for x in normed]
+
+ def bar(self, subset=None, axis=0, color='#FFC0CB', width=100):
+ """
+ Color the background `color` proptional to the values in each column.
+ Excludes non-numeric data by default.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ axis: int
+ color: str
+ width: float
+ A number between 0 or 100. The largest value will cover this
+ percent of the cell's width
+ """
+ subset = _maybe_numeric_slice(self.data, subset)
+ subset = _non_reducing_slice(subset)
+ self.apply(self._bar, subset=subset, axis=axis, color=color,
+ width=width)
+ return self
+
+ def highlight_max(self, subset=None, color='yellow', axis=None):
+ """
+ Highlight the maximum by shading the background
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ color: str, default 'yellow'
+ axis: int, str, or None
+ 0 or 'index' for columnwise, 1 or 'columns' for rowwise
+ or ``None`` for tablewise
+ """
+ return self._highlight_handler(subset=subset, color=color, axis=axis,
+ max_=True)
+
+ def highlight_min(self, subset=None, color='yellow', axis=None):
+ """
+ Highlight the minimum by shading the background
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ color: str, default 'yellow'
+ axis: int, str, or None
+ 0 or 'index' for columnwise, 1 or 'columns' for rowwise
+ or ``None`` for tablewise
+ """
+ return self._highlight_handler(subset=subset, color=color, axis=axis,
+ max_=False)
+
+ def _highlight_handler(self, subset=None, color='yellow', axis=None,
+ max_=True):
+ subset = _non_reducing_slice(_maybe_numeric_slice(self.data, subset))
+ self.apply(self._highlight_extrema, color=color, axis=axis,
+ subset=subset, max_=max_)
+ return self
+
+ @staticmethod
+ def _highlight_extrema(data, color='yellow', max_=True):
+ '''
+ highlight the min or max in a Series or DataFrame
+ '''
+ attr = 'background-color: {0}'.format(color)
+ if data.ndim == 1: # Series from .apply
+ if max_:
+ extrema = data == data.max()
+ else:
+ extrema = data == data.min()
+ return [attr if v else '' for v in extrema]
+ else: # DataFrame from .tee
+ if max_:
+ extrema = data == data.max().max()
+ else:
+ extrema = data == data.min().min()
+ return pd.DataFrame(np.where(extrema, attr, ''),
+ index=data.index, columns=data.columns)
+
+
+def _non_reducing_slice(slice_):
+ """
+ Ensurse that a slice doesn't reduce to a Series or Scalar.
+
+ Any user-paseed `subset` should have this called on it
+ to make sure we're always working with DataFrames.
+ """
+ # default to column slice, like DataFrame
+ # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
+ kinds = tuple(list(pd.compat.string_types) +
+ [pd.Series, np.ndarray, pd.Index, list])
+ if isinstance(slice_, kinds):
+ slice_ = pd.IndexSlice[:, slice_]
+
+ def pred(part):
+ # true when slice does *not* reduce
+ return isinstance(part, slice) or com.is_list_like(part)
+
+ if not com.is_list_like(slice_):
+ if not isinstance(slice_, slice):
+ # a 1-d slice, like df.loc[1]
+ slice_ = [[slice_]]
+ else:
+ # slice(a, b, c)
+ slice_ = [slice_] # to tuplize later
+ else:
+ slice_ = [part if pred(part) else [part] for part in slice_]
+ return tuple(slice_)
+
+
+def _maybe_numeric_slice(df, slice_, include_bool=False):
+ """
+ want nice defaults for background_gradient that don't break
+ with non-numeric data. But if slice_ is passed go with that.
+ """
+ if slice_ is None:
+ dtypes = [np.number]
+ if include_bool:
+ dtypes.append(bool)
+ slice_ = pd.IndexSlice[:, df.select_dtypes(include=dtypes).columns]
+ return slice_
diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py
new file mode 100644
index 0000000000000..cec12666b2462
--- /dev/null
+++ b/pandas/tests/test_style.py
@@ -0,0 +1,413 @@
+import os
+from nose import SkipTest
+
+def setUpModule():
+ try:
+ import jinja2
+ except ImportError:
+ raise SkipTest("No Jinja2")
+
+import copy
+from pandas.core.style import (Styler, _non_reducing_slice,
+ _maybe_numeric_slice)
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+from pandas.util.testing import TestCase
+import pandas.util.testing as tm
+import warnings
+
+class TestStyler(TestCase):
+
+ def setUp(self):
+ np.random.seed(24)
+ self.s = DataFrame({'A': np.random.permutation(range(6))})
+ self.df = DataFrame({'A': [0, 1], 'B': np.random.randn(2)})
+ self.f = lambda x: x
+ self.g = lambda x: x
+
+ def h(x, foo='bar'):
+ return pd.Series(['color: %s' % foo], index=x.index, name=x.name)
+
+ self.h = h
+ self.styler = Styler(self.df)
+ self.attrs = pd.DataFrame({'A': ['color: red', 'color: blue']})
+ self.dataframes = [
+ self.df,
+ pd.DataFrame({'f': [1., 2.], 'o': ['a', 'b'],
+ 'c': pd.Categorical(['a', 'b'])})
+ ]
+
+ def test_update_ctx(self):
+ self.styler._update_ctx(self.attrs)
+ expected = {(0, 0): ['color: red'],
+ (1, 0): ['color: blue']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_update_ctx_flatten_multi(self):
+ attrs = DataFrame({"A": ['color: red; foo: bar',
+ 'color: blue; foo: baz']})
+ self.styler._update_ctx(attrs)
+ expected = {(0, 0): ['color: red', ' foo: bar'],
+ (1, 0): ['color: blue', ' foo: baz']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_update_ctx_flatten_multi_traliing_semi(self):
+ attrs = DataFrame({"A": ['color: red; foo: bar;',
+ 'color: blue; foo: baz;']})
+ self.styler._update_ctx(attrs)
+ expected = {(0, 0): ['color: red', ' foo: bar'],
+ (1, 0): ['color: blue', ' foo: baz']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_copy(self):
+ s2 = copy.copy(self.styler)
+ self.assertTrue(self.styler is not s2)
+ self.assertTrue(self.styler.ctx is s2.ctx) # shallow
+
+ self.styler._update_ctx(self.attrs)
+ self.assertEqual(self.styler.ctx, s2.ctx)
+
+ def test_deepcopy(self):
+ s2 = copy.deepcopy(self.styler)
+ self.assertTrue(self.styler is not s2)
+ self.assertTrue(self.styler.ctx is not s2.ctx)
+
+ self.styler._update_ctx(self.attrs)
+ self.assertNotEqual(self.styler.ctx, s2.ctx)
+
+ def test_clear(self):
+ self.styler._update_ctx(self.attrs)
+ self.assertTrue(len(self.styler.ctx) > 0)
+ self.styler.clear()
+ self.assertTrue(len(self.styler.ctx) == 0)
+
+ def test_render(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
+ s = Styler(df, uuid='AB').apply(style).apply(style, axis=1)
+ s.render()
+ # it worked?
+
+ def test_render_double(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ style = lambda x: pd.Series(["color: red; border: 1px",
+ "color: blue; border: 2px"], name=x.name)
+ s = Styler(df, uuid='AB').apply(style)
+ s.render()
+ # it worked?
+
+ def test_set_properties(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ result = df.style.set_properties(color='white',
+ size='10px')._compute().ctx
+ # order is deterministic
+ v = ["color: white", "size: 10px"]
+ expected = {(0, 0): v, (1, 0): v}
+ self.assertEqual(result.keys(), expected.keys())
+ for v1, v2 in zip(result.values(), expected.values()):
+ self.assertEqual(sorted(v1), sorted(v2))
+
+ def test_set_properties_subset(self):
+ df = pd.DataFrame({'A': [0, 1]})
+ result = df.style.set_properties(subset=pd.IndexSlice[0, 'A'],
+ color='white')._compute().ctx
+ expected = {(0, 0): ['color: white']}
+ self.assertEqual(result, expected)
+
+ def test_apply_axis(self):
+ df = pd.DataFrame({'A': [0, 0], 'B': [1, 1]})
+ f = lambda x: ['val: %s' % x.max() for v in x]
+ result = df.style.apply(f, axis=1)
+ self.assertEqual(len(result._todo), 1)
+ self.assertEqual(len(result.ctx), 0)
+ result._compute()
+ expected = {(0, 0): ['val: 1'], (0, 1): ['val: 1'],
+ (1, 0): ['val: 1'], (1, 1): ['val: 1']}
+ self.assertEqual(result.ctx, expected)
+
+ result = df.style.apply(f, axis=0)
+ expected = {(0, 0): ['val: 0'], (0, 1): ['val: 1'],
+ (1, 0): ['val: 0'], (1, 1): ['val: 1']}
+ result._compute()
+ self.assertEqual(result.ctx, expected)
+ result = df.style.apply(f) # default
+ result._compute()
+ self.assertEqual(result.ctx, expected)
+
+ def test_apply_subset(self):
+ axes = [0, 1]
+ slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
+ pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
+ pd.IndexSlice[:2, ['A', 'B']]]
+ for ax in axes:
+ for slice_ in slices:
+ result = self.df.style.apply(self.h, axis=ax, subset=slice_,
+ foo='baz')._compute().ctx
+ expected = dict(((r, c), ['color: baz'])
+ for r, row in enumerate(self.df.index)
+ for c, col in enumerate(self.df.columns)
+ if row in self.df.loc[slice_].index
+ and col in self.df.loc[slice_].columns)
+ self.assertEqual(result, expected)
+
+ def test_applymap_subset(self):
+ def f(x):
+ return 'foo: bar'
+
+ slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
+ pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
+ pd.IndexSlice[:2, ['A', 'B']]]
+
+ for slice_ in slices:
+ result = self.df.style.applymap(f, subset=slice_)._compute().ctx
+ expected = dict(((r, c), ['foo: bar'])
+ for r, row in enumerate(self.df.index)
+ for c, col in enumerate(self.df.columns)
+ if row in self.df.loc[slice_].index
+ and col in self.df.loc[slice_].columns)
+ self.assertEqual(result, expected)
+
+ def test_non_reducing_slice(self):
+ df = pd.DataFrame([[0, 1], [2, 3]])
+
+ slices = [
+ # pd.IndexSlice[:, :],
+ pd.IndexSlice[:, 1],
+ pd.IndexSlice[1, :],
+ pd.IndexSlice[[1], [1]],
+ pd.IndexSlice[1, [1]],
+ pd.IndexSlice[[1], 1],
+ pd.IndexSlice[1],
+ pd.IndexSlice[1, 1],
+ slice(None, None, None),
+ [0, 1],
+ np.array([0, 1]),
+ pd.Series([0, 1])
+ ]
+ for slice_ in slices:
+ tslice_ = _non_reducing_slice(slice_)
+ self.assertTrue(isinstance(df.loc[tslice_], DataFrame))
+
+ def test_list_slice(self):
+ # like dataframe getitem
+ slices = [['A'], pd.Series(['A']), np.array(['A'])]
+ df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['A', 'B'])
+ expected = pd.IndexSlice[:, ['A']]
+ for subset in slices:
+ result = _non_reducing_slice(subset)
+ tm.assert_frame_equal(df.loc[result], df.loc[expected])
+
+ def test_empty(self):
+ df = pd.DataFrame({'A': [1, 0]})
+ s = df.style
+ s.ctx = {(0, 0): ['color: red'],
+ (1, 0): ['']}
+
+ result = s._translate()['cellstyle']
+ expected = [{'props': [['color', ' red']], 'selector': 'row0_col0'},
+ {'props': [['', '']], 'selector': 'row1_col0'}]
+ self.assertEqual(result, expected)
+
+ def test_bar(self):
+ df = pd.DataFrame({'A': [0, 1, 2]})
+ result = df.style.bar()._compute().ctx
+ expected = {
+ (0, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#FFC0CB 0.0%, transparent 0%)'],
+ (1, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#FFC0CB 50.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#FFC0CB 100.0%, transparent 0%)']
+ }
+ self.assertEqual(result, expected)
+
+ result = df.style.bar(color='red', width=50)._compute().ctx
+ expected = {
+ (0, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 0.0%, transparent 0%)'],
+ (1, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 25.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 50.0%, transparent 0%)']
+ }
+ self.assertEqual(result, expected)
+
+ df['C'] = ['a'] * len(df)
+ result = df.style.bar(color='red', width=50)._compute().ctx
+ self.assertEqual(result, expected)
+ df['C'] = df['C'].astype('category')
+ result = df.style.bar(color='red', width=50)._compute().ctx
+ self.assertEqual(result, expected)
+
+ def test_highlight_null(self, null_color='red'):
+ df = pd.DataFrame({'A': [0, np.nan]})
+ result = df.style.highlight_null()._compute().ctx
+ expected = {(0, 0): [''],
+ (1, 0): ['background-color: red']}
+ self.assertEqual(result, expected)
+
+ def test_nonunique_raises(self):
+ df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
+ with tm.assertRaises(ValueError):
+ df.style
+
+ with tm.assertRaises(ValueError):
+ Styler(df)
+
+ def test_caption(self):
+ styler = Styler(self.df, caption='foo')
+ result = styler.render()
+ self.assertTrue(all(['caption' in result, 'foo' in result]))
+
+ # override
+ result = styler.render(caption='bar')
+ self.assertTrue(all(['caption' in result, 'bar' in result,
+ 'foo' not in result]))
+
+ styler = self.df.style
+ result = styler.set_caption('baz')
+ self.assertTrue(styler is result)
+ self.assertEqual(styler.caption, 'baz')
+
+ def test_uuid(self):
+ styler = Styler(self.df, uuid='abc123')
+ result = styler.render()
+ self.assertTrue('abc123' in result)
+
+ result = styler.render(uuid='123abc')
+ self.assertTrue('123abc' in result)
+
+ uuid = styler.uuid
+ result = styler.render(uuid=None)
+ self.assertTrue(uuid in result)
+
+ styler = self.df.style
+ result = styler.set_uuid('aaa')
+ self.assertTrue(result is styler)
+ self.assertEqual(result.uuid, 'aaa')
+
+ def test_table_styles(self):
+ style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
+ styler = Styler(self.df, table_styles=style)
+ result = ' '.join(styler.render().split())
+ self.assertTrue('th { foo: bar; }' in result)
+
+ styler = self.df.style
+ result = styler.set_table_styles(style)
+ self.assertTrue(styler is result)
+ self.assertEqual(styler.table_styles, style)
+
+ def test_precision(self):
+ with pd.option_context('display.precision', 10):
+ s = Styler(self.df)
+ self.assertEqual(s.precision, 10)
+ s = Styler(self.df, precision=2)
+ self.assertEqual(s.precision, 2)
+
+ s2 = s.set_precision(4)
+ self.assertTrue(s is s2)
+ self.assertEqual(s.precision, 4)
+
+ def test_maybe_numeric_slice(self):
+ df = pd.DataFrame({'A': [1, 2], 'B': ['c', 'd'], 'C': [True, False]})
+ result = _maybe_numeric_slice(df, slice_=None)
+ expected = pd.IndexSlice[:, ['A']]
+ self.assertEqual(result, expected)
+
+ result = _maybe_numeric_slice(df, None, include_bool=True)
+ expected = pd.IndexSlice[:, ['A', 'C']]
+ result = _maybe_numeric_slice(df, [1])
+ expected = [1]
+ self.assertEqual(result, expected)
+
+ def test_apply_none(self):
+ def f(x):
+ return pd.DataFrame(np.where(x == x.max(), 'color: red', ''),
+ index=x.index, columns=x.columns)
+ result = (pd.DataFrame([[1, 2], [3, 4]])
+ .style.apply(f, axis=None)._compute().ctx)
+ self.assertEqual(result[(1, 1)], ['color: red'])
+
+ def test_trim(self):
+ result = self.df.style.render() # trim=True
+ self.assertEqual(result.count('#'), 0)
+
+ result = self.df.style.render(trim=False)
+ self.assertEqual(result.count('#'), self.df.size)
+
+ result = self.df.style.highlight_max().render()
+ self.assertEqual(result.count('#'), 1)
+
+ def test_highlight_max(self):
+ df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+ # max(df) = min(-df)
+ for max_ in [True, False]:
+ if max_:
+ attr = 'highlight_max'
+ else:
+ df = -df
+ attr = 'highlight_min'
+ result = getattr(df.style, attr)()._compute().ctx
+ self.assertEqual(result[(1, 1)], ['background-color: yellow'])
+
+ result = getattr(df.style, attr)(color='green')._compute().ctx
+ self.assertEqual(result[(1, 1)], ['background-color: green'])
+
+ result = getattr(df.style, attr)(subset='A')._compute().ctx
+ self.assertEqual(result[(1, 0)], ['background-color: yellow'])
+
+ result = getattr(df.style, attr)(axis=0)._compute().ctx
+ expected = {(1, 0): ['background-color: yellow'],
+ (1, 1): ['background-color: yellow'],
+ (0, 1): [''], (0, 0): ['']}
+ self.assertEqual(result, expected)
+
+ result = getattr(df.style, attr)(axis=1)._compute().ctx
+ expected = {(0, 1): ['background-color: yellow'],
+ (1, 1): ['background-color: yellow'],
+ (0, 0): [''], (1, 0): ['']}
+ self.assertEqual(result, expected)
+
+ # separate since we cant negate the strs
+ df['C'] = ['a', 'b']
+ result = df.style.highlight_max()._compute().ctx
+ expected = {(1, 1): ['background-color: yellow']}
+
+ result = df.style.highlight_min()._compute().ctx
+ expected = {(0, 0): ['background-color: yellow']}
+
+ def test_export(self):
+ f = lambda x: 'color: red' if x > 0 else 'color: blue'
+ g = lambda x, y, z: 'color: %s' if x > 0 else 'color: %s' % z
+ style1 = self.styler
+ style1.applymap(f)\
+ .applymap(g, y='a', z='b')\
+ .highlight_max()
+ result = style1.export()
+ style2 = self.df.style
+ style2.set(result)
+ self.assertEqual(style1._todo, style2._todo)
+ style2.render()
+
+@tm.mplskip
+class TestStylerMatplotlibDep(TestCase):
+
+ def test_background_gradient(self):
+ df = pd.DataFrame([[1, 2], [2, 4]], columns=['A', 'B'])
+ for axis in [0, 1, 'index', 'columns']:
+ for cmap in [None, 'YlOrRd']:
+ result = df.style.background_gradient(cmap=cmap)._compute().ctx
+ self.assertTrue(all("#" in x[0] for x in result.values()))
+ self.assertEqual(result[(0, 0)], result[(0, 1)])
+ self.assertEqual(result[(1, 0)], result[(1, 1)])
+
+ result = (df.style.background_gradient(subset=pd.IndexSlice[1, 'A'])
+ ._compute().ctx)
+ self.assertEqual(result[(1, 0)], ['background-color: #fff7fb'])
diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py
index 2b5443e6ff0d2..13c0b6a08f6e7 100644
--- a/pandas/tests/test_testing.py
+++ b/pandas/tests/test_testing.py
@@ -11,7 +11,8 @@
from pandas.util.testing import (
assert_almost_equal, assertRaisesRegexp, raise_with_traceback,
assert_index_equal, assert_series_equal, assert_frame_equal,
- assert_numpy_array_equal, assert_isinstance, RNGContext
+ assert_numpy_array_equal, assert_isinstance, RNGContext,
+ assertRaises, skip_if_no_package_deco
)
from pandas.compat import is_platform_windows
@@ -627,6 +628,23 @@ def test_locale(self):
self.assertTrue(len(locales) >= 1)
+def test_skiptest_deco():
+ from nose import SkipTest
+ @skip_if_no_package_deco("fakepackagename")
+ def f():
+ pass
+ with assertRaises(SkipTest):
+ f()
+
+ @skip_if_no_package_deco("numpy")
+ def f():
+ pass
+ # hack to ensure that SkipTest is *not* raised
+ with assertRaises(ValueError):
+ f()
+ raise ValueError
+
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index be8b0df73593f..ba32bc1f3e377 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -1578,7 +1578,18 @@ def skip_if_no_package(*args, **kwargs):
exc_failed_check=SkipTest,
*args, **kwargs)
-#
+def skip_if_no_package_deco(pkg_name, version=None, app='pandas'):
+ from nose import SkipTest
+
+ def deco(func):
+ @wraps(func)
+ def wrapper(*args, **kwargs):
+ package_check(pkg_name, version=version, app=app,
+ exc_failed_import=SkipTest, exc_failed_check=SkipTest)
+ return func(*args, **kwargs)
+ return wrapper
+ return deco
+ #
# Additional tags decorators for nose
#
| https://api.github.com/repos/pandas-dev/pandas/pulls/11605 | 2015-11-14T20:51:55Z | 2015-11-29T18:10:53Z | null | 2016-11-03T12:38:32Z | |
API: provide Rolling/Expanding/EWM objects for deferred rolling type calculations #10702 | diff --git a/doc/source/api.rst b/doc/source/api.rst
index 12dc0b0cb50b9..3c7ca6d5c2326 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -194,65 +194,6 @@ Top-level evaluation
eval
-Standard moving window functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autosummary::
- :toctree: generated/
-
- rolling_count
- rolling_sum
- rolling_mean
- rolling_median
- rolling_var
- rolling_std
- rolling_min
- rolling_max
- rolling_corr
- rolling_corr_pairwise
- rolling_cov
- rolling_skew
- rolling_kurt
- rolling_apply
- rolling_quantile
- rolling_window
-
-.. _api.functions_expanding:
-
-Standard expanding window functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autosummary::
- :toctree: generated/
-
- expanding_count
- expanding_sum
- expanding_mean
- expanding_median
- expanding_var
- expanding_std
- expanding_min
- expanding_max
- expanding_corr
- expanding_corr_pairwise
- expanding_cov
- expanding_skew
- expanding_kurt
- expanding_apply
- expanding_quantile
-
-Exponentially-weighted moving window functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autosummary::
- :toctree: generated/
-
- ewma
- ewmstd
- ewmvar
- ewmcorr
- ewmcov
-
.. _api.series:
Series
@@ -260,6 +201,9 @@ Series
Constructor
~~~~~~~~~~~
+
+.. currentmodule:: pandas
+
.. autosummary::
:toctree: generated/
@@ -344,14 +288,17 @@ Binary operator functions
Series.ne
Series.eq
-Function application, GroupBy
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Function application, GroupBy & Window
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated/
Series.apply
Series.map
Series.groupby
+ Series.rolling
+ Series.expanding
+ Series.ewm
.. _api.series.stats:
@@ -846,14 +793,17 @@ Binary operator functions
DataFrame.combine
DataFrame.combine_first
-Function application, GroupBy
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Function application, GroupBy & Window
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated/
DataFrame.apply
DataFrame.applymap
DataFrame.groupby
+ DataFrame.rolling
+ DataFrame.expanding
+ DataFrame.ewm
.. _api.dataframe.stats:
@@ -1551,6 +1501,78 @@ Conversion
TimedeltaIndex.to_series
TimedeltaIndex.round
+Window
+------
+.. currentmodule:: pandas.core.window
+
+Rolling objects are returned by ``.rolling`` calls: :func:`pandas.DataFrame.rolling`, :func:`pandas.Series.rolling`, etc.
+Expanding objects are returned by ``.expanding`` calls: :func:`pandas.DataFrame.expanding`, :func:`pandas.Series.expanding`, etc.
+EWM objects are returned by ``.ewm`` calls: :func:`pandas.DataFrame.ewm`, :func:`pandas.Series.ewm`, etc.
+
+Standard moving window functions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. currentmodule:: pandas.core.window
+
+.. autosummary::
+ :toctree: generated/
+
+ Rolling.count
+ Rolling.sum
+ Rolling.mean
+ Rolling.median
+ Rolling.var
+ Rolling.std
+ Rolling.min
+ Rolling.max
+ Rolling.corr
+ Rolling.cov
+ Rolling.skew
+ Rolling.kurt
+ Rolling.apply
+ Rolling.quantile
+ Window.mean
+ Window.sum
+
+.. _api.functions_expanding:
+
+Standard expanding window functions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. currentmodule:: pandas.core.window
+
+.. autosummary::
+ :toctree: generated/
+
+ Expanding.count
+ Expanding.sum
+ Expanding.mean
+ Expanding.median
+ Expanding.var
+ Expanding.std
+ Expanding.min
+ Expanding.max
+ Expanding.corr
+ Expanding.cov
+ Expanding.skew
+ Expanding.kurt
+ Expanding.apply
+ Expanding.quantile
+
+Exponentially-weighted moving window functions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. currentmodule:: pandas.core.window
+
+.. autosummary::
+ :toctree: generated/
+
+ EWM.mean
+ EWM.std
+ EWM.var
+ EWM.corr
+ EWM.cov
+
GroupBy
-------
.. currentmodule:: pandas.core.groupby
diff --git a/doc/source/computation.rst b/doc/source/computation.rst
index b2fa7f6749379..39587e82731b0 100644
--- a/doc/source/computation.rst
+++ b/doc/source/computation.rst
@@ -21,7 +21,7 @@
Computational tools
===================
-Statistical functions
+Statistical Functions
---------------------
.. _computation.pct_change:
@@ -196,90 +196,120 @@ parameter:
- ``max`` : highest rank in the group
- ``first`` : ranks assigned in the order they appear in the array
+.. _stats.moments:
-.. currentmodule:: pandas
+Window Functions
+----------------
-.. currentmodule:: pandas.stats.api
+.. warning::
-.. _stats.moments:
+ Prior to version 0.18.0, ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` were module level
+ functions and are now deprecated and replaced by the corresponding method call.
-Moving (rolling) statistics / moments
--------------------------------------
+ The deprecation warning will show the new syntax, see an example :ref:`here <whatsnew_0180.window_deprecations>`
+ You can view the previous documentation
+ `here <http://pandas.pydata.org/pandas-docs/version/0.17.1/computation.html#moving-rolling-statistics-moments>`__
-For working with time series data, a number of functions are provided for
-computing common *moving* or *rolling* statistics. Among these are count, sum,
+For working with data, a number of windows functions are provided for
+computing common *window* or *rolling* statistics. Among these are count, sum,
mean, median, correlation, variance, covariance, standard deviation, skewness,
-and kurtosis. All of these methods are in the :mod:`pandas` namespace, but
-otherwise they can be found in :mod:`pandas.stats.moments`.
+and kurtosis.
-.. currentmodule:: pandas
+.. currentmodule:: pandas.core.window
-.. csv-table::
- :header: "Function", "Description"
- :widths: 20, 80
+.. note::
+
+ The API for window statistics is quite similar to the way one works with ``GroupBy`` objects, see the documentation :ref:`here <groupby>`
+
+We work with ``rolling``, ``expanding`` and ``exponentially weighted`` data through the corresponding
+objects, :class:`~pandas.core.window.Rolling`, :class:`~pandas.core.window.Expanding` and :class:`~pandas.core.window.EWM`.
+
+.. ipython:: python
+
+ s = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
+ s = s.cumsum()
+ s
+
+These are created from methods on ``Series`` and ``DataFrame``.
- :func:`rolling_count`, Number of non-null observations
- :func:`rolling_sum`, Sum of values
- :func:`rolling_mean`, Mean of values
- :func:`rolling_median`, Arithmetic median of values
- :func:`rolling_min`, Minimum
- :func:`rolling_max`, Maximum
- :func:`rolling_std`, Unbiased standard deviation
- :func:`rolling_var`, Unbiased variance
- :func:`rolling_skew`, Unbiased skewness (3rd moment)
- :func:`rolling_kurt`, Unbiased kurtosis (4th moment)
- :func:`rolling_quantile`, Sample quantile (value at %)
- :func:`rolling_apply`, Generic apply
- :func:`rolling_cov`, Unbiased covariance (binary)
- :func:`rolling_corr`, Correlation (binary)
- :func:`rolling_window`, Moving window function
-
-Generally these methods all have the same interface. The binary operators
-(e.g. :func:`rolling_corr`) take two Series or DataFrames. Otherwise, they all
+.. ipython:: python
+
+ r = s.rolling(window=60)
+ r
+
+Generally these methods all have the same interface. They all
accept the following arguments:
- - ``window``: size of moving window
- - ``min_periods``: threshold of non-null data points to require (otherwise
- result is NA)
- - ``freq``: optionally specify a :ref:`frequency string <timeseries.alias>`
- or :ref:`DateOffset <timeseries.offsets>` to pre-conform the data to.
- Note that prior to pandas v0.8.0, a keyword argument ``time_rule`` was used
- instead of ``freq`` that referred to the legacy time rule constants
- - ``how``: optionally specify method for down or re-sampling. Default is
- is min for :func:`rolling_min`, max for :func:`rolling_max`, median for
- :func:`rolling_median`, and mean for all other rolling functions. See
- :meth:`DataFrame.resample`'s how argument for more information.
+- ``window``: size of moving window
+- ``min_periods``: threshold of non-null data points to require (otherwise
+ result is NA)
+- ``center``: boolean, whether to set the labels at the center (default is False)
+
+.. warning::
-These functions can be applied to ndarrays or Series objects:
+ 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.
+
+We can then call methods on these ``rolling`` objects. These return like-indexed objects:
.. ipython:: python
- ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
- ts = ts.cumsum()
+ r.mean()
+
+.. ipython:: python
- ts.plot(style='k--')
+ s.plot(style='k--')
@savefig rolling_mean_ex.png
- pd.rolling_mean(ts, 60).plot(style='k')
-
-They can also be applied to DataFrame objects. This is really just syntactic
-sugar for applying the moving window operator to all of the DataFrame's columns:
+ r.mean().plot(style='k')
.. ipython:: python
:suppress:
plt.close('all')
+They can also be applied to DataFrame objects. This is really just syntactic
+sugar for applying the moving window operator to all of the DataFrame's columns:
+
.. ipython:: python
- df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
- columns=['A', 'B', 'C', 'D'])
+ df = pd.DataFrame(np.random.randn(1000, 4), index=s.index,
+ columns=['A', 'B', 'C', 'D'])
df = df.cumsum()
@savefig rolling_mean_frame.png
- pd.rolling_sum(df, 60).plot(subplots=True)
+ df.rolling(window=60).sum().plot(subplots=True)
-The :func:`rolling_apply` function takes an extra ``func`` argument and performs
+.. _stats.summary:
+
+Method Summary
+~~~~~~~~~~~~~~
+
+We provide a number of the common statistical functions:
+
+.. currentmodule:: pandas.core.window
+
+.. csv-table::
+ :header: "Method", "Description"
+ :widths: 20, 80
+
+ :meth:`~Rolling.count`, Number of non-null observations
+ :meth:`~Rolling.sum`, Sum of values
+ :meth:`~Rolling.mean`, Mean of values
+ :meth:`~Rolling.median`, Arithmetic median of values
+ :meth:`~Rolling.min`, Minimum
+ :meth:`~Rolling.max`, Maximum
+ :meth:`~Rolling.std`, Unbiased standard deviation
+ :meth:`~Rolling.var`, Unbiased variance
+ :meth:`~Rolling.skew`, Unbiased skewness (3rd moment)
+ :meth:`~Rolling.kurt`, Unbiased kurtosis (4th moment)
+ :meth:`~Rolling.quantile`, Sample quantile (value at %)
+ :meth:`~Rolling.apply`, Generic apply
+ :meth:`~Rolling.cov`, Unbiased covariance (binary)
+ :meth:`~Rolling.corr`, Correlation (binary)
+
+The :meth:`~Rolling.apply` function takes an extra ``func`` argument and performs
generic rolling computations. The ``func`` argument should be a single function
that produces a single value from an ndarray input. Suppose we wanted to
compute the mean absolute deviation on a rolling basis:
@@ -288,79 +318,91 @@ compute the mean absolute deviation on a rolling basis:
mad = lambda x: np.fabs(x - x.mean()).mean()
@savefig rolling_apply_ex.png
- pd.rolling_apply(ts, 60, mad).plot(style='k')
-
-The :func:`rolling_window` function performs a generic rolling window computation
-on the input data. The weights used in the window are specified by the ``win_type``
-keyword. The list of recognized types are:
+ s.rolling(window=60).apply(mad).plot(style='k')
- - ``boxcar``
- - ``triang``
- - ``blackman``
- - ``hamming``
- - ``bartlett``
- - ``parzen``
- - ``bohman``
- - ``blackmanharris``
- - ``nuttall``
- - ``barthann``
- - ``kaiser`` (needs beta)
- - ``gaussian`` (needs std)
- - ``general_gaussian`` (needs power, width)
- - ``slepian`` (needs width).
+.. _stats.rolling_window:
-.. ipython:: python
+Rolling Windows
+~~~~~~~~~~~~~~~
- ser = pd.Series(np.random.randn(10), index=pd.date_range('1/1/2000', periods=10))
+Passing ``win_type`` to ``.rolling`` generates a generic rolling window computation, that is weighted according the ``win_type``.
+The following methods are available:
- pd.rolling_window(ser, 5, 'triang')
+.. csv-table::
+ :header: "Method", "Description"
+ :widths: 20, 80
-Note that the ``boxcar`` window is equivalent to :func:`rolling_mean`.
+ :meth:`~Window.sum`, Sum of values
+ :meth:`~Window.mean`, Mean of values
+
+The weights used in the window are specified by the ``win_type`` keyword. The list of recognized types are:
+
+- ``boxcar``
+- ``triang``
+- ``blackman``
+- ``hamming``
+- ``bartlett``
+- ``parzen``
+- ``bohman``
+- ``blackmanharris``
+- ``nuttall``
+- ``barthann``
+- ``kaiser`` (needs beta)
+- ``gaussian`` (needs std)
+- ``general_gaussian`` (needs power, width)
+- ``slepian`` (needs width).
.. ipython:: python
- pd.rolling_window(ser, 5, 'boxcar')
+ ser = pd.Series(np.random.randn(10), index=pd.date_range('1/1/2000', periods=10))
- pd.rolling_mean(ser, 5)
+ ser.rolling(window=5, win_type='triang').mean()
-For some windowing functions, additional parameters must be specified:
+Note that the ``boxcar`` window is equivalent to :meth:`~Rolling.mean`.
.. ipython:: python
- pd.rolling_window(ser, 5, 'gaussian', std=0.1)
+ ser.rolling(window=5, win_type='boxcar').mean()
+ ser.rolling(window=5).mean()
-By default the labels are set to the right edge of the window, but a
-``center`` keyword is available so the labels can be set at the center.
-This keyword is available in other rolling functions as well.
+For some windowing functions, additional parameters must be specified:
.. ipython:: python
- pd.rolling_window(ser, 5, 'boxcar')
-
- pd.rolling_window(ser, 5, 'boxcar', center=True)
-
- pd.rolling_mean(ser, 5, center=True)
+ ser.rolling(window=5, win_type='gaussian').mean(std=0.1)
.. _stats.moments.normalization:
.. note::
- In rolling sum mode (``mean=False``) there is no normalization done to the
- weights. Passing custom weights of ``[1, 1, 1]`` will yield a different
+ For ``.sum()`` with a ``win_type``, there is no normalization done to the
+ weights for the window. Passing custom weights of ``[1, 1, 1]`` will yield a different
result than passing weights of ``[2, 2, 2]``, for example. When passing a
``win_type`` instead of explicitly specifying the weights, the weights are
already normalized so that the largest weight is 1.
- In contrast, the nature of the rolling mean calculation (``mean=True``)is
+ In contrast, the nature of the ``.mean()`` calculation is
such that the weights are normalized with respect to each other. Weights
of ``[1, 1, 1]`` and ``[2, 2, 2]`` yield the same result.
+Centering Windows
+~~~~~~~~~~~~~~~~~
+
+By default the labels are set to the right edge of the window, but a
+``center`` keyword is available so the labels can be set at the center.
+This keyword is available in other rolling functions as well.
+
+.. ipython:: python
+
+ ser.rolling(window=5).mean()
+ ser.rolling(window=5, center=True).mean()
+
.. _stats.moments.binary:
-Binary rolling moments
-~~~~~~~~~~~~~~~~~~~~~~
+Binary Window Functions
+~~~~~~~~~~~~~~~~~~~~~~~
-:func:`rolling_cov` and :func:`rolling_corr` can compute moving window statistics about
+:meth:`~Rolling.cov` and :meth:`~Rolling.corr` can compute moving window statistics about
two ``Series`` or any combination of ``DataFrame/Series`` or
``DataFrame/DataFrame``. Here is the behavior in each case:
@@ -378,7 +420,7 @@ For example:
.. ipython:: python
df2 = df[:20]
- pd.rolling_corr(df2, df2['B'], window=5)
+ df2.rolling(window=5).corr(df2['B'])
.. _stats.moments.corr_pairwise:
@@ -403,23 +445,16 @@ can even be omitted:
.. ipython:: python
- covs = pd.rolling_cov(df[['B','C','D']], df[['A','B','C']], 50, pairwise=True)
+ covs = df[['B','C','D']].rolling(window=50).cov(df[['A','B','C']], pairwise=True)
covs[df.index[-50]]
.. ipython:: python
- correls = pd.rolling_corr(df, 50)
+ correls = df.rolling(window=50).corr()
correls[df.index[-50]]
-.. note::
-
- Prior to version 0.14 this was available through ``rolling_corr_pairwise``
- which is now simply syntactic sugar for calling ``rolling_corr(...,
- pairwise=True)`` and deprecated. This is likely to be removed in a future
- release.
-
You can efficiently retrieve the time series of correlations between two
-columns using ``ix`` indexing:
+columns using ``.loc`` indexing:
.. ipython:: python
:suppress:
@@ -429,62 +464,152 @@ columns using ``ix`` indexing:
.. ipython:: python
@savefig rolling_corr_pairwise_ex.png
- correls.ix[:, 'A', 'C'].plot()
+ correls.loc[:, 'A', 'C'].plot()
+
+.. _stats.aggregate:
+
+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:
+
+.. ipython:: python
+
+ dfa = pd.DataFrame(np.random.randn(1000, 3), index=s.index,
+ columns=['A', 'B', 'C'])
+ r = dfa.rolling(window=60,min_periods=1)
+ r
+
+We can aggregate by passing a function to the entire DataFrame, or select a Series (or multiple Series) via standard getitem.
+
+.. ipython:: python
+
+ r.aggregate(np.sum)
+
+ r['A'].aggregate(np.sum)
+
+ r[['A','B']].aggregate(np.sum)
+
+As you can see, the result of the aggregation will have the selected columns, or all
+columns if none are selected.
+
+.. _stats.aggregate.multifunc:
+
+Applying multiple functions at once
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+With windowed Series you can also pass a list or dict of functions to do
+aggregation with, outputting a DataFrame:
+
+.. ipython:: python
+
+ r['A'].agg([np.sum, np.mean, np.std])
+
+If a dict is passed, the keys will be used to name the columns. Otherwise the
+function's name (stored in the function object) will be used.
+
+.. ipython:: python
+
+ r['A'].agg({'result1' : np.sum,
+ 'result2' : np.mean})
+
+On a widowed DataFrame, you can pass a list of functions to apply to each
+column, which produces an aggregated result with a hierarchical index:
+
+.. ipython:: python
+
+ r.agg([np.sum, np.mean])
+
+Passing a dict of functions has different behavior by default, see the next
+section.
+
+Applying different functions to DataFrame columns
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+By passing a dict to ``aggregate`` you can apply a different aggregation to the
+columns of a DataFrame:
+
+.. ipython:: python
+
+ r.agg({'A' : np.sum,
+ '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
+
+.. ipython:: python
+
+ r.agg({'A' : 'sum', 'B' : 'std'})
+
+Furthermore you can pass a nested dict to indicate different aggregations on different columns.
+
+.. ipython:: python
+
+ r.agg({'A' : ['sum','std'], 'B' : ['mean','std'] })
+
.. _stats.moments.expanding:
-Expanding window moment functions
----------------------------------
+Expanding Windows
+-----------------
+
A common alternative to rolling statistics is to use an *expanding* window,
which yields the value of the statistic with all the data available up to that
-point in time. As these calculations are a special case of rolling statistics,
+point in time.
+
+These follow a similar interface to ``.rolling``, with the ``.expanding`` method
+returning an :class:`~pandas.core.window.Expanding` object.
+
+As these calculations are a special case of rolling statistics,
they are implemented in pandas such that the following two calls are equivalent:
.. ipython:: python
- pd.rolling_mean(df, window=len(df), min_periods=1)[:5]
+ df.rolling(window=len(df), min_periods=1).mean()[:5]
- pd.expanding_mean(df)[:5]
+ df.expanding(min_periods=1).mean()[:5]
-Like the ``rolling_`` functions, the following methods are included in the
-``pandas`` namespace or can be located in ``pandas.stats.moments``.
+These have a similar set of methods to ``.rolling`` methods.
-.. currentmodule:: pandas
+Method Summary
+~~~~~~~~~~~~~~
+
+.. currentmodule:: pandas.core.window
.. csv-table::
:header: "Function", "Description"
:widths: 20, 80
- :func:`expanding_count`, Number of non-null observations
- :func:`expanding_sum`, Sum of values
- :func:`expanding_mean`, Mean of values
- :func:`expanding_median`, Arithmetic median of values
- :func:`expanding_min`, Minimum
- :func:`expanding_max`, Maximum
- :func:`expanding_std`, Unbiased standard deviation
- :func:`expanding_var`, Unbiased variance
- :func:`expanding_skew`, Unbiased skewness (3rd moment)
- :func:`expanding_kurt`, Unbiased kurtosis (4th moment)
- :func:`expanding_quantile`, Sample quantile (value at %)
- :func:`expanding_apply`, Generic apply
- :func:`expanding_cov`, Unbiased covariance (binary)
- :func:`expanding_corr`, Correlation (binary)
+ :meth:`~Expanding.count`, Number of non-null observations
+ :meth:`~Expanding.sum`, Sum of values
+ :meth:`~Expanding.mean`, Mean of values
+ :meth:`~Expanding.median`, Arithmetic median of values
+ :meth:`~Expanding.min`, Minimum
+ :meth:`~Expanding.max`, Maximum
+ :meth:`~Expanding.std`, Unbiased standard deviation
+ :meth:`~Expanding.var`, Unbiased variance
+ :meth:`~Expanding.skew`, Unbiased skewness (3rd moment)
+ :meth:`~Expanding.kurt`, Unbiased kurtosis (4th moment)
+ :meth:`~Expanding.quantile`, Sample quantile (value at %)
+ :meth:`~Expanding.apply`, Generic apply
+ :meth:`~Expanding.cov`, Unbiased covariance (binary)
+ :meth:`~Expanding.corr`, Correlation (binary)
Aside from not having a ``window`` parameter, these functions have the same
-interfaces as their ``rolling_`` counterpart. Like above, the parameters they
+interfaces as their ``.rolling`` counterparts. Like above, the parameters they
all accept are:
- - ``min_periods``: threshold of non-null data points to require. Defaults to
- minimum needed to compute statistic. No ``NaNs`` will be output once
- ``min_periods`` non-null data points have been seen.
- - ``freq``: optionally specify a :ref:`frequency string <timeseries.alias>`
- or :ref:`DateOffset <timeseries.offsets>` to pre-conform the data to.
- Note that prior to pandas v0.8.0, a keyword argument ``time_rule`` was used
- instead of ``freq`` that referred to the legacy time rule constants
+- ``min_periods``: threshold of non-null data points to require. Defaults to
+ minimum needed to compute statistic. No ``NaNs`` will be output once
+ ``min_periods`` non-null data points have been seen.
+- ``center``: boolean, whether to set the labels at the center (default is False)
.. note::
- The output of the ``rolling_`` and ``expanding_`` functions do not return a
+ The output of the ``.rolling`` and ``.expanding`` methods do not return a
``NaN`` if there are at least ``min_periods`` non-null values in the current
window. This differs from ``cumsum``, ``cumprod``, ``cummax``, and
``cummin``, which return ``NaN`` in the output wherever a ``NaN`` is
@@ -493,7 +618,7 @@ all accept are:
An expanding window statistic will be more stable (and less responsive) than
its rolling window counterpart as the increasing window size decreases the
relative impact of an individual data point. As an example, here is the
-:func:`expanding_mean` output for the previous time series dataset:
+:meth:`~Expanding.mean` output for the previous time series dataset:
.. ipython:: python
:suppress:
@@ -502,31 +627,34 @@ relative impact of an individual data point. As an example, here is the
.. ipython:: python
- ts.plot(style='k--')
+ s.plot(style='k--')
@savefig expanding_mean_frame.png
- pd.expanding_mean(ts).plot(style='k')
+ s.expanding().mean().plot(style='k')
+
.. _stats.moments.exponentially_weighted:
-Exponentially weighted moment functions
----------------------------------------
+Exponentially Weighted Windows
+------------------------------
A related set of functions are exponentially weighted versions of several of
-the above statistics. A number of expanding EW (exponentially weighted)
-functions are provided:
+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.
+A number of expanding EW (exponentially weighted)
+methods are provided:
-.. currentmodule:: pandas
+.. currentmodule:: pandas.core.window
.. csv-table::
:header: "Function", "Description"
:widths: 20, 80
- :func:`ewma`, EW moving average
- :func:`ewmvar`, EW moving variance
- :func:`ewmstd`, EW moving standard deviation
- :func:`ewmcorr`, EW moving correlation
- :func:`ewmcov`, EW moving covariance
+ :meth:`~EWM.mean`, EW moving average
+ :meth:`~EWM.var`, EW moving variance
+ :meth:`~EWM.std`, EW moving standard deviation
+ :meth:`~EWM.corr`, EW moving correlation
+ :meth:`~EWM.cov`, EW moving covariance
In general, a weighted moving average is calculated as
@@ -621,20 +749,20 @@ Here is an example for a univariate time series:
.. ipython:: python
- ts.plot(style='k--')
+ s.plot(style='k--')
@savefig ewma_ex.png
- pd.ewma(ts, span=20).plot(style='k')
+ s.ewm(span=20).mean().plot(style='k')
-All the EW functions have a ``min_periods`` argument, which has the same
-meaning it does for all the ``expanding_`` and ``rolling_`` functions:
+EWM has a ``min_periods`` argument, which has the same
+meaning it does for all the ``.expanding`` and ``.rolling`` methods:
no output values will be set until at least ``min_periods`` non-null values
are encountered in the (expanding) window.
(This is a change from versions prior to 0.15.0, in which the ``min_periods``
argument affected only the ``min_periods`` consecutive entries starting at the
first non-null value.)
-All the EW functions also have an ``ignore_na`` argument, which deterines how
+EWM also has an ``ignore_na`` argument, which deterines how
intermediate null values affect the calculation of the weights.
When ``ignore_na=False`` (the default), weights are calculated based on absolute
positions, so that intermediate null values affect the result.
@@ -653,7 +781,7 @@ Whereas if ``ignore_na=True``, the weighted average would be calculated as
\frac{(1-\alpha) \cdot 3 + 1 \cdot 5}{(1-\alpha) + 1}.
-The :func:`ewmvar`, :func:`ewmstd`, and :func:`ewmcov` functions have a ``bias`` argument,
+The :meth:`~Ewm.var`, :meth:`~Ewm.std`, and :meth:`~Ewm.cov` functions have a ``bias`` argument,
specifying whether the result should contain biased or unbiased statistics.
For example, if ``bias=True``, ``ewmvar(x)`` is calculated as
``ewmvar(x) = ewma(x**2) - ewma(x)**2``;
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 23095b7f4d24b..709d9b32984c0 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -224,16 +224,7 @@
'pandas.io.pickle.read_pickle', 'pandas.io.pytables.HDFStore.append', 'pandas.io.pytables.HDFStore.get',
'pandas.io.pytables.HDFStore.put', 'pandas.io.pytables.HDFStore.select', 'pandas.io.pytables.read_hdf',
'pandas.io.sql.read_sql', 'pandas.io.sql.read_frame', 'pandas.io.sql.write_frame',
- 'pandas.io.stata.read_stata', 'pandas.stats.moments.ewma', 'pandas.stats.moments.ewmcorr',
- 'pandas.stats.moments.ewmcov', 'pandas.stats.moments.ewmstd', 'pandas.stats.moments.ewmvar',
- 'pandas.stats.moments.expanding_apply', 'pandas.stats.moments.expanding_corr', 'pandas.stats.moments.expanding_count',
- 'pandas.stats.moments.expanding_cov', 'pandas.stats.moments.expanding_kurt', 'pandas.stats.moments.expanding_mean',
- 'pandas.stats.moments.expanding_median', 'pandas.stats.moments.expanding_quantile', 'pandas.stats.moments.expanding_skew',
- 'pandas.stats.moments.expanding_std', 'pandas.stats.moments.expanding_sum', 'pandas.stats.moments.expanding_var',
- 'pandas.stats.moments.rolling_apply', 'pandas.stats.moments.rolling_corr', 'pandas.stats.moments.rolling_count',
- 'pandas.stats.moments.rolling_cov', 'pandas.stats.moments.rolling_kurt', 'pandas.stats.moments.rolling_mean',
- 'pandas.stats.moments.rolling_median', 'pandas.stats.moments.rolling_quantile', 'pandas.stats.moments.rolling_skew',
- 'pandas.stats.moments.rolling_std', 'pandas.stats.moments.rolling_sum', 'pandas.stats.moments.rolling_var']
+ 'pandas.io.stata.read_stata']
html_additional_pages = {'generated/' + page: 'api_redirect.html' for page in moved_api_pages}
diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst
index 92ed85071ecb8..4d6a7457bcf90 100644
--- a/doc/source/cookbook.rst
+++ b/doc/source/cookbook.rst
@@ -517,7 +517,7 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
def Red(x):
return functools.reduce(CumRet,x,1.0)
- pd.expanding_apply(S, Red)
+ S.expanding().apply(Red)
`Replacing some values with mean of the rest of a group
@@ -639,7 +639,7 @@ Create a list of dataframes, split using a delineation based on logic included i
df = pd.DataFrame(data={'Case' : ['A','A','A','B','A','A','B','A','A'],
'Data' : np.random.randn(9)})
- dfs = list(zip(*df.groupby(pd.rolling_median((1*(df['Case']=='B')).cumsum(),3,True))))[-1]
+ dfs = list(zip(*df.groupby((1*(df['Case']=='B')).cumsum().rolling(window=3,min_periods=1).median())))[-1]
dfs[0]
dfs[1]
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
index 4ae2ee1927d1a..61f87ebe0db1b 100644
--- a/doc/source/groupby.rst
+++ b/doc/source/groupby.rst
@@ -519,7 +519,7 @@ to standardize the data within each group:
index = pd.date_range('10/1/1999', periods=1100)
ts = pd.Series(np.random.normal(0.5, 2, 1100), index)
- ts = pd.rolling_mean(ts, 100, 100).dropna()
+ ts = ts.rolling(window=100,min_periods=100).mean().dropna()
ts.head()
ts.tail()
diff --git a/doc/source/whatsnew/v0.14.0.txt b/doc/source/whatsnew/v0.14.0.txt
index e2f96f204edab..67928af30bead 100644
--- a/doc/source/whatsnew/v0.14.0.txt
+++ b/doc/source/whatsnew/v0.14.0.txt
@@ -170,11 +170,18 @@ API changes
:ref:`Computing rolling pairwise covariances and correlations
<stats.moments.corr_pairwise>` in the docs.
- .. ipython:: python
+ .. code-block:: python
+
+ In [1]: df = DataFrame(np.random.randn(10,4),columns=list('ABCD'))
+
+ In [4]: covs = pd.rolling_cov(df[['A','B','C']], df[['B','C','D']], 5, pairwise=True)
- df = DataFrame(np.random.randn(10,4),columns=list('ABCD'))
- covs = rolling_cov(df[['A','B','C']], df[['B','C','D']], 5, pairwise=True)
- covs[df.index[-1]]
+ In [5]: covs[df.index[-1]]
+ Out[5]:
+ B C D
+ A 0.035310 0.326593 -0.505430
+ B 0.137748 -0.006888 -0.005383
+ C -0.006888 0.861040 0.020762
- ``Series.iteritems()`` is now lazy (returns an iterator rather than a list). This was the documented behavior prior to 0.14. (:issue:`6760`)
diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt
index a33e0f19961ab..9651c1efeff4a 100644
--- a/doc/source/whatsnew/v0.15.0.txt
+++ b/doc/source/whatsnew/v0.15.0.txt
@@ -68,7 +68,7 @@ For full docs, see the :ref:`categorical introduction <categorical>` and the
.. ipython:: python
:okwarning:
-
+
df = DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
df["grade"] = df["raw_grade"].astype("category")
@@ -353,9 +353,15 @@ Rolling/Expanding Moments improvements
New behavior
- .. ipython:: python
+ .. code-block:: python
- rolling_min(s, window=10, min_periods=5)
+ In [4]: pd.rolling_min(s, window=10, min_periods=5)
+ Out[4]:
+ 0 NaN
+ 1 NaN
+ 2 NaN
+ 3 NaN
+ dtype: float64
- :func:`rolling_max`, :func:`rolling_min`, :func:`rolling_sum`, :func:`rolling_mean`, :func:`rolling_median`,
:func:`rolling_std`, :func:`rolling_var`, :func:`rolling_skew`, :func:`rolling_kurt`, :func:`rolling_quantile`,
@@ -381,9 +387,15 @@ Rolling/Expanding Moments improvements
New behavior (note final value is ``5 = sum([2, 3, NaN])``):
- .. ipython:: python
+ .. code-block:: python
- rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)
+ In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)
+ Out[7]:
+ 0 1
+ 1 3
+ 2 6
+ 3 5
+ dtype: float64
- :func:`rolling_window` now normalizes the weights properly in rolling mean mode (`mean=True`) so that
the calculated weighted means (e.g. 'triang', 'gaussian') are distributed about the same means as those
@@ -397,20 +409,27 @@ Rolling/Expanding Moments improvements
.. code-block:: python
- In [39]: rolling_window(s, window=3, win_type='triang', center=True)
- Out[39]:
- 0 NaN
- 1 6.583333
- 2 6.883333
- 3 6.683333
- 4 NaN
- dtype: float64
+ In [39]: rolling_window(s, window=3, win_type='triang', center=True)
+ Out[39]:
+ 0 NaN
+ 1 6.583333
+ 2 6.883333
+ 3 6.683333
+ 4 NaN
+ dtype: float64
New behavior
.. ipython:: python
- rolling_window(s, window=3, win_type='triang', center=True)
+ In [10]: pd.rolling_window(s, window=3, win_type='triang', center=True)
+ Out[10]:
+ 0 NaN
+ 1 9.875
+ 2 10.325
+ 3 10.025
+ 4 NaN
+ dtype: float64
- Removed ``center`` argument from all :func:`expanding_ <expanding_apply>` functions (see :ref:`list <api.functions_expanding>`),
as the results produced when ``center=True`` did not make much sense. (:issue:`7925`)
@@ -449,9 +468,17 @@ Rolling/Expanding Moments improvements
New behavior (note values start at index ``4``, the location of the 2nd (since ``min_periods=2``) non-empty value):
- .. ipython:: python
+ .. code-block:: python
- ewma(s, com=3., min_periods=2)
+ In [2]: pd.ewma(s, com=3., min_periods=2)
+ Out[2]:
+ 0 NaN
+ 1 NaN
+ 2 NaN
+ 3 NaN
+ 4 1.759644
+ 5 2.383784
+ dtype: float64
- :func:`ewmstd`, :func:`ewmvol`, :func:`ewmvar`, :func:`ewmcov`, and :func:`ewmcorr`
now have an optional ``adjust`` argument, just like :func:`ewma` does,
@@ -465,11 +492,28 @@ Rolling/Expanding Moments improvements
When ``ignore_na=True`` (which reproduces the pre-0.15.0 behavior), missing values are ignored in the weights calculation.
(:issue:`7543`)
- .. ipython:: python
+ .. code-block:: python
+
+ In [7]: pd.ewma(Series([None, 1., 8.]), com=2.)
+ Out[7]:
+ 0 NaN
+ 1 1.0
+ 2 5.2
+ dtype: float64
+
+ In [8]: pd.ewma(Series([1., None, 8.]), com=2., ignore_na=True) # pre-0.15.0 behavior
+ Out[8]:
+ 0 1.0
+ 1 1.0
+ 2 5.2
+ dtype: float64
- ewma(Series([None, 1., 8.]), com=2.)
- ewma(Series([1., None, 8.]), com=2., ignore_na=True) # pre-0.15.0 behavior
- ewma(Series([1., None, 8.]), com=2., ignore_na=False) # new default
+ In [9]: pd.ewma(Series([1., None, 8.]), com=2., ignore_na=False) # new default
+ Out[9]:
+ 0 1.000000
+ 1 1.000000
+ 2 5.846154
+ dtype: float64
.. warning::
@@ -525,10 +569,23 @@ Rolling/Expanding Moments improvements
By comparison, the following 0.15.0 results have a ``NaN`` for entry ``0``,
and the debiasing factors are decreasing (towards 1.25):
- .. ipython:: python
+ .. code-block:: python
- ewmvar(s, com=2., bias=False)
- ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True)
+ In [14]: pd.ewmvar(s, com=2., bias=False)
+ Out[14]:
+ 0 NaN
+ 1 0.500000
+ 2 1.210526
+ 3 4.089069
+ dtype: float64
+
+ In [15]: pd.ewmvar(s, com=2., bias=False) / pd.ewmvar(s, com=2., bias=True)
+ Out[15]:
+ 0 NaN
+ 1 2.083333
+ 2 1.583333
+ 3 1.425439
+ dtype: float64
See :ref:`Exponentially weighted moment functions <stats.moments.exponentially_weighted>` for details. (:issue:`7912`)
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 86a7be5857035..7f63096d7c045 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -13,6 +13,8 @@ users upgrade to this version.
Highlights include:
+- Window functions are now methods on ``.groupby`` like objects, see :ref:`here <whatsnew_0180.moments>`.
+
Check the :ref:`API Changes <whatsnew_0180.api>` and :ref:`deprecations <whatsnew_0180.deprecations>` before updating.
.. contents:: What's new in v0.18.0
@@ -25,10 +27,67 @@ New features
~~~~~~~~~~~~
+.. _whatsnew_0180.enhancements.moments:
+
+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`)
+
+.. ipython:: python
+
+ np.random.seed(1234)
+ df = DataFrame({'A' : range(10), 'B' : np.random.randn(10)})
+ df
+
+Previous Behavior:
+
+.. code-block:: python
+
+ In [8]: pd.rolling_mean(df,window=3)
+ Out[8]:
+ A B
+ 0 NaN NaN
+ 1 NaN NaN
+ 2 1 0.237722
+ 3 2 -0.023640
+ 4 3 0.133155
+ 5 4 -0.048693
+ 6 5 0.342054
+ 7 6 0.370076
+ 8 7 0.079587
+ 9 8 -0.954504
+
+New Behavior:
+
+.. ipython:: python
+
+ r = df.rolling(window=3)
+
+These show a descriptive repr, with tab-completion of available methods
+
+.. ipython:: python
+
+ r
+
+The methods operate on this ``Rolling`` object itself
+
+.. ipython:: python
+
+ r.mean()
+They provide getitem accessors
+.. ipython:: python
+ r['A'].mean()
+And multiple aggregations
+
+.. ipython:: python
+
+ r.agg({'A' : ['mean','std'],
+ 'B' : ['mean','std']})
.. _whatsnew_0180.enhancements.other:
@@ -153,18 +212,44 @@ Other API Changes
Deprecations
^^^^^^^^^^^^
+.. _whatsnew_0180.window_deprecations:
+
+- The functions ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` are deprecated and replaced by the corresponding method call. Note that
+ the new suggested syntax includes all of the arguments (even if default) (:issue:`11603`)
+
+ .. code-block:: python
+ In [1]: s = Series(range(3))
+ In [2]: pd.rolling_mean(s,window=2,min_periods=1)
+ FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with
+ Series.rolling(min_periods=1,window=2,center=False).mean()
+ Out[2]:
+ 0 0.0
+ 1 0.5
+ 2 1.5
+ dtype: float64
+ In [3]: pd.rolling_cov(s, s, window=2)
+ FutureWarning: pd.rolling_cov is deprecated for Series and will be removed in a future version, replace with
+ Series.rolling(window=2).cov(other=<Series>)
+ Out[3]:
+ 0 NaN
+ 1 0.5
+ 2 0.5
+ dtype: float64
+- 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.
.. _whatsnew_0180.prior_deprecations:
Removal of prior version deprecations/changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-
+- Removal of ``rolling_corr_pairwise`` in favor of ``.rolling().corr(pairwise=True)`` (:issue:`4950`)
+- Removal of ``expanding_corr_pairwise`` in favor of ``.expanding().corr(pairwise=True)`` (:issue:`4950`)
@@ -195,6 +280,7 @@ Bug Fixes
- 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 consistency of passing nested dicts to ``.groupby(...).agg(...)`` (:issue:`9052`)
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 6996bb06065af..a1e1c20344ea4 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -2,11 +2,13 @@
Base and utility classes for pandas objects.
"""
from pandas import compat
+from pandas.compat import builtins
import numpy as np
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, Substitution,
+ cache_readonly, deprecate_kwarg)
from pandas.core.common import AbstractMethodError
_shared_docs = dict()
@@ -218,6 +220,288 @@ def __delete__(self, instance):
raise AttributeError("can't delete attribute")
+class GroupByError(Exception):
+ pass
+
+
+class DataError(GroupByError):
+ pass
+
+
+class SpecificationError(GroupByError):
+ pass
+
+
+class SelectionMixin(object):
+ """
+ mixin implementing the selection & aggregation interface on a group-like object
+ sub-classes need to define: obj, exclusions
+ """
+ _selection = None
+ _internal_names = ['_cache','__setstate__']
+ _internal_names_set = set(_internal_names)
+ _builtin_table = {
+ builtins.sum: np.sum,
+ builtins.max: np.max,
+ builtins.min: np.min,
+ }
+ _cython_table = {
+ builtins.sum: 'sum',
+ builtins.max: 'max',
+ builtins.min: 'min',
+ np.sum: 'sum',
+ np.mean: 'mean',
+ np.prod: 'prod',
+ np.std: 'std',
+ np.var: 'var',
+ np.median: 'median',
+ np.max: 'max',
+ np.min: 'min',
+ np.cumprod: 'cumprod',
+ np.cumsum: 'cumsum'
+ }
+
+ @property
+ def name(self):
+ if self._selection is None:
+ return None # 'result'
+ else:
+ return self._selection
+
+ @property
+ def _selection_list(self):
+ if not isinstance(self._selection, (list, tuple, com.ABCSeries, com.ABCIndex, np.ndarray)):
+ return [self._selection]
+ return self._selection
+
+ @cache_readonly
+ def _selected_obj(self):
+
+ if self._selection is None or isinstance(self.obj, com.ABCSeries):
+ return self.obj
+ else:
+ return self.obj[self._selection]
+
+ @cache_readonly
+ def _obj_with_exclusions(self):
+ if self._selection is not None and isinstance(self.obj, com.ABCDataFrame):
+ return self.obj.reindex(columns=self._selection_list)
+
+ if len(self.exclusions) > 0:
+ return self.obj.drop(self.exclusions, axis=1)
+ else:
+ return self.obj
+
+ def __getitem__(self, key):
+ if self._selection is not None:
+ raise Exception('Column(s) %s already selected' % self._selection)
+
+ if isinstance(key, (list, tuple, com.ABCSeries, com.ABCIndex, np.ndarray)):
+ if len(self.obj.columns.intersection(key)) != len(key):
+ bad_keys = list(set(key).difference(self.obj.columns))
+ raise KeyError("Columns not found: %s"
+ % str(bad_keys)[1:-1])
+ return self._gotitem(list(key), ndim=2)
+
+ elif not getattr(self,'as_index',False):
+ if key not in self.obj.columns:
+ raise KeyError("Column not found: %s" % key)
+ return self._gotitem(key, ndim=2)
+
+ else:
+ if key not in self.obj:
+ raise KeyError("Column not found: %s" % key)
+ return self._gotitem(key, ndim=1)
+
+ def _gotitem(self, key, ndim, subset=None):
+ """
+ sub-classes to define
+ return a sliced object
+
+ Parameters
+ ----------
+ key : string / list of selections
+ ndim : 1,2
+ requested ndim of result
+ subset : object, default None
+ subset to act on
+
+ """
+ raise AbstractMethodError(self)
+
+ _agg_doc = """Aggregate using input function or dict of {column -> function}
+
+Parameters
+----------
+arg : function or dict
+ Function to use for aggregating groups. If a function, must either
+ work when passed a DataFrame or when passed to DataFrame.apply. If
+ passed a dict, the keys must be DataFrame column names.
+
+ Accepted Combinations are:
+ - string cythonized function name
+ - function
+ - list of functions
+ - dict of columns -> functions
+ - nested dict of names -> dicts of functions
+
+Notes
+-----
+Numpy functions mean/median/prod/sum/std/var are special cased so the
+default behavior is applying the function along axis=0
+(e.g., np.mean(arr_2d, axis=0)) as opposed to
+mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).
+
+Returns
+-------
+aggregated : DataFrame
+"""
+
+ _see_also_template = """
+See also
+--------
+pandas.Series.%(name)s
+pandas.DataFrame.%(name)s
+"""
+
+ def aggregate(self, func, *args, **kwargs):
+ raise AbstractMethodError(self)
+
+ agg = aggregate
+
+ def _aggregate(self, arg, *args, **kwargs):
+ """
+ provide an implementation for the aggregators
+
+ Parameters
+ ----------
+ arg : string, dict, function
+ *args : args to pass on to the function
+ **kwargs : kwargs to pass on to the function
+
+
+ Returns
+ -------
+ tuple of result, how
+
+ Notes
+ -----
+ how can be a string describe the required post-processing, or
+ None if not required
+ """
+
+ _level = kwargs.pop('_level',None)
+ if isinstance(arg, compat.string_types):
+ return getattr(self, arg)(*args, **kwargs), None
+
+ result = compat.OrderedDict()
+ if isinstance(arg, dict):
+ if self.axis != 0: # pragma: no cover
+ raise ValueError('Can only pass dict with axis=0')
+
+ obj = self._selected_obj
+
+ if any(isinstance(x, (list, tuple, dict)) for x in arg.values()):
+ new_arg = compat.OrderedDict()
+ for k, v in compat.iteritems(arg):
+ if not isinstance(v, (tuple, list, dict)):
+ new_arg[k] = [v]
+ else:
+ new_arg[k] = v
+ arg = new_arg
+
+ keys = []
+ if self._selection is not None:
+ subset = obj
+
+ for fname, agg_how in compat.iteritems(arg):
+ colg = self._gotitem(self._selection, ndim=1, subset=subset)
+ result[fname] = colg.aggregate(agg_how, _level=None)
+ keys.append(fname)
+ else:
+ for col, agg_how in compat.iteritems(arg):
+ colg = self._gotitem(col, ndim=1)
+ result[col] = colg.aggregate(agg_how, _level=None)
+ keys.append(col)
+
+ if isinstance(list(result.values())[0], com.ABCDataFrame):
+ from pandas.tools.merge import concat
+ result = concat([ result[k] for k in keys ], keys=keys, axis=1)
+ else:
+ from pandas import DataFrame
+ result = DataFrame(result)
+
+ return result, True
+ elif hasattr(arg, '__iter__'):
+ return self._aggregate_multiple_funcs(arg, _level=_level), None
+ else:
+ result = None
+
+ cy_func = self._is_cython_func(arg)
+ if cy_func and not args and not kwargs:
+ return getattr(self, cy_func)(), None
+
+ # caller can react
+ return result, True
+
+ def _aggregate_multiple_funcs(self, arg, _level):
+ from pandas.tools.merge import concat
+
+ if self.axis != 0:
+ raise NotImplementedError("axis other than 0 is not supported")
+
+ if self._selected_obj.ndim == 1:
+ obj = self._selected_obj
+ else:
+ obj = self._obj_with_exclusions
+
+ results = []
+ keys = []
+
+ # degenerate case
+ if obj.ndim==1:
+ for a in arg:
+ try:
+ colg = self._gotitem(obj.name, ndim=1, subset=obj)
+ results.append(colg.aggregate(a))
+
+ # make sure we find a good name
+ name = com._get_callable_name(a) or a
+ keys.append(name)
+ except (TypeError, DataError):
+ pass
+ except SpecificationError:
+ raise
+
+ # multiples
+ else:
+ for col in obj:
+ try:
+ colg = self._gotitem(col, ndim=1, subset=obj[col])
+ results.append(colg.aggregate(arg))
+ keys.append(col)
+ except (TypeError, DataError):
+ pass
+ except SpecificationError:
+ raise
+
+ if _level:
+ keys = None
+ result = concat(results, keys=keys, axis=1)
+
+ return result
+
+ def _is_cython_func(self, arg):
+ """ if we define an internal function for this argument, return it """
+ return self._cython_table.get(arg)
+
+ def _is_builtin_func(self, arg):
+ """
+ if we define an builtin function for this argument, return it,
+ otherwise return the arg
+ """
+ return self._builtin_table.get(arg, arg)
+
class FrozenList(PandasObject, list):
"""
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index ff110880d34ba..2fc0786aa1e09 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -5149,6 +5149,7 @@ def combineMult(self, other):
DataFrame._setup_axes(['index', 'columns'], info_axis=1, stat_axis=0,
axes_are_reversed=True, aliases={'rows': 0})
DataFrame._add_numeric_operations()
+DataFrame._add_series_or_dataframe_operations()
_EMPTY_SERIES = Series([])
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index b75573edc7157..d3cd0840782b4 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -29,7 +29,6 @@
from pandas.util.decorators import Appender, Substitution, deprecate_kwarg
from pandas.core import config
-
# goal is to be able to define the docs close to function, while still being
# able to share
_shared_docs = dict()
@@ -4734,6 +4733,35 @@ def nanptp(values, axis=0, skipna=True):
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 """
+
+ from pandas.core import window as rwindow
+
+ @Appender(rwindow.rolling.__doc__)
+ 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)
+ 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)
+ 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):
+ 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)
+ cls.ewm = ewm
+
def _doc_parms(cls):
""" return a tuple of the doc parms """
axis_descr = "{%s}" % ', '.join([
@@ -4916,6 +4944,6 @@ def logical_func(self, axis=None, bool_only=None, skipna=None,
logical_func.__name__ = name
return logical_func
-# install the indexerse
+# install the indexes
for _name, _indexer in indexing.get_indexers_list():
NDFrame._create_indexer(_name, _indexer)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 28d95c40c7294..5428ee5484bfa 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -12,7 +12,7 @@
)
from pandas import compat
-from pandas.core.base import PandasObject
+from pandas.core.base import PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError
from pandas.core.categorical import Categorical
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
@@ -20,8 +20,9 @@
from pandas.core.internals import BlockManager, make_block
from pandas.core.series import Series
from pandas.core.panel import Panel
-from pandas.util.decorators import (cache_readonly, Appender, make_signature,
+from pandas.util.decorators import (cache_readonly, Substitution, Appender, make_signature,
deprecate_kwarg)
+from textwrap import dedent
import pandas.core.algorithms as algos
import pandas.core.common as com
from pandas.core.common import(_possibly_downcast_to_dtype, isnull,
@@ -37,27 +38,14 @@
import pandas.algos as _algos
import pandas.hashtable as _hash
-_agg_doc = """Aggregate using input function or dict of {column -> function}
-
-Parameters
-----------
-arg : function or dict
- Function to use for aggregating groups. If a function, must either
- work when passed a DataFrame or when passed to DataFrame.apply. If
- passed a dict, the keys must be DataFrame column names.
-
-Notes
------
-Numpy functions mean/median/prod/sum/std/var are special cased so the
-default behavior is applying the function along axis=0
-(e.g., np.mean(arr_2d, axis=0)) as opposed to
-mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).
-
-Returns
--------
-aggregated : DataFrame
-"""
+_doc_template = """
+ See also
+ --------
+ pandas.Series.%(name)s
+ pandas.DataFrame.%(name)s
+ pandas.Panel.%(name)s
+"""
# special case to prevent duplicate plots when catching exceptions when
# forwarding methods from NDFrames
@@ -91,20 +79,14 @@
_cython_transforms = frozenset(['cumprod', 'cumsum', 'shift'])
-class GroupByError(Exception):
- pass
-
-
-class DataError(GroupByError):
- pass
-
-
-class SpecificationError(GroupByError):
- pass
-
-
def _groupby_function(name, alias, npfunc, numeric_only=True,
_convert=False):
+
+ _local_template = "Compute %(f)s of group values"
+
+ @Substitution(name='groupby',f=name)
+ @Appender(_doc_template)
+ @Appender(_local_template)
def f(self):
self._set_selection_from_grouper()
try:
@@ -117,8 +99,7 @@ def f(self):
result = result._convert(datetime=True)
return result
- f.__doc__ = "Compute %s of group values" % name
- f.__name__ = name
+ f.__name__ = name
return f
@@ -319,7 +300,7 @@ def f(self):
return attr
-class GroupBy(PandasObject):
+class GroupBy(PandasObject, SelectionMixin):
"""
Class for grouping and aggregating relational data. See aggregate,
@@ -387,8 +368,6 @@ class GroupBy(PandasObject):
Number of groups
"""
_apply_whitelist = _common_apply_whitelist
- _internal_names = ['_cache']
- _internal_names_set = set(_internal_names)
_group_selection = None
def __init__(self, obj, keys=None, axis=0, level=None,
@@ -493,19 +472,6 @@ def _get_index(self, name):
""" safe get index, translate keys for datelike to underlying repr """
return self._get_indices([name])[0]
- @property
- def name(self):
- if self._selection is None:
- return None # 'result'
- else:
- return self._selection
-
- @property
- def _selection_list(self):
- if not isinstance(self._selection, (list, tuple, Series, Index, np.ndarray)):
- return [self._selection]
- return self._selection
-
@cache_readonly
def _selected_obj(self):
@@ -558,9 +524,6 @@ def __getattr__(self, attr):
raise AttributeError("%r object has no attribute %r" %
(type(self).__name__, attr))
- def __getitem__(self, key):
- raise NotImplementedError('Not implemented: %s' % key)
-
plot = property(GroupByPlot)
def _make_wrapper(self, name):
@@ -660,6 +623,7 @@ def __iter__(self):
"""
return self.grouper.get_iterator(self.obj, axis=self.axis)
+ @Substitution(name='groupby')
def apply(self, func, *args, **kwargs):
"""
Apply function and combine results together in an intelligent way. The
@@ -695,16 +659,14 @@ def apply(self, func, *args, **kwargs):
side-effects, as they will take effect twice for the first
group.
-
See also
--------
aggregate, transform
+ pandas.Series.%(name)s
+ pandas.DataFrame.%(name)s
+ pandas.Panel.%(name)s"""
- Returns
- -------
- applied : type depending on grouped object and function
- """
- func = _intercept_function(func)
+ func = self._is_builtin_func(func)
@wraps(func)
def f(g):
@@ -721,13 +683,6 @@ def _python_apply_general(self, f):
return self._wrap_applied_output(keys, values,
not_indexed_same=mutated)
- def aggregate(self, func, *args, **kwargs):
- raise AbstractMethodError(self)
-
- @Appender(_agg_doc)
- def agg(self, func, *args, **kwargs):
- return self.aggregate(func, *args, **kwargs)
-
def _iterate_slices(self):
yield self.name, self._selected_obj
@@ -744,12 +699,16 @@ def irow(self, i):
FutureWarning, stacklevel=2)
return self.nth(i)
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def count(self):
- """ Compute count of group, excluding missing values """
+ """Compute count of group, excluding missing values"""
# defined here for API doc
raise NotImplementedError
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def mean(self):
"""
Compute mean of groups, excluding missing values
@@ -765,6 +724,8 @@ def mean(self):
f = lambda x: x.mean(axis=self.axis)
return self._python_agg_general(f)
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def median(self):
"""
Compute median of groups, excluding missing values
@@ -784,21 +745,37 @@ def f(x):
return x.median(axis=self.axis)
return self._python_agg_general(f)
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def std(self, ddof=1):
"""
Compute standard deviation of groups, excluding missing values
For multiple groupings, the result index will be a MultiIndex
+
+ Parameters
+ ----------
+ ddof : integer, default 1
+ degrees of freedom
"""
+
# todo, implement at cython level?
return np.sqrt(self.var(ddof=ddof))
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def var(self, ddof=1):
"""
Compute variance of groups, excluding missing values
For multiple groupings, the result index will be a MultiIndex
+
+ Parameters
+ ----------
+ ddof : integer, default 1
+ degrees of freedom
"""
+
if ddof == 1:
return self._cython_agg_general('var')
else:
@@ -806,19 +783,26 @@ def var(self, ddof=1):
f = lambda x: x.var(ddof=ddof)
return self._python_agg_general(f)
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def sem(self, ddof=1):
"""
Compute standard error of the mean of groups, excluding missing values
For multiple groupings, the result index will be a MultiIndex
+
+ Parameters
+ ----------
+ ddof : integer, default 1
+ degrees of freedom
"""
+
return self.std(ddof=ddof)/np.sqrt(self.count())
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def size(self):
- """
- Compute group sizes
-
- """
+ """Compute group sizes"""
return self.grouper.size()
sum = _groupby_function('sum', 'add', np.sum)
@@ -830,14 +814,19 @@ def size(self):
last = _groupby_function('last', 'last', _last_compat, numeric_only=False,
_convert=True)
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def ohlc(self):
"""
Compute sum of values, excluding missing values
For multiple groupings, the result index will be a MultiIndex
"""
+
return self._apply_to_column_groupbys(
lambda x: x._cython_agg_general('ohlc'))
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def nth(self, n, dropna=None):
"""
Take the nth row from each group if n is an int, or a subset of rows
@@ -872,16 +861,16 @@ def nth(self, n, dropna=None):
2 5 6
>>> g.nth(0, dropna='any')
B
- A
+ A
1 4
5 6
>>> g.nth(1, dropna='any') # NaNs denote group exhausted when using dropna
B
- A
+ A
1 NaN
5 NaN
-
"""
+
if isinstance(n, int):
nth_values = [n]
elif isinstance(n, (set, list, tuple)):
@@ -973,6 +962,8 @@ def nth(self, n, dropna=None):
return result
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def cumcount(self, ascending=True):
"""
Number each item in each group from 0 to the length of that group - 1.
@@ -1015,37 +1006,44 @@ def cumcount(self, ascending=True):
4 0
5 0
dtype: int64
-
"""
+
self._set_selection_from_grouper()
index = self._selected_obj.index
cumcounts = self._cumcount_array(ascending=ascending)
return Series(cumcounts, index)
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def cumprod(self, axis=0):
- """
- Cumulative product for each group
-
- """
+ """Cumulative product for each group"""
if axis != 0:
return self.apply(lambda x: x.cumprod(axis=axis))
return self._cython_transform('cumprod')
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def cumsum(self, axis=0):
- """
- Cumulative sum for each group
-
- """
+ """Cumulative sum for each group"""
if axis != 0:
return self.apply(lambda x: x.cumprod(axis=axis))
return self._cython_transform('cumsum')
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def shift(self, periods=1, freq=None, axis=0):
"""
Shift each group by periods observations
+
+ Parameters
+ ----------
+ periods : integer, default 1
+ number of periods to shift
+ freq : frequency string
+ axis : axis to shift, default 0
"""
if freq is not None or axis != 0:
@@ -1062,6 +1060,8 @@ def shift(self, periods=1, freq=None, axis=0):
return self._wrap_transformed_output(output)
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def head(self, n=5):
"""
Returns first n rows of each group.
@@ -1073,7 +1073,7 @@ def head(self, n=5):
--------
>>> df = DataFrame([[1, 2], [1, 4], [5, 6]],
- columns=['A', 'B'])
+ columns=['A', 'B'])
>>> df.groupby('A', as_index=False).head(1)
A B
0 1 2
@@ -1082,13 +1082,15 @@ def head(self, n=5):
A B
0 1 2
2 5 6
-
"""
+
obj = self._selected_obj
in_head = self._cumcount_array() < n
head = obj[in_head]
return head
+ @Substitution(name='groupby')
+ @Appender(_doc_template)
def tail(self, n=5):
"""
Returns last n rows of each group
@@ -1100,7 +1102,7 @@ def tail(self, n=5):
--------
>>> df = DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
- columns=['A', 'B'])
+ columns=['A', 'B'])
>>> df.groupby('A').tail(1)
A B
1 a 2
@@ -1109,8 +1111,8 @@ def tail(self, n=5):
A B
0 a 1
2 b 1
-
"""
+
obj = self._selected_obj
rng = np.arange(0, -self.grouper._max_groupsize, -1, dtype='int64')
in_tail = self._cumcount_array(rng, ascending=False) > -n
@@ -1121,8 +1123,10 @@ def _cumcount_array(self, arr=None, ascending=True):
"""
arr is where cumcount gets its values from
- note: this is currently implementing sort=False (though the default is sort=True)
- for groupby in general
+ Note
+ ----
+ this is currently implementing sort=False (though the default is sort=True)
+ for groupby in general
"""
if arr is None:
arr = np.arange(self.grouper._max_groupsize, dtype='int64')
@@ -1217,7 +1221,7 @@ def _cython_agg_general(self, how, numeric_only=True):
return self._wrap_aggregated_output(output, names)
def _python_agg_general(self, func, *args, **kwargs):
- func = _intercept_function(func)
+ func = self._is_builtin_func(func)
f = lambda x: func(x, *args, **kwargs)
# iterate through "columns" ex exclusions to populate output dict
@@ -1733,7 +1737,7 @@ def agg_series(self, obj, func):
return self._aggregate_series_pure_python(obj, func)
def _aggregate_series_fast(self, obj, func):
- func = _intercept_function(func)
+ func = self._is_builtin_func(func)
if obj.index._has_complex_internals:
raise TypeError('Incompatible index for Cython grouper')
@@ -2421,13 +2425,14 @@ def aggregate(self, func_or_funcs, *args, **kwargs):
-------
Series or DataFrame
"""
+ _level = kwargs.pop('_level',None)
if isinstance(func_or_funcs, compat.string_types):
return getattr(self, func_or_funcs)(*args, **kwargs)
if hasattr(func_or_funcs, '__iter__'):
ret = self._aggregate_multiple_funcs(func_or_funcs)
else:
- cyfunc = _intercept_cython(func_or_funcs)
+ cyfunc = self._is_cython_func(func_or_funcs)
if cyfunc and not args and not kwargs:
return getattr(self, cyfunc)()
@@ -2447,6 +2452,8 @@ def aggregate(self, func_or_funcs, *args, **kwargs):
return ret
+ agg = aggregate
+
def _aggregate_multiple_funcs(self, arg):
if isinstance(arg, dict):
columns = list(arg.keys())
@@ -2470,11 +2477,18 @@ def _aggregate_multiple_funcs(self, arg):
results = {}
for name, func in arg:
+ obj = self
if name in results:
raise SpecificationError('Function names must be unique, '
'found multiple named %s' % name)
- results[name] = self.aggregate(func)
+ # reset the cache so that we
+ # only include the named selection
+ if name in self._selected_obj:
+ obj = copy.copy(obj)
+ obj._reset_cache()
+ obj._selection = name
+ results[name] = obj.aggregate(func)
return DataFrame(results, columns=columns)
@@ -2559,7 +2573,7 @@ def transform(self, func, *args, **kwargs):
transformed : Series
"""
- func = _intercept_cython(func) or func
+ func = self._is_cython_func(func) or func
# if string function
if isinstance(func, compat.string_types):
@@ -2912,68 +2926,16 @@ def _post_process_cython_aggregate(self, obj):
obj = obj.swapaxes(0, 1)
return obj
- @cache_readonly
- def _obj_with_exclusions(self):
- if self._selection is not None:
- return self.obj.reindex(columns=self._selection_list)
-
- if len(self.exclusions) > 0:
- return self.obj.drop(self.exclusions, axis=1)
- else:
- return self.obj
-
- @Appender(_agg_doc)
def aggregate(self, arg, *args, **kwargs):
- if isinstance(arg, compat.string_types):
- return getattr(self, arg)(*args, **kwargs)
-
- result = OrderedDict()
- if isinstance(arg, dict):
- if self.axis != 0: # pragma: no cover
- raise ValueError('Can only pass dict with axis=0')
- obj = self._selected_obj
+ _level = kwargs.pop('_level',None)
+ result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
+ if how is None:
+ return result
- if any(isinstance(x, (list, tuple, dict)) for x in arg.values()):
- new_arg = OrderedDict()
- for k, v in compat.iteritems(arg):
- if not isinstance(v, (tuple, list, dict)):
- new_arg[k] = [v]
- else:
- new_arg[k] = v
- arg = new_arg
-
- keys = []
- if self._selection is not None:
- subset = obj
- if isinstance(subset, DataFrame):
- raise NotImplementedError("Aggregating on a DataFrame is "
- "not supported")
-
- for fname, agg_how in compat.iteritems(arg):
- colg = SeriesGroupBy(subset, selection=self._selection,
- grouper=self.grouper)
- result[fname] = colg.aggregate(agg_how)
- keys.append(fname)
- else:
- for col, agg_how in compat.iteritems(arg):
- colg = SeriesGroupBy(obj[col], selection=col,
- grouper=self.grouper)
- result[col] = colg.aggregate(agg_how)
- keys.append(col)
-
- if isinstance(list(result.values())[0], DataFrame):
- from pandas.tools.merge import concat
- result = concat([result[k] for k in keys], keys=keys, axis=1)
- else:
- result = DataFrame(result)
- elif isinstance(arg, list):
- return self._aggregate_multiple_funcs(arg)
- else:
- cyfunc = _intercept_cython(arg)
- if cyfunc and not args and not kwargs:
- return getattr(self, cyfunc)()
+ if result is None:
+ # grouper specific aggregations
if self.grouper.nkeys > 1:
return self._python_agg_general(arg, *args, **kwargs)
else:
@@ -2981,7 +2943,7 @@ def aggregate(self, arg, *args, **kwargs):
# try to treat as if we are passing a list
try:
assert not args and not kwargs
- result = self._aggregate_multiple_funcs([arg])
+ result = self._aggregate_multiple_funcs([arg], _level=_level)
result.columns = Index(result.columns.levels[0],
name=self._selected_obj.columns.name)
except:
@@ -2993,29 +2955,7 @@ def aggregate(self, arg, *args, **kwargs):
return result._convert(datetime=True)
- def _aggregate_multiple_funcs(self, arg):
- from pandas.tools.merge import concat
-
- if self.axis != 0:
- raise NotImplementedError("axis other than 0 is not supported")
-
- obj = self._obj_with_exclusions
-
- results = []
- keys = []
- for col in obj:
- try:
- colg = SeriesGroupBy(obj[col], selection=col,
- grouper=self.grouper)
- results.append(colg.aggregate(arg))
- keys.append(col)
- except (TypeError, DataError):
- pass
- except SpecificationError:
- raise
- result = concat(results, keys=keys, axis=1)
-
- return result
+ agg = aggregate
def _aggregate_generic(self, func, *args, **kwargs):
if self.grouper.nkeys != 1:
@@ -3318,7 +3258,7 @@ def transform(self, func, *args, **kwargs):
"""
# optimized transforms
- func = _intercept_cython(func) or func
+ func = self._is_cython_func(func) or func
if isinstance(func, compat.string_types):
if func in _cython_transforms:
# cythonized transform
@@ -3463,35 +3403,42 @@ class DataFrameGroupBy(NDFrameGroupBy):
_block_agg_axis = 1
- def __getitem__(self, key):
- if self._selection is not None:
- raise Exception('Column(s) %s already selected' % self._selection)
+ @Substitution(name='groupby')
+ @Appender(SelectionMixin._see_also_template)
+ @Appender(SelectionMixin._agg_doc)
+ def aggregate(self, arg, *args, **kwargs):
+ return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
- if isinstance(key, (list, tuple, Series, Index, np.ndarray)):
- if len(self.obj.columns.intersection(key)) != len(key):
- bad_keys = list(set(key).difference(self.obj.columns))
- raise KeyError("Columns not found: %s"
- % str(bad_keys)[1:-1])
- return DataFrameGroupBy(self.obj, self.grouper, selection=key,
- grouper=self.grouper,
- exclusions=self.exclusions,
- as_index=self.as_index)
+ agg = aggregate
- elif not self.as_index:
- if key not in self.obj.columns:
- raise KeyError("Column not found: %s" % key)
- return DataFrameGroupBy(self.obj, self.grouper, selection=key,
+ def _gotitem(self, key, ndim, subset=None):
+ """
+ sub-classes to define
+ return a sliced object
+
+ Parameters
+ ----------
+ key : string / list of selections
+ ndim : 1,2
+ requested ndim of result
+ subset : object, default None
+ subset to act on
+ """
+
+ if ndim == 2:
+ if subset is None:
+ subset = self.obj
+ return DataFrameGroupBy(subset, self.grouper, selection=key,
grouper=self.grouper,
exclusions=self.exclusions,
as_index=self.as_index)
+ elif ndim == 1:
+ if subset is None:
+ subset = self.obj[key]
+ return SeriesGroupBy(subset, selection=key,
+ grouper=self.grouper)
- else:
- if key not in self.obj:
- raise KeyError("Column not found: %s" % key)
- # kind of a kludge
- return SeriesGroupBy(self.obj[key], selection=key,
- grouper=self.grouper,
- exclusions=self.exclusions)
+ raise AssertionError("invalid ndim for _gotitem")
def _wrap_generic_output(self, result, obj):
result_index = self.grouper.levels[0]
@@ -3627,6 +3574,14 @@ def count(self):
class PanelGroupBy(NDFrameGroupBy):
+ @Substitution(name='groupby')
+ @Appender(SelectionMixin._see_also_template)
+ @Appender(SelectionMixin._agg_doc)
+ def aggregate(self, arg, *args, **kwargs):
+ return super(PanelGroupBy, self).aggregate(arg, *args, **kwargs)
+
+ agg = aggregate
+
def _iterate_slices(self):
if self.axis == 0:
# kludge
@@ -4162,38 +4117,6 @@ def _reorder_by_uniques(uniques, labels):
return uniques, labels
-_func_table = {
- builtins.sum: np.sum,
- builtins.max: np.max,
- builtins.min: np.min
-}
-
-
-_cython_table = {
- builtins.sum: 'sum',
- builtins.max: 'max',
- builtins.min: 'min',
- np.sum: 'sum',
- np.mean: 'mean',
- np.prod: 'prod',
- np.std: 'std',
- np.var: 'var',
- np.median: 'median',
- np.max: 'max',
- np.min: 'min',
- np.cumprod: 'cumprod',
- np.cumsum: 'cumsum'
-}
-
-
-def _intercept_function(func):
- return _func_table.get(func, func)
-
-
-def _intercept_cython(func):
- return _cython_table.get(func)
-
-
def _groupby_indices(values):
return _algos.groupby_indices(_values_from_object(com._ensure_object(values)))
diff --git a/pandas/core/series.py b/pandas/core/series.py
index ca55a834a33d2..d6eb18396e14c 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2765,6 +2765,7 @@ def _dir_additions(self):
aliases={'rows': 0})
Series._add_numeric_operations()
Series._add_series_only_operations()
+Series._add_series_or_dataframe_operations()
_INDEX_TYPES = ndarray, Index, list, tuple
#------------------------------------------------------------------------------
diff --git a/pandas/core/window.py b/pandas/core/window.py
new file mode 100644
index 0000000000000..4bbdf444ac2a7
--- /dev/null
+++ b/pandas/core/window.py
@@ -0,0 +1,1364 @@
+"""
+
+provide a generic structure to support window functions,
+similar to how we have a Groupby object
+
+
+"""
+from __future__ import division
+
+import warnings
+import numpy as np
+from functools import wraps
+from collections import defaultdict
+
+import pandas as pd
+from pandas.lib import isscalar
+from pandas.core.base import PandasObject, SelectionMixin, AbstractMethodError
+import pandas.core.common as com
+import pandas.algos as algos
+from pandas import compat
+from pandas.util.decorators import Substitution, Appender
+from textwrap import dedent
+
+_shared_docs = dict()
+_doc_template = """
+
+Returns
+-------
+same type as input
+
+See also
+--------
+pandas.Series.%(name)s
+pandas.DataFrame.%(name)s
+"""
+
+class _Window(PandasObject, SelectionMixin):
+ _attributes = ['window','min_periods','freq','center','win_type','axis']
+ exclusions = set()
+
+ def __init__(self, obj, window=None, min_periods=None, freq=None, center=False,
+ win_type=None, axis=0):
+
+ if freq is not None:
+ warnings.warn("The freq kw is deprecated and will be removed in a future version. You can resample prior "
+ "to passing to a window function",
+ FutureWarning, stacklevel=3)
+
+ self.blocks = []
+ self.obj = obj
+ self.window = window
+ self.min_periods = min_periods
+ self.freq = freq
+ self.center = center
+ self.win_type = win_type
+ self.axis = axis
+ self._setup()
+
+ @property
+ def _constructor(self):
+ return Window
+
+ def _setup(self):
+ pass
+
+ def _convert_freq(self, how=None):
+ """ resample according to the how, return a new object """
+
+ obj = self._selected_obj
+ if self.freq is not None and isinstance(obj, (com.ABCSeries, com.ABCDataFrame)):
+ if how is not None:
+ warnings.warn("The how kw argument is deprecated and removed in a future version. You can resample prior "
+ "to passing to a window function",
+ FutureWarning, stacklevel=6)
+
+ obj = obj.resample(self.freq, how=how)
+ return obj
+
+ def _create_blocks(self, how):
+ """ split data into blocks & return conformed data """
+
+ obj = self._convert_freq(how)
+ return obj.as_blocks(copy=False).values(), obj
+
+ def _gotitem(self, key, ndim, subset=None):
+ """
+ sub-classes to define
+ return a sliced object
+
+ Parameters
+ ----------
+ key : string / list of selections
+ ndim : 1,2
+ requested ndim of result
+ subset : object, default None
+ subset to act on
+ """
+
+ # create a new object to prevent aliasing
+ if subset is None:
+ subset = self.obj
+ self = self._shallow_copy(subset)
+ self._reset_cache()
+ if subset.ndim==2:
+ if isscalar(key) and key in subset or com.is_list_like(key):
+ self._selection = key
+ return self
+
+ def __getattr__(self, attr):
+ if attr in self._internal_names_set:
+ return object.__getattribute__(self, attr)
+ if attr in self.obj:
+ return self[attr]
+
+ raise AttributeError("%r object has no attribute %r" %
+ (type(self).__name__, attr))
+
+ def _dir_additions(self):
+ return self.obj._dir_additions()
+
+ def _get_window(self, other=None):
+ return self.window
+
+ 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__,
+ attrs=','.join(attrs))
+
+ def _shallow_copy(self, obj=None, **kwargs):
+ """ return a new object with the replacement attributes """
+ if obj is None:
+ obj = self._selected_obj.copy()
+ if isinstance(obj, self.__class__):
+ obj = obj.obj
+ for attr in self._attributes:
+ if attr not in kwargs:
+ kwargs[attr] = getattr(self,attr)
+ return self._constructor(obj, **kwargs)
+
+ 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
+ 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:
+ try:
+ values = values.astype(float)
+ except (ValueError, TypeError):
+ raise TypeError("cannot handle this type -> {0}".format(values.dtype))
+
+ if kill_inf:
+ values = values.copy()
+ values[np.isinf(values)] = np.NaN
+
+ return values
+
+ def _wrap_result(self, result, block=None, obj=None):
+ """ wrap a single result """
+
+ if obj is None:
+ obj = self._selected_obj
+ if isinstance(result, np.ndarray):
+
+ # coerce if necessary
+ if block is not None:
+ if com.is_timedelta64_dtype(block.values.dtype):
+ result = pd.to_timedelta(result.ravel(),unit='ns').values.reshape(result.shape)
+
+ if result.ndim == 1:
+ from pandas import Series
+ return Series(result, obj.index, name=obj.name)
+
+ return type(obj)(result,
+ index=obj.index,
+ columns=block.columns)
+ return result
+
+ def _wrap_results(self, results, blocks, obj):
+ """
+ wrap the results
+
+ Paramters
+ ---------
+ results : list of ndarrays
+ blocks : list of blocks
+ obj : conformed data (may be resampled)
+ """
+
+ final = []
+ for result, block in zip(results, blocks):
+
+ result = self._wrap_result(result, block=block, obj=obj)
+ if result.ndim == 1:
+ return result
+ final.append(result)
+
+ if not len(final):
+ return obj.astype('float64')
+ return pd.concat(final,axis=1).reindex(columns=obj.columns)
+
+ def _center_window(self, result, window):
+ """ center the result in the window """
+ if self.axis > result.ndim-1:
+ raise ValueError("Requested axis is larger then no. of argument "
+ "dimensions")
+
+ from pandas import Series, DataFrame
+ offset = _offset(window, True)
+ if offset > 0:
+ if isinstance(result, (Series, DataFrame)):
+ result = result.slice_shift(-offset, axis=self.axis)
+ else:
+ lead_indexer = [slice(None)] * result.ndim
+ lead_indexer[self.axis] = slice(offset, None)
+ result = np.copy(result[tuple(lead_indexer)])
+ return result
+
+ def aggregate(self, arg, *args, **kwargs):
+ result, how = self._aggregate(arg, *args, **kwargs)
+ if result is None:
+ return self.apply(arg, args=args, kwargs=kwargs)
+ return result
+
+ agg = aggregate
+
+ _shared_docs['sum'] = dedent("""
+ %(name)s sum
+
+ Parameters
+ ----------
+ how : string, default None (DEPRECATED)
+ Method for down- or re-sampling""")
+
+ _shared_docs['mean'] = dedent("""
+ %(name)s mean
+
+ Parameters
+ ----------
+ how : string, default None (DEPRECATED)
+ Method for down- or re-sampling""")
+
+class Window(_Window):
+ """
+ Provides rolling transformations.
+
+ .. versionadded:: 0.18.0
+
+ Parameters
+ ----------
+ window : int
+ Size of the moving window. This is the number of observations used for
+ calculating the statistic.
+ min_periods : int, default None
+ Minimum number of observations in window required to have a value
+ (otherwise result is NA).
+ freq : string or DateOffset object, optional (default None) (DEPRECATED)
+ Frequency to conform the data to before computing the statistic. Specified
+ as a frequency string or DateOffset object.
+ center : boolean, default False
+ Set the labels at the center of the window.
+ win_type : string, default None
+ prove a window type, see the notes below
+ axis : int, default 0
+
+ Returns
+ -------
+ a Window sub-classed for the particular operation
+
+ Notes
+ -----
+ By default, the result is set to the right edge of the window. This can be
+ changed to the center of the window by setting ``center=True``.
+
+ 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
+ of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
+
+ The recognized window types are:
+
+ * ``boxcar``
+ * ``triang``
+ * ``blackman``
+ * ``hamming``
+ * ``bartlett``
+ * ``parzen``
+ * ``bohman``
+ * ``blackmanharris``
+ * ``nuttall``
+ * ``barthann``
+ * ``kaiser`` (needs beta)
+ * ``gaussian`` (needs std)
+ * ``general_gaussian`` (needs power, width)
+ * ``slepian`` (needs width).
+"""
+
+ def _prep_window(self, **kwargs):
+ """ provide validation for our window type, return the window """
+ window = self._get_window()
+
+ if isinstance(window, (list, tuple, np.ndarray)):
+ return com._asarray_tuplesafe(window).astype(float)
+ elif com.is_integer(window):
+ try:
+ import scipy.signal as sig
+ except ImportError:
+ raise ImportError('Please install scipy to generate window weight')
+ win_type = _validate_win_type(self.win_type, kwargs) # may pop from kwargs
+ return sig.get_window(win_type, window).astype(float)
+
+ raise ValueError('Invalid window %s' % str(window))
+
+ def _apply_window(self, mean=True, how=None, **kwargs):
+ """
+ Applies a moving window of type ``window_type`` on the data.
+
+ Parameters
+ ----------
+ mean : boolean, default True
+ If True computes weighted mean, else weighted sum
+ how : string, default to None (DEPRECATED)
+ how to resample
+
+ Returns
+ -------
+ y : type of input argument
+
+ """
+ window = self._prep_window(**kwargs)
+ center = self.center
+
+ blocks, obj = self._create_blocks(how=how)
+ results = []
+ for b in blocks:
+ try:
+ values = self._prep_values(b.values)
+ except TypeError:
+ results.append(b.values.copy())
+ continue
+
+ if values.size == 0:
+ results.append(values.copy())
+ continue
+
+ offset = _offset(window, center)
+ additional_nans = np.array([np.NaN] * offset)
+ def f(arg, *args, **kwargs):
+ minp = _use_window(self.min_periods, len(window))
+ return algos.roll_window(np.concatenate((arg, additional_nans)) if center else arg,
+ window, minp, avg=mean)
+
+ result = np.apply_along_axis(f, self.axis, values)
+
+ if center:
+ result = self._center_window(result, window)
+ results.append(result)
+
+ return self._wrap_results(results, blocks, obj)
+
+ @Substitution(name='rolling')
+ @Appender(SelectionMixin._see_also_template)
+ @Appender(SelectionMixin._agg_doc)
+ def aggregate(self, arg, *args, **kwargs):
+ result, how = self._aggregate(arg, *args, **kwargs)
+ if result is None:
+
+ # these must apply directly
+ result = arg(self)
+
+ return result
+
+ agg = aggregate
+
+ @Substitution(name='window')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['sum'])
+ def sum(self, **kwargs):
+ return self._apply_window(mean=False, **kwargs)
+
+ @Substitution(name='window')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['mean'])
+ def mean(self, **kwargs):
+ return self._apply_window(mean=True, **kwargs)
+
+class _Rolling(_Window):
+
+ @property
+ def _constructor(self):
+ return Rolling
+
+ def _apply(self, func, window=None, center=None, check_minp=None, how=None, **kwargs):
+ """
+ Rolling statistical measure using supplied function. Designed to be
+ used with passed-in Cython array-based functions.
+
+ Parameters
+ ----------
+ func : string/callable to apply
+ window : int/array, default to _get_window()
+ center : boolean, default to self.center
+ check_minp : function, default to _use_window
+ how : string, default to None (DEPRECATED)
+ how to resample
+
+ Returns
+ -------
+ y : type of input
+ """
+ if center is None:
+ center = self.center
+ if window is None:
+ window = self._get_window()
+
+ if check_minp is None:
+ check_minp = _use_window
+
+ blocks, obj = self._create_blocks(how=how)
+ results = []
+ for b in blocks:
+ try:
+ values = self._prep_values(b.values)
+ except TypeError:
+ results.append(b.values.copy())
+ continue
+
+ if values.size == 0:
+ results.append(values.copy())
+ continue
+
+ # if we have a string function name, wrap it
+ if isinstance(func, compat.string_types):
+ if not hasattr(algos, func):
+ raise ValueError("we do not support this function algos.{0}".format(func))
+
+ cfunc = getattr(algos, func)
+ def func(arg, window, min_periods=None):
+ minp = check_minp(min_periods, window)
+ return cfunc(arg, window, minp, **kwargs)
+
+ # calculation function
+ if center:
+ offset = _offset(window, center)
+ additional_nans = np.array([np.NaN] * offset)
+ def calc(x):
+ return func(np.concatenate((x, additional_nans)),
+ window, min_periods=self.min_periods)
+ else:
+ def calc(x):
+ return func(x,window, min_periods=self.min_periods)
+
+ if values.ndim > 1:
+ result = np.apply_along_axis(calc, self.axis, values)
+ else:
+ result = calc(values)
+
+ if center:
+ result = self._center_window(result, window)
+
+ results.append(result)
+
+ return self._wrap_results(results, blocks, obj)
+
+class _Rolling_and_Expanding(_Rolling):
+
+ _shared_docs['count'] = """%(name)s count of number of non-NaN observations inside provided window."""
+ 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
+
+ _shared_docs['apply'] = dedent("""
+ %(name)s function apply
+
+ Parameters
+ ----------
+ func : function
+ Must produce a single value from an ndarray input
+ *args and **kwargs are passed to the function""")
+
+ def apply(self, func, args=(), kwargs={}):
+ _level = kwargs.pop('_level',None)
+ window = self._get_window()
+ offset = _offset(window, self.center)
+ def f(arg, window, min_periods):
+ minp = _use_window(min_periods, window)
+ return algos.roll_generic(arg, window, minp, offset, func, args, kwargs)
+
+ return self._apply(f, center=False)
+
+ def sum(self, **kwargs):
+ return self._apply('roll_sum', **kwargs)
+
+ _shared_docs['max'] = dedent("""
+ %(name)s maximum
+
+ Parameters
+ ----------
+ how : string, default 'max' (DEPRECATED)
+ Method for down- or re-sampling""")
+ def max(self, how=None, **kwargs):
+ if self.freq is not None and how is None:
+ how = 'max'
+ return self._apply('roll_max', how=how, **kwargs)
+
+ _shared_docs['min'] = dedent("""
+ %(name)s minimum
+
+ Parameters
+ ----------
+ how : string, default 'min' (DEPRECATED)
+ Method for down- or re-sampling""")
+ def min(self, how=None, **kwargs):
+ if self.freq is not None and how is None:
+ how = 'min'
+ return self._apply('roll_min', how=how, **kwargs)
+
+ def mean(self, **kwargs):
+ return self._apply('roll_mean', **kwargs)
+
+ _shared_docs['median'] = dedent("""
+ %(name)s median
+
+ Parameters
+ ----------
+ how : string, default 'median' (DEPRECATED)
+ Method for down- or re-sampling""")
+ def median(self, how=None, **kwargs):
+ if self.freq is not None and how is None:
+ how = 'median'
+ return self._apply('roll_median_c', how=how, **kwargs)
+
+ _shared_docs['std'] = dedent("""
+ %(name)s standard deviation
+
+ Parameters
+ ----------
+ ddof : int, default 1
+ Delta Degrees of Freedom. The divisor used in calculations
+ is ``N - ddof``, where ``N`` represents the number of elements.""")
+
+ def std(self, ddof=1, **kwargs):
+ window = self._get_window()
+ def f(arg, *args, **kwargs):
+ minp = _require_min_periods(1)(self.min_periods, window)
+ return _zsqrt(algos.roll_var(arg, window, minp, ddof))
+
+ return self._apply(f, check_minp=_require_min_periods(1), **kwargs)
+
+ _shared_docs['var'] = dedent("""
+ %(name)s variance
+
+ Parameters
+ ----------
+ ddof : int, default 1
+ Delta Degrees of Freedom. The divisor used in calculations
+ is ``N - ddof``, where ``N`` represents the number of elements.""")
+
+ def var(self, ddof=1, **kwargs):
+ return self._apply('roll_var',
+ check_minp=_require_min_periods(1),
+ ddof=ddof,
+ **kwargs)
+
+ _shared_docs['skew'] = """Unbiased %(name)s skewness"""
+ def skew(self, **kwargs):
+ return self._apply('roll_skew',
+ check_minp=_require_min_periods(3),
+ **kwargs)
+
+ _shared_docs['kurt'] = """Unbiased %(name)s kurtosis"""
+ def kurt(self, **kwargs):
+ return self._apply('roll_kurt',
+ check_minp=_require_min_periods(4),
+ **kwargs)
+
+ _shared_docs['quantile'] = dedent("""
+ %(name)s quantile
+
+ Parameters
+ ----------
+ quantile : float
+ 0 <= quantile <= 1""")
+
+ def quantile(self, quantile, **kwargs):
+ window = self._get_window()
+ def f(arg, *args, **kwargs):
+ minp = _use_window(self.min_periods, window)
+ return algos.roll_quantile(arg, window, minp, quantile)
+
+ return self._apply(f, **kwargs)
+
+ _shared_docs['cov'] = dedent("""
+ %(name)s sample covariance
+
+ Parameters
+ ----------
+ other : Series, DataFrame, or ndarray, optional
+ if not supplied then will default to self and produce pairwise output
+ pairwise : bool, default None
+ If False then only matching columns between self and other will be used and
+ the output will be a DataFrame.
+ If True then all pairwise combinations will be calculated and the output
+ will be a Panel in the case of DataFrame inputs. In the case of missing
+ elements, only complete pairwise observations will be used.
+ ddof : int, default 1
+ Delta Degrees of Freedom. The divisor used in calculations
+ is ``N - ddof``, where ``N`` represents the number of elements.""")
+
+ def cov(self, other=None, pairwise=None, ddof=1, **kwargs):
+ if other is None:
+ other = self._selected_obj
+ pairwise = True if pairwise is None else pairwise # only default unset
+ other = self._shallow_copy(other)
+ window = self._get_window(other)
+
+ def _get_cov(X, Y):
+ mean = lambda x: x.rolling(window, self.min_periods, center=self.center).mean(**kwargs)
+ count = (X+Y).rolling(window=window, center=self.center).count(**kwargs)
+ bias_adj = count / (count - ddof)
+ return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj
+ return _flex_binary_moment(self._selected_obj, other._selected_obj, _get_cov, pairwise=bool(pairwise))
+
+ _shared_docs['corr'] = dedent("""
+ %(name)s sample correlation
+
+ Parameters
+ ----------
+ other : Series, DataFrame, or ndarray, optional
+ if not supplied then will default to self and produce pairwise output
+ pairwise : bool, default None
+ If False then only matching columns between self and other will be used and
+ the output will be a DataFrame.
+ If True then all pairwise combinations will be calculated and the output
+ will be a Panel in the case of DataFrame inputs. In the case of missing
+ elements, only complete pairwise observations will be used.""")
+
+ def corr(self, other=None, pairwise=None, **kwargs):
+ if other is None:
+ other = self._selected_obj
+ pairwise = True if pairwise is None else pairwise # only default unset
+ other = self._shallow_copy(other)
+ window = self._get_window(other)
+
+ def _get_corr(a, b):
+ a = a.rolling(window=window,
+ min_periods=self.min_periods,
+ freq=self.freq,
+ center=self.center)
+ b = b.rolling(window=window,
+ min_periods=self.min_periods,
+ freq=self.freq,
+ center=self.center)
+
+ return a.cov(b, **kwargs) / (a.std(**kwargs) * b.std(**kwargs))
+ return _flex_binary_moment(self._selected_obj, other._selected_obj, _get_corr, pairwise=bool(pairwise))
+
+class Rolling(_Rolling_and_Expanding):
+ """
+ Provides rolling window calculcations.
+
+ .. versionadded:: 0.18.0
+
+ Parameters
+ ----------
+ window : int
+ Size of the moving window. This is the number of observations used for
+ calculating the statistic.
+ min_periods : int, default None
+ Minimum number of observations in window required to have a value
+ (otherwise result is NA).
+ freq : string or DateOffset object, optional (default None) (DEPRECATED)
+ Frequency to conform the data to before computing the statistic. Specified
+ as a frequency string or DateOffset object.
+ center : boolean, default False
+ Set the labels at the center of the window.
+ axis : int, default 0
+
+ Returns
+ -------
+ a Window sub-classed for the particular operation
+
+ Notes
+ -----
+ By default, the result is set to the right edge of the window. This can be
+ changed to the center of the window by setting ``center=True``.
+
+ 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
+ of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
+ """
+
+ @Substitution(name='rolling')
+ @Appender(SelectionMixin._see_also_template)
+ @Appender(SelectionMixin._agg_doc)
+ def aggregate(self, arg, *args, **kwargs):
+ return super(Rolling, self).aggregate(arg, *args, **kwargs)
+
+ agg = aggregate
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['count'])
+ def count(self):
+ return super(Rolling, self).count()
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['apply'])
+ def apply(self, func, args=(), kwargs={}):
+ return super(Rolling, self).apply(func, args=args, kwargs=kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['sum'])
+ def sum(self, **kwargs):
+ return super(Rolling, self).sum(**kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['max'])
+ def max(self, **kwargs):
+ return super(Rolling, self).max(**kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['min'])
+ def min(self, **kwargs):
+ return super(Rolling, self).min(**kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['mean'])
+ def mean(self, **kwargs):
+ return super(Rolling, self).mean(**kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['median'])
+ def median(self, **kwargs):
+ return super(Rolling, self).median(**kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['std'])
+ def std(self, ddof=1, **kwargs):
+ return super(Rolling, self).std(ddof=ddof, **kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['var'])
+ def var(self, ddof=1, **kwargs):
+ return super(Rolling, self).var(ddof=ddof, **kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['skew'])
+ def skew(self, **kwargs):
+ return super(Rolling, self).skew(**kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['kurt'])
+ def kurt(self, **kwargs):
+ return super(Rolling, self).kurt(**kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['quantile'])
+ def quantile(self, quantile, **kwargs):
+ return super(Rolling, self).quantile(quantile=quantile, **kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['cov'])
+ def cov(self, other=None, pairwise=None, ddof=1, **kwargs):
+ return super(Rolling, self).cov(other=other, pairwise=pairwise, ddof=ddof, **kwargs)
+
+ @Substitution(name='rolling')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['corr'])
+ def corr(self, other=None, pairwise=None, **kwargs):
+ return super(Rolling, self).corr(other=other, pairwise=pairwise, **kwargs)
+
+class Expanding(_Rolling_and_Expanding):
+ """
+ Provides expanding transformations.
+
+ .. versionadded:: 0.18.0
+
+ Parameters
+ ----------
+ min_periods : int, default None
+ Minimum number of observations in window required to have a value
+ (otherwise result is NA).
+ freq : string or DateOffset object, optional (default None) (DEPRECATED)
+ Frequency to conform the data to before computing the statistic. Specified
+ as a frequency string or DateOffset object.
+ center : boolean, default False
+ Set the labels at the center of the window.
+ axis : int, default 0
+
+ Returns
+ -------
+ a Window sub-classed for the particular operation
+
+ Notes
+ -----
+ By default, the result is set to the right edge of the window. This can be
+ changed to the center of the window by setting ``center=True``.
+
+ 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
+ of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
+ """
+
+ _attributes = ['min_periods','freq','center','axis']
+
+ def __init__(self, obj, min_periods=1, freq=None, center=False, axis=0, **kwargs):
+ return super(Expanding, self).__init__(obj=obj, min_periods=min_periods, freq=freq, center=center, axis=axis)
+
+ @property
+ def _constructor(self):
+ return Expanding
+
+ def _get_window(self, other=None):
+ obj = self._selected_obj
+ if other is None:
+ return max(len(obj), self.min_periods) if self.min_periods else len(obj)
+ return max((len(obj) + len(obj)), self.min_periods) if self.min_periods else (len(obj) + len(obj))
+
+ @Substitution(name='expanding')
+ @Appender(SelectionMixin._see_also_template)
+ @Appender(SelectionMixin._agg_doc)
+ def aggregate(self, arg, *args, **kwargs):
+ return super(Expanding, self).aggregate(arg, *args, **kwargs)
+
+ agg = aggregate
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['count'])
+ def count(self, **kwargs):
+ return super(Expanding, self).count(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['apply'])
+ def apply(self, func, args=(), kwargs={}):
+ return super(Expanding, self).apply(func, args=args, kwargs=kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['sum'])
+ def sum(self, **kwargs):
+ return super(Expanding, self).sum(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['max'])
+ def max(self, **kwargs):
+ return super(Expanding, self).max(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['min'])
+ def min(self, **kwargs):
+ return super(Expanding, self).min(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['mean'])
+ def mean(self, **kwargs):
+ return super(Expanding, self).mean(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['median'])
+ def median(self, **kwargs):
+ return super(Expanding, self).median(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['std'])
+ def std(self, ddof=1, **kwargs):
+ return super(Expanding, self).std(ddof=ddof, **kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['var'])
+ def var(self, ddof=1, **kwargs):
+ return super(Expanding, self).var(ddof=ddof, **kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['skew'])
+ def skew(self, **kwargs):
+ return super(Expanding, self).skew(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['kurt'])
+ def kurt(self, **kwargs):
+ return super(Expanding, self).kurt(**kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['quantile'])
+ def quantile(self, quantile, **kwargs):
+ return super(Expanding, self).quantile(quantile=quantile, **kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['cov'])
+ def cov(self, other=None, pairwise=None, ddof=1, **kwargs):
+ return super(Expanding, self).cov(other=other, pairwise=pairwise, ddof=ddof, **kwargs)
+
+ @Substitution(name='expanding')
+ @Appender(_doc_template)
+ @Appender(_shared_docs['corr'])
+ def corr(self, other=None, pairwise=None, **kwargs):
+ return super(Expanding, self).corr(other=other, pairwise=pairwise, **kwargs)
+
+_bias_template = """
+
+Parameters
+----------
+bias : boolean, default False
+ Use a standard estimation bias correction
+"""
+
+_pairwise_template = """
+
+Parameters
+----------
+other : Series, DataFrame, or ndarray, optional
+ if not supplied then will default to self and produce pairwise output
+pairwise : bool, default None
+ If False then only matching columns between self and other will be used and
+ the output will be a DataFrame.
+ If True then all pairwise combinations will be calculated and the output
+ will be a Panel in the case of DataFrame inputs. In the case of missing
+ elements, only complete pairwise observations will be used.
+bias : boolean, default False
+ Use a standard estimation bias correction
+"""
+
+class EWM(_Rolling):
+ """
+ Provides exponential weighted functions
+
+ .. versionadded:: 0.18.0
+
+ Parameters
+ ----------
+ com : float. optional
+ Center of mass: :math:`\alpha = 1 / (1 + com)`,
+ span : float, optional
+ Specify decay in terms of span, :math:`\alpha = 2 / (span + 1)`
+ halflife : float, optional
+ Specify decay in terms of halflife, :math:`\alpha = 1 - exp(log(0.5) / halflife)`
+ min_periods : int, default 0
+ Minimum number of observations in window required to have a value
+ (otherwise result is NA).
+ freq : None or string alias / date offset object, default=None (DEPRECATED)
+ Frequency to conform to before computing statistic
+ adjust : boolean, default True
+ Divide by decaying adjustment factor in beginning periods to account for
+ imbalance in relative weightings (viewing EWMA as a moving average)
+ ignore_na : boolean, default False
+ Ignore missing values when calculating weights;
+ specify True to reproduce pre-0.15.0 behavior
+
+ Returns
+ -------
+ a Window sub-classed for the particular operation
+
+ 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.
+
+ 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
+ of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
+
+ When adjust is True (default), weighted averages are calculated using weights
+ (1-alpha)**(n-1), (1-alpha)**(n-2), ..., 1-alpha, 1.
+
+ When adjust is False, weighted averages are calculated recursively as:
+ weighted_average[0] = arg[0];
+ weighted_average[i] = (1-alpha)*weighted_average[i-1] + alpha*arg[i].
+
+ When ignore_na is False (default), weights are based on absolute positions.
+ For example, the weights of x and y used in calculating the final weighted
+ average of [x, None, y] are (1-alpha)**2 and 1 (if adjust is True), and
+ (1-alpha)**2 and alpha (if adjust is False).
+
+ When ignore_na is True (reproducing pre-0.15.0 behavior), weights are based on
+ relative positions. For example, the weights of x and y used in calculating
+ the final weighted average of [x, None, y] are 1-alpha and 1 (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
+ """
+ _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):
+ self.obj = obj
+ self.com = _get_center_of_mass(com, span, halflife)
+ self.min_periods = min_periods
+ self.freq = freq
+ self.adjust = adjust
+ self.ignore_na = ignore_na
+ self.axis = axis
+
+ @property
+ def _constructor(self):
+ return EWM
+
+ @Substitution(name='ewm')
+ @Appender(SelectionMixin._see_also_template)
+ @Appender(SelectionMixin._agg_doc)
+ def aggregate(self, arg, *args, **kwargs):
+ return super(EWM, self).aggregate(arg, *args, **kwargs)
+
+ agg = aggregate
+
+ def _apply(self, func, how=None, **kwargs):
+ """Rolling statistical measure using supplied function. Designed to be
+ used with passed-in Cython array-based functions.
+
+ Parameters
+ ----------
+ func : string/callable to apply
+ how : string, default to None (DEPRECATED)
+ how to resample
+
+ Returns
+ -------
+ y : type of input argument
+
+ """
+ blocks, obj = self._create_blocks(how=how)
+ results = []
+ for b in blocks:
+ try:
+ values = self._prep_values(b.values)
+ except TypeError:
+ results.append(b.values.copy())
+ continue
+
+ if values.size == 0:
+ results.append(values.copy())
+ continue
+
+ # if we have a string function name, wrap it
+ if isinstance(func, compat.string_types):
+ if not hasattr(algos, func):
+ raise ValueError("we do not support this function algos.{0}".format(func))
+
+ cfunc = getattr(algos, func)
+ def func(arg):
+ return cfunc(arg, self.com, int(self.adjust), int(self.ignore_na), int(self.min_periods))
+
+ results.append(np.apply_along_axis(func, self.axis, values))
+
+ return self._wrap_results(results, blocks, obj)
+
+ @Substitution(name='ewm')
+ @Appender(_doc_template)
+ def mean(self, **kwargs):
+ """exponential weighted moving average"""
+ return self._apply('ewma', **kwargs)
+
+ @Substitution(name='ewm')
+ @Appender(_doc_template)
+ @Appender(_bias_template)
+ def std(self, bias=False, **kwargs):
+ """exponential weighted moving stddev"""
+ return _zsqrt(self.var(bias=bias, **kwargs))
+ vol=std
+
+ @Substitution(name='ewm')
+ @Appender(_doc_template)
+ @Appender(_bias_template)
+ def var(self, bias=False, **kwargs):
+ """exponential weighted moving variance"""
+ def f(arg):
+ return algos.ewmcov(arg,
+ arg,
+ self.com,
+ int(self.adjust),
+ int(self.ignore_na),
+ int(self.min_periods),
+ int(bias))
+
+ return self._apply(f, **kwargs)
+
+ @Substitution(name='ewm')
+ @Appender(_doc_template)
+ @Appender(_pairwise_template)
+ def cov(self, other=None, pairwise=None, bias=False, **kwargs):
+ """exponential weighted sample covariance"""
+ if other is None:
+ other = self._selected_obj
+ pairwise = True if pairwise is None else pairwise # only default unset
+ other = self._shallow_copy(other)
+
+ def _get_cov(X, Y):
+ X = self._shallow_copy(X)
+ Y = self._shallow_copy(Y)
+ cov = algos.ewmcov(X._prep_values(),
+ Y._prep_values(),
+ self.com,
+ int(self.adjust),
+ int(self.ignore_na),
+ int(self.min_periods),
+ int(bias))
+ return X._wrap_result(cov)
+
+ return _flex_binary_moment(self._selected_obj, other._selected_obj, _get_cov, pairwise=bool(pairwise))
+
+ @Substitution(name='ewm')
+ @Appender(_doc_template)
+ @Appender(_pairwise_template)
+ def corr(self, other=None, pairwise=None, **kwargs):
+ """exponential weighted sample correlation"""
+ if other is None:
+ other = self._selected_obj
+ pairwise = True if pairwise is None else pairwise # only default unset
+ other = self._shallow_copy(other)
+
+ def _get_corr(X, Y):
+ X = self._shallow_copy(X)
+ Y = self._shallow_copy(Y)
+ def _cov(x, y):
+ return algos.ewmcov(x, y, self.com, int(self.adjust), int(self.ignore_na), int(self.min_periods), 1)
+
+ x_values = X._prep_values()
+ y_values = Y._prep_values()
+ cov = _cov(x_values, y_values)
+ x_var = _cov(x_values, x_values)
+ y_var = _cov(y_values, y_values)
+ corr = cov / _zsqrt(x_var * y_var)
+ return X._wrap_result(corr)
+
+ return _flex_binary_moment(self._selected_obj, other._selected_obj, _get_corr, pairwise=bool(pairwise))
+
+########################
+##### Helper Funcs #####
+########################
+
+def _flex_binary_moment(arg1, arg2, f, pairwise=False):
+ from pandas import Series, DataFrame, Panel
+ if not (isinstance(arg1,(np.ndarray, Series, DataFrame)) and
+ isinstance(arg2,(np.ndarray, Series, DataFrame))):
+ raise TypeError("arguments to moment function must be of type "
+ "np.ndarray/Series/DataFrame")
+
+ if isinstance(arg1, (np.ndarray, Series)) and \
+ isinstance(arg2, (np.ndarray,Series)):
+ X, Y = _prep_binary(arg1, arg2)
+ return f(X, Y)
+
+ elif isinstance(arg1, DataFrame):
+ def dataframe_from_int_dict(data, frame_template):
+ result = DataFrame(data, index=frame_template.index)
+ if len(result.columns) > 0:
+ result.columns = frame_template.columns[result.columns]
+ return result
+
+ results = {}
+ if isinstance(arg2, DataFrame):
+ if pairwise is False:
+ if arg1 is arg2:
+ # special case in order to handle duplicate column names
+ for i, col in enumerate(arg1.columns):
+ results[i] = f(arg1.iloc[:, i], arg2.iloc[:, i])
+ return dataframe_from_int_dict(results, arg1)
+ else:
+ if not arg1.columns.is_unique:
+ raise ValueError("'arg1' columns are not unique")
+ if not arg2.columns.is_unique:
+ raise ValueError("'arg2' columns are not unique")
+ X, Y = arg1.align(arg2, join='outer')
+ X = X + 0 * Y
+ Y = Y + 0 * X
+ res_columns = arg1.columns.union(arg2.columns)
+ for col in res_columns:
+ if col in X and col in Y:
+ results[col] = f(X[col], Y[col])
+ return DataFrame(results, index=X.index, columns=res_columns)
+ elif pairwise is True:
+ results = defaultdict(dict)
+ for i, k1 in enumerate(arg1.columns):
+ for j, k2 in enumerate(arg2.columns):
+ if j<i and arg2 is arg1:
+ # Symmetric case
+ results[i][j] = results[j][i]
+ else:
+ results[i][j] = f(*_prep_binary(arg1.iloc[:, i], arg2.iloc[:, j]))
+ p = Panel.from_dict(results).swapaxes('items', 'major')
+ if len(p.major_axis) > 0:
+ p.major_axis = arg1.columns[p.major_axis]
+ if len(p.minor_axis) > 0:
+ p.minor_axis = arg2.columns[p.minor_axis]
+ return p
+ else:
+ raise ValueError("'pairwise' is not True/False")
+ else:
+ results = {}
+ for i, col in enumerate(arg1.columns):
+ results[i] = f(*_prep_binary(arg1.iloc[:, i], arg2))
+ return dataframe_from_int_dict(results, arg1)
+
+ else:
+ 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])
+ if valid_count > 1:
+ raise Exception("com, span, and halflife are mutually exclusive")
+
+ if span is not None:
+ # convert span to center of mass
+ com = (span - 1) / 2.
+ elif halflife is not None:
+ # convert halflife to center of mass
+ 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")
+
+ return float(com)
+
+def _offset(window, center):
+ if not com.is_integer(window):
+ window = len(window)
+ offset = (window - 1) / 2. if center else 0
+ try:
+ return int(offset)
+ except:
+ return offset.astype(int)
+
+def _require_min_periods(p):
+ def _check_func(minp, window):
+ if minp is None:
+ return window
+ else:
+ return max(p, minp)
+ return _check_func
+
+def _use_window(minp, window):
+ if minp is None:
+ return window
+ else:
+ return minp
+
+def _zsqrt(x):
+ result = np.sqrt(x)
+ mask = x < 0
+
+ from pandas import DataFrame
+ if isinstance(x, DataFrame):
+ if mask.values.any():
+ result[mask] = 0
+ else:
+ if mask.any():
+ result[mask] = 0
+
+ return result
+
+def _prep_binary(arg1, arg2):
+ if not isinstance(arg2, type(arg1)):
+ raise Exception('Input arrays must be of the same type!')
+
+ # mask out values, this also makes a common index...
+ X = arg1 + 0 * arg2
+ Y = arg2 + 0 * arg1
+
+ return X, Y
+
+def _validate_win_type(win_type, kwargs):
+ # may pop from kwargs
+ arg_map = {'kaiser': ['beta'],
+ 'gaussian': ['std'],
+ 'general_gaussian': ['power', 'width'],
+ 'slepian': ['width']}
+ if win_type in arg_map:
+ return tuple([win_type] +
+ _pop_args(win_type, arg_map[win_type], kwargs))
+ return win_type
+
+
+def _pop_args(win_type, arg_names, kwargs):
+ msg = '%s window requires %%s' % win_type
+ all_args = []
+ for n in arg_names:
+ if n not in kwargs:
+ raise ValueError(msg % n)
+ all_args.append(kwargs.pop(n))
+ return all_args
+
+#############################
+##### top-level exports #####
+#############################
+
+def rolling(obj, win_type=None, **kwds):
+ from pandas import Series, DataFrame
+ if not isinstance(obj, (Series, DataFrame)):
+ raise TypeError('invalid type: %s' % type(obj))
+
+ if win_type is not None:
+ return Window(obj, win_type=win_type, **kwds)
+
+ return Rolling(obj, **kwds)
+rolling.__doc__ = Window.__doc__
+
+def expanding(obj, **kwds):
+ from pandas import Series, DataFrame
+ if not isinstance(obj, (Series, DataFrame)):
+ raise TypeError('invalid type: %s' % type(obj))
+
+ return Expanding(obj, **kwds)
+expanding.__doc__ = Expanding.__doc__
+
+def ewm(obj, **kwds):
+ from pandas import Series, DataFrame
+ if not isinstance(obj, (Series, DataFrame)):
+ raise TypeError('invalid type: %s' % type(obj))
+
+ return EWM(obj, **kwds)
+ewm.__doc__ = EWM.__doc__
diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py
index 3cddae45e7516..28f35cf26e582 100644
--- a/pandas/stats/moments.py
+++ b/pandas/stats/moments.py
@@ -4,29 +4,23 @@
"""
from __future__ import division
-from functools import wraps
-from collections import defaultdict
-
-from numpy import NaN
+import warnings
import numpy as np
-
-from pandas.core.api import DataFrame, Series, Panel, notnull
-import pandas.algos as algos
-import pandas.core.common as pdcom
-
+from pandas import lib
+from pandas.core.api import DataFrame, Series
from pandas.util.decorators import Substitution, Appender
__all__ = ['rolling_count', 'rolling_max', 'rolling_min',
'rolling_sum', 'rolling_mean', 'rolling_std', 'rolling_cov',
'rolling_corr', 'rolling_var', 'rolling_skew', 'rolling_kurt',
'rolling_quantile', 'rolling_median', 'rolling_apply',
- 'rolling_corr_pairwise', 'rolling_window',
+ 'rolling_window',
'ewma', 'ewmvar', 'ewmstd', 'ewmvol', 'ewmcorr', 'ewmcov',
'expanding_count', 'expanding_max', 'expanding_min',
'expanding_sum', 'expanding_mean', 'expanding_std',
'expanding_cov', 'expanding_corr', 'expanding_var',
'expanding_skew', 'expanding_kurt', 'expanding_quantile',
- 'expanding_median', 'expanding_apply', 'expanding_corr_pairwise']
+ 'expanding_median', 'expanding_apply' ]
#------------------------------------------------------------------------------
# Docs
@@ -179,8 +173,72 @@
Use a standard estimation bias correction
"""
+def ensure_compat(dispatch, name, arg, func_kw=None, *args, **kwargs):
+ """
+ wrapper function to dispatch to the appropriate window functions
+ wraps/unwraps ndarrays for compat
+
+ can be removed when ndarray support is removed
+ """
+ is_ndarray = isinstance(arg, np.ndarray)
+ if is_ndarray:
+ if arg.ndim == 1:
+ arg = Series(arg)
+ elif arg.ndim == 2:
+ arg = DataFrame(arg)
+ else:
+ raise AssertionError("cannot support ndim > 2 for ndarray compat")
+
+ warnings.warn("pd.{dispatch}_{name} is deprecated for ndarrays and will be removed "
+ "in a future version".format(dispatch=dispatch,name=name),
+ FutureWarning, stacklevel=3)
+
+ # get the functional keywords here
+ if func_kw is None:
+ func_kw = []
+ kwds = {}
+ for k in func_kw:
+ value = kwargs.pop(k,None)
+ if value is not None:
+ kwds[k] = value
+
+ # how is a keyword that if not-None should be in kwds
+ how = kwargs.pop('how',None)
+ if how is not None:
+ kwds['how'] = how
+
+ r = getattr(arg,dispatch)(**kwargs)
+
+ if not is_ndarray:
+
+ # give a helpful deprecation message
+ # with copy-pastable arguments
+ pargs = ','.join([ "{a}={b}".format(a=a,b=b) for a,b in kwargs.items() if b is not None ])
+ aargs = ','.join(args)
+ if len(aargs):
+ aargs += ','
+
+ def f(a,b):
+ if lib.isscalar(b):
+ return "{a}={b}".format(a=a,b=b)
+ return "{a}=<{b}>".format(a=a,b=type(b).__name__)
+ aargs = ','.join([ f(a,b) for a,b in kwds.items() if b is not None ])
+ warnings.warn("pd.{dispatch}_{name} is deprecated for {klass} "
+ "and will be removed in a future version, replace with "
+ "\n\t{klass}.{dispatch}({pargs}).{name}({aargs})".format(klass=type(arg).__name__,
+ pargs=pargs,
+ aargs=aargs,
+ dispatch=dispatch,
+ name=name),
+ FutureWarning, stacklevel=3)
+
+ result = getattr(r,name)(*args, **kwds)
+
+ if is_ndarray:
+ result = result.values
+ return result
-def rolling_count(arg, window, freq=None, center=False, how=None):
+def rolling_count(arg, window, **kwargs):
"""
Rolling count of number of non-NaN observations inside provided window.
@@ -208,26 +266,12 @@ def rolling_count(arg, window, freq=None, center=False, how=None):
frequency by resampling the data. This is done with the default parameters
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
- arg = _conv_timerule(arg, freq, how)
- if not center:
- window = min(window, len(arg))
-
- return_hook, values = _process_data_structure(arg, kill_inf=False)
-
- converted = np.isfinite(values).astype(float)
- result = rolling_sum(converted, window, min_periods=0,
- center=center) # already converted
-
- # putmask here?
- result[np.isnan(result)] = 0
- return return_hook(result)
-
+ return ensure_compat('rolling', 'count', arg, window=window, **kwargs)
@Substitution("Unbiased moving covariance.", _binary_arg_flex,
_roll_kw%'None'+_pairwise_kw+_ddof_kw, _flex_retval, _roll_notes)
@Appender(_doc_template)
-def rolling_cov(arg1, arg2=None, window=None, min_periods=None, freq=None,
- center=False, pairwise=None, how=None, ddof=1):
+def rolling_cov(arg1, arg2=None, window=None, pairwise=None, **kwargs):
if window is None and isinstance(arg2, (int, float)):
window = arg2
arg2 = arg1
@@ -235,23 +279,19 @@ def rolling_cov(arg1, arg2=None, window=None, min_periods=None, freq=None,
elif arg2 is None:
arg2 = arg1
pairwise = True if pairwise is None else pairwise # only default unset
- arg1 = _conv_timerule(arg1, freq, how)
- arg2 = _conv_timerule(arg2, freq, how)
-
- def _get_cov(X, Y):
- mean = lambda x: rolling_mean(x, window, min_periods, center=center)
- count = rolling_count(X + Y, window, center=center)
- bias_adj = count / (count - ddof)
- return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj
- rs = _flex_binary_moment(arg1, arg2, _get_cov, pairwise=bool(pairwise))
- return rs
-
+ return ensure_compat('rolling',
+ 'cov',
+ arg1,
+ other=arg2,
+ window=window,
+ pairwise=pairwise,
+ func_kw=['other','pairwise','ddof'],
+ **kwargs)
@Substitution("Moving sample correlation.", _binary_arg_flex,
_roll_kw%'None'+_pairwise_kw, _flex_retval, _roll_notes)
@Appender(_doc_template)
-def rolling_corr(arg1, arg2=None, window=None, min_periods=None, freq=None,
- center=False, pairwise=None, how=None):
+def rolling_corr(arg1, arg2=None, window=None, pairwise=None, **kwargs):
if window is None and isinstance(arg2, (int, float)):
window = arg2
arg2 = arg1
@@ -259,259 +299,74 @@ def rolling_corr(arg1, arg2=None, window=None, min_periods=None, freq=None,
elif arg2 is None:
arg2 = arg1
pairwise = True if pairwise is None else pairwise # only default unset
- arg1 = _conv_timerule(arg1, freq, how)
- arg2 = _conv_timerule(arg2, freq, how)
-
- def _get_corr(a, b):
- num = rolling_cov(a, b, window, min_periods, freq=freq,
- center=center)
- den = (rolling_std(a, window, min_periods, freq=freq,
- center=center) *
- rolling_std(b, window, min_periods, freq=freq,
- center=center))
- return num / den
-
- return _flex_binary_moment(arg1, arg2, _get_corr, pairwise=bool(pairwise))
-
-
-def _flex_binary_moment(arg1, arg2, f, pairwise=False):
- if not (isinstance(arg1,(np.ndarray, Series, DataFrame)) and
- isinstance(arg2,(np.ndarray, Series, DataFrame))):
- raise TypeError("arguments to moment function must be of type "
- "np.ndarray/Series/DataFrame")
-
- if isinstance(arg1, (np.ndarray, Series)) and \
- isinstance(arg2, (np.ndarray,Series)):
- X, Y = _prep_binary(arg1, arg2)
- return f(X, Y)
-
- elif isinstance(arg1, DataFrame):
- def dataframe_from_int_dict(data, frame_template):
- result = DataFrame(data, index=frame_template.index)
- if len(result.columns) > 0:
- result.columns = frame_template.columns[result.columns]
- return result
-
- results = {}
- if isinstance(arg2, DataFrame):
- if pairwise is False:
- if arg1 is arg2:
- # special case in order to handle duplicate column names
- for i, col in enumerate(arg1.columns):
- results[i] = f(arg1.iloc[:, i], arg2.iloc[:, i])
- return dataframe_from_int_dict(results, arg1)
- else:
- if not arg1.columns.is_unique:
- raise ValueError("'arg1' columns are not unique")
- if not arg2.columns.is_unique:
- raise ValueError("'arg2' columns are not unique")
- X, Y = arg1.align(arg2, join='outer')
- X = X + 0 * Y
- Y = Y + 0 * X
- res_columns = arg1.columns.union(arg2.columns)
- for col in res_columns:
- if col in X and col in Y:
- results[col] = f(X[col], Y[col])
- return DataFrame(results, index=X.index, columns=res_columns)
- elif pairwise is True:
- results = defaultdict(dict)
- for i, k1 in enumerate(arg1.columns):
- for j, k2 in enumerate(arg2.columns):
- if j<i and arg2 is arg1:
- # Symmetric case
- results[i][j] = results[j][i]
- else:
- results[i][j] = f(*_prep_binary(arg1.iloc[:, i], arg2.iloc[:, j]))
- p = Panel.from_dict(results).swapaxes('items', 'major')
- if len(p.major_axis) > 0:
- p.major_axis = arg1.columns[p.major_axis]
- if len(p.minor_axis) > 0:
- p.minor_axis = arg2.columns[p.minor_axis]
- return p
- else:
- raise ValueError("'pairwise' is not True/False")
- else:
- results = {}
- for i, col in enumerate(arg1.columns):
- results[i] = f(*_prep_binary(arg1.iloc[:, i], arg2))
- return dataframe_from_int_dict(results, arg1)
-
- else:
- return _flex_binary_moment(arg2, arg1, f)
-
-
-@Substitution("Deprecated. Use rolling_corr(..., pairwise=True) instead.\n\n"
- "Pairwise moving sample correlation", _pairwise_arg,
- _roll_kw%'None', _pairwise_retval, _roll_notes)
-@Appender(_doc_template)
-def rolling_corr_pairwise(df1, df2=None, window=None, min_periods=None,
- freq=None, center=False):
- import warnings
- msg = "rolling_corr_pairwise is deprecated, use rolling_corr(..., pairwise=True)"
- warnings.warn(msg, FutureWarning, stacklevel=2)
- return rolling_corr(df1, df2, window=window, min_periods=min_periods,
- freq=freq, center=center,
- pairwise=True)
-
-
-def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False,
- how=None, args=(), kwargs={}, **kwds):
- """
- Rolling statistical measure using supplied function. Designed to be
- used with passed-in Cython array-based functions.
-
- Parameters
- ----------
- arg : DataFrame or numpy ndarray-like
- window : Number of observations used for calculating statistic
- func : Cython function to compute rolling statistic on raw series
- minp : int
- Minimum number of observations required to have a value
- axis : int, default 0
- freq : None or string alias / date offset object, default=None
- Frequency to conform to before computing statistic
- center : boolean, default False
- Whether the label should correspond with center of window
- how : string, default 'mean'
- Method for down- or re-sampling
- args : tuple
- Passed on to func
- kwargs : dict
- Passed on to func
-
- Returns
- -------
- y : type of input
- """
- arg = _conv_timerule(arg, freq, how)
-
- return_hook, values = _process_data_structure(arg)
-
- if values.size == 0:
- result = values.copy()
- else:
- # actually calculate the moment. Faster way to do this?
- offset = int((window - 1) / 2.) if center else 0
- additional_nans = np.array([np.NaN] * offset)
- calc = lambda x: func(np.concatenate((x, additional_nans)) if center else x,
- window, minp=minp, args=args, kwargs=kwargs,
- **kwds)
- if values.ndim > 1:
- result = np.apply_along_axis(calc, axis, values)
- else:
- result = calc(values)
+ return ensure_compat('rolling',
+ 'corr',
+ arg1,
+ other=arg2,
+ window=window,
+ pairwise=pairwise,
+ func_kw=['other','pairwise'],
+ **kwargs)
- if center:
- result = _center_window(result, window, axis)
-
- return return_hook(result)
-
-
-def _center_window(rs, window, axis):
- if axis > rs.ndim-1:
- raise ValueError("Requested axis is larger then no. of argument "
- "dimensions")
-
- offset = int((window - 1) / 2.)
- if offset > 0:
- if isinstance(rs, (Series, DataFrame, Panel)):
- rs = rs.slice_shift(-offset, axis=axis)
- else:
- lead_indexer = [slice(None)] * rs.ndim
- lead_indexer[axis] = slice(offset, None)
- rs = np.copy(rs[tuple(lead_indexer)])
- return rs
-
-
-def _process_data_structure(arg, kill_inf=True):
- if isinstance(arg, DataFrame):
- return_hook = lambda v: type(arg)(v, index=arg.index,
- columns=arg.columns)
- values = arg.values
- elif isinstance(arg, Series):
- values = arg.values
- return_hook = lambda v: Series(v, arg.index, name=arg.name)
- else:
- return_hook = lambda v: v
- values = arg
-
- if not issubclass(values.dtype.type, float):
- values = values.astype(float)
-
- if kill_inf:
- values = values.copy()
- values[np.isinf(values)] = np.NaN
-
- return return_hook, values
#------------------------------------------------------------------------------
# Exponential moving moments
-def _get_center_of_mass(com, span, halflife):
- valid_count = len([x for x in [com, span, halflife] 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
- com = (span - 1) / 2.
- elif halflife is not None:
- # convert halflife to center of mass
- 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")
-
- return float(com)
-
-
@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):
- arg = _conv_timerule(arg, freq, how)
- com = _get_center_of_mass(com, span, halflife)
-
- def _ewma(v):
- return algos.ewma(v, com, int(adjust), int(ignore_na), int(min_periods))
-
- return_hook, values = _process_data_structure(arg)
- if values.size == 0:
- output = values.copy()
- else:
- output = np.apply_along_axis(_ewma, 0, values)
- return return_hook(output)
-
+ return ensure_compat('ewm',
+ 'mean',
+ arg,
+ com=com,
+ span=span,
+ halflife=halflife,
+ min_periods=min_periods,
+ freq=freq,
+ adjust=adjust,
+ how=how,
+ ignore_na=ignore_na)
@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):
- arg = _conv_timerule(arg, freq, how)
- com = _get_center_of_mass(com, span, halflife)
-
- def _ewmvar(v):
- return algos.ewmcov(v, v, com, int(adjust), int(ignore_na), int(min_periods), int(bias))
-
- return_hook, values = _process_data_structure(arg)
- if values.size == 0:
- output = values.copy()
- else:
- output = np.apply_along_axis(_ewmvar, 0, values)
- return return_hook(output)
-
+ return ensure_compat('ewm',
+ 'var',
+ arg,
+ com=com,
+ span=span,
+ halflife=halflife,
+ min_periods=min_periods,
+ freq=freq,
+ adjust=adjust,
+ how=how,
+ ignore_na=ignore_na,
+ bias=bias,
+ func_kw=['bias'])
@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,
- ignore_na=False, adjust=True):
- result = ewmvar(arg, com=com, span=span, halflife=halflife,
- min_periods=min_periods, bias=bias, adjust=adjust, ignore_na=ignore_na)
- return _zsqrt(result)
+ freq=None, how=None, ignore_na=False, adjust=True):
+ return ensure_compat('ewm',
+ 'std',
+ arg,
+ com=com,
+ span=span,
+ halflife=halflife,
+ min_periods=min_periods,
+ freq=freq,
+ adjust=adjust,
+ how=how,
+ ignore_na=ignore_na,
+ bias=bias,
+ func_kw=['bias'])
ewmvol = ewmstd
@@ -528,21 +383,22 @@ def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0,
com = arg2
arg2 = arg1
pairwise = True if pairwise is None else pairwise
- arg1 = _conv_timerule(arg1, freq, how)
- arg2 = _conv_timerule(arg2, freq, how)
- com = _get_center_of_mass(com, span, halflife)
-
- def _get_ewmcov(X, Y):
- # X and Y have the same structure (and NaNs) when called from _flex_binary_moment()
- return_hook, x_values = _process_data_structure(X)
- return_hook, y_values = _process_data_structure(Y)
- cov = algos.ewmcov(x_values, y_values, com, int(adjust), int(ignore_na), int(min_periods), int(bias))
- return return_hook(cov)
-
- result = _flex_binary_moment(arg1, arg2, _get_ewmcov,
- pairwise=bool(pairwise))
- return result
+ return ensure_compat('ewm',
+ 'cov',
+ arg1,
+ other=arg2,
+ com=com,
+ span=span,
+ halflife=halflife,
+ min_periods=min_periods,
+ bias=bias,
+ freq=freq,
+ how=how,
+ ignore_na=ignore_na,
+ adjust=adjust,
+ pairwise=pairwise,
+ func_kw=['other','pairwise','bias'])
@Substitution("Exponentially-weighted moving correlation", _binary_arg_flex,
_ewm_kw+_pairwise_kw, _type_of_input_retval, _ewm_notes)
@@ -556,80 +412,26 @@ def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0,
com = arg2
arg2 = arg1
pairwise = True if pairwise is None else pairwise
- arg1 = _conv_timerule(arg1, freq, how)
- arg2 = _conv_timerule(arg2, freq, how)
- com = _get_center_of_mass(com, span, halflife)
-
- def _get_ewmcorr(X, Y):
- # X and Y have the same structure (and NaNs) when called from _flex_binary_moment()
- return_hook, x_values = _process_data_structure(X)
- return_hook, y_values = _process_data_structure(Y)
- cov = algos.ewmcov(x_values, y_values, com, int(adjust), int(ignore_na), int(min_periods), 1)
- x_var = algos.ewmcov(x_values, x_values, com, int(adjust), int(ignore_na), int(min_periods), 1)
- y_var = algos.ewmcov(y_values, y_values, com, int(adjust), int(ignore_na), int(min_periods), 1)
- corr = cov / _zsqrt(x_var * y_var)
- return return_hook(corr)
-
- result = _flex_binary_moment(arg1, arg2, _get_ewmcorr,
- pairwise=bool(pairwise))
- return result
-
-
-def _zsqrt(x):
- result = np.sqrt(x)
- mask = x < 0
-
- if isinstance(x, DataFrame):
- if mask.values.any():
- result[mask] = 0
- else:
- if mask.any():
- result[mask] = 0
-
- return result
-
-
-def _prep_binary(arg1, arg2):
- if not isinstance(arg2, type(arg1)):
- raise Exception('Input arrays must be of the same type!')
-
- # mask out values, this also makes a common index...
- X = arg1 + 0 * arg2
- Y = arg2 + 0 * arg1
-
- return X, Y
+ return ensure_compat('ewm',
+ 'corr',
+ arg1,
+ other=arg2,
+ com=com,
+ span=span,
+ halflife=halflife,
+ min_periods=min_periods,
+ freq=freq,
+ how=how,
+ ignore_na=ignore_na,
+ adjust=adjust,
+ pairwise=pairwise,
+ func_kw=['other','pairwise'])
#----------------------------------------------------------------------
# Python interface to Cython functions
-def _conv_timerule(arg, freq, how):
-
- types = (DataFrame, Series)
- if freq is not None and isinstance(arg, types):
- # Conform to whatever frequency needed.
- arg = arg.resample(freq, how=how)
-
- return arg
-
-
-def _require_min_periods(p):
- def _check_func(minp, window):
- if minp is None:
- return window
- else:
- return max(p, minp)
- return _check_func
-
-
-def _use_window(minp, window):
- if minp is None:
- return window
- else:
- return minp
-
-
-def _rolling_func(func, desc, check_minp=_use_window, how=None, additional_kw=''):
+def _rolling_func(name, desc, how=None, func_kw=None, additional_kw=''):
if how is None:
how_arg_str = 'None'
else:
@@ -638,36 +440,33 @@ def _rolling_func(func, desc, check_minp=_use_window, how=None, additional_kw=''
@Substitution(desc, _unary_arg, _roll_kw%how_arg_str + additional_kw,
_type_of_input_retval, _roll_notes)
@Appender(_doc_template)
- @wraps(func)
- def f(arg, window, min_periods=None, freq=None, center=False, how=how,
+ def f(arg, window, min_periods=None, freq=None, center=False,
**kwargs):
- def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
- minp = check_minp(minp, window)
- return func(arg, window, minp, **kwds)
- return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
- center=center, how=how, **kwargs)
+ return ensure_compat('rolling',
+ name,
+ arg,
+ window=window,
+ min_periods=min_periods,
+ freq=freq,
+ center=center,
+ func_kw=func_kw,
+ **kwargs)
return f
-rolling_max = _rolling_func(algos.roll_max, 'Moving maximum.', how='max')
-rolling_min = _rolling_func(algos.roll_min, 'Moving minimum.', how='min')
-rolling_sum = _rolling_func(algos.roll_sum, 'Moving sum.')
-rolling_mean = _rolling_func(algos.roll_mean, 'Moving mean.')
-rolling_median = _rolling_func(algos.roll_median_c, 'Moving median.',
- how='median')
-
-_ts_std = lambda *a, **kw: _zsqrt(algos.roll_var(*a, **kw))
-rolling_std = _rolling_func(_ts_std, 'Moving standard deviation.',
- check_minp=_require_min_periods(1),
+rolling_max = _rolling_func('max', 'Moving maximum.', how='max')
+rolling_min = _rolling_func('min', 'Moving minimum.', how='min')
+rolling_sum = _rolling_func('sum', 'Moving sum.')
+rolling_mean = _rolling_func('mean', 'Moving mean.')
+rolling_median = _rolling_func('median', 'Moving median.', how='median')
+rolling_std = _rolling_func('std', 'Moving standard deviation.',
+ func_kw=['ddof'],
additional_kw=_ddof_kw)
-rolling_var = _rolling_func(algos.roll_var, 'Moving variance.',
- check_minp=_require_min_periods(1),
+rolling_var = _rolling_func('var', 'Moving variance.',
+ func_kw=['ddof'],
additional_kw=_ddof_kw)
-rolling_skew = _rolling_func(algos.roll_skew, 'Unbiased moving skewness.',
- check_minp=_require_min_periods(3))
-rolling_kurt = _rolling_func(algos.roll_kurt, 'Unbiased moving kurtosis.',
- check_minp=_require_min_periods(4))
-
+rolling_skew = _rolling_func('skew', 'Unbiased moving skewness.')
+rolling_kurt = _rolling_func('kurt', 'Unbiased moving kurtosis.')
def rolling_quantile(arg, window, quantile, min_periods=None, freq=None,
center=False):
@@ -703,12 +502,15 @@ def rolling_quantile(arg, window, quantile, min_periods=None, freq=None,
frequency by resampling the data. This is done with the default parameters
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
-
- def call_cython(arg, window, minp, args=(), kwargs={}):
- minp = _use_window(minp, window)
- return algos.roll_quantile(arg, window, minp, quantile)
- return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
- center=center)
+ return ensure_compat('rolling',
+ 'quantile',
+ arg,
+ window=window,
+ freq=freq,
+ center=center,
+ min_periods=min_periods,
+ func_kw=['quantile'],
+ quantile=quantile)
def rolling_apply(arg, window, func, min_periods=None, freq=None,
@@ -749,12 +551,17 @@ def rolling_apply(arg, window, func, min_periods=None, freq=None,
frequency by resampling the data. This is done with the default parameters
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
- offset = int((window - 1) / 2.) if center else 0
- def call_cython(arg, window, minp, args, kwargs):
- minp = _use_window(minp, window)
- return algos.roll_generic(arg, window, minp, offset, func, args, kwargs)
- return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
- center=False, args=args, kwargs=kwargs)
+ return ensure_compat('rolling',
+ 'apply',
+ arg,
+ window=window,
+ freq=freq,
+ center=center,
+ min_periods=min_periods,
+ func_kw=['func','args','kwargs'],
+ func=func,
+ args=args,
+ kwargs=kwargs)
def rolling_window(arg, window=None, win_type=None, min_periods=None,
@@ -816,97 +623,47 @@ def rolling_window(arg, window=None, win_type=None, min_periods=None,
frequency by resampling the data. This is done with the default parameters
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
- if isinstance(window, (list, tuple, np.ndarray)):
- if win_type is not None:
- raise ValueError(('Do not specify window type if using custom '
- 'weights'))
- window = pdcom._asarray_tuplesafe(window).astype(float)
- elif pdcom.is_integer(window): # window size
- if win_type is None:
- raise ValueError('Must specify window type')
- try:
- import scipy.signal as sig
- except ImportError:
- raise ImportError('Please install scipy to generate window weight')
- win_type = _validate_win_type(win_type, kwargs) # may pop from kwargs
- window = sig.get_window(win_type, window).astype(float)
- else:
- raise ValueError('Invalid window %s' % str(window))
-
- minp = _use_window(min_periods, len(window))
-
- arg = _conv_timerule(arg, freq, how)
- return_hook, values = _process_data_structure(arg)
-
- if values.size == 0:
- result = values.copy()
- else:
- offset = int((len(window) - 1) / 2.) if center else 0
- additional_nans = np.array([np.NaN] * offset)
- f = lambda x: algos.roll_window(np.concatenate((x, additional_nans)) if center else x,
- window, minp, avg=mean)
- result = np.apply_along_axis(f, axis, values)
-
- if center:
- result = _center_window(result, len(window), axis)
-
- return return_hook(result)
-
-
-def _validate_win_type(win_type, kwargs):
- # may pop from kwargs
- arg_map = {'kaiser': ['beta'],
- 'gaussian': ['std'],
- 'general_gaussian': ['power', 'width'],
- 'slepian': ['width']}
- if win_type in arg_map:
- return tuple([win_type] +
- _pop_args(win_type, arg_map[win_type], kwargs))
- return win_type
-
-
-def _pop_args(win_type, arg_names, kwargs):
- msg = '%s window requires %%s' % win_type
- all_args = []
- for n in arg_names:
- if n not in kwargs:
- raise ValueError(msg % n)
- all_args.append(kwargs.pop(n))
- return all_args
-
-
-def _expanding_func(func, desc, check_minp=_use_window, additional_kw=''):
+ func = 'mean' if mean else 'sum'
+ return ensure_compat('rolling',
+ func,
+ arg,
+ window=window,
+ win_type=win_type,
+ freq=freq,
+ center=center,
+ min_periods=min_periods,
+ axis=axis,
+ func_kw=kwargs.keys(),
+ **kwargs)
+
+def _expanding_func(name, desc, func_kw=None, additional_kw=''):
@Substitution(desc, _unary_arg, _expanding_kw + additional_kw,
_type_of_input_retval, "")
@Appender(_doc_template)
- @wraps(func)
def f(arg, min_periods=1, freq=None, **kwargs):
- window = max(len(arg), min_periods) if min_periods else len(arg)
-
- def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
- minp = check_minp(minp, window)
- return func(arg, window, minp, **kwds)
- return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
- **kwargs)
-
+ return ensure_compat('expanding',
+ name,
+ arg,
+ min_periods=min_periods,
+ freq=freq,
+ func_kw=func_kw,
+ **kwargs)
return f
-expanding_max = _expanding_func(algos.roll_max, 'Expanding maximum.')
-expanding_min = _expanding_func(algos.roll_min, 'Expanding minimum.')
-expanding_sum = _expanding_func(algos.roll_sum, 'Expanding sum.')
-expanding_mean = _expanding_func(algos.roll_mean, 'Expanding mean.')
-expanding_median = _expanding_func(algos.roll_median_c, 'Expanding median.')
+expanding_max = _expanding_func('max', 'Expanding maximum.')
+expanding_min = _expanding_func('min', 'Expanding minimum.')
+expanding_sum = _expanding_func('sum', 'Expanding sum.')
+expanding_mean = _expanding_func('mean', 'Expanding mean.')
+expanding_median = _expanding_func('median', 'Expanding median.')
-expanding_std = _expanding_func(_ts_std, 'Expanding standard deviation.',
- check_minp=_require_min_periods(1),
+expanding_std = _expanding_func('std', 'Expanding standard deviation.',
+ func_kw=['ddof'],
additional_kw=_ddof_kw)
-expanding_var = _expanding_func(algos.roll_var, 'Expanding variance.',
- check_minp=_require_min_periods(1),
+expanding_var = _expanding_func('var', 'Expanding variance.',
+ func_kw=['ddof'],
additional_kw=_ddof_kw)
-expanding_skew = _expanding_func(algos.roll_skew, 'Unbiased expanding skewness.',
- check_minp=_require_min_periods(3))
-expanding_kurt = _expanding_func(algos.roll_kurt, 'Unbiased expanding kurtosis.',
- check_minp=_require_min_periods(4))
+expanding_skew = _expanding_func('skew', 'Unbiased expanding skewness.')
+expanding_kurt = _expanding_func('kurt', 'Unbiased expanding kurtosis.')
def expanding_count(arg, freq=None):
@@ -930,7 +687,7 @@ def expanding_count(arg, freq=None):
frequency by resampling the data. This is done with the default parameters
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
- return rolling_count(arg, len(arg), freq=freq)
+ return ensure_compat('expanding', 'count', arg, freq=freq)
def expanding_quantile(arg, quantile, min_periods=1, freq=None):
@@ -958,9 +715,13 @@ def expanding_quantile(arg, quantile, min_periods=1, freq=None):
frequency by resampling the data. This is done with the default parameters
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
- return rolling_quantile(arg, len(arg), quantile, min_periods=min_periods,
- freq=freq)
-
+ return ensure_compat('expanding',
+ 'quantile',
+ arg,
+ freq=freq,
+ min_periods=min_periods,
+ func_kw=['quantile'],
+ quantile=quantile)
@Substitution("Unbiased expanding covariance.", _binary_arg_flex,
_expanding_kw+_pairwise_kw+_ddof_kw, _flex_retval, "")
@@ -973,10 +734,15 @@ def expanding_cov(arg1, arg2=None, min_periods=1, freq=None, pairwise=None, ddof
min_periods = arg2
arg2 = arg1
pairwise = True if pairwise is None else pairwise
- window = max((len(arg1) + len(arg2)), min_periods) if min_periods else (len(arg1) + len(arg2))
- return rolling_cov(arg1, arg2, window,
- min_periods=min_periods, freq=freq,
- pairwise=pairwise, ddof=ddof)
+ return ensure_compat('expanding',
+ 'cov',
+ arg1,
+ other=arg2,
+ min_periods=min_periods,
+ pairwise=pairwise,
+ freq=freq,
+ ddof=ddof,
+ func_kw=['other','pairwise','ddof'])
@Substitution("Expanding sample correlation.", _binary_arg_flex,
@@ -990,23 +756,14 @@ def expanding_corr(arg1, arg2=None, min_periods=1, freq=None, pairwise=None):
min_periods = arg2
arg2 = arg1
pairwise = True if pairwise is None else pairwise
- window = max((len(arg1) + len(arg2)), min_periods) if min_periods else (len(arg1) + len(arg2))
- return rolling_corr(arg1, arg2, window,
- min_periods=min_periods,
- freq=freq, pairwise=pairwise)
-
-
-@Substitution("Deprecated. Use expanding_corr(..., pairwise=True) instead.\n\n"
- "Pairwise expanding sample correlation", _pairwise_arg,
- _expanding_kw, _pairwise_retval, "")
-@Appender(_doc_template)
-def expanding_corr_pairwise(df1, df2=None, min_periods=1, freq=None):
- import warnings
- msg = "expanding_corr_pairwise is deprecated, use expanding_corr(..., pairwise=True)"
- warnings.warn(msg, FutureWarning, stacklevel=2)
- return expanding_corr(df1, df2, min_periods=min_periods,
- freq=freq, pairwise=True)
-
+ return ensure_compat('expanding',
+ 'corr',
+ arg1,
+ other=arg2,
+ min_periods=min_periods,
+ pairwise=pairwise,
+ freq=freq,
+ func_kw=['other','pairwise','ddof'])
def expanding_apply(arg, func, min_periods=1, freq=None,
args=(), kwargs={}):
@@ -1038,6 +795,12 @@ def expanding_apply(arg, func, min_periods=1, freq=None,
frequency by resampling the data. This is done with the default parameters
of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
"""
- window = max(len(arg), min_periods) if min_periods else len(arg)
- return rolling_apply(arg, window, func, min_periods=min_periods, freq=freq,
- args=args, kwargs=kwargs)
+ return ensure_compat('expanding',
+ 'apply',
+ arg,
+ freq=freq,
+ min_periods=min_periods,
+ func_kw=['func','args','kwargs'],
+ func=func,
+ args=args,
+ kwargs=kwargs)
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index bd21053f37568..d067b2fd7b969 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -1443,6 +1443,48 @@ def test_frame_set_name_single(self):
result = grouped['C'].agg({'foo': np.mean, 'bar': np.std})
self.assertEqual(result.index.name, 'A')
+ def test_aggregate_api_consistency(self):
+ # GH 9052
+ # make sure that the aggregates via dict
+ # are consistent
+
+
+ def compare(result, expected):
+ # if we ar passin dicts then ordering is not guaranteed for output columns
+ assert_frame_equal(result.reindex_like(expected), expected)
+
+
+ df = DataFrame({'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)})
+
+ grouped = df.groupby(['A', 'B'])
+ result = grouped[['D','C']].agg({'r':np.sum, 'r2':np.mean})
+ expected = pd.concat([grouped[['D','C']].sum(),
+ grouped[['D','C']].mean()],
+ keys=['r','r2'],
+ axis=1).stack(level=1)
+ compare(result, expected)
+
+ result = grouped[['D','C']].agg({'r': { 'C' : np.sum }, 'r2' : { 'D' : np.mean }})
+ expected = pd.concat([grouped[['C']].sum(),
+ grouped[['D']].mean()],
+ axis=1)
+ expected.columns = MultiIndex.from_tuples([('r','C'),('r2','D')])
+ compare(result, expected)
+
+ result = grouped[['D','C']].agg([np.sum, np.mean])
+ expected = pd.concat([grouped['D'].sum(),
+ grouped['D'].mean(),
+ grouped['C'].sum(),
+ grouped['C'].mean()],
+ axis=1)
+ expected.columns = MultiIndex.from_product([['D','C'],['sum','mean']])
+ compare(result, expected)
+
def test_multi_iter(self):
s = Series(np.arange(6))
k1 = np.array(['a', 'a', 'a', 'b', 'b', 'b'])
diff --git a/pandas/stats/tests/test_moments.py b/pandas/tests/test_window.py
similarity index 57%
rename from pandas/stats/tests/test_moments.py
rename to pandas/tests/test_window.py
index b9efa875735d2..4d7f9292705ad 100644
--- a/pandas/stats/tests/test_moments.py
+++ b/pandas/tests/test_window.py
@@ -9,12 +9,14 @@
import numpy as np
from distutils.version import LooseVersion
+import pandas as pd
from pandas import Series, DataFrame, Panel, bdate_range, isnull, notnull, concat
from pandas.util.testing import (
assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_index_equal
)
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
+import pandas.core.window as rwindow
import pandas.util.testing as tm
from pandas.compat import range, zip, PY3, StringIO
@@ -33,41 +35,267 @@ def _create_data(self):
self.arr = arr
self.rng = bdate_range(datetime(2009, 1, 1), periods=N)
-
self.series = Series(arr.copy(), index=self.rng)
-
self.frame = DataFrame(randn(N, K), index=self.rng,
columns=np.arange(K))
+class TestApi(Base):
+
+ def setUp(self):
+ self._create_data()
+
+ def test_getitem(self):
+
+ r = self.frame.rolling(window=5)
+ tm.assert_index_equal(r._selected_obj.columns,self.frame.columns)
+
+ r = self.frame.rolling(window=5)[1]
+ self.assertEqual(r._selected_obj.name,self.frame.columns[1])
+
+ # technically this is allowed
+ r = self.frame.rolling(window=5)[1,3]
+ tm.assert_index_equal(r._selected_obj.columns,self.frame.columns[[1,3]])
+
+ r = self.frame.rolling(window=5)[[1,3]]
+ tm.assert_index_equal(r._selected_obj.columns,self.frame.columns[[1,3]])
+
+ def test_select_bad_cols(self):
+ df = DataFrame([[1, 2]], columns=['A', 'B'])
+ g = df.rolling(window=5)
+ self.assertRaises(KeyError, g.__getitem__, ['C']) # g[['C']]
+
+ self.assertRaises(KeyError, g.__getitem__, ['A', 'C']) # g[['A', 'C']]
+ with tm.assertRaisesRegexp(KeyError, '^[^A]+$'):
+ # A should not be referenced as a bad column...
+ # will have to rethink regex if you change message!
+ g[['A', 'C']]
+
+ def test_attribute_access(self):
+
+ df = DataFrame([[1, 2]], columns=['A', 'B'])
+ r = df.rolling(window=5)
+ tm.assert_series_equal(r.A.sum(),r['A'].sum())
+ self.assertRaises(AttributeError, lambda : r.F)
+
+ def tests_skip_nuisance(self):
+
+ df = DataFrame({'A' : range(5), 'B' : range(5,10), 'C' : 'foo'})
+
+ r = df.rolling(window=3)
+ result = r[['A','B']].sum()
+ expected = DataFrame({'A' : [np.nan,np.nan,3,6,9],
+ 'B' : [np.nan,np.nan,18,21,24]},
+ columns=list('AB'))
+ assert_frame_equal(result, expected)
+
+ expected = pd.concat([r[['A','B']].sum(),df[['C']]],axis=1)
+ 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)})
+
+ r = df.rolling(window=3)
+ a_mean = r['A'].mean()
+ a_std = r['A'].std()
+ a_sum = r['A'].sum()
+ b_mean = r['B'].mean()
+ b_std = r['B'].std()
+ b_sum = r['B'].sum()
+
+ def compare(result, expected):
+ # if we are using dicts, the orderings is not guaranteed
+ assert_frame_equal(result.reindex_like(expected), expected)
+
+ result = r.aggregate([np.mean, np.std])
+ expected = pd.concat([a_mean,a_std,b_mean,b_std],axis=1)
+ expected.columns = pd.MultiIndex.from_product([['A','B'],['mean','std']])
+ assert_frame_equal(result, expected)
+
+ result = r.aggregate({'A': np.mean,
+ 'B': np.std})
+ expected = pd.concat([a_mean,b_std],axis=1)
+ compare(result, expected)
+
+ result = r.aggregate({'A': ['mean','std']})
+ expected = pd.concat([a_mean,a_std],axis=1)
+ expected.columns = pd.MultiIndex.from_tuples([('A','mean'),('A','std')])
+ assert_frame_equal(result, expected)
+
+ result = r['A'].aggregate(['mean','sum'])
+ expected = pd.concat([a_mean,a_sum],axis=1)
+ expected.columns = ['mean','sum']
+ assert_frame_equal(result, expected)
+
+ result = r.aggregate({'A': { 'mean' : 'mean', 'sum' : 'sum' } })
+ expected = pd.concat([a_mean,a_sum],axis=1)
+ expected.columns = pd.MultiIndex.from_tuples([('A','mean'),('A','sum')])
+ compare(result, expected)
+
+ result = r.aggregate({'A': { 'mean' : 'mean', 'sum' : 'sum' },
+ 'B': { 'mean2' : 'mean', 'sum2' : 'sum' }})
+ expected = pd.concat([a_mean,a_sum,b_mean,b_sum],axis=1)
+ expected.columns = pd.MultiIndex.from_tuples([('A','mean'),('A','sum'),
+ ('B','mean2'),('B','sum2')])
+ compare(result, expected)
+
+ result = r.aggregate({'A': ['mean','std'],
+ 'B': ['mean','std']})
+ expected = pd.concat([a_mean,a_std,b_mean,b_std],axis=1)
+ expected.columns = pd.MultiIndex.from_tuples([('A','mean'),('A','std'),
+ ('B','mean'),('B','std')])
+ compare(result, expected)
+
+ result = r.aggregate({'r1' : { 'A' : ['mean','sum'] },
+ 'r2' : { 'B' : ['mean','sum'] }})
+ expected = pd.concat([a_mean,a_sum,b_mean,b_sum],axis=1)
+ expected.columns = pd.MultiIndex.from_tuples([('r1','A','mean'),('r1','A','sum'),
+ ('r2','B','mean'),('r2','B','sum')])
+ compare(result, expected)
+
+ result = r.agg({'A' : {'ra' : ['mean','std']},
+ 'B' : {'rb' : ['mean','std']}})
+ expected = pd.concat([a_mean,a_std,b_mean,b_std],axis=1)
+ expected.columns = pd.MultiIndex.from_tuples([('A','ra','mean'),('A','ra','std'),
+ ('B','rb','mean'),('B','rb','std')])
+ compare(result, expected)
+
+
+ # passed lambda
+ result = r.agg({'A' : np.sum,
+ 'B' : lambda x: np.std(x, ddof=1)})
+ rcustom = r['B'].apply(lambda x: np.std(x,ddof=1))
+ expected = pd.concat([a_sum,rcustom],axis=1)
+ compare(result, expected)
+
+ def test_agg_consistency(self):
+
+ df = DataFrame({'A' : range(5),
+ 'B' : range(0,10,2)})
+ r = df.rolling(window=3)
+
+ result = r.agg([np.sum, np.mean]).columns
+ expected = pd.MultiIndex.from_product([list('AB'),['sum','mean']])
+ tm.assert_index_equal(result, expected)
+
+ result = r['A'].agg([np.sum, np.mean]).columns
+ expected = pd.Index(['sum','mean'])
+ tm.assert_index_equal(result, expected)
+
+ result = r.agg({'A' : [np.sum, np.mean]}).columns
+ expected = pd.MultiIndex.from_tuples([('A','sum'),('A','mean')])
+ tm.assert_index_equal(result, expected)
+
+ def test_window_with_args(self):
+ tm._skip_if_no_scipy()
+
+ # make sure that we are aggregating window functions correctly with arg
+ r = Series(np.random.randn(100)).rolling(window=10,min_periods=1,win_type='gaussian')
+ expected = pd.concat([r.mean(std=10),r.mean(std=.01)],axis=1)
+ expected.columns = ['<lambda>','<lambda>']
+ result = r.aggregate([lambda x: x.mean(std=10), lambda x: x.mean(std=.01)])
+ assert_frame_equal(result, expected)
+
+ def a(x):
+ return x.mean(std=10)
+ def b(x):
+ return x.mean(std=0.01)
+ expected = pd.concat([r.mean(std=10),r.mean(std=.01)],axis=1)
+ expected.columns = ['a','b']
+ result = r.aggregate([a,b])
+ assert_frame_equal(result, expected)
+
+ def test_preserve_metadata(self):
+ # GH 10565
+ s = Series(np.arange(100), name='foo')
+
+ s2 = s.rolling(30).sum()
+ s3 = s.rolling(20).sum()
+ self.assertEqual(s2.name, 'foo')
+ self.assertEqual(s3.name, 'foo')
+
+ def test_how_compat(self):
+ # in prior versions, we would allow how to be used in the resample
+ # now that its deprecated, we need to handle this in the actual
+ # aggregation functions
+ s = pd.Series(np.random.randn(20), index=pd.date_range('1/1/2000', periods=20, freq='12H'))
+
+ for how in ['min','max','median']:
+ for op in ['mean','sum','std','var','kurt','skew']:
+ for t in ['rolling','expanding']:
+
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+
+ dfunc = getattr(pd,"{0}_{1}".format(t,op))
+ if dfunc is None:
+ continue
+
+ if t == 'rolling':
+ kwargs = {'window' : 5}
+ else:
+ kwargs = {}
+ result = dfunc(s, freq='D', how=how, **kwargs)
+
+ expected = getattr(getattr(s,t)(freq='D', **kwargs),op)(how=how)
+ assert_series_equal(result, expected)
+
+class TestDeprecations(Base):
+ """ test that we are catching deprecation warnings """
+
+ def setUp(self):
+ self._create_data()
+
+
+ def test_deprecations(self):
+
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ mom.rolling_mean(np.ones(10),3,center=True ,axis=0)
+ mom.rolling_mean(Series(np.ones(10)),3,center=True ,axis=0)
+
class TestMoments(Base):
def setUp(self):
self._create_data()
def test_centered_axis_validation(self):
+
# ok
- mom.rolling_mean(Series(np.ones(10)),3,center=True ,axis=0)
+ Series(np.ones(10)).rolling(window=3,center=True ,axis=0).mean()
+
# bad axis
- self.assertRaises(ValueError, mom.rolling_mean,Series(np.ones(10)),3,center=True ,axis=1)
+ self.assertRaises(ValueError, lambda : Series(np.ones(10)).rolling(window=3,center=True ,axis=1).mean())
# ok ok
- mom.rolling_mean(DataFrame(np.ones((10,10))),3,center=True ,axis=0)
- mom.rolling_mean(DataFrame(np.ones((10,10))),3,center=True ,axis=1)
+ DataFrame(np.ones((10,10))).rolling(window=3,center=True ,axis=0).mean()
+ DataFrame(np.ones((10,10))).rolling(window=3,center=True ,axis=1).mean()
+
# bad axis
- self.assertRaises(ValueError, mom.rolling_mean,DataFrame(np.ones((10,10))),3,center=True ,axis=2)
+ self.assertRaises(ValueError, lambda : DataFrame(np.ones((10,10))).rolling(window=3,center=True ,axis=2).mean())
def test_rolling_sum(self):
- self._check_moment_func(mom.rolling_sum, np.sum)
+ self._check_moment_func(mom.rolling_sum, np.sum, name='sum')
def test_rolling_count(self):
counter = lambda x: np.isfinite(x).astype(float).sum()
self._check_moment_func(mom.rolling_count, counter,
+ name='count',
has_min_periods=False,
preserve_nan=False,
fill_value=0)
def test_rolling_mean(self):
- self._check_moment_func(mom.rolling_mean, np.mean)
+ self._check_moment_func(mom.rolling_mean, np.mean, name='mean')
def test_cmov_mean(self):
# GH 8238
@@ -78,11 +306,12 @@ def test_cmov_mean(self):
xp = np.array([np.nan, np.nan, 9.962, 11.27 , 11.564, 12.516,
12.818, 12.952, np.nan, np.nan])
- rs = mom.rolling_mean(vals, 5, center=True)
- assert_almost_equal(xp, rs)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ rs = mom.rolling_mean(vals, 5, center=True)
+ assert_almost_equal(xp, rs)
xp = Series(rs)
- rs = mom.rolling_mean(Series(vals), 5, center=True)
+ rs = Series(vals).rolling(5, center=True).mean()
assert_series_equal(xp, rs)
def test_cmov_window(self):
@@ -94,11 +323,12 @@ def test_cmov_window(self):
xp = np.array([np.nan, np.nan, 9.962, 11.27 , 11.564, 12.516,
12.818, 12.952, np.nan, np.nan])
- rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
- assert_almost_equal(xp, rs)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
+ assert_almost_equal(xp, rs)
xp = Series(rs)
- rs = mom.rolling_window(Series(vals), 5, 'boxcar', center=True)
+ rs = Series(vals).rolling(5, win_type='boxcar', center=True).mean()
assert_series_equal(xp, rs)
def test_cmov_window_corner(self):
@@ -108,19 +338,22 @@ def test_cmov_window_corner(self):
# all nan
vals = np.empty(10, dtype=float)
vals.fill(np.nan)
- rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
- self.assertTrue(np.isnan(rs).all())
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
+ self.assertTrue(np.isnan(rs).all())
# empty
vals = np.array([])
- rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
- self.assertEqual(len(rs), 0)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
+ self.assertEqual(len(rs), 0)
# shorter than window
vals = np.random.randn(5)
- rs = mom.rolling_window(vals, 10, 'boxcar')
- self.assertTrue(np.isnan(rs).all())
- self.assertEqual(len(rs), 5)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ rs = mom.rolling_window(vals, 10, 'boxcar')
+ self.assertTrue(np.isnan(rs).all())
+ self.assertEqual(len(rs), 5)
def test_cmov_window_frame(self):
# Gh 8238
@@ -149,7 +382,25 @@ def test_cmov_window_frame(self):
[ np.nan, np.nan]])
# DataFrame
- rs = mom.rolling_window(DataFrame(vals), 5, 'boxcar', center=True)
+ rs = DataFrame(vals).rolling(5, win_type='boxcar', center=True).mean()
+ assert_frame_equal(DataFrame(xp), rs)
+
+ # invalid method
+ self.assertRaises(AttributeError, lambda : DataFrame(vals).rolling(5, win_type='boxcar', center=True).std())
+
+ # sum
+ xp = np.array([[ np.nan, np.nan],
+ [ np.nan, np.nan],
+ [ 46.26, 46.96],
+ [ 43.22, 49.53],
+ [ 44.35, 51.04],
+ [ 34.05, 42.94],
+ [ 38.96, 43.22],
+ [ 45.25, 39.12],
+ [ np.nan, np.nan],
+ [ np.nan, np.nan]])
+
+ rs = DataFrame(vals).rolling(5, win_type='boxcar', center=True).sum()
assert_frame_equal(DataFrame(xp), rs)
def test_cmov_window_na_min_periods(self):
@@ -160,9 +411,8 @@ def test_cmov_window_na_min_periods(self):
vals[4] = np.nan
vals[8] = np.nan
- xp = mom.rolling_mean(vals, 5, min_periods=4, center=True)
- rs = mom.rolling_window(vals, 5, 'boxcar', min_periods=4, center=True)
-
+ xp = vals.rolling(5, min_periods=4, center=True).mean()
+ rs = vals.rolling(5, win_type='boxcar', min_periods=4, center=True).mean()
assert_series_equal(xp, rs)
def test_cmov_window_regular(self):
@@ -194,7 +444,7 @@ def test_cmov_window_regular(self):
for wt in win_types:
xp = Series(xps[wt])
- rs = mom.rolling_window(Series(vals), 5, wt, center=True)
+ rs = Series(vals).rolling(5, win_type=wt, center=True).mean()
assert_series_equal(xp, rs)
def test_cmov_window_regular_linear_range(self):
@@ -211,7 +461,7 @@ def test_cmov_window_regular_linear_range(self):
xp = Series(xp)
for wt in win_types:
- rs = mom.rolling_window(Series(vals), 5, wt, center=True)
+ rs = Series(vals).rolling(5, win_type=wt, center=True).mean()
assert_series_equal(xp, rs)
def test_cmov_window_regular_missing_data(self):
@@ -245,7 +495,7 @@ def test_cmov_window_regular_missing_data(self):
for wt in win_types:
xp = Series(xps[wt])
- rs = mom.rolling_window(Series(vals), 5, wt, min_periods=3)
+ rs = Series(vals).rolling(5, win_type=wt, min_periods=3).mean()
assert_series_equal(xp, rs)
def test_cmov_window_special(self):
@@ -273,9 +523,7 @@ def test_cmov_window_special(self):
for wt, k in zip(win_types, kwds):
xp = Series(xps[wt])
-
- rs = mom.rolling_window(Series(vals), 5, wt, center=True,
- **k)
+ rs = Series(vals).rolling(5, win_type=wt, center=True).mean(**k)
assert_series_equal(xp, rs)
def test_cmov_window_special_linear_range(self):
@@ -293,32 +541,36 @@ def test_cmov_window_special_linear_range(self):
xp = Series(xp)
for wt, k in zip(win_types, kwds):
- rs = mom.rolling_window(Series(vals), 5, wt, center=True,
- **k)
+ rs = Series(vals).rolling(5, win_type=wt, center=True).mean(**k)
assert_series_equal(xp, rs)
def test_rolling_median(self):
- self._check_moment_func(mom.rolling_median, np.median)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ self._check_moment_func(mom.rolling_median, np.median, name='median')
def test_rolling_min(self):
- self._check_moment_func(mom.rolling_min, np.min)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ self._check_moment_func(mom.rolling_min, np.min, name='min')
- a = np.array([1, 2, 3, 4, 5])
- b = mom.rolling_min(a, window=100, min_periods=1)
- assert_almost_equal(b, np.ones(len(a)))
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ a = np.array([1, 2, 3, 4, 5])
+ b = mom.rolling_min(a, window=100, min_periods=1)
+ assert_almost_equal(b, np.ones(len(a)))
- self.assertRaises(ValueError, mom.rolling_min, np.array([1,
- 2, 3]), window=3, min_periods=5)
+ self.assertRaises(ValueError, mom.rolling_min,
+ np.array([1,2, 3]), window=3, min_periods=5)
def test_rolling_max(self):
- self._check_moment_func(mom.rolling_max, np.max)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ self._check_moment_func(mom.rolling_max, np.max, name='max')
- a = np.array([1, 2, 3, 4, 5])
- b = mom.rolling_max(a, window=100, min_periods=1)
- assert_almost_equal(a, b)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ a = np.array([1, 2, 3, 4, 5])
+ b = mom.rolling_max(a, window=100, min_periods=1)
+ assert_almost_equal(a, b)
- self.assertRaises(ValueError, mom.rolling_max, np.array([1,
- 2, 3]), window=3, min_periods=5)
+ self.assertRaises(ValueError, mom.rolling_max, np.array([1,2, 3]),
+ window=3, min_periods=5)
def test_rolling_quantile(self):
qs = [.1, .5, .9]
@@ -330,8 +582,8 @@ def scoreatpercentile(a, per):
return values[int(idx)]
for q in qs:
- def f(x, window, min_periods=None, freq=None, center=False):
- return mom.rolling_quantile(x, window, q,
+ def f(x, window, quantile, min_periods=None, freq=None, center=False):
+ return mom.rolling_quantile(x, window, quantile,
min_periods=min_periods,
freq=freq,
center=center)
@@ -339,7 +591,7 @@ def f(x, window, min_periods=None, freq=None, center=False):
def alt(x):
return scoreatpercentile(x, q)
- self._check_moment_func(f, alt)
+ self._check_moment_func(f, alt, name='quantile', quantile=q)
def test_rolling_apply(self):
# suppress warnings about empty slices, as we are deliberately testing with a 0-length Series
@@ -347,52 +599,65 @@ def test_rolling_apply(self):
warnings.filterwarnings("ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning)
ser = Series([])
- assert_series_equal(ser, mom.rolling_apply(ser, 10, lambda x: x.mean()))
+ assert_series_equal(ser, ser.rolling(10).apply(lambda x: x.mean()))
- def roll_mean(x, window, min_periods=None, freq=None, center=False):
- return mom.rolling_apply(x, window,
- lambda x: x[np.isfinite(x)].mean(),
+ f = lambda x: x[np.isfinite(x)].mean()
+ def roll_mean(x, window, min_periods=None, freq=None, center=False, **kwargs):
+ return mom.rolling_apply(x,
+ window,
+ func=f,
min_periods=min_periods,
freq=freq,
center=center)
- self._check_moment_func(roll_mean, np.mean)
+ self._check_moment_func(roll_mean, np.mean, name='apply', func=f)
# GH 8080
s = Series([None, None, None])
- result = mom.rolling_apply(s, 2, lambda x: len(x), min_periods=0)
+ result = s.rolling(2,min_periods=0).apply(lambda x: len(x))
expected = Series([1., 2., 2.])
assert_series_equal(result, expected)
+ result = s.rolling(2, min_periods=0).apply(len)
+ assert_series_equal(result, expected)
+
def test_rolling_apply_out_of_bounds(self):
# #1850
arr = np.arange(4)
# it works!
- result = mom.rolling_apply(arr, 10, np.sum)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_apply(arr, 10, np.sum)
self.assertTrue(isnull(result).all())
- result = mom.rolling_apply(arr, 10, np.sum, min_periods=1)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_apply(arr, 10, np.sum, min_periods=1)
assert_almost_equal(result, result)
def test_rolling_std(self):
self._check_moment_func(mom.rolling_std,
- lambda x: np.std(x, ddof=1))
- self._check_moment_func(functools.partial(mom.rolling_std, ddof=0),
- lambda x: np.std(x, ddof=0))
+ lambda x: np.std(x, ddof=1),
+ name='std')
+ self._check_moment_func(mom.rolling_std,
+ lambda x: np.std(x, ddof=0),
+ name='std',
+ ddof=0)
def test_rolling_std_1obs(self):
- result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
- 1, min_periods=1)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
+ 1, min_periods=1)
expected = np.array([np.nan] * 5)
assert_almost_equal(result, expected)
- result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
- 1, min_periods=1, ddof=0)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
+ 1, min_periods=1, ddof=0)
expected = np.zeros(5)
assert_almost_equal(result, expected)
- result = mom.rolling_std(np.array([np.nan, np.nan, 3., 4., 5.]),
- 3, min_periods=2)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_std(np.array([np.nan, np.nan, 3., 4., 5.]),
+ 3, min_periods=2)
self.assertTrue(np.isnan(result[2]))
def test_rolling_std_neg_sqrt(self):
@@ -405,18 +670,23 @@ def test_rolling_std_neg_sqrt(self):
0.00028718669878572767,
0.00028718669878572767,
0.00028718669878572767])
- b = mom.rolling_std(a, window=3)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ b = mom.rolling_std(a, window=3)
self.assertTrue(np.isfinite(b[2:]).all())
- b = mom.ewmstd(a, span=3)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ b = mom.ewmstd(a, span=3)
self.assertTrue(np.isfinite(b[2:]).all())
def test_rolling_var(self):
self._check_moment_func(mom.rolling_var,
lambda x: np.var(x, ddof=1),
- test_stable=True)
- self._check_moment_func(functools.partial(mom.rolling_var, ddof=0),
- lambda x: np.var(x, ddof=0))
+ test_stable=True,
+ name='var')
+ self._check_moment_func(mom.rolling_var,
+ lambda x: np.var(x, ddof=0),
+ name='var',
+ ddof=0)
def test_rolling_skew(self):
try:
@@ -424,7 +694,8 @@ def test_rolling_skew(self):
except ImportError:
raise nose.SkipTest('no scipy')
self._check_moment_func(mom.rolling_skew,
- lambda x: skew(x, bias=False))
+ lambda x: skew(x, bias=False),
+ name='skew')
def test_rolling_kurt(self):
try:
@@ -432,7 +703,8 @@ def test_rolling_kurt(self):
except ImportError:
raise nose.SkipTest('no scipy')
self._check_moment_func(mom.rolling_kurt,
- lambda x: kurtosis(x, bias=False))
+ lambda x: kurtosis(x, bias=False),
+ name='kurt')
def test_fperr_robustness(self):
# TODO: remove this once python 2.5 out of picture
@@ -446,53 +718,79 @@ def test_fperr_robustness(self):
if sys.byteorder != "little":
arr = arr.byteswap().newbyteorder()
- result = mom.rolling_sum(arr, 2)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_sum(arr, 2)
self.assertTrue((result[1:] >= 0).all())
- result = mom.rolling_mean(arr, 2)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_mean(arr, 2)
self.assertTrue((result[1:] >= 0).all())
- result = mom.rolling_var(arr, 2)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_var(arr, 2)
self.assertTrue((result[1:] >= 0).all())
# #2527, ugh
arr = np.array([0.00012456, 0.0003, 0])
- result = mom.rolling_mean(arr, 1)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_mean(arr, 1)
self.assertTrue(result[-1] >= 0)
- result = mom.rolling_mean(-arr, 1)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.rolling_mean(-arr, 1)
self.assertTrue(result[-1] <= 0)
- def _check_moment_func(self, func, static_comp, window=50,
+ def _check_moment_func(self, f, static_comp,
+ name=None,
+ window=50,
has_min_periods=True,
has_center=True,
has_time_rule=True,
preserve_nan=True,
fill_value=None,
- test_stable=False):
-
- self._check_ndarray(func, static_comp, window=window,
- has_min_periods=has_min_periods,
- preserve_nan=preserve_nan,
- has_center=has_center,
- fill_value=fill_value,
- test_stable=test_stable)
-
- self._check_structures(func, static_comp,
- has_min_periods=has_min_periods,
- has_time_rule=has_time_rule,
- fill_value=fill_value,
- has_center=has_center)
-
- def _check_ndarray(self, func, static_comp, window=50,
+ test_stable=False,
+ **kwargs):
+
+ with warnings.catch_warnings(record=True):
+ self._check_ndarray(f, static_comp, window=window,
+ has_min_periods=has_min_periods,
+ preserve_nan=preserve_nan,
+ has_center=has_center,
+ fill_value=fill_value,
+ test_stable=test_stable,
+ **kwargs)
+
+ with warnings.catch_warnings(record=True):
+ self._check_structures(f, static_comp,
+ has_min_periods=has_min_periods,
+ has_time_rule=has_time_rule,
+ fill_value=fill_value,
+ has_center=has_center,
+ **kwargs)
+
+ # new API
+ if name is not None:
+ self._check_structures(f, static_comp,
+ name=name,
+ has_min_periods=has_min_periods,
+ has_time_rule=has_time_rule,
+ fill_value=fill_value,
+ has_center=has_center,
+ **kwargs)
+
+ def _check_ndarray(self, f, static_comp, window=50,
has_min_periods=True,
preserve_nan=True,
has_center=True,
fill_value=None,
test_stable=False,
- test_window=True):
+ test_window=True,
+ **kwargs):
+
+ def get_result(arr, window, min_periods=None, center=False):
+ return f(arr, window, min_periods=min_periods, center=center, **kwargs)
- result = func(self.arr, window)
+ result = get_result(self.arr, window)
assert_almost_equal(result[-1],
static_comp(self.arr[-50:]))
@@ -505,11 +803,11 @@ def _check_ndarray(self, func, static_comp, window=50,
arr[-10:] = np.NaN
if has_min_periods:
- result = func(arr, 50, min_periods=30)
+ result = get_result(arr, 50, min_periods=30)
assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# min_periods is working correctly
- result = func(arr, 20, min_periods=15)
+ result = get_result(arr, 20, min_periods=15)
self.assertTrue(np.isnan(result[23]))
self.assertFalse(np.isnan(result[24]))
@@ -517,31 +815,31 @@ def _check_ndarray(self, func, static_comp, window=50,
self.assertTrue(np.isnan(result[-5]))
arr2 = randn(20)
- result = func(arr2, 10, min_periods=5)
+ result = get_result(arr2, 10, min_periods=5)
self.assertTrue(isnull(result[3]))
self.assertTrue(notnull(result[4]))
# min_periods=0
- result0 = func(arr, 20, min_periods=0)
- result1 = func(arr, 20, min_periods=1)
+ result0 = get_result(arr, 20, min_periods=0)
+ result1 = get_result(arr, 20, min_periods=1)
assert_almost_equal(result0, result1)
else:
- result = func(arr, 50)
+ result = get_result(arr, 50)
assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# GH 7925
if has_center:
if has_min_periods:
- result = func(arr, 20, min_periods=15, center=True)
- expected = func(np.concatenate((arr, np.array([np.NaN] * 9))), 20, min_periods=15)[9:]
+ result = get_result(arr, 20, min_periods=15, center=True)
+ expected = get_result(np.concatenate((arr, np.array([np.NaN] * 9))), 20, min_periods=15)[9:]
else:
- result = func(arr, 20, center=True)
- expected = func(np.concatenate((arr, np.array([np.NaN] * 9))), 20)[9:]
+ result = get_result(arr, 20, center=True)
+ expected = get_result(np.concatenate((arr, np.array([np.NaN] * 9))), 20)[9:]
self.assert_numpy_array_equal(result, expected)
if test_stable:
- result = func(self.arr + 1e9, window)
+ result = get_result(self.arr + 1e9, window)
assert_almost_equal(result[-1],
static_comp(self.arr[-50:] + 1e9))
@@ -549,16 +847,16 @@ def _check_ndarray(self, func, static_comp, window=50,
if test_window:
if has_min_periods:
for minp in (0, len(self.arr)-1, len(self.arr)):
- result = func(self.arr, len(self.arr)+1, min_periods=minp)
- expected = func(self.arr, len(self.arr), min_periods=minp)
+ result = get_result(self.arr, len(self.arr)+1, min_periods=minp)
+ expected = get_result(self.arr, len(self.arr), min_periods=minp)
nan_mask = np.isnan(result)
self.assertTrue(np.array_equal(nan_mask,
np.isnan(expected)))
nan_mask = ~nan_mask
assert_almost_equal(result[nan_mask], expected[nan_mask])
else:
- result = func(self.arr, len(self.arr)+1)
- expected = func(self.arr, len(self.arr))
+ result = get_result(self.arr, len(self.arr)+1)
+ expected = get_result(self.arr, len(self.arr))
nan_mask = np.isnan(result)
self.assertTrue(np.array_equal(nan_mask, np.isnan(expected)))
nan_mask = ~nan_mask
@@ -567,15 +865,40 @@ def _check_ndarray(self, func, static_comp, window=50,
- def _check_structures(self, func, static_comp,
+ def _check_structures(self, f, static_comp,
+ name=None,
has_min_periods=True, has_time_rule=True,
has_center=True,
- fill_value=None):
+ fill_value=None,
+ **kwargs):
+
+ def get_result(obj, window, min_periods=None, freq=None, center=False):
+
+ # check via the API calls if name is provided
+ if name is not None:
+
+ # catch a freq deprecation warning if freq is provided and not None
+ w = FutureWarning if freq is not None else None
+ with tm.assert_produces_warning(w, check_stacklevel=False):
+ r = obj.rolling(window=window,
+ min_periods=min_periods,
+ freq=freq,
+ center=center)
+ return getattr(r,name)(**kwargs)
+
+ # check via the moments API
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ return f(obj,
+ window=window,
+ min_periods=min_periods,
+ freq=freq,
+ center=center,
+ **kwargs)
+
+ series_result = get_result(self.series, window=50)
+ frame_result = get_result(self.frame, window=50)
- series_result = func(self.series, 50)
tm.assertIsInstance(series_result, Series)
-
- frame_result = func(self.frame, 50)
self.assertEqual(type(frame_result), DataFrame)
# check time_rule works
@@ -584,13 +907,11 @@ def _check_structures(self, func, static_comp,
minp = 10
if has_min_periods:
- series_result = func(self.series[::2], win, min_periods=minp,
- freq='B')
- frame_result = func(self.frame[::2], win, min_periods=minp,
- freq='B')
+ series_result = get_result(self.series[::2], window=win, min_periods=minp, freq='B')
+ frame_result = get_result(self.frame[::2], window=win, min_periods=minp, freq='B')
else:
- series_result = func(self.series[::2], win, freq='B')
- frame_result = func(self.frame[::2], win, freq='B')
+ series_result = get_result(self.series[::2], window=win, freq='B')
+ frame_result = get_result(self.frame[::2], window=win, freq='B')
last_date = series_result.index[-1]
prev_date = last_date - 24 * datetools.bday
@@ -605,22 +926,41 @@ def _check_structures(self, func, static_comp,
# GH 7925
if has_center:
+
+ # shifter index
+ s = ['x%d'%x for x in range(12)]
+
if has_min_periods:
minp = 10
- series_xp = func(self.series.reindex(list(self.series.index)+['x%d'%x for x in range(12)]), 25, min_periods=minp).shift(-12).reindex(self.series.index)
- frame_xp = func(self.frame.reindex(list(self.frame.index)+['x%d'%x for x in range(12)]), 25, min_periods=minp).shift(-12).reindex(self.frame.index)
- series_rs = func(self.series, 25, min_periods=minp,
- center=True)
- frame_rs = func(self.frame, 25, min_periods=minp,
- center=True)
+ series_xp = get_result(self.series.reindex(list(self.series.index)+s),
+ window=25,
+ min_periods=minp).shift(-12).reindex(self.series.index)
+ frame_xp = get_result(self.frame.reindex(list(self.frame.index)+s),
+ window=25,
+ min_periods=minp).shift(-12).reindex(self.frame.index)
+
+ series_rs = get_result(self.series,
+ window=25,
+ min_periods=minp,
+ center=True)
+ frame_rs = get_result(self.frame,
+ window=25,
+ min_periods=minp,
+ center=True)
else:
- series_xp = func(self.series.reindex(list(self.series.index)+['x%d'%x for x in range(12)]), 25).shift(-12).reindex(self.series.index)
- frame_xp = func(self.frame.reindex(list(self.frame.index)+['x%d'%x for x in range(12)]), 25).shift(-12).reindex(self.frame.index)
-
- series_rs = func(self.series, 25, center=True)
- frame_rs = func(self.frame, 25, center=True)
+ series_xp = get_result(self.series.reindex(list(self.series.index)+s),
+ window=25).shift(-12).reindex(self.series.index)
+ frame_xp = get_result(self.frame.reindex(list(self.frame.index)+s),
+ window=25).shift(-12).reindex(self.frame.index)
+
+ series_rs = get_result(self.series,
+ window=25,
+ center=True)
+ frame_rs = get_result(self.frame,
+ window=25,
+ center=True)
if fill_value is not None:
series_xp = series_xp.fillna(fill_value)
@@ -629,38 +969,39 @@ def _check_structures(self, func, static_comp,
assert_frame_equal(frame_xp, frame_rs)
def test_ewma(self):
- self._check_ew(mom.ewma)
+ self._check_ew(mom.ewma,name='mean')
arr = np.zeros(1000)
arr[5] = 1
- result = mom.ewma(arr, span=100, adjust=False).sum()
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = mom.ewma(arr, span=100, adjust=False).sum()
self.assertTrue(np.abs(result - 1) < 1e-2)
s = Series([1.0, 2.0, 4.0, 8.0])
expected = Series([1.0, 1.6, 2.736842, 4.923077])
- for f in [lambda s: mom.ewma(s, com=2.0, adjust=True),
- lambda s: mom.ewma(s, com=2.0, adjust=True, ignore_na=False),
- lambda s: mom.ewma(s, com=2.0, adjust=True, ignore_na=True),
- ]:
+ for f in [lambda s: s.ewm(com=2.0, adjust=True).mean(),
+ lambda s: s.ewm(com=2.0, adjust=True, ignore_na=False).mean(),
+ lambda s: s.ewm(com=2.0, adjust=True, ignore_na=True).mean(),
+ ]:
result = f(s)
assert_series_equal(result, expected)
expected = Series([1.0, 1.333333, 2.222222, 4.148148])
- for f in [lambda s: mom.ewma(s, com=2.0, adjust=False),
- lambda s: mom.ewma(s, com=2.0, adjust=False, ignore_na=False),
- lambda s: mom.ewma(s, com=2.0, adjust=False, ignore_na=True),
+ for f in [lambda s: s.ewm(com=2.0, adjust=False).mean(),
+ lambda s: s.ewm(com=2.0, adjust=False, ignore_na=False).mean(),
+ lambda s: s.ewm(com=2.0, adjust=False, ignore_na=True).mean(),
]:
result = f(s)
assert_series_equal(result, expected)
def test_ewma_nan_handling(self):
s = Series([1.] + [np.nan] * 5 + [1.])
- result = mom.ewma(s, com=5)
+ result = s.ewm(com=5).mean()
assert_almost_equal(result, [1.] * len(s))
s = Series([np.nan] * 2 + [1.] + [np.nan] * 2 + [1.])
- result = mom.ewma(s, com=5)
+ result = s.ewm(com=5).mean()
assert_almost_equal(result, [np.nan] * 2 + [1.] * 4)
# GH 7603
@@ -693,58 +1034,55 @@ def simple_wma(s, w):
(s3, False, True, [(1. - alpha)**2, np.nan, (1. - alpha) * alpha, alpha]),
]:
expected = simple_wma(s, Series(w))
- result = mom.ewma(s, com=com, adjust=adjust, ignore_na=ignore_na)
+ result = s.ewm(com=com, adjust=adjust, ignore_na=ignore_na).mean()
+
assert_series_equal(result, expected)
if ignore_na is False:
# check that ignore_na defaults to False
- result = mom.ewma(s, com=com, adjust=adjust)
+ result = s.ewm(com=com, adjust=adjust).mean()
assert_series_equal(result, expected)
def test_ewmvar(self):
- self._check_ew(mom.ewmvar)
+ self._check_ew(mom.ewmvar, name='var')
def test_ewmvol(self):
- self._check_ew(mom.ewmvol)
+ self._check_ew(mom.ewmvol, name='vol')
def test_ewma_span_com_args(self):
- A = mom.ewma(self.arr, com=9.5)
- B = mom.ewma(self.arr, span=20)
- assert_almost_equal(A, B)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ A = mom.ewma(self.arr, com=9.5)
+ 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(Exception, mom.ewma, self.arr, com=9.5, span=20)
+ self.assertRaises(Exception, mom.ewma, self.arr)
def test_ewma_halflife_arg(self):
- A = mom.ewma(self.arr, com=13.932726172912965)
- B = mom.ewma(self.arr, halflife=10.0)
- assert_almost_equal(A, B)
-
- self.assertRaises(Exception, mom.ewma, self.arr, span=20, halflife=50)
- self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, halflife=50)
- self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20, halflife=50)
- self.assertRaises(Exception, mom.ewma, self.arr)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ A = mom.ewma(self.arr, com=13.932726172912965)
+ B = mom.ewma(self.arr, halflife=10.0)
+ assert_almost_equal(A, B)
- def test_moment_preserve_series_name(self):
- # GH 10565
- s = Series(np.arange(100), name='foo')
- s2 = mom.rolling_mean(s, 30)
- s3 = mom.rolling_sum(s, 20)
- self.assertEqual(s2.name, 'foo')
- self.assertEqual(s3.name, 'foo')
+ self.assertRaises(Exception, mom.ewma, self.arr, span=20, halflife=50)
+ self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, halflife=50)
+ self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20, halflife=50)
+ self.assertRaises(Exception, mom.ewma, self.arr)
def test_ew_empty_arrays(self):
arr = np.array([], dtype=np.float64)
funcs = [mom.ewma, mom.ewmvol, mom.ewmvar]
for f in funcs:
- result = f(arr, 3)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ result = f(arr, 3)
assert_almost_equal(result, arr)
- def _check_ew(self, func):
- self._check_ew_ndarray(func)
- self._check_ew_structures(func)
+ def _check_ew(self, func, name=None):
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ self._check_ew_ndarray(func, name=name)
+ self._check_ew_structures(func, name=name)
- def _check_ew_ndarray(self, func, preserve_nan=False):
+ def _check_ew_ndarray(self, func, preserve_nan=False, name=None):
result = func(self.arr, com=10)
if preserve_nan:
assert(np.isnan(result[self._nan_locs]).all())
@@ -787,10 +1125,11 @@ def _check_ew_ndarray(self, func, preserve_nan=False):
result2 = func(np.arange(50), span=10)
self.assertEqual(result2.dtype, np.float_)
- def _check_ew_structures(self, func):
- series_result = func(self.series, com=10)
+ def _check_ew_structures(self, func, name):
+ series_result = getattr(self.series.ewm(com=10),name)()
tm.assertIsInstance(series_result, Series)
- frame_result = func(self.frame, com=10)
+
+ frame_result = getattr(self.frame.ewm(com=10),name)()
self.assertEqual(type(frame_result), DataFrame)
# create the data only once as we are not setting it
@@ -1044,7 +1383,7 @@ def _variance_debiasing_factors(s, com, adjust, ignore_na):
def _ewma(s, com, min_periods, adjust, ignore_na):
weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
result = s.multiply(weights).cumsum().divide(weights.cumsum()).fillna(method='ffill')
- result[mom.expanding_count(s) < (max(min_periods, 1) if min_periods else 1)] = np.nan
+ result[s.expanding().count() < (max(min_periods, 1) if min_periods else 1)] = np.nan
return result
com = 3.
@@ -1054,16 +1393,16 @@ def _ewma(s, com, min_periods, adjust, ignore_na):
# test consistency between different ewm* moments
self._test_moments_consistency(
min_periods=min_periods,
- count=mom.expanding_count,
- mean=lambda x: mom.ewma(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na),
+ count=lambda x: x.expanding().count(),
+ mean=lambda x: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).mean(),
mock_mean=lambda x: _ewma(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na),
- corr=lambda x, y: mom.ewmcorr(x, y, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na),
- var_unbiased=lambda x: mom.ewmvar(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=False),
- std_unbiased=lambda x: mom.ewmstd(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=False),
- cov_unbiased=lambda x, y: mom.ewmcov(x, y, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=False),
- var_biased=lambda x: mom.ewmvar(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=True),
- std_biased=lambda x: mom.ewmstd(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=True),
- cov_biased=lambda x, y: mom.ewmcov(x, y, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=True),
+ corr=lambda x, y: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).corr(y),
+ var_unbiased=lambda x: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).var(bias=False),
+ std_unbiased=lambda x: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).std(bias=False),
+ cov_unbiased=lambda x, y: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).cov(y, bias=False),
+ var_biased=lambda x: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).var(bias=True),
+ std_biased=lambda x: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).std(bias=True),
+ cov_biased=lambda x, y: x.ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na).cov(y, bias=True),
var_debiasing_factors=lambda x: _variance_debiasing_factors(x, com=com, adjust=adjust, ignore_na=ignore_na))
@slow
@@ -1078,17 +1417,17 @@ def test_expanding_consistency(self):
# test consistency between different expanding_* moments
self._test_moments_consistency(
min_periods=min_periods,
- count=mom.expanding_count,
- mean=lambda x: mom.expanding_mean(x, min_periods=min_periods),
- mock_mean=lambda x: mom.expanding_sum(x, min_periods=min_periods) / mom.expanding_count(x),
- corr=lambda x, y: mom.expanding_corr(x, y, min_periods=min_periods),
- var_unbiased=lambda x: mom.expanding_var(x, min_periods=min_periods),
- std_unbiased=lambda x: mom.expanding_std(x, min_periods=min_periods),
- cov_unbiased=lambda x, y: mom.expanding_cov(x, y, min_periods=min_periods),
- var_biased=lambda x: mom.expanding_var(x, min_periods=min_periods, ddof=0),
- std_biased=lambda x: mom.expanding_std(x, min_periods=min_periods, ddof=0),
- cov_biased=lambda x, y: mom.expanding_cov(x, y, min_periods=min_periods, ddof=0),
- var_debiasing_factors=lambda x: mom.expanding_count(x) / (mom.expanding_count(x) - 1.).replace(0., np.nan)
+ count=lambda x: x.expanding().count(),
+ mean=lambda x: x.expanding(min_periods=min_periods).mean(),
+ mock_mean=lambda x: x.expanding(min_periods=min_periods).sum() / x.expanding().count(),
+ corr=lambda x, y: x.expanding(min_periods=min_periods).corr(y),
+ var_unbiased=lambda x: x.expanding(min_periods=min_periods).var(),
+ std_unbiased=lambda x: x.expanding(min_periods=min_periods).std(),
+ cov_unbiased=lambda x, y: x.expanding(min_periods=min_periods).cov(y),
+ var_biased=lambda x: x.expanding(min_periods=min_periods).var(ddof=0),
+ std_biased=lambda x: x.expanding(min_periods=min_periods).std(ddof=0),
+ cov_biased=lambda x, y: x.expanding(min_periods=min_periods).cov(y, ddof=0),
+ var_debiasing_factors=lambda x: x.expanding().count() / (x.expanding().count() - 1.).replace(0., np.nan)
)
# test consistency between expanding_xyz() and either (a) expanding_apply of Series.xyz(),
@@ -1101,117 +1440,120 @@ def test_expanding_consistency(self):
if no_nans:
functions = self.base_functions + self.no_nan_functions
for (f, require_min_periods, name) in functions:
- expanding_f = getattr(mom,'expanding_{0}'.format(name))
+ expanding_f = getattr(x.expanding(min_periods=min_periods),name)
if require_min_periods and (min_periods is not None) and (min_periods < require_min_periods):
continue
- if expanding_f is mom.expanding_count:
- expanding_f_result = expanding_f(x)
- expanding_apply_f_result = mom.expanding_apply(x, func=f, min_periods=0)
+ if name == 'count':
+ expanding_f_result = expanding_f()
+ expanding_apply_f_result = x.expanding(min_periods=0).apply(func=f)
else:
- if expanding_f in [mom.expanding_cov, mom.expanding_corr]:
- expanding_f_result = expanding_f(x, min_periods=min_periods, pairwise=False)
+ if name in ['cov','corr']:
+ expanding_f_result = expanding_f(pairwise=False)
else:
- expanding_f_result = expanding_f(x, min_periods=min_periods)
- expanding_apply_f_result = mom.expanding_apply(x, func=f, min_periods=min_periods)
+ expanding_f_result = expanding_f()
+ expanding_apply_f_result = x.expanding(min_periods=min_periods).apply(func=f)
if not tm._incompat_bottleneck_version(name):
assert_equal(expanding_f_result, expanding_apply_f_result)
- if (expanding_f in [mom.expanding_cov, mom.expanding_corr]) and isinstance(x, DataFrame):
+ if (name in ['cov','corr']) and isinstance(x, DataFrame):
# test pairwise=True
- expanding_f_result = expanding_f(x, x, min_periods=min_periods, pairwise=True)
+ expanding_f_result = expanding_f(x, pairwise=True)
expected = Panel(items=x.index, major_axis=x.columns, minor_axis=x.columns)
for i, _ in enumerate(x.columns):
for j, _ in enumerate(x.columns):
- expected.iloc[:, i, j] = expanding_f(x.iloc[:, i], x.iloc[:, j], min_periods=min_periods)
+ expected.iloc[:, i, j] = getattr(x.iloc[:, i].expanding(min_periods=min_periods),name)(x.iloc[:, j])
assert_panel_equal(expanding_f_result, expected)
@slow
def test_rolling_consistency(self):
- for window in [1, 2, 3, 10, 20]:
- for min_periods in set([0, 1, 2, 3, 4, window]):
- if min_periods and (min_periods > window):
- continue
- for center in [False, True]:
+ # suppress warnings about empty slices, as we are deliberately testing with empty/0-length Series/DataFrames
+ with warnings.catch_warnings():
+ warnings.filterwarnings("ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning)
- # test consistency between different rolling_* moments
- self._test_moments_consistency(
- min_periods=min_periods,
- count=lambda x: mom.rolling_count(x, window=window, center=center),
- mean=lambda x: mom.rolling_mean(x, window=window, min_periods=min_periods, center=center),
- mock_mean=lambda x: mom.rolling_sum(x, window=window, min_periods=min_periods, center=center).divide(
- mom.rolling_count(x, window=window, center=center)),
- corr=lambda x, y: mom.rolling_corr(x, y, window=window, min_periods=min_periods, center=center),
- var_unbiased=lambda x: mom.rolling_var(x, window=window, min_periods=min_periods, center=center),
- std_unbiased=lambda x: mom.rolling_std(x, window=window, min_periods=min_periods, center=center),
- cov_unbiased=lambda x, y: mom.rolling_cov(x, y, window=window, min_periods=min_periods, center=center),
- var_biased=lambda x: mom.rolling_var(x, window=window, min_periods=min_periods, center=center, ddof=0),
- std_biased=lambda x: mom.rolling_std(x, window=window, min_periods=min_periods, center=center, ddof=0),
- cov_biased=lambda x, y: mom.rolling_cov(x, y, window=window, min_periods=min_periods, center=center, ddof=0),
- var_debiasing_factors=lambda x: mom.rolling_count(x, window=window, center=center).divide(
- (mom.rolling_count(x, window=window, center=center) - 1.).replace(0., np.nan)),
- )
-
- # test consistency between rolling_xyz() and either (a) rolling_apply of Series.xyz(),
- # or (b) rolling_apply of np.nanxyz()
- for (x, is_constant, no_nans) in self.data:
-
- assert_equal = assert_series_equal if isinstance(x, Series) else assert_frame_equal
- functions = self.base_functions
-
- # GH 8269
- if no_nans:
- functions = self.base_functions + self.no_nan_functions
- for (f, require_min_periods, name) in functions:
- rolling_f = getattr(mom,'rolling_{0}'.format(name))
-
- if require_min_periods and (min_periods is not None) and (min_periods < require_min_periods):
- continue
-
- if rolling_f is mom.rolling_count:
- rolling_f_result = rolling_f(x, window=window, center=center)
- rolling_apply_f_result = mom.rolling_apply(x, window=window, func=f,
- min_periods=0, center=center)
- else:
- if rolling_f in [mom.rolling_cov, mom.rolling_corr]:
- rolling_f_result = rolling_f(x, window=window, min_periods=min_periods, center=center, pairwise=False)
+ for window in [1, 2, 3, 10, 20]:
+ for min_periods in set([0, 1, 2, 3, 4, window]):
+ if min_periods and (min_periods > window):
+ continue
+ for center in [False, True]:
+
+ # test consistency between different rolling_* moments
+ self._test_moments_consistency(
+ min_periods=min_periods,
+ count=lambda x: x.rolling(window=window, center=center).count(),
+ mean=lambda x: x.rolling(window=window, min_periods=min_periods, center=center).mean(),
+ mock_mean=lambda x: x.rolling(window=window, min_periods=min_periods, center=center).sum().divide(
+ x.rolling(window=window, min_periods=min_periods, center=center).count()),
+ corr=lambda x, y: x.rolling(window=window, min_periods=min_periods, center=center).corr(y),
+ var_unbiased=lambda x: x.rolling(window=window, min_periods=min_periods, center=center).var(),
+ std_unbiased=lambda x: x.rolling(window=window, min_periods=min_periods, center=center).std(),
+ cov_unbiased=lambda x, y: x.rolling(window=window, min_periods=min_periods, center=center).cov(y),
+ var_biased=lambda x: x.rolling(window=window, min_periods=min_periods, center=center).var(ddof=0),
+ std_biased=lambda x: x.rolling(window=window, min_periods=min_periods, center=center).std(ddof=0),
+ cov_biased=lambda x, y: x.rolling(window=window, min_periods=min_periods, center=center).cov(y, ddof=0),
+ var_debiasing_factors=lambda x: x.rolling(window=window, center=center).count().divide(
+ (x.rolling(window=window, center=center).count() - 1.).replace(0., np.nan)),
+ )
+
+ # test consistency between rolling_xyz() and either (a) rolling_apply of Series.xyz(),
+ # or (b) rolling_apply of np.nanxyz()
+ for (x, is_constant, no_nans) in self.data:
+
+ assert_equal = assert_series_equal if isinstance(x, Series) else assert_frame_equal
+ functions = self.base_functions
+
+ # GH 8269
+ if no_nans:
+ functions = self.base_functions + self.no_nan_functions
+ for (f, require_min_periods, name) in functions:
+ rolling_f = getattr(x.rolling(window=window, center=center, min_periods=min_periods),name)
+
+ if require_min_periods and (min_periods is not None) and (min_periods < require_min_periods):
+ continue
+
+ if name == 'count':
+ rolling_f_result = rolling_f()
+ rolling_apply_f_result = x.rolling(window=window,
+ min_periods=0, center=center).apply(func=f)
else:
- rolling_f_result = rolling_f(x, window=window, min_periods=min_periods, center=center)
- rolling_apply_f_result = mom.rolling_apply(x, window=window, func=f,
- min_periods=min_periods, center=center)
- if not tm._incompat_bottleneck_version(name):
- assert_equal(rolling_f_result, rolling_apply_f_result)
-
- if (rolling_f in [mom.rolling_cov, mom.rolling_corr]) and isinstance(x, DataFrame):
- # test pairwise=True
- rolling_f_result = rolling_f(x, x, window=window, min_periods=min_periods,
- center=center, pairwise=True)
- expected = Panel(items=x.index, major_axis=x.columns, minor_axis=x.columns)
- for i, _ in enumerate(x.columns):
- for j, _ in enumerate(x.columns):
- expected.iloc[:, i, j] = rolling_f(x.iloc[:, i], x.iloc[:, j],
- window=window, min_periods=min_periods, center=center)
- assert_panel_equal(rolling_f_result, expected)
+ if name in ['cov','corr']:
+ rolling_f_result = rolling_f(pairwise=False)
+ else:
+ rolling_f_result = rolling_f()
+ rolling_apply_f_result = x.rolling(window=window,
+ min_periods=min_periods, center=center).apply(func=f)
+ if not tm._incompat_bottleneck_version(name):
+ assert_equal(rolling_f_result, rolling_apply_f_result)
+
+ if (name in ['cov','corr']) and isinstance(x, DataFrame):
+ # test pairwise=True
+ rolling_f_result = rolling_f(x, pairwise=True)
+ expected = Panel(items=x.index, major_axis=x.columns, minor_axis=x.columns)
+ for i, _ in enumerate(x.columns):
+ for j, _ in enumerate(x.columns):
+ expected.iloc[:, i, j] = getattr(x.iloc[:, i].rolling(
+ window=window, min_periods=min_periods, center=center),name)(x.iloc[:, j])
+ assert_panel_equal(rolling_f_result, expected)
# binary moments
def test_rolling_cov(self):
A = self.series
B = A + randn(len(A))
- result = mom.rolling_cov(A, B, 50, min_periods=25)
+ result = A.rolling(window=50, min_periods=25).cov(B)
assert_almost_equal(result[-1], np.cov(A[-50:], B[-50:])[0, 1])
def test_rolling_cov_pairwise(self):
- self._check_pairwise_moment(mom.rolling_cov, 10, min_periods=5)
+ self._check_pairwise_moment('rolling','cov', window=10, min_periods=5)
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
- result = mom.rolling_corr(A, B, 50, min_periods=25)
+ result = A.rolling(window=50, min_periods=25).corr(B)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
# test for correct bias correction
@@ -1220,24 +1562,27 @@ def test_rolling_corr(self):
a[:5] = np.nan
b[:10] = np.nan
- result = mom.rolling_corr(a, b, len(a), min_periods=1)
+ result = a.rolling(window=len(a), min_periods=1).corr(b)
assert_almost_equal(result[-1], a.corr(b))
def test_rolling_corr_pairwise(self):
- self._check_pairwise_moment(mom.rolling_corr, 10, min_periods=5)
+ self._check_pairwise_moment('rolling', 'corr', window=10, min_periods=5)
+
+ def _check_pairwise_moment(self, dispatch, name, **kwargs):
- def _check_pairwise_moment(self, func, *args, **kwargs):
- panel = func(self.frame, *args, **kwargs)
+ def get_result(obj, obj2=None):
+ return getattr(getattr(obj,dispatch)(**kwargs),name)(obj2)
+ panel = get_result(self.frame)
actual = panel.ix[:, 1, 5]
- expected = func(self.frame[1], self.frame[5], *args, **kwargs)
+ expected = get_result(self.frame[1], self.frame[5])
tm.assert_series_equal(actual, expected, check_names=False)
self.assertEqual(actual.name, 5)
def test_flex_binary_moment(self):
# GH3155
# don't blow the stack
- self.assertRaises(TypeError, mom._flex_binary_moment,5,6,None)
+ self.assertRaises(TypeError, rwindow._flex_binary_moment,5,6,None)
def test_corr_sanity(self):
#GH 3155
@@ -1251,13 +1596,13 @@ def test_corr_sanity(self):
[ 0.78369152, 0.63919667]])
)
- res = mom.rolling_corr(df[0],df[1],5,center=True)
+ res = df[0].rolling(5,center=True).corr(df[1])
self.assertTrue(all([np.abs(np.nan_to_num(x)) <=1 for x in res]))
# and some fuzzing
for i in range(10):
df = DataFrame(np.random.rand(30,2))
- res = mom.rolling_corr(df[0],df[1],5,center=True)
+ res = df[0].rolling(5,center=True).corr(df[1])
try:
self.assertTrue(all([np.abs(np.nan_to_num(x)) <=1 for x in res]))
except:
@@ -1268,9 +1613,9 @@ def test_flex_binary_frame(self):
def _check(method):
series = self.frame[1]
- res = method(series, self.frame, 10)
- res2 = method(self.frame, series, 10)
- exp = self.frame.apply(lambda x: method(series, x, 10))
+ res = getattr(series.rolling(window=10),method)(self.frame)
+ res2 = getattr(self.frame.rolling(window=10),method)(series)
+ exp = self.frame.apply(lambda x: getattr(series.rolling(window=10),method)(x))
tm.assert_frame_equal(res, exp)
tm.assert_frame_equal(res2, exp)
@@ -1278,28 +1623,32 @@ def _check(method):
frame2 = self.frame.copy()
frame2.values[:] = np.random.randn(*frame2.shape)
- res3 = method(self.frame, frame2, 10)
- exp = DataFrame(dict((k, method(self.frame[k], frame2[k], 10))
+ res3 = getattr(self.frame.rolling(window=10),method)(frame2)
+ exp = DataFrame(dict((k, getattr(self.frame[k].rolling(window=10),method)(frame2[k]))
for k in self.frame))
tm.assert_frame_equal(res3, exp)
- methods = [mom.rolling_corr, mom.rolling_cov]
+ methods = ['corr','cov']
for meth in methods:
_check(meth)
def test_ewmcov(self):
- self._check_binary_ew(mom.ewmcov)
+ self._check_binary_ew('cov')
def test_ewmcov_pairwise(self):
- self._check_pairwise_moment(mom.ewmcov, span=10, min_periods=5)
+ self._check_pairwise_moment('ewm','cov', span=10, min_periods=5)
def test_ewmcorr(self):
- self._check_binary_ew(mom.ewmcorr)
+ self._check_binary_ew('corr')
def test_ewmcorr_pairwise(self):
- self._check_pairwise_moment(mom.ewmcorr, span=10, min_periods=5)
+ self._check_pairwise_moment('ewm','corr', span=10, min_periods=5)
+
+ def _check_binary_ew(self, name):
+
+ def func(A, B, com, **kwargs):
+ return getattr(A.ewm(com, **kwargs),name)(B)
- def _check_binary_ew(self, func):
A = Series(randn(50), index=np.arange(50))
B = A[2:] + randn(48)
@@ -1329,7 +1678,7 @@ def _check_binary_ew(self, func):
def test_expanding_apply(self):
ser = Series([])
- assert_series_equal(ser, mom.expanding_apply(ser, lambda x: x.mean()))
+ assert_series_equal(ser, ser.expanding().apply(lambda x: x.mean()))
def expanding_mean(x, min_periods=1, freq=None):
return mom.expanding_apply(x,
@@ -1340,7 +1689,7 @@ def expanding_mean(x, min_periods=1, freq=None):
# GH 8080
s = Series([None, None, None])
- result = mom.expanding_apply(s, lambda x: len(x), min_periods=0)
+ result = s.expanding(min_periods=0).apply(lambda x: len(x))
expected = Series([1., 2., 3.])
assert_series_equal(result, expected)
@@ -1350,36 +1699,34 @@ def mean_w_arg(x, const):
df = DataFrame(np.random.rand(20, 3))
- expected = mom.expanding_apply(df, np.mean) + 20.
+ expected = df.expanding().apply(np.mean) + 20.
- assert_frame_equal(mom.expanding_apply(df, mean_w_arg, args=(20,)),
- expected)
- assert_frame_equal(mom.expanding_apply(df, mean_w_arg,
- kwargs={'const' : 20}),
+ assert_frame_equal(df.expanding().apply(mean_w_arg, args=(20,)),
expected)
+ assert_frame_equal(df.expanding().apply(mean_w_arg,
+ kwargs={'const' : 20}),
+ expected)
def test_expanding_corr(self):
A = self.series.dropna()
B = (A + randn(len(A)))[:-5]
- result = mom.expanding_corr(A, B)
+ result = A.expanding().corr(B)
- rolling_result = mom.rolling_corr(A, B, len(A), min_periods=1)
+ rolling_result = A.rolling(window=len(A),min_periods=1).corr(B)
assert_almost_equal(rolling_result, result)
def test_expanding_count(self):
- result = mom.expanding_count(self.series)
- assert_almost_equal(result, mom.rolling_count(self.series,
- len(self.series)))
+ result = self.series.expanding().count()
+ assert_almost_equal(result, self.series.rolling(window=len(self.series)).count())
def test_expanding_quantile(self):
- result = mom.expanding_quantile(self.series, 0.5)
+ result = self.series.expanding().quantile(0.5)
- rolling_result = mom.rolling_quantile(self.series,
- len(self.series),
- 0.5, min_periods=1)
+ rolling_result = self.series.rolling(
+ window=len(self.series),min_periods=1).quantile(0.5)
assert_almost_equal(result, rolling_result)
@@ -1387,9 +1734,9 @@ def test_expanding_cov(self):
A = self.series
B = (A + randn(len(A)))[:-5]
- result = mom.expanding_cov(A, B)
+ result = A.expanding().cov(B)
- rolling_result = mom.rolling_cov(A, B, len(A), min_periods=1)
+ rolling_result = A.rolling(window=len(A), min_periods=1).cov(B)
assert_almost_equal(rolling_result, result)
@@ -1397,19 +1744,17 @@ def test_expanding_max(self):
self._check_expanding(mom.expanding_max, np.max, preserve_nan=False)
def test_expanding_cov_pairwise(self):
- result = mom.expanding_cov(self.frame)
+ result = self.frame.expanding().corr()
- rolling_result = mom.rolling_cov(self.frame, len(self.frame),
- min_periods=1)
+ rolling_result = self.frame.rolling(window=len(self.frame),min_periods=1).corr()
for i in result.items:
assert_almost_equal(result[i], rolling_result[i])
def test_expanding_corr_pairwise(self):
- result = mom.expanding_corr(self.frame)
+ result = self.frame.expanding().corr()
- rolling_result = mom.rolling_corr(self.frame, len(self.frame),
- min_periods=1)
+ rolling_result = self.frame.rolling(window=len(self.frame), min_periods=1).corr()
for i in result.items:
assert_almost_equal(result[i], rolling_result[i])
@@ -1418,17 +1763,17 @@ def test_expanding_cov_diff_index(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
- result = mom.expanding_cov(s1, s2)
+ result = s1.expanding().cov(s2)
expected = Series([None, None, 2.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
- result = mom.expanding_cov(s1, s2a)
+ result = s1.expanding().cov(s2a)
assert_series_equal(result, expected)
s1 = Series([7, 8, 10], index=[0, 1, 3])
s2 = Series([7, 9, 10], index=[0, 2, 3])
- result = mom.expanding_cov(s1, s2)
+ result = s1.expanding().cov(s2)
expected = Series([None, None, None, 4.5])
assert_series_equal(result, expected)
@@ -1436,17 +1781,17 @@ def test_expanding_corr_diff_index(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
- result = mom.expanding_corr(s1, s2)
+ result = s1.expanding().corr(s2)
expected = Series([None, None, 1.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
- result = mom.expanding_corr(s1, s2a)
+ result = s1.expanding().corr(s2a)
assert_series_equal(result, expected)
s1 = Series([7, 8, 10], index=[0, 1, 3])
s2 = Series([7, 9, 10], index=[0, 2, 3])
- result = mom.expanding_corr(s1, s2)
+ result = s1.expanding().corr(s2)
expected = Series([None, None, None, 1.])
assert_series_equal(result, expected)
@@ -1454,24 +1799,24 @@ def test_rolling_cov_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
- result = mom.rolling_cov(s1, s2, window=3, min_periods=2)
+ result = s1.rolling(window=3, min_periods=2).cov(s2)
expected = Series([None, None, 2.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
- result = mom.rolling_cov(s1, s2a, window=3, min_periods=2)
+ result = s1.rolling(window=3, min_periods=2).cov(s2a)
assert_series_equal(result, expected)
def test_rolling_corr_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
- result = mom.rolling_corr(s1, s2, window=3, min_periods=2)
+ result = s1.rolling(window=3, min_periods=2).corr(s2)
expected = Series([None, None, 1.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
- result = mom.rolling_corr(s1, s2a, window=3, min_periods=2)
+ result = s1.rolling(window=3, min_periods=2).corr(s2a)
assert_series_equal(result, expected)
def test_rolling_functions_window_non_shrinkage(self):
@@ -1482,20 +1827,20 @@ def test_rolling_functions_window_non_shrinkage(self):
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)
- functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
- lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
- lambda x: mom.rolling_max(x, window=10, min_periods=5),
- lambda x: mom.rolling_min(x, window=10, min_periods=5),
- lambda x: mom.rolling_sum(x, window=10, min_periods=5),
- lambda x: mom.rolling_mean(x, window=10, min_periods=5),
- lambda x: mom.rolling_std(x, window=10, min_periods=5),
- lambda x: mom.rolling_var(x, window=10, min_periods=5),
- lambda x: mom.rolling_skew(x, window=10, min_periods=5),
- lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
- lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
- lambda x: mom.rolling_median(x, window=10, min_periods=5),
- lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
- lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
+ functions = [lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=False),
+ lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=False),
+ lambda x: x.rolling(window=10, min_periods=5).max(),
+ lambda x: x.rolling(window=10, min_periods=5).min(),
+ lambda x: x.rolling(window=10, min_periods=5).sum(),
+ lambda x: x.rolling(window=10, min_periods=5).mean(),
+ lambda x: x.rolling(window=10, min_periods=5).std(),
+ lambda x: x.rolling(window=10, min_periods=5).var(),
+ lambda x: x.rolling(window=10, min_periods=5).skew(),
+ lambda x: x.rolling(window=10, min_periods=5).kurt(),
+ lambda x: x.rolling(window=10, min_periods=5).quantile(quantile=0.5),
+ lambda x: x.rolling(window=10, min_periods=5).median(),
+ lambda x: x.rolling(window=10, min_periods=5).apply(sum),
+ lambda x: x.rolling(win_type='boxcar', window=10, min_periods=5).mean(),
]
for f in functions:
try:
@@ -1509,8 +1854,8 @@ def test_rolling_functions_window_non_shrinkage(self):
# scipy needed for rolling_window
continue
- functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
- lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
+ functions = [lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=True),
+ lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=True),
]
for f in functions:
df_result_panel = f(df)
@@ -1528,35 +1873,35 @@ def test_moment_functions_zero_length(self):
df2_expected = df2
df2_expected_panel = Panel(items=df2.index, major_axis=df2.columns, minor_axis=df2.columns)
- functions = [lambda x: mom.expanding_count(x),
- lambda x: mom.expanding_cov(x, x, pairwise=False, min_periods=5),
- lambda x: mom.expanding_corr(x, x, pairwise=False, min_periods=5),
- lambda x: mom.expanding_max(x, min_periods=5),
- lambda x: mom.expanding_min(x, min_periods=5),
- lambda x: mom.expanding_sum(x, min_periods=5),
- lambda x: mom.expanding_mean(x, min_periods=5),
- lambda x: mom.expanding_std(x, min_periods=5),
- lambda x: mom.expanding_var(x, min_periods=5),
- lambda x: mom.expanding_skew(x, min_periods=5),
- lambda x: mom.expanding_kurt(x, min_periods=5),
- lambda x: mom.expanding_quantile(x, quantile=0.5, min_periods=5),
- lambda x: mom.expanding_median(x, min_periods=5),
- lambda x: mom.expanding_apply(x, func=sum, min_periods=5),
- lambda x: mom.rolling_count(x, window=10),
- lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
- lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
- lambda x: mom.rolling_max(x, window=10, min_periods=5),
- lambda x: mom.rolling_min(x, window=10, min_periods=5),
- lambda x: mom.rolling_sum(x, window=10, min_periods=5),
- lambda x: mom.rolling_mean(x, window=10, min_periods=5),
- lambda x: mom.rolling_std(x, window=10, min_periods=5),
- lambda x: mom.rolling_var(x, window=10, min_periods=5),
- lambda x: mom.rolling_skew(x, window=10, min_periods=5),
- lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
- lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
- lambda x: mom.rolling_median(x, window=10, min_periods=5),
- lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
- lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
+ functions = [lambda x: x.expanding().count(),
+ lambda x: x.expanding(min_periods=5).cov(x, pairwise=False),
+ lambda x: x.expanding(min_periods=5).corr(x, pairwise=False),
+ lambda x: x.expanding(min_periods=5).max(),
+ lambda x: x.expanding(min_periods=5).min(),
+ lambda x: x.expanding(min_periods=5).sum(),
+ lambda x: x.expanding(min_periods=5).mean(),
+ lambda x: x.expanding(min_periods=5).std(),
+ lambda x: x.expanding(min_periods=5).var(),
+ lambda x: x.expanding(min_periods=5).skew(),
+ lambda x: x.expanding(min_periods=5).kurt(),
+ lambda x: x.expanding(min_periods=5).quantile(0.5),
+ lambda x: x.expanding(min_periods=5).median(),
+ lambda x: x.expanding(min_periods=5).apply(sum),
+ lambda x: x.rolling(window=10).count(),
+ lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=False),
+ lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=False),
+ lambda x: x.rolling(window=10, min_periods=5).max(),
+ lambda x: x.rolling(window=10, min_periods=5).min(),
+ lambda x: x.rolling(window=10, min_periods=5).sum(),
+ lambda x: x.rolling(window=10, min_periods=5).mean(),
+ lambda x: x.rolling(window=10, min_periods=5).std(),
+ lambda x: x.rolling(window=10, min_periods=5).var(),
+ lambda x: x.rolling(window=10, min_periods=5).skew(),
+ lambda x: x.rolling(window=10, min_periods=5).kurt(),
+ lambda x: x.rolling(window=10, min_periods=5).quantile(0.5),
+ lambda x: x.rolling(window=10, min_periods=5).median(),
+ lambda x: x.rolling(window=10, min_periods=5).apply(sum),
+ lambda x: x.rolling(win_type='boxcar', window=10, min_periods=5).mean(),
]
for f in functions:
try:
@@ -1573,10 +1918,10 @@ def test_moment_functions_zero_length(self):
# scipy needed for rolling_window
continue
- functions = [lambda x: mom.expanding_cov(x, x, pairwise=True, min_periods=5),
- lambda x: mom.expanding_corr(x, x, pairwise=True, min_periods=5),
- lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
- lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
+ functions = [lambda x: x.expanding(min_periods=5).cov(x, pairwise=True),
+ lambda x: x.expanding(min_periods=5).corr(x, pairwise=True),
+ lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=True),
+ lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=True),
]
for f in functions:
df1_result_panel = f(df1)
@@ -1591,10 +1936,10 @@ def test_expanding_cov_pairwise_diff_length(self):
df1a = DataFrame([[1,5], [3,9]], index=[0,2], columns=['A','B'])
df2 = DataFrame([[5,6], [None,None], [2,1]], columns=['X','Y'])
df2a = DataFrame([[5,6], [2,1]], index=[0,2], columns=['X','Y'])
- result1 = mom.expanding_cov(df1, df2, pairwise=True)[2]
- result2 = mom.expanding_cov(df1, df2a, pairwise=True)[2]
- result3 = mom.expanding_cov(df1a, df2, pairwise=True)[2]
- result4 = mom.expanding_cov(df1a, df2a, pairwise=True)[2]
+ result1 = df1.expanding().cov(df2a, pairwise=True)[2]
+ result2 = df1.expanding().cov(df2a, pairwise=True)[2]
+ result3 = df1a.expanding().cov(df2, pairwise=True)[2]
+ result4 = df1a.expanding().cov(df2a, pairwise=True)[2]
expected = DataFrame([[-3., -5.], [-6., -10.]], index=['A','B'], columns=['X','Y'])
assert_frame_equal(result1, expected)
assert_frame_equal(result2, expected)
@@ -1607,10 +1952,10 @@ def test_expanding_corr_pairwise_diff_length(self):
df1a = DataFrame([[1,2], [3,4]], index=[0,2], columns=['A','B'])
df2 = DataFrame([[5,6], [None,None], [2,1]], columns=['X','Y'])
df2a = DataFrame([[5,6], [2,1]], index=[0,2], columns=['X','Y'])
- result1 = mom.expanding_corr(df1, df2, pairwise=True)[2]
- result2 = mom.expanding_corr(df1, df2a, pairwise=True)[2]
- result3 = mom.expanding_corr(df1a, df2, pairwise=True)[2]
- result4 = mom.expanding_corr(df1a, df2a, pairwise=True)[2]
+ result1 = df1.expanding().corr(df2, pairwise=True)[2]
+ result2 = df1.expanding().corr(df2a, pairwise=True)[2]
+ result3 = df1a.expanding().corr(df2, pairwise=True)[2]
+ result4 = df1a.expanding().corr(df2a, pairwise=True)[2]
expected = DataFrame([[-1.0, -1.0], [-1.0, -1.0]], index=['A','B'], columns=['X','Y'])
assert_frame_equal(result1, expected)
assert_frame_equal(result2, expected)
@@ -1650,12 +1995,12 @@ def test_pairwise_stats_column_names_order(self):
self.assert_numpy_array_equal(result, results[0])
# DataFrame with itself, pairwise=True
- for f in [lambda x: mom.expanding_cov(x, pairwise=True),
- lambda x: mom.expanding_corr(x, pairwise=True),
- lambda x: mom.rolling_cov(x, window=3, pairwise=True),
- lambda x: mom.rolling_corr(x, window=3, pairwise=True),
- lambda x: mom.ewmcov(x, com=3, pairwise=True),
- lambda x: mom.ewmcorr(x, com=3, pairwise=True),
+ for f in [lambda x: x.expanding().cov(pairwise=True),
+ lambda x: x.expanding().corr(pairwise=True),
+ lambda x: x.rolling(window=3).cov(pairwise=True),
+ lambda x: x.rolling(window=3).corr(pairwise=True),
+ lambda x: x.ewm(com=3).cov(pairwise=True),
+ lambda x: x.ewm(com=3).corr(pairwise=True),
]:
results = [f(df) for df in df1s]
for (df, result) in zip(df1s, results):
@@ -1667,12 +2012,12 @@ def test_pairwise_stats_column_names_order(self):
self.assert_numpy_array_equal(result, results[0])
# DataFrame with itself, pairwise=False
- for f in [lambda x: mom.expanding_cov(x, pairwise=False),
- lambda x: mom.expanding_corr(x, pairwise=False),
- lambda x: mom.rolling_cov(x, window=3, pairwise=False),
- lambda x: mom.rolling_corr(x, window=3, pairwise=False),
- lambda x: mom.ewmcov(x, com=3, pairwise=False),
- lambda x: mom.ewmcorr(x, com=3, pairwise=False),
+ for f in [lambda x: x.expanding().cov(pairwise=False),
+ lambda x: x.expanding().corr(pairwise=False),
+ lambda x: x.rolling(window=3).cov(pairwise=False),
+ lambda x: x.rolling(window=3).corr(pairwise=False),
+ lambda x: x.ewm(com=3).cov(pairwise=False),
+ lambda x: x.ewm(com=3).corr(pairwise=False),
]:
results = [f(df) for df in df1s]
for (df, result) in zip(df1s, results):
@@ -1683,12 +2028,12 @@ def test_pairwise_stats_column_names_order(self):
self.assert_numpy_array_equal(result, results[0])
# DataFrame with another DataFrame, pairwise=True
- for f in [lambda x, y: mom.expanding_cov(x, y, pairwise=True),
- lambda x, y: mom.expanding_corr(x, y, pairwise=True),
- lambda x, y: mom.rolling_cov(x, y, window=3, pairwise=True),
- lambda x, y: mom.rolling_corr(x, y, window=3, pairwise=True),
- lambda x, y: mom.ewmcov(x, y, com=3, pairwise=True),
- lambda x, y: mom.ewmcorr(x, y, com=3, pairwise=True),
+ for f in [lambda x, y: x.expanding().cov(y, pairwise=True),
+ lambda x, y: x.expanding().corr(y, pairwise=True),
+ lambda x, y: x.rolling(window=3).cov(y, pairwise=True),
+ lambda x, y: x.rolling(window=3).corr(y, pairwise=True),
+ lambda x, y: x.ewm(com=3).cov(y, pairwise=True),
+ lambda x, y: x.ewm(com=3).corr(y, pairwise=True),
]:
results = [f(df, df2) for df in df1s]
for (df, result) in zip(df1s, results):
@@ -1700,12 +2045,12 @@ def test_pairwise_stats_column_names_order(self):
self.assert_numpy_array_equal(result, results[0])
# DataFrame with another DataFrame, pairwise=False
- for f in [lambda x, y: mom.expanding_cov(x, y, pairwise=False),
- lambda x, y: mom.expanding_corr(x, y, pairwise=False),
- lambda x, y: mom.rolling_cov(x, y, window=3, pairwise=False),
- lambda x, y: mom.rolling_corr(x, y, window=3, pairwise=False),
- lambda x, y: mom.ewmcov(x, y, com=3, pairwise=False),
- lambda x, y: mom.ewmcorr(x, y, com=3, pairwise=False),
+ for f in [lambda x, y: x.expanding().cov(y, pairwise=False),
+ lambda x, y: x.expanding().corr(y, pairwise=False),
+ lambda x, y: x.rolling(window=3).cov(y, pairwise=False),
+ lambda x, y: x.rolling(window=3).corr(y, pairwise=False),
+ lambda x, y: x.ewm(com=3).cov(y, pairwise=False),
+ lambda x, y: x.ewm(com=3).corr(y, pairwise=False),
]:
results = [f(df, df2) if df.columns.is_unique else None for df in df1s]
for (df, result) in zip(df1s, results):
@@ -1719,12 +2064,12 @@ def test_pairwise_stats_column_names_order(self):
tm.assertRaisesRegexp(ValueError, "'arg2' columns are not unique", f, df2, df)
# DataFrame with a Series
- for f in [lambda x, y: mom.expanding_cov(x, y),
- lambda x, y: mom.expanding_corr(x, y),
- lambda x, y: mom.rolling_cov(x, y, window=3),
- lambda x, y: mom.rolling_corr(x, y, window=3),
- lambda x, y: mom.ewmcov(x, y, com=3),
- lambda x, y: mom.ewmcorr(x, y, com=3),
+ for f in [lambda x, y: x.expanding().cov(y),
+ lambda x, y: x.expanding().corr(y),
+ lambda x, y: x.rolling(window=3).cov(y),
+ lambda x, y: x.rolling(window=3).corr(y),
+ lambda x, y: x.ewm(com=3).cov(y),
+ lambda x, y: x.ewm(com=3).corr(y),
]:
results = [f(df, s) for df in df1s] + [f(s, df) for df in df1s]
for (df, result) in zip(df1s, results):
@@ -1740,12 +2085,12 @@ def test_rolling_skew_edge_cases(self):
# yields all NaN (0 variance)
d = Series([1] * 5)
- x = mom.rolling_skew(d, window=5)
+ x = d.rolling(window=5).skew()
assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
- x = mom.rolling_skew(d, window=2)
+ x = d.rolling(window=2).skew()
assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 0.177994, 1.548824]
@@ -1753,7 +2098,7 @@ def test_rolling_skew_edge_cases(self):
1.73508164, 0.41941401])
expected = Series([np.NaN, np.NaN, np.NaN,
0.177994, 1.548824])
- x = mom.rolling_skew(d, window=4)
+ x = d.rolling(window=4).skew()
assert_series_equal(expected, x)
def test_rolling_kurt_edge_cases(self):
@@ -1762,12 +2107,12 @@ def test_rolling_kurt_edge_cases(self):
# yields all NaN (0 variance)
d = Series([1] * 5)
- x = mom.rolling_kurt(d, window=5)
+ x = d.rolling(window=5).kurt()
assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
- x = mom.rolling_kurt(d, window=3)
+ x = d.rolling(window=3).kurt()
assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 1.224307, 2.671499]
@@ -1775,7 +2120,7 @@ def test_rolling_kurt_edge_cases(self):
1.73508164, 0.41941401])
expected = Series([np.NaN, np.NaN, np.NaN,
1.224307, 2.671499])
- x = mom.rolling_kurt(d, window=4)
+ x = d.rolling(window=4).kurt()
assert_series_equal(expected, x)
def _check_expanding_ndarray(self, func, static_comp, has_min_periods=True,
@@ -1822,11 +2167,13 @@ def _check_expanding_structures(self, func):
def _check_expanding(self, func, static_comp, has_min_periods=True,
has_time_rule=True,
preserve_nan=True):
- self._check_expanding_ndarray(func, static_comp,
- has_min_periods=has_min_periods,
- has_time_rule=has_time_rule,
- preserve_nan=preserve_nan)
- self._check_expanding_structures(func)
+ with warnings.catch_warnings(record=True):
+ self._check_expanding_ndarray(func, static_comp,
+ has_min_periods=has_min_periods,
+ has_time_rule=has_time_rule,
+ preserve_nan=preserve_nan)
+ with warnings.catch_warnings(record=True):
+ self._check_expanding_structures(func)
def test_rolling_max_gh6297(self):
"""Replicate result expected in GH #6297"""
@@ -1843,7 +2190,8 @@ def test_rolling_max_gh6297(self):
expected = Series([1.0, 2.0, 6.0, 4.0, 5.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
- x = mom.rolling_max(series, window=1, freq='D')
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ x = series.rolling(window=1, freq='D').max()
assert_series_equal(expected, x)
def test_rolling_max_how_resample(self):
@@ -1862,14 +2210,16 @@ def test_rolling_max_how_resample(self):
expected = Series([0.0, 1.0, 2.0, 3.0, 20.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
- x = mom.rolling_max(series, window=1, freq='D')
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ x = series.rolling(window=1, freq='D').max()
assert_series_equal(expected, x)
# Now specify median (10.0)
expected = Series([0.0, 1.0, 2.0, 3.0, 10.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
- x = mom.rolling_max(series, window=1, freq='D', how='median')
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ x = series.rolling(window=1, freq='D').max(how='median')
assert_series_equal(expected, x)
# Now specify mean (4+10+20)/3
@@ -1877,8 +2227,9 @@ def test_rolling_max_how_resample(self):
expected = Series([0.0, 1.0, 2.0, 3.0, v],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
- x = mom.rolling_max(series, window=1, freq='D', how='mean')
- assert_series_equal(expected, x)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ x = series.rolling(window=1, freq='D').max(how='mean')
+ assert_series_equal(expected, x)
def test_rolling_min_how_resample(self):
@@ -1897,8 +2248,9 @@ def test_rolling_min_how_resample(self):
expected = Series([0.0, 1.0, 2.0, 3.0, 4.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
- x = mom.rolling_min(series, window=1, freq='D')
- assert_series_equal(expected, x)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ r = series.rolling(window=1, freq='D')
+ assert_series_equal(expected, r.min())
def test_rolling_median_how_resample(self):
@@ -1916,14 +2268,15 @@ def test_rolling_median_how_resample(self):
expected = Series([0.0, 1.0, 2.0, 3.0, 10],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
- x = mom.rolling_median(series, window=1, freq='D')
- assert_series_equal(expected, x)
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ x = series.rolling(window=1, freq='D').median()
+ assert_series_equal(expected, x)
def test_rolling_median_memory_error(self):
# GH11722
n = 20000
- mom.rolling_median(Series(np.random.randn(n)), window=2, center=False)
- mom.rolling_median(Series(np.random.randn(n)), window=2, center=False)
+ Series(np.random.randn(n)).rolling(window=2, center=False).median()
+ Series(np.random.randn(n)).rolling(window=2, center=False).median()
if __name__ == '__main__':
import nose
diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py
index a6aa5ff66576c..5c3cb573766d7 100644
--- a/pandas/util/decorators.py
+++ b/pandas/util/decorators.py
@@ -2,6 +2,7 @@
from pandas.lib import cache_readonly
import sys
import warnings
+from textwrap import dedent
from functools import wraps
@@ -180,7 +181,7 @@ def __call__(self, func):
func.__doc__ = func.__doc__ if func.__doc__ else ''
self.addendum = self.addendum if self.addendum else ''
docitems = [func.__doc__, self.addendum]
- func.__doc__ = self.join.join(docitems)
+ func.__doc__ = dedent(self.join.join(docitems))
return func
| closes #10702
closes #9052
xref #4950, removal of depr
So this basically takes all of the `pd.rolling_*`,`pd.expanding_*`,`pd.ewma_*` routines and allows an object oriented interface, similar to groupby.
Some benefits:
- nice tab completions on the Rolling/Expanding/EWM objects
- much cleaner code internally
- complete back compat, e.g. everything just works like it did
- added a `.agg/aggregate` function, similar to groupby, where you can do multiple aggregations at once
- added `__getitem__` accessing, e.g. `df.rolling(....)['A','B'].sum()` for a nicer API
- allows for much of #8659 to be done very easily
- fix for coercing Timedeltas properly
- handling nuiscance (string) columns
Other:
- along with window doc rewrite, fixed doc-strings for groupby/window to provide back-refs
ToDO:
- [x] I think that all of the doc-strings are correct, but need check
- [x] implement `.agg`
- [x] update API.rst, what's new
- [x] deprecate the `pd.expanding_*`,`pd.rolling_*`,`pd.ewma_*` interface as this is polluting the top-level namespace quite a bit
- [x] change the docs to use the new API
```
In [4]: df = DataFrame({'A' : range(5), 'B' : pd.timedelta_range('1 day',periods=5), 'C' : 'foo'})
In [5]: df.rolling(window=2).sum()
Out[5]:
A B C
0 NaN NaT foo
1 1 3 days foo
2 3 5 days foo
3 5 7 days foo
4 7 9 days foo
In [6]: df.rolling(window=2)['A','C'].sum()
Out[6]:
A C
0 NaN foo
1 1 foo
2 3 foo
3 5 foo
4 7 foo
In [2]: r = df.rolling(window=3)
In [3]: r.
r.A r.C r.corr r.cov r.max r.median r.name r.skew r.sum
r.B r.apply r.count r.kurt r.mean r.min r.quantile r.std r.var
```
do rolling/expanding/ewma ops
```
In [1]: s = Series(range(5))
In [2]: r = s.rolling(2)
I# pd.rolling_sum
In [3]: r.sum()
Out[3]:
0 NaN
1 1
2 3
3 5
4 7
dtype: float64
# nicer repr
In [4]: r
Out[4]: Rolling [window->2,center->False,axis->0]
In [5]: e = s.expanding(min_periods=2)
# pd.expanding_sum
In [6]: e.sum()
Out[6]:
0 NaN
1 1
2 3
3 6
4 10
dtype: float64
In [7]: em = s.ewm(com=10)
# pd.ewma
In [8]: em.mean()
Out[8]:
0 0.000000
1 0.523810
2 1.063444
3 1.618832
4 2.189874
dtype: float64
```
and allow the various aggregation type of ops (similar to groupby)
```
In [1]: df = DataFrame({'A' : range(5), 'B' : pd.timedelta_range('1 day',periods=5), 'C' : 'foo'})
In [2]: r = df.rolling(2,min_periods=1)
In [3]: r.agg([np.sum,np.mean])
Out[3]:
A B C
sum mean sum mean sum mean
0 0 0.0 1 days 1 days 00:00:00 foo foo
1 1 0.5 3 days 1 days 12:00:00 foo foo
2 3 1.5 5 days 2 days 12:00:00 foo foo
3 5 2.5 7 days 3 days 12:00:00 foo foo
4 7 3.5 9 days 4 days 12:00:00 foo foo
In [4]: r.agg({'A' : 'sum', 'B' : 'mean'})
Out[4]:
A B
0 0 1 days 00:00:00
1 1 1 days 12:00:00
2 3 2 days 12:00:00
3 5 3 days 12:00:00
4 7 4 days 12:00:00
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/11603 | 2015-11-14T18:12:18Z | 2015-12-19T13:52:07Z | 2015-12-19T13:52:07Z | 2016-01-17T18:46:08Z |
PERF: Faster Series construction with no data and DatetimeIndex. | diff --git a/asv_bench/benchmarks/series_methods.py b/asv_bench/benchmarks/series_methods.py
index a40ed3f1d6482..4e368c6d7cde2 100644
--- a/asv_bench/benchmarks/series_methods.py
+++ b/asv_bench/benchmarks/series_methods.py
@@ -1,6 +1,20 @@
from .pandas_vb_common import *
+class series_constructor_no_data_datetime_index(object):
+ goal_time = 0.2
+
+ def setup(self):
+ self.dr = pd.date_range(
+ start=datetime(2015,10,26),
+ end=datetime(2016,1,1),
+ freq='10s'
+ ) # ~500k long
+
+ def time_series_constructor_no_data_datetime_index(self):
+ Series(data=None, index=self.dr)
+
+
class series_isin_int64(object):
goal_time = 0.2
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 3d10566e47075..1b8cd270b3e8c 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -86,6 +86,7 @@ Performance Improvements
- Improved performance to ``to_excel`` (:issue:`11352`)
- Performance bug in repr of ``Categorical`` categories, which was rendering the strings before chopping them for display (:issue:`11305`)
+- Improved performance of ``Series`` constructor with no data and ``DatetimeIndex`` (:issue:`11433`)
.. _whatsnew_0171.bug_fixes:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 5106225cdd3c9..bc9a2f466530e 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -171,17 +171,22 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
index = Index(_try_sort(data))
try:
if isinstance(index, DatetimeIndex):
- # coerce back to datetime objects for lookup
- data = _dict_compat(data)
- data = lib.fast_multiget(data, index.astype('O'),
- default=np.nan)
+ if len(data):
+ # coerce back to datetime objects for lookup
+ data = _dict_compat(data)
+ data = lib.fast_multiget(data, index.astype('O'),
+ default=np.nan)
+ else:
+ data = np.nan
elif isinstance(index, PeriodIndex):
- data = [data.get(i, nan) for i in index]
+ data = [data.get(i, nan)
+ for i in index] if data else np.nan
else:
data = lib.fast_multiget(data, index.values,
default=np.nan)
except TypeError:
- data = [data.get(i, nan) for i in index]
+ data = [data.get(i, nan)
+ for i in index] if data else np.nan
elif isinstance(data, SingleBlockManager):
if index is None:
| closes pydata/pandas#11433
Code. taken from @jreback comment on pydata/pandas#11433
asv benchmark:
```
before after ratio
[6c4d2c7d] [6439bd94]
- 1.68s 364.81μs 0.00 series_methods.series_constructor_no_data_datetime_index.time_series_constructor_no_data_datetime_index
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/11598 | 2015-11-13T21:40:37Z | 2015-11-15T16:55:16Z | 2015-11-15T16:55:16Z | 2015-11-15T16:55:19Z |
PERF/DOC: Option to .info() and .memory_usage() to provide for deep introspection of memory consumption #11595 | diff --git a/doc/source/api.rst b/doc/source/api.rst
index bfd1c92d14acd..6bee0a1ceafb8 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -284,6 +284,7 @@ Attributes
Series.itemsize
Series.base
Series.T
+ Series.memory_usage
Conversion
~~~~~~~~~~
@@ -772,6 +773,7 @@ Attributes and underlying data
DataFrame.ndim
DataFrame.size
DataFrame.shape
+ DataFrame.memory_usage
Conversion
~~~~~~~~~~
@@ -1333,6 +1335,7 @@ Attributes
Index.itemsize
Index.base
Index.T
+ Index.memory_usage
Modifying and Computations
~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/doc/source/faq.rst b/doc/source/faq.rst
index 7714d937e15d6..82102296c4198 100644
--- a/doc/source/faq.rst
+++ b/doc/source/faq.rst
@@ -50,6 +50,16 @@ The ``+`` symbol indicates that the true memory usage could be higher, because
pandas does not count the memory used by values in columns with
``dtype=object``.
+.. versionadded:: 0.17.1
+
+Passing ``memory_usage='deep'`` will enable a more accurate memory usage report,
+that accounts for the full usage of the contained objects. This is optional
+as it can be expensive to do this deeper introspection.
+
+.. ipython:: python
+
+ df.info(memory_usage='deep')
+
By default the display option is set to ``True`` but can be explicitly
overridden by passing the ``memory_usage`` argument when invoking ``df.info()``.
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 1d9b02e6a7bb1..f92ed4af50b6c 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -27,6 +27,19 @@ Enhancements
- Improve the error message displayed in :func:`pandas.io.gbq.to_gbq` when the DataFrame does not match the schema of the destination table (:issue:`11359`)
- Added ``axvlines_kwds`` to parallel coordinates plot (:issue:`10709`)
+- Option to ``.info()`` and ``.memory_usage()`` to provide for deep introspection of memory consumption. Note that this can be expensive to compute and therefore is an optional parameter. (:issue:``11595``)
+
+.. ipython:: python
+
+ df = DataFrame({'A' : ['foo']*1000})
+ df['B'] = df['A'].astype('category')
+
+ # shows the '+' as we have object dtypes
+ df.info()
+
+ # we have an accurate memory assessment (but can be expensive to compute this)
+ df.info(memory_usage='deep')
+
- ``Index`` now has ``fillna`` method (:issue:`10089`)
.. ipython:: python
diff --git a/pandas/core/base.py b/pandas/core/base.py
index d3850be13b6f0..173423007037a 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -489,6 +489,36 @@ def nunique(self, dropna=True):
n -= 1
return n
+ def memory_usage(self, deep=False):
+ """
+ Memory usage of my values
+
+ Parameters
+ ----------
+ deep : bool
+ Introspect the data deeply, interrogate
+ `object` dtypes for system-level memory consumption
+
+ Returns
+ -------
+ bytes used
+
+ Notes
+ -----
+ Memory usage does not include memory consumed by elements that
+ are not components of the array if deep=False
+
+ See Also
+ --------
+ numpy.ndarray.nbytes
+ """
+ if hasattr(self.values,'memory_usage'):
+ return self.values.memory_usage(deep=deep)
+
+ v = self.values.nbytes
+ if deep and com.is_object_dtype(self):
+ v += lib.memory_usage_of_objects(self.values)
+ return v
def factorize(self, sort=False, na_sentinel=-1):
"""
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py
index 514f907d943a8..ccfd4e657fe39 100644
--- a/pandas/core/categorical.py
+++ b/pandas/core/categorical.py
@@ -924,6 +924,31 @@ def T(self):
def nbytes(self):
return self._codes.nbytes + self._categories.values.nbytes
+ def memory_usage(self, deep=False):
+ """
+ Memory usage of my values
+
+ Parameters
+ ----------
+ deep : bool
+ Introspect the data deeply, interrogate
+ `object` dtypes for system-level memory consumption
+
+ Returns
+ -------
+ bytes used
+
+ Notes
+ -----
+ Memory usage does not include memory consumed by elements that
+ are not components of the array if deep=False
+
+ See Also
+ --------
+ numpy.ndarray.nbytes
+ """
+ return self._codes.nbytes + self._categories.memory_usage(deep=deep)
+
def searchsorted(self, v, side='left', sorter=None):
"""Find indices where elements should be inserted to maintain order.
diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py
index 751a530ce73cc..35689030d9c09 100644
--- a/pandas/core/config_init.py
+++ b/pandas/core/config_init.py
@@ -215,9 +215,9 @@
"""
pc_memory_usage_doc = """
-: bool or None
+: bool, string or None
This specifies if the memory usage of a DataFrame should be displayed when
- df.info() is called.
+ df.info() is called. Valid values True,False,'deep'
"""
style_backup = dict()
@@ -292,7 +292,7 @@ def mpl_style_cb(key):
cf.register_option('line_width', get_default_val('display.width'),
pc_line_width_doc)
cf.register_option('memory_usage', True, pc_memory_usage_doc,
- validator=is_instance_factory([type(None), bool]))
+ validator=is_one_of_factory([None, True, False, 'deep']))
cf.register_option('unicode.east_asian_width', False,
pc_east_asian_width_doc, validator=is_bool)
cf.register_option('unicode.ambiguous_as_wide', False,
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 538b9d3f8e712..22d0026f27742 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1582,11 +1582,12 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_co
max_cols : int, default None
Determines whether full summary or short summary is printed.
None follows the `display.max_info_columns` setting.
- memory_usage : boolean, default None
+ memory_usage : boolean/string, default None
Specifies whether total memory usage of the DataFrame
elements (including index) should be displayed. None follows
the `display.memory_usage` setting. True or False overrides
- the `display.memory_usage` setting. Memory usage is shown in
+ the `display.memory_usage` setting. A value of 'deep' is equivalent
+ of True, with deep introspection. Memory usage is shown in
human-readable units (base-2 representation).
null_counts : boolean, default None
Whether to show the non-null counts
@@ -1676,20 +1677,27 @@ def _sizeof_fmt(num, size_qualifier):
counts = self.get_dtype_counts()
dtypes = ['%s(%d)' % k for k in sorted(compat.iteritems(counts))]
lines.append('dtypes: %s' % ', '.join(dtypes))
+
if memory_usage is None:
memory_usage = get_option('display.memory_usage')
- if memory_usage: # append memory usage of df to display
- # size_qualifier is just a best effort; not guaranteed to catch all
- # cases (e.g., it misses categorical data even with object
- # categories)
- size_qualifier = ('+' if 'object' in counts
- or is_object_dtype(self.index) else '')
- mem_usage = self.memory_usage(index=True).sum()
+ if memory_usage:
+ # append memory usage of df to display
+ size_qualifier = ''
+ if memory_usage == 'deep':
+ deep=True
+ else:
+ # size_qualifier is just a best effort; not guaranteed to catch all
+ # cases (e.g., it misses categorical data even with object
+ # categories)
+ deep=False
+ if 'object' in counts or is_object_dtype(self.index):
+ size_qualifier = '+'
+ mem_usage = self.memory_usage(index=True, deep=deep).sum()
lines.append("memory usage: %s\n" %
_sizeof_fmt(mem_usage, size_qualifier))
_put_lines(buf, lines)
- def memory_usage(self, index=False):
+ def memory_usage(self, index=False, deep=False):
"""Memory usage of DataFrame columns.
Parameters
@@ -1698,6 +1706,9 @@ def memory_usage(self, index=False):
Specifies whether to include memory usage of DataFrame's
index in returned Series. If `index=True` (default is False)
the first index of the Series is `Index`.
+ deep : bool
+ Introspect the data deeply, interrogate
+ `object` dtypes for system-level memory consumption
Returns
-------
@@ -1708,17 +1719,17 @@ def memory_usage(self, index=False):
Notes
-----
Memory usage does not include memory consumed by elements that
- are not components of the array.
+ are not components of the array if deep=False
See Also
--------
numpy.ndarray.nbytes
"""
- result = Series([ c.values.nbytes for col, c in self.iteritems() ],
+ result = Series([ c.memory_usage(index=False, deep=deep) for col, c in self.iteritems() ],
index=self.columns)
if index:
- result = Series(self.index.nbytes,
- index=['Index']).append(result)
+ result = Series(self.index.memory_usage(deep=deep),
+ index=['Index']).append(result)
return result
def transpose(self):
diff --git a/pandas/core/series.py b/pandas/core/series.py
index b12a31d64eaf7..5106225cdd3c9 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2281,6 +2281,35 @@ def reindex_axis(self, labels, axis=0, **kwargs):
raise ValueError("cannot reindex series on non-zero axis!")
return self.reindex(index=labels, **kwargs)
+ def memory_usage(self, index=False, deep=False):
+ """Memory usage of the Series
+
+ Parameters
+ ----------
+ index : bool
+ Specifies whether to include memory usage of Series index
+ deep : bool
+ Introspect the data deeply, interrogate
+ `object` dtypes for system-level memory consumption
+
+ Returns
+ -------
+ scalar bytes of memory consumed
+
+ Notes
+ -----
+ Memory usage does not include memory consumed by elements that
+ are not components of the array if deep=False
+
+ See Also
+ --------
+ numpy.ndarray.nbytes
+ """
+ v = super(Series, self).memory_usage(deep=deep)
+ if index:
+ v += self.index.memory_usage(deep=deep)
+ return v
+
def take(self, indices, axis=0, convert=True, is_copy=False):
"""
return Series corresponding to requested indices
diff --git a/pandas/lib.pyx b/pandas/lib.pyx
index 74842d9a165fe..1a1f04cba1cb9 100644
--- a/pandas/lib.pyx
+++ b/pandas/lib.pyx
@@ -182,6 +182,19 @@ def ismember_int64(ndarray[int64_t] arr, set values):
return result.view(np.bool_)
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def memory_usage_of_objects(ndarray[object, ndim=1] arr):
+ """ return the memory usage of an object array in bytes,
+ does not include the actual bytes of the pointers """
+ cdef Py_ssize_t i, n
+ cdef int64_t s = 0
+
+ n = len(arr)
+ for i from 0 <= i < n:
+ s += arr[i].__sizeof__()
+ return s
+
#----------------------------------------------------------------------
# datetime / io related
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index 3a42059a63b0d..5fb37abbbb3d2 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -877,6 +877,28 @@ def get_fill_value(obj):
self.assertFalse(o is result)
+ def test_memory_usage(self):
+ for o in self.objs:
+ res = o.memory_usage()
+ res2 = o.memory_usage(deep=True)
+
+ if com.is_object_dtype(o):
+ self.assertTrue(res2 > res)
+ else:
+ self.assertEqual(res, res2)
+
+ if isinstance(o, Series):
+ res = o.memory_usage(index=True)
+ res2 = o.memory_usage(index=True, deep=True)
+ if com.is_object_dtype(o) or com.is_object_dtype(o.index):
+ self.assertTrue(res2 > res)
+ else:
+ self.assertEqual(res, res2)
+
+ self.assertEqual(o.memory_usage(index=False) + o.index.memory_usage(),
+ o.memory_usage(index=True))
+
+
class TestFloat64HashTable(tm.TestCase):
def test_lookup_nan(self):
from pandas.hashtable import Float64HashTable
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 1d143236e285b..23484ee8c7e05 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -1197,6 +1197,15 @@ def test_nbytes(self):
exp = cat._codes.nbytes + cat._categories.values.nbytes
self.assertEqual(cat.nbytes, exp)
+ def test_memory_usage(self):
+ cat = pd.Categorical([1,2,3])
+ self.assertEqual(cat.nbytes, cat.memory_usage())
+ self.assertEqual(cat.nbytes, cat.memory_usage(deep=True))
+
+ cat = pd.Categorical(['foo','foo','bar'])
+ self.assertEqual(cat.nbytes, cat.memory_usage())
+ self.assertTrue(cat.memory_usage(deep=True) > cat.nbytes)
+
def test_searchsorted(self):
# https://github.com/pydata/pandas/issues/8420
s1 = pd.Series(['apple', 'bread', 'bread', 'cheese', 'milk' ])
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index a924d17aa7e8f..a743ce4ffef61 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -7614,6 +7614,17 @@ def test_info_memory_usage(self):
res = buf.getvalue().splitlines()
self.assertTrue(re.match(r"memory usage: [^+]+\+", res[-1]))
+ df_with_object_index.info(buf=buf, memory_usage='deep')
+ res = buf.getvalue().splitlines()
+ self.assertTrue(re.match(r"memory usage: [^+]+$", res[-1]))
+
+ self.assertTrue(df_with_object_index.memory_usage(index=True, deep=True).sum() \
+ > df_with_object_index.memory_usage(index=True).sum())
+
+ df_object = pd.DataFrame({'a': ['a']})
+ self.assertTrue(df_object.memory_usage(deep=True).sum() \
+ > df_object.memory_usage().sum())
+
# Test a DataFrame with duplicate columns
dtypes = ['int64', 'int64', 'int64', 'float64']
data = {}
@@ -7630,6 +7641,9 @@ def test_info_memory_usage(self):
size_df = np.size(df.columns.values) # index=False; default
self.assertEqual(size_df, np.size(df.memory_usage()))
+ # assert deep works only on object
+ self.assertEqual(df.memory_usage().sum(),df.memory_usage(deep=True).sum())
+
# test for validity
DataFrame(1,index=['a'],columns=['A']).memory_usage(index=True)
DataFrame(1,index=['a'],columns=['A']).index.nbytes
| closes #11595
| https://api.github.com/repos/pandas-dev/pandas/pulls/11596 | 2015-11-13T18:23:01Z | 2015-11-13T21:34:59Z | 2015-11-13T21:34:59Z | 2015-11-13T21:34:59Z |
BUG/PERF: Stata value labels | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 9ebed15c05246..a56d3469cd50e 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -416,10 +416,9 @@ Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Improved performance of ``andrews_curves`` (:issue:`11534`)
-
- Improved huge ``DatetimeIndex``, ``PeriodIndex`` and ``TimedeltaIndex``'s ops performance including ``NaT`` (:issue:`10277`)
- Improved performance of ``pandas.concat`` (:issue:`11958`)
-
+- Improved performance of ``StataReader`` (PR #`11591`)
@@ -440,6 +439,7 @@ Bug Fixes
- 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`)
- Bug in vectorized ``DateOffset`` when ``n`` parameter is ``0`` (:issue:`11370`)
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 5afbc2671e3a7..67008a79bdeb5 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -39,8 +39,7 @@
_encoding_params = """\
encoding : string, None or encoding
- Encoding used to parse the files. Note that Stata doesn't
- support unicode. None defaults to iso-8859-1."""
+ Encoding used to parse the files. None defaults to iso-8859-1."""
_statafile_processing_params2 = """\
index : identifier of index column
@@ -431,6 +430,7 @@ def parse_dates_safe(dates, delta=False, year=False, days=False):
Column '%s' does not satisfy this restriction.
"""
+
class PossiblePrecisionLoss(Warning):
pass
@@ -440,6 +440,7 @@ class PossiblePrecisionLoss(Warning):
conversion range. This may result in a loss of precision in the saved data.
"""
+
class ValueLabelTypeMismatch(Warning):
pass
@@ -514,7 +515,6 @@ def _cast_to_stata_types(data):
data[col] = data[col].astype(dtype)
-
# Check values and upcast if necessary
if dtype == np.int8:
if data[col].max() > 100 or data[col].min() < -127:
@@ -788,19 +788,19 @@ class StataParser(object):
def __init__(self, encoding):
self._encoding = encoding
- #type code.
- #--------------------
- #str1 1 = 0x01
- #str2 2 = 0x02
- #...
- #str244 244 = 0xf4
- #byte 251 = 0xfb (sic)
- #int 252 = 0xfc
- #long 253 = 0xfd
- #float 254 = 0xfe
- #double 255 = 0xff
- #--------------------
- #NOTE: the byte type seems to be reserved for categorical variables
+ # type code.
+ # --------------------
+ # str1 1 = 0x01
+ # str2 2 = 0x02
+ # ...
+ # str244 244 = 0xf4
+ # byte 251 = 0xfb (sic)
+ # int 252 = 0xfc
+ # long 253 = 0xfd
+ # float 254 = 0xfe
+ # double 255 = 0xff
+ # --------------------
+ # NOTE: the byte type seems to be reserved for categorical variables
# with a label, but the underlying variable is -127 to 100
# we're going to drop the label and cast to int
self.DTYPE_MAP = \
@@ -837,7 +837,7 @@ def __init__(self, encoding):
(65530, 'b')
]
)
- #NOTE: technically, some of these are wrong. there are more numbers
+ # NOTE: technically, some of these are wrong. there are more numbers
# that can be represented. it's the 27 ABOVE and BELOW the max listed
# numeric data type in [U] 12.2.2 of the 11.2 manual
float32_min = b'\xff\xff\xff\xfe'
@@ -899,6 +899,7 @@ def _decode_bytes(self, str, errors=None):
else:
return str
+
class StataReader(StataParser):
__doc__ = _stata_reader_doc
@@ -932,7 +933,7 @@ def __init__(self, path_or_buf, convert_dates=True,
self._dtype = None
self._lines_read = 0
- self._native_byteorder = _set_endianness(sys.byteorder)
+ self._native_byteorder = _set_endianness(sys.byteorder)
if isinstance(path_or_buf, str):
path_or_buf, encoding, _ = get_filepath_or_buffer(
path_or_buf, encoding=self._default_encoding
@@ -951,7 +952,6 @@ def __init__(self, path_or_buf, convert_dates=True,
self._read_header()
-
def __enter__(self):
""" enter context manager """
return self
@@ -967,7 +967,6 @@ def close(self):
except IOError:
pass
-
def _read_header(self):
first_char = self.path_or_buf.read(1)
if struct.unpack('c', first_char)[0] == b'<':
@@ -984,7 +983,6 @@ def _read_header(self):
# remove format details from %td
self.fmtlist = ["%td" if x.startswith("%td") else x for x in self.fmtlist]
-
def _read_new_header(self, first_char):
# The first part of the header is common to 117 and 118.
self.path_or_buf.read(27) # stata_dta><header><release>
@@ -1049,7 +1047,6 @@ def _read_new_header(self, first_char):
self.path_or_buf.seek(self._seek_variable_labels)
self.vlblist = self._get_vlblist()
-
# Get data type information, works for versions 117-118.
def _get_dtypes(self, seek_vartypes):
@@ -1082,7 +1079,6 @@ def f(typ):
return typlist, dtyplist
-
def _get_varlist(self):
if self.format_version == 117:
b = 33
@@ -1092,7 +1088,6 @@ def _get_varlist(self):
return [self._null_terminate(self.path_or_buf.read(b))
for i in range(self.nvar)]
-
# Returns the format list
def _get_fmtlist(self):
if self.format_version == 118:
@@ -1107,7 +1102,6 @@ def _get_fmtlist(self):
return [self._null_terminate(self.path_or_buf.read(b))
for i in range(self.nvar)]
-
# Returns the label list
def _get_lbllist(self):
if self.format_version >= 118:
@@ -1119,7 +1113,6 @@ def _get_lbllist(self):
return [self._null_terminate(self.path_or_buf.read(b))
for i in range(self.nvar)]
-
def _get_vlblist(self):
if self.format_version == 118:
vlblist = [self._decode(self.path_or_buf.read(321))
@@ -1132,7 +1125,6 @@ def _get_vlblist(self):
for i in range(self.nvar)]
return vlblist
-
def _get_nobs(self):
if self.format_version == 118:
return struct.unpack(self.byteorder + 'Q',
@@ -1141,7 +1133,6 @@ def _get_nobs(self):
return struct.unpack(self.byteorder + 'I',
self.path_or_buf.read(4))[0]
-
def _get_data_label(self):
if self.format_version == 118:
strlen = struct.unpack(self.byteorder + 'H', self.path_or_buf.read(2))[0]
@@ -1154,7 +1145,6 @@ def _get_data_label(self):
else:
return self._null_terminate(self.path_or_buf.read(32))
-
def _get_time_stamp(self):
if self.format_version == 118:
strlen = struct.unpack('b', self.path_or_buf.read(1))[0]
@@ -1167,20 +1157,18 @@ def _get_time_stamp(self):
else:
raise ValueError()
-
def _get_seek_variable_labels(self):
if self.format_version == 117:
self.path_or_buf.read(8) # <variable_lables>, throw away
# Stata 117 data files do not follow the described format. This is
# a work around that uses the previous label, 33 bytes for each
# variable, 20 for the closing tag and 17 for the opening tag
- return self._seek_value_label_names + (33*self.nvar) + 20 + 17
+ return self._seek_value_label_names + (33 * self.nvar) + 20 + 17
elif self.format_version == 118:
return struct.unpack(self.byteorder + 'q', self.path_or_buf.read(8))[0] + 17
else:
raise ValueError()
-
def _read_old_header(self, first_char):
self.format_version = struct.unpack('b', first_char)[0]
if self.format_version not in [104, 105, 108, 113, 114, 115]:
@@ -1258,20 +1246,17 @@ def _read_old_header(self, first_char):
# necessary data to continue parsing
self.data_location = self.path_or_buf.tell()
-
def _calcsize(self, fmt):
return (type(fmt) is int and fmt
or struct.calcsize(self.byteorder + fmt))
-
def _decode(self, s):
s = s.partition(b"\0")[0]
return s.decode('utf-8')
-
def _null_terminate(self, s):
- if compat.PY3 or self._encoding is not None: # have bytes not strings,
- # so must decode
+ if compat.PY3 or self._encoding is not None:
+ # have bytes not strings, so must decode
s = s.partition(b"\0")[0]
return s.decode(self._encoding or self._default_encoding)
else:
@@ -1316,33 +1301,32 @@ def _read_value_labels(self):
self.path_or_buf.read(4))[0]
txtlen = struct.unpack(self.byteorder + 'I',
self.path_or_buf.read(4))[0]
- off = []
- for i in range(n):
- off.append(struct.unpack(self.byteorder + 'I',
- self.path_or_buf.read(4))[0])
- val = []
- for i in range(n):
- val.append(struct.unpack(self.byteorder + 'I',
- self.path_or_buf.read(4))[0])
+ off = np.frombuffer(self.path_or_buf.read(4 * n),
+ dtype=self.byteorder + "i4",
+ count=n)
+ val = np.frombuffer(self.path_or_buf.read(4 * n),
+ dtype=self.byteorder + "i4",
+ count=n)
+ ii = np.argsort(off)
+ off = off[ii]
+ val = val[ii]
txt = self.path_or_buf.read(txtlen)
self.value_label_dict[labname] = dict()
for i in range(n):
+ end = off[i + 1] if i < n - 1 else txtlen
if self.format_version <= 117:
self.value_label_dict[labname][val[i]] = (
- self._null_terminate(txt[off[i]:])
- )
+ self._null_terminate(txt[off[i]:end]))
else:
self.value_label_dict[labname][val[i]] = (
- self._decode(txt[off[i]:])
- )
+ self._decode(txt[off[i]:end]))
if self.format_version >= 117:
self.path_or_buf.read(6) # </lbl>
self._value_labels_read = True
-
def _read_strls(self):
self.path_or_buf.seek(self.seek_strls)
- self.GSO = {0 : ''}
+ self.GSO = {0: ''}
while True:
if self.path_or_buf.read(3) != b'GSO':
break
@@ -1381,7 +1365,6 @@ def data(self, **kwargs):
return self.read(None, **kwargs)
-
def __iter__(self):
try:
if self._chunksize:
@@ -1392,7 +1375,6 @@ def __iter__(self):
except StopIteration:
pass
-
def get_chunk(self, size=None):
"""
Reads lines from Stata file and returns as dataframe
@@ -1410,7 +1392,6 @@ def get_chunk(self, size=None):
size = self._chunksize
return self.read(nrows=size)
-
@Appender(_read_method_doc)
def read(self, nrows=None, convert_dates=None,
convert_categoricals=None, index=None,
@@ -1465,7 +1446,8 @@ def read(self, nrows=None, convert_dates=None,
if read_len <= 0:
# Iterator has finished, should never be here unless
# we are reading the file incrementally
- self._read_value_labels()
+ if convert_categoricals:
+ self._read_value_labels()
raise StopIteration
offset = self._lines_read * dtype.itemsize
self.path_or_buf.seek(self.data_location + offset)
@@ -1484,7 +1466,7 @@ def read(self, nrows=None, convert_dates=None,
if convert_categoricals:
self._read_value_labels()
- if len(data)==0:
+ if len(data) == 0:
data = DataFrame(columns=self.varlist, index=index)
else:
data = DataFrame.from_records(data, index=index)
@@ -1536,7 +1518,7 @@ def read(self, nrows=None, convert_dates=None,
if convert_categoricals and self.value_label_dict:
data = self._do_convert_categoricals(data, self.value_label_dict, self.lbllist,
- order_categoricals)
+ order_categoricals)
if not preserve_dtypes:
retyped_data = []
@@ -1629,7 +1611,6 @@ def _do_select_columns(self, data, columns):
return data[columns]
-
def _do_convert_categoricals(self, data, value_label_dict, lbllist, order_categoricals):
"""
Converts categorical columns to Categorical type.
@@ -1677,7 +1658,7 @@ def value_labels(self):
def _open_file_binary_write(fname, encoding):
if hasattr(fname, 'write'):
- #if 'b' not in fname.mode:
+ # if 'b' not in fname.mode:
return fname
return open(fname, "wb")
@@ -2003,7 +1984,7 @@ def _check_column_names(self, data):
return data
def _prepare_pandas(self, data):
- #NOTE: we might need a different API / class for pandas objects so
+ # NOTE: we might need a different API / class for pandas objects so
# we can set different semantics - handle this with a PR to pandas.io
data = data.copy()
@@ -2076,9 +2057,9 @@ def _write_header(self, data_label=None, time_stamp=None):
# unused
self._write("\x00")
# number of vars, 2 bytes
- self._file.write(struct.pack(byteorder+"h", self.nvar)[:2])
+ self._file.write(struct.pack(byteorder + "h", self.nvar)[:2])
# number of obs, 4 bytes
- self._file.write(struct.pack(byteorder+"i", self.nobs)[:4])
+ self._file.write(struct.pack(byteorder + "i", self.nobs)[:4])
# data label 81 bytes, char, null terminated
if data_label is None:
self._file.write(self._null_terminate(_pad_bytes("", 80)))
@@ -2111,7 +2092,7 @@ def _write_descriptors(self, typlist=None, varlist=None, srtlist=None,
self._write(name)
# srtlist, 2*(nvar+1), int array, encoded by byteorder
- srtlist = _pad_bytes("", (2*(nvar+1)))
+ srtlist = _pad_bytes("", 2 * (nvar + 1))
self._write(srtlist)
# fmtlist, 49*nvar, char array
@@ -2156,11 +2137,11 @@ def _prepare_data(self):
has_strings = True
data[col] = data[col].fillna('').apply(_pad_bytes, args=(typ,))
stype = 'S%d' % typ
- dtype.append(('c'+str(i), stype))
+ dtype.append(('c' + str(i), stype))
string = data[col].str.encode(self._encoding)
data_cols.append(string.values.astype(stype))
else:
- dtype.append(('c'+str(i), data[col].dtype))
+ dtype.append(('c' + str(i), data[col].dtype))
data_cols.append(data[col].values)
dtype = np.dtype(dtype)
| closes #12014
This PR fixes a minor bug and introduces some performance enhancements, all related to value label reading in Stata files.
The bug is that when reading a Stata file incrementally, the value labels will be read even when specifying convert_categoricals=False (this does not happen when reading the entire file at once).
The performance enhancements are:
1. Read key/value information as an ndarray using np.frombuffer rather than as a Python loop.
2. When splitting the value labels at the offsets, we currently pass txt[off[i]:] to null_terminate, which creates many copies of large segments of a potentially large byte array. I changed it so that
only the relevant part of the string is copied.
Relating to 2, further performance improvements might be possible since there is no trailing null byte to remove except for the last element of `txt` (thus some of the work in `_null_terminate` is superfluous).
Background: This is an issue when processing large Stata files with millions of distinct value labels.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11591 | 2015-11-13T14:17:48Z | 2016-01-13T13:41:59Z | null | 2016-01-13T13:41:59Z |
Update Vizualisation.rst - Fixes #11589 | diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst
index 88ae4a227fe2a..30f91b495b8a9 100644
--- a/doc/source/visualization.rst
+++ b/doc/source/visualization.rst
@@ -1638,7 +1638,7 @@ Trellis plotting interface
.. note::
The tips data set can be downloaded `here
- <http://wesmckinney.com/files/tips.csv>`__. Once you download it execute
+ <https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv>`__. Once you download it execute
.. code-block:: python
| closes #11589
Fixes a bad url
| https://api.github.com/repos/pandas-dev/pandas/pulls/11590 | 2015-11-13T14:05:36Z | 2015-11-13T14:07:05Z | 2015-11-13T14:07:05Z | 2015-11-13T14:07:31Z |
TST: Enable Index dtype comparison by default | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 28819c522c696..609c14ad92fba 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -128,3 +128,6 @@ Bug Fixes
- Bug in the link-time error caused by C ``inline`` functions on FreeBSD 10+ (with ``clang``) (:issue:`10510`)
- Bug in ``DataFrame.to_csv`` in passing through arguments for formatting ``MultiIndexes``, including ``date_format`` (:issue:`7791`)
- Bug in ``DataFrame.join()`` with ``how='right'`` producing a ``TypeError`` (:issue:`11519`)
+- Bug in ``Series.quantile`` with empty list results has ``Index`` with ``object`` dtype (:issue:`11588`)
+- Bug in ``pd.merge`` results in empty ``Int64Index`` rather than ``Index(dtype=object)`` when the merge result is empty (:issue:`11588`)
+
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 99ee50a9ae7fb..929e07eea98c4 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1779,7 +1779,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
avoid duplicating data
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
method to use for filling holes in reindexed DataFrame.
- Please note: this is only applicable to DataFrames/Series with a
+ 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
@@ -1822,7 +1822,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
- records in the dataframe are assigned ``NaN``.
+ records in the dataframe are assigned ``NaN``.
>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
@@ -1836,8 +1836,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
We can fill in the missing values by passing a value to
the keyword ``fill_value``. Because the index is not monotonically
- increasing or decreasing, we cannot use arguments to the keyword
- ``method`` to fill the ``NaN`` values.
+ increasing or decreasing, we cannot use arguments to the keyword
+ ``method`` to fill the ``NaN`` values.
>>> df.reindex(new_index, fill_value=0)
http_status response_time
@@ -1855,8 +1855,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
IE10 404 0.08
Chrome 200 0.02
- To further illustrate the filling functionality in
- ``reindex``, we will create a dataframe with a
+ To further illustrate the filling functionality in
+ ``reindex``, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
@@ -1873,7 +1873,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
2010-01-06 88
Suppose we decide to expand the dataframe to cover a wider
- date range.
+ date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
@@ -1890,10 +1890,10 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
2010-01-07 NaN
The index entries that did not have a value in the original data frame
- (for example, '2009-12-29') are by default filled with ``NaN``.
+ (for example, '2009-12-29') are by default filled with ``NaN``.
If desired, we can fill in the missing values using one of several
- options.
-
+ options.
+
For example, to backpropagate the last valid value to fill the ``NaN``
values, pass ``bfill`` as an argument to the ``method`` keyword.
@@ -1911,7 +1911,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
2010-01-07 NaN
Please note that the ``NaN`` value present in the original dataframe
- (at index value 2010-01-03) will not be filled by any of the
+ (at index value 2010-01-03) will not be filled by any of the
value propagation schemes. This is because filling while reindexing
does not look at dataframe values, but only compares the original and
desired indexes. If you do want to fill in the ``NaN`` values present
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 0f3795fcad0c3..fd4e680fef431 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -524,7 +524,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False):
Parameters
----------
indexer : tuple, slice, scalar
- The indexer used to get the locations that will be set to
+ The indexer used to get the locations that will be set to
`ser`
ser : pd.Series
@@ -532,7 +532,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False):
multiindex_indexer : boolean, optional
Defaults to False. Should be set to True if `indexer` was from
- a `pd.MultiIndex`, to avoid unnecessary broadcasting.
+ a `pd.MultiIndex`, to avoid unnecessary broadcasting.
Returns:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index b12a31d64eaf7..000e8c4c2dab8 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -27,7 +27,7 @@
_maybe_box_datetimelike, ABCDataFrame,
_dict_compat)
from pandas.core.index import (Index, MultiIndex, InvalidIndexError,
- _ensure_index)
+ Float64Index, _ensure_index)
from pandas.core.indexing import check_bool_indexer, maybe_convert_indices
from pandas.core import generic, base
from pandas.core.internals import SingleBlockManager
@@ -1271,6 +1271,8 @@ def quantile(self, q=0.5):
def multi(values, qs):
if com.is_list_like(qs):
values = [_quantile(values, x*100) 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)
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py
index a03e54f8078a7..27e607870cebc 100644
--- a/pandas/io/tests/test_excel.py
+++ b/pandas/io/tests/test_excel.py
@@ -846,7 +846,8 @@ def test_int_types(self):
# test with convert_float=False comes back as float
float_frame = frame.astype(float)
recons = read_excel(path, 'test1', convert_float=False)
- tm.assert_frame_equal(recons, float_frame)
+ tm.assert_frame_equal(recons, float_frame,
+ check_index_type=False, check_column_type=False)
def test_float_types(self):
_skip_if_no_xlrd()
@@ -1186,9 +1187,11 @@ def test_to_excel_output_encoding(self):
_skip_if_no_xlrd()
ext = self.ext
filename = '__tmp_to_excel_float_format__.' + ext
- df = DataFrame([[u('\u0192'), u('\u0193'), u('\u0194')],
- [u('\u0195'), u('\u0196'), u('\u0197')]],
- index=[u('A\u0192'), 'B'], columns=[u('X\u0193'), 'Y', 'Z'])
+
+ # avoid mixed inferred_type
+ df = DataFrame([[u'\u0192', u'\u0193', u'\u0194'],
+ [u'\u0195', u'\u0196', u'\u0197']],
+ index=[u'A\u0192', u'B'], columns=[u'X\u0193', u'Y', u'Z'])
with ensure_clean(filename) as filename:
df.to_excel(filename, sheet_name='TestSheet', encoding='utf8')
diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/test_json/test_pandas.py
index 40cdc8fe8478c..5f41a803538e6 100644
--- a/pandas/io/tests/test_json/test_pandas.py
+++ b/pandas/io/tests/test_json/test_pandas.py
@@ -134,7 +134,8 @@ def _check(df):
def test_frame_from_json_to_json(self):
def _check_orient(df, orient, dtype=None, numpy=False,
convert_axes=True, check_dtype=True, raise_ok=None,
- sort=None):
+ sort=None, check_index_type=True,
+ check_column_type=True):
if sort is not None:
df = df.sort_values(sort)
else:
@@ -191,20 +192,29 @@ def _check_orient(df, orient, dtype=None, numpy=False,
assert_almost_equal(df.values, unser.values)
else:
if convert_axes:
- assert_frame_equal(df, unser, check_dtype=check_dtype)
+ assert_frame_equal(df, unser, check_dtype=check_dtype,
+ check_index_type=check_index_type,
+ check_column_type=check_column_type)
else:
assert_frame_equal(df, unser, check_less_precise=False,
check_dtype=check_dtype)
- def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None, sort=None):
+ def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None,
+ sort=None, check_index_type=True,
+ check_column_type=True):
# numpy=False
if convert_axes:
- _check_orient(df, "columns", dtype=dtype, sort=sort)
- _check_orient(df, "records", dtype=dtype, sort=sort)
- _check_orient(df, "split", dtype=dtype, sort=sort)
- _check_orient(df, "index", dtype=dtype, sort=sort)
- _check_orient(df, "values", dtype=dtype, sort=sort)
+ _check_orient(df, "columns", dtype=dtype, sort=sort,
+ check_index_type=False, check_column_type=False)
+ _check_orient(df, "records", dtype=dtype, sort=sort,
+ check_index_type=False, check_column_type=False)
+ _check_orient(df, "split", dtype=dtype, sort=sort,
+ check_index_type=False, check_column_type=False)
+ _check_orient(df, "index", dtype=dtype, sort=sort,
+ check_index_type=False, check_column_type=False)
+ _check_orient(df, "values", dtype=dtype, sort=sort,
+ check_index_type=False, check_column_type=False)
_check_orient(df, "columns", dtype=dtype, convert_axes=False, sort=sort)
_check_orient(df, "records", dtype=dtype, convert_axes=False, sort=sort)
@@ -215,15 +225,20 @@ def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None, sort=No
# numpy=True and raise_ok might be not None, so ignore the error
if convert_axes:
_check_orient(df, "columns", dtype=dtype, numpy=True,
- raise_ok=raise_ok, sort=sort)
+ raise_ok=raise_ok, sort=sort,
+ check_index_type=False, check_column_type=False)
_check_orient(df, "records", dtype=dtype, numpy=True,
- raise_ok=raise_ok, sort=sort)
+ raise_ok=raise_ok, sort=sort,
+ check_index_type=False, check_column_type=False)
_check_orient(df, "split", dtype=dtype, numpy=True,
- raise_ok=raise_ok, sort=sort)
+ raise_ok=raise_ok, sort=sort,
+ check_index_type=False, check_column_type=False)
_check_orient(df, "index", dtype=dtype, numpy=True,
- raise_ok=raise_ok, sort=sort)
+ raise_ok=raise_ok, sort=sort,
+ check_index_type=False, check_column_type=False)
_check_orient(df, "values", dtype=dtype, numpy=True,
- raise_ok=raise_ok, sort=sort)
+ raise_ok=raise_ok, sort=sort,
+ check_index_type=False, check_column_type=False)
_check_orient(df, "columns", dtype=dtype, numpy=True,
convert_axes=False, raise_ok=raise_ok, sort=sort)
@@ -250,7 +265,7 @@ def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None, sort=No
biggie = DataFrame(np.zeros((200, 4)),
columns=[str(i) for i in range(4)],
index=[str(i) for i in range(200)])
- _check_all_orients(biggie,dtype=False,convert_axes=False)
+ _check_all_orients(biggie,dtype=False, convert_axes=False)
# dtypes
_check_all_orients(DataFrame(biggie, dtype=np.float64),
@@ -264,7 +279,8 @@ def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None, sort=No
_check_all_orients(self.categorical, sort='sort', raise_ok=ValueError)
# empty
- _check_all_orients(self.empty_frame)
+ _check_all_orients(self.empty_frame, check_index_type=False,
+ check_column_type=False)
# time series data
_check_all_orients(self.tsframe)
@@ -354,14 +370,16 @@ def test_frame_to_json_except(self):
def test_frame_empty(self):
df = DataFrame(columns=['jim', 'joe'])
self.assertFalse(df._is_mixed_type)
- assert_frame_equal(read_json(df.to_json(), dtype=dict(df.dtypes)), df)
+ assert_frame_equal(read_json(df.to_json(), dtype=dict(df.dtypes)), df,
+ check_index_type=False)
def test_frame_empty_mixedtype(self):
# mixed type
df = DataFrame(columns=['jim', 'joe'])
df['joe'] = df['joe'].astype('i8')
self.assertTrue(df._is_mixed_type)
- assert_frame_equal(read_json(df.to_json(), dtype=dict(df.dtypes)), df)
+ assert_frame_equal(read_json(df.to_json(), dtype=dict(df.dtypes)), df,
+ check_index_type=False)
def test_frame_mixedtype_orient(self): # GH10289
vals = [[10, 1, 'foo', .1, .01],
@@ -457,7 +475,8 @@ def test_series_non_unique_index(self):
def test_series_from_json_to_json(self):
- def _check_orient(series, orient, dtype=None, numpy=False):
+ def _check_orient(series, orient, dtype=None, numpy=False,
+ check_index_type=True):
series = series.sort_index()
unser = read_json(series.to_json(orient=orient),
typ='series', orient=orient, numpy=numpy,
@@ -467,22 +486,33 @@ def _check_orient(series, orient, dtype=None, numpy=False):
assert_almost_equal(series.values, unser.values)
else:
if orient == "split":
- assert_series_equal(series, unser)
+ assert_series_equal(series, unser,
+ check_index_type=check_index_type)
else:
- assert_series_equal(series, unser, check_names=False)
-
- def _check_all_orients(series, dtype=None):
- _check_orient(series, "columns", dtype=dtype)
- _check_orient(series, "records", dtype=dtype)
- _check_orient(series, "split", dtype=dtype)
- _check_orient(series, "index", dtype=dtype)
+ assert_series_equal(series, unser, check_names=False,
+ check_index_type=check_index_type)
+
+ def _check_all_orients(series, dtype=None, check_index_type=True):
+ _check_orient(series, "columns", dtype=dtype,
+ check_index_type=check_index_type)
+ _check_orient(series, "records", dtype=dtype,
+ check_index_type=check_index_type)
+ _check_orient(series, "split", dtype=dtype,
+ check_index_type=check_index_type)
+ _check_orient(series, "index", dtype=dtype,
+ check_index_type=check_index_type)
_check_orient(series, "values", dtype=dtype)
- _check_orient(series, "columns", dtype=dtype, numpy=True)
- _check_orient(series, "records", dtype=dtype, numpy=True)
- _check_orient(series, "split", dtype=dtype, numpy=True)
- _check_orient(series, "index", dtype=dtype, numpy=True)
- _check_orient(series, "values", dtype=dtype, numpy=True)
+ _check_orient(series, "columns", dtype=dtype, numpy=True,
+ check_index_type=check_index_type)
+ _check_orient(series, "records", dtype=dtype, numpy=True,
+ check_index_type=check_index_type)
+ _check_orient(series, "split", dtype=dtype, numpy=True,
+ check_index_type=check_index_type)
+ _check_orient(series, "index", dtype=dtype, numpy=True,
+ check_index_type=check_index_type)
+ _check_orient(series, "values", dtype=dtype, numpy=True,
+ check_index_type=check_index_type)
# basic
_check_all_orients(self.series)
@@ -493,7 +523,12 @@ def _check_all_orients(series, dtype=None):
index=self.objSeries.index,
name=self.objSeries.name)
_check_all_orients(objSeries, dtype=False)
- _check_all_orients(self.empty_series)
+
+ # empty_series has empty index with object dtype
+ # which cannot be revert
+ self.assertEqual(self.empty_series.index.dtype, np.object_)
+ _check_all_orients(self.empty_series, check_index_type=False)
+
_check_all_orients(self.ts)
# dtype
@@ -508,25 +543,30 @@ def test_series_to_json_except(self):
def test_series_from_json_precise_float(self):
s = Series([4.56, 4.56, 4.56])
result = read_json(s.to_json(), typ='series', precise_float=True)
- assert_series_equal(result, s)
+ assert_series_equal(result, s, check_index_type=False)
def test_frame_from_json_precise_float(self):
df = DataFrame([[4.56, 4.56, 4.56], [4.56, 4.56, 4.56]])
result = read_json(df.to_json(), precise_float=True)
- assert_frame_equal(result, df)
+ assert_frame_equal(result, df, check_index_type=False, check_column_type=False)
def test_typ(self):
s = Series(lrange(6), index=['a','b','c','d','e','f'], dtype='int64')
result = read_json(s.to_json(),typ=None)
- assert_series_equal(result,s)
+ assert_series_equal(result, s)
def test_reconstruction_index(self):
df = DataFrame([[1, 2, 3], [4, 5, 6]])
result = read_json(df.to_json())
- # the index is serialized as strings....correct?
+ self.assertEqual(result.index.dtype, np.float64)
+ self.assertEqual(result.columns.dtype, np.float64)
+ assert_frame_equal(result, df, check_index_type=False, check_column_type=False)
+
+ df = DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=['A', 'B', 'C'])
+ result = read_json(df.to_json())
assert_frame_equal(result, df)
def test_path(self):
@@ -691,7 +731,7 @@ def test_misc_example(self):
\\[left\\]: Index\\(\\[u?'a', u?'b'\\], dtype='object'\\)
\\[right\\]: Int64Index\\(\\[0, 1\\], dtype='int64'\\)"""
with tm.assertRaisesRegexp(AssertionError, error_msg):
- assert_frame_equal(result, expected)
+ assert_frame_equal(result, expected, check_index_type=False)
result = read_json('[{"a": 1, "b": 2}, {"b":2, "a" :1}]')
expected = DataFrame([[1,2], [1,2]], columns=['a','b'])
@@ -717,13 +757,19 @@ def test_timedelta(self):
converter = lambda x: pd.to_timedelta(x,unit='ms')
s = Series([timedelta(23), timedelta(seconds=5)])
- self.assertEqual(s.dtype,'timedelta64[ns]')
- assert_series_equal(s, pd.read_json(s.to_json(),typ='series').apply(converter))
+ self.assertEqual(s.dtype, 'timedelta64[ns]')
+ # index will be float dtype
+ assert_series_equal(s, pd.read_json(s.to_json(),typ='series').apply(converter),
+ check_index_type=False)
+
+ s = Series([timedelta(23), timedelta(seconds=5)], index=pd.Index([0, 1], dtype=float))
+ self.assertEqual(s.dtype, 'timedelta64[ns]')
+ assert_series_equal(s, pd.read_json(s.to_json(), typ='series').apply(converter))
frame = DataFrame([timedelta(23), timedelta(seconds=5)])
self.assertEqual(frame[0].dtype,'timedelta64[ns]')
- assert_frame_equal(
- frame, pd.read_json(frame.to_json()).apply(converter))
+ assert_frame_equal(frame, pd.read_json(frame.to_json()).apply(converter),
+ check_index_type=False, check_column_type=False)
frame = DataFrame({'a': [timedelta(days=23), timedelta(seconds=5)],
'b': [1, 2],
@@ -732,7 +778,7 @@ def test_timedelta(self):
result = pd.read_json(frame.to_json(date_unit='ns'))
result['a'] = pd.to_timedelta(result.a, unit='ns')
result['c'] = pd.to_datetime(result.c)
- assert_frame_equal(frame, result)
+ assert_frame_equal(frame, result, check_index_type=False)
def test_mixed_timedelta_datetime(self):
frame = DataFrame({'a': [timedelta(23), pd.Timestamp('20130101')]},
@@ -742,14 +788,14 @@ def test_mixed_timedelta_datetime(self):
pd.Timestamp(frame.a[1]).value]})
result = pd.read_json(frame.to_json(date_unit='ns'),
dtype={'a': 'int64'})
- assert_frame_equal(result, expected)
+ assert_frame_equal(result, expected, check_index_type=False)
def test_default_handler(self):
value = object()
frame = DataFrame({'a': ['a', value]})
expected = frame.applymap(str)
result = pd.read_json(frame.to_json(default_handler=str))
- assert_frame_equal(expected, result)
+ assert_frame_equal(expected, result, check_index_type=False)
def test_default_handler_raises(self):
def my_handler_raises(obj):
diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py
index 799c573b13c8b..87450ddde636e 100755
--- a/pandas/io/tests/test_parsers.py
+++ b/pandas/io/tests/test_parsers.py
@@ -2313,14 +2313,14 @@ def test_emtpy_with_multiindex(self):
result = self.read_csv(StringIO(data), index_col=['x', 'y'])
expected = DataFrame([], columns=['z'],
index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y']))
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_with_reversed_multiindex(self):
data = 'x,y,z'
result = self.read_csv(StringIO(data), index_col=[1, 0])
expected = DataFrame([], columns=['z'],
index=MultiIndex.from_arrays([[]] * 2, names=['y', 'x']))
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_index_col_scenarios(self):
data = 'x,y,z'
@@ -2352,28 +2352,26 @@ def test_empty_index_col_scenarios(self):
# list of int
index_col, expected = [0, 1], DataFrame([], columns=['z'],
index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y']))
- tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected)
+ tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected,
+ check_index_type=False)
# list of str
- index_col, expected = (
- ['x', 'y'],
- DataFrame([], columns=['z'], index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y']))
- )
- tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected)
+ index_col = ['x', 'y']
+ expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays([[]] * 2, names=['x', 'y']))
+ tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected,
+ check_index_type=False)
# list of int, reversed sequence
- index_col, expected = (
- [1, 0],
- DataFrame([], columns=['z'], index=MultiIndex.from_arrays([[]] * 2, names=['y', 'x']))
- )
- tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected)
+ index_col = [1, 0]
+ expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays([[]] * 2, names=['y', 'x']))
+ tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected,
+ check_index_type=False)
# list of str, reversed sequence
- index_col, expected = (
- ['y', 'x'],
- DataFrame([], columns=['z'], index=MultiIndex.from_arrays([[]] * 2, names=['y', 'x']))
- )
- tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected)
+ index_col = ['y', 'x']
+ expected = DataFrame([], columns=['z'], index=MultiIndex.from_arrays([[]] * 2, names=['y', 'x']))
+ tm.assert_frame_equal(self.read_csv(StringIO(data), index_col=index_col), expected,
+ check_index_type=False)
def test_empty_with_index_col_false(self):
# GH 10413
@@ -2434,11 +2432,11 @@ def test_empty_with_nrows_chunksize(self):
result = pd.read_csv(StringIO('foo,bar\n'), nrows=10, as_recarray=True)
result = pd.DataFrame(result[2], columns=result[1], index=result[0])
- tm.assert_frame_equal(pd.DataFrame.from_records(result), expected)
+ tm.assert_frame_equal(pd.DataFrame.from_records(result), expected, check_index_type=False)
result = next(iter(pd.read_csv(StringIO('foo,bar\n'), chunksize=10, as_recarray=True)))
result = pd.DataFrame(result[2], columns=result[1], index=result[0])
- tm.assert_frame_equal(pd.DataFrame.from_records(result), expected)
+ tm.assert_frame_equal(pd.DataFrame.from_records(result), expected, check_index_type=False)
def test_eof_states(self):
# GH 10728 and 10548
@@ -3697,7 +3695,7 @@ def test_empty_pass_dtype(self):
expected = DataFrame({'one': np.empty(0, dtype='u1'),
'two': np.empty(0, dtype=np.object)})
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_with_index_pass_dtype(self):
data = 'one,two'
@@ -3706,38 +3704,37 @@ def test_empty_with_index_pass_dtype(self):
expected = DataFrame({'two': np.empty(0, dtype='f')},
index=Index([], dtype='u1', name='one'))
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_with_multiindex_pass_dtype(self):
data = 'one,two,three'
result = self.read_csv(StringIO(data), index_col=['one', 'two'],
dtype={'one': 'u1', 1: 'f8'})
- expected = DataFrame({'three': np.empty(0, dtype=np.object)}, index=MultiIndex.from_arrays(
- [np.empty(0, dtype='u1'), np.empty(0, dtype='O')],
- names=['one', 'two'])
- )
- tm.assert_frame_equal(result, expected)
+ exp_idx = MultiIndex.from_arrays([np.empty(0, dtype='u1'), np.empty(0, dtype='O')],
+ names=['one', 'two'])
+ expected = DataFrame({'three': np.empty(0, dtype=np.object)}, index=exp_idx)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_with_mangled_column_pass_dtype_by_names(self):
data = 'one,one'
result = self.read_csv(StringIO(data), dtype={'one': 'u1', 'one.1': 'f'})
expected = DataFrame({'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')})
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_with_mangled_column_pass_dtype_by_indexes(self):
data = 'one,one'
result = self.read_csv(StringIO(data), dtype={0: 'u1', 1: 'f'})
expected = DataFrame({'one': np.empty(0, dtype='u1'), 'one.1': np.empty(0, dtype='f')})
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_with_dup_column_pass_dtype_by_names(self):
data = 'one,one'
result = self.read_csv(StringIO(data), mangle_dupe_cols=False, dtype={'one': 'u1'})
expected = pd.concat([Series([], name='one', dtype='u1')] * 2, axis=1)
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_empty_with_dup_column_pass_dtype_by_indexes(self):
### FIXME in GH9424
@@ -3747,7 +3744,7 @@ def test_empty_with_dup_column_pass_dtype_by_indexes(self):
result = self.read_csv(StringIO(data), mangle_dupe_cols=False, dtype={0: 'u1', 1: 'f'})
expected = pd.concat([Series([], name='one', dtype='u1'),
Series([], name='one', dtype='f')], axis=1)
- tm.assert_frame_equal(result, expected)
+ tm.assert_frame_equal(result, expected, check_index_type=False)
def test_usecols_dtypes(self):
data = """\
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index 6c78f9cf3937c..afe76ee1746da 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -2506,7 +2506,7 @@ def test_series(self):
ts3 = Series(ts.values, Index(np.asarray(ts.index, dtype=object),
dtype=object))
- self._check_roundtrip(ts3, tm.assert_series_equal)
+ self._check_roundtrip(ts3, tm.assert_series_equal, check_index_type=False)
def test_sparse_series(self):
@@ -3049,7 +3049,7 @@ def test_select_dtypes(self):
result = store.select(
'df4', where='values>2.0')
tm.assert_frame_equal(expected, result)
-
+
# test selection with comparison against numpy scalar
# GH 11283
with ensure_clean_store(self.path) as store:
diff --git a/pandas/io/tests/test_sas.py b/pandas/io/tests/test_sas.py
index 0e08252fdce97..8d1041229bf3c 100644
--- a/pandas/io/tests/test_sas.py
+++ b/pandas/io/tests/test_sas.py
@@ -60,17 +60,17 @@ def test1_index(self):
# Read full file
data = XportReader(self.file01, index="SEQN").read()
- tm.assert_frame_equal(data, data_csv)
+ tm.assert_frame_equal(data, data_csv, check_index_type=False)
# Test incremental read with `read` method.
reader = XportReader(self.file01, index="SEQN")
data = reader.read(10)
- tm.assert_frame_equal(data, data_csv.iloc[0:10, :])
+ tm.assert_frame_equal(data, data_csv.iloc[0:10, :], check_index_type=False)
# Test incremental read with `get_chunk` method.
reader = XportReader(self.file01, index="SEQN", chunksize=10)
data = reader.get_chunk()
- tm.assert_frame_equal(data, data_csv.iloc[0:10, :])
+ tm.assert_frame_equal(data, data_csv.iloc[0:10, :], check_index_type=False)
def test1_incremental(self):
@@ -85,7 +85,7 @@ def test1_incremental(self):
all_data = [x for x in reader]
data = pd.concat(all_data, axis=0)
- tm.assert_frame_equal(data, data_csv)
+ tm.assert_frame_equal(data, data_csv, check_index_type=False)
def test2(self):
diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index aced92ec8abd0..3736bcecfca9f 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -1480,7 +1480,7 @@ def test_get_schema_create_table(self):
self.drop_table(tbl)
self.conn.execute(create_sql)
returned_df = sql.read_sql_table(tbl, self.conn)
- tm.assert_frame_equal(returned_df, blank_test_df)
+ tm.assert_frame_equal(returned_df, blank_test_df, check_index_type=False)
self.drop_table(tbl)
def test_dtype(self):
diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py
index aff9cd6c558e2..86dfbc8f76a9b 100644
--- a/pandas/io/tests/test_stata.py
+++ b/pandas/io/tests/test_stata.py
@@ -300,7 +300,7 @@ def test_write_dta6(self):
original.to_stata(path, None)
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'),
- original)
+ original, check_index_type=False)
def test_read_write_dta10(self):
original = DataFrame(data=[["string", "object", 1, 1.1,
@@ -315,8 +315,9 @@ def test_read_write_dta10(self):
with tm.ensure_clean() as path:
original.to_stata(path, {'datetime': 'tc'})
written_and_read_again = self.read_dta(path)
+ # original.index is np.int32, readed index is np.int64
tm.assert_frame_equal(written_and_read_again.set_index('index'),
- original)
+ original, check_index_type=False)
def test_stata_doc_examples(self):
with tm.ensure_clean() as path:
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index fb255f300ebdd..948c7a524f266 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -577,7 +577,7 @@ def test_value_counts_inferred(self):
s = klass({})
expected = Series([], dtype=np.int64)
- tm.assert_series_equal(s.value_counts(), expected)
+ tm.assert_series_equal(s.value_counts(), expected, check_index_type=False)
self.assert_numpy_array_equal(s.unique(), np.array([]))
self.assertEqual(s.nunique(), 0)
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 1d143236e285b..812951b126320 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -434,64 +434,57 @@ def test_categories_none(self):
def test_describe(self):
# string type
desc = self.factor.describe()
- expected = DataFrame.from_dict(dict(counts=[3, 2, 3],
- freqs=[3/8., 2/8., 3/8.],
- categories=['a', 'b', 'c'])
- ).set_index('categories')
+ expected = DataFrame({'counts': [3, 2, 3],
+ 'freqs': [3/8., 2/8., 3/8.]},
+ index=pd.CategoricalIndex(['a', 'b', 'c'], name='categories'))
tm.assert_frame_equal(desc, expected)
# check unused categories
cat = self.factor.copy()
cat.set_categories(["a","b","c","d"], inplace=True)
desc = cat.describe()
- expected = DataFrame.from_dict(dict(counts=[3, 2, 3, 0],
- freqs=[3/8., 2/8., 3/8., 0],
- categories=['a', 'b', 'c', 'd'])
- ).set_index('categories')
+ expected = DataFrame({'counts': [3, 2, 3, 0],
+ 'freqs': [3/8., 2/8., 3/8., 0]},
+ index=pd.CategoricalIndex(['a', 'b', 'c', 'd'], name='categories'))
tm.assert_frame_equal(desc, expected)
# check an integer one
desc = Categorical([1,2,3,1,2,3,3,2,1,1,1]).describe()
- expected = DataFrame.from_dict(dict(counts=[5, 3, 3],
- freqs=[5/11., 3/11., 3/11.],
- categories=[1,2,3]
- )
- ).set_index('categories')
+ expected = DataFrame({'counts': [5, 3, 3],
+ 'freqs': [5/11., 3/11., 3/11.]},
+ index=pd.CategoricalIndex([1, 2, 3], name='categories'))
tm.assert_frame_equal(desc, expected)
# https://github.com/pydata/pandas/issues/3678
# describe should work with NaN
cat = pd.Categorical([np.nan,1, 2, 2])
desc = cat.describe()
- expected = DataFrame.from_dict(dict(counts=[1, 2, 1],
- freqs=[1/4., 2/4., 1/4.],
- categories=Categorical([1,2,np.nan],
- [1, 2])
- )
- ).set_index('categories')
+ expected = DataFrame({'counts': [1, 2, 1],
+ 'freqs': [1/4., 2/4., 1/4.]},
+ index=pd.CategoricalIndex([1, 2, np.nan], categories=[1, 2],
+ name='categories'))
tm.assert_frame_equal(desc, expected)
# NA as a category
with tm.assert_produces_warning(FutureWarning):
- cat = pd.Categorical(["a","c","c",np.nan], categories=["b","a","c",np.nan])
+ cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c", np.nan])
result = cat.describe()
expected = DataFrame([[0,0],[1,0.25],[2,0.5],[1,0.25]],
columns=['counts','freqs'],
- index=Index(['b','a','c',np.nan],name='categories'))
+ index=pd.CategoricalIndex(['b', 'a', 'c', np.nan], name='categories'))
tm.assert_frame_equal(result,expected)
# NA as an unused category
with tm.assert_produces_warning(FutureWarning):
- cat = pd.Categorical(["a","c","c"], categories=["b","a","c",np.nan])
+ cat = pd.Categorical(["a", "c", "c"], categories=["b", "a", "c", np.nan])
result = cat.describe()
- expected = DataFrame([[0,0],[1,1/3.],[2,2/3.],[0,0]],
- columns=['counts','freqs'],
- index=Index(['b','a','c',np.nan],name='categories'))
+ exp_idx = pd.CategoricalIndex(['b', 'a', 'c', np.nan], name='categories')
+ expected = DataFrame([[0, 0], [1, 1/3.], [2, 2/3.], [0, 0]],
+ columns=['counts', 'freqs'], index=exp_idx)
tm.assert_frame_equal(result,expected)
-
def test_print(self):
expected = ["[a, b, b, a, a, c, c, c]",
"Categories (3, object): [a < b < c]"]
@@ -2373,6 +2366,7 @@ def test_groupby_sort(self):
res = self.cat.groupby(['value_group'])['value_group'].count()
exp = res[sorted(res.index, key=lambda x: float(x.split()[0]))]
+ exp.index = pd.CategoricalIndex(exp.index, name=exp.index.name)
tm.assert_series_equal(res, exp)
def test_min_max(self):
@@ -2423,10 +2417,10 @@ def test_value_counts(self):
s = pd.Series(pd.Categorical(["a","b","c","c","c","b"], categories=["c","a","b","d"]))
res = s.value_counts(sort=False)
- exp = Series([3,1,2,0], index=["c","a","b","d"])
+ exp = Series([3,1,2,0], index=pd.CategoricalIndex(["c","a","b","d"]))
tm.assert_series_equal(res, exp)
res = s.value_counts(sort=True)
- exp = Series([3,2,1,0], index=["c","b","a","d"])
+ exp = Series([3,2,1,0], index=pd.CategoricalIndex(["c","b","a","d"]))
tm.assert_series_equal(res, exp)
def test_value_counts_with_nan(self):
@@ -2435,42 +2429,42 @@ def test_value_counts_with_nan(self):
s = pd.Series(["a", "b", "a"], dtype="category")
tm.assert_series_equal(
s.value_counts(dropna=True),
- pd.Series([2, 1], index=["a", "b"]))
+ pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])))
tm.assert_series_equal(
s.value_counts(dropna=False),
- pd.Series([2, 1], index=["a", "b"]))
+ pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])))
s = pd.Series(["a", "b", None, "a", None, None], dtype="category")
tm.assert_series_equal(
s.value_counts(dropna=True),
- pd.Series([2, 1], index=["a", "b"]))
+ pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])))
tm.assert_series_equal(
s.value_counts(dropna=False),
- pd.Series([3, 2, 1], index=[np.nan, "a", "b"]))
+ pd.Series([3, 2, 1], index=pd.CategoricalIndex([np.nan, "a", "b"])))
# When we aren't sorting by counts, and np.nan isn't a
# category, it should be last.
tm.assert_series_equal(
s.value_counts(dropna=False, sort=False),
- pd.Series([2, 1, 3], index=["a", "b", np.nan]))
+ pd.Series([2, 1, 3], index=pd.CategoricalIndex(["a", "b", np.nan])))
with tm.assert_produces_warning(FutureWarning):
s = pd.Series(pd.Categorical(["a", "b", "a"], categories=["a", "b", np.nan]))
tm.assert_series_equal(
s.value_counts(dropna=True),
- pd.Series([2, 1], index=["a", "b"]))
+ pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])))
tm.assert_series_equal(
s.value_counts(dropna=False),
- pd.Series([2, 1, 0], index=["a", "b", np.nan]))
+ pd.Series([2, 1, 0], index=pd.CategoricalIndex(["a", "b", np.nan])))
with tm.assert_produces_warning(FutureWarning):
s = pd.Series(pd.Categorical(["a", "b", None, "a", None, None],
categories=["a", "b", np.nan]))
tm.assert_series_equal(
s.value_counts(dropna=True),
- pd.Series([2, 1], index=["a", "b"]))
+ pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])))
tm.assert_series_equal(
s.value_counts(dropna=False),
- pd.Series([3, 2, 1], index=[np.nan, "a", "b"]))
+ pd.Series([3, 2, 1], index=pd.CategoricalIndex([np.nan, "a", "b"])))
def test_groupby(self):
@@ -2478,7 +2472,7 @@ def test_groupby(self):
data = DataFrame({"a":[1,1,1,2,2,2,3,4,5], "b":cats})
expected = DataFrame({'a': Series([1, 2, 4, np.nan],
- index=Index(['a', 'b', 'c', 'd'], name='b'))})
+ index=pd.CategoricalIndex(['a', 'b', 'c', 'd'], name='b'))})
result = data.groupby("b").mean()
tm.assert_frame_equal(result, expected)
@@ -2488,7 +2482,8 @@ def test_groupby(self):
# single grouper
gb = df.groupby("A")
- expected = DataFrame({ 'values' : Series([3,7,np.nan],index=Index(['a','b','z'],name='A')) })
+ exp_idx = pd.CategoricalIndex(['a', 'b', 'z'], name='A')
+ expected = DataFrame({'values': Series([3, 7, np.nan], index=exp_idx)})
result = gb.sum()
tm.assert_frame_equal(result, expected)
@@ -2568,7 +2563,7 @@ def f(x):
df = pd.DataFrame({'a': [1, 0, 0, 0]})
c = pd.cut(df.a, [0, 1, 2, 3, 4])
result = df.groupby(c).apply(len)
- expected = pd.Series([1, 0, 0, 0], index=c.values.categories)
+ expected = pd.Series([1, 0, 0, 0], index=pd.CategoricalIndex(c.values.categories))
expected.index.name = 'a'
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 8a01b1ca17373..8fab5151f9c9a 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -1429,7 +1429,12 @@ def test_getitem_setitem_float_labels(self):
self.assertEqual(len(result), 4)
result = df.ix[4:5]
- expected = df.reindex([4, 5])
+ expected = df.reindex([4, 5]) # reindex with int
+ assert_frame_equal(result, expected, check_index_type=False)
+ self.assertEqual(len(result), 2)
+
+ result = df.ix[4:5]
+ expected = df.reindex([4.0, 5.0]) # reindex with float
assert_frame_equal(result, expected)
self.assertEqual(len(result), 2)
@@ -1978,30 +1983,34 @@ def test_reindex_level(self):
from itertools import permutations
icol = ['jim', 'joe', 'jolie']
- def verify_first_level(df, level, idx):
+ def verify_first_level(df, level, idx, check_index_type=True):
f = lambda val: np.nonzero(df[level] == val)[0]
i = np.concatenate(list(map(f, idx)))
left = df.set_index(icol).reindex(idx, level=level)
right = df.iloc[i].set_index(icol)
- assert_frame_equal(left, right)
+ assert_frame_equal(left, right, check_index_type=check_index_type)
- def verify(df, level, idx, indexer):
+ def verify(df, level, idx, indexer, check_index_type=True):
left = df.set_index(icol).reindex(idx, level=level)
right = df.iloc[indexer].set_index(icol)
- assert_frame_equal(left, right)
+ assert_frame_equal(left, right, check_index_type=check_index_type)
df = pd.DataFrame({'jim':list('B' * 4 + 'A' * 2 + 'C' * 3),
'joe':list('abcdeabcd')[::-1],
'jolie':[10, 20, 30] * 3,
'joline': np.random.randint(0, 1000, 9)})
- target = [['C', 'B', 'A'], ['F', 'C', 'A', 'D'], ['A'], ['D', 'F'],
+ target = [['C', 'B', 'A'], ['F', 'C', 'A', 'D'], ['A'],
['A', 'B', 'C'], ['C', 'A', 'B'], ['C', 'B'], ['C', 'A'],
- ['A', 'B'], ['B', 'A', 'C'], ['A', 'C', 'B']]
+ ['A', 'B'], ['B', 'A', 'C']]
for idx in target:
verify_first_level(df, 'jim', idx)
+ # reindex by these causes different MultiIndex levels
+ for idx in [['D', 'F'], ['A', 'C', 'B']]:
+ verify_first_level(df, 'jim', idx, check_index_type=False)
+
verify(df, 'joe', list('abcde'), [3, 2, 1, 0, 5, 4, 8, 7, 6])
verify(df, 'joe', list('abcd'), [3, 2, 1, 0, 5, 8, 7, 6])
verify(df, 'joe', list('abc'), [3, 2, 1, 8, 7, 6])
@@ -2009,7 +2018,7 @@ def verify(df, level, idx, indexer):
verify(df, 'joe', list('edc'), [0, 1, 4, 5, 6])
verify(df, 'joe', list('eadbc'), [3, 0, 2, 1, 4, 5, 8, 7, 6])
verify(df, 'joe', list('edwq'), [0, 4, 5])
- verify(df, 'joe', list('wq'), [])
+ verify(df, 'joe', list('wq'), [], check_index_type=False)
df = DataFrame({'jim':['mid'] * 5 + ['btm'] * 8 + ['top'] * 7,
'joe':['3rd'] * 2 + ['1st'] * 3 + ['2nd'] * 3 +
@@ -10205,10 +10214,10 @@ def test_xs_corner(self):
xs = df.xs(0)
assert_almost_equal(xs, [1., 'foo', 2., 'bar', 3.])
- # no columns but index
+ # no columns but Index(dtype=object)
df = DataFrame(index=['a', 'b', 'c'])
result = df.xs('a')
- expected = Series([], name='a')
+ expected = Series([], name='a', index=pd.Index([], dtype=object))
assert_series_equal(result, expected)
def test_xs_duplicates(self):
@@ -10394,7 +10403,7 @@ def test_reindex_nan(self):
tm.assert_frame_equal(df.reindex(i), df.iloc[j])
df.index = df.index.astype('object')
- tm.assert_frame_equal(df.reindex(i), df.iloc[j])
+ tm.assert_frame_equal(df.reindex(i), df.iloc[j], check_index_type=False)
# GH10388
df = pd.DataFrame({'other':['a', 'b', np.nan, 'c'],
@@ -11527,13 +11536,13 @@ def test_apply_empty(self):
result = self.empty.apply(x.append, axis=1, reduce=False)
assert_frame_equal(result, self.empty)
result = self.empty.apply(x.append, axis=1, reduce=True)
- assert_series_equal(result, Series([]))
+ assert_series_equal(result, Series([], index=pd.Index([], dtype=object)))
empty_with_cols = DataFrame(columns=['a', 'b', 'c'])
result = empty_with_cols.apply(x.append, axis=1, reduce=False)
assert_frame_equal(result, empty_with_cols)
result = empty_with_cols.apply(x.append, axis=1, reduce=True)
- assert_series_equal(result, Series([]))
+ assert_series_equal(result, Series([], index=pd.Index([], dtype=object)))
# Ensure that x.append hasn't been called
self.assertEqual(x, [])
@@ -11592,7 +11601,7 @@ def test_apply_mixed_dtype_corner(self):
result = df[:0].apply(np.mean, axis=1)
# the result here is actually kind of ambiguous, should it be a Series
# or a DataFrame?
- expected = Series(np.nan, index=[])
+ expected = Series(np.nan, index=pd.Index([], dtype=int))
assert_series_equal(result, expected)
df = DataFrame({'A': ['foo'],
@@ -11881,7 +11890,7 @@ def test_filter(self):
# regex with ints in column names
# from PR #10384
df = DataFrame(0., index=[0, 1, 2], columns=['A1', 1, 'B', 2, 'C'])
- expected = DataFrame(0., index=[0, 1, 2], columns=[1, 2])
+ expected = DataFrame(0., index=[0, 1, 2], columns=pd.Index([1, 2], dtype=object))
filtered = df.filter(regex='^[0-9]+$')
assert_frame_equal(filtered, expected)
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index 22a1c0573d45a..7d41ba060717a 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -612,26 +612,26 @@ def test_rename_mi(self):
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
- o = Series([1,2,3])
+ o = Series([1, 2, 3])
result = o._get_numeric_data()
self._compare(result, o)
- o = Series([1,'2',3.])
+ o = Series([1, '2', 3.])
result = o._get_numeric_data()
- expected = Series([],dtype=object)
+ expected = Series([], dtype=object, index=pd.Index([], dtype=object))
self._compare(result, expected)
- o = Series([True,False,True])
+ o = Series([True, False, True])
result = o._get_numeric_data()
self._compare(result, o)
- o = Series([True,False,True])
+ o = Series([True, False, True])
result = o._get_bool_data()
self._compare(result, o)
o = Series(date_range('20130101',periods=3))
result = o._get_numeric_data()
- expected = Series([],dtype='M8[ns]')
+ expected = Series([],dtype='M8[ns]', index=pd.Index([], dtype=object))
self._compare(result, expected)
def test_nonzero_single_element(self):
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index 7e6aaa8213667..cace8aed24107 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -10,6 +10,7 @@
from datetime import datetime, date
+import pandas as pd
from pandas import (Series, DataFrame, MultiIndex, PeriodIndex, date_range,
bdate_range)
from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip,
@@ -1482,6 +1483,14 @@ def test_unsorted_index(self):
l = ax.get_lines()[0]
rs = l.get_xydata()
rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64, name='y')
+ tm.assert_series_equal(rs, df.y, check_index_type=False)
+ tm.close()
+
+ df.index = pd.Index(np.arange(99, -1, -1), dtype=np.float64)
+ ax = df.plot()
+ l = ax.get_lines()[0]
+ rs = l.get_xydata()
+ rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64, name='y')
tm.assert_series_equal(rs, df.y)
@slow
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 46026a4c887a6..ff2dd63b01a98 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -129,7 +129,8 @@ def checkit(dtype):
assert_series_equal(transformed, expected)
value_grouped = data.groupby(data)
- assert_series_equal(value_grouped.aggregate(np.mean), agged)
+ assert_series_equal(value_grouped.aggregate(np.mean), agged,
+ check_index_type=False)
# complex agg
agged = grouped.aggregate([np.mean, np.std])
@@ -390,6 +391,9 @@ def test_grouper_multilevel_freq(self):
# Check string level
expected = df.reset_index().groupby([pd.Grouper(key='foo', freq='W'),
pd.Grouper(key='bar', freq='W')]).sum()
+ # reset index changes columns dtype to object
+ expected.columns = pd.Index([0], dtype=int)
+
result = df.groupby([pd.Grouper(level='foo', freq='W'),
pd.Grouper(level='bar', freq='W')]).sum()
assert_frame_equal(result, expected)
@@ -746,14 +750,18 @@ def test_get_group_grouped_by_tuple(self):
def test_agg_apply_corner(self):
# nothing to group, all NA
grouped = self.ts.groupby(self.ts * np.nan)
+ self.assertEqual(self.ts.dtype, np.float64)
- assert_series_equal(grouped.sum(), Series([]))
- assert_series_equal(grouped.agg(np.sum), Series([]))
- assert_series_equal(grouped.apply(np.sum), Series([]))
+ # groupby float64 values results in Float64Index
+ exp = Series([], dtype=np.float64, index=pd.Index([], dtype=np.float64))
+ assert_series_equal(grouped.sum(), exp)
+ assert_series_equal(grouped.agg(np.sum), exp)
+ assert_series_equal(grouped.apply(np.sum), exp, check_index_type=False)
# DataFrame
grouped = self.tsframe.groupby(self.tsframe['A'] * np.nan)
- exp_df = DataFrame(columns=self.tsframe.columns, dtype=float)
+ exp_df = DataFrame(columns=self.tsframe.columns, dtype=float,
+ index=pd.Index([], dtype=np.float64))
assert_frame_equal(grouped.sum(), exp_df, check_names=False)
assert_frame_equal(grouped.agg(np.sum), exp_df, check_names=False)
assert_frame_equal(grouped.apply(np.sum), DataFrame({}, dtype=float))
@@ -1831,7 +1839,8 @@ def test_groupby_head_tail(self):
assert_frame_equal(df.loc[[0, 2]], g_not_as.head(1))
assert_frame_equal(df.loc[[1, 2]], g_not_as.tail(1))
- empty_not_as = DataFrame(columns=df.columns)
+ empty_not_as = DataFrame(columns=df.columns, index=pd.Index([],
+ dtype=df.index.dtype))
empty_not_as['A'] = empty_not_as['A'].astype(df.A.dtype)
empty_not_as['B'] = empty_not_as['B'].astype(df.B.dtype)
assert_frame_equal(empty_not_as, g_not_as.head(0))
@@ -2972,9 +2981,12 @@ def test_groupby_aggregation_mixed_dtype(self):
def test_groupby_dtype_inference_empty(self):
# GH 6733
df = DataFrame({'x': [], 'range': np.arange(0,dtype='int64')})
+ self.assertEqual(df['x'].dtype, np.float64)
+
result = df.groupby('x').first()
- expected = DataFrame({'range' : Series([],index=Index([],name='x'),dtype='int64') })
- assert_frame_equal(result,expected,by_blocks=True)
+ exp_index = Index([], name='x', dtype=np.float64)
+ expected = DataFrame({'range' : Series([], index=exp_index, dtype='int64')})
+ assert_frame_equal(result,expected, by_blocks=True)
def test_groupby_list_infer_array_like(self):
result = self.df.groupby(list(self.df['A'])).mean()
@@ -3535,33 +3547,27 @@ def test_groupby_sort_categorical(self):
['(2.5, 5]', 4, 50],
['(0, 2.5]', 1, 60],
['(5, 7.5]', 7, 70]], columns=['range', 'foo', 'bar'])
- df['range'] = Categorical(df['range'],ordered=True)
- index = Index(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], dtype='object')
- index.name = 'range'
- result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
- result_sort.index = index
- index = Index(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', '(0, 2.5]'], dtype='object')
- index.name = 'range'
- result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], index=index, columns=['foo', 'bar'])
- result_nosort.index = index
+ df['range'] = Categorical(df['range'], ordered=True)
+ index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], name='range')
+ result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]],
+ columns=['foo', 'bar'], index=index)
col = 'range'
assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
# when categories is ordered, group is ordered by category's order
assert_frame_equal(result_sort, df.groupby(col, sort=False).first())
- df['range'] = Categorical(df['range'],ordered=False)
- index = Index(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], dtype='object')
- index.name = 'range'
- result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
- result_sort.index = index
- index = Index(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', '(0, 2.5]'], dtype='object')
- index.name = 'range'
- result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]], index=index, columns=['foo', 'bar'])
- result_nosort.index = index
+ df['range'] = Categorical(df['range'], ordered=False)
+ index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], name='range')
+ result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]],
+ columns=['foo', 'bar'], index=index)
- col = 'range'
+ index = CategoricalIndex(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', '(0, 2.5]'],
+ name='range')
+ result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
+ index=index, columns=['foo', 'bar'])
+ col = 'range'
#### this is an unordered categorical, but we allow this ####
assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
assert_frame_equal(result_nosort, df.groupby(col, sort=False).first())
@@ -3644,7 +3650,8 @@ def test_groupby_categorical(self):
result = data.groupby(cats).mean()
expected = data.groupby(np.asarray(cats)).mean()
- expected = expected.reindex(levels)
+ exp_idx = CategoricalIndex(levels, ordered=True)
+ expected = expected.reindex(exp_idx)
assert_frame_equal(result, expected)
@@ -3654,7 +3661,7 @@ def test_groupby_categorical(self):
idx = cats.codes.argsort()
ord_labels = np.asarray(cats).take(idx)
ord_data = data.take(idx)
- expected = ord_data.groupby(ord_labels, sort=False).describe()
+ expected = ord_data.groupby(Categorical(ord_labels), sort=False).describe()
expected.index.names = [None, None]
assert_frame_equal(desc_result, expected)
@@ -4026,8 +4033,12 @@ def test_groupby_levels_and_columns(self):
df = pd.DataFrame(np.arange(12).reshape(-1, 3), index=idx)
by_levels = df.groupby(level=idx_names).mean()
+ # reset_index changes columns dtype to object
by_columns = df.reset_index().groupby(idx_names).mean()
+ tm.assert_frame_equal(by_levels, by_columns, check_column_type=False)
+
+ by_columns.columns = pd.Index(by_columns.columns, dtype=np.int64)
tm.assert_frame_equal(by_levels, by_columns)
def test_gb_apply_list_of_unequal_len_arrays(self):
@@ -5443,7 +5454,7 @@ def test_groupby_categorical_two_columns(self):
groups_single_key = test.groupby("cat")
res = groups_single_key.agg('mean')
exp = DataFrame({"ints":[1.5,1.5,np.nan], "val":[20,30,np.nan]},
- index=pd.Index(["a", "b", "c"], name="cat"))
+ index=pd.CategoricalIndex(["a", "b", "c"], name="cat"))
tm.assert_frame_equal(res, exp)
# Grouping on two columns
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index 36e825924995a..e5b8aee75ad17 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -1880,7 +1880,8 @@ def test_ix_general(self):
# this is ok
df.sortlevel(inplace=True)
res = df.ix[key]
- index = MultiIndex.from_arrays([[4] * 3, [2012] * 3],
+ # col has float dtype, result should be Float64Index
+ index = MultiIndex.from_arrays([[4.] * 3, [2012] * 3],
names=['col', 'year'])
expected = DataFrame({'amount': [222, 333, 444]}, index=index)
tm.assert_frame_equal(res, expected)
@@ -2592,26 +2593,28 @@ def test_dups_fancy_indexing(self):
result = dfnu.ix[['E']]
assert_frame_equal(result, expected)
+ # ToDo: check_index_type can be True after GH 11497
+
# GH 4619; duplicate indexer with missing label
df = DataFrame({"A": [0, 1, 2]})
- result = df.ix[[0,8,0]]
- expected = DataFrame({"A": [0, np.nan, 0]},index=[0,8,0])
- assert_frame_equal(result,expected)
+ result = df.ix[[0, 8, 0]]
+ expected = DataFrame({"A": [0, np.nan, 0]}, index=[0, 8, 0])
+ assert_frame_equal(result, expected, check_index_type=False)
df = DataFrame({"A": list('abc')})
result = df.ix[[0,8,0]]
- expected = DataFrame({"A": ['a', np.nan, 'a']},index=[0,8,0])
- assert_frame_equal(result,expected)
+ expected = DataFrame({"A": ['a', np.nan, 'a']}, index=[0, 8, 0])
+ assert_frame_equal(result, expected, check_index_type=False)
# non unique with non unique selector
- df = DataFrame({'test': [5,7,9,11]}, index=['A','A','B','C'])
- expected = DataFrame({'test' : [5,7,5,7,np.nan]},index=['A','A','A','A','E'])
- result = df.ix[['A','A','E']]
+ df = DataFrame({'test': [5, 7, 9, 11]}, index=['A', 'A', 'B', 'C'])
+ expected = DataFrame({'test' : [5, 7, 5, 7, np.nan]}, index=['A', 'A', 'A', 'A', 'E'])
+ result = df.ix[['A', 'A', 'E']]
assert_frame_equal(result, expected)
# GH 5835
# dups on index and missing values
- df = DataFrame(np.random.randn(5,5),columns=['A','B','B','B','A'])
+ df = DataFrame(np.random.randn(5, 5), columns=['A', 'B', 'B', 'B', 'A'])
expected = pd.concat([df.ix[:,['A','B']],DataFrame(np.nan,columns=['C'],index=df.index)],axis=1)
result = df.ix[:,['A','B','C']]
@@ -3168,7 +3171,7 @@ def test_iloc_non_unique_indexing(self):
expected = DataFrame(new_list)
expected = pd.concat([ expected, DataFrame(index=idx[idx>sidx.max()]) ])
result = df2.loc[idx]
- assert_frame_equal(result, expected)
+ assert_frame_equal(result, expected, check_index_type=False)
def test_mi_access(self):
@@ -3504,7 +3507,7 @@ def test_loc_setitem_datetime(self):
df.loc[conv(dt1),'one'] = 100
df.loc[conv(dt2),'one'] = 200
- expected = DataFrame({'one' : [100.0,200.0]},index=[dt1,dt2])
+ expected = DataFrame({'one' : [100.0, 200.0]},index=[dt1, dt2])
assert_frame_equal(df, expected)
def test_series_partial_set(self):
@@ -3512,42 +3515,44 @@ def test_series_partial_set(self):
# Regression from GH4825
ser = Series([0.1, 0.2], index=[1, 2])
+ # ToDo: check_index_type can be True after GH 11497
+
# loc
expected = Series([np.nan, 0.2, np.nan], index=[3, 2, 3])
result = ser.loc[[3, 2, 3]]
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_index_type=False)
# raises as nothing in in the index
self.assertRaises(KeyError, lambda : ser.loc[[3, 3, 3]])
expected = Series([0.2, 0.2, np.nan], index=[2, 2, 3])
result = ser.loc[[2, 2, 3]]
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_index_type=False)
expected = Series([0.3, np.nan, np.nan], index=[3, 4, 4])
- result = Series([0.1, 0.2, 0.3], index=[1,2,3]).loc[[3,4,4]]
- assert_series_equal(result, expected)
+ result = Series([0.1, 0.2, 0.3], index=[1, 2, 3]).loc[[3, 4, 4]]
+ assert_series_equal(result, expected, check_index_type=False)
expected = Series([np.nan, 0.3, 0.3], index=[5, 3, 3])
- result = Series([0.1, 0.2, 0.3, 0.4], index=[1,2,3,4]).loc[[5,3,3]]
- assert_series_equal(result, expected)
+ result = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4]).loc[[5, 3, 3]]
+ assert_series_equal(result, expected, check_index_type=False)
expected = Series([np.nan, 0.4, 0.4], index=[5, 4, 4])
- result = Series([0.1, 0.2, 0.3, 0.4], index=[1,2,3,4]).loc[[5,4,4]]
- assert_series_equal(result, expected)
+ result = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4]).loc[[5, 4, 4]]
+ assert_series_equal(result, expected, check_index_type=False)
expected = Series([0.4, np.nan, np.nan], index=[7, 2, 2])
- result = Series([0.1, 0.2, 0.3, 0.4], index=[4,5,6,7]).loc[[7,2,2]]
- assert_series_equal(result, expected)
+ result = Series([0.1, 0.2, 0.3, 0.4], index=[4, 5, 6, 7]).loc[[7, 2, 2]]
+ assert_series_equal(result, expected, check_index_type=False)
expected = Series([0.4, np.nan, np.nan], index=[4, 5, 5])
- result = Series([0.1, 0.2, 0.3, 0.4], index=[1,2,3,4]).loc[[4,5,5]]
- assert_series_equal(result, expected)
+ result = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4]).loc[[4, 5, 5]]
+ assert_series_equal(result, expected, check_index_type=False)
# iloc
- expected = Series([0.2,0.2,0.1,0.1], index=[2,2,1,1])
- result = ser.iloc[[1,1,0,0]]
- assert_series_equal(result, expected)
+ expected = Series([0.2, 0.2, 0.1, 0.1], index=[2, 2, 1, 1])
+ result = ser.iloc[[1, 1, 0, 0]]
+ assert_series_equal(result, expected, check_index_type=False)
def test_partial_set_invalid(self):
@@ -3617,7 +3622,7 @@ def f():
# these work as they don't really change
# anything but the index
# GH5632
- expected = DataFrame(columns=['foo'])
+ expected = DataFrame(columns=['foo'], index=pd.Index([], dtype=int))
def f():
df = DataFrame()
df['foo'] = Series([], dtype='object')
@@ -3634,7 +3639,7 @@ def f():
return df
assert_frame_equal(f(), expected)
- expected = DataFrame(columns=['foo'])
+ expected = DataFrame(columns=['foo'], index=pd.Index([], dtype=int))
expected['foo'] = expected['foo'].astype('float64')
def f():
df = DataFrame()
@@ -3654,16 +3659,16 @@ def f():
df = DataFrame()
df2 = DataFrame()
- df2[1] = Series([1],index=['foo'])
- df.loc[:,1] = Series([1],index=['foo'])
- assert_frame_equal(df,DataFrame([[1]],index=['foo'],columns=[1]))
+ df2[1] = Series([1], index=['foo'])
+ df.loc[:,1] = Series([1], index=['foo'])
+ assert_frame_equal(df,DataFrame([[1]], index=['foo'], columns=[1]))
assert_frame_equal(df,df2)
# no index to start
- expected = DataFrame({ 0 : Series(1,index=range(4)) },columns=['A','B',0])
+ expected = DataFrame({ 0 : Series(1,index=range(4)) }, columns=['A','B',0])
df = DataFrame(columns=['A','B'])
- df[0] = Series(1,index=range(4))
+ df[0] = Series(1, index=range(4))
df.dtypes
str(df)
assert_frame_equal(df,expected)
@@ -3676,28 +3681,28 @@ def f():
# GH5720, GH5744
# don't create rows when empty
- expected = DataFrame(columns=['A','B','New'])
+ expected = DataFrame(columns=['A', 'B', 'New'], index=pd.Index([], dtype=int))
expected['A'] = expected['A'].astype('int64')
expected['B'] = expected['B'].astype('float64')
expected['New'] = expected['New'].astype('float64')
df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
y = df[df.A > 5]
y['New'] = np.nan
- assert_frame_equal(y,expected)
+ assert_frame_equal(y, expected)
#assert_frame_equal(y,expected)
- expected = DataFrame(columns=['a','b','c c','d'])
+ expected = DataFrame(columns=['a', 'b', 'c c', 'd'])
expected['d'] = expected['d'].astype('int64')
df = DataFrame(columns=['a', 'b', 'c c'])
df['d'] = 3
- assert_frame_equal(df,expected)
+ assert_frame_equal(df, expected)
assert_series_equal(df['c c'],Series(name='c c',dtype=object))
# reindex columns is ok
df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
y = df[df.A > 5]
result = y.reindex(columns=['A','B','C'])
- expected = DataFrame(columns=['A','B','C'])
+ expected = DataFrame(columns=['A','B','C'], index=pd.Index([], dtype=int))
expected['A'] = expected['A'].astype('int64')
expected['B'] = expected['B'].astype('float64')
expected['C'] = expected['C'].astype('float64')
@@ -4140,7 +4145,13 @@ def test_floating_index(self):
# 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], [5, 0], np.array([5.0, 0.0]), np.array([5, 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=int))
+ 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)
@@ -4778,10 +4789,12 @@ def test_loc_listlike(self):
expected = self.df.iloc[[4,0,1,5]]
assert_frame_equal(result, expected)
+ # ToDo: check_index_type can be True after GH XXX
+
result = self.df2.loc[['a','b','e']]
- expected = DataFrame({'A' : [0,1,5,2,3,np.nan],
- 'B' : Series(list('aaabbe')).astype('category',categories=list('cabe')) }).set_index('B')
- assert_frame_equal(result, expected)
+ 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=False)
# element in the categories but not in the values
self.assertRaises(KeyError, lambda : self.df2.loc['e'])
@@ -4790,15 +4803,15 @@ def test_loc_listlike(self):
df = self.df2.copy()
df.loc['e'] = 20
result = df.loc[['a','b','e']]
- expected = DataFrame({'A' : [0,1,5,2,3,20],
- 'B' : Series(list('aaabbe')).astype('category',categories=list('cabe')) }).set_index('B')
+ 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']]
expected = DataFrame({'A' : [0,1,5,2,3,np.nan],
'B' : Series(list('aaabbe')).astype('category',categories=list('cabe')) }).set_index('B')
- assert_frame_equal(result, expected)
+ assert_frame_equal(result, expected, check_index_type=False)
# not all labels in the categories
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 1c8cbac60e7c7..2720435a20c01 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -471,11 +471,20 @@ def f():
# only 1 left, del, add, del
s = Series(1)
del s[0]
- assert_series_equal(s, Series(dtype='int64'))
+ assert_series_equal(s, Series(dtype='int64', index=Index([], dtype='int64')))
s[0] = 1
assert_series_equal(s, Series(1))
del s[0]
- assert_series_equal(s, Series(dtype='int64'))
+ assert_series_equal(s, Series(dtype='int64', index=Index([], dtype='int64')))
+
+ # Index(dtype=object)
+ s = Series(1, index=['a'])
+ del s['a']
+ assert_series_equal(s, Series(dtype='int64', index=Index([], dtype='object')))
+ s['a'] = 1
+ assert_series_equal(s, Series(1, index=['a']))
+ del s['a']
+ assert_series_equal(s, Series(dtype='int64', index=Index([], dtype='object')))
def test_getitem_preserve_name(self):
result = self.ts[self.ts > 0]
@@ -755,7 +764,7 @@ def test_constructor(self):
def test_constructor_empty(self):
empty = Series()
empty2 = Series([])
- assert_series_equal(empty, empty2)
+ assert_series_equal(empty, empty2, check_index_type=False)
empty = Series(index=lrange(10))
empty2 = Series(np.nan, index=lrange(10))
@@ -1448,7 +1457,7 @@ def test_getitem_boolean_empty(self):
assert_series_equal(result, expected)
s = Series(['A', 'B'])
- expected = Series(dtype=object)
+ expected = Series(dtype=object, index=Index([], dtype=int))
result = s[Series([], dtype=object)]
assert_series_equal(result, expected)
@@ -3031,7 +3040,7 @@ def test_quantile_multi(self):
assert_series_equal(result, expected)
result = self.ts.quantile([])
- expected = pd.Series([], name=self.ts.name)
+ expected = pd.Series([], name=self.ts.name, index=Index([], dtype=float))
assert_series_equal(result, expected)
def test_append(self):
@@ -4751,12 +4760,23 @@ def f(x):
# compress
# GH 6658
- s = Series([0,1.,-1],index=list('abc'))
- result = np.compress(s>0,s)
- assert_series_equal(result, Series([1.],index=['b']))
+ s = Series([0, 1., -1], index=list('abc'))
+ result = np.compress(s > 0, s)
+ assert_series_equal(result, Series([1.], index=['b']))
+
+ result = np.compress(s < -1, s)
+ # result empty Index(dtype=object) as the same as original
+ exp = Series([], dtype='float64', index=Index([], dtype='object'))
+ assert_series_equal(result, exp)
- result = np.compress(s<-1,s)
- assert_series_equal(result, Series([],dtype='float64'))
+ s = Series([0, 1., -1], index=[.1, .2, .3])
+ result = np.compress(s > 0, s)
+ assert_series_equal(result, Series([1.], index=[.2]))
+
+ result = np.compress(s < -1, s)
+ # result empty Float64Index as the same as original
+ exp = Series([], dtype='float64', index=Index([], dtype='float64'))
+ assert_series_equal(result, exp)
def test_complexx(self):
@@ -7031,7 +7051,8 @@ def test_reindex_nan(self):
assert_series_equal(ts.reindex(i), ts.iloc[j])
ts.index = ts.index.astype('object')
- assert_series_equal(ts.reindex(i), ts.iloc[j])
+ # reindex coerces index.dtype to float, loc/iloc doesn't
+ assert_series_equal(ts.reindex(i), ts.iloc[j], check_index_type=False)
def test_reindex_corner(self):
# (don't forget to fix this) I think it's fixed
diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py
index 722ce439722c9..9399f537191e7 100644
--- a/pandas/tools/merge.py
+++ b/pandas/tools/merge.py
@@ -304,6 +304,7 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
def _get_join_info(self):
left_ax = self.left._data.axes[self.axis]
right_ax = self.right._data.axes[self.axis]
+
if self.left_index and self.right_index:
join_index, left_indexer, right_indexer = \
left_ax.join(right_ax, how=self.how, return_indexers=True)
@@ -321,7 +322,6 @@ def _get_join_info(self):
right_indexer) = _get_join_indexers(self.left_join_keys,
self.right_join_keys,
sort=self.sort, how=self.how)
-
if self.right_index:
if len(self.left) > 0:
join_index = self.left.index.take(left_indexer)
@@ -337,6 +337,8 @@ def _get_join_info(self):
else:
join_index = Index(np.arange(len(left_indexer)))
+ if len(join_index) == 0:
+ join_index = join_index.astype(object)
return join_index, left_indexer, right_indexer
def _get_merge_data(self):
diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py
index 0f920fc5aa5bc..6db2d2e15f699 100644
--- a/pandas/tools/tests/test_merge.py
+++ b/pandas/tools/tests/test_merge.py
@@ -762,6 +762,7 @@ def test_merge_left_empty_right_empty(self):
right = pd.DataFrame([], columns=['x', 'y', 'z'])
exp_in = pd.DataFrame([], columns=['a', 'b', 'c', 'x', 'y', 'z'],
+ index=pd.Index([], dtype=object),
dtype=object)
for kwarg in [dict(left_index=True, right_index=True),
@@ -792,6 +793,8 @@ def test_merge_left_empty_right_notempty(self):
'z': [3, 6, 9]},
columns=['a', 'b', 'c', 'x', 'y', 'z'])
exp_in = exp_out[0:0] # make empty DataFrame keeping dtype
+ # result will have object dtype
+ exp_in.index = exp_in.index.astype(object)
for kwarg in [dict(left_index=True, right_index=True),
dict(left_index=True, right_on='x'),
@@ -822,6 +825,8 @@ def test_merge_left_notempty_right_empty(self):
'z': np.array([np.nan]*3, dtype=object)},
columns=['a', 'b', 'c', 'x', 'y', 'z'])
exp_in = exp_out[0:0] # make empty DataFrame keeping dtype
+ # result will have object dtype
+ exp_in.index = exp_in.index.astype(object)
for kwarg in [dict(left_index=True, right_index=True),
dict(left_index=True, right_on='x'),
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index be8b0df73593f..a50700813d2ea 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -628,7 +628,12 @@ def _check_types(l, r, obj='Index'):
msg = '{0} classes are different'.format(obj)
raise_assert_detail(obj, msg, l, r)
assert_attr_equal('dtype', l, r, obj=obj)
- assert_attr_equal('inferred_type', l, r, obj=obj)
+
+ # allow string-like to have different inferred_types
+ if l.inferred_type in ('string', 'unicode'):
+ assertIn(r.inferred_type, ('string', 'unicode'))
+ else:
+ assert_attr_equal('inferred_type', l, r, obj=obj)
def _get_ilevel_values(index, level):
# accept level number only
@@ -865,8 +870,8 @@ def assert_numpy_array_equal(left, right,
# This could be refactored to use the NDFrame.equals method
def assert_series_equal(left, right, check_dtype=True,
- check_index_type=False,
- check_series_type=False,
+ check_index_type=True,
+ check_series_type=True,
check_less_precise=False,
check_names=True,
check_exact=False,
@@ -947,9 +952,9 @@ def assert_series_equal(left, right, check_dtype=True,
# This could be refactored to use the NDFrame.equals method
def assert_frame_equal(left, right, check_dtype=True,
- check_index_type=False,
- check_column_type=False,
- check_frame_type=False,
+ check_index_type=True,
+ check_column_type=True,
+ check_frame_type=True,
check_less_precise=False,
check_names=True,
by_blocks=False,
| Enable `check_index_type` and `check_columns_type` by default to detect dtype related problems.
Fixed following 2 minor bugs at the same time, but I'm willing to split them to separate PR if required.
### 1. `Series.quantile` with empty list results in `object` index rather than `float`.
```
pd.Series([1, 2, 3]).quantile([]).index
# Index([], dtype='object')
```
### 2. `pd.merge` includes empty data may result in `Int64Index` rather than `object`
Sorry, #10826 was incomplete.
When fixing #11497, #11586, we can enable corresponding `check_index_type`
| https://api.github.com/repos/pandas-dev/pandas/pulls/11588 | 2015-11-13T13:45:17Z | 2015-11-17T12:17:29Z | 2015-11-17T12:17:29Z | 2015-11-17T13:19:12Z |
Make .str/.dt available for Series of type category with string/datetime | diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst
index 4ba52694980d3..6207366b96f63 100644
--- a/doc/source/categorical.rst
+++ b/doc/source/categorical.rst
@@ -515,6 +515,50 @@ To get a single value `Series` of type ``category`` pass in a list with a single
df.loc[["h"],"cats"]
+String and datetime accessors
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. versionadded:: 0.17.1
+
+The accessors ``.dt`` and ``.str`` will work if the ``s.cat.categories`` are of an appropriate
+type:
+
+
+.. ipython:: python
+
+ str_s = pd.Series(list('aabb'))
+ str_cat = str_s.astype('category')
+ str_cat.str.contains("a")
+
+ date_s = pd.Series(date_range('1/1/2015', periods=5))
+ date_cat = date_s.astype('category')
+ date_cat.dt.day
+
+.. note::
+
+ The returned ``Series`` (or ``DataFrame``) is of the same type as if you used the
+ ``.str.<method>`` / ``.dt.<method>`` on a ``Series`` of that type (and not of
+ type ``category``!).
+
+That means, that the returned values from methods and properties on the accessors of a
+``Series`` and the returned values from methods and properties on the accessors of this
+``Series`` transformed to one of type `category` will be equal:
+
+.. ipython:: python
+
+ ret_s = str_s.str.contains("a")
+ ret_cat = str_cat.str.contains("a")
+ ret_s.dtype == ret_cat.dtype
+ ret_s == ret_cat
+
+.. note::
+
+ The work is done on the ``categories`` and then a new ``Series`` is constructed. This has
+ some performance implication if you have a ``Series`` of type string, where lots of elements
+ are repeated (i.e. the number of unique elements in the ``Series`` is a lot smaller than the
+ length of the ``Series``). In this case it can be faster to convert the original ``Series``
+ to one of type ``category`` and use ``.str.<method>`` or ``.dt.<property>`` on that.
+
Setting
~~~~~~~
diff --git a/doc/source/text.rst b/doc/source/text.rst
index ee4f96b41c7de..68ac82a5383c2 100644
--- a/doc/source/text.rst
+++ b/doc/source/text.rst
@@ -63,6 +63,22 @@ and replacing any remaining whitespaces with underscores:
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')
df
+.. note::
+
+ If you do a lot of string munging and have a ``Series`` where lots of elements are repeated
+ (i.e. the number of unique elements in the ``Series`` is a lot smaller than the length of the
+ ``Series``), it can be faster to convert the original ``Series`` to one of type
+ ``category`` and then use ``.str.<method>`` or ``.dt.<property>`` on that. The
+ performance difference comes from the fact that, for ``Series`` of type ``category``, the
+ string operations are done on the ``.categories`` and not on each element of the
+ ``Series``. Please note that a ``Series`` of type ``category`` with string ``.categories`` has
+ some limitations in comparison of ``Series`` of type string (e.g. you can't add strings to
+ each other: ``s + " " + s`` won't work if ``s`` is a ``Series`` of type ``category``). Also,
+ ``.str`` methods which operate on elements of type ``list`` are not available on such a
+ ``Series``. If you are interested in having these performance gains on all string ``Series``,
+ please look at `this bug report <https://github.com/pydata/pandas/issues/8640>`_.
+
+
Splitting and Replacing Strings
-------------------------------
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 046791d4287c9..b8702034cd464 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -65,6 +65,8 @@ Enhancements
pd.Index([1, np.nan, 3]).fillna(2)
+- Series of type ``"category"`` now make ``.str.<...>`` and ``.dt.<...>`` accessor methods / properties available, if the categories are of that type. (:issue:`10661`)
+
- ``pivot_table`` now has a ``margins_name`` argument so you can use something other than the default of 'All' (:issue:`3335`)
.. _whatsnew_0171.api:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index cab231e8fb09c..29e9a81d19cd6 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2704,12 +2704,10 @@ def _dir_deletions(self):
def _dir_additions(self):
rv = set()
- # these accessors are mutually exclusive, so break loop when one exists
for accessor in self._accessors:
try:
getattr(self, accessor)
rv.add(accessor)
- break
except AttributeError:
pass
return rv
diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index f1ff7e2178a04..a8907ac192707 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -2,7 +2,7 @@
from pandas.compat import zip
from pandas.core.common import (isnull, _values_from_object, is_bool_dtype, is_list_like,
- is_categorical_dtype, is_object_dtype)
+ is_categorical_dtype, is_object_dtype, take_1d)
import pandas.compat as compat
from pandas.core.base import AccessorProperty, NoNewAttributesMixin
from pandas.util.decorators import Appender, deprecate_kwarg
@@ -1003,7 +1003,7 @@ def str_encode(arr, encoding, errors="strict"):
def _noarg_wrapper(f, docstring=None, **kargs):
def wrapper(self):
- result = _na_map(f, self.series, **kargs)
+ result = _na_map(f, self._data, **kargs)
return self._wrap_result(result)
wrapper.__name__ = f.__name__
@@ -1017,15 +1017,15 @@ def wrapper(self):
def _pat_wrapper(f, flags=False, na=False, **kwargs):
def wrapper1(self, pat):
- result = f(self.series, pat)
+ result = f(self._data, pat)
return self._wrap_result(result)
def wrapper2(self, pat, flags=0, **kwargs):
- result = f(self.series, pat, flags=flags, **kwargs)
+ result = f(self._data, pat, flags=flags, **kwargs)
return self._wrap_result(result)
def wrapper3(self, pat, na=np.nan):
- result = f(self.series, pat, na=na)
+ result = f(self._data, pat, na=na)
return self._wrap_result(result)
wrapper = wrapper3 if na else wrapper2 if flags else wrapper1
@@ -1059,8 +1059,11 @@ class StringMethods(NoNewAttributesMixin):
>>> s.str.replace('_', '')
"""
- def __init__(self, series):
- self.series = series
+ def __init__(self, data):
+ self._is_categorical = is_categorical_dtype(data)
+ self._data = data.cat.categories if self._is_categorical else data
+ # save orig to blow up categoricals to the right type
+ self._orig = data
self._freeze()
def __getitem__(self, key):
@@ -1078,7 +1081,15 @@ def __iter__(self):
i += 1
g = self.get(i)
- def _wrap_result(self, result, **kwargs):
+ def _wrap_result(self, result, use_codes=True, name=None):
+
+ # for category, we do the stuff on the categories, so blow it up
+ # to the full series again
+ # But for some operations, we have to do the stuff on the full values,
+ # so make it possible to skip this step as the method already did this before
+ # the transformation...
+ 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
@@ -1088,29 +1099,34 @@ def _wrap_result(self, result, **kwargs):
if not hasattr(result, 'ndim'):
return result
- name = kwargs.get('name') or getattr(result, 'name', None) or self.series.name
+ name = name or getattr(result, 'name', None) or self._orig.name
if result.ndim == 1:
- if isinstance(self.series, Index):
+ 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.series.index, name=name)
+ return Series(result, index=self._orig.index, name=name)
else:
assert result.ndim < 3
- return DataFrame(result, index=self.series.index)
+ return DataFrame(result, index=self._orig.index)
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 isinstance(self.series, Index):
+ 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)
@@ -1123,36 +1139,38 @@ def _wrap_result_expand(self, result, expand=False):
else:
return Index(result, name=name)
else:
- index = self.series.index
+ index = self._orig.index
if expand:
def cons_row(x):
if is_list_like(x):
return x
else:
return [ x ]
- cons = self.series._constructor_expanddim
+ cons = self._orig._constructor_expanddim
data = [cons_row(x) for x in result]
return cons(data, index=index)
else:
name = getattr(result, 'name', None)
- cons = self.series._constructor
+ cons = self._orig._constructor
return cons(result, name=name, index=index)
@copy(str_cat)
def cat(self, others=None, sep=None, na_rep=None):
- result = str_cat(self.series, others=others, sep=sep, na_rep=na_rep)
- return self._wrap_result(result)
+ data = self._orig if self._is_categorical else self._data
+ result = str_cat(data, others=others, sep=sep, na_rep=na_rep)
+ return self._wrap_result(result, use_codes=(not self._is_categorical))
+
@deprecate_kwarg('return_type', 'expand',
mapping={'series': False, 'frame': True})
@copy(str_split)
def split(self, pat=None, n=-1, expand=False):
- result = str_split(self.series, pat, n=n)
+ result = str_split(self._data, pat, n=n)
return self._wrap_result_expand(result, expand=expand)
@copy(str_rsplit)
def rsplit(self, pat=None, n=-1, expand=False):
- result = str_rsplit(self.series, pat, n=n)
+ result = str_rsplit(self._data, pat, n=n)
return self._wrap_result_expand(result, expand=expand)
_shared_docs['str_partition'] = ("""
@@ -1203,7 +1221,7 @@ def rsplit(self, pat=None, n=-1, expand=False):
'also': 'rpartition : Split the string at the last occurrence of `sep`'})
def partition(self, pat=' ', expand=True):
f = lambda x: x.partition(pat)
- result = _na_map(f, self.series)
+ result = _na_map(f, self._data)
return self._wrap_result_expand(result, expand=expand)
@Appender(_shared_docs['str_partition'] % {'side': 'last',
@@ -1211,45 +1229,45 @@ def partition(self, pat=' ', expand=True):
'also': 'partition : Split the string at the first occurrence of `sep`'})
def rpartition(self, pat=' ', expand=True):
f = lambda x: x.rpartition(pat)
- result = _na_map(f, self.series)
+ result = _na_map(f, self._data)
return self._wrap_result_expand(result, expand=expand)
@copy(str_get)
def get(self, i):
- result = str_get(self.series, i)
+ result = str_get(self._data, i)
return self._wrap_result(result)
@copy(str_join)
def join(self, sep):
- result = str_join(self.series, sep)
+ result = str_join(self._data, sep)
return self._wrap_result(result)
@copy(str_contains)
def contains(self, pat, case=True, flags=0, na=np.nan, regex=True):
- result = str_contains(self.series, pat, case=case, flags=flags,
+ result = str_contains(self._data, pat, case=case, flags=flags,
na=na, regex=regex)
return self._wrap_result(result)
@copy(str_match)
def match(self, pat, case=True, flags=0, na=np.nan, as_indexer=False):
- result = str_match(self.series, pat, case=case, flags=flags,
+ result = str_match(self._data, pat, case=case, flags=flags,
na=na, as_indexer=as_indexer)
return self._wrap_result(result)
@copy(str_replace)
def replace(self, pat, repl, n=-1, case=True, flags=0):
- result = str_replace(self.series, pat, repl, n=n, case=case,
+ result = str_replace(self._data, pat, repl, n=n, case=case,
flags=flags)
return self._wrap_result(result)
@copy(str_repeat)
def repeat(self, repeats):
- result = str_repeat(self.series, repeats)
+ result = str_repeat(self._data, repeats)
return self._wrap_result(result)
@copy(str_pad)
def pad(self, width, side='left', fillchar=' '):
- result = str_pad(self.series, width, side=side, fillchar=fillchar)
+ result = str_pad(self._data, width, side=side, fillchar=fillchar)
return self._wrap_result(result)
_shared_docs['str_pad'] = ("""
@@ -1297,27 +1315,27 @@ def zfill(self, width):
-------
filled : Series/Index of objects
"""
- result = str_pad(self.series, width, side='left', fillchar='0')
+ result = str_pad(self._data, width, side='left', fillchar='0')
return self._wrap_result(result)
@copy(str_slice)
def slice(self, start=None, stop=None, step=None):
- result = str_slice(self.series, start, stop, step)
+ result = str_slice(self._data, start, stop, step)
return self._wrap_result(result)
@copy(str_slice_replace)
def slice_replace(self, start=None, stop=None, repl=None):
- result = str_slice_replace(self.series, start, stop, repl)
+ result = str_slice_replace(self._data, start, stop, repl)
return self._wrap_result(result)
@copy(str_decode)
def decode(self, encoding, errors="strict"):
- result = str_decode(self.series, encoding, errors)
+ result = str_decode(self._data, encoding, errors)
return self._wrap_result(result)
@copy(str_encode)
def encode(self, encoding, errors="strict"):
- result = str_encode(self.series, encoding, errors)
+ result = str_encode(self._data, encoding, errors)
return self._wrap_result(result)
_shared_docs['str_strip'] = ("""
@@ -1332,34 +1350,37 @@ def encode(self, encoding, errors="strict"):
@Appender(_shared_docs['str_strip'] % dict(side='left and right sides',
method='strip'))
def strip(self, to_strip=None):
- result = str_strip(self.series, to_strip, side='both')
+ result = str_strip(self._data, to_strip, side='both')
return self._wrap_result(result)
@Appender(_shared_docs['str_strip'] % dict(side='left side',
method='lstrip'))
def lstrip(self, to_strip=None):
- result = str_strip(self.series, to_strip, side='left')
+ result = str_strip(self._data, to_strip, side='left')
return self._wrap_result(result)
@Appender(_shared_docs['str_strip'] % dict(side='right side',
method='rstrip'))
def rstrip(self, to_strip=None):
- result = str_strip(self.series, to_strip, side='right')
+ result = str_strip(self._data, to_strip, side='right')
return self._wrap_result(result)
@copy(str_wrap)
def wrap(self, width, **kwargs):
- result = str_wrap(self.series, width, **kwargs)
+ result = str_wrap(self._data, width, **kwargs)
return self._wrap_result(result)
@copy(str_get_dummies)
def get_dummies(self, sep='|'):
- result = str_get_dummies(self.series, sep)
- return self._wrap_result(result)
+ # we need to cast to Series of strings as only that has all
+ # 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))
@copy(str_translate)
def translate(self, table, deletechars=None):
- result = str_translate(self.series, table, deletechars)
+ result = str_translate(self._data, table, deletechars)
return self._wrap_result(result)
count = _pat_wrapper(str_count, flags=True)
@@ -1369,7 +1390,7 @@ def translate(self, table, deletechars=None):
@copy(str_extract)
def extract(self, pat, flags=0):
- result, name = str_extract(self.series, pat, flags=flags)
+ result, name = str_extract(self._data, pat, flags=flags)
return self._wrap_result(result, name=name)
_shared_docs['find'] = ("""
@@ -1398,13 +1419,13 @@ def extract(self, pat, flags=0):
@Appender(_shared_docs['find'] % dict(side='lowest', method='find',
also='rfind : Return highest indexes in each strings'))
def find(self, sub, start=0, end=None):
- result = str_find(self.series, sub, start=start, end=end, side='left')
+ result = str_find(self._data, sub, start=start, end=end, side='left')
return self._wrap_result(result)
@Appender(_shared_docs['find'] % dict(side='highest', method='rfind',
also='find : Return lowest indexes in each strings'))
def rfind(self, sub, start=0, end=None):
- result = str_find(self.series, sub, start=start, end=end, side='right')
+ result = str_find(self._data, sub, start=start, end=end, side='right')
return self._wrap_result(result)
def normalize(self, form):
@@ -1423,7 +1444,7 @@ def normalize(self, form):
"""
import unicodedata
f = lambda x: unicodedata.normalize(form, compat.u_safe(x))
- result = _na_map(f, self.series)
+ result = _na_map(f, self._data)
return self._wrap_result(result)
_shared_docs['index'] = ("""
@@ -1453,13 +1474,13 @@ def normalize(self, form):
@Appender(_shared_docs['index'] % dict(side='lowest', similar='find', method='index',
also='rindex : Return highest indexes in each strings'))
def index(self, sub, start=0, end=None):
- result = str_index(self.series, sub, start=start, end=end, side='left')
+ result = str_index(self._data, sub, start=start, end=end, side='left')
return self._wrap_result(result)
@Appender(_shared_docs['index'] % dict(side='highest', similar='rfind', method='rindex',
also='index : Return lowest indexes in each strings'))
def rindex(self, sub, start=0, end=None):
- result = str_index(self.series, sub, start=start, end=end, side='right')
+ result = str_index(self._data, sub, start=start, end=end, side='right')
return self._wrap_result(result)
_shared_docs['len'] = ("""
@@ -1553,9 +1574,14 @@ class StringAccessorMixin(object):
def _make_str_accessor(self):
from pandas.core.series import Series
from pandas.core.index import Index
- if isinstance(self, Series) and not is_object_dtype(self.dtype):
- # this really should exclude all series with any non-string values,
- # but that isn't practical for performance reasons until we have a
+ if isinstance(self, Series) and not(
+ (is_categorical_dtype(self.dtype) and
+ is_object_dtype(self.values.categories)) or
+ (is_object_dtype(self.dtype))):
+ # it's neither a string series not a categorical series with strings
+ # inside the categories.
+ # this really should exclude all series with any non-string values (instead of test
+ # for object dtype), but that isn't practical for performance reasons until we have a
# str dtype (GH 9343)
raise AttributeError("Can only use .str accessor with string "
"values, which use np.object_ dtype in "
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 0da4d0e68621d..ac2f9e77c3674 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -3640,6 +3640,153 @@ def test_cat_accessor_no_new_attributes(self):
with tm.assertRaisesRegexp(AttributeError, "You cannot add any new attribute"):
c.cat.xlabel = "a"
+ def test_str_accessor_api_for_categorical(self):
+ # https://github.com/pydata/pandas/issues/10661
+ from pandas.core.strings import StringMethods
+ s = Series(list('aabb'))
+ s = s + " " + s
+ c = s.astype('category')
+ self.assertIsInstance(c.str, StringMethods)
+
+ # str functions, which need special arguments
+ special_func_defs = [
+ ('cat', (list("zyxw"),), {"sep": ","}),
+ ('center', (10,), {}),
+ ('contains', ("a",), {}),
+ ('count', ("a",), {}),
+ ('decode', ("UTF-8",), {}),
+ ('encode', ("UTF-8",), {}),
+ ('endswith', ("a",), {}),
+ ('extract', ("([a-z]*) ",), {}),
+ ('find', ("a",), {}),
+ ('findall', ("a",), {}),
+ ('index', (" ",), {}),
+ ('ljust', (10,), {}),
+ ('match', ("a"), {}), # deprecated...
+ ('normalize', ("NFC",), {}),
+ ('pad', (10,), {}),
+ ('partition', (" ",), {"expand": False}), # not default
+ ('partition', (" ",), {"expand": True}), # default
+ ('repeat', (3,), {}),
+ ('replace', ("a", "z"), {}),
+ ('rfind', ("a",), {}),
+ ('rindex', (" ",), {}),
+ ('rjust', (10,), {}),
+ ('rpartition', (" ",), {"expand": False}), # not default
+ ('rpartition', (" ",), {"expand": True}), # default
+ ('slice', (0,1), {}),
+ ('slice_replace', (0,1,"z"), {}),
+ ('split', (" ",), {"expand":False}), #default
+ ('split', (" ",), {"expand":True}), # not default
+ ('startswith', ("a",), {}),
+ ('wrap', (2,), {}),
+ ('zfill', (10,), {})
+ ]
+ _special_func_names = [f[0] for f in special_func_defs]
+
+ # * get, join: they need a individual elements of type lists, but
+ # we can't make a categorical with lists as individual categories.
+ # -> `s.str.split(" ").astype("category")` will error!
+ # * `translate` has different interfaces for py2 vs. py3
+ _ignore_names = ["get", "join", "translate"]
+
+ str_func_names = [f for f in dir(s.str) if not (f.startswith("_") or
+ f in _special_func_names or
+ f in _ignore_names)]
+
+ func_defs = [(f, (), {}) for f in str_func_names]
+ func_defs.extend(special_func_defs)
+
+
+ for func, args, kwargs in func_defs:
+ res = getattr(c.str, func)(*args, **kwargs)
+ exp = getattr(s.str, func)(*args, **kwargs)
+
+ if isinstance(res, pd.DataFrame):
+ tm.assert_frame_equal(res, exp)
+ else:
+ tm.assert_series_equal(res, exp)
+
+ invalid = Series([1,2,3]).astype('category')
+ with tm.assertRaisesRegexp(AttributeError, "Can only use .str accessor with string"):
+ invalid.str
+ self.assertFalse(hasattr(invalid, 'str'))
+
+ def test_dt_accessor_api_for_categorical(self):
+ # https://github.com/pydata/pandas/issues/10661
+ from pandas.tseries.common import Properties
+ from pandas.tseries.index import date_range, DatetimeIndex
+ from pandas.tseries.period import period_range, PeriodIndex
+ from pandas.tseries.tdi import timedelta_range, TimedeltaIndex
+
+ s_dr = Series(date_range('1/1/2015', periods=5, tz="MET"))
+ c_dr = s_dr.astype("category")
+
+ s_pr = Series(period_range('1/1/2015', freq='D', periods=5))
+ c_pr = s_pr.astype("category")
+
+ s_tdr = Series(timedelta_range('1 days','10 days'))
+ c_tdr = s_tdr.astype("category")
+
+ test_data = [
+ ("Datetime", DatetimeIndex._datetimelike_ops, s_dr, c_dr),
+ ("Period", PeriodIndex._datetimelike_ops, s_pr, c_pr),
+ ("Timedelta", TimedeltaIndex._datetimelike_ops, s_tdr, c_tdr)]
+
+ self.assertIsInstance(c_dr.dt, Properties)
+
+ special_func_defs = [
+ ('strftime', ("%Y-%m-%d",), {}),
+ ('tz_convert', ("EST",), {}),
+ #('tz_localize', ("UTC",), {}),
+ ]
+ _special_func_names = [f[0] for f in special_func_defs]
+
+ # the series is already localized
+ _ignore_names = ['tz_localize']
+
+ for name, attr_names, s, c in test_data:
+ func_names = [f for f in dir(s.dt) if not (f.startswith("_") or
+ f in attr_names or
+ f in _special_func_names or
+ f in _ignore_names)]
+
+ func_defs = [(f, (), {}) for f in func_names]
+ for f_def in special_func_defs:
+ if f_def[0] in dir(s.dt):
+ func_defs.append(f_def)
+
+ for func, args, kwargs in func_defs:
+ res = getattr(c.dt, func)(*args, **kwargs)
+ exp = getattr(s.dt, func)(*args, **kwargs)
+
+ if isinstance(res, pd.DataFrame):
+ tm.assert_frame_equal(res, exp)
+ elif isinstance(res, pd.Series):
+ tm.assert_series_equal(res, exp)
+ else:
+ tm.assert_numpy_array_equal(res, exp)
+
+ for attr in attr_names:
+ try:
+ res = getattr(c.dt, attr)
+ exp = getattr(s.dt, attr)
+ except Exception as e:
+ print(name, attr)
+ raise e
+
+ if isinstance(res, pd.DataFrame):
+ tm.assert_frame_equal(res, exp)
+ elif isinstance(res, pd.Series):
+ tm.assert_series_equal(res, exp)
+ else:
+ tm.assert_numpy_array_equal(res, exp)
+
+ invalid = Series([1,2,3]).astype('category')
+ with tm.assertRaisesRegexp(AttributeError, "Can only use .dt accessor with datetimelike"):
+ invalid.dt
+ self.assertFalse(hasattr(invalid, 'str'))
+
def test_pickle_v0_14_1(self):
# we have the name warning
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index f30481ee17f75..9be0784c709bc 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -362,12 +362,20 @@ def test_tab_completion(self):
self.assertTrue('str' not in dir(s))
self.assertTrue('cat' not in dir(s))
- # similiarly for .cat
+ # similiarly for .cat, but with the twist that str and dt should be there
+ # if the categories are of that type
+ # first cat and str
s = Series(list('abbcd'), dtype="category")
self.assertTrue('cat' in dir(s))
- self.assertTrue('str' not in dir(s))
+ self.assertTrue('str' in dir(s)) # as it is a string categorical
self.assertTrue('dt' not in dir(s))
+ # similar to cat and str
+ s = Series(date_range('1/1/2015', periods=5)).astype("category")
+ self.assertTrue('cat' in dir(s))
+ self.assertTrue('str' not in dir(s))
+ self.assertTrue('dt' in dir(s)) # as it is a datetime categorical
+
def test_binop_maybe_preserve_name(self):
# names match, preserve
result = self.ts * self.ts
diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py
index 171f72d37cdd8..31b5281aa86a6 100644
--- a/pandas/tseries/common.py
+++ b/pandas/tseries/common.py
@@ -10,8 +10,8 @@
from pandas.core.common import (_NS_DTYPE, _TD_DTYPE, is_period_arraylike,
is_datetime_arraylike, is_integer_dtype, is_list_like,
is_datetime64_dtype, is_datetime64tz_dtype,
- is_timedelta64_dtype,
- get_dtype_kinds)
+ is_timedelta64_dtype, is_categorical_dtype,
+ get_dtype_kinds, take_1d)
def is_datetimelike(data):
""" return a boolean if we can be successfully converted to a datetimelike """
@@ -45,26 +45,36 @@ def maybe_to_datetimelike(data, copy=False):
raise TypeError("cannot convert an object of type {0} to a datetimelike index".format(type(data)))
index = data.index
+ name = data.name
+ orig = data if is_categorical_dtype(data) else None
+ if orig is not None:
+ data = orig.values.categories
+
if is_datetime64_dtype(data.dtype):
- return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index, name=data.name)
+ return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index, name=name,
+ orig=orig)
elif is_datetime64tz_dtype(data.dtype):
- return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer', ambiguous='infer'), index, name=data.name)
+ return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer', ambiguous='infer'),
+ index, data.name, orig=orig)
elif is_timedelta64_dtype(data.dtype):
- return TimedeltaProperties(TimedeltaIndex(data, copy=copy, freq='infer'), index, name=data.name)
+ return TimedeltaProperties(TimedeltaIndex(data, copy=copy, freq='infer'), index,
+ name=name, orig=orig)
else:
if is_period_arraylike(data):
- return PeriodProperties(PeriodIndex(data, copy=copy), index, name=data.name)
+ return PeriodProperties(PeriodIndex(data, copy=copy), index, name=name, orig=orig)
if is_datetime_arraylike(data):
- return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index, name=data.name)
+ return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index,
+ name=name, orig=orig)
raise TypeError("cannot convert an object of type {0} to a datetimelike index".format(type(data)))
class Properties(PandasDelegate, NoNewAttributesMixin):
- def __init__(self, values, index, name):
+ def __init__(self, values, index, name, orig=None):
self.values = values
self.index = index
self.name = name
+ self.orig = orig
self._freeze()
def _delegate_property_get(self, name):
@@ -79,6 +89,10 @@ def _delegate_property_get(self, name):
elif not is_list_like(result):
return result
+ # blow up if we operate on categories
+ if self.orig is not None:
+ result = take_1d(result, self.orig.cat.codes)
+
# return the result as a Series, which is by definition a copy
result = Series(result, index=self.index, name=self.name)
| If a series is a type category and the underlying Categorical has categories of type string or datetime, then make it possible to use the `.str`/`.dt` assessor on such a series.
The string/dt methods work on the categories (and therefore fast if we have
only a few categories), but return a Series with a dtype other than
category (integer, boolean, string,...), so that it is no different if we use
`.str` / `.dt` on a series of type string or of type category.
The main reason for that is that I think things like `s.str.slice(...) + s.str.slice(...)` should work.
Closes: https://github.com/pydata/pandas/issues/10661
| https://api.github.com/repos/pandas-dev/pandas/pulls/11582 | 2015-11-12T13:21:27Z | 2015-11-18T11:46:16Z | null | 2015-11-18T13:39:25Z |
ENH: #3335 Pivot table support for setting name of margins column. | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 3d10566e47075..3463435262692 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -46,6 +46,8 @@ Enhancements
pd.Index([1, np.nan, 3]).fillna(2)
+- ``pivot_table`` now has a ``margins_name`` argument so you can use something other than the default of 'All' (:issue:`3335`)
+
.. _whatsnew_0171.api:
API changes
diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py
index de7a5f5a73f3d..97bd1f86d01cf 100644
--- a/pandas/tools/pivot.py
+++ b/pandas/tools/pivot.py
@@ -1,6 +1,5 @@
# pylint: disable=E1103
-import warnings
from pandas import Series, DataFrame
from pandas.core.index import MultiIndex, Index
@@ -8,13 +7,14 @@
from pandas.tools.merge import concat
from pandas.tools.util import cartesian_product
from pandas.compat import range, lrange, zip
-from pandas.util.decorators import deprecate_kwarg
from pandas import compat
import pandas.core.common as com
import numpy as np
+
def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
- fill_value=None, margins=False, dropna=True):
+ fill_value=None, margins=False, dropna=True,
+ margins_name='All'):
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in the
pivot table will be stored in MultiIndex objects (hierarchical indexes) on
@@ -40,6 +40,9 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
+ margins_name : string, default 'All'
+ Name of the row / column that will contain the totals
+ when margins is True.
Examples
--------
@@ -127,7 +130,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
m = MultiIndex.from_arrays(cartesian_product(table.columns.levels))
table = table.reindex_axis(m, axis=1)
except AttributeError:
- pass # it's a single level or a series
+ pass # it's a single level or a series
if isinstance(table, DataFrame):
if isinstance(table.columns, MultiIndex):
@@ -140,7 +143,8 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
if margins:
table = _add_margins(table, data, values, rows=index,
- cols=columns, aggfunc=aggfunc)
+ cols=columns, aggfunc=aggfunc,
+ margins_name=margins_name)
# discard the top level
if values_passed and not values_multi:
@@ -155,29 +159,49 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
DataFrame.pivot_table = pivot_table
-def _add_margins(table, data, values, rows, cols, aggfunc):
+def _add_margins(table, data, values, rows, cols, aggfunc,
+ margins_name='All'):
+ if not isinstance(margins_name, compat.string_types):
+ raise ValueError('margins_name argument must be a string')
+
+ exception_msg = 'Conflicting name "{0}" in margins'.format(margins_name)
+ for level in table.index.names:
+ if margins_name in table.index.get_level_values(level):
+ raise ValueError(exception_msg)
- grand_margin = _compute_grand_margin(data, values, aggfunc)
+ grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name)
+
+ # could be passed a Series object with no 'columns'
+ if hasattr(table, 'columns'):
+ for level in table.columns.names[1:]:
+ if margins_name in table.columns.get_level_values(level):
+ raise ValueError(exception_msg)
+
+ if len(rows) > 1:
+ key = (margins_name,) + ('',) * (len(rows) - 1)
+ else:
+ key = margins_name
if not values and isinstance(table, Series):
# If there are no values and the table is a series, then there is only
# one column in the data. Compute grand margin and return it.
- row_key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All'
- return table.append(Series({row_key: grand_margin['All']}))
+ return table.append(Series({key: grand_margin[margins_name]}))
if values:
- marginal_result_set = _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin)
+ marginal_result_set = _generate_marginal_results(table, data, values,
+ rows, cols, aggfunc,
+ grand_margin,
+ margins_name)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
else:
- marginal_result_set = _generate_marginal_results_without_values(table, data, rows, cols, aggfunc)
+ marginal_result_set = _generate_marginal_results_without_values(
+ table, data, rows, cols, aggfunc, margins_name)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
- key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All'
-
row_margin = row_margin.reindex(result.columns)
# populate grand margin
for k in margin_keys:
@@ -201,7 +225,8 @@ def _add_margins(table, data, values, rows, cols, aggfunc):
return result
-def _compute_grand_margin(data, values, aggfunc):
+def _compute_grand_margin(data, values, aggfunc,
+ margins_name='All'):
if values:
grand_margin = {}
@@ -220,18 +245,19 @@ def _compute_grand_margin(data, values, aggfunc):
pass
return grand_margin
else:
- return {'All': aggfunc(data.index)}
-
+ return {margins_name: aggfunc(data.index)}
-def _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin):
+def _generate_marginal_results(table, data, values, rows, cols, aggfunc,
+ grand_margin,
+ margins_name='All'):
if len(cols) > 0:
# need to "interleave" the margins
table_pieces = []
margin_keys = []
def _all_key(key):
- return (key, 'All') + ('',) * (len(cols) - 1)
+ return (key, margins_name) + ('',) * (len(cols) - 1)
if len(rows) > 0:
margin = data[rows + values].groupby(rows).agg(aggfunc)
@@ -282,15 +308,17 @@ def _all_key(key):
return result, margin_keys, row_margin
-def _generate_marginal_results_without_values(table, data, rows, cols, aggfunc):
+def _generate_marginal_results_without_values(
+ table, data, rows, cols, aggfunc,
+ margins_name='All'):
if len(cols) > 0:
# need to "interleave" the margins
margin_keys = []
def _all_key():
if len(cols) == 1:
- return 'All'
- return ('All', ) + ('', ) * (len(cols) - 1)
+ return margins_name
+ return (margins_name, ) + ('', ) * (len(cols) - 1)
if len(rows) > 0:
margin = data[rows].groupby(rows).apply(aggfunc)
diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py
index f0052774d66a2..cb7e9102b21a0 100644
--- a/pandas/tools/tests/test_pivot.py
+++ b/pandas/tools/tests/test_pivot.py
@@ -224,32 +224,44 @@ def test_pivot_with_tz(self):
tm.assert_frame_equal(pv, expected)
def test_margins(self):
- def _check_output(res, col, index=['A', 'B'], columns=['C']):
- cmarg = res['All'][:-1]
- exp = self.data.groupby(index)[col].mean()
- tm.assert_series_equal(cmarg, exp, check_names=False)
- self.assertEqual(cmarg.name, 'All')
-
- res = res.sortlevel()
- rmarg = res.xs(('All', ''))[:-1]
- exp = self.data.groupby(columns)[col].mean()
- tm.assert_series_equal(rmarg, exp, check_names=False)
- self.assertEqual(rmarg.name, ('All', ''))
-
- gmarg = res['All']['All', '']
- exp = self.data[col].mean()
- self.assertEqual(gmarg, exp)
+ def _check_output(result, values_col, index=['A', 'B'],
+ columns=['C'],
+ margins_col='All'):
+ col_margins = result.ix[:-1, margins_col]
+ expected_col_margins = self.data.groupby(index)[values_col].mean()
+ tm.assert_series_equal(col_margins, expected_col_margins,
+ check_names=False)
+ self.assertEqual(col_margins.name, margins_col)
+
+ result = result.sortlevel()
+ index_margins = result.ix[(margins_col, '')].iloc[:-1]
+ expected_ix_margins = self.data.groupby(columns)[values_col].mean()
+ tm.assert_series_equal(index_margins, expected_ix_margins,
+ check_names=False)
+ self.assertEqual(index_margins.name, (margins_col, ''))
+
+ grand_total_margins = result.loc[(margins_col, ''), margins_col]
+ expected_total_margins = self.data[values_col].mean()
+ self.assertEqual(grand_total_margins, expected_total_margins)
# column specified
- table = self.data.pivot_table('D', index=['A', 'B'], columns='C',
- margins=True, aggfunc=np.mean)
- _check_output(table, 'D')
+ result = self.data.pivot_table(values='D', index=['A', 'B'],
+ columns='C',
+ margins=True, aggfunc=np.mean)
+ _check_output(result, 'D')
+
+ # Set a different margins_name (not 'All')
+ result = self.data.pivot_table(values='D', index=['A', 'B'],
+ columns='C',
+ margins=True, aggfunc=np.mean,
+ margins_name='Totals')
+ _check_output(result, 'D', margins_col='Totals')
# no column specified
table = self.data.pivot_table(index=['A', 'B'], columns='C',
margins=True, aggfunc=np.mean)
- for valcol in table.columns.levels[0]:
- _check_output(table[valcol], valcol)
+ for value_col in table.columns.levels[0]:
+ _check_output(table[value_col], value_col)
# no col
@@ -257,49 +269,61 @@ def _check_output(res, col, index=['A', 'B'], columns=['C']):
self.data.columns = [k * 2 for k in self.data.columns]
table = self.data.pivot_table(index=['AA', 'BB'], margins=True,
aggfunc=np.mean)
- for valcol in table.columns:
- gmarg = table[valcol]['All', '']
- self.assertEqual(gmarg, self.data[valcol].mean())
-
- # this is OK
- table = self.data.pivot_table(index=['AA', 'BB'], margins=True,
- aggfunc='mean')
+ for value_col in table.columns:
+ totals = table.loc[('All', ''), value_col]
+ self.assertEqual(totals, self.data[value_col].mean())
# no rows
rtable = self.data.pivot_table(columns=['AA', 'BB'], margins=True,
aggfunc=np.mean)
tm.assertIsInstance(rtable, Series)
+
+ table = self.data.pivot_table(index=['AA', 'BB'], margins=True,
+ aggfunc='mean')
for item in ['DD', 'EE', 'FF']:
- gmarg = table[item]['All', '']
- self.assertEqual(gmarg, self.data[item].mean())
+ totals = table.loc[('All', ''), item]
+ self.assertEqual(totals, self.data[item].mean())
# issue number #8349: pivot_table with margins and dictionary aggfunc
+ data = [
+ {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2013,
+ 'MONTH': 12, 'DAYS': 3, 'SALARY': 17},
+ {'JOB': 'Employ', 'NAME':
+ 'Mary', 'YEAR': 2013, 'MONTH': 12, 'DAYS': 5, 'SALARY': 23},
+ {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2014,
+ 'MONTH': 1, 'DAYS': 10, 'SALARY': 100},
+ {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2014,
+ 'MONTH': 1, 'DAYS': 11, 'SALARY': 110},
+ {'JOB': 'Employ', 'NAME': 'Mary', 'YEAR': 2014,
+ 'MONTH': 1, 'DAYS': 15, 'SALARY': 200},
+ {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2014,
+ 'MONTH': 2, 'DAYS': 8, 'SALARY': 80},
+ {'JOB': 'Employ', 'NAME': 'Mary', 'YEAR': 2014,
+ 'MONTH': 2, 'DAYS': 5, 'SALARY': 190},
+ ]
- df=DataFrame([ {'JOB':'Worker','NAME':'Bob' ,'YEAR':2013,'MONTH':12,'DAYS': 3,'SALARY': 17},
- {'JOB':'Employ','NAME':'Mary','YEAR':2013,'MONTH':12,'DAYS': 5,'SALARY': 23},
- {'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':10,'SALARY':100},
- {'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':11,'SALARY':110},
- {'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 1,'DAYS':15,'SALARY':200},
- {'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 2,'DAYS': 8,'SALARY': 80},
- {'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 2,'DAYS': 5,'SALARY':190} ])
-
- df=df.set_index(['JOB','NAME','YEAR','MONTH'],drop=False,append=False)
-
- rs=df.pivot_table( index=['JOB','NAME'],
- columns=['YEAR','MONTH'],
- values=['DAYS','SALARY'],
- aggfunc={'DAYS':'mean','SALARY':'sum'},
- margins=True)
+ df = DataFrame(data)
- ex=df.pivot_table(index=['JOB','NAME'],columns=['YEAR','MONTH'],values=['DAYS'],aggfunc='mean',margins=True)
+ df = df.set_index(['JOB', 'NAME', 'YEAR', 'MONTH'], drop=False,
+ append=False)
- tm.assert_frame_equal(rs['DAYS'], ex['DAYS'])
+ result = df.pivot_table(index=['JOB', 'NAME'],
+ columns=['YEAR', 'MONTH'],
+ values=['DAYS', 'SALARY'],
+ aggfunc={'DAYS': 'mean', 'SALARY': 'sum'},
+ margins=True)
- ex=df.pivot_table(index=['JOB','NAME'],columns=['YEAR','MONTH'],values=['SALARY'],aggfunc='sum',margins=True)
+ expected = df.pivot_table(index=['JOB', 'NAME'],
+ columns=['YEAR', 'MONTH'], values=['DAYS'],
+ aggfunc='mean', margins=True)
- tm.assert_frame_equal(rs['SALARY'], ex['SALARY'])
+ tm.assert_frame_equal(result['DAYS'], expected['DAYS'])
+ expected = df.pivot_table(index=['JOB', 'NAME'],
+ columns=['YEAR', 'MONTH'], values=['SALARY'],
+ aggfunc='sum', margins=True)
+ tm.assert_frame_equal(result['SALARY'], expected['SALARY'])
def test_pivot_integer_columns(self):
# caused by upstream bug in unstack
@@ -402,6 +426,25 @@ def test_margins_no_values_two_row_two_cols(self):
result = self.data[['A', 'B', 'C', 'D']].pivot_table(index=['A', 'B'], columns=['C', 'D'], aggfunc=len, margins=True)
self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0])
+ def test_pivot_table_with_margins_set_margin_name(self):
+ # GH 3335
+ for margin_name in ['foo', 'one', 666, None, ['a', 'b']]:
+ with self.assertRaises(ValueError):
+ # multi-index index
+ pivot_table(self.data, values='D', index=['A', 'B'],
+ columns=['C'], margins=True,
+ margins_name=margin_name)
+ with self.assertRaises(ValueError):
+ # multi-index column
+ pivot_table(self.data, values='D', index=['C'],
+ columns=['A', 'B'], margins=True,
+ margins_name=margin_name)
+ with self.assertRaises(ValueError):
+ # non-multi-index index/column
+ pivot_table(self.data, values='D', index=['A'],
+ columns=['B'], margins=True,
+ margins_name=margin_name)
+
def test_pivot_timegrouper(self):
df = DataFrame({
'Branch' : 'A A A A A A A B'.split(),
| closes #3335.
Adds margin_column parameter to pivot_table so that user can set it to
something other than 'All'.
Raises ValueError exception if there is a conflict between the value of
margin_column and one of the other values appearing in the indices of
the pivot table.
take into account notes from closed pull request: #10296
| https://api.github.com/repos/pandas-dev/pandas/pulls/11581 | 2015-11-12T11:11:18Z | 2015-11-15T16:46:44Z | 2015-11-15T16:46:44Z | 2015-11-15T16:46:47Z |
CI: Ignore this | just trying from a different branch.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11577 | 2015-11-12T01:07:49Z | 2015-11-14T20:49:13Z | null | 2017-04-05T02:06:36Z | |
Prevent adding new attributes to the accessors .str, .dt and .cat | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 28819c522c696..2fde26451ad5c 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -76,6 +76,8 @@ Bug Fixes
- Bug in merging ``datetime64[ns, tz]`` dtypes (:issue:`11405`)
- Bug in ``HDFStore.select`` when comparing with a numpy scalar in a where clause (:issue:`11283`)
- Bug in using ``DataFrame.ix`` with a multi-index indexer(:issue:`11372`)
+- Prevent adding new attributes to the accessors ``.str``, ``.dt`` and ``.cat``. Retrieving such
+ a value was not possible, so error out on setting it. (:issue:`10673`)
- Bug in tz-conversions with an ambiguous time and ``.dt`` accessors (:issue:`11295`)
diff --git a/pandas/core/base.py b/pandas/core/base.py
index d3850be13b6f0..0f5c43b8e1fff 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -7,7 +7,6 @@
import pandas.core.nanops as nanops
import pandas.lib as lib
from pandas.util.decorators import Appender, cache_readonly, deprecate_kwarg
-from pandas.core.strings import StringMethods
from pandas.core.common import AbstractMethodError
_shared_docs = dict()
@@ -111,6 +110,31 @@ def _reset_cache(self, key=None):
else:
self._cache.pop(key, None)
+class NoNewAttributesMixin(object):
+ """Mixin which prevents adding new attributes.
+
+ Prevents additional attributes via xxx.attribute = "something" after a call to
+ `self.__freeze()`. Mainly used to prevent the user from using wrong attrirbutes
+ on a accessor (`Series.cat/.str/.dt`).
+
+ If you really want to add a new attribute at a later time, you need to use
+ `object.__setattr__(self, key, value)`.
+ """
+
+ def _freeze(self):
+ """Prevents setting additional attributes"""
+ object.__setattr__(self, "__frozen", True)
+
+
+ # prevent adding any attribute via s.xxx.new_attribute = ...
+ def __setattr__(self, key, value):
+ # _cache is used by a decorator
+ # dict lookup instead of getattr as getattr is false for getter which error
+ if getattr(self, "__frozen", False) and not (key in type(self).__dict__ or key == "_cache"):
+ raise AttributeError( "You cannot add any new attribute '{key}'".format(key=key))
+ object.__setattr__(self, key, value)
+
+
class PandasDelegate(PandasObject):
""" an abstract base class for delegating methods/properties """
@@ -517,41 +541,6 @@ def searchsorted(self, key, side='left'):
#### needs tests/doc-string
return self.values.searchsorted(key, side=side)
- # string methods
- def _make_str_accessor(self):
- from pandas.core.series import Series
- from pandas.core.index import Index
- if isinstance(self, Series) and not com.is_object_dtype(self.dtype):
- # this really should exclude all series with any non-string values,
- # but that isn't practical for performance reasons until we have a
- # str dtype (GH 9343)
- raise AttributeError("Can only use .str accessor with string "
- "values, which use np.object_ dtype in "
- "pandas")
- elif isinstance(self, Index):
- # see scc/inferrence.pyx which can contain string values
- allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
- if self.inferred_type not in allowed_types:
- message = ("Can only use .str accessor with string values "
- "(i.e. inferred_type is 'string', 'unicode' or 'mixed')")
- raise AttributeError(message)
- if self.nlevels > 1:
- message = "Can only use .str accessor with Index, not MultiIndex"
- raise AttributeError(message)
- return StringMethods(self)
-
- str = AccessorProperty(StringMethods, _make_str_accessor)
-
- def _dir_additions(self):
- return set()
-
- def _dir_deletions(self):
- try:
- getattr(self, 'str')
- except AttributeError:
- return set(['str'])
- return set()
-
_shared_docs['drop_duplicates'] = (
"""Return %(klass)s with duplicate values removed
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py
index e304684036766..1d9d347f5e5a7 100644
--- a/pandas/core/categorical.py
+++ b/pandas/core/categorical.py
@@ -8,7 +8,7 @@
from pandas.compat import u
from pandas.core.algorithms import factorize
-from pandas.core.base import PandasObject, PandasDelegate
+from pandas.core.base import PandasObject, PandasDelegate, NoNewAttributesMixin
import pandas.core.common as com
from pandas.core.missing import interpolate_2d
from pandas.util.decorators import cache_readonly, deprecate_kwarg
@@ -1717,7 +1717,7 @@ def repeat(self, repeats):
##### The Series.cat accessor #####
-class CategoricalAccessor(PandasDelegate):
+class CategoricalAccessor(PandasDelegate, NoNewAttributesMixin):
"""
Accessor object for categorical properties of the Series values.
@@ -1742,6 +1742,7 @@ class CategoricalAccessor(PandasDelegate):
def __init__(self, values, index):
self.categorical = values
self.index = index
+ self._freeze()
def _delegate_property_get(self, name):
return getattr(self.categorical, name)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 644b6a411c79a..b55d583ce63cf 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -25,6 +25,7 @@
_values_from_object, is_float, is_integer, is_iterator, is_categorical_dtype,
_ensure_object, _ensure_int64, is_bool_indexer,
is_list_like, is_bool_dtype, is_null_slice, is_integer_dtype)
+from pandas.core.strings import StringAccessorMixin
from pandas.core.config import get_option
from pandas.io.common import PerformanceWarning
@@ -64,7 +65,7 @@ def _new_Index(cls, d):
and breaks __new__ """
return cls.__new__(cls, **d)
-class Index(IndexOpsMixin, PandasObject):
+class Index(IndexOpsMixin, StringAccessorMixin, PandasObject):
"""
Immutable ndarray implementing an ordered, sliceable set. The basic object
diff --git a/pandas/core/series.py b/pandas/core/series.py
index b12a31d64eaf7..7eb8859979ad9 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -32,6 +32,7 @@
from pandas.core import generic, base
from pandas.core.internals import SingleBlockManager
from pandas.core.categorical import Categorical, CategoricalAccessor
+import pandas.core.strings as strings
from pandas.tseries.common import (maybe_to_datetimelike,
CombinedDatetimelikeProperties)
from pandas.tseries.index import DatetimeIndex
@@ -85,7 +86,7 @@ def wrapper(self):
# Series class
-class Series(base.IndexOpsMixin, generic.NDFrame):
+class Series(base.IndexOpsMixin, strings.StringAccessorMixin, generic.NDFrame,):
"""
One-dimensional ndarray with axis labels (including time series).
diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index dddc1f4898908..f1ff7e2178a04 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -1,8 +1,10 @@
import numpy as np
from pandas.compat import zip
-from pandas.core.common import isnull, _values_from_object, is_bool_dtype, is_list_like
+from pandas.core.common import (isnull, _values_from_object, is_bool_dtype, is_list_like,
+ is_categorical_dtype, is_object_dtype)
import pandas.compat as compat
+from pandas.core.base import AccessorProperty, NoNewAttributesMixin
from pandas.util.decorators import Appender, deprecate_kwarg
import re
import pandas.lib as lib
@@ -1044,7 +1046,7 @@ def do_copy(target):
return do_copy
-class StringMethods(object):
+class StringMethods(NoNewAttributesMixin):
"""
Vectorized string functions for Series and Index. NAs stay NA unless
@@ -1059,6 +1061,7 @@ class StringMethods(object):
def __init__(self, series):
self.series = series
+ self._freeze()
def __getitem__(self, key):
if isinstance(key, slice):
@@ -1542,3 +1545,41 @@ def rindex(self, sub, start=0, end=None):
isdecimal = _noarg_wrapper(lambda x: compat.u_safe(x).isdecimal(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isdecimal'])
+
+class StringAccessorMixin(object):
+ """ Mixin to add a `.str` acessor to the class."""
+
+ # string methods
+ def _make_str_accessor(self):
+ from pandas.core.series import Series
+ from pandas.core.index import Index
+ if isinstance(self, Series) and not is_object_dtype(self.dtype):
+ # this really should exclude all series with any non-string values,
+ # but that isn't practical for performance reasons until we have a
+ # str dtype (GH 9343)
+ raise AttributeError("Can only use .str accessor with string "
+ "values, which use np.object_ dtype in "
+ "pandas")
+ elif isinstance(self, Index):
+ # see scc/inferrence.pyx which can contain string values
+ allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
+ if self.inferred_type not in allowed_types:
+ message = ("Can only use .str accessor with string values "
+ "(i.e. inferred_type is 'string', 'unicode' or 'mixed')")
+ raise AttributeError(message)
+ if self.nlevels > 1:
+ message = "Can only use .str accessor with Index, not MultiIndex"
+ raise AttributeError(message)
+ return StringMethods(self)
+
+ str = AccessorProperty(StringMethods, _make_str_accessor)
+
+ def _dir_additions(self):
+ return set()
+
+ def _dir_deletions(self):
+ try:
+ getattr(self, 'str')
+ except AttributeError:
+ return set(['str'])
+ return set()
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index fb255f300ebdd..fa60633a70a53 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -6,7 +6,7 @@
import pandas.compat as compat
import pandas as pd
from pandas.compat import u, StringIO
-from pandas.core.base import FrozenList, FrozenNDArray, PandasDelegate
+from pandas.core.base import FrozenList, FrozenNDArray, PandasDelegate, NoNewAttributesMixin
import pandas.core.common as com
from pandas.tseries.base import DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assertIsInstance
@@ -825,6 +825,25 @@ def test_lookup_nan(self):
self.assert_numpy_array_equal(m.lookup(xs), np.arange(len(xs)))
+class TestNoNewAttributesMixin(tm.TestCase):
+
+ def test_mixin(self):
+ class T(NoNewAttributesMixin):
+ pass
+
+ t = T()
+ self.assertFalse(hasattr(t, "__frozen"))
+ t.a = "test"
+ self.assertEqual(t.a, "test")
+ t._freeze()
+ #self.assertTrue("__frozen" not in dir(t))
+ self.assertIs(getattr(t, "__frozen"), True)
+ def f():
+ t.b = "test"
+ self.assertRaises(AttributeError, f)
+ self.assertFalse(hasattr(t, "b"))
+
+
if __name__ == '__main__':
import nose
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 1d143236e285b..6f1311b44d6b5 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -3625,6 +3625,12 @@ def test_cat_accessor_api(self):
invalid.cat
self.assertFalse(hasattr(invalid, 'cat'))
+ def test_cat_accessor_no_new_attributes(self):
+ # https://github.com/pydata/pandas/issues/10673
+ c = Series(list('aabbcde')).astype('category')
+ with tm.assertRaisesRegexp(AttributeError, "You cannot add any new attribute"):
+ c.cat.xlabel = "a"
+
def test_pickle_v0_14_1(self):
# we have the name warning
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 1c8cbac60e7c7..60fe2bece628d 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -247,6 +247,12 @@ def f():
s.dt.hour[0] = 5
self.assertRaises(com.SettingWithCopyError, f)
+ def test_dt_accessor_no_new_attributes(self):
+ # https://github.com/pydata/pandas/issues/10673
+ s = Series(date_range('20130101',periods=5,freq='D'))
+ with tm.assertRaisesRegexp(AttributeError, "You cannot add any new attribute"):
+ s.dt.xlabel = "a"
+
def test_strftime(self):
# GH 10086
s = Series(date_range('20130101', periods=5))
diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py
index 31623d5c277c4..1017704379811 100644
--- a/pandas/tests/test_strings.py
+++ b/pandas/tests/test_strings.py
@@ -2034,6 +2034,12 @@ def test_index_str_accessor_visibility(self):
with self.assertRaisesRegexp(AttributeError, message):
idx.str
+ def test_str_accessor_no_new_attributes(self):
+ # https://github.com/pydata/pandas/issues/10673
+ s = Series(list('aabbcde'))
+ with tm.assertRaisesRegexp(AttributeError, "You cannot add any new attribute"):
+ s.str.xlabel = "a"
+
def test_method_on_bytes(self):
lhs = Series(np.array(list('abc'), 'S1').astype(object))
rhs = Series(np.array(list('def'), 'S1').astype(object))
diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py
index dcfe809074a0b..171f72d37cdd8 100644
--- a/pandas/tseries/common.py
+++ b/pandas/tseries/common.py
@@ -1,7 +1,7 @@
## datetimelike delegation ##
import numpy as np
-from pandas.core.base import PandasDelegate
+from pandas.core.base import PandasDelegate, NoNewAttributesMixin
from pandas.core import common as com
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
@@ -59,12 +59,13 @@ def maybe_to_datetimelike(data, copy=False):
raise TypeError("cannot convert an object of type {0} to a datetimelike index".format(type(data)))
-class Properties(PandasDelegate):
+class Properties(PandasDelegate, NoNewAttributesMixin):
def __init__(self, values, index, name):
self.values = values
self.index = index
self.name = name
+ self._freeze()
def _delegate_property_get(self, name):
from pandas import Series
| assigning to `Series.str`, `Series.dt`, or `Series.cat` was not failing
although you couldn't get the value back:
```
In[10]: a = pandas.Series(pandas.Categorical(list("abc")))
In[11]: a.cat.labels = [1,2]
In[12]: a.cat.labels
Traceback (most recent call last):
File "C:\portabel\miniconda\envs\pandas_dev\lib\site-packages\IPython\core\interactiveshell.py", line 2883, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-a68ee763e4e8>", line 1, in <module>
a.cat.labels
AttributeError: 'CategoricalAccessor' object has no attribute 'labels'
```
Now we fail early:
```
In[10]: a = pandas.Series(pandas.Categorical(list("abc")))
In[11]: a.cat.labels = [1,2]
Traceback (most recent call last):
File "C:\data\external\pandas\pandas\tests\test_categorical.py", line 3633, in test_cat_accessor_no_new_attributes
c.cat.labels = [1,2]
File "C:\data\external\pandas\pandas\core\base.py", line 121, in __setattr__
raise AttributeError( "You cannot add any new attribute '{key}'".format(key=key))
AttributeError: You cannot add any new attribute 'labels'
```
Closes: https://github.com/pydata/pandas/issues/10673
| https://api.github.com/repos/pandas-dev/pandas/pulls/11575 | 2015-11-11T22:04:25Z | 2015-11-14T15:08:07Z | 2015-11-14T15:08:07Z | 2015-11-14T15:08:24Z |
Fixed typo in plotting documentation | diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst
index 4430dd6d38155..88ae4a227fe2a 100644
--- a/doc/source/visualization.rst
+++ b/doc/source/visualization.rst
@@ -109,7 +109,7 @@ You can plot one column versus another using the `x` and `y` keywords in
.. note::
- For more formatting and sytling options, see :ref:`below <visualization.formatting>`.
+ For more formatting and styling options, see :ref:`below <visualization.formatting>`.
.. ipython:: python
:suppress:
| https://api.github.com/repos/pandas-dev/pandas/pulls/11572 | 2015-11-11T16:04:32Z | 2015-11-11T22:07:18Z | 2015-11-11T22:07:18Z | 2015-11-11T23:03:04Z | |
BUG: GH11349 where Series.apply and Series.map did not box timedelta64 | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 4dd8a1d19c383..58d003b5c9dc7 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -167,6 +167,64 @@ Backwards incompatible API changes
- The parameter ``out`` has been removed from the ``Series.round()`` method. (:issue:`11763`)
- ``DataFrame.round()`` leaves non-numeric columns unchanged in its return, rather than raises. (:issue:`11885`)
+NaT and Timedelta operations
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``NaT`` and ``Timedelta`` have expanded arithmetic operations, which are extended to ``Series``
+arithmetic where applicable. Operations defined for ``datetime64[ns]`` or ``timedelta64[ns]``
+are now also defined for ``NaT`` (:issue:`11564`).
+
+``NaT`` now supports arithmetic operations with integers and floats.
+
+.. ipython:: python
+
+ pd.NaT * 1
+ pd.NaT * 1.5
+ pd.NaT / 2
+ pd.NaT * np.nan
+
+``NaT`` defines more arithmetic operations with ``datetime64[ns]`` and ``timedelta64[ns]``.
+
+.. ipython:: python
+
+ pd.NaT / pd.NaT
+ pd.Timedelta('1s') / pd.NaT
+
+``NaT`` may represent either a ``datetime64[ns]`` null or a ``timedelta64[ns]`` null.
+Given the ambiguity, it is treated as a `timedelta64[ns]`, which allows more operations
+to succeed.
+
+.. ipython:: python
+ :okexcept:
+
+ pd.NaT + pd.NaT
+ # same as
+ pd.Timedelta('1s') + pd.Timedelta('1s')
+ # as opposed to
+ pd.Timestamp('1990315') + pd.Timestamp('19900315')
+
+However, when wrapped in a ``Series`` whose ``dtype`` is ``datetime64[ns]`` or ``timedelta64[ns]``,
+the ``dtype`` information is respected.
+
+.. ipython:: python
+
+ pd.Series([pd.NaT], dtype='<M8[ns]') + pd.Series([pd.NaT], dtype='<M8[ns]')
+ pd.Series([pd.NaT], dtype='<m8[ns]') + pd.Series([pd.NaT], dtype='<m8[ns]')
+
+``Timedelta`` division by ``float``s now works.
+
+.. ipython:: python
+
+ pd.Timedelta('1s') / 2.0
+
+Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`11925`)
+
+.. ipython:: python
+
+ ser = pd.Series(pd.timedelta_range('1 day', periods=3))
+ ser
+ pd.Timestamp('2012-01-01') - ser
+
Bug in QuarterBegin with n=0
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -312,6 +370,7 @@ 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`)
diff --git a/pandas/core/ops.py b/pandas/core/ops.py
index bf331ff1b781c..4d003456f8102 100644
--- a/pandas/core/ops.py
+++ b/pandas/core/ops.py
@@ -291,6 +291,7 @@ def __init__(self, left, right, name, na_op):
self.is_datetime64tz_lhs = is_datetime64tz_dtype(lvalues)
self.is_datetime_lhs = self.is_datetime64_lhs or self.is_datetime64tz_lhs
self.is_integer_lhs = left.dtype.kind in ['i', 'u']
+ self.is_floating_lhs = left.dtype.kind == 'f'
# right
self.right = right
@@ -300,6 +301,7 @@ def __init__(self, left, right, name, na_op):
self.is_datetime_rhs = self.is_datetime64_rhs or self.is_datetime64tz_rhs
self.is_timedelta_rhs = is_timedelta64_dtype(rvalues)
self.is_integer_rhs = rvalues.dtype.kind in ('i', 'u')
+ self.is_floating_rhs = rvalues.dtype.kind == 'f'
self._validate(lvalues, rvalues, name)
self.lvalues, self.rvalues = self._convert_for_datetime(lvalues, rvalues)
@@ -307,25 +309,17 @@ def __init__(self, left, right, name, na_op):
def _validate(self, lvalues, rvalues, name):
# timedelta and integer mul/div
- if (self.is_timedelta_lhs and self.is_integer_rhs) or (
- self.is_integer_lhs and self.is_timedelta_rhs):
+ if (self.is_timedelta_lhs and
+ (self.is_integer_rhs or self.is_floating_rhs)) or (
+ self.is_timedelta_rhs and
+ (self.is_integer_lhs or self.is_floating_lhs)):
- if name not in ('__div__', '__truediv__', '__mul__'):
+ if name not in ('__div__', '__truediv__', '__mul__', '__rmul__'):
raise TypeError("can only operate on a timedelta and an "
- "integer for division, but the operator [%s]"
- "was passed" % name)
+ "integer or a float for division and "
+ "multiplication, but the operator [%s] was"
+ "passed" % name)
- # 2 datetimes
- elif self.is_datetime_lhs and self.is_datetime_rhs:
-
- if name not in ('__sub__','__rsub__'):
- raise TypeError("can only operate on a datetimes for"
- " subtraction, but the operator [%s] was"
- " passed" % name)
-
- # if tz's must be equal (same or None)
- if getattr(lvalues,'tz',None) != getattr(rvalues,'tz',None):
- raise ValueError("Incompatbile tz's on datetime subtraction ops")
# 2 timedeltas
elif ((self.is_timedelta_lhs and
@@ -339,6 +333,7 @@ def _validate(self, lvalues, rvalues, name):
"addition, subtraction, and division, but the"
" operator [%s] was passed" % name)
+
# datetime and timedelta/DateOffset
elif (self.is_datetime_lhs and
(self.is_timedelta_rhs or self.is_offset_rhs)):
@@ -349,6 +344,28 @@ def _validate(self, lvalues, rvalues, name):
" but the operator [%s] was passed" %
name)
+ elif (self.is_datetime_rhs and
+ (self.is_timedelta_lhs or self.is_offset_lhs)):
+ if name not in ('__add__', '__radd__', '__rsub__'):
+ raise TypeError("can only operate on a timedelta/DateOffset with a rhs of"
+ " a datetime for addition,"
+ " but the operator [%s] was passed" %
+ name)
+
+
+ # 2 datetimes
+ elif self.is_datetime_lhs and self.is_datetime_rhs:
+
+ if name not in ('__sub__','__rsub__'):
+ raise TypeError("can only operate on a datetimes for"
+ " subtraction, but the operator [%s] was"
+ " passed" % name)
+
+ # if tz's must be equal (same or None)
+ if getattr(lvalues,'tz',None) != getattr(rvalues,'tz',None):
+ raise ValueError("Incompatbile tz's on datetime subtraction ops")
+
+
elif ((self.is_timedelta_lhs or self.is_offset_lhs)
and self.is_datetime_rhs):
@@ -357,7 +374,7 @@ def _validate(self, lvalues, rvalues, name):
" a datetime for addition, but the operator"
" [%s] was passed" % name)
else:
- raise TypeError('cannot operate on a series with out a rhs '
+ raise TypeError('cannot operate on a series without a rhs '
'of a series/ndarray of type datetime64[ns] '
'or a timedelta')
@@ -366,17 +383,25 @@ def _convert_to_array(self, values, name=None, other=None):
from pandas.tseries.timedeltas import to_timedelta
ovalues = values
+ supplied_dtype = None
if not is_list_like(values):
values = np.array([values])
-
- inferred_type = lib.infer_dtype(values)
-
- if inferred_type in ('datetime64', 'datetime', 'date', 'time'):
+ # if this is a Series that contains relevant dtype info, then use this
+ # instead of the inferred type; this avoids coercing Series([NaT],
+ # dtype='datetime64[ns]') to Series([NaT], dtype='timedelta64[ns]')
+ elif isinstance(values, pd.Series) and (
+ is_timedelta64_dtype(values) or is_datetime64_dtype(values)):
+ supplied_dtype = values.dtype
+ inferred_type = supplied_dtype or lib.infer_dtype(values)
+ if (inferred_type in ('datetime64', 'datetime', 'date', 'time')
+ or com.is_datetimetz(inferred_type)):
# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
- if (other is not None and other.dtype == 'timedelta64[ns]' and
- all(isnull(v) for v in values)):
- values = np.empty(values.shape, dtype=other.dtype)
+ if (supplied_dtype is None
+ and other is not None
+ and (other.dtype in ('timedelta64[ns]', 'datetime64[ns]'))
+ and isnull(values).all()):
+ values = np.empty(values.shape, dtype='timedelta64[ns]')
values[:] = iNaT
# a datelike
@@ -401,18 +426,15 @@ def _convert_to_array(self, values, name=None, other=None):
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
- elif name not in ('__truediv__', '__div__', '__mul__'):
+ elif name not in ('__truediv__', '__div__', '__mul__', '__rmul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif inferred_type == 'floating':
- # all nan, so ok, use the other dtype (e.g. timedelta or datetime)
- if isnull(values).all():
+ if isnull(values).all() and name in ('__add__', '__radd__',
+ '__sub__', '__rsub__'):
values = np.empty(values.shape, dtype=other.dtype)
values[:] = iNaT
- else:
- raise TypeError(
- 'incompatible type [{0}] for a datetime/timedelta '
- 'operation'.format(np.array(values).dtype))
+ return values
elif self._is_offset(values):
return values
else:
@@ -431,7 +453,10 @@ def _convert_for_datetime(self, lvalues, rvalues):
# datetime subtraction means timedelta
if self.is_datetime_lhs and self.is_datetime_rhs:
- self.dtype = 'timedelta64[ns]'
+ if self.name in ('__sub__', '__rsub__'):
+ self.dtype = 'timedelta64[ns]'
+ else:
+ self.dtype = 'datetime64[ns]'
elif self.is_datetime64tz_lhs:
self.dtype = lvalues.dtype
elif self.is_datetime64tz_rhs:
@@ -482,7 +507,8 @@ def _offset(lvalues, rvalues):
rvalues = to_timedelta(rvalues)
lvalues = lvalues.astype(np.int64)
- rvalues = rvalues.astype(np.int64)
+ if not self.is_floating_rhs:
+ rvalues = rvalues.astype(np.int64)
# time delta division -> unit less
# integer gets converted to timedelta in np < 1.6
@@ -580,7 +606,7 @@ def wrapper(left, right, name=name, na_op=na_op):
lvalues, rvalues = left, right
dtype = None
wrap_results = lambda x: x
- elif time_converted == NotImplemented:
+ elif time_converted is NotImplemented:
return NotImplemented
else:
left, right = time_converted.left, time_converted.right
diff --git a/pandas/core/series.py b/pandas/core/series.py
index d6eb18396e14c..29abd8f031206 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2078,8 +2078,9 @@ def map(self, arg, na_action=None):
same index as caller
"""
values = self._values
- if com.is_datetime64_dtype(values.dtype):
- values = lib.map_infer(values, lib.Timestamp)
+ if needs_i8_conversion(values.dtype):
+ boxer = i8_boxer(values)
+ values = lib.map_infer(values, boxer)
if na_action == 'ignore':
mask = isnull(values)
@@ -2210,8 +2211,9 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
return f(self)
values = _values_from_object(self)
- if com.is_datetime64_dtype(values.dtype):
- values = lib.map_infer(values, lib.Timestamp)
+ if needs_i8_conversion(values.dtype):
+ boxer = i8_boxer(values)
+ values = lib.map_infer(values, boxer)
mapped = lib.map_infer(values, f, convert=convert_dtype)
if len(mapped) and isinstance(mapped[0], Series):
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 099e86a44d188..ea9ee8fc5b235 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -19,7 +19,7 @@
import numpy.ma as ma
import pandas as pd
-from pandas import (Index, Series, DataFrame, isnull, notnull, bdate_range,
+from pandas import (Index, Series, DataFrame, isnull, notnull, bdate_range, NaT,
date_range, period_range, timedelta_range, _np_version_under1p8)
from pandas.core.index import MultiIndex
from pandas.core.indexing import IndexingError
@@ -3466,6 +3466,13 @@ def test_timedeltas_with_DateOffset(self):
assert_series_equal(result, expected)
assert_series_equal(result2, expected)
+ result = s - pd.offsets.Second(5)
+ result2 = -pd.offsets.Second(5) + s
+ expected = Series(
+ [Timestamp('20130101 9:00:55'), Timestamp('20130101 9:01:55')])
+ assert_series_equal(result, expected)
+ assert_series_equal(result2, expected)
+
result = s + pd.offsets.Milli(5)
result2 = pd.offsets.Milli(5) + s
expected = Series(
@@ -3500,6 +3507,19 @@ def test_timedeltas_with_DateOffset(self):
s + op(5)
op(5) + s
+ def test_timedelta_series_ops(self):
+ #GH11925
+
+ s = Series(timedelta_range('1 day', periods=3))
+ ts = Timestamp('2012-01-01')
+ expected = Series(date_range('2012-01-02', periods=3))
+ assert_series_equal(ts + s, expected)
+ assert_series_equal(s + ts, expected)
+
+ expected2 = Series(date_range('2011-12-31', periods=3, freq='-1D'))
+ assert_series_equal(ts - s, expected2)
+ assert_series_equal(ts + (-s), expected2)
+
def test_timedelta64_operations_with_DateOffset(self):
# GH 10699
@@ -3619,11 +3639,14 @@ def test_timedelta64_operations_with_integers(self):
assert_series_equal(result,expected)
# invalid ops
- for op in ['__true_div__','__div__','__mul__']:
- sop = getattr(s1,op,None)
- if sop is not None:
- self.assertRaises(TypeError, sop, s2.astype(float))
- self.assertRaises(TypeError, sop, 2.)
+ assert_series_equal(s1 / s2.astype(float),
+ Series([Timedelta('2 days 22:48:00'),
+ Timedelta('1 days 23:12:00'),
+ Timedelta('NaT')]))
+ assert_series_equal(s1 / 2.0,
+ Series([Timedelta('29 days 12:00:00'),
+ Timedelta('29 days 12:00:00'),
+ Timedelta('NaT')]))
for op in ['__add__','__sub__']:
sop = getattr(s1,op,None)
@@ -3653,7 +3676,7 @@ def test_timedelta64_conversions(self):
assert_series_equal(result, expected)
# reverse op
- expected = s1.apply(lambda x: np.timedelta64(m,unit) / x)
+ expected = s1.apply(lambda x: Timedelta(np.timedelta64(m,unit)) / x)
result = np.timedelta64(m,unit) / s1
# astype
@@ -3759,7 +3782,7 @@ def run_ops(ops, get_ser, test_ser):
### timetimedelta with datetime64 ###
ops = ['__sub__', '__mul__', '__floordiv__', '__truediv__', '__div__',
- '__pow__', '__rsub__', '__rmul__', '__rfloordiv__',
+ '__pow__', '__rmul__', '__rfloordiv__',
'__rtruediv__', '__rdiv__', '__rpow__']
run_ops(ops, td1, dt1)
td1 + dt1
@@ -3825,6 +3848,151 @@ def run_ops(ops, get_ser, test_ser):
self.assertRaises(TypeError, lambda: td1 - dt1)
self.assertRaises(TypeError, lambda: td2 - dt2)
+ def test_ops_nat(self):
+ # GH 11349
+ timedelta_series = Series([NaT, Timedelta('1s')])
+ datetime_series = Series([NaT, Timestamp('19900315')])
+ nat_series_dtype_timedelta = Series([NaT, NaT], dtype='timedelta64[ns]')
+ nat_series_dtype_timestamp = Series([NaT, NaT], dtype='datetime64[ns]')
+ single_nat_dtype_datetime = Series([NaT], dtype='datetime64[ns]')
+ single_nat_dtype_timedelta = Series([NaT], dtype='timedelta64[ns]')
+
+ # subtraction
+ assert_series_equal(timedelta_series - NaT, nat_series_dtype_timedelta)
+ assert_series_equal(-NaT + timedelta_series, nat_series_dtype_timedelta)
+
+ assert_series_equal(timedelta_series - single_nat_dtype_timedelta,
+ nat_series_dtype_timedelta)
+ assert_series_equal(-single_nat_dtype_timedelta + timedelta_series,
+ nat_series_dtype_timedelta)
+
+ assert_series_equal(datetime_series - NaT, nat_series_dtype_timestamp)
+ assert_series_equal(-NaT + datetime_series, nat_series_dtype_timestamp)
+
+ assert_series_equal(datetime_series - single_nat_dtype_datetime,
+ nat_series_dtype_timedelta)
+ with tm.assertRaises(TypeError):
+ -single_nat_dtype_datetime + datetime_series
+
+ assert_series_equal(datetime_series - single_nat_dtype_timedelta,
+ nat_series_dtype_timestamp)
+ assert_series_equal(-single_nat_dtype_timedelta + datetime_series ,
+ nat_series_dtype_timestamp)
+
+ # without a Series wrapping the NaT, it is ambiguous
+ # whether it is a datetime64 or timedelta64
+ # defaults to interpreting it as timedelta64
+ assert_series_equal(nat_series_dtype_timestamp - NaT,
+ nat_series_dtype_timestamp)
+ assert_series_equal(-NaT + nat_series_dtype_timestamp,
+ nat_series_dtype_timestamp)
+
+ assert_series_equal(nat_series_dtype_timestamp - single_nat_dtype_datetime,
+ nat_series_dtype_timedelta)
+ with tm.assertRaises(TypeError):
+ -single_nat_dtype_datetime + nat_series_dtype_timestamp
+
+ assert_series_equal(nat_series_dtype_timestamp - single_nat_dtype_timedelta,
+ nat_series_dtype_timestamp)
+ assert_series_equal(-single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+ nat_series_dtype_timestamp)
+
+ with tm.assertRaises(TypeError):
+ timedelta_series - single_nat_dtype_datetime
+
+ # addition
+ assert_series_equal(nat_series_dtype_timestamp + NaT,
+ nat_series_dtype_timestamp)
+ assert_series_equal(NaT + nat_series_dtype_timestamp,
+ nat_series_dtype_timestamp)
+
+ assert_series_equal(nat_series_dtype_timestamp + single_nat_dtype_timedelta,
+ nat_series_dtype_timestamp)
+ assert_series_equal(single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+ nat_series_dtype_timestamp)
+
+ assert_series_equal(nat_series_dtype_timedelta + NaT,
+ nat_series_dtype_timedelta)
+ assert_series_equal(NaT + nat_series_dtype_timedelta,
+ nat_series_dtype_timedelta)
+
+ assert_series_equal(nat_series_dtype_timedelta + single_nat_dtype_timedelta,
+ nat_series_dtype_timedelta)
+ assert_series_equal(single_nat_dtype_timedelta + nat_series_dtype_timedelta,
+ nat_series_dtype_timedelta)
+
+ assert_series_equal(timedelta_series + NaT, nat_series_dtype_timedelta)
+ assert_series_equal(NaT + timedelta_series, nat_series_dtype_timedelta)
+
+ assert_series_equal(timedelta_series + single_nat_dtype_timedelta,
+ nat_series_dtype_timedelta)
+ assert_series_equal(single_nat_dtype_timedelta + timedelta_series,
+ nat_series_dtype_timedelta)
+
+ assert_series_equal(nat_series_dtype_timestamp + NaT,
+ nat_series_dtype_timestamp)
+ assert_series_equal(NaT + nat_series_dtype_timestamp,
+ nat_series_dtype_timestamp)
+
+ assert_series_equal(nat_series_dtype_timestamp + single_nat_dtype_timedelta,
+ nat_series_dtype_timestamp)
+ assert_series_equal(single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+ nat_series_dtype_timestamp)
+
+ assert_series_equal(nat_series_dtype_timedelta + NaT,
+ nat_series_dtype_timedelta)
+ assert_series_equal(NaT + nat_series_dtype_timedelta,
+ nat_series_dtype_timedelta)
+
+ assert_series_equal(nat_series_dtype_timedelta + single_nat_dtype_timedelta,
+ nat_series_dtype_timedelta)
+ assert_series_equal(single_nat_dtype_timedelta + nat_series_dtype_timedelta,
+ nat_series_dtype_timedelta)
+
+ assert_series_equal(nat_series_dtype_timedelta + single_nat_dtype_datetime,
+ nat_series_dtype_timestamp)
+ assert_series_equal(single_nat_dtype_datetime + nat_series_dtype_timedelta,
+ nat_series_dtype_timestamp)
+
+ # multiplication
+ assert_series_equal(nat_series_dtype_timedelta * 1.0,
+ nat_series_dtype_timedelta)
+ assert_series_equal(1.0 * nat_series_dtype_timedelta,
+ nat_series_dtype_timedelta)
+
+ assert_series_equal(timedelta_series * 1, timedelta_series)
+ assert_series_equal(1 * timedelta_series, timedelta_series)
+
+ assert_series_equal(timedelta_series * 1.5,
+ Series([NaT, Timedelta('1.5s')]))
+ assert_series_equal(1.5 * timedelta_series,
+ Series([NaT, Timedelta('1.5s')]))
+
+ assert_series_equal(timedelta_series * nan, nat_series_dtype_timedelta)
+ assert_series_equal(nan * timedelta_series, nat_series_dtype_timedelta)
+
+ with tm.assertRaises(TypeError):
+ datetime_series * 1
+ with tm.assertRaises(TypeError):
+ nat_series_dtype_timestamp * 1
+ with tm.assertRaises(TypeError):
+ datetime_series * 1.0
+ with tm.assertRaises(TypeError):
+ nat_series_dtype_timestamp * 1.0
+
+ # division
+ assert_series_equal(timedelta_series / 2,
+ Series([NaT, Timedelta('0.5s')]))
+ assert_series_equal(timedelta_series / 2.0,
+ Series([NaT, Timedelta('0.5s')]))
+ assert_series_equal(timedelta_series / nan,
+ nat_series_dtype_timedelta)
+ with tm.assertRaises(TypeError):
+ nat_series_dtype_timestamp / 1.0
+ with tm.assertRaises(TypeError):
+ nat_series_dtype_timestamp / 1
+
+
def test_ops_datetimelike_align(self):
# GH 7500
# datetimelike ops need to align
diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py
index 67f1b12ec8ead..cb050f2589673 100644
--- a/pandas/tseries/tests/test_timedeltas.py
+++ b/pandas/tseries/tests/test_timedeltas.py
@@ -270,7 +270,7 @@ def test_ops(self):
self.assertEqual(abs(td), td)
self.assertEqual(abs(-td), td)
self.assertEqual(td / td, 1)
- self.assertTrue((td / pd.NaT) is pd.NaT)
+ self.assertTrue((td / pd.NaT) is np.nan)
# invert
self.assertEqual(-td,Timedelta('-10d'))
@@ -881,20 +881,32 @@ def test_timedelta_ops_with_missing_values(self):
actual = s1 + scalar1
assert_series_equal(actual, s2)
+ actual = scalar1 + s1
+ assert_series_equal(actual, s2)
actual = s2 - scalar1
assert_series_equal(actual, s1)
+ actual = -scalar1 + s2
+ assert_series_equal(actual, s1)
actual = s1 + timedelta_NaT
assert_series_equal(actual, sn)
+ actual = timedelta_NaT + s1
+ assert_series_equal(actual, sn)
actual = s1 - timedelta_NaT
assert_series_equal(actual, sn)
+ actual = -timedelta_NaT + s1
+ assert_series_equal(actual, sn)
actual = s1 + NA
assert_series_equal(actual, sn)
+ actual = NA + s1
+ assert_series_equal(actual, sn)
actual = s1 - NA
assert_series_equal(actual, sn)
+ actual = -NA + s1
+ assert_series_equal(actual, sn)
- actual = s1 + pd.NaT # NaT is datetime, not timedelta
+ actual = s1 + pd.NaT
assert_series_equal(actual, sn)
actual = s2 - pd.NaT
assert_series_equal(actual, sn)
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index 74b9f52a7eb0a..cf970807999e0 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -11,7 +11,8 @@
from pandas import (Index, Series, DataFrame,
isnull, date_range, Timestamp, Period, DatetimeIndex,
- Int64Index, to_datetime, bdate_range, Float64Index, TimedeltaIndex, NaT)
+ Int64Index, to_datetime, bdate_range, Float64Index,
+ TimedeltaIndex, NaT, timedelta_range, Timedelta)
import pandas.core.datetools as datetools
import pandas.tseries.offsets as offsets
@@ -369,6 +370,12 @@ def test_series_box_timestamp(self):
tm.assertIsInstance(s.iat[5], Timestamp)
+ def test_series_box_timedelta(self):
+ rng = timedelta_range('1 day 1 s',periods=5,freq='h')
+ s = Series(rng)
+ tm.assertIsInstance(s[1], Timedelta)
+ tm.assertIsInstance(s.iat[2], Timedelta)
+
def test_date_range_ambiguous_arguments(self):
# #2538
start = datetime(2011, 1, 1, 5, 3, 40)
@@ -2086,6 +2093,16 @@ def f(x):
s.apply(f)
DataFrame(s).applymap(f)
+ def test_series_map_box_timedelta(self):
+ # GH 11349
+ s = Series(timedelta_range('1 day 1 s',periods=5,freq='h'))
+
+ def f(x):
+ return x.total_seconds()
+ s.map(f)
+ s.apply(f)
+ DataFrame(s).applymap(f)
+
def test_concat_datetime_datetime64_frame(self):
# #2624
rows = []
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index 7e772aeb14f6e..d27bddf8879db 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -860,31 +860,34 @@ def test_nat_arithmetic(self):
t = Timestamp('2014-01-01')
dt = datetime.datetime(2014, 1, 1)
delta = datetime.timedelta(3600)
-
- # Timestamp / datetime
- for (left, right) in [(nat, nat), (nat, t), (dt, nat)]:
- # NaT + Timestamp-like should raise TypeError
- with tm.assertRaises(TypeError):
- left + right
+ td = Timedelta('5s')
+ i = 2
+ f = 1.5
+
+ for (left, right) in [(nat, i), (nat, f), (nat, np.nan)]:
+ self.assertTrue((left / right) is nat)
+ self.assertTrue((left * right) is nat)
+ self.assertTrue((right * left) is nat)
with tm.assertRaises(TypeError):
- right + left
+ right / left
- # NaT - Timestamp-like (or inverse) returns NaT
- self.assertTrue((left - right) is tslib.NaT)
- self.assertTrue((right - left) is tslib.NaT)
+ # Timestamp / datetime
+ for (left, right) in [(nat, nat), (nat, t), (nat, dt)]:
+ # NaT __add__ or __sub__ Timestamp-like (or inverse) returns NaT
+ self.assertTrue((right + left) is nat)
+ self.assertTrue((left + right) is nat)
+ self.assertTrue((left - right) is nat)
+ self.assertTrue((right - left) is nat)
# timedelta-like
# offsets are tested in test_offsets.py
- for (left, right) in [(nat, delta)]:
+ for (left, right) in [(nat, delta), (nat, td)]:
# NaT + timedelta-like returns NaT
- self.assertTrue((left + right) is tslib.NaT)
- # timedelta-like + NaT should raise TypeError
- with tm.assertRaises(TypeError):
- right + left
+ self.assertTrue((right + left) is nat)
+ self.assertTrue((left + right) is nat)
+ self.assertTrue((right - left) is nat)
+ self.assertTrue((left - right) is nat)
- self.assertTrue((left - right) is tslib.NaT)
- with tm.assertRaises(TypeError):
- right - left
class TestTslib(tm.TestCase):
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index a6908a0c36ad4..43f3c3add160a 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -665,6 +665,21 @@ class NaTType(_NaT):
# GH 10939
return np.nan
+ def __rdiv__(self, other):
+ return _nat_rdivide_op(self, other)
+
+ def __rtruediv__(self, other):
+ return _nat_rdivide_op(self, other)
+
+ def __rfloordiv__(self, other):
+ return _nat_rdivide_op(self, other)
+
+ def __rmul__(self, other):
+ if is_integer_object(other) or is_float_object(other):
+ return NaT
+ return NotImplemented
+
+
fields = ['year', 'quarter', 'month', 'day', 'hour',
'minute', 'second', 'millisecond', 'microsecond', 'nanosecond',
@@ -1001,7 +1016,7 @@ cdef class _Timestamp(datetime):
# index/series like
elif hasattr(other, '_typ'):
- return other + self
+ return NotImplemented
result = datetime.__add__(self, other)
if isinstance(result, datetime):
@@ -1081,6 +1096,18 @@ _nat_scalar_rules[Py_GT] = False
_nat_scalar_rules[Py_GE] = False
+cdef _nat_divide_op(self, other):
+ if isinstance(other, (Timedelta, np.timedelta64)) or other is NaT:
+ return np.nan
+ if is_integer_object(other) or is_float_object(other):
+ return NaT
+ return NotImplemented
+
+cdef _nat_rdivide_op(self, other):
+ if isinstance(other, Timedelta):
+ return np.nan
+ return NotImplemented
+
cdef class _NaT(_Timestamp):
def __hash__(_NaT self):
@@ -1103,6 +1130,8 @@ cdef class _NaT(_Timestamp):
def __add__(self, other):
try:
+ if isinstance(other, datetime):
+ return NaT
result = _Timestamp.__add__(self, other)
if result is NotImplemented:
return result
@@ -1112,6 +1141,9 @@ cdef class _NaT(_Timestamp):
def __sub__(self, other):
+ if other is NaT:
+ return NaT
+
if type(self) is datetime:
other, self = self, other
try:
@@ -1122,6 +1154,26 @@ cdef class _NaT(_Timestamp):
pass
return NaT
+ def __pos__(self):
+ return NaT
+
+ def __neg__(self):
+ return NaT
+
+ def __div__(self, other):
+ return _nat_divide_op(self, other)
+
+ def __truediv__(self, other):
+ return _nat_divide_op(self, other)
+
+ def __floordiv__(self, other):
+ return _nat_divide_op(self, other)
+
+ def __mul__(self, other):
+ if is_integer_object(other) or is_float_object(other):
+ return NaT
+ return NotImplemented
+
def _delta_to_nanoseconds(delta):
if isinstance(delta, np.ndarray):
@@ -2541,8 +2593,8 @@ class Timedelta(_Timedelta):
if other is NaT:
return NaT
- # only integers allowed
- if not is_integer_object(other):
+ # only integers and floats allowed
+ if not (is_integer_object(other) or is_float_object(other)):
return NotImplemented
return Timedelta(other*self.value, unit='ns')
@@ -2554,8 +2606,8 @@ class Timedelta(_Timedelta):
if hasattr(other, 'dtype'):
return self.to_timedelta64() / other
- # pure integers
- if is_integer_object(other):
+ # integers or floats
+ if is_integer_object(other) or is_float_object(other):
return Timedelta(self.value/other, unit='ns')
if not self._validate_ops_compat(other):
@@ -2563,7 +2615,7 @@ class Timedelta(_Timedelta):
other = Timedelta(other)
if other is NaT:
- return NaT
+ return np.nan
return self.value/float(other.value)
def __rtruediv__(self, other):
| closes #11349
closes #11925
| https://api.github.com/repos/pandas-dev/pandas/pulls/11564 | 2015-11-10T05:53:32Z | 2015-12-31T13:46:31Z | 2015-12-31T13:46:31Z | 2016-01-05T18:44:24Z |
VIS: only apply shared axes handling on actual SubplotAxes | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index e8078accba176..5e38d3b81abb1 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -102,7 +102,8 @@ Bug Fixes
- Fix regression in setting of ``xticks`` in ``plot`` (:issue:`11529`).
-
+- Fix plotting issues when having plain ``Axes`` instances instead of
+ ``SubplotAxes`` (:issue:`11520`, :issue:`11556`).
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index 7e6aaa8213667..93f365baa86e3 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -3678,6 +3678,35 @@ def test_invalid_colormap(self):
with tm.assertRaises(ValueError):
df.plot(colormap='invalid_colormap')
+ def test_plain_axes(self):
+
+ # supplied ax itself is a SubplotAxes, but figure contains also
+ # a plain Axes object (GH11556)
+ fig, ax = self.plt.subplots()
+ fig.add_axes([0.2, 0.2, 0.2, 0.2])
+ Series(rand(10)).plot(ax=ax)
+
+ # suppliad ax itself is a plain Axes, but because the cmap keyword
+ # a new ax is created for the colorbar -> also multiples axes (GH11520)
+ df = DataFrame({'a': randn(8), 'b': randn(8)})
+ fig = self.plt.figure()
+ ax = fig.add_axes((0,0,1,1))
+ df.plot(kind='scatter', ax=ax, x='a', y='b', c='a', cmap='hsv')
+
+ # other examples
+ fig, ax = self.plt.subplots()
+ from mpl_toolkits.axes_grid1 import make_axes_locatable
+ divider = make_axes_locatable(ax)
+ cax = divider.append_axes("right", size="5%", pad=0.05)
+ Series(rand(10)).plot(ax=ax)
+ Series(rand(10)).plot(ax=cax)
+
+ fig, ax = self.plt.subplots()
+ from mpl_toolkits.axes_grid.inset_locator import inset_axes
+ iax = inset_axes(ax, width="30%", height=1., loc=3)
+ Series(rand(10)).plot(ax=ax)
+ Series(rand(10)).plot(ax=iax)
+
@tm.mplskip
class TestDataFrameGroupByPlots(TestPlotBase):
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index c6a29c7d5bb9d..4570dc24151c7 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -1135,7 +1135,7 @@ def _post_plot_logic(self, ax, data):
def _adorn_subplots(self):
"""Common post process unrelated to data"""
if len(self.axes) > 0:
- all_axes = self._get_axes()
+ all_axes = self._get_subplots()
nrows, ncols = self._get_axes_layout()
_handle_shared_axes(axarr=all_axes, nplots=len(all_axes),
naxes=nrows * ncols, nrows=nrows,
@@ -1467,11 +1467,13 @@ def _get_errorbars(self, label=None, index=None, xerr=True, yerr=True):
errors[kw] = err
return errors
- def _get_axes(self):
- return self.axes[0].get_figure().get_axes()
+ def _get_subplots(self):
+ from matplotlib.axes import Subplot
+ return [ax for ax in self.axes[0].get_figure().get_axes()
+ if isinstance(ax, Subplot)]
def _get_axes_layout(self):
- axes = self._get_axes()
+ axes = self._get_subplots()
x_set = set()
y_set = set()
for ax in axes:
| closes #11556
closes #11520
| https://api.github.com/repos/pandas-dev/pandas/pulls/11561 | 2015-11-09T14:48:37Z | 2015-11-15T18:36:39Z | null | 2015-11-15T18:36:39Z |
BUG: Fix bug for kendall corr when in DF num and bool | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index ff45d28802885..1f6b17dac6632 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -123,6 +123,6 @@ Bug Fixes
- Bug in ``DataFrame.to_dict()`` produces a ``np.datetime64`` object instead of ``Timestamp`` when only datetime is present in data (:issue:`11327`)
-
+- Bug in ``DataFrame.corr()`` raises exception when computes Kendall correlation for DataFrames with boolean and not boolean columns (:issue:`11560`)
- Bug in the link-time error caused by C ``inline`` functions on FreeBSD 10+ (with ``clang``) (:issue:`10510`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index de74b70cdfaac..538b9d3f8e712 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4411,16 +4411,21 @@ def corr(self, method='pearson', min_periods=1):
else:
if min_periods is None:
min_periods = 1
- mat = mat.T
+ mat = com._ensure_float64(mat).T
corrf = nanops.get_corr_func(method)
K = len(cols)
correl = np.empty((K, K), dtype=float)
mask = np.isfinite(mat)
for i, ac in enumerate(mat):
for j, bc in enumerate(mat):
+ if i > j:
+ continue
+
valid = mask[i] & mask[j]
if valid.sum() < min_periods:
c = NA
+ elif i == j:
+ c = 1.
elif not valid.all():
c = corrf(ac[valid], bc[valid])
else:
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 1b57d53a548f3..b290b6b84ad18 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -8002,12 +8002,14 @@ def test_corr_nooverlap(self):
# nothing in common
for meth in ['pearson', 'kendall', 'spearman']:
df = DataFrame({'A': [1, 1.5, 1, np.nan, np.nan, np.nan],
- 'B': [np.nan, np.nan, np.nan, 1, 1.5, 1]})
+ 'B': [np.nan, np.nan, np.nan, 1, 1.5, 1],
+ 'C': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]})
rs = df.corr(meth)
self.assertTrue(isnull(rs.ix['A', 'B']))
self.assertTrue(isnull(rs.ix['B', 'A']))
self.assertEqual(rs.ix['A', 'A'], 1)
self.assertEqual(rs.ix['B', 'B'], 1)
+ self.assertTrue(isnull(rs.ix['C', 'C']))
def test_corr_constant(self):
tm._skip_if_no_scipy()
@@ -8028,6 +8030,18 @@ def test_corr_int(self):
df3.cov()
df3.corr()
+ def test_corr_int_and_boolean(self):
+ tm._skip_if_no_scipy()
+
+ # when dtypes of pandas series are different
+ # then ndarray will have dtype=object,
+ # so it need to be properly handled
+ df = DataFrame({"a": [True, False], "b": [1, 0]})
+
+ expected = DataFrame(np.ones((2, 2)), index=['a', 'b'], columns=['a', 'b'])
+ for meth in ['pearson', 'kendall', 'spearman']:
+ assert_frame_equal(df.corr(meth), expected)
+
def test_cov(self):
# min_periods no NAs (corner case)
expected = self.frame.cov()
| Hi,
1. When DataFrame contain Numerics and Booleans, than numpy will have type object,
so `np.isfinite(mat)` will raise Exception.
I've fixed this by using `com._ensure_float64` like for other correlation.
1. I've skipped half of computation, because correlation is symmetrical
| https://api.github.com/repos/pandas-dev/pandas/pulls/11560 | 2015-11-09T12:04:53Z | 2015-11-13T15:06:26Z | 2015-11-13T15:06:26Z | 2015-11-13T15:16:48Z |
Style2: ignore this, just for CI | diff --git a/ci/requirements-2.6.run b/ci/requirements-2.6.run
index 5f8a2fde1409f..52c12b80d0ec7 100644
--- a/ci/requirements-2.6.run
+++ b/ci/requirements-2.6.run
@@ -14,3 +14,5 @@ psycopg2=2.5.1
pymysql=0.6.0
sqlalchemy=0.7.8
xlsxwriter=0.4.6
+jinja2=2.8
+markupsafe=0.23
diff --git a/ci/requirements-2.7.run b/ci/requirements-2.7.run
index 10049179912da..9863e7135f407 100644
--- a/ci/requirements-2.7.run
+++ b/ci/requirements-2.7.run
@@ -19,3 +19,5 @@ pymysql=0.6.3
html5lib=1.0b2
beautiful-soup=4.2.1
statsmodels
+jinja2=2.8
+markupsafe=0.23
diff --git a/ci/requirements-2.7_LOCALE.run b/ci/requirements-2.7_LOCALE.run
index 9bb37ee10f8db..36f426e523dba 100644
--- a/ci/requirements-2.7_LOCALE.run
+++ b/ci/requirements-2.7_LOCALE.run
@@ -15,3 +15,5 @@ scipy=0.11.0
beautiful-soup=4.2.1
statsmodels=0.4.3
bigquery=2.0.17
+jinja2=2.8
+markupsafe=0.23
diff --git a/ci/requirements-2.7_SLOW.run b/ci/requirements-2.7_SLOW.run
index f02a7cb8a309a..47a67cb29eb2f 100644
--- a/ci/requirements-2.7_SLOW.run
+++ b/ci/requirements-2.7_SLOW.run
@@ -19,3 +19,5 @@ psycopg2
pymysql
html5lib
beautiful-soup
+jinja2=2.8
+markupsafe=0.23
diff --git a/ci/requirements-3.3.run b/ci/requirements-3.3.run
index 0256802a69eba..f7782efc0f2de 100644
--- a/ci/requirements-3.3.run
+++ b/ci/requirements-3.3.run
@@ -14,3 +14,5 @@ lxml=3.2.1
scipy
beautiful-soup=4.2.1
statsmodels
+jinja2=2.8
+markupsafe=0.23
diff --git a/ci/requirements-3.4.run b/ci/requirements-3.4.run
index 45d082022713e..a9726afa83b56 100644
--- a/ci/requirements-3.4.run
+++ b/ci/requirements-3.4.run
@@ -16,3 +16,5 @@ sqlalchemy
bottleneck
pymysql=0.6.3
psycopg2
+jinja2=2.8
+markupsafe=0.23
diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run
index 1eca130ecd96a..d9b6ff35f7cf8 100644
--- a/ci/requirements-3.4_SLOW.run
+++ b/ci/requirements-3.4_SLOW.run
@@ -18,3 +18,5 @@ bottleneck
pymysql
psycopg2
statsmodels
+jinja2=2.8
+markupsafe=0.23
diff --git a/ci/requirements-3.5.run b/ci/requirements-3.5.run
index 8de8f7d8f0630..057acad1ff7ae 100644
--- a/ci/requirements-3.5.run
+++ b/ci/requirements-3.5.run
@@ -12,7 +12,8 @@ pytables
html5lib
lxml
matplotlib
-
+jinja2
+markupsafe
# currently causing some warnings
#sqlalchemy
#pymysql
diff --git a/ci/requirements_all.txt b/ci/requirements_all.txt
index 6a0b695c5de87..6a05f2db8901f 100644
--- a/ci/requirements_all.txt
+++ b/ci/requirements_all.txt
@@ -20,3 +20,4 @@ lxml
sqlalchemy
bottleneck
pymysql
+Jinja2
diff --git a/doc/source/install.rst b/doc/source/install.rst
index 54e7b2d4df350..9accc188d567f 100644
--- a/doc/source/install.rst
+++ b/doc/source/install.rst
@@ -254,6 +254,7 @@ Optional Dependencies
* 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.
* `boto <https://pypi.python.org/pypi/boto>`__: necessary for Amazon S3
access.
* `blosc <https://pypi.python.org/pypi/blosc>`__: for msgpack compression using ``blosc``
diff --git a/doc/source/style.rst b/doc/source/style.rst
new file mode 100644
index 0000000000000..e3e0346d91c03
--- /dev/null
+++ b/doc/source/style.rst
@@ -0,0 +1,45 @@
+.. _style:
+
+.. currentmodule:: pandas
+
+.. ipython:: python
+ :suppress:
+
+ import numpy as np
+ import pandas as pd
+ np.set_printoptions(precision=4, suppress=True)
+ pd.options.display.max_rows = 15
+
+*******
+Styling
+*******
+
+.. versionadded:: 0.17.1
+
+
+You can apply **conditional formatting**, the visual styling of a DataFrame
+depending on the data within, by using the ``.style`` property.
+This is a property on every object that returns a ``Styler`` object, which has
+useful methods for formatting and displaying DataFrames.
+
+The styling is accomplished through CSS_.
+You write functions that take DataFrames or Series, and return like-indexed
+objects with CSS ``"attribute: value"`` pairs.
+You can build up your styles incrementally using method chains, before rending.
+
+.. _CSS: https://developer.mozilla.org/en-US/docs/Web/CSS
+
+Let's create some dummy data to work with
+
+.. ipython:: python
+
+ np.random.seed(24)
+ df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
+ df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
+ axis=1)
+ df.iloc[0, 2] = np.nan
+
+.. ipython:: python
+
+ df.style.bar().render()
+
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index de74b70cdfaac..4750d1a918223 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -577,6 +577,12 @@ def _repr_html_(self):
else:
return None
+ @property
+ def style(self):
+ """ return our styler """
+ from pandas.core.style import Styler
+ return Styler(self)
+
def iteritems(self):
"""
Iterator over (column name, Series) pairs.
@@ -1518,6 +1524,7 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions)
+ # TODO: a generic formatter wld b in DataFrameFormatter
formatter.to_html(classes=classes, notebook=notebook)
if buf is None:
diff --git a/pandas/core/style.py b/pandas/core/style.py
new file mode 100644
index 0000000000000..43715cf07776f
--- /dev/null
+++ b/pandas/core/style.py
@@ -0,0 +1,648 @@
+"""
+Module for applying conditional formatting to
+DataFrames and Series.
+"""
+from functools import partial
+from contextlib import contextmanager
+from uuid import uuid1
+import copy
+from collections import defaultdict
+
+try:
+ from jinja2 import Template
+ has_jinja = True
+except ImportError:
+ has_jinja = False
+
+import numpy as np
+import pandas as pd
+import pandas.core.common as com
+try:
+ import matplotlib.pyplot as plt
+ from matplotlib import colors
+ has_mpl = True
+except ImportError:
+ has_mpl = False
+ no_mpl_message = "{0} requires matplotlib."
+
+
+@contextmanager
+def _mpl(func):
+ if has_mpl:
+ yield plt, colors
+ else:
+ raise ImportError(no_mpl_message.format(func.__name__))
+
+
+class Styler(object):
+ """
+ Helps style a DataFrame or Series according to the
+ data.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ data: Series or DataFrame
+ precision: int
+ precision to round floats to, defaults to pd.options.display.precision
+ table_styles: list-like, default None
+ list of {selector: (attr, value)} dicts; see Notes
+ uuid: str, default None
+ a unique identifier to avoid CSS collisons; generated automatically
+ caption: str, default None
+ caption to attach to the table
+
+ Attributes
+ ----------
+ tempate: Jinja Template
+ ctx : defaultdict
+ maps (row, column) -> [styles] where each style is a
+ TODO: <(attribute, value)> paire | <string with 'attribute: value'>
+
+ Notes
+ -----
+ Most styling will be done through by passing style functions into
+ Styler.tee, Styler.apply, or Styler.applymap. Style functions should
+ return values with strings containing 'attr: value' that will be applied
+ to the indicated cells.
+
+ If using in the Jupyter notebook, Styler has defined a _repr_html_
+ to automatically render itself. Otherwise call Styler.render to get
+ the genterated HTML.
+ """
+ if has_jinja:
+ template = Template("""
+ <style type="text/css" >
+ {% for s in table_styles %}
+ #T_{{uuid}} {{s.selector}} {
+ {% for p,val in s.props %}
+ {{p}}: {{val}};
+ {% endfor %}
+ }
+ {% endfor %}
+ {% for s in cellstyle %}
+ #T_{{uuid}}{{s.selector}} {
+ {% for p,val in s.props %}
+ {{p}}: {{val}};
+ {% endfor %}
+ }
+ {% endfor %}
+ </style>
+
+ <table id="T_{{uuid}}">
+ {% if caption %}
+ <caption>{{caption}}</caption>
+ {% endif %}
+
+ <thead>
+ {% for r in head %}
+ <tr>
+ {% for c in r %}
+ <{{c.type}} class="{{c.class}}">{{c.value}}
+ {% endfor %}
+ </tr>
+ {% endfor %}
+ </thead>
+ <tbody>
+ {% for r in body %}
+ <tr>
+ {% for c in r %}
+ <{{c.type}} id="T_{{uuid}}{{c.id}}" class="{{c.class}}">
+ {% if c.value is number %}
+ {{c.value|round(precision)}}
+ {% else %}
+ {{c.value}}
+ {% endif %}
+ {% endfor %}
+ </tr>
+ {% endfor %}
+ </tbody>
+ </table>
+ """)
+
+ def __init__(self, data, precision=None, table_styles=None, uuid=None,
+ caption=None):
+ if not has_jinja:
+ msg = "pandas.Styler requires jinja2. "\
+ "Please install with `conda install Jinja2`\n"\
+ "or `pip install Jinja2`"
+ raise ImportError(msg)
+ self.ctx = defaultdict(list)
+
+ if not isinstance(data, (pd.Series, pd.DataFrame)):
+ raise TypeError
+ if data.ndim == 1:
+ data = data.to_frame()
+ if not data.index.is_unique or not data.columns.is_unique:
+ raise ValueError("Style is not supported for non-unique indicies.")
+
+ self.data = data
+ self.index = data.index
+ self.columns = data.columns
+
+ self.uuid = uuid
+ self.table_styles = table_styles
+ self.caption = caption
+ if precision is None:
+ precision = pd.options.display.precision
+ self.precision = precision
+
+ def _repr_html_(self):
+ '''
+ Hooks into Jupyter notebook rich display system.
+ '''
+ return self.render()
+
+ def _translate(self, table_styles=None, caption=None, uuid=None,
+ precision=None):
+ """
+ Convert the DataFrame in `self.data` and the attrs from `_build_styles`
+ into a dictionary of {head, body, uuid, cellstyle}
+ """
+ table_styles = table_styles or self.table_styles
+ if table_styles is None:
+ table_styles = []
+ caption = caption or self.caption
+ precision = precision or self.precision
+ ctx = self.ctx
+ uuid = uuid or str(uuid1()).replace("-", "_")
+ ROW_HEADING_CLASS = "row_heading"
+ COL_HEADING_CLASS = "col_heading"
+ DATA_CLASS = "data"
+ BLANK_CLASS = "blank"
+ BLANK_VALUE = ""
+
+ cell_context = dict()
+
+ n_rlvls = self.data.index.nlevels
+ n_clvls = self.data.columns.nlevels
+ rlabels = self.data.index.tolist()
+ clabels = self.data.columns.tolist()
+
+ if n_rlvls == 1:
+ rlabels = [[x] for x in rlabels]
+ if n_clvls == 1:
+ clabels = [[x] for x in clabels]
+ clabels = list(zip(*clabels))
+
+ cellstyle = []
+ head = []
+
+ for r in range(n_clvls):
+ row_es = [{"type": "th", "value": BLANK_VALUE,
+ "class": " ".join([BLANK_CLASS])}] * n_rlvls
+ for c in range(len(clabels[0])):
+ cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c]
+ cs.extend(cell_context.get(
+ "col_headings", {}).get(r, {}).get(c, []))
+ row_es.append({"type": "th", "value": clabels[r][c],
+ "class": " ".join(cs)})
+ head.append(row_es)
+
+ body = []
+ for r, idx in enumerate(self.data.index):
+ cs = [ROW_HEADING_CLASS, "level%s" % c, "row%s" % r]
+ cs.extend(cell_context.get(
+ "row_headings", {}).get(r, {}).get(c, []))
+ row_es = [{"type": "th",
+ "value": rlabels[r][c],
+ "class": " ".join(cs)}
+ for c in range(len(rlabels[r]))]
+
+ for c, col in enumerate(self.data.columns):
+ cs = [DATA_CLASS, "row%s" % r, "col%s" % c]
+ cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
+ row_es.append({"type": "td", "value": self.data.iloc[r][c],
+ "class": " ".join(cs), "id": "_".join(cs[1:])})
+ props = []
+ for x in ctx[r, c]:
+ # have to handle empty styles like ['']
+ if x.count(":"):
+ props.append(x.split(":"))
+ else:
+ props.append(['', ''])
+ cellstyle.append(
+ {'props': props,
+ 'selector': "row%s_col%s" % (r, c)}
+ )
+ body.append(row_es)
+
+ # uuid required to isolate table styling from others
+ # in same notebook in ipnb
+ return dict(head=head, cellstyle=cellstyle, body=body, uuid=uuid,
+ precision=self.precision, table_styles=table_styles,
+ caption=caption)
+
+ def render(self, table_styles=None, caption=None, uuid=None,
+ precision=None, trim=True):
+ """
+ Render the built up styles to HTML
+
+ Parameters
+ ----------
+ table_styles: list or None
+ defaults to ``self.table_styles``
+ caption: str or None
+ defaults to ``self.caption``
+ uuid: str or None
+ defaults to random uuid
+ precision: int or None
+ defaults to self.precision
+ trim: bool
+ Small optimization to not create classes for cells that
+ have no styles applied to them.
+
+ Returns
+ -------
+ rendered: str
+ the rendered HTML
+
+ Notes
+ -----
+ ``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
+ the rendered HTML in the notebook.
+ """
+ table_styles = table_styles or self.table_styles
+ caption = caption or self.caption
+ uuid = uuid or self.uuid
+ d = self._translate(table_styles=table_styles, caption=caption,
+ uuid=uuid)
+ if trim:
+ # filter out empty styles, every cell will have a class
+ # but the list of props may just be [['', '']].
+ # so we have the neested anys below
+ trimmed = [x for x in d['cellstyle'] if
+ any(any(y) for y in x['props'])]
+ d['cellstyle'] = trimmed
+ return self.template.render(**d)
+
+ def _update_ctx(self, attrs):
+ """
+ update the state of the Styler. Collects a mapping
+ of {index_label: ['<property>: <value>']}
+
+ attrs: Series or DataFrame
+ should contain strings of '<property>: <value>;<prop2>: <val2>'
+ Whitespace shouldn't matter and the final trailing ';' shouldn't
+ matter.
+ """
+ for row_label, v in attrs.iterrows():
+ for col_label, col in v.iteritems():
+ i = self.index.get_indexer([row_label])[0]
+ j = self.columns.get_indexer([col_label])[0]
+ for pair in col.rstrip(";").split(";"):
+ self.ctx[(i, j)].append(pair)
+
+ def _copy(self, deepcopy=False):
+ styler = Styler(self.data, precision=self.precision,
+ caption=self.caption, uuid=self.uuid,
+ table_styles=self.table_styles)
+ if deepcopy:
+ styler.ctx = copy.deepcopy(self.ctx)
+ else:
+ styler.ctx = self.ctx
+ return styler
+
+ def __copy__(self):
+ """
+ Deep copy by default.
+ """
+ return self._copy(deepcopy=False)
+
+ def __deepcopy__(self, memo):
+ return self._copy(deepcopy=True)
+
+ def clear(self):
+ self.ctx.clear()
+
+ def apply(self, func, axis=0, subset=None, **kwargs):
+ """
+ Apply a function, updating the HTML representation with the result.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ func: function
+ axis: int, str or None
+ apply to each column (``axis=0`` or ``'index'``)
+ or to each row (``axis=1`` or ``'columns'``) or
+ to the entire DataFrame at once with ``axis=None``.
+ subset: IndexSlice
+ a valid indexer to limit ``data`` to *before* applying the
+ function. Consider using a pandas.IndexSlice
+ kwargs: dict
+ pass along to ``func``
+
+ Returns
+ -------
+ self
+
+ Notes
+ -----
+ This is similar to DataFrame.apply, except that axis=None applies
+ the function to the entire DataFrame at once, rather tha column
+ or rowwise.
+
+ Examples
+ --------
+
+ """
+ subset = slice(None) if subset is None else subset
+ subset = _non_reducing_slice(subset)
+ if axis is not None:
+ result = self.data.loc[subset].apply(func, axis=axis, **kwargs)
+ else:
+ # like tee
+ result = func(self.data.loc[subset], **kwargs)
+ self._update_ctx(result)
+ return self
+
+ def applymap(self, func, subset=None, **kwargs):
+ """
+ Apply a function elementwise, updating the HTML
+ representation with the result.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ func : function
+ subset : IndexSlice
+ a valid indexer to limit ``data`` to *before* applying the
+ function. Consider using a pandas.IndexSlice
+ kwargs : dict
+ pass along to ``func``
+
+ Returns
+ -------
+ self
+
+ Notes
+ -----
+ Examples
+ --------
+ """
+
+ func = partial(func, **kwargs) # applymap doesn't take kwargs?
+ if subset is None:
+ subset = pd.IndexSlice[:]
+ subset = _non_reducing_slice(subset)
+ result = self.data.loc[subset].applymap(func)
+ self._update_ctx(result)
+ return self
+
+ def set_precision(self, precision):
+ self.precision = precision
+ return self
+
+ def set_uuid(self, uuid):
+ self.uuid = uuid
+ return self
+
+ def set_caption(self, caption):
+ self.caption = caption
+ return self
+
+ def set_table_styles(self, table_styles):
+ self.table_styles = table_styles
+ return self
+
+ # -----------------------------------------------------------------------
+ # A collection of "builtin" styles
+ # -----------------------------------------------------------------------
+
+ @staticmethod
+ def _highlight_null(v, null_color):
+ return 'background-color: %s' % null_color if pd.isnull(v) else ''
+
+ def highlight_null(self, null_color='red'):
+ """
+ Shade the background ``null_color`` for missing values.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ null_color: str
+
+ """
+ self.applymap(self._highlight_null, null_color=null_color)
+ return self
+
+ def background_gradient(self, cmap='PuBu', low=0, high=0,
+ axis=0, subset=None):
+ """
+ Color the background in a gradient according to
+ the data in each column (optionally row).
+ Requires matplotlib.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ cmap: str or colormap
+ matplotlib colormap
+ low, high: float
+ compress the range by these values.
+ axis: int or str
+ 1 or 'columns' for colunwise, 0 or 'index' for rowwise
+ subset: IndexSlice
+
+ Notes
+ -----
+ Tune ``low`` and ``high`` to keep the text legible by
+ not using the entire range of the color map. These extend
+ the range of the data by ``low * (x.max() - x.min())``
+ and ``high * (x.max() - x.min())`` before normalizing.
+ """
+ subset = _maybe_numeric_slice(self.data, subset)
+ subset = _non_reducing_slice(subset)
+ self.apply(self._background_gradient, cmap=cmap, subset=subset,
+ axis=axis, low=low, high=high)
+ return self
+
+ @staticmethod
+ def _background_gradient(s, cmap='PuBu', low=0, high=0):
+ """Color background in a range according to the data."""
+ with _mpl(Styler.background_gradient) as (plt, colors):
+ rng = s.max() - s.min()
+ # extend lower / upper bounds, compresses color range
+ norm = colors.Normalize(s.min() - (rng * low),
+ s.max() + (rng * high))
+ # matplotlib modifies inplace?
+ # https://github.com/matplotlib/matplotlib/issues/5427
+ normed = norm(s.values)
+ c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
+ return ['background-color: %s' % color for color in c]
+
+ def text_shadow(self, color='black'):
+ self.set_properties(**{"text-shadow": "-1px 0 {color}, 0 1px {color}, "
+ "1px 0 {color}, 0 -1px {color};".format(
+ color=color)})
+ return self
+
+ def set_properties(self, subset=None, **kwargs):
+ """
+ Convience method for setting a non-data dependent properties
+ on each element.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ kwargs: dict
+ property: value pairs to be set for each cell
+ subset: IndexSlice
+ a valid slice for ``data``
+
+ Returns
+ -------
+ self : Styler
+
+ Examples
+ --------
+ df.stle.set_properties(color="white", align="right")
+ """
+ values = ';'.join('{p}: {v}'.format(p=p, v=v) for p, v in
+ kwargs.items())
+ f = lambda x: values
+ return self.applymap(f, subset=subset)
+
+ @staticmethod
+ def _bar(s, color, width):
+ normed = width * (s - s.min()) / (s.max() - s.min())
+ attrs = 'width: 10em; height: 80%;'\
+ 'background: linear-gradient(90deg,'\
+ '{c} {w}%, transparent 0%)'
+ return [attrs.format(c=color, w=x) for x in normed]
+
+ def bar(self, subset=None, axis=0, color='#FFC0CB', width=100):
+ """
+ Color the background `color` proptional to the values in each column.
+ Excludes non-numeric data by default.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ axis: int
+ color: str
+ width: float
+ A number between 0 or 100. The largest value will cover this
+ percent of the cell's width
+ """
+ subset = _maybe_numeric_slice(self.data, subset)
+ subset = _non_reducing_slice(subset)
+ self.apply(self._bar, subset=subset, axis=axis, color=color,
+ width=width)
+ return self
+
+ def highlight_max(self, subset=None, color='yellow', axis=None):
+ """
+ Highlight the maximum by shading the background
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ color: str, default 'yellow'
+ axis: int, str, or None
+ 0 or 'index' for columnwise, 1 or 'columns' for rowwise
+ or ``None`` for tablewise
+ """
+ return self._highlight_handler(subset=subset, color=color, axis=axis,
+ max_=True)
+
+ def highlight_min(self, subset=None, color='yellow', axis=None):
+ """
+ Highlight the minimum by shading the background
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ color: str, default 'yellow'
+ axis: int, str, or None
+ 0 or 'index' for columnwise, 1 or 'columns' for rowwise
+ or ``None`` for tablewise
+ """
+ return self._highlight_handler(subset=subset, color=color, axis=axis,
+ max_=False)
+
+ def _highlight_handler(self, subset=None, color='yellow', axis=None,
+ max_=True):
+ subset = _non_reducing_slice(_maybe_numeric_slice(self.data, subset))
+ self.apply(self._highlight_extrema, color=color, axis=axis,
+ subset=subset, max_=max_)
+ return self
+
+ @staticmethod
+ def _highlight_extrema(data, color='yellow', max_=True):
+ '''
+ highlight the min or max in a Series or DataFrame
+ '''
+ attr = 'background-color: {0}'.format(color)
+ if data.ndim == 1: # Series from .apply
+ if max_:
+ extrema = data == data.max()
+ else:
+ extrema = data == data.min()
+ return [attr if v else '' for v in extrema]
+ else: # DataFrame from .tee
+ if max_:
+ extrema = data == data.max().max()
+ else:
+ extrema = data == data.min().min()
+ return pd.DataFrame(np.where(extrema, attr, ''),
+ index=data.index, columns=data.columns)
+
+
+def _non_reducing_slice(slice_):
+ """
+ Ensurse that a slice doesn't reduce to a Series or Scalar.
+
+ Any user-paseed `subset` should have this called on it
+ to make sure we're always working with DataFrames.
+ """
+ # default to column slice, like DataFrame
+ # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
+ kinds = tuple(list(pd.compat.string_types) +
+ [pd.Series, np.ndarray, pd.Index, list])
+ if isinstance(slice_, kinds):
+ slice_ = pd.IndexSlice[:, slice_]
+
+ def pred(part):
+ # true when slice does *not* reduce
+ return isinstance(part, slice) or com.is_list_like(part)
+
+ if not com.is_list_like(slice_):
+ if not isinstance(slice_, slice):
+ # a 1-d slice, like df.loc[1]
+ slice_ = [[slice_]]
+ else:
+ # slice(a, b, c)
+ slice_ = [slice_] # to tuplize later
+ else:
+ slice_ = [part if pred(part) else [part] for part in slice_]
+ return tuple(slice_)
+
+
+def _maybe_numeric_slice(df, slice_, include_bool=False):
+ """
+ want nice defaults for background_gradient that don't break
+ with non-numeric data. But if slice_ is passed go with that.
+ """
+ if slice_ is None:
+ dtypes = [np.number]
+ if include_bool:
+ dtypes.append(bool)
+ slice_ = pd.IndexSlice[:, df.select_dtypes(include=dtypes).columns]
+ return slice_
diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py
new file mode 100644
index 0000000000000..73713886df414
--- /dev/null
+++ b/pandas/tests/test_style.py
@@ -0,0 +1,395 @@
+import copy
+
+import numpy as np
+import pandas as pd
+from nose import SkipTest
+from pandas import DataFrame
+from pandas.core.style import (Styler, _non_reducing_slice, has_jinja,
+ _maybe_numeric_slice)
+from pandas.util.testing import TestCase
+import pandas.util.testing as tm
+
+
+def test_no_jinja():
+ # Separate so it isn't skipped by setUp
+ df = pd.DataFrame({'A': [1]})
+ if not has_jinja:
+ with tm.assertRaises(ImportError):
+ Styler(df)
+ with tm.assertRaises(ImportError):
+ df.style
+
+
+class TestStyler(TestCase):
+
+ @tm.skip_if_no_package_deco('jinja2')
+ def setUp(self):
+ np.random.seed(24)
+ self.s = DataFrame({'A': np.random.permutation(range(6))})
+ self.df = DataFrame({'A': [0, 1], 'B': np.random.randn(2)})
+ self.f = lambda x: x
+ self.g = lambda x: x
+
+ def h(x, foo='bar'):
+ return pd.Series(['color: %s' % foo], index=x.index, name=x.name)
+
+ self.h = h
+ self.styler = Styler(self.df)
+ self.attrs = pd.DataFrame({'A': ['color: red', 'color: blue']})
+ self.dataframes = [
+ self.df,
+ pd.DataFrame({'f': [1., 2.], 'o': ['a', 'b'],
+ 'c': pd.Categorical(['a', 'b'])})
+ ]
+
+ def test_update_ctx(self):
+ self.styler._update_ctx(self.attrs)
+ expected = {(0, 0): ['color: red'],
+ (1, 0): ['color: blue']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_update_ctx_flatten_multi(self):
+ attrs = DataFrame({"A": ['color: red; foo: bar',
+ 'color: blue; foo: baz']})
+ self.styler._update_ctx(attrs)
+ expected = {(0, 0): ['color: red', ' foo: bar'],
+ (1, 0): ['color: blue', ' foo: baz']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_update_ctx_flatten_multi_traliing_semi(self):
+ attrs = DataFrame({"A": ['color: red; foo: bar;',
+ 'color: blue; foo: baz;']})
+ self.styler._update_ctx(attrs)
+ expected = {(0, 0): ['color: red', ' foo: bar'],
+ (1, 0): ['color: blue', ' foo: baz']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_copy(self):
+ s2 = copy.copy(self.styler)
+ self.assertTrue(self.styler is not s2)
+ self.assertTrue(self.styler.ctx is s2.ctx) # shallow
+
+ self.styler._update_ctx(self.attrs)
+ self.assertEqual(self.styler.ctx, s2.ctx)
+
+ def test_deepcopy(self):
+ s2 = copy.deepcopy(self.styler)
+ self.assertTrue(self.styler is not s2)
+ self.assertTrue(self.styler.ctx is not s2.ctx)
+
+ self.styler._update_ctx(self.attrs)
+ self.assertNotEqual(self.styler.ctx, s2.ctx)
+
+ def test_clear(self):
+ self.styler._update_ctx(self.attrs)
+ self.assertTrue(len(self.styler.ctx) > 0)
+ self.styler.clear()
+ self.assertTrue(len(self.styler.ctx) == 0)
+
+ def test_render(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
+ s = Styler(df, uuid='AB').apply(style).apply(style, axis=1)
+ s.render()
+ # it worked?
+
+ def test_render_double(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ style = lambda x: pd.Series(["color: red; border: 1px",
+ "color: blue; border: 2px"], name=x.name)
+ s = Styler(df, uuid='AB').apply(style)
+ s.render()
+ # it worked?
+
+ def test_set_properties(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ result = df.style.set_properties(color='white', size='10px').ctx
+ # order is deterministic
+ v = ["color: white", "size: 10px"]
+ expected = {(0, 0): v, (1, 0): v}
+ self.assertEqual(result.keys(), expected.keys())
+ for v1, v2 in zip(result.values(), expected.values()):
+ self.assertEqual(sorted(v1), sorted(v2))
+
+ def test_set_properties_subset(self):
+ df = pd.DataFrame({'A': [0, 1]})
+ result = df.style.set_properties(subset=pd.IndexSlice[0, 'A'],
+ color='white').ctx
+ expected = {(0, 0): ['color: white']}
+ self.assertEqual(result, expected)
+
+ def test_apply_axis(self):
+ df = pd.DataFrame({'A': [0, 0], 'B': [1, 1]})
+ f = lambda x: ['val: %s' % x.max() for v in x]
+ result = df.style.apply(f, axis=1)
+ expected = {(0, 0): ['val: 1'], (0, 1): ['val: 1'],
+ (1, 0): ['val: 1'], (1, 1): ['val: 1']}
+ self.assertEqual(result.ctx, expected)
+
+ result = df.style.apply(f, axis=0)
+ expected = {(0, 0): ['val: 0'], (0, 1): ['val: 1'],
+ (1, 0): ['val: 0'], (1, 1): ['val: 1']}
+ self.assertEqual(result.ctx, expected)
+ result = df.style.apply(f) # default
+ self.assertEqual(result.ctx, expected)
+
+ def test_apply_subset(self):
+ axes = [0, 1]
+ slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
+ pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
+ pd.IndexSlice[:2, ['A', 'B']]]
+ for ax in axes:
+ for slice_ in slices:
+ result = self.df.style.apply(self.h, axis=ax, subset=slice_,
+ foo='baz').ctx
+ expected = dict(((r, c), ['color: baz'])
+ for r, row in enumerate(self.df.index)
+ for c, col in enumerate(self.df.columns)
+ if row in self.df.loc[slice_].index
+ and col in self.df.loc[slice_].columns)
+ self.assertEqual(result, expected)
+
+ def test_applymap_subset(self):
+ def f(x):
+ return 'foo: bar'
+
+ slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
+ pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
+ pd.IndexSlice[:2, ['A', 'B']]]
+
+ for slice_ in slices:
+ result = self.df.style.applymap(f, subset=slice_).ctx
+ expected = dict(((r, c), ['foo: bar'])
+ for r, row in enumerate(self.df.index)
+ for c, col in enumerate(self.df.columns)
+ if row in self.df.loc[slice_].index
+ and col in self.df.loc[slice_].columns)
+ self.assertEqual(result, expected)
+
+ def test_non_reducing_slice(self):
+ df = pd.DataFrame([[0, 1], [2, 3]])
+
+ slices = [
+ # pd.IndexSlice[:, :],
+ pd.IndexSlice[:, 1],
+ pd.IndexSlice[1, :],
+ pd.IndexSlice[[1], [1]],
+ pd.IndexSlice[1, [1]],
+ pd.IndexSlice[[1], 1],
+ pd.IndexSlice[1],
+ pd.IndexSlice[1, 1],
+ slice(None, None, None),
+ [0, 1],
+ np.array([0, 1]),
+ pd.Series([0, 1])
+ ]
+ for slice_ in slices:
+ tslice_ = _non_reducing_slice(slice_)
+ self.assertTrue(isinstance(df.loc[tslice_], DataFrame))
+
+ def test_list_slice(self):
+ # like dataframe getitem
+ slices = [['A'], pd.Series(['A']), np.array(['A'])]
+ df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['A', 'B'])
+ expected = pd.IndexSlice[:, ['A']]
+ for subset in slices:
+ result = _non_reducing_slice(subset)
+ tm.assert_frame_equal(df.loc[result], df.loc[expected])
+
+ def test_empty(self):
+ df = pd.DataFrame({'A': [1, 0]})
+ s = df.style
+ s.ctx = {(0, 0): ['color: red'],
+ (1, 0): ['']}
+
+ result = s._translate()['cellstyle']
+ expected = [{'props': [['color', ' red']], 'selector': 'row0_col0'},
+ {'props': [['', '']], 'selector': 'row1_col0'}]
+ self.assertEqual(result, expected)
+
+ def test_bar(self):
+ df = pd.DataFrame({'A': [0, 1, 2]})
+ result = df.style.bar().ctx
+ expected = {
+ (0, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#FFC0CB 0.0%, transparent 0%)'],
+ (1, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#FFC0CB 50.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#FFC0CB 100.0%, transparent 0%)']
+ }
+ self.assertEqual(result, expected)
+
+ result = df.style.bar(color='red', width=50).ctx
+ expected = {
+ (0, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 0.0%, transparent 0%)'],
+ (1, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 25.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 50.0%, transparent 0%)']
+ }
+ self.assertEqual(result, expected)
+
+ df['C'] = ['a'] * len(df)
+ result = df.style.bar(color='red', width=50).ctx
+ self.assertEqual(result, expected)
+ df['C'] = df['C'].astype('category')
+ result = df.style.bar(color='red', width=50).ctx
+ self.assertEqual(result, expected)
+
+ def test_highlight_null(self, null_color='red'):
+ df = pd.DataFrame({'A': [0, np.nan]})
+ result = df.style.highlight_null().ctx
+ expected = {(0, 0): [''],
+ (1, 0): ['background-color: red']}
+
+ def test_nonunique_raises(self):
+ df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
+ with tm.assertRaises(ValueError):
+ df.style
+
+ with tm.assertRaises(ValueError):
+ Styler(df)
+
+ def test_caption(self):
+ styler = Styler(self.df, caption='foo')
+ result = styler.render()
+ self.assertTrue(all(['caption' in result, 'foo' in result]))
+
+ # override
+ result = styler.render(caption='bar')
+ self.assertTrue(all(['caption' in result, 'bar' in result,
+ 'foo' not in result]))
+
+ styler = self.df.style
+ result = styler.set_caption('baz')
+ self.assertTrue(styler is result)
+ self.assertEqual(styler.caption, 'baz')
+
+ def test_uuid(self):
+ styler = Styler(self.df, uuid='abc123')
+ result = styler.render()
+ self.assertTrue('abc123' in result)
+
+ result = styler.render(uuid='123abc')
+ self.assertTrue('123abc' in result)
+
+ uuid = styler.uuid
+ result = styler.render(uuid=None)
+ self.assertTrue(uuid in result)
+
+ styler = self.df.style
+ result = styler.set_uuid('aaa')
+ self.assertTrue(result is styler)
+ self.assertEqual(result.uuid, 'aaa')
+
+ def test_table_styles(self):
+ style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
+ styler = Styler(self.df, table_styles=style)
+ result = ' '.join(styler.render().split())
+ self.assertTrue('th { foo: bar; }' in result)
+
+ styler = self.df.style
+ result = styler.set_table_styles(style)
+ self.assertTrue(styler is result)
+ self.assertEqual(styler.table_styles, style)
+
+ def test_precision(self):
+ with pd.option_context('display.precision', 10):
+ s = Styler(self.df)
+ self.assertEqual(s.precision, 10)
+ s = Styler(self.df, precision=2)
+ self.assertEqual(s.precision, 2)
+
+ s2 = s.set_precision(4)
+ self.assertTrue(s is s2)
+ self.assertEqual(s.precision, 4)
+
+ def test_maybe_numeric_slice(self):
+ df = pd.DataFrame({'A': [1, 2], 'B': ['c', 'd'], 'C': [True, False]})
+ result = _maybe_numeric_slice(df, slice_=None)
+ expected = pd.IndexSlice[:, ['A']]
+ self.assertEqual(result, expected)
+
+ result = _maybe_numeric_slice(df, None, include_bool=True)
+ expected = pd.IndexSlice[:, ['A', 'C']]
+ result = _maybe_numeric_slice(df, [1])
+ expected = [1]
+ self.assertEqual(result, expected)
+
+ def test_apply_none(self):
+ def f(x):
+ return pd.DataFrame(np.where(x == x.max(), 'color: red', ''),
+ index=x.index, columns=x.columns)
+ result = pd.DataFrame([[1, 2], [3, 4]]).style.apply(f, axis=None).ctx
+ self.assertEqual(result[(1, 1)], ['color: red'])
+
+ def test_trim(self):
+ result = self.df.style.render() # trim=True
+ self.assertEqual(result.count('#'), 0)
+
+ result = self.df.style.render(trim=False)
+ self.assertEqual(result.count('#'), self.df.size)
+
+ result = self.df.style.highlight_max().render()
+ self.assertEqual(result.count('#'), 1)
+
+ def test_highlight_max(self):
+ df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+ for max_ in [True, False]:
+ if max_:
+ attr = 'highlight_max'
+ else:
+ df = -df
+ attr = 'highlight_min'
+ result = getattr(df.style, attr)().ctx
+ self.assertEqual(result[(1, 1)], ['background-color: yellow'])
+
+ result = getattr(df.style, attr)(color='green').ctx
+ self.assertEqual(result[(1, 1)], ['background-color: green'])
+
+ result = getattr(df.style, attr)(subset='A').ctx
+ self.assertEqual(result[(1, 0)], ['background-color: yellow'])
+
+ result = getattr(df.style, attr)(axis=0).ctx
+ expected = {(1, 0): ['background-color: yellow'],
+ (1, 1): ['background-color: yellow'],
+ (0, 1): [''], (0, 0): ['']}
+ self.assertEqual(result, expected)
+
+ result = getattr(df.style, attr)(axis=1).ctx
+ expected = {(0, 1): ['background-color: yellow'],
+ (1, 1): ['background-color: yellow'],
+ (0, 0): [''], (1, 0): ['']}
+ self.assertEqual(result, expected)
+
+ df['C'] = ['a', 'b']
+ result = df.style.highlight_max().ctx
+ expected = {(1, 1): ['background-color: yellow']}
+
+ result = df.style.highlight_min().ctx
+ expected = {(0, 0): ['background-color: yellow']}
+
+
+@tm.mplskip
+class TestStylerMatplotlibDep(TestCase):
+
+ @tm.skip_if_no_package_deco('jinja2')
+ def test_background_gradient(self):
+ df = pd.DataFrame([[1, 2], [2, 4]], columns=['A', 'B'])
+ for axis in [0, 1, 'index', 'columns']:
+ for cmap in [None, 'YlOrRd']:
+ result = df.style.background_gradient(cmap=cmap).ctx
+ self.assertTrue(all("#" in x[0] for x in result.values()))
+ self.assertEqual(result[(0, 0)], result[(0, 1)])
+ self.assertEqual(result[(1, 0)], result[(1, 1)])
+
+ result = df.style.background_gradient(subset=pd.IndexSlice[1, 'A']).ctx
+ self.assertEqual(result[(1, 0)], ['background-color: #fff7fb'])
diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py
index 2b5443e6ff0d2..13c0b6a08f6e7 100644
--- a/pandas/tests/test_testing.py
+++ b/pandas/tests/test_testing.py
@@ -11,7 +11,8 @@
from pandas.util.testing import (
assert_almost_equal, assertRaisesRegexp, raise_with_traceback,
assert_index_equal, assert_series_equal, assert_frame_equal,
- assert_numpy_array_equal, assert_isinstance, RNGContext
+ assert_numpy_array_equal, assert_isinstance, RNGContext,
+ assertRaises, skip_if_no_package_deco
)
from pandas.compat import is_platform_windows
@@ -627,6 +628,23 @@ def test_locale(self):
self.assertTrue(len(locales) >= 1)
+def test_skiptest_deco():
+ from nose import SkipTest
+ @skip_if_no_package_deco("fakepackagename")
+ def f():
+ pass
+ with assertRaises(SkipTest):
+ f()
+
+ @skip_if_no_package_deco("numpy")
+ def f():
+ pass
+ # hack to ensure that SkipTest is *not* raised
+ with assertRaises(ValueError):
+ f()
+ raise ValueError
+
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index be8b0df73593f..ba32bc1f3e377 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -1578,7 +1578,18 @@ def skip_if_no_package(*args, **kwargs):
exc_failed_check=SkipTest,
*args, **kwargs)
-#
+def skip_if_no_package_deco(pkg_name, version=None, app='pandas'):
+ from nose import SkipTest
+
+ def deco(func):
+ @wraps(func)
+ def wrapper(*args, **kwargs):
+ package_check(pkg_name, version=version, app=app,
+ exc_failed_import=SkipTest, exc_failed_check=SkipTest)
+ return func(*args, **kwargs)
+ return wrapper
+ return deco
+ #
# Additional tags decorators for nose
#
diff --git a/style.ipynb b/style.ipynb
new file mode 100644
index 0000000000000..e15c2b40562bc
--- /dev/null
+++ b/style.ipynb
@@ -0,0 +1,15699 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Conditional Formatting\n",
+ "\n",
+ "*New in version 0.17.1*\n",
+ "\n",
+ "<p style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future release. We'd love to hear your [feedback](https://github.com/pydata/pandas/issues).*<p style=\"color: red\">\n",
+ "\n",
+ "You can apply **conditional formatting**, the visual styling of a DataFrame\n",
+ "depending on the data within, by using the ``DataFrame.style`` property.\n",
+ "This is a property that returns a ``pandas.Styler`` object, which has\n",
+ "useful methods for formatting and displaying DataFrames.\n",
+ "\n",
+ "The styling is accomplished using CSS.\n",
+ "You write functions that take scalars, `DataFrame`s or `Series`, and return *like-indexed* DataFrames or Series with CSS `\"attribute: value\"` pairs for the values.\n",
+ "These functions can be incrementally passed to the `Styler` which collects the styles before rendering."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Building Styles\n",
+ "\n",
+ "Pass your style functions into one of the following methods:\n",
+ "\n",
+ "- `Styler.applymap`: elementwise\n",
+ "- `Styler.apply`: column/row-wise/tablewise\n",
+ "\n",
+ "Each of those methods take a function (and some other keyword arguments) and apply your function to the DataFrame in a certain way. `applymap` works through the DataFrame elementwise, `apply` passes each column or row into your DataFrame one-at-a-time or the entire table at once, depending on the `axis` keyword argument. For columnwise use `axis=0`, rowwise use `axis=1`, and for the entire table at once use `axis=None`.\n",
+ "\n",
+ "The result of the function application, a CSS attribute-value pair, is stored in an internal dictionary on your ``Styler`` object.\n",
+ "\n",
+ "Let's create a DataFrame to work with."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "np.random.seed(24)\n",
+ "df = pd.DataFrame({'A': np.linspace(1, 10, 10)})\n",
+ "df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],\n",
+ " axis=1)\n",
+ "df.iloc[0, 2] = np.nan"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's a (boring) example of rendering a DataFrame, without any visible styles:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_b367d0a2_867e_11e5_85ae_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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_b367d0a2_867e_11e5_85ae_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5fa710>"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Note*: The `DataFrame.style` is a propetry that returns a `Styler` object. `Styler` has a `_repr_html_` method defined on it so they are rendered automatically. If you want the actual HTML back for further processing or for writing to file call the `.render()` method which returns a string.\n",
+ "\n",
+ "The above output looks very similar to the standard DataFrame HTML representation. But we've done some work behind the scenes to attach CSS classes to each cell. We can view these by calling the `.render` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['',\n",
+ " ' <style type=\"text/css\" >',\n",
+ " ' ',\n",
+ " ' ',\n",
+ " ' #T_9a2da04a_8644_11e5_9f81_a45e60bd97fbrow0_col0 {',\n",
+ " ' ',\n",
+ " ' }',\n",
+ " ' ',\n",
+ " ' #T_9a2da04a_8644_11e5_9f81_a45e60bd97fbrow0_col1 {',\n",
+ " ' ']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.render(trim=False).split('\\n')[:10] # ignore trim for now"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `row0_col0` is the identifier for that particular cell. We've also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn't collied with the styling from another within the same notebook or page (you can set the `uuid` if you'd like to tie together the styling of two DataFrames)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's write a simple style function that will color negative numbers red and positive numbers black."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def color_negative_red(val):\n",
+ " \"\"\"\n",
+ " Takes a scalar and returns a string with\n",
+ " the css property `'color: red'` for negative\n",
+ " strings, black otherwise.\n",
+ " \"\"\"\n",
+ " color = 'red' if val < 0 else 'black'\n",
+ " return 'color: %s' % color"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, the cell's style depends only on it's own value.\n",
+ "That means we should use the `Styler.applymap` method which works elementwise."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col4 {\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " color: red;\n",
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+ " }\n",
+ " \n",
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+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow5_col0 {\n",
+ " \n",
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+ " \n",
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+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow5_col2 {\n",
+ " \n",
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+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
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+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " color: black;\n",
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+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_2e437422_8680_11e5_9571_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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_2e437422_8680_11e5_9571_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_2e437422_8680_11e5_9571_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_2e437422_8680_11e5_9571_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_2e437422_8680_11e5_9571_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_2e437422_8680_11e5_9571_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
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+ " \n",
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+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " <th id=\"T_2e437422_8680_11e5_9571_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " <th id=\"T_2e437422_8680_11e5_9571_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
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+ " \n",
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+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
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+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_2e437422_8680_11e5_9571_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x1174b4b00>"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.applymap(color_negative_red)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice the similarity with the standard `df.applymap`, which operates on DataFrames elementwise. We want you to be able to resuse your existing knowledge of how to interact with DataFrames.\n",
+ "\n",
+ "Notice also that our function returned a string containing the CSS attribute and value, separated by a colon just like in a `<style>` tag. This will be a common theme."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now suppose you wanted to highlight the maximum value in each column.\n",
+ "We can't use `.applymap` anymore since that operated elementwise.\n",
+ "Instead, we'll turn to `.apply` which operates columnwise (or rowwise using the `axis` keyword). Later on we'll see that something like `highlight_max` is already defined on `Styler` so you wouldn't need to write this yourself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def highlight_max(s):\n",
+ " '''\n",
+ " highlight the maximum in a Series yellow.\n",
+ " '''\n",
+ " is_max = s == s.max()\n",
+ " return ['background-color: yellow' if v else '' for v in is_max]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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+ " \n",
+ " \n",
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+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
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+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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+ " \n",
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+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
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+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
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+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c243c74_8644_11e5_9472_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " \n",
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+ "<pandas.core.style.Styler at 0x11a5f2128>"
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+ },
+ "execution_count": 7,
+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "df.style.apply(highlight_max)"
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+ },
+ {
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+ "metadata": {},
+ "source": [
+ "We encourage you to use method chains to build up a style piecewise, before finally rending at the end of the chain."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
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+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
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+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " <tr>\n",
+ " \n",
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+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9c979e26_8644_11e5_9d5a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f25c0>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.\\\n",
+ " applymap(color_negative_red).\\\n",
+ " apply(highlight_max)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Above we used `.apply` to pass in each column one at a time.\n",
+ "What if you wanted to highlight just the maximum value in the entire table?\n",
+ "Use `.apply(function, axis=None)` to indicate that your function wants the entire table, not one column or row at a time.\n",
+ "\n",
+ "Let's rewrite our `highlight-max` to handle either Series (from `.apply(axis=0)`) or DataFrames (from `.apply(axis=1)`). We'll also allow the color to be adjustable, to demonstrate that `.apply`, and `.applymap` pass along keyword arguments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def highlight_max(data, color='yellow'):\n",
+ " '''\n",
+ " highlight the maximum in a Series or DataFrame\n",
+ " '''\n",
+ " attr = 'background-color: {}'.format(color)\n",
+ " if data.ndim == 1: # Series from .apply(axis=0) or axis=1\n",
+ " is_max = data == data.max()\n",
+ " return [attr if v else '' for v in is_max]\n",
+ " else: # from .apply(axis=None)\n",
+ " is_max = data == data.max().max()\n",
+ " return pd.DataFrame(np.where(is_max, attr, ''),\n",
+ " index=data.index, columns=data.columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: darkorange;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_9d4dbbd4_8644_11e5_9f27_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",
+ " \n",
+ " <tr>\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9d4dbbd4_8644_11e5_9f27_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
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+ " \n",
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+ " \n",
+ " </tbody>\n",
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+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5fa940>"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.apply(highlight_max, color='darkorange', axis=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can control the precision of floats using pandas' regular `display.precision` option."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
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+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.32\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.07\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.3\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.89\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.96\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.48\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.85\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.52\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.06\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.12\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.63\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.52\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.69\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.09\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9dccb06c_8644_11e5_ac5a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f2cc0>"
+ ]
+ },
+ "execution_count": 11,
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Finer Control: Slicing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The signatures for `Styler.apply`, `Styler.applymap` and `Styler.tee` all include a `subset` keyword.\n",
+ "This allows you to apply styles to specific rows or columns, without having to code that logic into your `style` function.\n",
+ "\n",
+ "The value passed to `subset` behaves simlar to slicing a DataFrame. You can pass a list (or Series or numpy array) of column lables to select just those columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_9e80a0a4_8644_11e5_a667_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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " <th id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9e80a0a4_8644_11e5_a667_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5e3b38>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.apply(highlight_max, subset=['B', 'C', 'D'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For row and column slicing, any valid indexer to `.loc` will work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9eebd674_8644_11e5_a13b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f2e48>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.applymap(color_negative_red,\n",
+ " subset=pd.IndexSlice[2:5, ['B', 'D']])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Consider using `pd.IndexSlice` to make writing the slices easier.\n",
+ "\n",
+ "**N.B.** Only label-based slicing is supported right now, not positional.\n",
+ "\n",
+ "**N.B.** If your style function uses a `subset` keyword argument, consider wrapping your function in a `functools.partial`, partialing out that keyword.\n",
+ "\n",
+ "```python\n",
+ "my_func2 = functools.partial(my_func, subset=42)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Builtins"
+ ]
+ },
+ {
+ "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": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_9f9ec89c_8644_11e5_9b1f_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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9f9ec89c_8644_11e5_9b1f_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5e3a58>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.highlight_null(null_color='red')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Gradients for the background color. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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+ " \n",
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+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
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+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
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+ " \n",
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+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
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+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
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+ " <th id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
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+ " \n",
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+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
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+ " \n",
+ " <th id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
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+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
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+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_9ff11018_8644_11e5_a790_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f29b0>"
+ ]
+ },
+ "execution_count": 15,
+ "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"
+ ]
+ },
+ {
+ "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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ " }\n",
+ " \n",
+ " #T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " background-color: #e5e419;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " background-color: #3b528b;\n",
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+ " \n",
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+ " \n",
+ " background-color: #433e85;\n",
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+ " \n",
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+ " \n",
+ " background-color: #bddf26;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow1_col4 {\n",
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+ " \n",
+ " }\n",
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+ " background-color: #fde725;\n",
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+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #1f9e89;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " background-color: #1f958b;\n",
+ " \n",
+ " }\n",
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+ " </style>\n",
+ "\n",
+ " <table id=\"T_a82a9b8a_8644_11e5_9576_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",
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+ " <th id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
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+ " 0\n",
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+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
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+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
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+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
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+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_a82a9b8a_8644_11e5_9576_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f2358>"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Uses the full color range\n",
+ "df.loc[:4].style.background_gradient(cmap='viridis')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_abf96c78_8644_11e5_a949_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " background-color: #31688e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_abf96c78_8644_11e5_a949_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " background-color: #efe51c;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_abf96c78_8644_11e5_a949_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_abf96c78_8644_11e5_a949_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " background-color: #277f8e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_abf96c78_8644_11e5_a949_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " background-color: #31688e;\n",
+ " \n",
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+ " #T_abf96c78_8644_11e5_a949_a45e60bd97fbrow1_col0 {\n",
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+ " #T_abf96c78_8644_11e5_a949_a45e60bd97fbrow1_col2 {\n",
+ " \n",
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+ " background-color: #d5e21a;\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_abf96c78_8644_11e5_a949_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_abf96c78_8644_11e5_a949_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_abf96c78_8644_11e5_a949_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f26d8>"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Compreesses\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 got your barchart in my DataFrame!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " \n",
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+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#7F7FFF 52.764243600289866%, transparent 0%);\n",
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+ " #T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
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+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
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+ " background: linear-gradient(90deg,#7F7FFF 45.17326030334935%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fb\">\n",
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+ " \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",
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+ " \n",
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+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
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+ " \n",
+ " <th id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
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+ " \n",
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+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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+ " \n",
+ " -1.438713\n",
+ " \n",
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+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
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+ " \n",
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+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
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+ " \n",
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+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_ae7e684a_8644_11e5_9aa2_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f2320>"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.bar(subset=['A', 'B'], color='#7F7FFF')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use `Styler.set_properties` when the style doesn't actually depend on the values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_af50ad98_8644_11e5_a3a4_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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af50ad98_8644_11e5_a3a4_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5f29e8>"
+ ]
+ },
+ "execution_count": 22,
+ "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": [
+ "## Other options\n",
+ "\n",
+ "You've seen a few methods for data-driven styling.\n",
+ "`Styler` also provides a few other options for styling that don't depend on the data.\n",
+ "\n",
+ "- captions\n",
+ "- table-wide styles\n",
+ "\n",
+ "Each of these can be specified in two ways:\n",
+ "\n",
+ "- A keyword argument to `pd.Styler`\n",
+ "- A keyword argument to `.render`\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Captions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ " \n",
+ " \n",
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+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " background-color: #188d18;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
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+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " background-color: #c7eec7;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " background-color: #a6dca6;\n",
+ " \n",
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+ " \n",
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+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow2_col2 {\n",
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+ " \n",
+ " background-color: #5fb55f;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " background-color: #daf9da;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " background-color: #3ba13b;\n",
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+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " background-color: #80c780;\n",
+ " \n",
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+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " background-color: #108910;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " background-color: #0d870d;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " background-color: #90d090;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " background-color: #4fac4f;\n",
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+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " background-color: #66b866;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " background-color: #d2f4d2;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " background-color: #389f38;\n",
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+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col3 {\n",
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+ " \n",
+ " background-color: #6cbc6c;\n",
+ " \n",
+ " }\n",
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+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
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+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " background-color: #a3daa3;\n",
+ " \n",
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+ " \n",
+ " background-color: #0e880e;\n",
+ " \n",
+ " }\n",
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+ " \n",
+ " background-color: #329c32;\n",
+ " \n",
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+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " background-color: #5cb35c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " background-color: #0e880e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " background-color: #cff3cf;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " background-color: #4fac4f;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " background-color: #198e19;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " background-color: #dcfadc;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " background-color: #7ec67e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " background-color: #319b31;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " background-color: #5cb35c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\">\n",
+ " \n",
+ " <caption>Colormaps, with a caption.</caption>\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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_af94e362_8644_11e5_b382_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_af94e362_8644_11e5_b382_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5fa7b8>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pandas.core.style import Styler\n",
+ "s = Styler(df, caption='Colormaps, with a caption.')\n",
+ "s.background_gradient(cmap=cm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternativly, pass the caption in when rendering."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " background-color: #188d18;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " background-color: #c7eec7;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " background-color: #a6dca6;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " background-color: #ccf1cc;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " background-color: #c0eac0;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " background-color: #62b662;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col4 {\n",
+ " \n",
+ " background-color: #5cb35c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col0 {\n",
+ " \n",
+ " background-color: #b3e3b3;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col1 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col2 {\n",
+ " \n",
+ " background-color: #56af56;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col3 {\n",
+ " \n",
+ " background-color: #56af56;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col0 {\n",
+ " \n",
+ " background-color: #99d599;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " background-color: #329c32;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col2 {\n",
+ " \n",
+ " background-color: #5fb55f;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " background-color: #daf9da;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " background-color: #3ba13b;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " background-color: #80c780;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " background-color: #108910;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " background-color: #0d870d;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " background-color: #90d090;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " background-color: #4fac4f;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " background-color: #66b866;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " background-color: #d2f4d2;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " background-color: #389f38;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " background-color: #048204;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " background-color: #70be70;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " background-color: #4daa4d;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " background-color: #6cbc6c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " background-color: #a3daa3;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " background-color: #0e880e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " background-color: #329c32;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " background-color: #5cb35c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " background-color: #0e880e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " background-color: #cff3cf;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " background-color: #4fac4f;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " background-color: #198e19;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " background-color: #dcfadc;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " background-color: #7ec67e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " background-color: #319b31;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " background-color: #5cb35c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\">\n",
+ " \n",
+ " <caption>Caption, from render.</caption>\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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afa85968_8644_11e5_85a7_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import HTML\n",
+ "HTML(\n",
+ " df.style.background_gradient(cmap=cm).render(caption=\"Caption, from render.\")\n",
+ ")"
+ ]
+ },
+ {
+ "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, and 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": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " #T_afba79f6_8644_11e5_b8b0_a45e60bd97fb tr:hover {\n",
+ " \n",
+ " background-color: #ffff99;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afba79f6_8644_11e5_b8b0_a45e60bd97fb th {\n",
+ " \n",
+ " font-size: 150%;\n",
+ " \n",
+ " text-align: center;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\">\n",
+ " \n",
+ " <caption>Hover to highlight.</caption>\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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afba79f6_8644_11e5_b8b0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def hover(hover_color=\"#ffff99\"):\n",
+ " return dict(selector=\"tr:hover\", props=[(\"background-color\", \"%s\" % hover_color)])\n",
+ "\n",
+ "styles = [hover(), dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n",
+ " (\"text-align\", \"center\")])]\n",
+ "html = df.style.render(table_styles=styles, caption=\"Hover to highlight.\")\n",
+ "HTML(html)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fun stuff."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# https://developer.mozilla.org/en-US/docs/Web/CSS/animation#Cylon_Eye\n",
+ "# no animation yet :(\n",
+ "\n",
+ "def cylon(s):\n",
+ " tpl = \"\"\"\n",
+ " background-color: red;\n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " color: white;\n",
+ " height: 100%;\n",
+ " width: 20\"\"\"\n",
+ " return pd.Series([tpl for i in s], index=s.index, name=s.name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col4 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: linear-gradient(to right, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow2_col0 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow4_col2 {\n",
+ " \n",
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+ " background-color: red;\n",
+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow4_col3 {\n",
+ " \n",
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+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow4_col4 {\n",
+ " \n",
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+ " background-color: red;\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow5_col0 {\n",
+ " \n",
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+ " background-color: red;\n",
+ " \n",
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+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow5_col1 {\n",
+ " \n",
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+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow5_col2 {\n",
+ " \n",
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+ " background-color: red;\n",
+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow5_col3 {\n",
+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col2 {\n",
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+ " background-color: red;\n",
+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col3 {\n",
+ " \n",
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+ " background-color: red;\n",
+ " \n",
+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col4 {\n",
+ " \n",
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+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow8_col0 {\n",
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+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " \n",
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+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " color: white;\n",
+ " \n",
+ " \n",
+ " height: 100%;\n",
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+ " width: 20;\n",
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+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " color: white;\n",
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+ " \n",
+ " height: 100%;\n",
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+ " width: 20;\n",
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+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -moz-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
+ " background-image: -o-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
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+ " color: white;\n",
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+ " \n",
+ " height: 100%;\n",
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+ " \n",
+ " width: 20;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow9_col1 {\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " \n",
+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " \n",
+ " height: 100%;\n",
+ " \n",
+ " \n",
+ " width: 20;\n",
+ " \n",
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+ " \n",
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+ " background-image: -webkit-linear-gradient( left, rgba( 0,0,0,0.9 ) 25%, rgba( 0,0,0,0.1 ) 50%, rgba( 0,0,0,0.9 ) 75%);\n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
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+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
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+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
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+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
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+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
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+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
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+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " <th id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
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+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afcf4fb6_8644_11e5_b381_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x1174a9908>"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.apply(cylon)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Interacts pretty well with widgets. If you're viewing this on NBViewer you're missing out on some interactivity."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " background-color: #8ba7a3;\n",
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+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col2 {\n",
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+ " background-color: #557e79;\n",
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+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col3 {\n",
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+ " background-color: #dfe8e7;\n",
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+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col4 {\n",
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+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col0 {\n",
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+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col1 {\n",
+ " \n",
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+ " \n",
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+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col0 {\n",
+ " \n",
+ " background-color: #c1d1cf;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " background-color: #9cb5b1;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col2 {\n",
+ " \n",
+ " background-color: #dce5e4;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " background-color: #668b86;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " background-color: #a9bebb;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " background-color: #e5eceb;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " background-color: #6c908b;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " background-color: #678c87;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " background-color: #cedbd9;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " background-color: #c5d4d2;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " background-color: #e5eceb;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " background-color: #71948f;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " background-color: #a4bbb8;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " background-color: #5a827d;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " background-color: #c1d1cf;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " background-color: #edf3f2;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " background-color: #b3c6c4;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " background-color: #698e89;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " background-color: #9cb5b1;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " background-color: #d7e2e0;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " background-color: #698e89;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " background-color: #759792;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " background-color: #c5d4d2;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " background-color: #799995;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " background-color: #628882;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " background-color: #e7eeed;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " background-color: #9bb4b0;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " background-color: #d7e2e0;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_afe47602_8644_11e5_be9f_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",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_afe47602_8644_11e5_be9f_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11a5fa748>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
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+ " \n",
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+ " color: white;\n",
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+ " \n",
+ " background-color: white;\n",
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+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " \n",
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+ " color: black;\n",
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+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col5 {\n",
+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
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+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col8 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
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+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col10 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
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+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col0 {\n",
+ " \n",
+ " background-color: white;\n",
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+ " color: black;\n",
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+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
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+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col2 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
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+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col3 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
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+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col4 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col5 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col6 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col7 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col8 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col9 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col10 {\n",
+ " \n",
+ " background-color: white;\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fb\">\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",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fb\" class=\"row_heading level10 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.26\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 0.29\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " -1.97\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " 0.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " 1.03\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n",
+ " \n",
+ " 0.12\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n",
+ " \n",
+ " -0.02\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n",
+ " \n",
+ " 0.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n",
+ " \n",
+ " -1.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n",
+ " \n",
+ " -0.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n",
+ " \n",
+ " 1.7\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fb\" class=\"row_heading level10 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 1.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " 0.7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -0.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " -0.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n",
+ " \n",
+ " 0.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n",
+ " \n",
+ " 0.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n",
+ " \n",
+ " -0.54\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n",
+ " \n",
+ " -0.26\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n",
+ " \n",
+ " -1.01\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n",
+ " \n",
+ " 0.13\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fb\" class=\"row_heading level10 row18\">\n",
+ " \n",
+ " 18\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n",
+ " \n",
+ " -0.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n",
+ " \n",
+ " 2.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n",
+ " \n",
+ " 0.51\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n",
+ " \n",
+ " -0.94\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n",
+ " \n",
+ " -0.18\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n",
+ " \n",
+ " -0.7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n",
+ " \n",
+ " -0.95\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n",
+ " \n",
+ " -0.93\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n",
+ " \n",
+ " -0.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n",
+ " \n",
+ " 0.25\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n",
+ " \n",
+ " -0.07\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fb\" class=\"row_heading level10 row19\">\n",
+ " \n",
+ " 19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n",
+ " \n",
+ " -0.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n",
+ " \n",
+ " 0.32\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n",
+ " \n",
+ " 0.98\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n",
+ " \n",
+ " -0.55\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n",
+ " \n",
+ " 1.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n",
+ " \n",
+ " 1.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n",
+ " \n",
+ " -0.98\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n",
+ " \n",
+ " 0.09\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n",
+ " \n",
+ " -0.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n",
+ " \n",
+ " 0.5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_aff57ab0_8644_11e5_ac1f_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n",
+ " \n",
+ " 1.56\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x1174c62b0>"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Don't do this\n",
+ "def panda(df):\n",
+ " spots = [\n",
+ " # left ear\n",
+ " (0, 0), (1, 0), (0, 1),\n",
+ " # right ear\n",
+ " (0, 10), (1, 10), (0, 9),\n",
+ " # left eye\n",
+ " (5, 2), (4, 3), (5, 3),\n",
+ " # right eye\n",
+ " (5, 7), (4, 7), (5, 8),\n",
+ " # nose\n",
+ " (10, 4), (10, 5), (10, 6), (11, 5),\n",
+ " (15, 5), (16, 5), (17, 5), (17, 4), (17, 6), (17, 3), (17, 7), (16, 2), (16, 8)\n",
+ " ]\n",
+ "\n",
+ " rows = []\n",
+ " for r in df.index:\n",
+ " cols = []\n",
+ " for c in df.columns:\n",
+ " if (r, c) in spots:\n",
+ " cols.append(\"background-color: black; color: white\")\n",
+ " else:\n",
+ " cols.append(\"background-color: white; color: black\")\n",
+ " rows.append(cols)\n",
+ "\n",
+ " return pd.DataFrame(rows)\n",
+ "\n",
+ "df = pd.DataFrame(np.random.randn(20, 11))\n",
+ "s = df.round(2).style\n",
+ "s._update_ctx(panda(df))\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Subclassing\n",
+ "\n",
+ "Unlike most objects that pandas exposes, `Styler` should be designed with subclassing in mind. There's no particular reason why CSS should be used over other backend, other than it's convinient for users of Notebooks, and relatively straightforward compared to, say, $\\LaTeX$. I've laid out a bit of the internal implementation here.\n",
+ "\n",
+ "As users apply styles (via `.apply`, `.applymap` and `.tee`), we modify an internal dict, `self.ctx`. This maps `(row_position, col_position)` to a list of (for CSS) `'attribute: value:` strings. Given this `ctx`, the rendering roughly consists of two steps\n",
+ "\n",
+ "- `translate` to a dictionary ready to be passed into the template `Styler.t`\n",
+ "- render template with the dictionary from `Styler.translate`\n",
+ "\n",
+ "This is done in `Styler.render`, but most of the heavy lifitng is done in `translate`.\n",
+ "\n",
+ "\n",
+ "## Alternate templates\n",
+ "\n",
+ "We've used [Jinja](http://jinja.pocoo.org/) templates to build up the HTML.\n",
+ "The template is stored as a class variable on `Styler` (called `Styler.t` for now) Subclasses can override that.\n",
+ "\n",
+ "```python\n",
+ "class CustomStyle(Styler):\n",
+ " t = Template(\"\"\"...\"\"\")\n",
+ "```"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.4.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
| https://api.github.com/repos/pandas-dev/pandas/pulls/11557 | 2015-11-09T01:35:53Z | 2015-11-09T13:18:51Z | null | 2015-11-12T01:08:46Z | |
BUG: multi-index to_native_types is not passing thru parameters | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 0d0f4c66c1fec..48eca9fd6f96d 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -120,3 +120,6 @@ Bug Fixes
- Bug in ``to_excel`` with openpyxl 2.2+ and merging (:issue:`11408`)
- Bug in ``DataFrame.to_dict()`` produces a ``np.datetime64`` object instead of ``Timestamp`` when only datetime is present in data (:issue:`11327`)
+
+
+- Bug in ``DataFrame.to_csv`` in passing through arguments for formatting ``MultiIndexes``, including ``date_format`` (:issue:`7791`)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 855e3f013bfd3..edca7893c12d8 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -4327,7 +4327,12 @@ def _reference_duplicate_name(self, name):
return sum(name == n for n in self.names) > 1
def _format_native_types(self, **kwargs):
- return self.values
+ # we go through the levels and format them
+ levels = [level._format_native_types(**kwargs)
+ for level in self.levels]
+ mi = MultiIndex(levels=levels, labels=self.labels, names=self.names,
+ sortorder=self.sortorder, verify_integrity=False)
+ return mi.values
@property
def _constructor(self):
diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py
index 140b54225b8e8..22555a84c55de 100644
--- a/pandas/tests/test_format.py
+++ b/pandas/tests/test_format.py
@@ -2952,6 +2952,17 @@ def test_to_csv_date_format(self):
self.assertEqual(df_day.to_csv(), expected_default_day)
self.assertEqual(df_day.to_csv(date_format='%Y-%m-%d'), expected_default_day)
+ # testing if date_format parameter is taken into account for
+ # multi-indexed dataframes (GH 7791)
+ df_sec['B'] = 0
+ df_sec['C'] = 1
+ expected_ymd_sec = 'A,B,C\n2013-01-01,0,1\n'
+ df_sec_grouped = df_sec.groupby([pd.Grouper(key='A', freq='1h'), 'B'])
+ self.assertEqual(
+ df_sec_grouped.mean().to_csv(date_format='%Y-%m-%d'),
+ expected_ymd_sec
+ )
+
# deprecation GH11274
def test_to_csv_engine_kw_deprecation(self):
with tm.assert_produces_warning(FutureWarning):
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 1b57d53a548f3..8a01b1ca17373 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -7341,6 +7341,13 @@ def test_to_csv_quoting(self):
df.to_csv(buf, encoding='utf-8', index=False)
self.assertEqual(buf.getvalue(), text)
+ # testing if quoting parameter is passed through with multi-indexes
+ # related to issue #7791
+ df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
+ df = df.set_index(['a', 'b'])
+ expected = '"a","b","c"\n"1","3","5"\n"2","4","6"\n'
+ self.assertEqual(df.to_csv(quoting=csv.QUOTE_ALL), expected)
+
def test_to_csv_unicodewriter_quoting(self):
df = DataFrame({'A': [1, 2, 3], 'B': ['foo', 'bar', 'baz']})
| closes #7791
xref #6797
| https://api.github.com/repos/pandas-dev/pandas/pulls/11551 | 2015-11-08T17:37:41Z | 2015-11-11T03:20:12Z | null | 2015-11-16T09:31:41Z |
ENH: allow dataframe get to take an axis argument | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f46296bb6f70c..56511489c86f4 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1062,23 +1062,51 @@ def _indexer(self):
# add to our internal names set
cls._internal_names_set.add(iname)
- def get(self, key, default=None):
+ def get(self, key, default=None, axis=None):
"""
Get item from object for given key (DataFrame column, Panel slice,
- etc.). Returns default value if not found
+ etc.) along the given axis. Returns default value if not found
Parameters
----------
key : object
+ default : object, optional
+ Value to return if key is not present
+ 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]]})
Returns
-------
- value : type of items contained in object
+ value : type of items contained in object, or default
"""
+ # special case (GH 5652)
+ if key is None:
+ return default
+ # general case
+ if axis is None:
+ axis = self._info_axis_number
+ else:
+ axis = self._get_axis_number(axis)
+ slices = [slice(None)] * self.ndim
+ slices[axis] = key
try:
- return self[key]
+ return self.loc[tuple(slices)]
except (KeyError, ValueError, IndexError):
- return default
+ pass
+ # Two possibilities:
+ # 1) the key is not present, and we should return default
+ # 2) self.loc does not like our slice (which we have to deal with the
+ # axis parameter). This happens for instance with a series with a
+ # string index and a negative key.
+ # To cover this last case, we revert to the previous implementation:
+ if axis == self._info_axis_number:
+ try:
+ return self[key]
+ except (KeyError, ValueError, IndexError):
+ pass
+ return default
def __getitem__(self, item):
return self._get_item_cache(item)
@@ -1779,7 +1807,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
avoid duplicating data
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
method to use for filling holes in reindexed DataFrame.
- Please note: this is only applicable to DataFrames/Series with a
+ 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
@@ -1822,7 +1850,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
- records in the dataframe are assigned ``NaN``.
+ records in the dataframe are assigned ``NaN``.
>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
@@ -1836,8 +1864,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
We can fill in the missing values by passing a value to
the keyword ``fill_value``. Because the index is not monotonically
- increasing or decreasing, we cannot use arguments to the keyword
- ``method`` to fill the ``NaN`` values.
+ increasing or decreasing, we cannot use arguments to the keyword
+ ``method`` to fill the ``NaN`` values.
>>> df.reindex(new_index, fill_value=0)
http_status response_time
@@ -1855,8 +1883,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
IE10 404 0.08
Chrome 200 0.02
- To further illustrate the filling functionality in
- ``reindex``, we will create a dataframe with a
+ To further illustrate the filling functionality in
+ ``reindex``, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
@@ -1873,7 +1901,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
2010-01-06 88
Suppose we decide to expand the dataframe to cover a wider
- date range.
+ date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
@@ -1890,10 +1918,10 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
2010-01-07 NaN
The index entries that did not have a value in the original data frame
- (for example, '2009-12-29') are by default filled with ``NaN``.
+ (for example, '2009-12-29') are by default filled with ``NaN``.
If desired, we can fill in the missing values using one of several
- options.
-
+ options.
+
For example, to backpropagate the last valid value to fill the ``NaN``
values, pass ``bfill`` as an argument to the ``method`` keyword.
@@ -1911,7 +1939,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
2010-01-07 NaN
Please note that the ``NaN`` value present in the original dataframe
- (at index value 2010-01-03) will not be filled by any of the
+ (at index value 2010-01-03) will not be filled by any of the
value propagation schemes. This is because filling while reindexing
does not look at dataframe values, but only compares the original and
desired indexes. If you do want to fill in the ``NaN`` values present
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index d29673e96ecdd..b36082cbe4120 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -99,6 +99,14 @@ def test_rename(self):
# multiple axes at once
+ def test_get(self):
+ # GH 6703
+ # testing the axis parameter
+ df = DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3], 'c': [1, 2, 3]})
+ x = df.set_index(['a', 'b'])
+ assert_series_equal(x.get((1, 1), axis=0), x.T.get((1, 1)))
+ assert_series_equal(x.get('c', axis=1), x.get('c'))
+
def test_get_numeric_data(self):
n = 4
| closes #6703
I would have like to have a solution also working for panels, but I've never used them, so I'm not sure how it should behave.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11550 | 2015-11-08T17:14:36Z | 2015-11-13T13:55:10Z | null | 2023-05-11T01:13:14Z |
BUG: to_sql with datetime.time values with sqlite | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index b3bbc5cf5ef8c..f71421a2f43a6 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -1094,7 +1094,7 @@ 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`` failing when index has a ``CustomBusinessDay`` frequency (:issue:`7222`).
-
+- Bug in ``.to_sql`` for ``datetime.time`` values with sqlite fallback (:issue:`8341`)
- 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`)
- Bug in ``.read_csv`` with dtype specified on empty data producing an error (:issue:`12048`)
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index c29286016a34f..addc88bebebe1 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -5,7 +5,7 @@
"""
from __future__ import print_function, division
-from datetime import datetime, date
+from datetime import datetime, date, time
import warnings
import traceback
@@ -1403,6 +1403,15 @@ class SQLiteTable(SQLTable):
Instead of a table variable just use the Create Table statement.
"""
+ def __init__(self, *args, **kwargs):
+ # GH 8341
+ # register an adapter callable for datetime.time object
+ import sqlite3
+ # this will transform time(12,34,56,789) into '12:34:56.000789'
+ # (this is what sqlalchemy does)
+ sqlite3.register_adapter(time, lambda _: _.strftime("%H:%M:%S.%f"))
+ super(SQLiteTable, self).__init__(*args, **kwargs)
+
def sql_schema(self):
return str(";\n".join(self.table))
diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index c2d6f5af48388..b72258cbf588d 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -1429,6 +1429,22 @@ def test_datetime_time(self):
res = read_sql_table('test_time', self.conn)
tm.assert_frame_equal(res, df)
+ # GH8341
+ # first, use the fallback to have the sqlite adapter put in place
+ sqlite_conn = TestSQLiteFallback.connect()
+ sql.to_sql(df, "test_time2", sqlite_conn, index=False)
+ res = sql.read_sql_query("SELECT * FROM test_time2", sqlite_conn)
+ ref = df.applymap(lambda _: _.strftime("%H:%M:%S.%f"))
+ tm.assert_frame_equal(ref, res) # check if adapter is in place
+ # then test if sqlalchemy is unaffected by the sqlite adapter
+ sql.to_sql(df, "test_time3", self.conn, index=False)
+ if self.flavor == 'sqlite':
+ res = sql.read_sql_query("SELECT * FROM test_time3", self.conn)
+ ref = df.applymap(lambda _: _.strftime("%H:%M:%S.%f"))
+ tm.assert_frame_equal(ref, res)
+ res = sql.read_sql_table("test_time3", self.conn)
+ tm.assert_frame_equal(df, res)
+
def test_mixed_dtype_insert(self):
# see GH6509
s1 = Series(2**25 + 1, dtype=np.int32)
@@ -1957,12 +1973,14 @@ def test_datetime_date(self):
tm.assert_frame_equal(res, df)
def test_datetime_time(self):
- # test support for datetime.time
+ # test support for datetime.time, GH #8341
df = DataFrame([time(9, 0, 0), time(9, 1, 30)], columns=["a"])
- # test it raises an error and not fails silently (GH8341)
+ df.to_sql('test_time', self.conn, index=False, flavor=self.flavor)
+ res = read_sql_query('SELECT * FROM test_time', self.conn)
if self.flavor == 'sqlite':
- self.assertRaises(sqlite3.InterfaceError, sql.to_sql, df,
- 'test_time', self.conn)
+ # comes back as strings
+ expected = df.applymap(lambda _: _.strftime("%H:%M:%S.%f"))
+ tm.assert_frame_equal(res, expected)
def _get_index_columns(self, tbl_name):
ixs = sql.read_sql_query(
| I'm proposing a solution to #8341
As @jorisvandenbossche suggested, I use a sqlite3 adapter to transform `datetime.time` objects into string (`hh:mm:ss.ffffff`, as this what sqlalchemy... I think?)
I added a test in the class `_TestSQLApi`, so that the solution is tested with both sqlalchemy and the sqlite3 fallback. Thus, the result should be consistent.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11547 | 2015-11-08T00:06:08Z | 2016-02-24T12:53:12Z | null | 2016-02-24T12:54:28Z |
BUG: GH11517 add multiindex column names after describe() | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 0d0f4c66c1fec..3c652aafd7d2a 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -95,6 +95,8 @@ Bug Fixes
- Bug in ``pd.eval`` where unary ops in a list error (:issue:`11235`)
- Bug in ``squeeze()`` with zero length arrays (:issue:`11230`, :issue:`8999`)
+- Bug in ``describe()`` dropping column names for hierarchical indexes (:issue:`11517`)
+
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f46296bb6f70c..99ee50a9ae7fb 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -4516,6 +4516,7 @@ def describe_1d(data, percentiles):
if name not in names:
names.append(name)
d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)
+ d.columns.names = data.columns.names
return d
def _check_percentile(self, q):
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index d29673e96ecdd..22a1c0573d45a 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -1471,6 +1471,25 @@ def test_describe_typefiltering_groupby(self):
self.assertTrue(G.describe(include=['number', 'object']).shape == (22, 3))
self.assertTrue(G.describe(include='all').shape == (26, 4))
+ def test_describe_multi_index_df_column_names(self):
+ """ Test that column names persist after the describe operation."""
+
+ df = pd.DataFrame({'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)})
+
+ # GH 11517
+ # test for hierarchical index
+ hierarchical_index_df = df.groupby(['A', 'B']).mean().T
+ self.assertTrue(hierarchical_index_df.columns.names == ['A', 'B'])
+ self.assertTrue(hierarchical_index_df.describe().columns.names == ['A', 'B'])
+
+ # test for non-hierarchical index
+ non_hierarchical_index_df = df.groupby(['A']).mean().T
+ self.assertTrue(non_hierarchical_index_df.columns.names == ['A'])
+ self.assertTrue(non_hierarchical_index_df.describe().columns.names == ['A'])
+
def test_no_order(self):
tm._skip_if_no_scipy()
s = Series([0, 1, np.nan, 3])
| Hi,
This fixes the bug mentioned in https://github.com/pydata/pandas/issues/11517
I have added column names after the describe operation.
Request you to review the change and merge it.
Thanks
| https://api.github.com/repos/pandas-dev/pandas/pulls/11546 | 2015-11-07T22:30:01Z | 2015-11-10T02:27:23Z | 2015-11-10T02:27:22Z | 2015-11-10T04:52:18Z |
BUG: df.join(df2, how='right') TypeError | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 0d0f4c66c1fec..7bd9ea43abefb 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -120,3 +120,5 @@ Bug Fixes
- Bug in ``to_excel`` with openpyxl 2.2+ and merging (:issue:`11408`)
- Bug in ``DataFrame.to_dict()`` produces a ``np.datetime64`` object instead of ``Timestamp`` when only datetime is present in data (:issue:`11327`)
+
+- Bug in ``DataFrame.join()`` with ``how='right'`` producing a ``TypeError`` (:issue:`11519`)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 855e3f013bfd3..0002c41854bf7 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -2490,7 +2490,7 @@ def _join_monotonic(self, other, how='left', return_indexers=False):
if how == 'left':
join_index, lidx, ridx = self._left_indexer(sv, ov)
elif how == 'right':
- join_index, ridx, lidx = self._left_indexer(other, self)
+ join_index, ridx, lidx = self._left_indexer(ov, sv)
elif how == 'inner':
join_index, lidx, ridx = self._inner_indexer(sv, ov)
elif how == 'outer':
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index f7d93a978a46a..228ce9c01c0d3 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -1682,6 +1682,7 @@ def test_join_self(self):
for kind in kinds:
joined = res.join(res, how=kind)
self.assertIs(res, joined)
+
def test_str_attribute(self):
# GH9068
methods = ['strip', 'rstrip', 'lstrip']
@@ -3056,17 +3057,15 @@ def test_join_left(self):
tm.assert_numpy_array_equal(ridx, eridx)
# non-unique
- """
- idx = Index([1,1,2,5])
- idx2 = Index([1,2,5,7,9])
+ idx = Index([1, 1, 2, 5])
+ idx2 = Index([1, 2, 5, 7, 9])
res, lidx, ridx = idx2.join(idx, how='left', return_indexers=True)
- eres = idx2
- eridx = np.array([0, 2, 3, -1, -1])
- elidx = np.array([0, 1, 2, 3, 4])
+ eres = Index([1, 1, 2, 5, 7, 9]) # 1 is in idx2, so it should be x2
+ eridx = np.array([0, 1, 2, 3, -1, -1])
+ elidx = np.array([0, 0, 1, 2, 3, 4])
self.assertTrue(res.equals(eres))
tm.assert_numpy_array_equal(lidx, elidx)
tm.assert_numpy_array_equal(ridx, eridx)
- """
def test_join_right(self):
other = Int64Index([7, 12, 25, 1, 2, 5])
@@ -3096,24 +3095,16 @@ def test_join_right(self):
self.assertIsNone(ridx)
# non-unique
- """
- idx = Index([1,1,2,5])
- idx2 = Index([1,2,5,7,9])
+ idx = Index([1, 1, 2, 5])
+ idx2 = Index([1, 2, 5, 7, 9])
res, lidx, ridx = idx.join(idx2, how='right', return_indexers=True)
- eres = idx2
- elidx = np.array([0, 2, 3, -1, -1])
- eridx = np.array([0, 1, 2, 3, 4])
+ eres = Index([1, 1, 2, 5, 7, 9]) # 1 is in idx2, so it should be x2
+ elidx = np.array([0, 1, 2, 3, -1, -1])
+ eridx = np.array([0, 0, 1, 2, 3, 4])
self.assertTrue(res.equals(eres))
tm.assert_numpy_array_equal(lidx, elidx)
tm.assert_numpy_array_equal(ridx, eridx)
- idx = Index([1,1,2,5])
- idx2 = Index([1,2,5,9,7])
- res = idx.join(idx2, how='right', return_indexers=False)
- eres = idx2
- self.assert(res.equals(eres))
- """
-
def test_join_non_int_index(self):
other = Index([3, 6, 7, 8, 10], dtype=object)
diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py
index 3a77cfec5fbc3..0f920fc5aa5bc 100644
--- a/pandas/tools/tests/test_merge.py
+++ b/pandas/tools/tests/test_merge.py
@@ -517,6 +517,23 @@ def test_join_many_non_unique_index(self):
assert_frame_equal(result, expected.ix[:, result.columns])
+ # GH 11519
+ df = DataFrame({'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)})
+ s = Series(np.repeat(np.arange(8), 2),
+ index=np.repeat(np.arange(8), 2), name='TEST')
+ inner = df.join(s, how='inner')
+ outer = df.join(s, how='outer')
+ left = df.join(s, how='left')
+ right = df.join(s, how='right')
+ assert_frame_equal(inner, outer)
+ assert_frame_equal(inner, left)
+ assert_frame_equal(inner, right)
+
def test_merge_index_singlekey_right_vs_left(self):
left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'],
'v1': np.random.randn(7)})
| Issue #11519
Somehow right joins had been forgotten in a previous bugfix. There were tests already written that should have seen the problem, but they had been commented out because the expected results were wrong, because of a subtlety in the way non-unique index are handled.
Thanks for the labels "difficulty novice" and "effort low": I'm using pandas every day, and I'm glad I could contribute that easily.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11545 | 2015-11-07T22:14:36Z | 2015-11-11T03:23:40Z | null | 2015-11-16T09:31:54Z |
DOC: Clean up colons in CONTRIBUTING.md | diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 284ac2fc5b169..9e85045d0be4b 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -40,7 +40,7 @@ Bug reports must:
1. Include a short, self-contained Python snippet reproducing the
problem. You can have the code formatted nicely by using [GitHub
Flavored
- Markdown](http://github.github.com/github-flavored-markdown/): :
+ Markdown](http://github.github.com/github-flavored-markdown/):
```python
>>> from pandas import DataFrame
@@ -50,12 +50,12 @@ Bug reports must:
2. Include the full version string of *pandas* and its dependencies. In
recent (\>0.12) versions of *pandas* you can use a built in
- function: :
+ function:
>>> from pandas.util.print_versions import show_versions
>>> show_versions()
- and in 0.13.1 onwards: :
+ and in 0.13.1 onwards:
>>> pd.show_versions()
@@ -105,7 +105,7 @@ local repository and GitHub.
You will need your own fork to work on the code. Go to the [pandas
project page](https://github.com/pydata/pandas) and hit the *fork*
-button. You will want to clone your fork to your machine: :
+button. You will want to clone your fork to your machine:
git clone git@github.com:your-user-name/pandas.git pandas-yourname
cd pandas-yourname
@@ -438,7 +438,7 @@ the actual result to the expected correct result:
#### Running the test suite
The tests can then be run directly inside your git clone (without having
-to install *pandas*) by typing::
+to install *pandas*) by typing:
nosetests pandas
@@ -507,11 +507,11 @@ Once you've made changes, you can see them by typing:
git status
If you've created a new file, it is not being tracked by git. Add it by
-typing :
+typing:
git add path/to/file-to-be-added.py
-Doing 'git status' again should give something like :
+Doing 'git status' again should give something like:
# On branch shiny-new-feature
#
@@ -556,17 +556,17 @@ the relevant commit message and discard others.
### Pushing your changes
When you want your changes to appear publicly on your GitHub page, push
-your forked feature branch's commits :
+your forked feature branch's commits:
git push origin shiny-new-feature
Here origin is the default name given to your remote repository on
-GitHub. You can see the remote repositories :
+GitHub. You can see the remote repositories:
git remote -v
If you added the upstream repository as described above you will see
-something like :
+something like:
origin git@github.com:yourname/pandas.git (fetch)
origin git@github.com:yourname/pandas.git (push)
@@ -622,7 +622,7 @@ and restart the Travis-CI tests.
Once your feature branch is accepted into upstream, you'll probably want
to get rid of the branch. First, merge upstream master into your branch
-so git knows it is safe to delete your branch :
+so git knows it is safe to delete your branch:
git fetch upstream
git checkout master
@@ -635,6 +635,6 @@ Then you can just do:
Make sure you use a lower-case -d, or else git won't warn you if your
feature branch has not actually been merged.
-The branch will still exist on GitHub, so to delete it there do :
+The branch will still exist on GitHub, so to delete it there do:
git push origin --delete shiny-new-feature
| Turn RST's :: and other variants like ': :' or '<word> :' into simple
colons.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11542 | 2015-11-07T10:49:42Z | 2015-11-07T14:49:52Z | null | 2015-11-07T14:49:56Z |
DOC: Document the args of DataFrame.apply in the same order as in the signature | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index b06f1b947bbe7..de74b70cdfaac 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3882,6 +3882,11 @@ def apply(self, func, axis=0, broadcast=False, raw=False, reduce=None,
broadcast : boolean, default False
For aggregation functions, return object of same size with values
propagated
+ raw : boolean, default False
+ If False, convert each row or column into a Series. If raw=True the
+ passed function will receive ndarray objects instead. If you are
+ just applying a NumPy reduction function this will achieve much
+ better performance
reduce : boolean or None, default None
Try to apply reduction procedures. If the DataFrame is empty,
apply will use reduce to determine whether the result should be a
@@ -3890,11 +3895,6 @@ def apply(self, func, axis=0, broadcast=False, raw=False, reduce=None,
while guessing, exceptions raised by func will be ignored). If
reduce is True a Series will always be returned, and if False a
DataFrame will always be returned.
- raw : boolean, default False
- If False, convert each row or column into a Series. If raw=True the
- passed function will receive ndarray objects instead. If you are
- just applying a NumPy reduction function this will achieve much
- better performance
args : tuple
Positional arguments to pass to function in addition to the
array/series
| https://api.github.com/repos/pandas-dev/pandas/pulls/11541 | 2015-11-07T10:42:17Z | 2015-11-08T14:04:23Z | 2015-11-08T14:04:23Z | 2015-11-08T14:04:28Z | |
Remove useless semicolon | diff --git a/pandas/io/tests/test_json/test_ujson.py b/pandas/io/tests/test_json/test_ujson.py
index 8e8a798b2e792..9590dbb90e5c6 100644
--- a/pandas/io/tests/test_json/test_ujson.py
+++ b/pandas/io/tests/test_json/test_ujson.py
@@ -1004,7 +1004,7 @@ def testFloatMax(self):
assert_approx_equal(np.float64(ujson.decode(ujson.encode(num, double_precision=15))), num, 15)
def testArrays(self):
- arr = np.arange(100);
+ arr = np.arange(100)
arr = arr.reshape((10, 10))
tm.assert_numpy_array_equal(np.array(ujson.decode(ujson.encode(arr))), arr)
@@ -1018,7 +1018,7 @@ def testArrays(self):
tm.assert_numpy_array_equal(np.array(ujson.decode(ujson.encode(arr))), arr)
tm.assert_numpy_array_equal(ujson.decode(ujson.encode(arr), numpy=True), arr)
- arr = np.arange(96);
+ arr = np.arange(96)
arr = arr.reshape((2, 2, 2, 2, 3, 2))
tm.assert_numpy_array_equal(np.array(ujson.decode(ujson.encode(arr))), arr)
tm.assert_numpy_array_equal(ujson.decode(ujson.encode(arr), numpy=True), arr)
@@ -1028,7 +1028,7 @@ def testArrays(self):
arr = np.array(l)
tm.assert_numpy_array_equal(np.array(ujson.decode(ujson.encode(arr))), arr)
- arr = np.arange(100.202, 200.202, 1, dtype=np.float32);
+ arr = np.arange(100.202, 200.202, 1, dtype=np.float32)
arr = arr.reshape((5, 5, 4))
outp = np.array(ujson.decode(ujson.encode(arr)), dtype=np.float32)
assert_array_almost_equal_nulp(arr, outp)
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 8aed49a7e6c3f..1c8cbac60e7c7 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -6074,7 +6074,7 @@ def test_getitem_setitem_datetimeindex(self):
assert_series_equal(result, ts)
def test_getitem_setitem_datetime_tz_pytz(self):
- tm._skip_if_no_pytz();
+ tm._skip_if_no_pytz()
from pytz import timezone as tz
from pandas import date_range
@@ -6110,7 +6110,7 @@ def test_getitem_setitem_datetime_tz_pytz(self):
def test_getitem_setitem_datetime_tz_dateutil(self):
- tm._skip_if_no_dateutil();
+ tm._skip_if_no_dateutil()
from dateutil.tz import tzutc
from pandas.tslib import _dateutil_gettz as gettz
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index a278c4d0f9045..be8b0df73593f 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -1886,7 +1886,7 @@ def assertRaises(_exception, _callable=None, *args, **kwargs):
In addition to using it as a contextmanager, you can also use it as a
function, just like the normal assertRaises
- >>> assertRaises(TypeError, ",".join, [1, 3, 5]);
+ >>> assertRaises(TypeError, ",".join, [1, 3, 5])
"""
manager = _AssertRaisesContextmanager(exception=_exception)
# don't return anything if used in function form
@@ -1907,18 +1907,18 @@ def assertRaisesRegexp(_exception, _regexp, _callable=None, *args, **kwargs):
You can pass either a regular expression or a compiled regular expression object.
>>> assertRaisesRegexp(ValueError, 'invalid literal for.*XYZ',
- ... int, 'XYZ');
+ ... int, 'XYZ')
>>> import re
- >>> assertRaisesRegexp(ValueError, re.compile('literal'), int, 'XYZ');
+ >>> assertRaisesRegexp(ValueError, re.compile('literal'), int, 'XYZ')
If an exception of a different type is raised, it bubbles up.
- >>> assertRaisesRegexp(TypeError, 'literal', int, 'XYZ');
+ >>> assertRaisesRegexp(TypeError, 'literal', int, 'XYZ')
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: 'XYZ'
>>> dct = dict()
- >>> assertRaisesRegexp(KeyError, 'pear', dct.__getitem__, 'apple');
+ >>> assertRaisesRegexp(KeyError, 'pear', dct.__getitem__, 'apple')
Traceback (most recent call last):
...
AssertionError: "pear" does not match "'apple'"
| https://api.github.com/repos/pandas-dev/pandas/pulls/11539 | 2015-11-07T09:40:57Z | 2015-11-07T14:34:10Z | 2015-11-07T14:34:10Z | 2015-11-07T14:34:13Z | |
Fix dictionary declaration to litteral | diff --git a/pandas/_version.py b/pandas/_version.py
index 61e9f3ff187ea..26812a086c86d 100644
--- a/pandas/_version.py
+++ b/pandas/_version.py
@@ -205,10 +205,7 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
- pieces = {}
- pieces["long"] = full_out
- pieces["short"] = full_out[:7] # maybe improved later
- pieces["error"] = None
+ pieces = {"long": full_out, "short": full_out[:7], "error": None}
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 605c95bfb0ba2..621092edabbf3 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -632,8 +632,7 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False):
renamed : type of caller
"""
axis = self._get_axis_name(axis)
- d = {'copy': copy, 'inplace': inplace}
- d[axis] = mapper
+ d = {'copy': copy, 'inplace': inplace, axis: mapper}
return self.rename(**d)
#----------------------------------------------------------------------
diff --git a/pandas/io/tests/test_clipboard.py b/pandas/io/tests/test_clipboard.py
index 4855b715ebbe2..e40f3575f1875 100644
--- a/pandas/io/tests/test_clipboard.py
+++ b/pandas/io/tests/test_clipboard.py
@@ -22,19 +22,17 @@ class TestClipboard(tm.TestCase):
@classmethod
def setUpClass(cls):
super(TestClipboard, cls).setUpClass()
- cls.data = {}
- cls.data['string'] = mkdf(5, 3, c_idx_type='s', r_idx_type='i',
- c_idx_names=[None], r_idx_names=[None])
- cls.data['int'] = mkdf(5, 3, data_gen_f=lambda *args: randint(2),
- c_idx_type='s', r_idx_type='i',
- c_idx_names=[None], r_idx_names=[None])
- cls.data['float'] = mkdf(5, 3,
- data_gen_f=lambda r, c: float(r) + 0.01,
- c_idx_type='s', r_idx_type='i',
- c_idx_names=[None], r_idx_names=[None])
- cls.data['mixed'] = DataFrame({'a': np.arange(1.0, 6.0) + 0.01,
- 'b': np.arange(1, 6),
- 'c': list('abcde')})
+ cls.data = {'string': mkdf(5, 3, c_idx_type='s', r_idx_type='i',
+ c_idx_names=[None], r_idx_names=[None]),
+ 'int': mkdf(5, 3, data_gen_f=lambda *args: randint(2),
+ c_idx_type='s', r_idx_type='i',
+ c_idx_names=[None], r_idx_names=[None]),
+ 'float': mkdf(5, 3, data_gen_f=lambda r, c: float(r) + 0.01,
+ c_idx_type='s', r_idx_type='i',
+ c_idx_names=[None], r_idx_names=[None]),
+ 'mixed': DataFrame({'a': np.arange(1.0, 6.0) + 0.01,
+ 'b': np.arange(1, 6),
+ 'c': list('abcde')})}
# Test columns exceeding "max_colwidth" (GH8305)
_cw = get_option('display.max_colwidth') + 1
diff --git a/pandas/stats/ols.py b/pandas/stats/ols.py
index d1d74442d8961..a175f66f155b0 100644
--- a/pandas/stats/ols.py
+++ b/pandas/stats/ols.py
@@ -1355,10 +1355,7 @@ def _y_converter(y):
def f_stat_to_dict(result):
f_stat, shape, p_value = result
- result = {}
- result['f-stat'] = f_stat
- result['DF X'] = shape[0]
- result['DF Resid'] = shape[1]
- result['p-value'] = p_value
-
- return result
+ return {'f-stat': f_stat,
+ 'DF X': shape[0],
+ 'DF Resid': shape[1],
+ 'p-value': p_value}
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index a743ce4ffef61..a2cf84476f559 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -3542,9 +3542,9 @@ def test_constructor_Series_copy_bug(self):
df.copy()
def test_constructor_mixed_dict_and_Series(self):
- data = {}
- data['A'] = {'foo': 1, 'bar': 2, 'baz': 3}
- data['B'] = Series([4, 3, 2, 1], index=['bar', 'qux', 'baz', 'foo'])
+ data = {'A': {'foo': 1, 'bar': 2, 'baz': 3},
+ 'B': Series([4, 3, 2, 1],
+ index=['bar', 'qux', 'baz', 'foo'])}
result = DataFrame(data)
self.assertTrue(result.index.is_monotonic)
@@ -16115,9 +16115,8 @@ def test_date_index_query_with_NaT(self):
def test_date_index_query_with_NaT_duplicates(self):
engine, parser = self.engine, self.parser
n = 10
- d = {}
- d['dates1'] = date_range('1/1/2012', periods=n)
- d['dates3'] = date_range('1/1/2014', periods=n)
+ d = {'dates1': date_range('1/1/2012', periods=n),
+ 'dates3': date_range('1/1/2014', periods=n)}
df = DataFrame(d)
df.loc[np.random.rand(n) > 0.5, 'dates1'] = pd.NaT
df.set_index('dates1', inplace=True, drop=True)
| https://api.github.com/repos/pandas-dev/pandas/pulls/11538 | 2015-11-07T09:40:31Z | 2015-11-20T18:42:15Z | null | 2015-11-20T18:42:15Z | |
improved cross-linking in computation.rst | diff --git a/doc/source/computation.rst b/doc/source/computation.rst
index dfb9fab19bf31..c49b477f3a498 100644
--- a/doc/source/computation.rst
+++ b/doc/source/computation.rst
@@ -212,28 +212,30 @@ mean, median, correlation, variance, covariance, standard deviation, skewness,
and kurtosis. All of these methods are in the :mod:`pandas` namespace, but
otherwise they can be found in :mod:`pandas.stats.moments`.
+.. currentmodule:: pandas
+
.. csv-table::
:header: "Function", "Description"
:widths: 20, 80
- ``rolling_count``, Number of non-null observations
- ``rolling_sum``, Sum of values
- ``rolling_mean``, Mean of values
- ``rolling_median``, Arithmetic median of values
- ``rolling_min``, Minimum
- ``rolling_max``, Maximum
- ``rolling_std``, Unbiased standard deviation
- ``rolling_var``, Unbiased variance
- ``rolling_skew``, Unbiased skewness (3rd moment)
- ``rolling_kurt``, Unbiased kurtosis (4th moment)
- ``rolling_quantile``, Sample quantile (value at %)
- ``rolling_apply``, Generic apply
- ``rolling_cov``, Unbiased covariance (binary)
- ``rolling_corr``, Correlation (binary)
- ``rolling_window``, Moving window function
+ :func:`rolling_count`, Number of non-null observations
+ :func:`rolling_sum`, Sum of values
+ :func:`rolling_mean`, Mean of values
+ :func:`rolling_median`, Arithmetic median of values
+ :func:`rolling_min`, Minimum
+ :func:`rolling_max`, Maximum
+ :func:`rolling_std`, Unbiased standard deviation
+ :func:`rolling_var`, Unbiased variance
+ :func:`rolling_skew`, Unbiased skewness (3rd moment)
+ :func:`rolling_kurt`, Unbiased kurtosis (4th moment)
+ :func:`rolling_quantile`, Sample quantile (value at %)
+ :func:`rolling_apply`, Generic apply
+ :func:`rolling_cov`, Unbiased covariance (binary)
+ :func:`rolling_corr`, Correlation (binary)
+ :func:`rolling_window`, Moving window function
Generally these methods all have the same interface. The binary operators
-(e.g. ``rolling_corr``) take two Series or DataFrames. Otherwise, they all
+(e.g. :func:`rolling_corr`) take two Series or DataFrames. Otherwise, they all
accept the following arguments:
- ``window``: size of moving window
@@ -244,8 +246,8 @@ accept the following arguments:
Note that prior to pandas v0.8.0, a keyword argument ``time_rule`` was used
instead of ``freq`` that referred to the legacy time rule constants
- ``how``: optionally specify method for down or re-sampling. Default is
- is min for ``rolling_min``, max for ``rolling_max``, median for
- ``rolling_median``, and mean for all other rolling functions. See
+ is min for :func:`rolling_min`, max for :func:`rolling_max`, median for
+ :func:`rolling_median`, and mean for all other rolling functions. See
:meth:`DataFrame.resample`'s how argument for more information.
These functions can be applied to ndarrays or Series objects:
@@ -265,7 +267,6 @@ sugar for applying the moving window operator to all of the DataFrame's columns:
.. ipython:: python
:suppress:
-
plt.close('all')
.. ipython:: python
@@ -277,7 +278,7 @@ sugar for applying the moving window operator to all of the DataFrame's columns:
@savefig rolling_mean_frame.png
pd.rolling_sum(df, 60).plot(subplots=True)
-The ``rolling_apply`` function takes an extra ``func`` argument and performs
+The :func:`rolling_apply` function takes an extra ``func`` argument and performs
generic rolling computations. The ``func`` argument should be a single function
that produces a single value from an ndarray input. Suppose we wanted to
compute the mean absolute deviation on a rolling basis:
@@ -288,7 +289,7 @@ compute the mean absolute deviation on a rolling basis:
@savefig rolling_apply_ex.png
pd.rolling_apply(ts, 60, mad).plot(style='k')
-The ``rolling_window`` function performs a generic rolling window computation
+The :func:`rolling_window` function performs a generic rolling window computation
on the input data. The weights used in the window are specified by the ``win_type``
keyword. The list of recognized types are:
@@ -313,7 +314,7 @@ keyword. The list of recognized types are:
pd.rolling_window(ser, 5, 'triang')
-Note that the ``boxcar`` window is equivalent to ``rolling_mean``.
+Note that the ``boxcar`` window is equivalent to :func:`rolling_mean`.
.. ipython:: python
@@ -358,7 +359,7 @@ This keyword is available in other rolling functions as well.
Binary rolling moments
~~~~~~~~~~~~~~~~~~~~~~
-``rolling_cov`` and ``rolling_corr`` can compute moving window statistics about
+:func:`rolling_cov` and :func:`rolling_corr` can compute moving window statistics about
two ``Series`` or any combination of ``DataFrame/Series`` or
``DataFrame/DataFrame``. Here is the behavior in each case:
@@ -447,24 +448,26 @@ they are implemented in pandas such that the following two calls are equivalent:
Like the ``rolling_`` functions, the following methods are included in the
``pandas`` namespace or can be located in ``pandas.stats.moments``.
+.. currentmodule:: pandas
+
.. csv-table::
:header: "Function", "Description"
:widths: 20, 80
- ``expanding_count``, Number of non-null observations
- ``expanding_sum``, Sum of values
- ``expanding_mean``, Mean of values
- ``expanding_median``, Arithmetic median of values
- ``expanding_min``, Minimum
- ``expanding_max``, Maximum
- ``expanding_std``, Unbiased standard deviation
- ``expanding_var``, Unbiased variance
- ``expanding_skew``, Unbiased skewness (3rd moment)
- ``expanding_kurt``, Unbiased kurtosis (4th moment)
- ``expanding_quantile``, Sample quantile (value at %)
- ``expanding_apply``, Generic apply
- ``expanding_cov``, Unbiased covariance (binary)
- ``expanding_corr``, Correlation (binary)
+ :func:`expanding_count`, Number of non-null observations
+ :func:`expanding_sum`, Sum of values
+ :func:`expanding_mean`, Mean of values
+ :func:`expanding_median`, Arithmetic median of values
+ :func:`expanding_min`, Minimum
+ :func:`expanding_max`, Maximum
+ :func:`expanding_std`, Unbiased standard deviation
+ :func:`expanding_var`, Unbiased variance
+ :func:`expanding_skew`, Unbiased skewness (3rd moment)
+ :func:`expanding_kurt`, Unbiased kurtosis (4th moment)
+ :func:`expanding_quantile`, Sample quantile (value at %)
+ :func:`expanding_apply`, Generic apply
+ :func:`expanding_cov`, Unbiased covariance (binary)
+ :func:`expanding_corr`, Correlation (binary)
Aside from not having a ``window`` parameter, these functions have the same
interfaces as their ``rolling_`` counterpart. Like above, the parameters they
@@ -489,7 +492,7 @@ all accept are:
An expanding window statistic will be more stable (and less responsive) than
its rolling window counterpart as the increasing window size decreases the
relative impact of an individual data point. As an example, here is the
-``expanding_mean`` output for the previous time series dataset:
+:func:`expanding_mean` output for the previous time series dataset:
.. ipython:: python
:suppress:
@@ -512,15 +515,17 @@ A related set of functions are exponentially weighted versions of several of
the above statistics. A number of expanding EW (exponentially weighted)
functions are provided:
+.. currentmodule:: pandas
+
.. csv-table::
:header: "Function", "Description"
:widths: 20, 80
- ``ewma``, EW moving average
- ``ewmvar``, EW moving variance
- ``ewmstd``, EW moving standard deviation
- ``ewmcorr``, EW moving correlation
- ``ewmcov``, EW moving covariance
+ :func:`ewma`, EW moving average
+ :func:`ewmvar`, EW moving variance
+ :func:`ewmstd`, EW moving standard deviation
+ :func:`ewmcorr`, EW moving correlation
+ :func:`ewmcov`, EW moving covariance
In general, a weighted moving average is calculated as
@@ -612,7 +617,7 @@ Whereas if ``ignore_na=True``, the weighted average would be calculated as
\frac{(1-\alpha) \cdot 3 + 1 \cdot 5}{(1-\alpha) + 1}.
-The ``ewmvar``, ``ewmstd``, and ``ewmcov`` functions have a ``bias`` argument,
+The :func:`ewmvar`, :func:`ewmstd`, and :func:`ewmcov` functions have a ``bias`` argument,
specifying whether the result should contain biased or unbiased statistics.
For example, if ``bias=True``, ``ewmvar(x)`` is calculated as
``ewmvar(x) = ewma(x**2) - ewma(x)**2``;
| As discussed in _gitter_, I added a couple of cross-links to the _Computational tools_ section in the docs.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11535 | 2015-11-06T17:39:29Z | 2015-11-07T09:40:13Z | 2015-11-07T09:40:13Z | 2015-11-07T09:40:44Z |
PERF: Updated andrews_curves to use Numpy arrays for its samples | diff --git a/asv_bench/benchmarks/plotting.py b/asv_bench/benchmarks/plotting.py
index f46082ac6f288..dc84df8f45975 100644
--- a/asv_bench/benchmarks/plotting.py
+++ b/asv_bench/benchmarks/plotting.py
@@ -2,9 +2,9 @@
try:
from pandas import date_range
except ImportError:
-
def date_range(start=None, end=None, periods=None, freq=None):
return DatetimeIndex(start, end, periods=periods, offset=freq)
+from pandas.tools.plotting import andrews_curves
class plot_timeseries_period(object):
@@ -16,4 +16,17 @@ def setup(self):
self.df = DataFrame(np.random.randn(self.N, self.M), index=date_range('1/1/1975', periods=self.N))
def time_plot_timeseries_period(self):
- self.df.plot()
\ No newline at end of file
+ self.df.plot()
+
+class plot_andrews_curves(object):
+ goal_time = 0.6
+
+ def setup(self):
+ self.N = 500
+ self.M = 10
+ data_dict = {x: np.random.randn(self.N) for x in range(self.M)}
+ data_dict["Name"] = ["A"] * self.N
+ self.df = DataFrame(data_dict)
+
+ def time_plot_andrews_curves(self):
+ andrews_curves(self.df, "Name")
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 5ccf829fd5a42..eaa6b4405a07b 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -91,7 +91,7 @@ Removal of prior version deprecations/changes
Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
-
+- Improved performance of ``andrews_curves`` (:issue:`11534`)
diff --git a/pandas/tests/test_graphics_others.py b/pandas/tests/test_graphics_others.py
index b18cbae600b43..0fb1864f998b2 100644
--- a/pandas/tests/test_graphics_others.py
+++ b/pandas/tests/test_graphics_others.py
@@ -463,6 +463,26 @@ def test_andrews_curves(self):
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
+ length = 10
+ df = DataFrame({"A": random.rand(length),
+ "B": random.rand(length),
+ "C": random.rand(length),
+ "Name": ["A"] * length})
+
+ _check_plot_works(andrews_curves, frame=df, class_column='Name')
+
+ rgba = ('#556270', '#4ECDC4', '#C7F464')
+ ax = _check_plot_works(andrews_curves, frame=df, class_column='Name', color=rgba)
+ self._check_colors(ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])
+
+ cnames = ['dodgerblue', 'aquamarine', 'seagreen']
+ ax = _check_plot_works(andrews_curves, frame=df, class_column='Name', color=cnames)
+ self._check_colors(ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])
+
+ ax = _check_plot_works(andrews_curves, frame=df, class_column='Name', colormap=cm.jet)
+ cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
+ self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
+
colors = ['b', 'g', 'r']
df = DataFrame({"A": [1, 2, 3],
"B": [1, 2, 3],
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 50e0ab66a8726..a4398c692c7c0 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -507,6 +507,15 @@ def normalize(series):
def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
colormap=None, **kwds):
"""
+ Generates a matplotlib plot of Andrews curves, for visualising clusters of multivariate data.
+
+ Andrews curves have the functional form:
+
+ f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) + x_4 sin(2t) + x_5 cos(2t) + ...
+
+ Where x coefficients correspond to the values of each dimension and t is linearly spaced between -pi and +pi. Each
+ row of frame then corresponds to a single curve.
+
Parameters:
-----------
frame : DataFrame
@@ -527,20 +536,26 @@ def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
ax: Matplotlib axis object
"""
- from math import sqrt, pi, sin, cos
+ from math import sqrt, pi
import matplotlib.pyplot as plt
def function(amplitudes):
- def f(x):
+ def f(t):
x1 = amplitudes[0]
result = x1 / sqrt(2.0)
- harmonic = 1.0
- for x_even, x_odd in zip(amplitudes[1::2], amplitudes[2::2]):
- result += (x_even * sin(harmonic * x) +
- x_odd * cos(harmonic * x))
- harmonic += 1.0
- if len(amplitudes) % 2 != 0:
- result += amplitudes[-1] * sin(harmonic * x)
+
+ # Take the rest of the coefficients and resize them appropriately. Take a copy of amplitudes as otherwise
+ # numpy deletes the element from amplitudes itself.
+ coeffs = np.delete(np.copy(amplitudes), 0)
+ coeffs.resize((coeffs.size + 1) / 2, 2)
+
+ # Generate the harmonics and arguments for the sin and cos functions.
+ harmonics = np.arange(0, coeffs.shape[0]) + 1
+ trig_args = np.outer(harmonics, t)
+
+ result += np.sum(coeffs[:, 0, np.newaxis] * np.sin(trig_args) +
+ coeffs[:, 1, np.newaxis] * np.cos(trig_args),
+ axis=0)
return result
return f
@@ -548,7 +563,7 @@ def f(x):
class_col = frame[class_column]
classes = frame[class_column].drop_duplicates()
df = frame.drop(class_column, axis=1)
- x = [-pi + 2.0 * pi * (t / float(samples)) for t in range(samples)]
+ t = np.linspace(-pi, pi, samples)
used_legends = set([])
color_values = _get_standard_colors(num_colors=len(classes),
@@ -560,14 +575,14 @@ def f(x):
for i in range(n):
row = df.iloc[i].values
f = function(row)
- y = [f(t) for t in x]
+ y = f(t)
kls = class_col.iat[i]
label = com.pprint_thing(kls)
if label not in used_legends:
used_legends.add(label)
- ax.plot(x, y, color=colors[kls], label=label, **kwds)
+ ax.plot(t, y, color=colors[kls], label=label, **kwds)
else:
- ax.plot(x, y, color=colors[kls], **kwds)
+ ax.plot(t, y, color=colors[kls], **kwds)
ax.legend(loc='upper right')
ax.grid()
| Hello,
I hope I've followed the contribution guidelines correctly, but am happy to change things if necessary.
I noticed that andrews_curves doesn't make use of numpy arrays in what I thought was a sensible use case: for generating its samples.
I added a test which uses variable length random data, so that I could check the timing changes between the numpy and non-numpy versions and found the following (rough data):
| Length of data | Time / s (non-numpy) | Time / s (numpy) |
| --- | --- | --- |
| Without test | 3.43 | 3.84 |
| 10 | 4.69 | 4.21 |
| 100 | 6.58 | 5.20 |
| 1000 | 20.67 | 16.41 |
| 10000 | 162.17 | 125.37 |
The test adds some overhead (though it is decorated with `@slow`), so I'm happy to amend the commit and remove it. Otherwise, the changes seem to have resulted in a small speed up, which becomes more important for larger data (my original motivation, since I was trying to do it with a 100k x 5 dataframe).
Thanks,
Kyle
| https://api.github.com/repos/pandas-dev/pandas/pulls/11534 | 2015-11-06T17:32:41Z | 2015-11-24T13:11:20Z | 2015-11-24T13:11:20Z | 2015-11-24T14:55:23Z |
BUG: fix incorrect xticklabels when specifying xticks (GH11529) | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 0d0f4c66c1fec..e8078accba176 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -101,7 +101,7 @@ Bug Fixes
-
+- Fix regression in setting of ``xticks`` in ``plot`` (:issue:`11529`).
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index b85f4628ae013..7e6aaa8213667 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -1237,6 +1237,13 @@ def test_time_series_plot_color_with_empty_kwargs(self):
ax = s.plot()
self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors])
+ def test_xticklabels(self):
+ # GH11529
+ s = Series(np.arange(10), index=['P%02d' % i for i in range(10)])
+ ax = s.plot(xticks=[0,3,5,9])
+ exp = ['P%02d' % i for i in [0,3,5,9]]
+ self._check_text_labels(ax.get_xticklabels(), exp)
+
@tm.mplskip
class TestDataFramePlots(TestPlotBase):
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 98d6f5e8eb797..c6a29c7d5bb9d 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -985,11 +985,11 @@ def generate(self):
self._make_plot()
self._add_table()
self._make_legend()
+ self._adorn_subplots()
for ax in self.axes:
self._post_plot_logic_common(ax, self.data)
self._post_plot_logic(ax, self.data)
- self._adorn_subplots()
def _args_adjust(self):
pass
| Fixes #11529
@sinhrks in PR #10717 you moved the setting of the ticklabels from `_adorn_subplots` to its own function `_post_plot_logic_common`. But this is called before `_adorn_subplots`, and the specified `xticks` are only set in `_adorn_subplots`, so now it does not use the new xticks to determine the xticklabels.
I just switched the order of the two functions. At first sight this seems OK, but do you know if there could be any side effects of switching the order?
| https://api.github.com/repos/pandas-dev/pandas/pulls/11531 | 2015-11-06T10:50:01Z | 2015-11-09T21:11:09Z | 2015-11-09T21:11:09Z | 2015-11-09T21:11:09Z |
Updated read_excel docstring to include parse_dates and date_parser | diff --git a/pandas/io/excel.py b/pandas/io/excel.py
index ffd2768c78824..3c836d3a53f8f 100644
--- a/pandas/io/excel.py
+++ b/pandas/io/excel.py
@@ -127,6 +127,21 @@ def read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0,
* If list of ints then indicates list of column numbers to be parsed
* If string then indicates comma separated list of column names and
column ranges (e.g. "A:E" or "A,C,E:F")
+ parse_dates : boolean, list of ints or names, list of lists, or dict, default False
+ If True -> try parsing the index.
+ If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
+ If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
+ {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo'
+ A fast-path exists for iso8601-formatted dates.
+ date_parser : function, default None
+ Function to use for converting a sequence of string columns to an
+ array of datetime instances. The default uses dateutil.parser.parser
+ to do the conversion. Pandas will try to call date_parser in three different
+ ways, advancing to the next if an exception occurs: 1) Pass one or more arrays
+ (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string
+ values from the columns defined by parse_dates into a single array and pass
+ that; and 3) call date_parser once for each row using one or more strings
+ (corresponding to the columns defined by parse_dates) as arguments.
na_values : list-like, default None
List of additional strings to recognize as NA/NaN
thousands : str, default None
| https://api.github.com/repos/pandas-dev/pandas/pulls/11527 | 2015-11-06T01:29:04Z | 2015-11-07T14:45:39Z | null | 2015-12-22T11:21:48Z | |
fix _is_view for multi-block frames | diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 1b08140ebec09..e509b3dfa55fd 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -2950,10 +2950,10 @@ def is_datelike_mixed_type(self):
@property
def is_view(self):
- """ return a boolean if we are a single block and are a view """
- if len(self.blocks) == 1:
- return self.blocks[0].is_view
-
+ """ return a boolean if any block is a view """
+ for b in self.blocks:
+ if b.is_view: return True
+
# It is technically possible to figure out which blocks are views
# e.g. [ b.values.base is not None for b in self.blocks ]
# but then we have the case of possibly some blocks being a view
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index d29673e96ecdd..26b9ccf203eeb 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -1679,7 +1679,16 @@ def test_set_attribute(self):
assert_equal(df.y, 5)
assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
-
+
+ def test_is_view(self):
+ # Ensure that if even if only one block of DF is view,
+ # returns _is_view = True. Test for PR #11518
+ df = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
+ s = pd.Series([0.5, 0.3, 0.4])
+ df['col3'] = s[0:1]
+ self.assertTrue(df._is_view )
+
+
class TestPanel(tm.TestCase, Generic):
_typ = Panel
| _is_view previously only testing whether first block was a view, when should return `_is_view = True` if _any_ block is a view.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11518 | 2015-11-04T20:37:39Z | 2015-11-04T20:53:02Z | null | 2015-11-04T21:21:08Z |
BUG: GH10355 groupby std() doesnt sqrt grouping cols | diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 3ca56ecc00d36..fd9d4a81833e8 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -75,6 +75,7 @@ Bug Fixes
- Bug in merging ``datetime64[ns, tz]`` dtypes (:issue:`11405`)
- Bug in ``HDFStore.select`` when comparing with a numpy scalar in a where clause (:issue:`11283`)
- Bug in using ``DataFrame.ix`` with a multi-index indexer(:issue:`11372`)
+- Bug in ``groupby.var()`` when using ``DataFrame.groupby`` with as_index=False (:issue:`10355`)
- Bug in tz-conversions with an ambiguous time and ``.dt`` accessors (:issue:`11295`)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index add5080a69ee4..3274e01c1bcc5 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -789,7 +789,25 @@ def std(self, ddof=1):
For multiple groupings, the result index will be a MultiIndex
"""
# todo, implement at cython level?
- return np.sqrt(self.var(ddof=ddof))
+ if self.as_index:
+ return np.sqrt(self.var(ddof=ddof))
+ else:
+ df = self.var(ddof=ddof)
+
+ # if we are selecting columns we musn't root them
+ try:
+ if type(self.keys) in (list, tuple) and all(k in df.columns for
+ k in self.keys):
+ to_root = [c for c in df.columns if c not in self.keys]
+ elif self.keys in df.columns:
+ to_root = [c for c in df.columns if c != self.keys]
+ else:
+ return np.sqrt(df)
+ except TypeError:
+ return np.sqrt(df)
+
+ df[to_root] = np.sqrt(df[to_root])
+ return df
def var(self, ddof=1):
"""
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 46026a4c887a6..3541ff2337349 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -5546,6 +5546,60 @@ def test_nunique_with_object(self):
expected = pd.Series([1] * 5, name='name', index=index)
tm.assert_series_equal(result, expected)
+ def test_std_with_as_index_false_and_column_grouping(self):
+ # GH 10355
+ df = pd.DataFrame({
+ 'a': [1, 1, 1, 2, 2, 2, 3, 3, 3],
+ 'b': [1, 2, 3, 4, 5, 6, 7, 8, 9],
+ })
+ sd = df.groupby('a', as_index=False).std()
+ sd2 = df.groupby(['a'], as_index=False).std()
+ sd3 = df.groupby(('a'), as_index=False).std()
+
+ expected = pd.DataFrame({
+ 'a': [1, 2, 3],
+ 'b': [1.0, 1.0, 1.0],
+ })
+ tm.assert_frame_equal(expected, sd)
+ tm.assert_frame_equal(expected, sd2)
+ tm.assert_frame_equal(expected, sd3)
+
+ def test_std_with_as_index_false_and_index_grouping(self):
+ df = pd.DataFrame({
+ 'a': [1, 1, 1, 2, 2, 2, 3, 4, 5],
+ 'b': [1, 2, 3, 4, 5, 6, 7, 8, 9],
+ })
+ sd = df.groupby(lambda x: 1+int(x/3), as_index=False).std()
+ sd2 = df.groupby([1, 1, 1, 2, 2, 2, 3, 3, 3], as_index=False).std()
+ s = pd.Series([1, 1, 1, 2, 2, 2, 3, 3, 3])
+ sd3 = df.groupby(s, as_index=False).std()
+ sd4 = df.groupby(dict(s), as_index=False).std()
+
+ expected = pd.DataFrame({
+ 'a': [0.0, 0.0, 1.0],
+ 'b': [1.0, 1.0, 1.0],
+ })
+ tm.assert_frame_equal(expected, sd)
+ tm.assert_frame_equal(expected, sd2)
+ tm.assert_frame_equal(expected, sd3)
+ tm.assert_frame_equal(expected, sd4)
+
+ def test_std_with_as_index_false_and_ddof_zero(self):
+ df = pd.DataFrame({
+ 1: [1, 1, 1, 2, 2, 2, 3, 3, 3],
+ 2: [1, 2, 3, 1, 5, 6, 7, 8, 10],
+ })
+ sd = df.groupby(1, as_index=False).std(ddof=0)
+
+ expected = pd.DataFrame({
+ 1: [1, 2, 3],
+ 2: [
+ np.std([1, 2, 3], ddof=0),
+ np.std([1, 5, 6], ddof=0),
+ np.std([7, 8, 10], ddof=0)],
+ })
+ tm.assert_frame_equal(expected, sd)
+
def assert_fp_equal(a, b):
assert (np.abs(a - b) < 1e-12).all()
| New attempt at #10355 Hopefully should address the issues raised in #11300
Previously, grouping columns were square rooted when as_index=False
We now test whether the grouping keys are in the columns, and
if so don't square root those columns.
Note that we squash TypeError which occurs when self.keys is not
Hashable, and so we can't check for existence in columns.
| https://api.github.com/repos/pandas-dev/pandas/pulls/11507 | 2015-11-03T08:17:46Z | 2016-01-06T17:20:51Z | null | 2019-02-14T05:24:07Z |
DOC: Link to IO Tools docs | diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 3cf91d36da2b3..bfe262a99cb76 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -34,6 +34,9 @@ class ParserWarning(Warning):
_parser_params = """Also supports optionally iterating or breaking of the file
into chunks.
+Additional help can be found in the `online docs for IO Tools
+<http://pandas.pydata.org/pandas-docs/stable/io.html>`_.
+
Parameters
----------
filepath_or_buffer : string or file handle / StringIO
@@ -133,9 +136,12 @@ class ParserWarning(Warning):
nrows : int, default None
Number of rows of file to read. Useful for reading pieces of large files
iterator : boolean, default False
- Return TextFileReader object
+ Return TextFileReader object for iteration with get_chunk().
chunksize : int, default None
- Return TextFileReader object for iteration
+ Return TextFileReader object for iteration. `See IO Tools docs for more
+ information
+ <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ on
+ ``iterator`` and ``chunksize``.
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with engine='c')
converters : dict, default None
| Addressing issue #11464 - Provide links to help for iterator and chunksize usage (since there's no docstring help for TextFileReader).
| https://api.github.com/repos/pandas-dev/pandas/pulls/11505 | 2015-11-02T22:52:33Z | 2015-11-13T17:17:46Z | null | 2015-11-13T17:17:46Z |
Fixed typos | diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst
index 76f8f6aa59b71..b550f59b8c5e6 100644
--- a/doc/source/advanced.rst
+++ b/doc/source/advanced.rst
@@ -686,7 +686,7 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.
.. warning::
- Reshaping and Comparision operations on a ``CategoricalIndex`` must have the same categories
+ Reshaping and Comparison operations on a ``CategoricalIndex`` must have the same categories
or a ``TypeError`` will be raised.
.. code-block:: python
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst
index ddd4fb81ed1f1..4ba52694980d3 100644
--- a/doc/source/categorical.rst
+++ b/doc/source/categorical.rst
@@ -718,7 +718,7 @@ Old style constructor usage
In earlier versions than pandas 0.15, a `Categorical` could be constructed by passing in precomputed
`codes` (called then `labels`) instead of values with categories. The `codes` were interpreted as
-pointers to the categories with `-1` as `NaN`. This type of constructor useage is replaced by
+pointers to the categories with `-1` as `NaN`. This type of constructor usage is replaced by
the special constructor :func:`Categorical.from_codes`.
Unfortunately, in some special cases, using code which assumes the old style constructor usage
diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst
index d98801f4a7afe..ead4c10341fe9 100644
--- a/doc/source/enhancingperf.rst
+++ b/doc/source/enhancingperf.rst
@@ -699,7 +699,7 @@ ol' Python.
.. note::
Using the ``'python'`` engine is generally *not* useful, except for testing
- other evaluation engines against it. You will acheive **no** performance
+ other evaluation engines against it. You will achieve **no** performance
benefits using :func:`~pandas.eval` with ``engine='python'`` and in fact may
incur a performance hit.
diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index 38629ee7baaea..95cdc89702877 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -97,7 +97,7 @@ of multi-axis indexing.
axis is of integer type. ``.ix`` is the most general and will
support any of the inputs in ``.loc`` and ``.iloc``. ``.ix`` also supports floating point
label schemes. ``.ix`` is exceptionally useful when dealing with mixed positional
- and label based hierachical indexes.
+ and label based hierarchical indexes.
However, when an axis is integer based, ONLY
label based access and not positional access is supported.
@@ -1585,5 +1585,5 @@ This will **not** work at all, and so should be avoided
.. warning::
The chained assignment warnings / exceptions are aiming to inform the user of a possibly invalid
- assignment. There may be false positives; situations where a chained assignment is inadvertantly
+ assignment. There may be false positives; situations where a chained assignment is inadvertently
reported.
diff --git a/doc/source/io.rst b/doc/source/io.rst
index a7c0d31189a75..a003f2e7de2d9 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -2006,7 +2006,7 @@ file, and the ``sheetname`` indicating which sheet to parse.
``ExcelFile`` class
+++++++++++++++++++
-To faciliate working with multiple sheets from the same file, the ``ExcelFile``
+To facilitate working with multiple sheets from the same file, the ``ExcelFile``
class can be used to wrap the file and can be be passed into ``read_excel``
There will be a performance benefit for reading multiple sheets as the file is
read into memory only once.
diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst
index d1a2ba59d7fdf..01eba8e826039 100644
--- a/doc/source/remote_data.rst
+++ b/doc/source/remote_data.rst
@@ -373,7 +373,7 @@ The first thing you need to do is to setup accesses to Google Analytics API. Fol
#. rename it to ``client_secrets.json``
#. move it to the ``pandas/io`` module directory
-The first time you use the :func:`read_ga` funtion, a browser window will open to ask you to authentify to the Google API. Do proceed.
+The first time you use the :func:`read_ga` function, a browser window will open to ask you to authentify to the Google API. Do proceed.
Using the Google Analytics API
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/doc/source/timedeltas.rst b/doc/source/timedeltas.rst
index e62f4f9387526..620270c8307b8 100644
--- a/doc/source/timedeltas.rst
+++ b/doc/source/timedeltas.rst
@@ -220,7 +220,7 @@ Frequency Conversion
.. versionadded:: 0.13
Timedelta Series, ``TimedeltaIndex``, and ``Timedelta`` scalars can be converted to other 'frequencies' by dividing by another timedelta,
-or by astyping to a specific timedelta type. These operations yield Series and propogate ``NaT`` -> ``nan``.
+or by astyping to a specific timedelta type. These operations yield Series and propagate ``NaT`` -> ``nan``.
Note that division by the numpy scalar is true division, while astyping is equivalent of floor division.
.. ipython:: python
diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst
index b6ee2d83fd131..4430dd6d38155 100644
--- a/doc/source/visualization.rst
+++ b/doc/source/visualization.rst
@@ -1264,7 +1264,7 @@ with the ``subplots`` keyword:
plt.close('all')
-Using Layout and Targetting Multiple Axes
+Using Layout and Targeting Multiple Axes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The layout of subplots can be specified by ``layout`` keyword. It can accept
| Thanks to pypi.python.org/pypi/misspellings
| https://api.github.com/repos/pandas-dev/pandas/pulls/11504 | 2015-11-02T09:45:26Z | 2015-11-02T10:06:53Z | 2015-11-02T10:06:53Z | 2015-11-02T11:32:44Z |
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