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ENH/TST: Add TestBaseReshaping tests for ArrowExtensionArray
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py index 861cc44310751..7e0792a6010a7 100644 --- a/pandas/tests/extension/test_arrow.py +++ b/pandas/tests/extension/test_arrow.py @@ -563,13 +563,209 @@ def test_fillna_frame(self, data_missing, using_array_manager, request): super().test_fillna_frame(data_missing) +class TestBasePrinting(base.BasePrintingTests): + def test_series_repr(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + if ( + pa.types.is_date(pa_dtype) + or pa.types.is_duration(pa_dtype) + or (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None) + ): + request.node.add_marker( + pytest.mark.xfail( + raises=TypeError, + reason="GH 47514: _concat_datetime expects axis arg.", + ) + ) + super().test_series_repr(data) + + def test_dataframe_repr(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + if ( + pa.types.is_date(pa_dtype) + or pa.types.is_duration(pa_dtype) + or (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None) + ): + request.node.add_marker( + pytest.mark.xfail( + raises=TypeError, + reason="GH 47514: _concat_datetime expects axis arg.", + ) + ) + super().test_dataframe_repr(data) + + +class TestBaseReshaping(base.BaseReshapingTests): + @pytest.mark.parametrize("in_frame", [True, False]) + def test_concat(self, data, in_frame, request): + pa_dtype = data.dtype.pyarrow_dtype + if ( + pa.types.is_date(pa_dtype) + or pa.types.is_duration(pa_dtype) + or (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None) + ): + request.node.add_marker( + pytest.mark.xfail( + raises=TypeError, + reason="GH 47514: _concat_datetime expects axis arg.", + ) + ) + super().test_concat(data, in_frame) + + @pytest.mark.parametrize("in_frame", [True, False]) + def test_concat_all_na_block(self, data_missing, in_frame, request): + pa_dtype = data_missing.dtype.pyarrow_dtype + if ( + pa.types.is_date(pa_dtype) + or pa.types.is_duration(pa_dtype) + or (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None) + ): + request.node.add_marker( + pytest.mark.xfail( + raises=TypeError, + reason="GH 47514: _concat_datetime expects axis arg.", + ) + ) + super().test_concat_all_na_block(data_missing, in_frame) + + def test_concat_columns(self, data, na_value, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_concat_columns(data, na_value) + + def test_concat_extension_arrays_copy_false(self, data, na_value, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_concat_extension_arrays_copy_false(data, na_value) + + def test_concat_with_reindex(self, data, request, using_array_manager): + pa_dtype = data.dtype.pyarrow_dtype + if pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=TypeError, + reason="GH 47514: _concat_datetime expects axis arg.", + ) + ) + elif pa.types.is_date(pa_dtype) or ( + pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None + ): + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError if not using_array_manager else TypeError, + reason="GH 34986", + ) + ) + super().test_concat_with_reindex(data) + + def test_align(self, data, na_value, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_align(data, na_value) + + def test_align_frame(self, data, na_value, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_align_frame(data, na_value) + + def test_align_series_frame(self, data, na_value, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_align_series_frame(data, na_value) + + def test_merge(self, data, na_value, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_merge(data, na_value) + + def test_merge_on_extension_array(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + if pa.types.is_date(pa_dtype) or ( + pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None + ): + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError, + reason="GH 34986", + ) + ) + super().test_merge_on_extension_array(data) + + def test_merge_on_extension_array_duplicates(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + if pa.types.is_date(pa_dtype) or ( + pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None + ): + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError, + reason="GH 34986", + ) + ) + super().test_merge_on_extension_array_duplicates(data) + + def test_ravel(self, data, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_ravel(data) + + @pytest.mark.xfail(reason="GH 45419: pyarrow.ChunkedArray does not support views") + def test_transpose(self, data): + super().test_transpose(data) + + def test_transpose_frame(self, data, request): + tz = getattr(data.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_transpose_frame(data) + + class TestBaseSetitem(base.BaseSetitemTests): def test_setitem_scalar_series(self, data, box_in_series, request): tz = getattr(data.dtype.pyarrow_dtype, "tz", None) if pa_version_under2p0 and tz not in (None, "UTC"): request.node.add_marker( pytest.mark.xfail( - reason=(f"Not supported by pyarrow < 2.0 with timestamp type {tz}") + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" ) ) super().test_setitem_scalar_series(data, box_in_series)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
https://api.github.com/repos/pandas-dev/pandas/pulls/47601
2022-07-05T20:38:37Z
2022-07-08T13:00:56Z
2022-07-08T13:00:56Z
2022-07-08T16:12:16Z
CI: flake8-pyi
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 13e0ecd33359f..8d1e13c1e2212 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -229,3 +229,11 @@ repos: entry: python scripts/validate_min_versions_in_sync.py language: python files: ^(ci/deps/actions-.*-minimum_versions\.yaml|pandas/compat/_optional\.py)$ + - id: flake8-pyi + name: flake8-pyi + entry: flake8 --extend-ignore=E301,E302,E305,E701,E704 + types: [pyi] + language: python + additional_dependencies: + - flake8==4.0.1 + - flake8-pyi==22.5.1 diff --git a/pandas/_libs/index.pyi b/pandas/_libs/index.pyi index 68ecf201285c7..575f83847b1b6 100644 --- a/pandas/_libs/index.pyi +++ b/pandas/_libs/index.pyi @@ -69,7 +69,7 @@ class BaseMultiIndexCodesEngine: ) -> npt.NDArray[np.intp]: ... class ExtensionEngine: - def __init__(self, values: "ExtensionArray"): ... + def __init__(self, values: ExtensionArray): ... def __contains__(self, val: object) -> bool: ... def get_loc(self, val: object) -> int | slice | np.ndarray: ... def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ... diff --git a/pandas/_libs/interval.pyi b/pandas/_libs/interval.pyi index d177e597478d9..a40a6a1a3e323 100644 --- a/pandas/_libs/interval.pyi +++ b/pandas/_libs/interval.pyi @@ -4,7 +4,6 @@ from typing import ( Any, Generic, TypeVar, - Union, overload, ) @@ -81,11 +80,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]): self: Interval[_OrderableTimesT], key: _OrderableTimesT ) -> bool: ... @overload - def __contains__( - self: Interval[_OrderableScalarT], key: Union[int, float] - ) -> bool: ... - def __repr__(self) -> str: ... - def __str__(self) -> str: ... + def __contains__(self: Interval[_OrderableScalarT], key: int | float) -> bool: ... @overload def __add__( self: Interval[_OrderableTimesT], y: Timedelta @@ -95,7 +90,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]): self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __add__(self: Interval[float], y: Union[int, float]) -> Interval[float]: ... + def __add__(self: Interval[float], y: int | float) -> Interval[float]: ... @overload def __radd__( self: Interval[_OrderableTimesT], y: Timedelta @@ -105,7 +100,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]): self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __radd__(self: Interval[float], y: Union[int, float]) -> Interval[float]: ... + def __radd__(self: Interval[float], y: int | float) -> Interval[float]: ... @overload def __sub__( self: Interval[_OrderableTimesT], y: Timedelta @@ -115,7 +110,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]): self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __sub__(self: Interval[float], y: Union[int, float]) -> Interval[float]: ... + def __sub__(self: Interval[float], y: int | float) -> Interval[float]: ... @overload def __rsub__( self: Interval[_OrderableTimesT], y: Timedelta @@ -125,33 +120,31 @@ class Interval(IntervalMixin, Generic[_OrderableT]): self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __rsub__(self: Interval[float], y: Union[int, float]) -> Interval[float]: ... + def __rsub__(self: Interval[float], y: int | float) -> Interval[float]: ... @overload def __mul__( self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __mul__(self: Interval[float], y: Union[int, float]) -> Interval[float]: ... + def __mul__(self: Interval[float], y: int | float) -> Interval[float]: ... @overload def __rmul__( self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __rmul__(self: Interval[float], y: Union[int, float]) -> Interval[float]: ... + def __rmul__(self: Interval[float], y: int | float) -> Interval[float]: ... @overload def __truediv__( self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __truediv__(self: Interval[float], y: Union[int, float]) -> Interval[float]: ... + def __truediv__(self: Interval[float], y: int | float) -> Interval[float]: ... @overload def __floordiv__( self: Interval[int], y: _OrderableScalarT ) -> Interval[_OrderableScalarT]: ... @overload - def __floordiv__( - self: Interval[float], y: Union[int, float] - ) -> Interval[float]: ... + def __floordiv__(self: Interval[float], y: int | float) -> Interval[float]: ... def overlaps(self: Interval[_OrderableT], other: Interval[_OrderableT]) -> bool: ... def intervals_to_interval_bounds( diff --git a/pandas/_libs/lib.pyi b/pandas/_libs/lib.pyi index 02f021128cbed..77d3cbe92bef9 100644 --- a/pandas/_libs/lib.pyi +++ b/pandas/_libs/lib.pyi @@ -213,7 +213,7 @@ def count_level_2d( def get_level_sorter( label: np.ndarray, # const int64_t[:] starts: np.ndarray, # const intp_t[:] -) -> np.ndarray: ... # np.ndarray[np.intp, ndim=1] +) -> np.ndarray: ... # np.ndarray[np.intp, ndim=1] def generate_bins_dt64( values: npt.NDArray[np.int64], binner: np.ndarray, # const int64_t[:] diff --git a/pandas/_libs/sparse.pyi b/pandas/_libs/sparse.pyi index aa5388025f6f2..be5d251b2aea6 100644 --- a/pandas/_libs/sparse.pyi +++ b/pandas/_libs/sparse.pyi @@ -7,7 +7,7 @@ import numpy as np from pandas._typing import npt -SparseIndexT = TypeVar("SparseIndexT", bound="SparseIndex") +_SparseIndexT = TypeVar("_SparseIndexT", bound=SparseIndex) class SparseIndex: length: int @@ -24,8 +24,8 @@ class SparseIndex: def lookup_array(self, indexer: npt.NDArray[np.int32]) -> npt.NDArray[np.int32]: ... def to_int_index(self) -> IntIndex: ... def to_block_index(self) -> BlockIndex: ... - def intersect(self: SparseIndexT, y_: SparseIndex) -> SparseIndexT: ... - def make_union(self: SparseIndexT, y_: SparseIndex) -> SparseIndexT: ... + def intersect(self: _SparseIndexT, y_: SparseIndex) -> _SparseIndexT: ... + def make_union(self: _SparseIndexT, y_: SparseIndex) -> _SparseIndexT: ... class IntIndex(SparseIndex): indices: npt.NDArray[np.int32] diff --git a/pandas/_libs/tslibs/conversion.pyi b/pandas/_libs/tslibs/conversion.pyi index 7e2ebb1b3bad3..d564d767f7f05 100644 --- a/pandas/_libs/tslibs/conversion.pyi +++ b/pandas/_libs/tslibs/conversion.pyi @@ -5,8 +5,6 @@ from datetime import ( import numpy as np -from pandas._typing import npt - DT64NS_DTYPE: np.dtype TD64NS_DTYPE: np.dtype diff --git a/pandas/_libs/tslibs/nattype.pyi b/pandas/_libs/tslibs/nattype.pyi index efadd8f0220b3..e5a7e0223e534 100644 --- a/pandas/_libs/tslibs/nattype.pyi +++ b/pandas/_libs/tslibs/nattype.pyi @@ -3,10 +3,6 @@ from datetime import ( timedelta, tzinfo as _tzinfo, ) -from typing import ( - Any, - Union, -) import numpy as np @@ -18,7 +14,7 @@ nat_strings: set[str] def is_null_datetimelike(val: object, inat_is_null: bool = ...) -> bool: ... -_NaTComparisonTypes = Union[datetime, timedelta, Period, np.datetime64, np.timedelta64] +_NaTComparisonTypes = datetime | timedelta | Period | np.datetime64 | np.timedelta64 class _NatComparison: def __call__(self, other: _NaTComparisonTypes) -> bool: ... @@ -117,8 +113,8 @@ class NaTType: # inject Period properties @property def qyear(self) -> float: ... - def __eq__(self, other: Any) -> bool: ... - def __ne__(self, other: Any) -> bool: ... + def __eq__(self, other: object) -> bool: ... + def __ne__(self, other: object) -> bool: ... __lt__: _NatComparison __le__: _NatComparison __gt__: _NatComparison diff --git a/pandas/_libs/tslibs/offsets.pyi b/pandas/_libs/tslibs/offsets.pyi index 058bbcdd346e0..12b113f0b73b1 100644 --- a/pandas/_libs/tslibs/offsets.pyi +++ b/pandas/_libs/tslibs/offsets.pyi @@ -21,7 +21,7 @@ from .timedeltas import Timedelta if TYPE_CHECKING: from pandas.core.indexes.datetimes import DatetimeIndex -_BaseOffsetT = TypeVar("_BaseOffsetT", bound="BaseOffset") +_BaseOffsetT = TypeVar("_BaseOffsetT", bound=BaseOffset) _DatetimeT = TypeVar("_DatetimeT", bound=datetime) _TimedeltaT = TypeVar("_TimedeltaT", bound=timedelta) @@ -76,13 +76,12 @@ class BaseOffset: def __rmul__(self: _BaseOffsetT, other: int) -> _BaseOffsetT: ... def __neg__(self: _BaseOffsetT) -> _BaseOffsetT: ... def copy(self: _BaseOffsetT) -> _BaseOffsetT: ... - def __repr__(self) -> str: ... @property def name(self) -> str: ... @property def rule_code(self) -> str: ... def freqstr(self) -> str: ... - def apply_index(self, dtindex: "DatetimeIndex") -> "DatetimeIndex": ... + def apply_index(self, dtindex: DatetimeIndex) -> DatetimeIndex: ... def _apply_array(self, dtarr) -> None: ... def rollback(self, dt: datetime) -> datetime: ... def rollforward(self, dt: datetime) -> datetime: ... diff --git a/pandas/_libs/tslibs/timedeltas.pyi b/pandas/_libs/tslibs/timedeltas.pyi index cc649e5a62660..1921329da9e24 100644 --- a/pandas/_libs/tslibs/timedeltas.pyi +++ b/pandas/_libs/tslibs/timedeltas.pyi @@ -2,7 +2,6 @@ from datetime import timedelta from typing import ( ClassVar, Literal, - Type, TypeVar, overload, ) @@ -84,7 +83,7 @@ class Timedelta(timedelta): resolution: ClassVar[Timedelta] value: int # np.int64 def __new__( - cls: Type[_S], + cls: type[_S], value=..., unit: str = ..., **kwargs: int | float | np.integer | np.floating, diff --git a/pandas/_libs/tslibs/timestamps.pyi b/pandas/_libs/tslibs/timestamps.pyi index 4de51d4dc7dd8..f6a62688fc72d 100644 --- a/pandas/_libs/tslibs/timestamps.pyi +++ b/pandas/_libs/tslibs/timestamps.pyi @@ -104,7 +104,9 @@ class Timestamp(datetime): def utcnow(cls: type[_DatetimeT]) -> _DatetimeT: ... # error: Signature of "combine" incompatible with supertype "datetime" @classmethod - def combine(cls, date: _date, time: _time) -> datetime: ... # type: ignore[override] + def combine( # type: ignore[override] + cls, date: _date, time: _time + ) -> datetime: ... @classmethod def fromisoformat(cls: type[_DatetimeT], date_string: str) -> _DatetimeT: ... def strftime(self, format: str) -> str: ... diff --git a/pandas/_libs/writers.pyi b/pandas/_libs/writers.pyi index 930322fcbeb77..0d2096eee3573 100644 --- a/pandas/_libs/writers.pyi +++ b/pandas/_libs/writers.pyi @@ -17,7 +17,7 @@ def max_len_string_array( ) -> int: ... def word_len(val: object) -> int: ... def string_array_replace_from_nan_rep( - arr: np.ndarray, # np.ndarray[object, ndim=1] + arr: np.ndarray, # np.ndarray[object, ndim=1] nan_rep: object, replace: object = ..., ) -> None: ... diff --git a/setup.cfg b/setup.cfg index ded05cbb4e413..b191930acf4c5 100644 --- a/setup.cfg +++ b/setup.cfg @@ -107,9 +107,19 @@ ignore = # false positives B301, # single-letter variables - PDF023 + PDF023, # "use 'pandas._testing' instead" in non-test code - PDF025 + PDF025, + # If test must be a simple comparison against sys.platform or sys.version_info + Y002, + # Use "_typeshed.Self" instead of class-bound TypeVar + Y019, + # Docstrings should not be included in stubs + Y021, + # Use typing_extensions.TypeAlias for type aliases + Y026, + # Use "collections.abc.*" instead of "typing.*" (PEP 585 syntax) + Y027 exclude = doc/sphinxext/*.py, doc/build/*.py,
null
https://api.github.com/repos/pandas-dev/pandas/pulls/47595
2022-07-04T19:48:47Z
2022-07-05T17:36:45Z
2022-07-05T17:36:45Z
2022-09-21T15:28:31Z
TST: added test for handling repeated keys when using Series.loc with Multiindex
diff --git a/pandas/tests/indexing/multiindex/test_multiindex.py b/pandas/tests/indexing/multiindex/test_multiindex.py index b88c411636610..193f3eb1a590f 100644 --- a/pandas/tests/indexing/multiindex/test_multiindex.py +++ b/pandas/tests/indexing/multiindex/test_multiindex.py @@ -213,3 +213,12 @@ def test_subtracting_two_series_with_unordered_index_and_all_nan_index( tm.assert_series_equal(result[0], a_series_expected) tm.assert_series_equal(result[1], b_series_expected) + + def test_multiindex_repeated_keys(self): + # GH19414 + tm.assert_series_equal( + Series([1, 2], MultiIndex.from_arrays([["a", "b"]])).loc[ + ["a", "a", "b", "b"] + ], + Series([1, 1, 2, 2], MultiIndex.from_arrays([["a", "a", "b", "b"]])), + )
- [x] closes #19414. - [x] Tests added and passed. - [x] All code checks passed. Added test for handling repeated keys when using Series.loc with Multiindex. Tests and linter pass locally.
https://api.github.com/repos/pandas-dev/pandas/pulls/47594
2022-07-04T16:55:42Z
2022-07-05T17:08:22Z
2022-07-05T17:08:22Z
2022-07-05T17:08:29Z
ENH: Move CSSWarning to error/__init__.py per GH27656
diff --git a/doc/source/reference/testing.rst b/doc/source/reference/testing.rst index c3ce267ff9dc7..249c2c56cfe57 100644 --- a/doc/source/reference/testing.rst +++ b/doc/source/reference/testing.rst @@ -26,6 +26,7 @@ Exceptions and warnings errors.AbstractMethodError errors.AccessorRegistrationWarning + errors.CSSWarning errors.DataError errors.DtypeWarning errors.DuplicateLabelError diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py index 04d52e2c5853c..98a9d2b35f09d 100644 --- a/pandas/errors/__init__.py +++ b/pandas/errors/__init__.py @@ -395,3 +395,21 @@ def __init__(self, message: str) -> None: # attr only exists on Windows, so typing fails on other platforms message += f" ({ctypes.WinError()})" # type: ignore[attr-defined] super().__init__(message) + + +class CSSWarning(UserWarning): + """ + Warning is raised when converting css styling fails. + This can be due to the styling not having an equivalent value or because the + styling isn't properly formatted. + + Examples + -------- + >>> df = pd.DataFrame({'A': [1, 1, 1]}) + >>> df.style.applymap(lambda x: 'background-color: blueGreenRed;') + ... .to_excel('styled.xlsx') # doctest: +SKIP + ... # CSSWarning: Unhandled color format: 'blueGreenRed' + >>> df.style.applymap(lambda x: 'border: 1px solid red red;') + ... .to_excel('styled.xlsx') # doctest: +SKIP + ... # CSSWarning: Too many tokens provided to "border" (expected 1-3) + """ diff --git a/pandas/io/formats/css.py b/pandas/io/formats/css.py index 92dafffc9c3de..778df087d28d8 100644 --- a/pandas/io/formats/css.py +++ b/pandas/io/formats/css.py @@ -12,11 +12,7 @@ ) import warnings - -class CSSWarning(UserWarning): - """ - This CSS syntax cannot currently be parsed. - """ +from pandas.errors import CSSWarning def _side_expander(prop_fmt: str) -> Callable: diff --git a/pandas/tests/io/formats/test_css.py b/pandas/tests/io/formats/test_css.py index c93694481ef53..70c91dd02751a 100644 --- a/pandas/tests/io/formats/test_css.py +++ b/pandas/tests/io/formats/test_css.py @@ -1,11 +1,10 @@ import pytest +from pandas.errors import CSSWarning + import pandas._testing as tm -from pandas.io.formats.css import ( - CSSResolver, - CSSWarning, -) +from pandas.io.formats.css import CSSResolver def assert_resolves(css, props, inherited=None): diff --git a/pandas/tests/io/formats/test_to_excel.py b/pandas/tests/io/formats/test_to_excel.py index b98fd74643207..7481baaee94f6 100644 --- a/pandas/tests/io/formats/test_to_excel.py +++ b/pandas/tests/io/formats/test_to_excel.py @@ -6,11 +6,11 @@ import pytest +from pandas.errors import CSSWarning import pandas.util._test_decorators as td import pandas._testing as tm -from pandas.io.formats.css import CSSWarning from pandas.io.formats.excel import ( CssExcelCell, CSSToExcelConverter, diff --git a/pandas/tests/test_errors.py b/pandas/tests/test_errors.py index e0ce798fec021..177ff566e347a 100644 --- a/pandas/tests/test_errors.py +++ b/pandas/tests/test_errors.py @@ -29,6 +29,7 @@ "NumExprClobberingError", "IndexingError", "PyperclipException", + "CSSWarning", ], ) def test_exception_importable(exc):
- [x] xref #27656. this GitHub issue is being done in multiple parts - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
https://api.github.com/repos/pandas-dev/pandas/pulls/47591
2022-07-03T20:07:01Z
2022-07-05T17:14:46Z
2022-07-05T17:14:46Z
2022-07-05T17:14:54Z
Backport PR #47474 on branch 1.4.x (BUG: to_sql with method=callable not returning int raising TypeError)
diff --git a/doc/source/whatsnew/v1.4.4.rst b/doc/source/whatsnew/v1.4.4.rst index 0af25daf0468a..6ee140f59e096 100644 --- a/doc/source/whatsnew/v1.4.4.rst +++ b/doc/source/whatsnew/v1.4.4.rst @@ -24,7 +24,7 @@ Fixed regressions Bug fixes ~~~~~~~~~ - The :class:`errors.FutureWarning` raised when passing arguments (other than ``filepath_or_buffer``) as positional in :func:`read_csv` is now raised at the correct stacklevel (:issue:`47385`) -- +- Bug in :meth:`DataFrame.to_sql` when ``method`` was a ``callable`` that did not return an ``int`` and would raise a ``TypeError`` (:issue:`46891`) .. --------------------------------------------------------------------------- diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 6357a670e6ba6..c5bdddd9f9aa7 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2847,7 +2847,7 @@ def to_sql( ------- None or int Number of rows affected by to_sql. None is returned if the callable - passed into ``method`` does not return the number of rows. + passed into ``method`` does not return an integer number of rows. The number of returned rows affected is the sum of the ``rowcount`` attribute of ``sqlite3.Cursor`` or SQLAlchemy connectable which may not diff --git a/pandas/io/sql.py b/pandas/io/sql.py index cf0769acde393..248fde4bfe5bc 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -33,6 +33,7 @@ from pandas.core.dtypes.common import ( is_datetime64tz_dtype, is_dict_like, + is_integer, is_list_like, ) from pandas.core.dtypes.dtypes import DatetimeTZDtype @@ -670,7 +671,7 @@ def to_sql( ------- None or int Number of rows affected by to_sql. None is returned if the callable - passed into ``method`` does not return the number of rows. + passed into ``method`` does not return an integer number of rows. .. versionadded:: 1.4.0 @@ -938,7 +939,7 @@ def insert( raise ValueError("chunksize argument should be non-zero") chunks = (nrows // chunksize) + 1 - total_inserted = 0 + total_inserted = None with self.pd_sql.run_transaction() as conn: for i in range(chunks): start_i = i * chunksize @@ -948,10 +949,12 @@ def insert( chunk_iter = zip(*(arr[start_i:end_i] for arr in data_list)) num_inserted = exec_insert(conn, keys, chunk_iter) - if num_inserted is None: - total_inserted = None - else: - total_inserted += num_inserted + # GH 46891 + if is_integer(num_inserted): + if total_inserted is None: + total_inserted = num_inserted + else: + total_inserted += num_inserted return total_inserted def _query_iterator( diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py index 4d483099157ae..2ac7f18f116ae 100644 --- a/pandas/tests/io/test_sql.py +++ b/pandas/tests/io/test_sql.py @@ -621,7 +621,8 @@ def test_read_procedure(conn, request): @pytest.mark.db @pytest.mark.parametrize("conn", postgresql_connectable) -def test_copy_from_callable_insertion_method(conn, request): +@pytest.mark.parametrize("expected_count", [2, "Success!"]) +def test_copy_from_callable_insertion_method(conn, expected_count, request): # GH 8953 # Example in io.rst found under _io.sql.method # not available in sqlite, mysql @@ -642,10 +643,18 @@ def psql_insert_copy(table, conn, keys, data_iter): sql_query = f"COPY {table_name} ({columns}) FROM STDIN WITH CSV" cur.copy_expert(sql=sql_query, file=s_buf) + return expected_count conn = request.getfixturevalue(conn) expected = DataFrame({"col1": [1, 2], "col2": [0.1, 0.2], "col3": ["a", "n"]}) - expected.to_sql("test_frame", conn, index=False, method=psql_insert_copy) + result_count = expected.to_sql( + "test_frame", conn, index=False, method=psql_insert_copy + ) + # GH 46891 + if not isinstance(expected_count, int): + assert result_count is None + else: + assert result_count == expected_count result = sql.read_sql_table("test_frame", conn) tm.assert_frame_equal(result, expected)
Backport PR #47474: BUG: to_sql with method=callable not returning int raising TypeError
https://api.github.com/repos/pandas-dev/pandas/pulls/47590
2022-07-03T15:13:33Z
2022-07-04T10:19:22Z
2022-07-04T10:19:22Z
2022-07-04T10:19:23Z
TST: added test for to_json when called on numbers exceeding the int64 limit
diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py index eaffbc60ead32..026c3bc68ce34 100644 --- a/pandas/tests/io/json/test_pandas.py +++ b/pandas/tests/io/json/test_pandas.py @@ -1908,3 +1908,13 @@ def test_complex_data_tojson(self, data, expected): # GH41174 result = data.to_json() assert result == expected + + def test_json_uint64(self): + # GH21073 + expected = ( + '{"columns":["col1"],"index":[0,1],' + '"data":[[13342205958987758245],[12388075603347835679]]}' + ) + df = DataFrame(data={"col1": [13342205958987758245, 12388075603347835679]}) + result = df.to_json(orient="split") + assert result == expected
- [x] closes #21073 - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). Added test for to_json() when called on numbers exceeding the int64 limit. Tests and linter pass locally.
https://api.github.com/repos/pandas-dev/pandas/pulls/47589
2022-07-03T14:35:49Z
2022-07-05T16:43:47Z
2022-07-05T16:43:46Z
2022-07-05T16:43:57Z
CLN: concat union_indexes now respecting sort keyword
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 82ae93ad31763..8dc838a5d61a5 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -9523,7 +9523,6 @@ def _append( verify_integrity: bool = False, sort: bool = False, ) -> DataFrame: - combined_columns = None if isinstance(other, (Series, dict)): if isinstance(other, dict): if not ignore_index: @@ -9536,8 +9535,6 @@ def _append( ) index = Index([other.name], name=self.index.name) - idx_diff = other.index.difference(self.columns) - combined_columns = self.columns.append(idx_diff) row_df = other.to_frame().T # infer_objects is needed for # test_append_empty_frame_to_series_with_dateutil_tz @@ -9563,16 +9560,6 @@ def _append( verify_integrity=verify_integrity, sort=sort, ) - if ( - combined_columns is not None - and not sort - and not combined_columns.equals(result.columns) - ): - # TODO: reindexing here is a kludge bc union_indexes does not - # pass sort to index.union, xref #43375 - # combined_columns.equals check is necessary for preserving dtype - # in test_crosstab_normalize - result = result.reindex(combined_columns, axis=1) return result.__finalize__(self, method="append") def join(
null
https://api.github.com/repos/pandas-dev/pandas/pulls/47588
2022-07-03T12:09:47Z
2022-07-03T14:14:16Z
2022-07-03T14:14:16Z
2022-07-03T17:10:08Z
TYP: return values in core/*.py
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 7e292f4ccf8cb..159c0bb2e72c0 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -948,7 +948,7 @@ def value_counts( # Called once from SparseArray, otherwise could be private def value_counts_arraylike( values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = None -): +) -> tuple[ArrayLike, npt.NDArray[np.int64]]: """ Parameters ---------- diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py index 0a25be38e81df..2fce5fc747312 100644 --- a/pandas/core/arrays/masked.py +++ b/pandas/core/arrays/masked.py @@ -945,9 +945,9 @@ def value_counts(self, dropna: bool = True) -> Series: index = index.astype(self.dtype) mask = np.zeros(len(counts), dtype="bool") - counts = IntegerArray(counts, mask) + counts_array = IntegerArray(counts, mask) - return Series(counts, index=index) + return Series(counts_array, index=index) @doc(ExtensionArray.equals) def equals(self, other) -> bool: diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py index 26c577886f174..e7c745e902a49 100644 --- a/pandas/core/arrays/sparse/array.py +++ b/pandas/core/arrays/sparse/array.py @@ -889,12 +889,20 @@ def value_counts(self, dropna: bool = True) -> Series: if mask.any(): counts[mask] += fcounts else: - keys = np.insert(keys, 0, self.fill_value) + # error: Argument 1 to "insert" has incompatible type "Union[ + # ExtensionArray,ndarray[Any, Any]]"; expected "Union[ + # _SupportsArray[dtype[Any]], Sequence[_SupportsArray[dtype + # [Any]]], Sequence[Sequence[_SupportsArray[dtype[Any]]]], + # Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]], Sequence + # [Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]]]]" + keys = np.insert(keys, 0, self.fill_value) # type: ignore[arg-type] counts = np.insert(counts, 0, fcounts) if not isinstance(keys, ABCIndex): - keys = Index(keys) - return Series(counts, index=keys) + index = Index(keys) + else: + index = keys + return Series(counts, index=index) def _quantile(self, qs: npt.NDArray[np.float64], interpolation: str): diff --git a/pandas/core/base.py b/pandas/core/base.py index 33dbc292660d0..1fa840bcbd51f 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -81,7 +81,10 @@ NumpyValueArrayLike, ) - from pandas import Categorical + from pandas import ( + Categorical, + Series, + ) _shared_docs: dict[str, str] = {} @@ -161,7 +164,7 @@ def _freeze(self): object.__setattr__(self, "__frozen", True) # prevent adding any attribute via s.xxx.new_attribute = ... - def __setattr__(self, key: str, value): + def __setattr__(self, key: str, value) -> None: # _cache is used by a decorator # We need to check both 1.) cls.__dict__ and 2.) getattr(self, key) # because @@ -765,7 +768,7 @@ def hasnans(self) -> bool: # has no attribute "any" return bool(isna(self).any()) # type: ignore[union-attr] - def isna(self): + def isna(self) -> npt.NDArray[np.bool_]: return isna(self._values) def _reduce( @@ -890,7 +893,7 @@ def value_counts( ascending: bool = False, bins=None, dropna: bool = True, - ): + ) -> Series: """ Return a Series containing counts of unique values. diff --git a/pandas/core/common.py b/pandas/core/common.py index 707201153e44a..980e7a79414ba 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -553,7 +553,7 @@ def temp_setattr(obj, attr: str, value) -> Iterator[None]: setattr(obj, attr, old_value) -def require_length_match(data, index: Index): +def require_length_match(data, index: Index) -> None: """ Check the length of data matches the length of the index. """ @@ -665,7 +665,9 @@ def resolve_numeric_only(numeric_only: bool | None | lib.NoDefault) -> bool: return result -def deprecate_numeric_only_default(cls: type, name: str, deprecate_none: bool = False): +def deprecate_numeric_only_default( + cls: type, name: str, deprecate_none: bool = False +) -> None: """Emit FutureWarning message for deprecation of numeric_only. See GH#46560 for details on the deprecation. diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 47cf64ba24022..a49e35539656f 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -37,7 +37,7 @@ """ -def use_bottleneck_cb(key): +def use_bottleneck_cb(key) -> None: from pandas.core import nanops nanops.set_use_bottleneck(cf.get_option(key)) @@ -51,7 +51,7 @@ def use_bottleneck_cb(key): """ -def use_numexpr_cb(key): +def use_numexpr_cb(key) -> None: from pandas.core.computation import expressions expressions.set_use_numexpr(cf.get_option(key)) @@ -65,7 +65,7 @@ def use_numexpr_cb(key): """ -def use_numba_cb(key): +def use_numba_cb(key) -> None: from pandas.core.util import numba_ numba_.set_use_numba(cf.get_option(key)) @@ -329,7 +329,7 @@ def use_numba_cb(key): """ -def table_schema_cb(key): +def table_schema_cb(key) -> None: from pandas.io.formats.printing import enable_data_resource_formatter enable_data_resource_formatter(cf.get_option(key)) @@ -500,7 +500,7 @@ def _deprecate_negative_int_max_colwidth(key): # or we'll hit circular deps. -def use_inf_as_na_cb(key): +def use_inf_as_na_cb(key) -> None: from pandas.core.dtypes.missing import _use_inf_as_na _use_inf_as_na(key) @@ -720,7 +720,7 @@ def use_inf_as_na_cb(key): """ -def register_plotting_backend_cb(key): +def register_plotting_backend_cb(key) -> None: if key == "matplotlib": # We defer matplotlib validation, since it's the default return @@ -746,7 +746,7 @@ def register_plotting_backend_cb(key): """ -def register_converter_cb(key): +def register_converter_cb(key) -> None: from pandas.plotting import ( deregister_matplotlib_converters, register_matplotlib_converters, diff --git a/pandas/core/flags.py b/pandas/core/flags.py index 001cd3d41177a..b4e1039e216c0 100644 --- a/pandas/core/flags.py +++ b/pandas/core/flags.py @@ -81,7 +81,7 @@ def allows_duplicate_labels(self) -> bool: return self._allows_duplicate_labels @allows_duplicate_labels.setter - def allows_duplicate_labels(self, value: bool): + def allows_duplicate_labels(self, value: bool) -> None: value = bool(value) obj = self._obj() if obj is None: @@ -99,12 +99,12 @@ def __getitem__(self, key): return getattr(self, key) - def __setitem__(self, key, value): + def __setitem__(self, key, value) -> None: if key not in self._keys: raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}") setattr(self, key, value) - def __repr__(self): + def __repr__(self) -> str: return f"<Flags(allows_duplicate_labels={self.allows_duplicate_labels})>" def __eq__(self, other): diff --git a/pandas/core/frame.py b/pandas/core/frame.py index c96e306c78347..ead4ea744c647 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1601,7 +1601,7 @@ def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series: """ return self.dot(other) - def __rmatmul__(self, other): + def __rmatmul__(self, other) -> DataFrame: """ Matrix multiplication using binary `@` operator in Python>=3.5. """ @@ -2791,6 +2791,32 @@ def to_markdown( handles.handle.write(result) return None + @overload + def to_parquet( + self, + path: None = ..., + engine: str = ..., + compression: str | None = ..., + index: bool | None = ..., + partition_cols: list[str] | None = ..., + storage_options: StorageOptions = ..., + **kwargs, + ) -> bytes: + ... + + @overload + def to_parquet( + self, + path: FilePath | WriteBuffer[bytes], + engine: str = ..., + compression: str | None = ..., + index: bool | None = ..., + partition_cols: list[str] | None = ..., + storage_options: StorageOptions = ..., + **kwargs, + ) -> None: + ... + @doc(storage_options=_shared_docs["storage_options"]) @deprecate_kwarg(old_arg_name="fname", new_arg_name="path") def to_parquet( @@ -2988,6 +3014,64 @@ def to_orc( self, path, engine=engine, index=index, engine_kwargs=engine_kwargs ) + @overload + def to_html( + self, + buf: FilePath | WriteBuffer[str], + columns: Sequence[Level] | None = ..., + col_space: ColspaceArgType | None = ..., + header: bool | Sequence[str] = ..., + index: bool = ..., + na_rep: str = ..., + formatters: FormattersType | None = ..., + float_format: FloatFormatType | None = ..., + sparsify: bool | None = ..., + index_names: bool = ..., + justify: str | None = ..., + max_rows: int | None = ..., + max_cols: int | None = ..., + show_dimensions: bool | str = ..., + decimal: str = ..., + bold_rows: bool = ..., + classes: str | list | tuple | None = ..., + escape: bool = ..., + notebook: bool = ..., + border: int | bool | None = ..., + table_id: str | None = ..., + render_links: bool = ..., + encoding: str | None = ..., + ) -> None: + ... + + @overload + def to_html( + self, + buf: None = ..., + columns: Sequence[Level] | None = ..., + col_space: ColspaceArgType | None = ..., + header: bool | Sequence[str] = ..., + index: bool = ..., + na_rep: str = ..., + formatters: FormattersType | None = ..., + float_format: FloatFormatType | None = ..., + sparsify: bool | None = ..., + index_names: bool = ..., + justify: str | None = ..., + max_rows: int | None = ..., + max_cols: int | None = ..., + show_dimensions: bool | str = ..., + decimal: str = ..., + bold_rows: bool = ..., + classes: str | list | tuple | None = ..., + escape: bool = ..., + notebook: bool = ..., + border: int | bool | None = ..., + table_id: str | None = ..., + render_links: bool = ..., + encoding: str | None = ..., + ) -> str: + ... + @Substitution( header_type="bool", header="Whether to print column labels, default True", @@ -3001,7 +3085,7 @@ def to_orc( def to_html( self, buf: FilePath | WriteBuffer[str] | None = None, - columns: Sequence[str] | None = None, + columns: Sequence[Level] | None = None, col_space: ColspaceArgType | None = None, header: bool | Sequence[str] = True, index: bool = True, @@ -3023,7 +3107,7 @@ def to_html( table_id: str | None = None, render_links: bool = False, encoding: str | None = None, - ): + ) -> str | None: """ Render a DataFrame as an HTML table. %(shared_params)s @@ -4136,7 +4220,20 @@ def _maybe_cache_changed(self, item, value: Series, inplace: bool) -> None: # ---------------------------------------------------------------------- # Unsorted - def query(self, expr: str, inplace: bool = False, **kwargs): + @overload + def query(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> DataFrame: + ... + + @overload + def query(self, expr: str, *, inplace: Literal[True], **kwargs) -> None: + ... + + @overload + def query(self, expr: str, *, inplace: bool = ..., **kwargs) -> DataFrame | None: + ... + + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "expr"]) + def query(self, expr: str, inplace: bool = False, **kwargs) -> DataFrame | None: """ Query the columns of a DataFrame with a boolean expression. @@ -4283,7 +4380,7 @@ def query(self, expr: str, inplace: bool = False, **kwargs): if not isinstance(expr, str): msg = f"expr must be a string to be evaluated, {type(expr)} given" raise ValueError(msg) - kwargs["level"] = kwargs.pop("level", 0) + 1 + kwargs["level"] = kwargs.pop("level", 0) + 2 kwargs["target"] = None res = self.eval(expr, **kwargs) @@ -4300,7 +4397,16 @@ def query(self, expr: str, inplace: bool = False, **kwargs): else: return result - def eval(self, expr: str, inplace: bool = False, **kwargs): + @overload + def eval(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> Any: + ... + + @overload + def eval(self, expr: str, *, inplace: Literal[True], **kwargs) -> None: + ... + + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "expr"]) + def eval(self, expr: str, inplace: bool = False, **kwargs) -> Any | None: """ Evaluate a string describing operations on DataFrame columns. @@ -4406,7 +4512,7 @@ def eval(self, expr: str, inplace: bool = False, **kwargs): from pandas.core.computation.eval import eval as _eval inplace = validate_bool_kwarg(inplace, "inplace") - kwargs["level"] = kwargs.pop("level", 0) + 1 + kwargs["level"] = kwargs.pop("level", 0) + 2 index_resolvers = self._get_index_resolvers() column_resolvers = self._get_cleaned_column_resolvers() resolvers = column_resolvers, index_resolvers @@ -4903,21 +5009,17 @@ def align( @overload def set_axis( - self, labels, axis: Axis = ..., inplace: Literal[False] = ... + self, labels, *, axis: Axis = ..., inplace: Literal[False] = ... ) -> DataFrame: ... @overload - def set_axis(self, labels, axis: Axis, inplace: Literal[True]) -> None: - ... - - @overload - def set_axis(self, labels, *, inplace: Literal[True]) -> None: + def set_axis(self, labels, *, axis: Axis = ..., inplace: Literal[True]) -> None: ... @overload def set_axis( - self, labels, axis: Axis = ..., inplace: bool = ... + self, labels, *, axis: Axis = ..., inplace: bool = ... ) -> DataFrame | None: ... @@ -4988,11 +5090,11 @@ def reindex(self, *args, **kwargs) -> DataFrame: @overload def drop( self, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[True], errors: IgnoreRaise = ..., @@ -5002,11 +5104,11 @@ def drop( @overload def drop( self, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[False] = ..., errors: IgnoreRaise = ..., @@ -5016,11 +5118,11 @@ def drop( @overload def drop( self, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: bool = ..., errors: IgnoreRaise = ..., @@ -5032,10 +5134,10 @@ def drop( @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"]) def drop( # type: ignore[override] self, - labels: Hashable | list[Hashable] = None, + labels: IndexLabel = None, axis: Axis = 0, - index: Hashable | list[Hashable] = None, - columns: Hashable | list[Hashable] = None, + index: IndexLabel = None, + columns: IndexLabel = None, level: Level | None = None, inplace: bool = False, errors: IgnoreRaise = "raise", @@ -5540,16 +5642,47 @@ def pop(self, item: Hashable) -> Series: """ return super().pop(item=item) - @doc(NDFrame.replace, **_shared_doc_kwargs) + # error: Signature of "replace" incompatible with supertype "NDFrame" + @overload # type: ignore[override] def replace( + self, + to_replace=..., + value=..., + *, + inplace: Literal[False] = ..., + limit: int | None = ..., + regex: bool = ..., + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., + ) -> DataFrame: + ... + + @overload + def replace( + self, + to_replace=..., + value=..., + *, + inplace: Literal[True], + limit: int | None = ..., + regex: bool = ..., + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., + ) -> None: + ... + + # error: Signature of "replace" incompatible with supertype "NDFrame" + @deprecate_nonkeyword_arguments( + version=None, allowed_args=["self", "to_replace", "value"] + ) + @doc(NDFrame.replace, **_shared_doc_kwargs) + def replace( # type: ignore[override] self, to_replace=None, value=lib.no_default, inplace: bool = False, - limit=None, + limit: int | None = None, regex: bool = False, method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default, - ): + ) -> DataFrame | None: return super().replace( to_replace=to_replace, value=value, @@ -5681,6 +5814,30 @@ def shift( periods=periods, freq=freq, axis=axis, fill_value=fill_value ) + @overload + def set_index( + self, + keys, + *, + drop: bool = ..., + append: bool = ..., + inplace: Literal[False] = ..., + verify_integrity: bool = ..., + ) -> DataFrame: + ... + + @overload + def set_index( + self, + keys, + *, + drop: bool = ..., + append: bool = ..., + inplace: Literal[True], + verify_integrity: bool = ..., + ) -> None: + ... + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "keys"]) def set_index( self, @@ -5689,7 +5846,7 @@ def set_index( append: bool = False, inplace: bool = False, verify_integrity: bool = False, - ): + ) -> DataFrame | None: """ Set the DataFrame index using existing columns. @@ -5882,6 +6039,7 @@ def set_index( if not inplace: return frame + return None @overload def reset_index( @@ -6232,6 +6390,30 @@ def notnull(self) -> DataFrame: """ return ~self.isna() + @overload + def dropna( + self, + *, + axis: Axis = ..., + how: str | NoDefault = ..., + thresh: int | NoDefault = ..., + subset: IndexLabel = ..., + inplace: Literal[False] = ..., + ) -> DataFrame: + ... + + @overload + def dropna( + self, + *, + axis: Axis = ..., + how: str | NoDefault = ..., + thresh: int | NoDefault = ..., + subset: IndexLabel = ..., + inplace: Literal[True], + ) -> None: + ... + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) def dropna( self, @@ -6240,7 +6422,7 @@ def dropna( thresh: int | NoDefault = no_default, subset: IndexLabel = None, inplace: bool = False, - ): + ) -> DataFrame | None: """ Remove missing values. @@ -6389,10 +6571,10 @@ def dropna( else: result = self.loc(axis=axis)[mask] - if inplace: - self._update_inplace(result) - else: + if not inplace: return result + self._update_inplace(result) + return None @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "subset"]) def drop_duplicates( @@ -6637,11 +6819,42 @@ def f(vals) -> tuple[np.ndarray, int]: # ---------------------------------------------------------------------- # Sorting + # error: Signature of "sort_values" incompatible with supertype "NDFrame" + @overload # type: ignore[override] + def sort_values( + self, + by, + *, + axis: Axis = ..., + ascending=..., + inplace: Literal[False] = ..., + kind: str = ..., + na_position: str = ..., + ignore_index: bool = ..., + key: ValueKeyFunc = ..., + ) -> DataFrame: + ... + + @overload + def sort_values( + self, + by, + *, + axis: Axis = ..., + ascending=..., + inplace: Literal[True], + kind: str = ..., + na_position: str = ..., + ignore_index: bool = ..., + key: ValueKeyFunc = ..., + ) -> None: + ... + # TODO: Just move the sort_values doc here. + # error: Signature of "sort_values" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "by"]) @Substitution(**_shared_doc_kwargs) @Appender(NDFrame.sort_values.__doc__) - # error: Signature of "sort_values" incompatible with supertype "NDFrame" def sort_values( # type: ignore[override] self, by, @@ -6652,7 +6865,7 @@ def sort_values( # type: ignore[override] na_position: str = "last", ignore_index: bool = False, key: ValueKeyFunc = None, - ): + ) -> DataFrame | None: inplace = validate_bool_kwarg(inplace, "inplace") axis = self._get_axis_number(axis) ascending = validate_ascending(ascending) @@ -6884,7 +7097,7 @@ def value_counts( sort: bool = True, ascending: bool = False, dropna: bool = True, - ): + ) -> Series: """ Return a Series containing counts of unique rows in the DataFrame. @@ -11407,25 +11620,93 @@ def values(self) -> np.ndarray: self._consolidate_inplace() return self._mgr.as_array() - @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + @overload def ffill( - self: DataFrame, + self, + *, + axis: None | Axis = ..., + inplace: Literal[False] = ..., + limit: None | int = ..., + downcast=..., + ) -> DataFrame: + ... + + @overload + def ffill( + self, + *, + axis: None | Axis = ..., + inplace: Literal[True], + limit: None | int = ..., + downcast=..., + ) -> None: + ... + + @overload + def ffill( + self, + *, + axis: None | Axis = ..., + inplace: bool = ..., + limit: None | int = ..., + downcast=..., + ) -> DataFrame | None: + ... + + # error: Signature of "ffill" incompatible with supertype "NDFrame" + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + def ffill( # type: ignore[override] + self, axis: None | Axis = None, inplace: bool = False, limit: None | int = None, downcast=None, ) -> DataFrame | None: - return super().ffill(axis, inplace, limit, downcast) + return super().ffill(axis=axis, inplace=inplace, limit=limit, downcast=downcast) - @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + @overload def bfill( - self: DataFrame, + self, + *, + axis: None | Axis = ..., + inplace: Literal[False] = ..., + limit: None | int = ..., + downcast=..., + ) -> DataFrame: + ... + + @overload + def bfill( + self, + *, + axis: None | Axis = ..., + inplace: Literal[True], + limit: None | int = ..., + downcast=..., + ) -> None: + ... + + @overload + def bfill( + self, + *, + axis: None | Axis = ..., + inplace: bool = ..., + limit: None | int = ..., + downcast=..., + ) -> DataFrame | None: + ... + + # error: Signature of "bfill" incompatible with supertype "NDFrame" + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + def bfill( # type: ignore[override] + self, axis: None | Axis = None, inplace: bool = False, limit: None | int = None, downcast=None, ) -> DataFrame | None: - return super().bfill(axis, inplace, limit, downcast) + return super().bfill(axis=axis, inplace=inplace, limit=limit, downcast=downcast) @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "lower", "upper"] @@ -11464,35 +11745,137 @@ def interpolate( **kwargs, ) + @overload + def where( + self, + cond, + other=..., + *, + inplace: Literal[False] = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> DataFrame: + ... + + @overload + def where( + self, + cond, + other=..., + *, + inplace: Literal[True], + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> None: + ... + + @overload + def where( + self, + cond, + other=..., + *, + inplace: bool = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> DataFrame | None: + ... + + # error: Signature of "where" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "cond", "other"] ) - def where( + def where( # type: ignore[override] self, cond, other=lib.no_default, - inplace=False, + inplace: bool = False, axis=None, level=None, - errors: IgnoreRaise = "raise", + errors: IgnoreRaise | lib.NoDefault = "raise", try_cast=lib.no_default, - ): - return super().where(cond, other, inplace, axis, level, errors, try_cast) + ) -> DataFrame | None: + return super().where( + cond, + other, + inplace=inplace, + axis=axis, + level=level, + errors=errors, + try_cast=try_cast, + ) + @overload + def mask( + self, + cond, + other=..., + *, + inplace: Literal[False] = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> DataFrame: + ... + + @overload + def mask( + self, + cond, + other=..., + *, + inplace: Literal[True], + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> None: + ... + + @overload + def mask( + self, + cond, + other=..., + *, + inplace: bool = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> DataFrame | None: + ... + + # error: Signature of "mask" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "cond", "other"] ) - def mask( + def mask( # type: ignore[override] self, cond, other=np.nan, - inplace=False, + inplace: bool = False, axis=None, level=None, - errors: IgnoreRaise = "raise", + errors: IgnoreRaise | lib.NoDefault = "raise", try_cast=lib.no_default, - ): - return super().mask(cond, other, inplace, axis, level, errors, try_cast) + ) -> DataFrame | None: + return super().mask( + cond, + other, + inplace=inplace, + axis=axis, + level=level, + errors=errors, + try_cast=try_cast, + ) DataFrame._add_numeric_operations() diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 76a473dad79a4..ba3474a2513fb 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -699,25 +699,24 @@ def size(self) -> int: @overload def set_axis( - self: NDFrameT, labels, axis: Axis = ..., inplace: Literal[False] = ... + self: NDFrameT, labels, *, axis: Axis = ..., inplace: Literal[False] = ... ) -> NDFrameT: ... @overload - def set_axis(self, labels, axis: Axis, inplace: Literal[True]) -> None: - ... - - @overload - def set_axis(self, labels, *, inplace: Literal[True]) -> None: + def set_axis(self, labels, *, axis: Axis = ..., inplace: Literal[True]) -> None: ... @overload def set_axis( - self: NDFrameT, labels, axis: Axis = ..., inplace: bool_t = ... + self: NDFrameT, labels, *, axis: Axis = ..., inplace: bool_t = ... ) -> NDFrameT | None: ... - def set_axis(self, labels, axis: Axis = 0, inplace: bool_t = False): + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"]) + def set_axis( + self: NDFrameT, labels, axis: Axis = 0, inplace: bool_t = False + ) -> NDFrameT | None: """ Assign desired index to given axis. @@ -1049,10 +1048,44 @@ def _rename( else: return result.__finalize__(self, method="rename") - @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)]) + @overload def rename_axis( - self, mapper: IndexLabel | lib.NoDefault = lib.no_default, **kwargs - ): + self: NDFrameT, + mapper: IndexLabel | lib.NoDefault = ..., + *, + inplace: Literal[False] = ..., + **kwargs, + ) -> NDFrameT: + ... + + @overload + def rename_axis( + self, + mapper: IndexLabel | lib.NoDefault = ..., + *, + inplace: Literal[True], + **kwargs, + ) -> None: + ... + + @overload + def rename_axis( + self: NDFrameT, + mapper: IndexLabel | lib.NoDefault = ..., + *, + inplace: bool_t = ..., + **kwargs, + ) -> NDFrameT | None: + ... + + @rewrite_axis_style_signature("mapper", [("copy", True)]) + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "mapper"]) + def rename_axis( + self: NDFrameT, + mapper: IndexLabel | lib.NoDefault = lib.no_default, + inplace: bool_t = False, + **kwargs, + ) -> NDFrameT | None: """ Set the name of the axis for the index or columns. @@ -1176,6 +1209,7 @@ class name cat 4 0 monkey 2 2 """ + kwargs["inplace"] = inplace axes, kwargs = self._construct_axes_from_arguments( (), kwargs, sentinel=lib.no_default ) @@ -1221,6 +1255,7 @@ class name result._set_axis_name(newnames, axis=axis, inplace=True) if not inplace: return result + return None @final def _set_axis_name(self, name, axis=0, inplace=False): @@ -1379,7 +1414,7 @@ def equals(self, other: object) -> bool_t: # Unary Methods @final - def __neg__(self): + def __neg__(self: NDFrameT) -> NDFrameT: def blk_func(values: ArrayLike): if is_bool_dtype(values.dtype): # error: Argument 1 to "inv" has incompatible type "Union @@ -1397,7 +1432,7 @@ def blk_func(values: ArrayLike): return res.__finalize__(self, method="__neg__") @final - def __pos__(self): + def __pos__(self: NDFrameT) -> NDFrameT: def blk_func(values: ArrayLike): if is_bool_dtype(values.dtype): return values.copy() @@ -1412,7 +1447,7 @@ def blk_func(values: ArrayLike): return res.__finalize__(self, method="__pos__") @final - def __invert__(self): + def __invert__(self: NDFrameT) -> NDFrameT: if not self.size: # inv fails with 0 len return self @@ -1430,7 +1465,7 @@ def __nonzero__(self): __bool__ = __nonzero__ @final - def bool(self): + def bool(self) -> bool_t: """ Return the bool of a single element Series or DataFrame. @@ -1473,6 +1508,8 @@ def bool(self): ) self.__nonzero__() + # for mypy (__nonzero__ raises) + return True @final def abs(self: NDFrameT) -> NDFrameT: @@ -1733,7 +1770,14 @@ def _get_label_or_level_values(self, key: str, axis: int = 0) -> np.ndarray: self._check_label_or_level_ambiguity(key, axis=axis) values = self.xs(key, axis=other_axes[0])._values elif self._is_level_reference(key, axis=axis): - values = self.axes[axis].get_level_values(key)._values + # error: Incompatible types in assignment (expression has type "Union[ + # ExtensionArray, ndarray[Any, Any]]", variable has type "ndarray[Any, + # Any]") + values = ( + self.axes[axis] + .get_level_values(key) # type: ignore[assignment] + ._values + ) else: raise KeyError(key) @@ -1852,7 +1896,7 @@ def __iter__(self): return iter(self._info_axis) # can we get a better explanation of this? - def keys(self): + def keys(self) -> Index: """ Get the 'info axis' (see Indexing for more). @@ -3293,6 +3337,60 @@ def to_latex( position=position, ) + @overload + def to_csv( + self, + path_or_buf: None = ..., + sep: str = ..., + na_rep: str = ..., + float_format: str | Callable | None = ..., + columns: Sequence[Hashable] | None = ..., + header: bool_t | list[str] = ..., + index: bool_t = ..., + index_label: IndexLabel | None = ..., + mode: str = ..., + encoding: str | None = ..., + compression: CompressionOptions = ..., + quoting: int | None = ..., + quotechar: str = ..., + lineterminator: str | None = ..., + chunksize: int | None = ..., + date_format: str | None = ..., + doublequote: bool_t = ..., + escapechar: str | None = ..., + decimal: str = ..., + errors: str = ..., + storage_options: StorageOptions = ..., + ) -> str: + ... + + @overload + def to_csv( + self, + path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str], + sep: str = ..., + na_rep: str = ..., + float_format: str | Callable | None = ..., + columns: Sequence[Hashable] | None = ..., + header: bool_t | list[str] = ..., + index: bool_t = ..., + index_label: IndexLabel | None = ..., + mode: str = ..., + encoding: str | None = ..., + compression: CompressionOptions = ..., + quoting: int | None = ..., + quotechar: str = ..., + lineterminator: str | None = ..., + chunksize: int | None = ..., + date_format: str | None = ..., + doublequote: bool_t = ..., + escapechar: str | None = ..., + decimal: str = ..., + errors: str = ..., + storage_options: StorageOptions = ..., + ) -> None: + ... + @final @doc( storage_options=_shared_docs["storage_options"], @@ -3666,7 +3764,9 @@ def _take_with_is_copy(self: NDFrameT, indices, axis=0) -> NDFrameT: return result @final - def xs(self, key, axis=0, level=None, drop_level: bool_t = True): + def xs( + self: NDFrameT, key, axis=0, level=None, drop_level: bool_t = True + ) -> NDFrameT: """ Return cross-section from the Series/DataFrame. @@ -4181,11 +4281,11 @@ def reindex_like( @overload def drop( self, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[True], errors: IgnoreRaise = ..., @@ -4195,11 +4295,11 @@ def drop( @overload def drop( self: NDFrameT, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[False] = ..., errors: IgnoreRaise = ..., @@ -4209,11 +4309,11 @@ def drop( @overload def drop( self: NDFrameT, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: bool_t = ..., errors: IgnoreRaise = ..., @@ -4223,10 +4323,10 @@ def drop( @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"]) def drop( self: NDFrameT, - labels: Hashable | list[Hashable] = None, + labels: IndexLabel = None, axis: Axis = 0, - index: Hashable | list[Hashable] = None, - columns: Hashable | list[Hashable] = None, + index: IndexLabel = None, + columns: IndexLabel = None, level: Level | None = None, inplace: bool_t = False, errors: IgnoreRaise = "raise", @@ -4489,16 +4589,59 @@ def add_suffix(self: NDFrameT, suffix: str) -> NDFrameT: # "**Dict[str, partial[str]]"; expected "Union[str, int, None]" return self._rename(**mapper) # type: ignore[return-value, arg-type] + @overload + def sort_values( + self: NDFrameT, + *, + axis: Axis = ..., + ascending=..., + inplace: Literal[False] = ..., + kind: str = ..., + na_position: str = ..., + ignore_index: bool_t = ..., + key: ValueKeyFunc = ..., + ) -> NDFrameT: + ... + + @overload def sort_values( self, - axis=0, + *, + axis: Axis = ..., + ascending=..., + inplace: Literal[True], + kind: str = ..., + na_position: str = ..., + ignore_index: bool_t = ..., + key: ValueKeyFunc = ..., + ) -> None: + ... + + @overload + def sort_values( + self: NDFrameT, + *, + axis: Axis = ..., + ascending=..., + inplace: bool_t = ..., + kind: str = ..., + na_position: str = ..., + ignore_index: bool_t = ..., + key: ValueKeyFunc = ..., + ) -> NDFrameT | None: + ... + + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + def sort_values( + self: NDFrameT, + axis: Axis = 0, ascending=True, inplace: bool_t = False, kind: str = "quicksort", na_position: str = "last", ignore_index: bool_t = False, key: ValueKeyFunc = None, - ): + ) -> NDFrameT | None: """ Sort by the values along either axis. @@ -5741,7 +5884,7 @@ def _get_bool_data(self): # Internal Interface Methods @property - def values(self) -> np.ndarray: + def values(self): raise AbstractMethodError(self) @property @@ -6553,6 +6696,40 @@ def fillna( else: return result.__finalize__(self, method="fillna") + @overload + def ffill( + self: NDFrameT, + *, + axis: None | Axis = ..., + inplace: Literal[False] = ..., + limit: None | int = ..., + downcast=..., + ) -> NDFrameT: + ... + + @overload + def ffill( + self, + *, + axis: None | Axis = ..., + inplace: Literal[True], + limit: None | int = ..., + downcast=..., + ) -> None: + ... + + @overload + def ffill( + self: NDFrameT, + *, + axis: None | Axis = ..., + inplace: bool_t = ..., + limit: None | int = ..., + downcast=..., + ) -> NDFrameT | None: + ... + + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) @doc(klass=_shared_doc_kwargs["klass"]) def ffill( self: NDFrameT, @@ -6575,6 +6752,40 @@ def ffill( pad = ffill + @overload + def bfill( + self: NDFrameT, + *, + axis: None | Axis = ..., + inplace: Literal[False] = ..., + limit: None | int = ..., + downcast=..., + ) -> NDFrameT: + ... + + @overload + def bfill( + self, + *, + axis: None | Axis = ..., + inplace: Literal[True], + limit: None | int = ..., + downcast=..., + ) -> None: + ... + + @overload + def bfill( + self: NDFrameT, + *, + axis: None | Axis = ..., + inplace: bool_t = ..., + limit: None | int = ..., + downcast=..., + ) -> NDFrameT | None: + ... + + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) @doc(klass=_shared_doc_kwargs["klass"]) def bfill( self: NDFrameT, @@ -6597,6 +6808,48 @@ def bfill( backfill = bfill + @overload + def replace( + self: NDFrameT, + to_replace=..., + value=..., + *, + inplace: Literal[False] = ..., + limit: int | None = ..., + regex=..., + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., + ) -> NDFrameT: + ... + + @overload + def replace( + self, + to_replace=..., + value=..., + *, + inplace: Literal[True], + limit: int | None = ..., + regex=..., + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., + ) -> None: + ... + + @overload + def replace( + self: NDFrameT, + to_replace=..., + value=..., + *, + inplace: bool_t = ..., + limit: int | None = ..., + regex=..., + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., + ) -> NDFrameT | None: + ... + + @deprecate_nonkeyword_arguments( + version=None, allowed_args=["self", "to_replace", "value"] + ) @doc( _shared_docs["replace"], klass=_shared_doc_kwargs["klass"], @@ -6604,14 +6857,14 @@ def bfill( replace_iloc=_shared_doc_kwargs["replace_iloc"], ) def replace( - self, + self: NDFrameT, to_replace=None, value=lib.no_default, inplace: bool_t = False, limit: int | None = None, regex=False, method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default, - ): + ) -> NDFrameT | None: if not ( is_scalar(to_replace) or is_re_compilable(to_replace) @@ -6652,9 +6905,8 @@ def replace( args=(to_replace, method, inplace, limit), ) if inplace: - return + return None return result - self = cast("Series", self) return self._replace_single(to_replace, method, inplace, limit) if not is_dict_like(to_replace): @@ -6703,7 +6955,7 @@ def replace( # need a non-zero len on all axes if not self.size: if inplace: - return + return None return self.copy() if is_dict_like(to_replace): @@ -9124,7 +9376,7 @@ def _where( inplace=False, axis=None, level=None, - errors: IgnoreRaise = "raise", + errors: IgnoreRaise | lib.NoDefault = "raise", ): """ Equivalent to public method `where`, except that `other` is not @@ -9249,6 +9501,51 @@ def _where( result = self._constructor(new_data) return result.__finalize__(self) + @overload + def where( + self: NDFrameT, + cond, + other=..., + *, + inplace: Literal[False] = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> NDFrameT: + ... + + @overload + def where( + self, + cond, + other=..., + *, + inplace: Literal[True], + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> None: + ... + + @overload + def where( + self: NDFrameT, + cond, + other=..., + *, + inplace: bool_t = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> NDFrameT | None: + ... + + @deprecate_nonkeyword_arguments( + version=None, allowed_args=["self", "cond", "other"] + ) @doc( klass=_shared_doc_kwargs["klass"], cond="True", @@ -9257,15 +9554,15 @@ def _where( name_other="mask", ) def where( - self, + self: NDFrameT, cond, other=np.nan, - inplace=False, + inplace: bool_t = False, axis=None, level=None, - errors: IgnoreRaise = "raise", + errors: IgnoreRaise | lib.NoDefault = "raise", try_cast=lib.no_default, - ): + ) -> NDFrameT | None: """ Replace values where the condition is {cond_rev}. @@ -9402,6 +9699,51 @@ def where( return self._where(cond, other, inplace, axis, level, errors=errors) + @overload + def mask( + self: NDFrameT, + cond, + other=..., + *, + inplace: Literal[False] = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> NDFrameT: + ... + + @overload + def mask( + self, + cond, + other=..., + *, + inplace: Literal[True], + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> None: + ... + + @overload + def mask( + self: NDFrameT, + cond, + other=..., + *, + inplace: bool_t = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> NDFrameT | None: + ... + + @deprecate_nonkeyword_arguments( + version=None, allowed_args=["self", "cond", "other"] + ) @doc( where, klass=_shared_doc_kwargs["klass"], @@ -9411,15 +9753,15 @@ def where( name_other="where", ) def mask( - self, + self: NDFrameT, cond, other=np.nan, - inplace=False, + inplace: bool_t = False, axis=None, level=None, - errors: IgnoreRaise = "raise", + errors: IgnoreRaise | lib.NoDefault = "raise", try_cast=lib.no_default, - ): + ) -> NDFrameT | None: inplace = validate_bool_kwarg(inplace, "inplace") cond = com.apply_if_callable(cond, self) @@ -11365,7 +11707,7 @@ def rolling( closed: str | None = None, step: int | None = None, method: str = "single", - ): + ) -> Window | Rolling: axis = self._get_axis_number(axis) if win_type is not None: @@ -11477,47 +11819,47 @@ def _inplace_method(self, other, op): ) return self - def __iadd__(self, other): + def __iadd__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for + ("Type[NDFrame]") return self._inplace_method(other, type(self).__add__) # type: ignore[operator] - def __isub__(self, other): + def __isub__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for - ("Type[NDFrame]") return self._inplace_method(other, type(self).__sub__) # type: ignore[operator] - def __imul__(self, other): + def __imul__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for * ("Type[NDFrame]") return self._inplace_method(other, type(self).__mul__) # type: ignore[operator] - def __itruediv__(self, other): + def __itruediv__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for / ("Type[NDFrame]") return self._inplace_method( other, type(self).__truediv__ # type: ignore[operator] ) - def __ifloordiv__(self, other): + def __ifloordiv__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for // ("Type[NDFrame]") return self._inplace_method( other, type(self).__floordiv__ # type: ignore[operator] ) - def __imod__(self, other): + def __imod__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for % ("Type[NDFrame]") return self._inplace_method(other, type(self).__mod__) # type: ignore[operator] - def __ipow__(self, other): + def __ipow__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for ** ("Type[NDFrame]") return self._inplace_method(other, type(self).__pow__) # type: ignore[operator] - def __iand__(self, other): + def __iand__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for & ("Type[NDFrame]") return self._inplace_method(other, type(self).__and__) # type: ignore[operator] - def __ior__(self, other): + def __ior__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for | ("Type[NDFrame]") return self._inplace_method(other, type(self).__or__) # type: ignore[operator] - def __ixor__(self, other): + def __ixor__(self: NDFrameT, other) -> NDFrameT: # error: Unsupported left operand type for ^ ("Type[NDFrame]") return self._inplace_method(other, type(self).__xor__) # type: ignore[operator] diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index b70904db8ae25..665333d0d7b4f 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -5,6 +5,7 @@ TYPE_CHECKING, Hashable, Sequence, + TypeVar, cast, final, ) @@ -74,6 +75,8 @@ Series, ) +_LocationIndexerT = TypeVar("_LocationIndexerT", bound="_LocationIndexer") + # "null slice" _NS = slice(None, None) _one_ellipsis_message = "indexer may only contain one '...' entry" @@ -654,7 +657,7 @@ class _LocationIndexer(NDFrameIndexerBase): _takeable: bool @final - def __call__(self, axis=None): + def __call__(self: _LocationIndexerT, axis=None) -> _LocationIndexerT: # we need to return a copy of ourselves new_self = type(self)(self.name, self.obj) @@ -798,7 +801,7 @@ def _ensure_listlike_indexer(self, key, axis=None, value=None): self.obj._mgr = self.obj._mgr.reindex_axis(keys, axis=0, only_slice=True) @final - def __setitem__(self, key, value): + def __setitem__(self, key, value) -> None: check_deprecated_indexers(key) if isinstance(key, tuple): key = tuple(list(x) if is_iterator(x) else x for x in key) @@ -2362,7 +2365,7 @@ def __getitem__(self, key): key = self._convert_key(key) return self.obj._get_value(*key, takeable=self._takeable) - def __setitem__(self, key, value): + def __setitem__(self, key, value) -> None: if isinstance(key, tuple): key = tuple(com.apply_if_callable(x, self.obj) for x in key) else: diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 57b0a95f803b1..6005e11efbac4 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -104,7 +104,7 @@ def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]: return mask -def clean_fill_method(method, allow_nearest: bool = False): +def clean_fill_method(method: str | None, allow_nearest: bool = False): # asfreq is compat for resampling if method in [None, "asfreq"]: return None @@ -907,7 +907,7 @@ def get_fill_func(method, ndim: int = 1): return {"pad": _pad_2d, "backfill": _backfill_2d}[method] -def clean_reindex_fill_method(method): +def clean_reindex_fill_method(method) -> str | None: return clean_fill_method(method, allow_nearest=True) diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index a96fb9c8129dd..05a9bde700e32 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -5,6 +5,7 @@ import operator from typing import ( Any, + Callable, cast, ) import warnings @@ -1527,7 +1528,7 @@ def _zero_out_fperr(arg): @disallow("M8", "m8") def nancorr( a: np.ndarray, b: np.ndarray, *, method="pearson", min_periods: int | None = None -): +) -> float: """ a, b: ndarrays """ @@ -1549,7 +1550,7 @@ def nancorr( return f(a, b) -def get_corr_func(method): +def get_corr_func(method) -> Callable[[np.ndarray, np.ndarray], float]: if method == "kendall": from scipy.stats import kendalltau @@ -1586,7 +1587,7 @@ def nancov( *, min_periods: int | None = None, ddof: int | None = 1, -): +) -> float: if len(a) != len(b): raise AssertionError("Operands to nancov must have same size") diff --git a/pandas/core/resample.py b/pandas/core/resample.py index 0a62861cdaba7..7306d13e44982 100644 --- a/pandas/core/resample.py +++ b/pandas/core/resample.py @@ -1491,7 +1491,9 @@ def _constructor(self): return TimedeltaIndexResampler -def get_resampler(obj, kind=None, **kwds): +def get_resampler( + obj, kind=None, **kwds +) -> DatetimeIndexResampler | PeriodIndexResampler | TimedeltaIndexResampler: """ Create a TimeGrouper and return our resampler. """ diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py index 8c861c199169b..03aad0ef64dec 100644 --- a/pandas/core/reshape/pivot.py +++ b/pandas/core/reshape/pivot.py @@ -481,6 +481,7 @@ def pivot( columns_listlike = com.convert_to_list_like(columns) + indexed: DataFrame | Series if values is None: if index is not None: cols = com.convert_to_list_like(index) @@ -517,7 +518,10 @@ def pivot( ) else: indexed = data._constructor_sliced(data[values]._values, index=multiindex) - return indexed.unstack(columns_listlike) + # error: Argument 1 to "unstack" of "DataFrame" has incompatible type "Union + # [List[Any], ExtensionArray, ndarray[Any, Any], Index, Series]"; expected + # "Hashable" + return indexed.unstack(columns_listlike) # type: ignore[arg-type] def crosstab( diff --git a/pandas/core/series.py b/pandas/core/series.py index dd52ec855240e..ef4ea0172c505 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -38,9 +38,11 @@ Axis, Dtype, DtypeObj, + FilePath, FillnaOptions, IgnoreRaise, IndexKeyFunc, + IndexLabel, Level, NaPosition, QuantileInterpolation, @@ -51,6 +53,7 @@ TimedeltaConvertibleTypes, TimestampConvertibleTypes, ValueKeyFunc, + WriteBuffer, npt, ) from pandas.compat.numpy import function as nv @@ -1369,15 +1372,39 @@ def repeat(self, repeats, axis=None) -> Series: self, method="repeat" ) + @overload + def reset_index( + self, + level: Level = ..., + *, + drop: bool = ..., + name: Level = ..., + inplace: Literal[False] = ..., + allow_duplicates: bool = ..., + ) -> Series: + ... + + @overload + def reset_index( + self, + level: Level = ..., + *, + drop: bool = ..., + name: Level = ..., + inplace: Literal[True], + allow_duplicates: bool = ..., + ) -> None: + ... + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "level"]) def reset_index( self, - level=None, - drop=False, - name=lib.no_default, - inplace=False, + level: Level = None, + drop: bool = False, + name: Level = lib.no_default, + inplace: bool = False, allow_duplicates: bool = False, - ): + ) -> Series | None: """ Generate a new DataFrame or Series with the index reset. @@ -1493,11 +1520,14 @@ def reset_index( if drop: new_index = default_index(len(self)) if level is not None: + level_list: Sequence[Hashable] if not isinstance(level, (tuple, list)): - level = [level] - level = [self.index._get_level_number(lev) for lev in level] - if len(level) < self.index.nlevels: - new_index = self.index.droplevel(level) + level_list = [level] + else: + level_list = level + level_list = [self.index._get_level_number(lev) for lev in level_list] + if len(level_list) < self.index.nlevels: + new_index = self.index.droplevel(level_list) if inplace: self.index = new_index @@ -1519,9 +1549,12 @@ def reset_index( name = self.name df = self.to_frame(name) - return df.reset_index( + # error: Incompatible return value type (got "DataFrame", expected + # "Optional[Series]") + return df.reset_index( # type: ignore[return-value] level=level, drop=drop, allow_duplicates=allow_duplicates ) + return None # ---------------------------------------------------------------------- # Rendering Methods @@ -1533,19 +1566,51 @@ def __repr__(self) -> str: repr_params = fmt.get_series_repr_params() return self.to_string(**repr_params) + @overload + def to_string( + self, + buf: None = ..., + na_rep: str = ..., + float_format: str | None = ..., + header: bool = ..., + index: bool = ..., + length=..., + dtype=..., + name=..., + max_rows: int | None = ..., + min_rows: int | None = ..., + ) -> str: + ... + + @overload def to_string( self, - buf=None, - na_rep="NaN", - float_format=None, - header=True, - index=True, + buf: FilePath | WriteBuffer[str], + na_rep: str = ..., + float_format: str | None = ..., + header: bool = ..., + index: bool = ..., + length=..., + dtype=..., + name=..., + max_rows: int | None = ..., + min_rows: int | None = ..., + ) -> None: + ... + + def to_string( + self, + buf: FilePath | WriteBuffer[str] | None = None, + na_rep: str = "NaN", + float_format: str | None = None, + header: bool = True, + index: bool = True, length=False, dtype=False, name=False, - max_rows=None, - min_rows=None, - ): + max_rows: int | None = None, + min_rows: int | None = None, + ) -> str | None: """ Render a string representation of the Series. @@ -1604,11 +1669,17 @@ def to_string( if buf is None: return result else: - try: - buf.write(result) - except AttributeError: - with open(buf, "w") as f: + if hasattr(buf, "write"): + # error: Item "str" of "Union[str, PathLike[str], WriteBuffer + # [str]]" has no attribute "write" + buf.write(result) # type: ignore[union-attr] + else: + # error: Argument 1 to "open" has incompatible type "Union[str, + # PathLike[str], WriteBuffer[str]]"; expected "Union[Union[str, + # bytes, PathLike[str], PathLike[bytes]], int]" + with open(buf, "w") as f: # type: ignore[arg-type] f.write(result) + return None @doc( klass=_shared_doc_kwargs["klass"], @@ -2922,7 +2993,7 @@ def searchsorted( # type: ignore[override] def append( self, to_append, ignore_index: bool = False, verify_integrity: bool = False - ): + ) -> Series: """ Concatenate two or more Series. @@ -3400,17 +3471,47 @@ def update(self, other) -> None: # ---------------------------------------------------------------------- # Reindexing, sorting - @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + # error: Signature of "sort_values" incompatible with supertype "NDFrame" + @overload # type: ignore[override] def sort_values( self, - axis=0, - ascending: bool | int | Sequence[bool | int] = True, + *, + axis: Axis = ..., + ascending: bool | int | Sequence[bool] | Sequence[int] = ..., + inplace: Literal[False] = ..., + kind: str = ..., + na_position: str = ..., + ignore_index: bool = ..., + key: ValueKeyFunc = ..., + ) -> Series: + ... + + @overload + def sort_values( + self, + *, + axis: Axis = ..., + ascending: bool | int | Sequence[bool] | Sequence[int] = ..., + inplace: Literal[True], + kind: str = ..., + na_position: str = ..., + ignore_index: bool = ..., + key: ValueKeyFunc = ..., + ) -> None: + ... + + # error: Signature of "sort_values" incompatible with supertype "NDFrame" + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + def sort_values( # type: ignore[override] + self, + axis: Axis = 0, + ascending: bool | int | Sequence[bool] | Sequence[int] = True, inplace: bool = False, kind: str = "quicksort", na_position: str = "last", ignore_index: bool = False, key: ValueKeyFunc = None, - ): + ) -> Series | None: """ Sort by the values. @@ -3604,10 +3705,10 @@ def sort_values( if ignore_index: result.index = default_index(len(sorted_index)) - if inplace: - self._update_inplace(result) - else: + if not inplace: return result.__finalize__(self, method="sort_values") + self._update_inplace(result) + return None @overload def sort_index( @@ -4819,22 +4920,21 @@ def rename( @overload def set_axis( - self, labels, axis: Axis = ..., inplace: Literal[False] = ... + self, labels, *, axis: Axis = ..., inplace: Literal[False] = ... ) -> Series: ... @overload - def set_axis(self, labels, axis: Axis, inplace: Literal[True]) -> None: - ... - - @overload - def set_axis(self, labels, *, inplace: Literal[True]) -> None: + def set_axis(self, labels, *, axis: Axis = ..., inplace: Literal[True]) -> None: ... @overload - def set_axis(self, labels, axis: Axis = ..., inplace: bool = ...) -> Series | None: + def set_axis( + self, labels, *, axis: Axis = ..., inplace: bool = ... + ) -> Series | None: ... + # error: Signature of "set_axis" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"]) @Appender( """ @@ -4861,7 +4961,9 @@ def set_axis(self, labels, axis: Axis = ..., inplace: bool = ...) -> Series | No see_also_sub="", ) @Appender(NDFrame.set_axis.__doc__) - def set_axis(self, labels, axis: Axis = 0, inplace: bool = False): + def set_axis( # type: ignore[override] + self, labels, axis: Axis = 0, inplace: bool = False + ) -> Series | None: return super().set_axis(labels, axis=axis, inplace=inplace) # error: Cannot determine type of 'reindex' @@ -4887,11 +4989,11 @@ def reindex(self, *args, **kwargs) -> Series: @overload def drop( self, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[True], errors: IgnoreRaise = ..., @@ -4901,11 +5003,11 @@ def drop( @overload def drop( self, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[False] = ..., errors: IgnoreRaise = ..., @@ -4915,11 +5017,11 @@ def drop( @overload def drop( self, - labels: Hashable | list[Hashable] = ..., + labels: IndexLabel = ..., *, axis: Axis = ..., - index: Hashable | list[Hashable] = ..., - columns: Hashable | list[Hashable] = ..., + index: IndexLabel = ..., + columns: IndexLabel = ..., level: Level | None = ..., inplace: bool = ..., errors: IgnoreRaise = ..., @@ -4931,10 +5033,10 @@ def drop( @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"]) def drop( # type: ignore[override] self, - labels: Hashable | list[Hashable] = None, + labels: IndexLabel = None, axis: Axis = 0, - index: Hashable | list[Hashable] = None, - columns: Hashable | list[Hashable] = None, + index: IndexLabel = None, + columns: IndexLabel = None, level: Level | None = None, inplace: bool = False, errors: IgnoreRaise = "raise", @@ -5198,22 +5300,52 @@ def pop(self, item: Hashable) -> Any: """ return super().pop(item=item) - # error: Cannot determine type of 'replace' + # error: Signature of "replace" incompatible with supertype "NDFrame" + @overload # type: ignore[override] + def replace( + self, + to_replace=..., + value=..., + *, + inplace: Literal[False] = ..., + limit: int | None = ..., + regex=..., + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., + ) -> Series: + ... + + @overload + def replace( + self, + to_replace=..., + value=..., + *, + inplace: Literal[True], + limit: int | None = ..., + regex=..., + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., + ) -> None: + ... + + # error: Signature of "replace" incompatible with supertype "NDFrame" + @deprecate_nonkeyword_arguments( + version=None, allowed_args=["self", "to_replace", "value"] + ) @doc( - NDFrame.replace, # type: ignore[has-type] + NDFrame.replace, klass=_shared_doc_kwargs["klass"], inplace=_shared_doc_kwargs["inplace"], replace_iloc=_shared_doc_kwargs["replace_iloc"], ) - def replace( + def replace( # type: ignore[override] self, to_replace=None, value=lib.no_default, - inplace=False, - limit=None, + inplace: bool = False, + limit: int | None = None, regex=False, method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default, - ): + ) -> Series | None: return super().replace( to_replace=to_replace, value=value, @@ -5530,8 +5662,10 @@ def _convert_dtypes( return result # error: Cannot determine type of 'isna' + # error: Return type "Series" of "isna" incompatible with return type "ndarray + # [Any, dtype[bool_]]" in supertype "IndexOpsMixin" @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"]) # type: ignore[has-type] - def isna(self) -> Series: + def isna(self) -> Series: # type: ignore[override] return NDFrame.isna(self) # error: Cannot determine type of 'isna' @@ -5555,8 +5689,22 @@ def notnull(self) -> Series: """ return super().notnull() + @overload + def dropna( + self, *, axis: Axis = ..., inplace: Literal[False] = ..., how: str | None = ... + ) -> Series: + ... + + @overload + def dropna( + self, *, axis: Axis = ..., inplace: Literal[True], how: str | None = ... + ) -> None: + ... + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) - def dropna(self, axis=0, inplace=False, how=None): + def dropna( + self, axis: Axis = 0, inplace: bool = False, how: str | None = None + ) -> Series | None: """ Return a new Series with missing values removed. @@ -5638,11 +5786,9 @@ def dropna(self, axis=0, inplace=False, how=None): else: return result else: - if inplace: - # do nothing - pass - else: + if not inplace: return self.copy() + return None # ---------------------------------------------------------------------- # Time series-oriented methods @@ -5755,25 +5901,93 @@ def to_period(self, freq=None, copy=True) -> Series: self, method="to_period" ) - @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + @overload def ffill( - self: Series, + self, + *, + axis: None | Axis = ..., + inplace: Literal[False] = ..., + limit: None | int = ..., + downcast=..., + ) -> Series: + ... + + @overload + def ffill( + self, + *, + axis: None | Axis = ..., + inplace: Literal[True], + limit: None | int = ..., + downcast=..., + ) -> None: + ... + + @overload + def ffill( + self, + *, + axis: None | Axis = ..., + inplace: bool = ..., + limit: None | int = ..., + downcast=..., + ) -> Series | None: + ... + + # error: Signature of "ffill" incompatible with supertype "NDFrame" + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + def ffill( # type: ignore[override] + self, axis: None | Axis = None, inplace: bool = False, limit: None | int = None, downcast=None, ) -> Series | None: - return super().ffill(axis, inplace, limit, downcast) + return super().ffill(axis=axis, inplace=inplace, limit=limit, downcast=downcast) - @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + @overload def bfill( - self: Series, + self, + *, + axis: None | Axis = ..., + inplace: Literal[False] = ..., + limit: None | int = ..., + downcast=..., + ) -> Series: + ... + + @overload + def bfill( + self, + *, + axis: None | Axis = ..., + inplace: Literal[True], + limit: None | int = ..., + downcast=..., + ) -> None: + ... + + @overload + def bfill( + self, + *, + axis: None | Axis = ..., + inplace: bool = ..., + limit: None | int = ..., + downcast=..., + ) -> Series | None: + ... + + # error: Signature of "bfill" incompatible with supertype "NDFrame" + @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) + def bfill( # type: ignore[override] + self, axis: None | Axis = None, inplace: bool = False, limit: None | int = None, downcast=None, ) -> Series | None: - return super().bfill(axis, inplace, limit, downcast) + return super().bfill(axis=axis, inplace=inplace, limit=limit, downcast=downcast) @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "lower", "upper"] @@ -5812,35 +6026,137 @@ def interpolate( **kwargs, ) + @overload + def where( + self, + cond, + other=..., + *, + inplace: Literal[False] = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> Series: + ... + + @overload + def where( + self, + cond, + other=..., + *, + inplace: Literal[True], + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> None: + ... + + @overload + def where( + self, + cond, + other=..., + *, + inplace: bool = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> Series | None: + ... + + # error: Signature of "where" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "cond", "other"] ) - def where( + def where( # type: ignore[override] self, cond, other=lib.no_default, - inplace=False, + inplace: bool = False, axis=None, level=None, - errors=lib.no_default, + errors: IgnoreRaise | lib.NoDefault = lib.no_default, try_cast=lib.no_default, - ): - return super().where(cond, other, inplace, axis, level, errors, try_cast) + ) -> Series | None: + return super().where( + cond, + other, + inplace=inplace, + axis=axis, + level=level, + errors=errors, + try_cast=try_cast, + ) + + @overload + def mask( + self, + cond, + other=..., + *, + inplace: Literal[False] = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> Series: + ... + + @overload + def mask( + self, + cond, + other=..., + *, + inplace: Literal[True], + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> None: + ... + @overload + def mask( + self, + cond, + other=..., + *, + inplace: bool = ..., + axis=..., + level=..., + errors: IgnoreRaise | lib.NoDefault = ..., + try_cast=..., + ) -> Series | None: + ... + + # error: Signature of "mask" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "cond", "other"] ) - def mask( + def mask( # type: ignore[override] self, cond, other=np.nan, - inplace=False, + inplace: bool = False, axis=None, level=None, - errors=lib.no_default, + errors: IgnoreRaise | lib.NoDefault = lib.no_default, try_cast=lib.no_default, - ): - return super().mask(cond, other, inplace, axis, level, errors, try_cast) + ) -> Series | None: + return super().mask( + cond, + other, + inplace=inplace, + axis=axis, + level=level, + errors=errors, + try_cast=try_cast, + ) # ---------------------------------------------------------------------- # Add index diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py index 497523211f2d8..6d497f7d9bb94 100644 --- a/pandas/io/formats/format.py +++ b/pandas/io/formats/format.py @@ -564,7 +564,7 @@ class DataFrameFormatter: def __init__( self, frame: DataFrame, - columns: Sequence[str] | None = None, + columns: Sequence[Hashable] | None = None, col_space: ColspaceArgType | None = None, header: bool | Sequence[str] = True, index: bool = True, @@ -686,7 +686,7 @@ def _initialize_justify(self, justify: str | None) -> str: else: return justify - def _initialize_columns(self, columns: Sequence[str] | None) -> Index: + def _initialize_columns(self, columns: Sequence[Hashable] | None) -> Index: if columns is not None: cols = ensure_index(columns) self.frame = self.frame[cols]
This PR is quite a bit larger since many functions needed overloads.
https://api.github.com/repos/pandas-dev/pandas/pulls/47587
2022-07-03T00:36:12Z
2022-07-06T19:57:07Z
2022-07-06T19:57:07Z
2022-09-21T15:28:31Z
BUG: concat losing columns dtypes for join=outer
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 6fc1ec9c6ff90..2f5c2bb36f405 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -995,6 +995,7 @@ Reshaping - Bug in :func:`get_dummies` that selected object and categorical dtypes but not string (:issue:`44965`) - Bug in :meth:`DataFrame.align` when aligning a :class:`MultiIndex` to a :class:`Series` with another :class:`MultiIndex` (:issue:`46001`) - Bug in concatenation with ``IntegerDtype``, or ``FloatingDtype`` arrays where the resulting dtype did not mirror the behavior of the non-nullable dtypes (:issue:`46379`) +- Bug in :func:`concat` losing dtype of columns when ``join="outer"`` and ``sort=True`` (:issue:`47329`) - Bug in :func:`concat` not sorting the column names when ``None`` is included (:issue:`47331`) - Bug in :func:`concat` with identical key leads to error when indexing :class:`MultiIndex` (:issue:`46519`) - Bug in :meth:`DataFrame.join` with a list when using suffixes to join DataFrames with duplicate column names (:issue:`46396`) diff --git a/pandas/core/indexes/api.py b/pandas/core/indexes/api.py index a9e24bd64922b..6820b7b5360a5 100644 --- a/pandas/core/indexes/api.py +++ b/pandas/core/indexes/api.py @@ -11,6 +11,7 @@ ) from pandas.errors import InvalidIndexError +from pandas.core.dtypes.cast import find_common_type from pandas.core.dtypes.common import is_dtype_equal from pandas.core.algorithms import safe_sort @@ -223,7 +224,7 @@ def union_indexes(indexes, sort: bool | None = True) -> Index: indexes, kind = _sanitize_and_check(indexes) - def _unique_indices(inds) -> Index: + def _unique_indices(inds, dtype) -> Index: """ Convert indexes to lists and concatenate them, removing duplicates. @@ -243,7 +244,30 @@ def conv(i): i = i.tolist() return i - return Index(lib.fast_unique_multiple_list([conv(i) for i in inds], sort=sort)) + return Index( + lib.fast_unique_multiple_list([conv(i) for i in inds], sort=sort), + dtype=dtype, + ) + + def _find_common_index_dtype(inds): + """ + Finds a common type for the indexes to pass through to resulting index. + + Parameters + ---------- + inds: list of Index or list objects + + Returns + ------- + The common type or None if no indexes were given + """ + dtypes = [idx.dtype for idx in indexes if isinstance(idx, Index)] + if dtypes: + dtype = find_common_type(dtypes) + else: + dtype = None + + return dtype if kind == "special": result = indexes[0] @@ -283,16 +307,18 @@ def conv(i): return result elif kind == "array": + dtype = _find_common_index_dtype(indexes) index = indexes[0] if not all(index.equals(other) for other in indexes[1:]): - index = _unique_indices(indexes) + index = _unique_indices(indexes, dtype) name = get_unanimous_names(*indexes)[0] if name != index.name: index = index.rename(name) return index else: # kind='list' - return _unique_indices(indexes) + dtype = _find_common_index_dtype(indexes) + return _unique_indices(indexes, dtype) def _sanitize_and_check(indexes): diff --git a/pandas/tests/reshape/concat/test_index.py b/pandas/tests/reshape/concat/test_index.py index 74a3e93c32ebe..66382eb0e95a9 100644 --- a/pandas/tests/reshape/concat/test_index.py +++ b/pandas/tests/reshape/concat/test_index.py @@ -398,3 +398,14 @@ def test_concat_range_index_result(self): tm.assert_frame_equal(result, expected) expected_index = pd.RangeIndex(0, 2) tm.assert_index_equal(result.index, expected_index, exact=True) + + @pytest.mark.parametrize("dtype", ["Int64", "object"]) + def test_concat_index_keep_dtype(self, dtype): + # GH#47329 + df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype)) + df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=dtype)) + result = concat([df1, df2], ignore_index=True, join="outer", sort=True) + expected = DataFrame( + [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype=dtype) + ) + tm.assert_frame_equal(result, expected)
- [x] closes #47329 (Replace xxxx with the Github issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. @simonjayhawkins This also happens for regular dtypes, so would not backport
https://api.github.com/repos/pandas-dev/pandas/pulls/47586
2022-07-02T23:26:10Z
2022-07-03T14:25:18Z
2022-07-03T14:25:18Z
2022-07-08T13:32:05Z
BUG: Series map ignoring na_action for dict or series mapper
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 090fea57872c5..16fcb34fdb7d1 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -914,6 +914,7 @@ Missing ^^^^^^^ - Bug in :meth:`Series.fillna` and :meth:`DataFrame.fillna` with ``downcast`` keyword not being respected in some cases where there are no NA values present (:issue:`45423`) - Bug in :meth:`Series.fillna` and :meth:`DataFrame.fillna` with :class:`IntervalDtype` and incompatible value raising instead of casting to a common (usually object) dtype (:issue:`45796`) +- Bug in :meth:`Series.map` not respecting ``na_action`` argument if mapper is a ``dict`` or :class:`Series` (:issue:`47527`) - Bug in :meth:`DataFrame.interpolate` with object-dtype column not returning a copy with ``inplace=False`` (:issue:`45791`) - Bug in :meth:`DataFrame.dropna` allows to set both ``how`` and ``thresh`` incompatible arguments (:issue:`46575`) - Bug in :meth:`DataFrame.fillna` ignored ``axis`` when :class:`DataFrame` is single block (:issue:`47713`) diff --git a/pandas/core/base.py b/pandas/core/base.py index 1fa840bcbd51f..2fa3f57f950b5 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -843,6 +843,10 @@ def _map_values(self, mapper, na_action=None): f"{na_action} was passed" ) raise ValueError(msg) + + if na_action == "ignore": + mapper = mapper[mapper.index.notna()] + # Since values were input this means we came from either # a dict or a series and mapper should be an index if is_categorical_dtype(self.dtype): diff --git a/pandas/tests/apply/test_series_apply.py b/pandas/tests/apply/test_series_apply.py index 69f7bebb63986..8900aa0060559 100644 --- a/pandas/tests/apply/test_series_apply.py +++ b/pandas/tests/apply/test_series_apply.py @@ -598,6 +598,34 @@ def test_map_dict_na_key(): tm.assert_series_equal(result, expected) +@pytest.mark.parametrize("arg_func", [dict, Series]) +def test_map_dict_ignore_na(arg_func): + # GH#47527 + mapping = arg_func({1: 10, np.nan: 42}) + ser = Series([1, np.nan, 2]) + result = ser.map(mapping, na_action="ignore") + expected = Series([10, np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + +def test_map_defaultdict_ignore_na(): + # GH#47527 + mapping = defaultdict(int, {1: 10, np.nan: 42}) + ser = Series([1, np.nan, 2]) + result = ser.map(mapping) + expected = Series([10, 0, 0]) + tm.assert_series_equal(result, expected) + + +def test_map_categorical_na_ignore(): + # GH#47527 + values = pd.Categorical([1, np.nan, 2], categories=[10, 1]) + ser = Series(values) + result = ser.map({1: 10, np.nan: 42}) + expected = Series([10, np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + def test_map_dict_subclass_with_missing(): """ Test Series.map with a dictionary subclass that defines __missing__,
- [x] closes #47527 (Replace xxxx with the Github issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47585
2022-07-02T22:48:26Z
2022-07-21T17:26:42Z
2022-07-21T17:26:42Z
2022-08-01T18:16:18Z
REF: share DTA/TDA.__init__
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py index 00dc8b2a4a13b..48de7771cd8d7 100644 --- a/pandas/core/arrays/datetimelike.py +++ b/pandas/core/arrays/datetimelike.py @@ -25,6 +25,7 @@ algos, lib, ) +from pandas._libs.arrays import NDArrayBacked from pandas._libs.tslibs import ( BaseOffset, IncompatibleFrequency, @@ -1906,6 +1907,81 @@ class TimelikeOps(DatetimeLikeArrayMixin): Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex. """ + _default_dtype: np.dtype + + def __init__(self, values, dtype=None, freq=lib.no_default, copy: bool = False): + values = extract_array(values, extract_numpy=True) + if isinstance(values, IntegerArray): + values = values.to_numpy("int64", na_value=iNaT) + + inferred_freq = getattr(values, "_freq", None) + explicit_none = freq is None + freq = freq if freq is not lib.no_default else None + + if isinstance(values, type(self)): + if explicit_none: + # don't inherit from values + pass + elif freq is None: + freq = values.freq + elif freq and values.freq: + freq = to_offset(freq) + freq, _ = validate_inferred_freq(freq, values.freq, False) + + if dtype is not None: + dtype = pandas_dtype(dtype) + if not is_dtype_equal(dtype, values.dtype): + # TODO: we only have tests for this for DTA, not TDA (2022-07-01) + raise TypeError( + f"dtype={dtype} does not match data dtype {values.dtype}" + ) + + dtype = values.dtype + values = values._ndarray + + elif dtype is None: + dtype = self._default_dtype + + if not isinstance(values, np.ndarray): + raise ValueError( + f"Unexpected type '{type(values).__name__}'. 'values' must be a " + f"{type(self).__name__}, ndarray, or Series or Index " + "containing one of those." + ) + if values.ndim not in [1, 2]: + raise ValueError("Only 1-dimensional input arrays are supported.") + + if values.dtype == "i8": + # for compat with datetime/timedelta/period shared methods, + # we can sometimes get here with int64 values. These represent + # nanosecond UTC (or tz-naive) unix timestamps + values = values.view(self._default_dtype) + + dtype = self._validate_dtype(values, dtype) + + if freq == "infer": + raise ValueError( + f"Frequency inference not allowed in {type(self).__name__}.__init__. " + "Use 'pd.array()' instead." + ) + + if copy: + values = values.copy() + if freq: + freq = to_offset(freq) + + NDArrayBacked.__init__(self, values=values, dtype=dtype) + self._freq = freq + + if inferred_freq is None and freq is not None: + type(self)._validate_frequency(self, freq) + + @classmethod + def _validate_dtype(cls, values, dtype): + raise AbstractMethodError(cls) + + # -------------------------------------------------------------- + @cache_readonly def _reso(self) -> int: return get_unit_from_dtype(self._ndarray.dtype) @@ -1917,6 +1993,8 @@ def _unit(self) -> str: # "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]" return dtype_to_unit(self.dtype) # type: ignore[arg-type] + # -------------------------------------------------------------- + def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs): if ( ufunc in [np.isnan, np.isinf, np.isfinite] diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py index c019edea5eee0..ccb20b1dfaf1a 100644 --- a/pandas/core/arrays/datetimes.py +++ b/pandas/core/arrays/datetimes.py @@ -19,7 +19,6 @@ lib, tslib, ) -from pandas._libs.arrays import NDArrayBacked from pandas._libs.tslibs import ( BaseOffset, NaT, @@ -30,7 +29,6 @@ fields, get_resolution, get_unit_from_dtype, - iNaT, ints_to_pydatetime, is_date_array_normalized, is_unitless, @@ -72,9 +70,7 @@ from pandas.core.arrays import datetimelike as dtl from pandas.core.arrays._ranges import generate_regular_range -from pandas.core.arrays.integer import IntegerArray import pandas.core.common as com -from pandas.core.construction import extract_array from pandas.tseries.frequencies import get_period_alias from pandas.tseries.offsets import ( @@ -254,75 +250,14 @@ def _scalar_type(self) -> type[Timestamp]: _dtype: np.dtype | DatetimeTZDtype _freq = None + _default_dtype = DT64NS_DTYPE # used in TimeLikeOps.__init__ - def __init__( - self, values, dtype=None, freq=lib.no_default, copy: bool = False - ) -> None: - values = extract_array(values, extract_numpy=True) - if isinstance(values, IntegerArray): - values = values.to_numpy("int64", na_value=iNaT) - - inferred_freq = getattr(values, "_freq", None) - explicit_none = freq is None - freq = freq if freq is not lib.no_default else None - - if isinstance(values, type(self)): - if explicit_none: - # don't inherit from values - pass - elif freq is None: - freq = values.freq - elif freq and values.freq: - freq = to_offset(freq) - freq, _ = dtl.validate_inferred_freq(freq, values.freq, False) - - if dtype is not None: - dtype = pandas_dtype(dtype) - if not is_dtype_equal(dtype, values.dtype): - raise TypeError( - f"dtype={dtype} does not match data dtype {values.dtype}" - ) - - dtype = values.dtype - values = values._ndarray - - elif dtype is None: - dtype = DT64NS_DTYPE - - if not isinstance(values, np.ndarray): - raise ValueError( - f"Unexpected type '{type(values).__name__}'. 'values' must be a " - f"{type(self).__name__}, ndarray, or Series or Index " - "containing one of those." - ) - if values.ndim not in [1, 2]: - raise ValueError("Only 1-dimensional input arrays are supported.") - - if values.dtype == "i8": - # for compat with datetime/timedelta/period shared methods, - # we can sometimes get here with int64 values. These represent - # nanosecond UTC (or tz-naive) unix timestamps - values = values.view(DT64NS_DTYPE) - + @classmethod + def _validate_dtype(cls, values, dtype): + # used in TimeLikeOps.__init__ _validate_dt64_dtype(values.dtype) dtype = _validate_dt64_dtype(dtype) - - if freq == "infer": - raise ValueError( - f"Frequency inference not allowed in {type(self).__name__}.__init__. " - "Use 'pd.array()' instead." - ) - - if copy: - values = values.copy() - if freq: - freq = to_offset(freq) - - NDArrayBacked.__init__(self, values=values, dtype=dtype) - self._freq = freq - - if inferred_freq is None and freq is not None: - type(self)._validate_frequency(self, freq) + return dtype # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked" @classmethod diff --git a/pandas/core/arrays/timedeltas.py b/pandas/core/arrays/timedeltas.py index 0a43c93cb6c9b..fbd27aa026a37 100644 --- a/pandas/core/arrays/timedeltas.py +++ b/pandas/core/arrays/timedeltas.py @@ -12,7 +12,6 @@ lib, tslibs, ) -from pandas._libs.arrays import NDArrayBacked from pandas._libs.tslibs import ( BaseOffset, NaT, @@ -54,13 +53,9 @@ from pandas.core.dtypes.missing import isna from pandas.core import nanops -from pandas.core.arrays import ( - IntegerArray, - datetimelike as dtl, -) +from pandas.core.arrays import datetimelike as dtl from pandas.core.arrays._ranges import generate_regular_range import pandas.core.common as com -from pandas.core.construction import extract_array from pandas.core.ops.common import unpack_zerodim_and_defer if TYPE_CHECKING: @@ -171,64 +166,14 @@ def dtype(self) -> np.dtype: # type: ignore[override] # Constructors _freq = None + _default_dtype = TD64NS_DTYPE # used in TimeLikeOps.__init__ - def __init__( - self, values, dtype=TD64NS_DTYPE, freq=lib.no_default, copy: bool = False - ) -> None: - values = extract_array(values, extract_numpy=True) - if isinstance(values, IntegerArray): - values = values.to_numpy("int64", na_value=tslibs.iNaT) - - inferred_freq = getattr(values, "_freq", None) - explicit_none = freq is None - freq = freq if freq is not lib.no_default else None - - if isinstance(values, type(self)): - if explicit_none: - # don't inherit from values - pass - elif freq is None: - freq = values.freq - elif freq and values.freq: - freq = to_offset(freq) - freq, _ = dtl.validate_inferred_freq(freq, values.freq, False) - - values = values._ndarray - - if not isinstance(values, np.ndarray): - raise ValueError( - f"Unexpected type '{type(values).__name__}'. 'values' must be a " - f"{type(self).__name__}, ndarray, or Series or Index " - "containing one of those." - ) - if values.ndim not in [1, 2]: - raise ValueError("Only 1-dimensional input arrays are supported.") - - if values.dtype == "i8": - # for compat with datetime/timedelta/period shared methods, - # we can sometimes get here with int64 values. These represent - # nanosecond UTC (or tz-naive) unix timestamps - values = values.view(TD64NS_DTYPE) - + @classmethod + def _validate_dtype(cls, values, dtype): + # used in TimeLikeOps.__init__ _validate_td64_dtype(values.dtype) dtype = _validate_td64_dtype(dtype) - - if freq == "infer": - raise ValueError( - f"Frequency inference not allowed in {type(self).__name__}.__init__. " - "Use 'pd.array()' instead." - ) - - if copy: - values = values.copy() - if freq: - freq = to_offset(freq) - - NDArrayBacked.__init__(self, values=values, dtype=dtype) - self._freq = freq - - if inferred_freq is None and freq is not None: - type(self)._validate_frequency(self, freq) + return dtype # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked" @classmethod
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47583
2022-07-02T15:05:45Z
2022-07-05T17:19:00Z
2022-07-05T17:19:00Z
2022-07-05T18:41:01Z
BUG: DataFrame.loc not aligning rhs df for single block case
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 6fc1ec9c6ff90..4e22ec263289f 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -882,6 +882,7 @@ Indexing - Bug in :meth:`Series.__setitem__` when setting ``boolean`` dtype values containing ``NA`` incorrectly raising instead of casting to ``boolean`` dtype (:issue:`45462`) - Bug in :meth:`Series.__setitem__` where setting :attr:`NA` into a numeric-dtype :class:`Series` would incorrectly upcast to object-dtype rather than treating the value as ``np.nan`` (:issue:`44199`) - Bug in :meth:`DataFrame.loc` when setting values to a column and right hand side is a dictionary (:issue:`47216`) +- Bug in :meth:`DataFrame.loc` when setting a :class:`DataFrame` not aligning index in some cases (:issue:`47578`) - Bug in :meth:`Series.__setitem__` with ``datetime64[ns]`` dtype, an all-``False`` boolean mask, and an incompatible value incorrectly casting to ``object`` instead of retaining ``datetime64[ns]`` dtype (:issue:`45967`) - Bug in :meth:`Index.__getitem__` raising ``ValueError`` when indexer is from boolean dtype with ``NA`` (:issue:`45806`) - Bug in :meth:`Series.__setitem__` losing precision when enlarging :class:`Series` with scalar (:issue:`32346`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 82ae93ad31763..75844ec8f2d3d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4706,10 +4706,11 @@ def _sanitize_column(self, value) -> ArrayLike: """ self._ensure_valid_index(value) - # We should never get here with DataFrame value - if isinstance(value, Series): + # We can get there through isetitem with a DataFrame + # or through loc single_block_path + if isinstance(value, DataFrame): return _reindex_for_setitem(value, self.index) - elif isinstance(value, dict): + elif is_dict_like(value): return _reindex_for_setitem(Series(value), self.index) if is_list_like(value): diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py index 0fbf375e441ac..994181eac8199 100644 --- a/pandas/tests/frame/indexing/test_indexing.py +++ b/pandas/tests/frame/indexing/test_indexing.py @@ -1298,6 +1298,15 @@ def test_loc_expand_empty_frame_keep_midx_names(self): ) tm.assert_frame_equal(df, expected) + @pytest.mark.parametrize("val", ["x", 1]) + @pytest.mark.parametrize("idxr", ["a", ["a"]]) + def test_loc_setitem_rhs_frame(self, idxr, val): + # GH#47578 + df = DataFrame({"a": [1, 2]}) + df.loc[:, "a"] = DataFrame({"a": [val, 11]}, index=[1, 2]) + expected = DataFrame({"a": [np.nan, val]}) + tm.assert_frame_equal(df, expected) + class TestDataFrameIndexingUInt64: def test_setitem(self, uint64_frame): diff --git a/pandas/tests/frame/indexing/test_setitem.py b/pandas/tests/frame/indexing/test_setitem.py index d30121f8f6271..9e9310d735f6a 100644 --- a/pandas/tests/frame/indexing/test_setitem.py +++ b/pandas/tests/frame/indexing/test_setitem.py @@ -721,6 +721,17 @@ def test_setitem_aligning_dict_with_index(self, vals): ) tm.assert_frame_equal(df, expected) + def test_setitem_rhs_dataframe(self): + # GH#47578 + df = DataFrame({"a": [1, 2]}) + df["a"] = DataFrame({"a": [10, 11]}, index=[1, 2]) + expected = DataFrame({"a": [np.nan, 10]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"a": [1, 2]}) + df.isetitem(0, DataFrame({"a": [10, 11]}, index=[1, 2])) + tm.assert_frame_equal(df, expected) + class TestSetitemTZAwareValues: @pytest.fixture
- [x] closes #47578 (Replace xxxx with the Github issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47581
2022-07-02T11:55:29Z
2022-07-03T14:18:45Z
2022-07-03T14:18:45Z
2022-08-08T13:16:49Z
ENH: DTI/DTA.astype support non-nano
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 3b40ad8caf372..0b450fab53137 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -275,7 +275,9 @@ Other enhancements - :class:`.DataError`, :class:`.SpecificationError`, :class:`.SettingWithCopyError`, :class:`.SettingWithCopyWarning`, :class:`.NumExprClobberingError`, :class:`.UndefinedVariableError`, and :class:`.IndexingError` are now exposed in ``pandas.errors`` (:issue:`27656`) - Added ``check_like`` argument to :func:`testing.assert_series_equal` (:issue:`47247`) - Allow reading compressed SAS files with :func:`read_sas` (e.g., ``.sas7bdat.gz`` files) +- :meth:`DatetimeIndex.astype` now supports casting timezone-naive indexes to ``datetime64[s]``, ``datetime64[ms]``, and ``datetime64[us]``, and timezone-aware indexes to the corresponding ``datetime64[unit, tzname]`` dtypes (:issue:`47579`) - :class:`Series` reducers (e.g. ``min``, ``max``, ``sum``, ``mean``) will now successfully operate when the dtype is numeric and ``numeric_only=True`` is provided; previously this would raise a ``NotImplementedError`` (:issue:`47500`) +- .. --------------------------------------------------------------------------- .. _whatsnew_150.notable_bug_fixes: diff --git a/pandas/_libs/tslibs/__init__.py b/pandas/_libs/tslibs/__init__.py index 599ddfec5a268..47143b32d6dbe 100644 --- a/pandas/_libs/tslibs/__init__.py +++ b/pandas/_libs/tslibs/__init__.py @@ -30,12 +30,14 @@ "get_unit_from_dtype", "periods_per_day", "periods_per_second", + "is_supported_unit", ] from pandas._libs.tslibs import dtypes from pandas._libs.tslibs.conversion import localize_pydatetime from pandas._libs.tslibs.dtypes import ( Resolution, + is_supported_unit, periods_per_day, periods_per_second, ) diff --git a/pandas/_libs/tslibs/dtypes.pyi b/pandas/_libs/tslibs/dtypes.pyi index f8c6a36a63c7e..dd439ebfc4798 100644 --- a/pandas/_libs/tslibs/dtypes.pyi +++ b/pandas/_libs/tslibs/dtypes.pyi @@ -7,6 +7,7 @@ _period_code_map: dict[str, int] def periods_per_day(reso: int) -> int: ... def periods_per_second(reso: int) -> int: ... +def is_supported_unit(reso: int) -> bool: ... class PeriodDtypeBase: _dtype_code: int # PeriodDtypeCode diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx index 6cbe31cb1dc16..01616666bba3f 100644 --- a/pandas/_libs/tslibs/dtypes.pyx +++ b/pandas/_libs/tslibs/dtypes.pyx @@ -277,6 +277,15 @@ class NpyDatetimeUnit(Enum): NPY_FR_GENERIC = NPY_DATETIMEUNIT.NPY_FR_GENERIC +def is_supported_unit(NPY_DATETIMEUNIT reso): + return ( + reso == NPY_DATETIMEUNIT.NPY_FR_ns + or reso == NPY_DATETIMEUNIT.NPY_FR_us + or reso == NPY_DATETIMEUNIT.NPY_FR_ms + or reso == NPY_DATETIMEUNIT.NPY_FR_s + ) + + cdef str npy_unit_to_abbrev(NPY_DATETIMEUNIT unit): if unit == NPY_DATETIMEUNIT.NPY_FR_ns or unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC: # generic -> default to nanoseconds diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py index ccb20b1dfaf1a..c9f5946c30c8c 100644 --- a/pandas/core/arrays/datetimes.py +++ b/pandas/core/arrays/datetimes.py @@ -31,6 +31,7 @@ get_unit_from_dtype, ints_to_pydatetime, is_date_array_normalized, + is_supported_unit, is_unitless, normalize_i8_timestamps, timezones, @@ -603,12 +604,26 @@ def astype(self, dtype, copy: bool = True): return self.copy() return self + elif ( + self.tz is None + and is_datetime64_dtype(dtype) + and not is_unitless(dtype) + and is_supported_unit(get_unit_from_dtype(dtype)) + ): + # unit conversion e.g. datetime64[s] + res_values = astype_overflowsafe(self._ndarray, dtype, copy=True) + return type(self)._simple_new(res_values, dtype=res_values.dtype) + # TODO: preserve freq? + elif is_datetime64_ns_dtype(dtype): return astype_dt64_to_dt64tz(self, dtype, copy, via_utc=False) - elif self.tz is None and is_datetime64_dtype(dtype) and dtype != self.dtype: - # unit conversion e.g. datetime64[s] - return self._ndarray.astype(dtype) + elif self.tz is not None and isinstance(dtype, DatetimeTZDtype): + # tzaware unit conversion e.g. datetime64[s, UTC] + np_dtype = np.dtype(dtype.str) + res_values = astype_overflowsafe(self._ndarray, np_dtype, copy=copy) + return type(self)._simple_new(res_values, dtype=dtype) + # TODO: preserve freq? elif is_period_dtype(dtype): return self.to_period(freq=dtype.freq) diff --git a/pandas/core/dtypes/astype.py b/pandas/core/dtypes/astype.py index 8d1427976276c..7fb58468746a8 100644 --- a/pandas/core/dtypes/astype.py +++ b/pandas/core/dtypes/astype.py @@ -15,6 +15,7 @@ import numpy as np from pandas._libs import lib +from pandas._libs.tslibs import is_unitless from pandas._libs.tslibs.timedeltas import array_to_timedelta64 from pandas._typing import ( ArrayLike, @@ -280,6 +281,20 @@ def astype_array_safe( # Ensure we don't end up with a PandasArray dtype = dtype.numpy_dtype + if ( + is_datetime64_dtype(values.dtype) + # need to do np.dtype check instead of is_datetime64_dtype + # otherwise pyright complains + and isinstance(dtype, np.dtype) + and dtype.kind == "M" + and not is_unitless(dtype) + and not is_dtype_equal(dtype, values.dtype) + ): + # unit conversion, we would re-cast to nanosecond, so this is + # effectively just a copy (regardless of copy kwd) + # TODO(2.0): remove special-case + return values.copy() + try: new_values = astype_array(values, dtype, copy=copy) except (ValueError, TypeError): diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py index a192337daf59b..519dfd9269df5 100644 --- a/pandas/core/dtypes/common.py +++ b/pandas/core/dtypes/common.py @@ -966,7 +966,9 @@ def is_datetime64_ns_dtype(arr_or_dtype) -> bool: tipo = get_dtype(arr_or_dtype.dtype) else: return False - return tipo == DT64NS_DTYPE or getattr(tipo, "base", None) == DT64NS_DTYPE + return tipo == DT64NS_DTYPE or ( + isinstance(tipo, DatetimeTZDtype) and tipo._unit == "ns" + ) def is_timedelta64_ns_dtype(arr_or_dtype) -> bool: diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index 0393c9d07cc74..87f2ae41cc98e 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -1064,16 +1064,6 @@ def astype(self, dtype, copy: bool = True): # Ensure that self.astype(self.dtype) is self return self.copy() if copy else self - if ( - self.dtype == np.dtype("M8[ns]") - and isinstance(dtype, np.dtype) - and dtype.kind == "M" - and dtype != np.dtype("M8[ns]") - ): - # For now DatetimeArray supports this by unwrapping ndarray, - # but DatetimeIndex doesn't - raise TypeError(f"Cannot cast {type(self).__name__} to dtype") - values = self._data if isinstance(values, ExtensionArray): with rewrite_exception(type(values).__name__, type(self).__name__): diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py index c1009d63ab8cc..18c0d56abbeb4 100644 --- a/pandas/core/indexes/datetimes.py +++ b/pandas/core/indexes/datetimes.py @@ -48,6 +48,7 @@ from pandas.core.dtypes.common import ( is_datetime64_dtype, is_datetime64tz_dtype, + is_dtype_equal, is_scalar, ) from pandas.core.dtypes.missing import is_valid_na_for_dtype @@ -338,6 +339,18 @@ def __new__( if copy: data = data.copy() return cls._simple_new(data, name=name) + elif ( + isinstance(data, DatetimeArray) + and freq is lib.no_default + and tz is None + and is_dtype_equal(data.dtype, dtype) + ): + # Reached via Index.__new__ when we call .astype + # TODO(2.0): special casing can be removed once _from_sequence_not_strict + # no longer chokes on non-nano + if copy: + data = data.copy() + return cls._simple_new(data, name=name) dtarr = DatetimeArray._from_sequence_not_strict( data, diff --git a/pandas/tests/arrays/test_datetimes.py b/pandas/tests/arrays/test_datetimes.py index 6c6a8b269aee8..63601ff963609 100644 --- a/pandas/tests/arrays/test_datetimes.py +++ b/pandas/tests/arrays/test_datetimes.py @@ -207,6 +207,36 @@ def test_cmp_dt64_arraylike_tznaive(self, comparison_op): class TestDatetimeArray: + def test_astype_non_nano_tznaive(self): + dti = pd.date_range("2016-01-01", periods=3) + + res = dti.astype("M8[s]") + assert res.dtype == "M8[s]" + + dta = dti._data + res = dta.astype("M8[s]") + assert res.dtype == "M8[s]" + assert isinstance(res, pd.core.arrays.DatetimeArray) # used to be ndarray + + def test_astype_non_nano_tzaware(self): + dti = pd.date_range("2016-01-01", periods=3, tz="UTC") + + res = dti.astype("M8[s, US/Pacific]") + assert res.dtype == "M8[s, US/Pacific]" + + dta = dti._data + res = dta.astype("M8[s, US/Pacific]") + assert res.dtype == "M8[s, US/Pacific]" + + # from non-nano to non-nano, preserving reso + res2 = res.astype("M8[s, UTC]") + assert res2.dtype == "M8[s, UTC]" + assert not tm.shares_memory(res2, res) + + res3 = res.astype("M8[s, UTC]", copy=False) + assert res2.dtype == "M8[s, UTC]" + assert tm.shares_memory(res3, res) + def test_astype_to_same(self): arr = DatetimeArray._from_sequence( ["2000"], dtype=DatetimeTZDtype(tz="US/Central") diff --git a/pandas/tests/dtypes/test_common.py b/pandas/tests/dtypes/test_common.py index c5d0567b6dfc0..92b99ba6d1fe2 100644 --- a/pandas/tests/dtypes/test_common.py +++ b/pandas/tests/dtypes/test_common.py @@ -474,6 +474,9 @@ def test_is_datetime64_ns_dtype(): pd.DatetimeIndex([1, 2, 3], dtype=np.dtype("datetime64[ns]")) ) + # non-nano dt64tz + assert not com.is_datetime64_ns_dtype(DatetimeTZDtype("us", "US/Eastern")) + def test_is_timedelta64_ns_dtype(): assert not com.is_timedelta64_ns_dtype(np.dtype("m8[ps]")) diff --git a/pandas/tests/tslibs/test_api.py b/pandas/tests/tslibs/test_api.py index 5891c28c11a68..2d195fad83644 100644 --- a/pandas/tests/tslibs/test_api.py +++ b/pandas/tests/tslibs/test_api.py @@ -55,6 +55,7 @@ def test_namespace(): "get_unit_from_dtype", "periods_per_day", "periods_per_second", + "is_supported_unit", ] expected = set(submodules + api)
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47579
2022-07-02T02:56:02Z
2022-07-05T20:00:51Z
2022-07-05T20:00:51Z
2022-07-05T20:04:52Z
Adding testcase for quartile performed on a timestamp column group by ( closes #33168 )
diff --git a/pandas/tests/groupby/test_quantile.py b/pandas/tests/groupby/test_quantile.py index 20328426a69b2..2b7e71d9619a4 100644 --- a/pandas/tests/groupby/test_quantile.py +++ b/pandas/tests/groupby/test_quantile.py @@ -343,3 +343,38 @@ def test_columns_groupby_quantile(): ) tm.assert_frame_equal(result, expected) + + +def test_timestamp_groupby_quantile(): + # GH 33168 + df = DataFrame( + { + "timestamp": pd.date_range( + start="2020-04-19 00:00:00", freq="1T", periods=100, tz="UTC" + ).floor("1H"), + "category": list(range(1, 101)), + "value": list(range(101, 201)), + } + ) + + result = df.groupby("timestamp").quantile([0.2, 0.8]) + + expected = DataFrame( + [ + {"category": 12.8, "value": 112.8}, + {"category": 48.2, "value": 148.2}, + {"category": 68.8, "value": 168.8}, + {"category": 92.2, "value": 192.2}, + ], + index=pd.MultiIndex.from_tuples( + [ + (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.2), + (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.8), + (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.2), + (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.8), + ], + names=("timestamp", None), + ), + ) + + tm.assert_frame_equal(result, expected)
Have added a testcase for quartile performed on a timestamp column group by. This change closes #33168
https://api.github.com/repos/pandas-dev/pandas/pulls/47575
2022-07-01T19:57:41Z
2022-07-03T14:20:33Z
2022-07-03T14:20:33Z
2022-07-03T14:20:37Z
REF: simplify DTA.__init__
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py index e3a1d1d0705c4..c019edea5eee0 100644 --- a/pandas/core/arrays/datetimes.py +++ b/pandas/core/arrays/datetimes.py @@ -256,7 +256,7 @@ def _scalar_type(self) -> type[Timestamp]: _freq = None def __init__( - self, values, dtype=DT64NS_DTYPE, freq=lib.no_default, copy: bool = False + self, values, dtype=None, freq=lib.no_default, copy: bool = False ) -> None: values = extract_array(values, extract_numpy=True) if isinstance(values, IntegerArray): @@ -276,22 +276,19 @@ def __init__( freq = to_offset(freq) freq, _ = dtl.validate_inferred_freq(freq, values.freq, False) - # validation - dtz = getattr(dtype, "tz", None) - if dtz and values.tz is None: - dtype = DatetimeTZDtype(tz=dtype.tz) - elif dtz and values.tz: - if not timezones.tz_compare(dtz, values.tz): - msg = ( - "Timezone of the array and 'dtype' do not match. " - f"'{dtz}' != '{values.tz}'" + if dtype is not None: + dtype = pandas_dtype(dtype) + if not is_dtype_equal(dtype, values.dtype): + raise TypeError( + f"dtype={dtype} does not match data dtype {values.dtype}" ) - raise TypeError(msg) - elif values.tz: - dtype = values.dtype + dtype = values.dtype values = values._ndarray + elif dtype is None: + dtype = DT64NS_DTYPE + if not isinstance(values, np.ndarray): raise ValueError( f"Unexpected type '{type(values).__name__}'. 'values' must be a " diff --git a/pandas/tests/arrays/datetimes/test_constructors.py b/pandas/tests/arrays/datetimes/test_constructors.py index 684b478d1de08..cb2d8f31f0f9c 100644 --- a/pandas/tests/arrays/datetimes/test_constructors.py +++ b/pandas/tests/arrays/datetimes/test_constructors.py @@ -77,9 +77,16 @@ def test_mismatched_timezone_raises(self): dtype=DatetimeTZDtype(tz="US/Central"), ) dtype = DatetimeTZDtype(tz="US/Eastern") - with pytest.raises(TypeError, match="Timezone of the array"): + msg = r"dtype=datetime64\[ns.*\] does not match data dtype datetime64\[ns.*\]" + with pytest.raises(TypeError, match=msg): DatetimeArray(arr, dtype=dtype) + # also with mismatched tzawareness + with pytest.raises(TypeError, match=msg): + DatetimeArray(arr, dtype=np.dtype("M8[ns]")) + with pytest.raises(TypeError, match=msg): + DatetimeArray(arr.tz_localize(None), dtype=arr.dtype) + def test_non_array_raises(self): with pytest.raises(ValueError, match="list"): DatetimeArray([1, 2, 3])
After this we can share DTA/TDA.`__init__`
https://api.github.com/repos/pandas-dev/pandas/pulls/47574
2022-07-01T19:10:36Z
2022-07-01T22:16:23Z
2022-07-01T22:16:23Z
2022-07-01T22:16:36Z
CLN: Move monotonic check to safe_sort_index
diff --git a/pandas/core/indexes/api.py b/pandas/core/indexes/api.py index 71ecb358cf575..a9e24bd64922b 100644 --- a/pandas/core/indexes/api.py +++ b/pandas/core/indexes/api.py @@ -157,7 +157,7 @@ def _get_combined_index( index = union_indexes(indexes, sort=False) index = ensure_index(index) - if sort and not index.is_monotonic_increasing: + if sort: index = safe_sort_index(index) # GH 29879 if copy: @@ -180,6 +180,9 @@ def safe_sort_index(index: Index) -> Index: ------- Index """ + if index.is_monotonic_increasing: + return index + try: array_sorted = safe_sort(index) except TypeError:
- [x] xref #47508 (Replace xxxx with the Github issue number) as discussed there
https://api.github.com/repos/pandas-dev/pandas/pulls/47573
2022-07-01T18:09:03Z
2022-07-01T22:16:44Z
2022-07-01T22:16:44Z
2022-07-01T22:17:13Z
Use license_files instead of license_file
diff --git a/setup.cfg b/setup.cfg index 05b7975cbc0a8..ded05cbb4e413 100644 --- a/setup.cfg +++ b/setup.cfg @@ -7,7 +7,7 @@ url = https://pandas.pydata.org author = The Pandas Development Team author_email = pandas-dev@python.org license = BSD-3-Clause -license_file = LICENSE +license_files = LICENSE platforms = any classifiers = Development Status :: 5 - Production/Stable
The license_file parameter is deprecated, use license_files instead We get this deprecation warning when rebuilding the extensions
https://api.github.com/repos/pandas-dev/pandas/pulls/47572
2022-07-01T18:01:54Z
2022-07-01T22:12:23Z
2022-07-01T22:12:23Z
2022-07-01T22:17:22Z
BUG: Fixed `tm.set_locale` context manager, it could error and leak when category LC_ALL was used
diff --git a/pandas/_config/localization.py b/pandas/_config/localization.py index fa5503029fd4b..c4355e954c67c 100644 --- a/pandas/_config/localization.py +++ b/pandas/_config/localization.py @@ -39,7 +39,8 @@ def set_locale( particular locale, without globally setting the locale. This probably isn't thread-safe. """ - current_locale = locale.getlocale() + # getlocale is not always compliant with setlocale, use setlocale. GH#46595 + current_locale = locale.setlocale(lc_var) try: locale.setlocale(lc_var, new_locale) diff --git a/pandas/tests/config/test_localization.py b/pandas/tests/config/test_localization.py index 21b1b7ed6ee65..f972a9ee3b497 100644 --- a/pandas/tests/config/test_localization.py +++ b/pandas/tests/config/test_localization.py @@ -10,31 +10,67 @@ set_locale, ) -from pandas.compat import is_platform_windows - import pandas as pd _all_locales = get_locales() or [] -_current_locale = locale.getlocale() +_current_locale = locale.setlocale(locale.LC_ALL) # getlocale() is wrong, see GH#46595 -# Don't run any of these tests if we are on Windows or have no locales. -pytestmark = pytest.mark.skipif( - is_platform_windows() or not _all_locales, reason="Need non-Windows and locales" -) +# Don't run any of these tests if we have no locales. +pytestmark = pytest.mark.skipif(not _all_locales, reason="Need locales") _skip_if_only_one_locale = pytest.mark.skipif( len(_all_locales) <= 1, reason="Need multiple locales for meaningful test" ) -def test_can_set_locale_valid_set(): +def _get_current_locale(lc_var: int = locale.LC_ALL) -> str: + # getlocale is not always compliant with setlocale, use setlocale. GH#46595 + return locale.setlocale(lc_var) + + +@pytest.mark.parametrize("lc_var", (locale.LC_ALL, locale.LC_CTYPE, locale.LC_TIME)) +def test_can_set_current_locale(lc_var): + # Can set the current locale + before_locale = _get_current_locale(lc_var) + assert can_set_locale(before_locale, lc_var=lc_var) + after_locale = _get_current_locale(lc_var) + assert before_locale == after_locale + + +@pytest.mark.parametrize("lc_var", (locale.LC_ALL, locale.LC_CTYPE, locale.LC_TIME)) +def test_can_set_locale_valid_set(lc_var): # Can set the default locale. - assert can_set_locale("") + before_locale = _get_current_locale(lc_var) + assert can_set_locale("", lc_var=lc_var) + after_locale = _get_current_locale(lc_var) + assert before_locale == after_locale -def test_can_set_locale_invalid_set(): +@pytest.mark.parametrize("lc_var", (locale.LC_ALL, locale.LC_CTYPE, locale.LC_TIME)) +def test_can_set_locale_invalid_set(lc_var): # Cannot set an invalid locale. - assert not can_set_locale("non-existent_locale") + before_locale = _get_current_locale(lc_var) + assert not can_set_locale("non-existent_locale", lc_var=lc_var) + after_locale = _get_current_locale(lc_var) + assert before_locale == after_locale + + +@pytest.mark.parametrize( + "lang,enc", + [ + ("it_CH", "UTF-8"), + ("en_US", "ascii"), + ("zh_CN", "GB2312"), + ("it_IT", "ISO-8859-1"), + ], +) +@pytest.mark.parametrize("lc_var", (locale.LC_ALL, locale.LC_CTYPE, locale.LC_TIME)) +def test_can_set_locale_no_leak(lang, enc, lc_var): + # Test that can_set_locale does not leak even when returning False. See GH#46595 + before_locale = _get_current_locale(lc_var) + can_set_locale((lang, enc), locale.LC_ALL) + after_locale = _get_current_locale(lc_var) + assert before_locale == after_locale def test_can_set_locale_invalid_get(monkeypatch): @@ -72,10 +108,7 @@ def test_get_locales_prefix(): ], ) def test_set_locale(lang, enc): - if all(x is None for x in _current_locale): - # Not sure why, but on some Travis runs with pytest, - # getlocale() returned (None, None). - pytest.skip("Current locale is not set.") + before_locale = _get_current_locale() enc = codecs.lookup(enc).name new_locale = lang, enc @@ -95,8 +128,8 @@ def test_set_locale(lang, enc): assert normalized_locale == new_locale # Once we exit the "with" statement, locale should be back to what it was. - current_locale = locale.getlocale() - assert current_locale == _current_locale + after_locale = _get_current_locale() + assert before_locale == after_locale def test_encoding_detected(): diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py index f712b4a24e5e5..afa06bf1a79af 100644 --- a/pandas/tests/tools/test_to_datetime.py +++ b/pandas/tests/tools/test_to_datetime.py @@ -284,6 +284,8 @@ def test_to_datetime_format_microsecond(self, cache): "%m/%d/%Y %H:%M:%S", Timestamp("2010-01-10 13:56:01"), ], + # The 3 tests below are locale-dependent. + # They pass, except when the machine locale is zh_CN or it_IT . pytest.param( "01/10/2010 08:14 PM", "%m/%d/%Y %I:%M %p", @@ -291,6 +293,7 @@ def test_to_datetime_format_microsecond(self, cache): marks=pytest.mark.xfail( locale.getlocale()[0] in ("zh_CN", "it_IT"), reason="fail on a CI build with LC_ALL=zh_CN.utf8/it_IT.utf8", + strict=False, ), ), pytest.param( @@ -300,6 +303,7 @@ def test_to_datetime_format_microsecond(self, cache): marks=pytest.mark.xfail( locale.getlocale()[0] in ("zh_CN", "it_IT"), reason="fail on a CI build with LC_ALL=zh_CN.utf8/it_IT.utf8", + strict=False, ), ), pytest.param( @@ -309,6 +313,7 @@ def test_to_datetime_format_microsecond(self, cache): marks=pytest.mark.xfail( locale.getlocale()[0] in ("zh_CN", "it_IT"), reason="fail on a CI build with LC_ALL=zh_CN.utf8/it_IT.utf8", + strict=False, ), ), ], diff --git a/pandas/tests/tools/test_to_time.py b/pandas/tests/tools/test_to_time.py index 7983944d4384d..a8316e0f3970c 100644 --- a/pandas/tests/tools/test_to_time.py +++ b/pandas/tests/tools/test_to_time.py @@ -9,9 +9,12 @@ from pandas.core.tools.datetimes import to_time as to_time_alias from pandas.core.tools.times import to_time +# The tests marked with this are locale-dependent. +# They pass, except when the machine locale is zh_CN or it_IT. fails_on_non_english = pytest.mark.xfail( locale.getlocale()[0] in ("zh_CN", "it_IT"), reason="fail on a CI build with LC_ALL=zh_CN.utf8/it_IT.utf8", + strict=False, )
- [x] closes #46595 - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47570
2022-07-01T14:47:13Z
2022-07-08T22:56:58Z
2022-07-08T22:56:58Z
2022-07-11T07:16:06Z
[DOC] improving the ExcelWriter docstring
diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py index 58bb4b1a8f849..0c4268626099a 100644 --- a/pandas/io/excel/_base.py +++ b/pandas/io/excel/_base.py @@ -871,11 +871,14 @@ class ExcelWriter(metaclass=abc.ABCMeta): """ Class for writing DataFrame objects into excel sheets. - Default is to use : - * xlwt for xls - * xlsxwriter for xlsx if xlsxwriter is installed otherwise openpyxl - * odf for ods. - See DataFrame.to_excel for typical usage. + Default is to use: + + * `xlwt <https://pypi.org/project/xlwt/>`__ for xls files + * `xlsxwriter <https://pypi.org/project/XlsxWriter/>`__ for xlsx files if xlsxwriter + is installed otherwise `openpyxl <https://pypi.org/project/openpyxl/>`__ + * `odswriter <https://pypi.org/project/odswriter/>`__ for ods files + + See ``DataFrame.to_excel`` for typical usage. The writer should be used as a context manager. Otherwise, call `close()` to save and close any opened file handles.
- [x] closes #47526 - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
https://api.github.com/repos/pandas-dev/pandas/pulls/47568
2022-07-01T12:26:35Z
2022-07-01T17:52:44Z
2022-07-01T17:52:43Z
2022-10-20T08:44:36Z
"Backport PR #47508 on branch 1.4.x REGR: concat materializing index even if sort is not necessary )"
diff --git a/doc/source/whatsnew/v1.4.4.rst b/doc/source/whatsnew/v1.4.4.rst index b462f0c6a8ffe..0f3d7a55fd6d7 100644 --- a/doc/source/whatsnew/v1.4.4.rst +++ b/doc/source/whatsnew/v1.4.4.rst @@ -14,7 +14,7 @@ including other versions of pandas. Fixed regressions ~~~~~~~~~~~~~~~~~ -- +- Fixed regression in :func:`concat` materializing :class:`Index` during sorting even if :class:`Index` was already sorted (:issue:`47501`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/indexes/api.py b/pandas/core/indexes/api.py index 1e740132e3464..733dd2088ecac 100644 --- a/pandas/core/indexes/api.py +++ b/pandas/core/indexes/api.py @@ -156,7 +156,7 @@ def _get_combined_index( index = union_indexes(indexes, sort=False) index = ensure_index(index) - if sort: + if sort and not index.is_monotonic_increasing: try: array_sorted = safe_sort(index) array_sorted = cast(np.ndarray, array_sorted) diff --git a/pandas/tests/reshape/concat/test_index.py b/pandas/tests/reshape/concat/test_index.py index 1692446627914..7c74a918ba18b 100644 --- a/pandas/tests/reshape/concat/test_index.py +++ b/pandas/tests/reshape/concat/test_index.py @@ -306,3 +306,14 @@ def test_concat_with_various_multiindex_dtypes( result_df = concat((df1, df2), axis=1) tm.assert_frame_equal(expected_df, result_df) + + def test_concat_range_index_result(self): + # GH#47501 + df1 = DataFrame({"a": [1, 2]}) + df2 = DataFrame({"b": [1, 2]}) + + result = concat([df1, df2], sort=True, axis=1) + expected = DataFrame({"a": [1, 2], "b": [1, 2]}) + tm.assert_frame_equal(result, expected) + expected_index = pd.RangeIndex(0, 2) + tm.assert_index_equal(result.index, expected_index, exact=True)
Backport PR #47508
https://api.github.com/repos/pandas-dev/pandas/pulls/47565
2022-07-01T07:21:56Z
2022-07-02T16:41:44Z
2022-07-02T16:41:44Z
2022-07-02T19:18:41Z
⬆️ UPGRADE: Autoupdate pre-commit config
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 13e0ecd33359f..3444bc8de6296 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,7 +18,7 @@ repos: pass_filenames: true require_serial: false - repo: https://github.com/python/black - rev: 22.3.0 + rev: 22.6.0 hooks: - id: black - repo: https://github.com/codespell-project/codespell @@ -27,7 +27,7 @@ repos: - id: codespell types_or: [python, rst, markdown] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.2.0 + rev: v4.3.0 hooks: - id: debug-statements - id: end-of-file-fixer @@ -59,7 +59,7 @@ repos: hooks: - id: isort - repo: https://github.com/asottile/pyupgrade - rev: v2.32.1 + rev: v2.34.0 hooks: - id: pyupgrade args: [--py38-plus] @@ -74,7 +74,7 @@ repos: types: [text] # overwrite types: [rst] types_or: [python, rst] - repo: https://github.com/sphinx-contrib/sphinx-lint - rev: v0.6 + rev: v0.6.1 hooks: - id: sphinx-lint - repo: https://github.com/asottile/yesqa
<!-- START pr-commits --> <!-- END pr-commits --> ## Base PullRequest default branch (https://github.com/pandas-dev/pandas/tree/main) ## Command results <details> <summary>Details: </summary> <details> <summary><em>add path</em></summary> ```Shell /home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/build/src/bin ``` </details> <details> <summary><em>pip install pre-commit</em></summary> ```Shell Collecting pre-commit Downloading pre_commit-2.19.0-py2.py3-none-any.whl (199 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 199.3/199.3 kB 9.2 MB/s eta 0:00:00 Collecting virtualenv>=20.0.8 Downloading virtualenv-20.15.1-py2.py3-none-any.whl (10.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 10.1/10.1 MB 107.9 MB/s eta 0:00:00 Collecting nodeenv>=0.11.1 Downloading nodeenv-1.7.0-py2.py3-none-any.whl (21 kB) Collecting cfgv>=2.0.0 Downloading cfgv-3.3.1-py2.py3-none-any.whl (7.3 kB) Collecting toml Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB) Collecting pyyaml>=5.1 Downloading PyYAML-6.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (682 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 682.2/682.2 kB 99.2 MB/s eta 0:00:00 Collecting identify>=1.0.0 Downloading identify-2.5.1-py2.py3-none-any.whl (98 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 98.6/98.6 kB 35.2 MB/s eta 0:00:00 Requirement already satisfied: setuptools in /opt/hostedtoolcache/Python/3.10.5/x64/lib/python3.10/site-packages (from nodeenv>=0.11.1->pre-commit) (58.1.0) Collecting six<2,>=1.9.0 Downloading six-1.16.0-py2.py3-none-any.whl (11 kB) Collecting distlib<1,>=0.3.1 Downloading distlib-0.3.4-py2.py3-none-any.whl (461 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 461.2/461.2 kB 89.3 MB/s eta 0:00:00 Collecting filelock<4,>=3.2 Downloading filelock-3.7.1-py3-none-any.whl (10 kB) Collecting platformdirs<3,>=2 Downloading platformdirs-2.5.2-py3-none-any.whl (14 kB) Installing collected packages: distlib, toml, six, pyyaml, platformdirs, nodeenv, identify, filelock, cfgv, virtualenv, pre-commit Successfully installed cfgv-3.3.1 distlib-0.3.4 filelock-3.7.1 identify-2.5.1 nodeenv-1.7.0 platformdirs-2.5.2 pre-commit-2.19.0 pyyaml-6.0 six-1.16.0 toml-0.10.2 virtualenv-20.15.1 ``` </details> <details> <summary><em>pre-commit autoupdate || (exit 0);</em></summary> ```Shell Updating https://github.com/MarcoGorelli/absolufy-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/absolufy-imports. already up to date. Updating https://github.com/jendrikseipp/vulture ... [INFO] Initializing environment for https://github.com/jendrikseipp/vulture. already up to date. Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black. updating 22.3.0 -> 22.6.0. Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell. already up to date. Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks. updating v4.2.0 -> v4.3.0. Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint. already up to date. Updating https://github.com/PyCQA/flake8 ... 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[INFO] Initializing environment for https://github.com/asottile/yesqa. already up to date. ``` </details> <details> <summary><em>pre-commit run -a || (exit 0);</em></summary> ```Shell [INFO] Initializing environment for https://github.com/PyCQA/flake8:flake8-bugbear==21.3.2,flake8-comprehensions==3.7.0,flake8==4.0.1,pandas-dev-flaker==0.5.0. [INFO] Initializing environment for https://github.com/asottile/yesqa:flake8-bugbear==21.3.2,flake8-comprehensions==3.7.0,flake8==4.0.1,pandas-dev-flaker==0.5.0. [INFO] Initializing environment for local:pyright@1.1.253. [INFO] Initializing environment for local:flake8-rst==0.7.0,flake8==3.7.9. [INFO] Initializing environment for local:pyyaml,toml. [INFO] Initializing environment for local:pyyaml. [INFO] Initializing environment for local. [INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/jendrikseipp/vulture. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/python/black. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/codespell-project/codespell. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/cpplint/cpplint. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/PyCQA/flake8. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/PyCQA/isort. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/asottile/pyupgrade. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/sphinx-contrib/sphinx-lint. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/asottile/yesqa. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for local. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for local. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for local. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for local. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... absolufy-imports........................................................................................Passed vulture.................................................................................................Passed black...................................................................................................Passed codespell...............................................................................................Passed debug statements (python)...............................................................................Passed fix end of files........................................................................................Passed trim trailing whitespace................................................................................Passed cpplint.................................................................................................Passed flake8..................................................................................................Passed isort...................................................................................................Passed pyupgrade...............................................................................................Passed rst ``code`` is two backticks...........................................................................Passed rst directives end with two colons......................................................................Passed rst ``inline code`` next to normal text.................................................................Passed Sphinx lint.............................................................................................Passed Strip unnecessary `# noqa`s.............................................................................Passed flake8-rst..............................................................................................Failed - hook id: flake8-rst - exit code: 1 Traceback (most recent call last): File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 486, in run_ast_checks ast = self.processor.build_ast() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/processor.py", line 212, in build_ast return compile("".join(self.lines), "", "exec", PyCF_ONLY_AST) File "", line 27 df2.<TAB> # noqa: E225, E999 ^ SyntaxError: invalid syntax During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/bin/flake8-rst", line 8, in <module> sys.exit(main()) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8_rst/cli.py", line 16, in main app.run(argv) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/main/application.py", line 393, in run self._run(argv) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/main/application.py", line 381, in _run self.run_checks() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/main/application.py", line 300, in run_checks self.file_checker_manager.run() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 331, in run self.run_serial() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 315, in run_serial checker.run_checks() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 598, in run_checks self.run_ast_checks() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 488, in run_ast_checks row, column = self._extract_syntax_information(e) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 473, in _extract_syntax_information lines = physical_line.rstrip("\n").split("\n") AttributeError: 'int' object has no attribute 'rstrip' Traceback (most recent call last): File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 486, in run_ast_checks ast = self.processor.build_ast() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/processor.py", line 212, in build_ast return compile("".join(self.lines), "", "exec", PyCF_ONLY_AST) File "", line 203 df.foo<TAB> # noqa: E225, E999 ^^^^^^^^^^^^^^^^^^ SyntaxError: invalid syntax During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/bin/flake8-rst", line 8, in <module> sys.exit(main()) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8_rst/cli.py", line 16, in main app.run(argv) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/main/application.py", line 393, in run self._run(argv) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/main/application.py", line 381, in _run self.run_checks() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/main/application.py", line 300, in run_checks self.file_checker_manager.run() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 331, in run self.run_serial() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 315, in run_serial checker.run_checks() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 598, in run_checks self.run_ast_checks() File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 488, in run_ast_checks row, column = self._extract_syntax_information(e) File "/home/runner/.cache/pre-commit/repoqgu3q7dm/py_env-python3.10/lib/python3.10/site-packages/flake8/checker.py", line 473, in _extract_syntax_information lines = physical_line.rstrip("\n").split("\n") AttributeError: 'int' object has no attribute 'rstrip' Unwanted patterns.......................................................................................Passed Check Cython casting is `<type>obj`, not `<type> obj`...................................................Passed Check for backticks incorrectly rendering because of missing spaces.....................................Passed Check for unnecessary random seeds in asv benchmarks....................................................Passed Check for usage of numpy testing or array_equal.........................................................Passed Check for invalid EA testing............................................................................Passed Generate pip dependency from conda......................................................................Passed Check flake8 version is synced across flake8, yesqa, and environment.yml................................Passed Validate correct capitalization among titles in documentation...........................................Passed Import pandas.array as pd_array in core.................................................................Passed Use pandas.io.common.urlopen instead of urllib.request.urlopen..........................................Passed Use bool_t instead of bool in pandas/core/generic.py....................................................Passed Ensure pandas errors are documented in doc/source/reference/testing.rst.................................Passed Check for pg8000 not installed on CI for test_pg8000_sqlalchemy_passthrough_error.......................Passed Check minimum version of dependencies are aligned.......................................................Passed ``` </details> </details> ## Changed files <details> <summary>Changed file: </summary> - .pre-commit-config.yaml </details> <hr> [:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action)
https://api.github.com/repos/pandas-dev/pandas/pulls/47564
2022-07-01T07:08:08Z
2022-07-06T16:59:53Z
2022-07-06T16:59:53Z
2022-07-06T16:59:56Z
TYP: fix a few errors found by pandas-stub
diff --git a/pandas/_testing/asserters.py b/pandas/_testing/asserters.py index 90ee600c1967d..7c89946a0c12f 100644 --- a/pandas/_testing/asserters.py +++ b/pandas/_testing/asserters.py @@ -864,7 +864,7 @@ def assert_series_equal( check_dtype: bool | Literal["equiv"] = True, check_index_type="equiv", check_series_type=True, - check_less_precise=no_default, + check_less_precise: bool | int | NoDefault = no_default, check_names=True, check_exact=False, check_datetimelike_compat=False, diff --git a/pandas/core/generic.py b/pandas/core/generic.py index f896169d0ae44..ef7e8421a0cdd 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1050,7 +1050,9 @@ def _rename( return result.__finalize__(self, method="rename") @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)]) - def rename_axis(self, mapper=lib.no_default, **kwargs): + def rename_axis( + self, mapper: IndexLabel | lib.NoDefault = lib.no_default, **kwargs + ): """ Set the name of the axis for the index or columns. diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py index 4da39318579eb..583612b4659b6 100644 --- a/pandas/core/indexes/multi.py +++ b/pandas/core/indexes/multi.py @@ -578,7 +578,10 @@ def from_tuples( @classmethod def from_product( - cls, iterables, sortorder=None, names=lib.no_default + cls, + iterables: Sequence[Iterable[Hashable]], + sortorder: int | None = None, + names: Sequence[Hashable] | lib.NoDefault = lib.no_default, ) -> MultiIndex: """ Make a MultiIndex from the cartesian product of multiple iterables. diff --git a/pandas/plotting/_matplotlib/groupby.py b/pandas/plotting/_matplotlib/groupby.py index 1b16eefb360ae..4f1cd3f38343a 100644 --- a/pandas/plotting/_matplotlib/groupby.py +++ b/pandas/plotting/_matplotlib/groupby.py @@ -112,7 +112,9 @@ def reconstruct_data_with_by( data_list = [] for key, group in grouped: - columns = MultiIndex.from_product([[key], cols]) + # error: List item 1 has incompatible type "Union[Hashable, + # Sequence[Hashable]]"; expected "Iterable[Hashable]" + columns = MultiIndex.from_product([[key], cols]) # type: ignore[list-item] sub_group = group[cols] sub_group.columns = columns data_list.append(sub_group)
There are (unsurprisingly) many errors when trying to use pyright/mypy to validate pandas-stub's tests against the pandas annotations. This PR fixes three of them.
https://api.github.com/repos/pandas-dev/pandas/pulls/47562
2022-07-01T03:30:09Z
2022-07-03T14:21:18Z
2022-07-03T14:21:18Z
2022-09-21T15:28:31Z
DEPR: numeic_only in Series and SeriesGroupBy consistency
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index d237ee9921356..28abfebce1ffc 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -275,6 +275,7 @@ Other enhancements - :class:`.DataError`, :class:`.SpecificationError`, :class:`.SettingWithCopyError`, :class:`.SettingWithCopyWarning`, :class:`.NumExprClobberingError`, :class:`.UndefinedVariableError`, and :class:`.IndexingError` are now exposed in ``pandas.errors`` (:issue:`27656`) - Added ``check_like`` argument to :func:`testing.assert_series_equal` (:issue:`47247`) - Allow reading compressed SAS files with :func:`read_sas` (e.g., ``.sas7bdat.gz`` files) +- :class:`Series` reducers (e.g. ``min``, ``max``, ``sum``, ``mean``) will now successfully operate when the dtype is numeric and ``numeric_only=True`` is provided; previously this would raise a ``NotImplementedError`` (:issue:`47500`) .. --------------------------------------------------------------------------- .. _whatsnew_150.notable_bug_fixes: @@ -766,7 +767,8 @@ Other Deprecations - Deprecated the argument ``na_sentinel`` in :func:`factorize`, :meth:`Index.factorize`, and :meth:`.ExtensionArray.factorize`; pass ``use_na_sentinel=True`` instead to use the sentinel ``-1`` for NaN values and ``use_na_sentinel=False`` instead of ``na_sentinel=None`` to encode NaN values (:issue:`46910`) - Deprecated :meth:`DataFrameGroupBy.transform` not aligning the result when the UDF returned DataFrame (:issue:`45648`) - Clarified warning from :func:`to_datetime` when delimited dates can't be parsed in accordance to specified ``dayfirst`` argument (:issue:`46210`) - +- Deprecated :class:`Series` and :class:`Resampler` reducers (e.g. ``min``, ``max``, ``sum``, ``mean``) raising a ``NotImplementedError`` when the dtype is non-numric and ``numeric_only=True`` is provided; this will raise a ``TypeError`` in a future version (:issue:`47500`) +- Deprecated :meth:`Series.rank` returning an empty result when the dtype is non-numeric and ``numeric_only=True`` is provided; this will raise a ``TypeError`` in a future version (:issue:`47500`) .. --------------------------------------------------------------------------- .. _whatsnew_150.performance: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index f896169d0ae44..1264d139df299 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -8689,6 +8689,15 @@ def ranker(data): ) if numeric_only: + if self.ndim == 1 and not is_numeric_dtype(self.dtype): + # GH#47500 + warnings.warn( + f"Calling Series.rank with numeric_only={numeric_only} and dtype " + f"{self.dtype} is deprecated and will raise a TypeError in a " + "future version of pandas", + category=FutureWarning, + stacklevel=find_stack_level(), + ) data = self._get_numeric_data() else: data = self diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py index a4462a292f4f9..63c861e084eda 100644 --- a/pandas/core/groupby/generic.py +++ b/pandas/core/groupby/generic.py @@ -1145,7 +1145,7 @@ def _cython_transform( ) -> DataFrame: assert axis == 0 # handled by caller # TODO: no tests with self.ndim == 1 for DataFrameGroupBy - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis) + numeric_only_bool = self._resolve_numeric_only(how, numeric_only, axis) # With self.axis == 0, we have multi-block tests # e.g. test_rank_min_int, test_cython_transform_frame diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py index c2098fbe93a56..7e8a732a2e30d 100644 --- a/pandas/core/groupby/groupby.py +++ b/pandas/core/groupby/groupby.py @@ -1291,7 +1291,7 @@ def _wrap_applied_output( raise AbstractMethodError(self) def _resolve_numeric_only( - self, numeric_only: bool | lib.NoDefault, axis: int + self, how: str, numeric_only: bool | lib.NoDefault, axis: int ) -> bool: """ Determine subclass-specific default value for 'numeric_only'. @@ -1328,6 +1328,20 @@ def _resolve_numeric_only( else: numeric_only = False + if numeric_only and self.obj.ndim == 1 and not is_numeric_dtype(self.obj.dtype): + # GH#47500 + how = "sum" if how == "add" else how + warnings.warn( + f"{type(self).__name__}.{how} called with " + f"numeric_only={numeric_only} and dtype {self.obj.dtype}. This will " + "raise a TypeError in a future version of pandas", + category=FutureWarning, + stacklevel=find_stack_level(), + ) + raise NotImplementedError( + f"{type(self).__name__}.{how} does not implement numeric_only" + ) + return numeric_only def _maybe_warn_numeric_only_depr( @@ -1704,7 +1718,7 @@ def _cython_agg_general( ): # Note: we never get here with how="ohlc" for DataFrameGroupBy; # that goes through SeriesGroupBy - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) + numeric_only_bool = self._resolve_numeric_only(how, numeric_only, axis=0) data = self._get_data_to_aggregate() is_ser = data.ndim == 1 @@ -2100,7 +2114,7 @@ def mean( 2 4.0 Name: B, dtype: float64 """ - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) + numeric_only_bool = self._resolve_numeric_only("mean", numeric_only, axis=0) if maybe_use_numba(engine): from pandas.core._numba.kernels import sliding_mean @@ -2134,7 +2148,7 @@ def median(self, numeric_only: bool | lib.NoDefault = lib.no_default): Series or DataFrame Median of values within each group. """ - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) + numeric_only_bool = self._resolve_numeric_only("median", numeric_only, axis=0) result = self._cython_agg_general( "median", @@ -2196,10 +2210,15 @@ def std( return np.sqrt(self._numba_agg_general(sliding_var, engine_kwargs, ddof)) else: # Resolve numeric_only so that var doesn't warn - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) - if numeric_only_bool and self.obj.ndim == 1: - raise NotImplementedError( - f"{type(self).__name__}.std does not implement numeric_only." + numeric_only_bool = self._resolve_numeric_only("std", numeric_only, axis=0) + if ( + numeric_only_bool + and self.obj.ndim == 1 + and not is_numeric_dtype(self.obj.dtype) + ): + raise TypeError( + f"{type(self).__name__}.std called with " + f"numeric_only={numeric_only} and dtype {self.obj.dtype}" ) result = self._get_cythonized_result( libgroupby.group_var, @@ -2264,7 +2283,7 @@ def var( return self._numba_agg_general(sliding_var, engine_kwargs, ddof) else: - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) + numeric_only_bool = self._resolve_numeric_only("var", numeric_only, axis=0) if ddof == 1: return self._cython_agg_general( "var", @@ -2304,10 +2323,15 @@ def sem(self, ddof: int = 1, numeric_only: bool | lib.NoDefault = lib.no_default Standard error of the mean of values within each group. """ # Reolve numeric_only so that std doesn't warn - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) - if numeric_only_bool and self.obj.ndim == 1: - raise NotImplementedError( - f"{type(self).__name__}.sem does not implement numeric_only." + numeric_only_bool = self._resolve_numeric_only("sem", numeric_only, axis=0) + if ( + numeric_only_bool + and self.obj.ndim == 1 + and not is_numeric_dtype(self.obj.dtype) + ): + raise TypeError( + f"{type(self).__name__}.sem called with " + f"numeric_only={numeric_only} and dtype {self.obj.dtype}" ) result = self.std(ddof=ddof, numeric_only=numeric_only_bool) self._maybe_warn_numeric_only_depr("sem", result, numeric_only) @@ -3179,10 +3203,15 @@ def quantile( a 2.0 b 3.0 """ - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) - if numeric_only_bool and self.obj.ndim == 1: - raise NotImplementedError( - f"{type(self).__name__}.quantile does not implement numeric_only" + numeric_only_bool = self._resolve_numeric_only("quantile", numeric_only, axis=0) + if ( + numeric_only_bool + and self.obj.ndim == 1 + and not is_numeric_dtype(self.obj.dtype) + ): + raise TypeError( + f"{type(self).__name__}.quantile called with " + f"numeric_only={numeric_only} and dtype {self.obj.dtype}" ) def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, np.dtype | None]: @@ -3671,7 +3700,8 @@ def _get_cythonized_result( ------- `Series` or `DataFrame` with filled values """ - numeric_only_bool = self._resolve_numeric_only(numeric_only, axis=0) + how = base_func.__name__ + numeric_only_bool = self._resolve_numeric_only(how, numeric_only, axis=0) if post_processing and not callable(post_processing): raise ValueError("'post_processing' must be a callable!") @@ -3682,7 +3712,6 @@ def _get_cythonized_result( ids, _, ngroups = grouper.group_info - how = base_func.__name__ base_func = partial(base_func, labels=ids) def blk_func(values: ArrayLike) -> ArrayLike: diff --git a/pandas/core/series.py b/pandas/core/series.py index cdebb67e1b6dc..dd52ec855240e 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -80,6 +80,7 @@ is_integer, is_iterator, is_list_like, + is_numeric_dtype, is_object_dtype, is_scalar, pandas_dtype, @@ -4616,10 +4617,17 @@ def _reduce( else: # dispatch to numpy arrays - if numeric_only: + if numeric_only and not is_numeric_dtype(self.dtype): kwd_name = "numeric_only" if name in ["any", "all"]: kwd_name = "bool_only" + # GH#47500 - change to TypeError to match other methods + warnings.warn( + f"Calling Series.{name} with {kwd_name}={numeric_only} and " + f"dtype {self.dtype} will raise a TypeError in the future", + FutureWarning, + stacklevel=find_stack_level(), + ) raise NotImplementedError( f"Series.{name} does not implement {kwd_name}." ) diff --git a/pandas/tests/groupby/aggregate/test_cython.py b/pandas/tests/groupby/aggregate/test_cython.py index 9631de7833cf4..869ed31b6a2d9 100644 --- a/pandas/tests/groupby/aggregate/test_cython.py +++ b/pandas/tests/groupby/aggregate/test_cython.py @@ -92,8 +92,9 @@ def test_cython_agg_boolean(): def test_cython_agg_nothing_to_agg(): frame = DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25}) - with pytest.raises(NotImplementedError, match="does not implement"): - frame.groupby("a")["b"].mean(numeric_only=True) + with tm.assert_produces_warning(FutureWarning, match="This will raise a TypeError"): + with pytest.raises(NotImplementedError, match="does not implement"): + frame.groupby("a")["b"].mean(numeric_only=True) with pytest.raises(TypeError, match="Could not convert (foo|bar)*"): frame.groupby("a")["b"].mean() @@ -114,8 +115,9 @@ def test_cython_agg_nothing_to_agg_with_dates(): "dates": pd.date_range("now", periods=50, freq="T"), } ) - with pytest.raises(NotImplementedError, match="does not implement"): - frame.groupby("b").dates.mean(numeric_only=True) + with tm.assert_produces_warning(FutureWarning, match="This will raise a TypeError"): + with pytest.raises(NotImplementedError, match="does not implement"): + frame.groupby("b").dates.mean(numeric_only=True) def test_cython_agg_frame_columns(): diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py index 7f24143920b84..7d6c5310942e2 100644 --- a/pandas/tests/groupby/test_function.py +++ b/pandas/tests/groupby/test_function.py @@ -1356,7 +1356,8 @@ def test_deprecate_numeric_only( method(*args, **kwargs) -def test_deprecate_numeric_only_series(groupby_func, request): +@pytest.mark.parametrize("dtype", [bool, int, float, object]) +def test_deprecate_numeric_only_series(dtype, groupby_func, request): # GH#46560 if groupby_func in ("backfill", "mad", "pad", "tshift"): pytest.skip("method is deprecated") @@ -1364,8 +1365,15 @@ def test_deprecate_numeric_only_series(groupby_func, request): msg = "corrwith is not implemented on SeriesGroupBy" request.node.add_marker(pytest.mark.xfail(reason=msg)) - ser = Series(list("xyz")) - gb = ser.groupby([0, 0, 1]) + grouper = [0, 0, 1] + + ser = Series([1, 0, 0], dtype=dtype) + gb = ser.groupby(grouper) + method = getattr(gb, groupby_func) + + expected_ser = Series([1, 0, 0]) + expected_gb = expected_ser.groupby(grouper) + expected_method = getattr(expected_gb, groupby_func) if groupby_func == "corrwith": args = (ser,) @@ -1383,48 +1391,95 @@ def test_deprecate_numeric_only_series(groupby_func, request): args = (0.5,) else: args = () - method = getattr(gb, groupby_func) - try: - _ = method(*args) - except (TypeError, ValueError) as err: - # ops that only work on numeric dtypes - assert groupby_func in ( - "corr", - "cov", - "cummax", - "cummin", - "cumprod", - "cumsum", - "diff", - "idxmax", - "idxmin", - "mean", - "median", - "pct_change", - "prod", - "quantile", - "sem", - "skew", - "std", - "var", - ) - assert ( - "could not convert" in str(err).lower() - or "unsupported operand type" in str(err) - or "not allowed for this dtype" in str(err) - or "can't multiply sequence by non-int" in str(err) - or "cannot be performed against 'object' dtypes" in str(err) - or "is not supported for object dtype" in str(err) - ), str(err) - - msgs = ( - "got an unexpected keyword argument 'numeric_only'", - f"{groupby_func} does not implement numeric_only", - f"{groupby_func} is not supported for object dtype", + fails_on_numeric_object = ( + "corr", + "cov", + "cummax", + "cummin", + "cumprod", + "cumsum", + "idxmax", + "idxmin", + "quantile", + ) + # ops that give an object result on object input + obj_result = ( + "first", + "last", + "nth", + "bfill", + "ffill", + "shift", + "sum", + "diff", + "pct_change", + ) + + # Test default behavior; kernels that fail may be enabled in the future but kernels + # that succeed should not be allowed to fail (without deprecation, at least) + if groupby_func in fails_on_numeric_object and dtype is object: + if groupby_func in ("idxmax", "idxmin"): + msg = "not allowed for this dtype" + elif groupby_func == "quantile": + msg = "cannot be performed against 'object' dtypes" + else: + msg = "is not supported for object dtype" + with pytest.raises(TypeError, match=msg): + method(*args) + elif dtype is object: + result = method(*args) + expected = expected_method(*args) + if groupby_func in obj_result: + expected = expected.astype(object) + tm.assert_series_equal(result, expected) + + has_numeric_only = ( + "first", + "last", + "max", + "mean", + "median", + "min", + "prod", + "quantile", + "sem", + "skew", + "std", + "sum", + "var", + "cummax", + "cummin", + "cumprod", + "cumsum", ) - with pytest.raises((NotImplementedError, TypeError), match=f"({'|'.join(msgs)})"): - _ = method(*args, numeric_only=True) + if groupby_func not in has_numeric_only: + msg = "got an unexpected keyword argument 'numeric_only'" + with pytest.raises(TypeError, match=msg): + method(*args, numeric_only=True) + elif dtype is object: + err_category = NotImplementedError + err_msg = f"{groupby_func} does not implement numeric_only" + if groupby_func.startswith("cum"): + # cum ops already exhibit future behavior + warn_category = None + warn_msg = "" + err_category = TypeError + err_msg = f"{groupby_func} is not supported for object dtype" + elif groupby_func == "skew": + warn_category = FutureWarning + warn_msg = "will raise a TypeError in the future" + else: + warn_category = FutureWarning + warn_msg = "This will raise a TypeError" + + with tm.assert_produces_warning(warn_category, match=warn_msg): + with pytest.raises(err_category, match=err_msg): + method(*args, numeric_only=True) + else: + result = method(*args, numeric_only=True) + expected = method(*args, numeric_only=False) + tm.assert_series_equal(result, expected) @pytest.mark.parametrize("dtype", [int, float, object]) diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py index a68595546eb83..fa53ed47dbdba 100644 --- a/pandas/tests/reductions/test_reductions.py +++ b/pandas/tests/reductions/test_reductions.py @@ -935,13 +935,9 @@ def test_all_any_params(self): with tm.assert_produces_warning(FutureWarning): s.all(bool_only=True, level=0) - # GH#38810 bool_only is not implemented alone. - msg = "Series.any does not implement bool_only" - with pytest.raises(NotImplementedError, match=msg): - s.any(bool_only=True) - msg = "Series.all does not implement bool_only." - with pytest.raises(NotImplementedError, match=msg): - s.all(bool_only=True) + # GH#47500 - test bool_only works + assert s.any(bool_only=True) + assert not s.all(bool_only=True) @pytest.mark.parametrize("bool_agg_func", ["any", "all"]) @pytest.mark.parametrize("skipna", [True, False]) diff --git a/pandas/tests/reductions/test_stat_reductions.py b/pandas/tests/reductions/test_stat_reductions.py index 0a6c0ccc891bb..be40d7ca631eb 100644 --- a/pandas/tests/reductions/test_stat_reductions.py +++ b/pandas/tests/reductions/test_stat_reductions.py @@ -149,10 +149,9 @@ def _check_stat_op( with pytest.raises(ValueError, match=msg): f(string_series_, axis=1) - # Unimplemented numeric_only parameter. if "numeric_only" in inspect.getfullargspec(f).args: - with pytest.raises(NotImplementedError, match=name): - f(string_series_, numeric_only=True) + # only the index is string; dtype is float + f(string_series_, numeric_only=True) def test_sum(self): string_series = tm.makeStringSeries().rename("series") diff --git a/pandas/tests/resample/test_resample_api.py b/pandas/tests/resample/test_resample_api.py index 5e10b9ee5277c..2d74b703b9bb1 100644 --- a/pandas/tests/resample/test_resample_api.py +++ b/pandas/tests/resample/test_resample_api.py @@ -922,8 +922,11 @@ def test_series_downsample_method(method, numeric_only, expected_data): func = getattr(resampled, method) if numeric_only and numeric_only is not lib.no_default: - with pytest.raises(NotImplementedError, match="not implement numeric_only"): - func(numeric_only=numeric_only) + with tm.assert_produces_warning( + FutureWarning, match="This will raise a TypeError" + ): + with pytest.raises(NotImplementedError, match="not implement numeric_only"): + func(numeric_only=numeric_only) elif method == "prod": with pytest.raises(TypeError, match="can't multiply sequence by non-int"): func(numeric_only=numeric_only) diff --git a/pandas/tests/series/test_api.py b/pandas/tests/series/test_api.py index 9a0c3fd5e9fed..0aab381d6e076 100644 --- a/pandas/tests/series/test_api.py +++ b/pandas/tests/series/test_api.py @@ -209,3 +209,97 @@ def test_series_iteritems_deprecated(self): ser = Series([1]) with tm.assert_produces_warning(FutureWarning): next(ser.iteritems()) + + @pytest.mark.parametrize( + "kernel, has_numeric_only", + [ + ("skew", True), + ("var", True), + ("all", False), + ("prod", True), + ("any", False), + ("idxmin", False), + ("quantile", False), + ("idxmax", False), + ("min", True), + ("sem", True), + ("mean", True), + ("nunique", False), + ("max", True), + ("sum", True), + ("count", False), + ("median", True), + ("std", True), + ("backfill", False), + ("rank", True), + ("pct_change", False), + ("cummax", False), + ("shift", False), + ("diff", False), + ("cumsum", False), + ("cummin", False), + ("cumprod", False), + ("fillna", False), + ("ffill", False), + ("pad", False), + ("bfill", False), + ("sample", False), + ("tail", False), + ("take", False), + ("head", False), + ("cov", False), + ("corr", False), + ], + ) + @pytest.mark.parametrize("dtype", [bool, int, float, object]) + def test_numeric_only(self, kernel, has_numeric_only, dtype): + # GH#47500 + ser = Series([0, 1, 1], dtype=dtype) + if kernel == "corrwith": + args = (ser,) + elif kernel == "corr": + args = (ser,) + elif kernel == "cov": + args = (ser,) + elif kernel == "nth": + args = (0,) + elif kernel == "fillna": + args = (True,) + elif kernel == "fillna": + args = ("ffill",) + elif kernel == "take": + args = ([0],) + elif kernel == "quantile": + args = (0.5,) + else: + args = () + method = getattr(ser, kernel) + if not has_numeric_only: + msg = ( + "(got an unexpected keyword argument 'numeric_only'" + "|too many arguments passed in)" + ) + with pytest.raises(TypeError, match=msg): + method(*args, numeric_only=True) + elif dtype is object: + if kernel == "rank": + msg = "Calling Series.rank with numeric_only=True and dtype object" + with tm.assert_produces_warning(FutureWarning, match=msg): + method(*args, numeric_only=True) + else: + warn_msg = ( + f"Calling Series.{kernel} with numeric_only=True and dtype object" + ) + err_msg = f"Series.{kernel} does not implement numeric_only" + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + with pytest.raises(NotImplementedError, match=err_msg): + method(*args, numeric_only=True) + else: + result = method(*args, numeric_only=True) + expected = method(*args, numeric_only=False) + if isinstance(expected, Series): + # transformer + tm.assert_series_equal(result, expected) + else: + # reducer + assert result == expected
Part of #47500 - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. Behavior changes are to functions which gained the numeric_only argument in 1.5.
https://api.github.com/repos/pandas-dev/pandas/pulls/47561
2022-07-01T03:05:31Z
2022-07-05T16:42:33Z
2022-07-05T16:42:32Z
2022-07-06T22:14:07Z
TYP: fix type of level in DataFrame.groupby
diff --git a/pandas/_typing.py b/pandas/_typing.py index a1bb119c32ba6..ac1237f8841be 100644 --- a/pandas/_typing.py +++ b/pandas/_typing.py @@ -109,7 +109,7 @@ Axis = Union[str, int] IndexLabel = Union[Hashable, Sequence[Hashable]] -Level = Union[Hashable, int] +Level = Hashable Shape = Tuple[int, ...] Suffixes = Tuple[Optional[str], Optional[str]] Ordered = Optional[bool] diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 0a255fdd49590..82ae93ad31763 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -8125,7 +8125,7 @@ def groupby( self, by=None, axis: Axis = 0, - level: Level | None = None, + level: IndexLabel | None = None, as_index: bool = True, sort: bool = True, group_keys: bool | lib.NoDefault = no_default,
fixes #47548
https://api.github.com/repos/pandas-dev/pandas/pulls/47560
2022-07-01T02:12:49Z
2022-07-01T17:50:11Z
2022-07-01T17:50:11Z
2022-09-21T15:28:31Z
TST: Test aggregate with list values #25581
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py index d52b6ceaf8990..54ee32502bbc9 100644 --- a/pandas/tests/groupby/aggregate/test_aggregate.py +++ b/pandas/tests/groupby/aggregate/test_aggregate.py @@ -1413,3 +1413,28 @@ def test_multi_axis_1_raises(func): gb = df.groupby("a", axis=1) with pytest.raises(NotImplementedError, match="axis other than 0 is not supported"): gb.agg(func) + + +@pytest.mark.parametrize( + "test, constant", + [ + ([[20, "A"], [20, "B"], [10, "C"]], {0: [10, 20], 1: ["C", ["A", "B"]]}), + ([[20, "A"], [20, "B"], [30, "C"]], {0: [20, 30], 1: [["A", "B"], "C"]}), + ([["a", 1], ["a", 1], ["b", 2], ["b", 3]], {0: ["a", "b"], 1: [1, [2, 3]]}), + pytest.param( + [["a", 1], ["a", 2], ["b", 3], ["b", 3]], + {0: ["a", "b"], 1: [[1, 2], 3]}, + marks=pytest.mark.xfail, + ), + ], +) +def test_agg_of_mode_list(test, constant): + # GH#25581 + df1 = DataFrame(test) + result = df1.groupby(0).agg(Series.mode) + # Mode usually only returns 1 value, but can return a list in the case of a tie. + + expected = DataFrame(constant) + expected = expected.set_index(0) + + tm.assert_frame_equal(result, expected)
- [ ] closes #25581 - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47559
2022-07-01T01:46:37Z
2022-07-05T22:22:19Z
2022-07-05T22:22:19Z
2022-07-05T22:22:29Z
TYP: misc return type annotations
diff --git a/pandas/__init__.py b/pandas/__init__.py index 1c4151372273b..01ff2e1e1f181 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -1,4 +1,5 @@ # flake8: noqa +from __future__ import annotations __docformat__ = "restructuredtext" @@ -185,7 +186,7 @@ __deprecated_num_index_names = ["Float64Index", "Int64Index", "UInt64Index"] -def __dir__(): +def __dir__() -> list[str]: # GH43028 # Int64Index etc. are deprecated, but we still want them to be available in the dir. # Remove in Pandas 2.0, when we remove Int64Index etc. from the code base. diff --git a/pandas/_config/config.py b/pandas/_config/config.py index 756c3b2d4b2b6..eacbf1b016432 100644 --- a/pandas/_config/config.py +++ b/pandas/_config/config.py @@ -60,6 +60,7 @@ Callable, Generic, Iterable, + Iterator, NamedTuple, cast, ) @@ -435,13 +436,13 @@ def __init__(self, *args) -> None: self.ops = list(zip(args[::2], args[1::2])) - def __enter__(self): + def __enter__(self) -> None: self.undo = [(pat, _get_option(pat, silent=True)) for pat, val in self.ops] for pat, val in self.ops: _set_option(pat, val, silent=True) - def __exit__(self, *args): + def __exit__(self, *args) -> None: if self.undo: for pat, val in self.undo: _set_option(pat, val, silent=True) @@ -733,7 +734,7 @@ def pp(name: str, ks: Iterable[str]) -> list[str]: @contextmanager -def config_prefix(prefix): +def config_prefix(prefix) -> Iterator[None]: """ contextmanager for multiple invocations of API with a common prefix diff --git a/pandas/_testing/__init__.py b/pandas/_testing/__init__.py index 9d3e84dc964d6..0fcea111716ec 100644 --- a/pandas/_testing/__init__.py +++ b/pandas/_testing/__init__.py @@ -238,7 +238,7 @@ _testing_mode_warnings = (DeprecationWarning, ResourceWarning) -def set_testing_mode(): +def set_testing_mode() -> None: # set the testing mode filters testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None") if "deprecate" in testing_mode: @@ -246,7 +246,7 @@ def set_testing_mode(): warnings.simplefilter("always", category) -def reset_testing_mode(): +def reset_testing_mode() -> None: # reset the testing mode filters testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None") if "deprecate" in testing_mode: @@ -257,7 +257,7 @@ def reset_testing_mode(): set_testing_mode() -def reset_display_options(): +def reset_display_options() -> None: """ Reset the display options for printing and representing objects. """ @@ -333,16 +333,16 @@ def to_array(obj): # Others -def getCols(k): +def getCols(k) -> str: return string.ascii_uppercase[:k] # make index -def makeStringIndex(k=10, name=None): +def makeStringIndex(k=10, name=None) -> Index: return Index(rands_array(nchars=10, size=k), name=name) -def makeCategoricalIndex(k=10, n=3, name=None, **kwargs): +def makeCategoricalIndex(k=10, n=3, name=None, **kwargs) -> CategoricalIndex: """make a length k index or n categories""" x = rands_array(nchars=4, size=n, replace=False) return CategoricalIndex( @@ -350,13 +350,13 @@ def makeCategoricalIndex(k=10, n=3, name=None, **kwargs): ) -def makeIntervalIndex(k=10, name=None, **kwargs): +def makeIntervalIndex(k=10, name=None, **kwargs) -> IntervalIndex: """make a length k IntervalIndex""" x = np.linspace(0, 100, num=(k + 1)) return IntervalIndex.from_breaks(x, name=name, **kwargs) -def makeBoolIndex(k=10, name=None): +def makeBoolIndex(k=10, name=None) -> Index: if k == 1: return Index([True], name=name) elif k == 2: @@ -364,7 +364,7 @@ def makeBoolIndex(k=10, name=None): return Index([False, True] + [False] * (k - 2), name=name) -def makeNumericIndex(k=10, name=None, *, dtype): +def makeNumericIndex(k=10, name=None, *, dtype) -> NumericIndex: dtype = pandas_dtype(dtype) assert isinstance(dtype, np.dtype) @@ -382,21 +382,21 @@ def makeNumericIndex(k=10, name=None, *, dtype): return NumericIndex(values, dtype=dtype, name=name) -def makeIntIndex(k=10, name=None): +def makeIntIndex(k=10, name=None) -> Int64Index: base_idx = makeNumericIndex(k, name=name, dtype="int64") return Int64Index(base_idx) -def makeUIntIndex(k=10, name=None): +def makeUIntIndex(k=10, name=None) -> UInt64Index: base_idx = makeNumericIndex(k, name=name, dtype="uint64") return UInt64Index(base_idx) -def makeRangeIndex(k=10, name=None, **kwargs): +def makeRangeIndex(k=10, name=None, **kwargs) -> RangeIndex: return RangeIndex(0, k, 1, name=name, **kwargs) -def makeFloatIndex(k=10, name=None): +def makeFloatIndex(k=10, name=None) -> Float64Index: base_idx = makeNumericIndex(k, name=name, dtype="float64") return Float64Index(base_idx) @@ -456,34 +456,34 @@ def all_timeseries_index_generator(k: int = 10) -> Iterable[Index]: # make series -def make_rand_series(name=None, dtype=np.float64): +def make_rand_series(name=None, dtype=np.float64) -> Series: index = makeStringIndex(_N) data = np.random.randn(_N) data = data.astype(dtype, copy=False) return Series(data, index=index, name=name) -def makeFloatSeries(name=None): +def makeFloatSeries(name=None) -> Series: return make_rand_series(name=name) -def makeStringSeries(name=None): +def makeStringSeries(name=None) -> Series: return make_rand_series(name=name) -def makeObjectSeries(name=None): +def makeObjectSeries(name=None) -> Series: data = makeStringIndex(_N) data = Index(data, dtype=object) index = makeStringIndex(_N) return Series(data, index=index, name=name) -def getSeriesData(): +def getSeriesData() -> dict[str, Series]: index = makeStringIndex(_N) return {c: Series(np.random.randn(_N), index=index) for c in getCols(_K)} -def makeTimeSeries(nper=None, freq="B", name=None): +def makeTimeSeries(nper=None, freq="B", name=None) -> Series: if nper is None: nper = _N return Series( @@ -491,22 +491,22 @@ def makeTimeSeries(nper=None, freq="B", name=None): ) -def makePeriodSeries(nper=None, name=None): +def makePeriodSeries(nper=None, name=None) -> Series: if nper is None: nper = _N return Series(np.random.randn(nper), index=makePeriodIndex(nper), name=name) -def getTimeSeriesData(nper=None, freq="B"): +def getTimeSeriesData(nper=None, freq="B") -> dict[str, Series]: return {c: makeTimeSeries(nper, freq) for c in getCols(_K)} -def getPeriodData(nper=None): +def getPeriodData(nper=None) -> dict[str, Series]: return {c: makePeriodSeries(nper) for c in getCols(_K)} # make frame -def makeTimeDataFrame(nper=None, freq="B"): +def makeTimeDataFrame(nper=None, freq="B") -> DataFrame: data = getTimeSeriesData(nper, freq) return DataFrame(data) @@ -533,14 +533,19 @@ def makeMixedDataFrame(): return DataFrame(getMixedTypeDict()[1]) -def makePeriodFrame(nper=None): +def makePeriodFrame(nper=None) -> DataFrame: data = getPeriodData(nper) return DataFrame(data) def makeCustomIndex( - nentries, nlevels, prefix="#", names=False, ndupe_l=None, idx_type=None -): + nentries, + nlevels, + prefix="#", + names: bool | str | list[str] | None = False, + ndupe_l=None, + idx_type=None, +) -> Index: """ Create an index/multindex with given dimensions, levels, names, etc' @@ -637,7 +642,8 @@ def keyfunc(x): # convert tuples to index if nentries == 1: # we have a single level of tuples, i.e. a regular Index - index = Index(tuples[0], name=names[0]) + name = None if names is None else names[0] + index = Index(tuples[0], name=name) elif nlevels == 1: name = None if names is None else names[0] index = Index((x[0] for x in tuples), name=name) @@ -659,7 +665,7 @@ def makeCustomDataframe( dtype=None, c_idx_type=None, r_idx_type=None, -): +) -> DataFrame: """ Create a DataFrame using supplied parameters. @@ -780,7 +786,7 @@ def _gen_unique_rand(rng, _extra_size): return i.tolist(), j.tolist() -def makeMissingDataframe(density=0.9, random_state=None): +def makeMissingDataframe(density=0.9, random_state=None) -> DataFrame: df = makeDataFrame() i, j = _create_missing_idx(*df.shape, density=density, random_state=random_state) df.values[i, j] = np.nan @@ -854,7 +860,7 @@ def skipna_wrapper(x): return skipna_wrapper -def convert_rows_list_to_csv_str(rows_list: list[str]): +def convert_rows_list_to_csv_str(rows_list: list[str]) -> str: """ Convert list of CSV rows to single CSV-formatted string for current OS. diff --git a/pandas/_testing/_io.py b/pandas/_testing/_io.py index 46f1545a67fab..d1acdff8d2fd7 100644 --- a/pandas/_testing/_io.py +++ b/pandas/_testing/_io.py @@ -250,7 +250,7 @@ def wrapper(*args, **kwargs): return wrapper -def can_connect(url, error_classes=None): +def can_connect(url, error_classes=None) -> bool: """ Try to connect to the given url. True if succeeds, False if OSError raised @@ -424,7 +424,7 @@ def write_to_compressed(compression, path, data, dest="test"): # Plotting -def close(fignum=None): +def close(fignum=None) -> None: from matplotlib.pyplot import ( close as _close, get_fignums, diff --git a/pandas/_testing/_random.py b/pandas/_testing/_random.py index cce6bf8da7d3e..880fffea21bd1 100644 --- a/pandas/_testing/_random.py +++ b/pandas/_testing/_random.py @@ -14,7 +14,7 @@ def randbool(size=(), p: float = 0.5): ) -def rands_array(nchars, size, dtype="O", replace=True): +def rands_array(nchars, size, dtype="O", replace=True) -> np.ndarray: """ Generate an array of byte strings. """ @@ -26,7 +26,7 @@ def rands_array(nchars, size, dtype="O", replace=True): return retval.astype(dtype) -def rands(nchars): +def rands(nchars) -> str: """ Generate one random byte string. diff --git a/pandas/_testing/asserters.py b/pandas/_testing/asserters.py index 90ee600c1967d..5e8b553b352c8 100644 --- a/pandas/_testing/asserters.py +++ b/pandas/_testing/asserters.py @@ -68,7 +68,7 @@ def assert_almost_equal( rtol: float = 1.0e-5, atol: float = 1.0e-8, **kwargs, -): +) -> None: """ Check that the left and right objects are approximately equal. @@ -239,7 +239,7 @@ def _check_isinstance(left, right, cls): ) -def assert_dict_equal(left, right, compare_keys: bool = True): +def assert_dict_equal(left, right, compare_keys: bool = True) -> None: _check_isinstance(left, right, dict) _testing.assert_dict_equal(left, right, compare_keys=compare_keys) @@ -428,7 +428,7 @@ def _get_ilevel_values(index, level): assert_categorical_equal(left._values, right._values, obj=f"{obj} category") -def assert_class_equal(left, right, exact: bool | str = True, obj="Input"): +def assert_class_equal(left, right, exact: bool | str = True, obj="Input") -> None: """ Checks classes are equal. """ @@ -455,7 +455,7 @@ def repr_class(x): raise_assert_detail(obj, msg, repr_class(left), repr_class(right)) -def assert_attr_equal(attr: str, left, right, obj: str = "Attributes"): +def assert_attr_equal(attr: str, left, right, obj: str = "Attributes") -> None: """ Check attributes are equal. Both objects must have attribute. @@ -474,11 +474,9 @@ def assert_attr_equal(attr: str, left, right, obj: str = "Attributes"): left_attr = getattr(left, attr) right_attr = getattr(right, attr) - if left_attr is right_attr: - return True - elif is_matching_na(left_attr, right_attr): + if left_attr is right_attr or is_matching_na(left_attr, right_attr): # e.g. both np.nan, both NaT, both pd.NA, ... - return True + return None try: result = left_attr == right_attr @@ -490,14 +488,13 @@ def assert_attr_equal(attr: str, left, right, obj: str = "Attributes"): elif not isinstance(result, bool): result = result.all() - if result: - return True - else: + if not result: msg = f'Attribute "{attr}" are different' raise_assert_detail(obj, msg, left_attr, right_attr) + return None -def assert_is_valid_plot_return_object(objs): +def assert_is_valid_plot_return_object(objs) -> None: import matplotlib.pyplot as plt if isinstance(objs, (Series, np.ndarray)): @@ -516,7 +513,7 @@ def assert_is_valid_plot_return_object(objs): assert isinstance(objs, (plt.Artist, tuple, dict)), msg -def assert_is_sorted(seq): +def assert_is_sorted(seq) -> None: """Assert that the sequence is sorted.""" if isinstance(seq, (Index, Series)): seq = seq.values @@ -526,7 +523,7 @@ def assert_is_sorted(seq): def assert_categorical_equal( left, right, check_dtype=True, check_category_order=True, obj="Categorical" -): +) -> None: """ Test that Categoricals are equivalent. @@ -581,7 +578,9 @@ def assert_categorical_equal( assert_attr_equal("ordered", left, right, obj=obj) -def assert_interval_array_equal(left, right, exact="equiv", obj="IntervalArray"): +def assert_interval_array_equal( + left, right, exact="equiv", obj="IntervalArray" +) -> None: """ Test that two IntervalArrays are equivalent. @@ -610,14 +609,16 @@ def assert_interval_array_equal(left, right, exact="equiv", obj="IntervalArray") assert_attr_equal("inclusive", left, right, obj=obj) -def assert_period_array_equal(left, right, obj="PeriodArray"): +def assert_period_array_equal(left, right, obj="PeriodArray") -> None: _check_isinstance(left, right, PeriodArray) assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data") assert_attr_equal("freq", left, right, obj=obj) -def assert_datetime_array_equal(left, right, obj="DatetimeArray", check_freq=True): +def assert_datetime_array_equal( + left, right, obj="DatetimeArray", check_freq=True +) -> None: __tracebackhide__ = True _check_isinstance(left, right, DatetimeArray) @@ -627,7 +628,9 @@ def assert_datetime_array_equal(left, right, obj="DatetimeArray", check_freq=Tru assert_attr_equal("tz", left, right, obj=obj) -def assert_timedelta_array_equal(left, right, obj="TimedeltaArray", check_freq=True): +def assert_timedelta_array_equal( + left, right, obj="TimedeltaArray", check_freq=True +) -> None: __tracebackhide__ = True _check_isinstance(left, right, TimedeltaArray) assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data") @@ -682,7 +685,7 @@ def assert_numpy_array_equal( check_same=None, obj="numpy array", index_values=None, -): +) -> None: """ Check that 'np.ndarray' is equivalent. @@ -762,7 +765,7 @@ def assert_extension_array_equal( check_exact=False, rtol: float = 1.0e-5, atol: float = 1.0e-8, -): +) -> None: """ Check that left and right ExtensionArrays are equal. @@ -878,7 +881,7 @@ def assert_series_equal( *, check_index=True, check_like=False, -): +) -> None: """ Check that left and right Series are equal. @@ -1145,7 +1148,7 @@ def assert_frame_equal( rtol=1.0e-5, atol=1.0e-8, obj="DataFrame", -): +) -> None: """ Check that left and right DataFrame are equal. @@ -1352,7 +1355,7 @@ def assert_frame_equal( ) -def assert_equal(left, right, **kwargs): +def assert_equal(left, right, **kwargs) -> None: """ Wrapper for tm.assert_*_equal to dispatch to the appropriate test function. @@ -1393,7 +1396,7 @@ def assert_equal(left, right, **kwargs): assert_almost_equal(left, right) -def assert_sp_array_equal(left, right): +def assert_sp_array_equal(left, right) -> None: """ Check that the left and right SparseArray are equal. @@ -1426,12 +1429,12 @@ def assert_sp_array_equal(left, right): assert_numpy_array_equal(left.to_dense(), right.to_dense()) -def assert_contains_all(iterable, dic): +def assert_contains_all(iterable, dic) -> None: for k in iterable: assert k in dic, f"Did not contain item: {repr(k)}" -def assert_copy(iter1, iter2, **eql_kwargs): +def assert_copy(iter1, iter2, **eql_kwargs) -> None: """ iter1, iter2: iterables that produce elements comparable with assert_almost_equal @@ -1463,7 +1466,7 @@ def is_extension_array_dtype_and_needs_i8_conversion(left_dtype, right_dtype) -> return is_extension_array_dtype(left_dtype) and needs_i8_conversion(right_dtype) -def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice): +def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice) -> None: """ Check that ser.iloc[i_slc] matches ser.loc[l_slc] and, if applicable, ser[l_slc]. @@ -1477,7 +1480,7 @@ def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice): assert_series_equal(ser[l_slc], expected) -def assert_metadata_equivalent(left, right): +def assert_metadata_equivalent(left, right) -> None: """ Check that ._metadata attributes are equivalent. """ diff --git a/pandas/_testing/contexts.py b/pandas/_testing/contexts.py index 7df9afd68b432..e64adb06bea7a 100644 --- a/pandas/_testing/contexts.py +++ b/pandas/_testing/contexts.py @@ -8,6 +8,7 @@ from typing import ( IO, Any, + Iterator, ) import uuid @@ -19,7 +20,7 @@ @contextmanager -def decompress_file(path, compression): +def decompress_file(path, compression) -> Iterator[IO[bytes]]: """ Open a compressed file and return a file object. @@ -40,7 +41,7 @@ def decompress_file(path, compression): @contextmanager -def set_timezone(tz: str): +def set_timezone(tz: str) -> Iterator[None]: """ Context manager for temporarily setting a timezone. @@ -126,7 +127,7 @@ def ensure_clean(filename=None, return_filelike: bool = False, **kwargs: Any): @contextmanager -def ensure_clean_dir(): +def ensure_clean_dir() -> Iterator[str]: """ Get a temporary directory path and agrees to remove on close. @@ -145,7 +146,7 @@ def ensure_clean_dir(): @contextmanager -def ensure_safe_environment_variables(): +def ensure_safe_environment_variables() -> Iterator[None]: """ Get a context manager to safely set environment variables @@ -161,7 +162,7 @@ def ensure_safe_environment_variables(): @contextmanager -def with_csv_dialect(name, **kwargs): +def with_csv_dialect(name, **kwargs) -> Iterator[None]: """ Context manager to temporarily register a CSV dialect for parsing CSV. @@ -195,7 +196,7 @@ def with_csv_dialect(name, **kwargs): @contextmanager -def use_numexpr(use, min_elements=None): +def use_numexpr(use, min_elements=None) -> Iterator[None]: from pandas.core.computation import expressions as expr if min_elements is None: @@ -231,11 +232,11 @@ class RNGContext: def __init__(self, seed) -> None: self.seed = seed - def __enter__(self): + def __enter__(self) -> None: self.start_state = np.random.get_state() np.random.seed(self.seed) - def __exit__(self, exc_type, exc_value, traceback): + def __exit__(self, exc_type, exc_value, traceback) -> None: np.random.set_state(self.start_state) diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index bb4787f07b2f0..2ab710a5762d3 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -7,9 +7,12 @@ Other items: * platform checker """ +from __future__ import annotations + import os import platform import sys +from typing import TYPE_CHECKING from pandas._typing import F from pandas.compat.numpy import ( @@ -26,6 +29,9 @@ pa_version_under7p0, ) +if TYPE_CHECKING: + import lzma + PY39 = sys.version_info >= (3, 9) PY310 = sys.version_info >= (3, 10) PYPY = platform.python_implementation() == "PyPy" @@ -117,7 +123,7 @@ def is_ci_environment() -> bool: return os.environ.get("PANDAS_CI", "0") == "1" -def get_lzma_file(): +def get_lzma_file() -> type[lzma.LZMAFile]: """ Importing the `LZMAFile` class from the `lzma` module. diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py index 2333324a7e22d..c8db82500d0d6 100644 --- a/pandas/compat/pickle_compat.py +++ b/pandas/compat/pickle_compat.py @@ -7,7 +7,10 @@ import copy import io import pickle as pkl -from typing import TYPE_CHECKING +from typing import ( + TYPE_CHECKING, + Iterator, +) import warnings import numpy as np @@ -291,7 +294,7 @@ def loads( @contextlib.contextmanager -def patch_pickle(): +def patch_pickle() -> Iterator[None]: """ Temporarily patch pickle to use our unpickler. """ diff --git a/pandas/conftest.py b/pandas/conftest.py index dfe8c5f1778d3..eb17aac99a904 100644 --- a/pandas/conftest.py +++ b/pandas/conftest.py @@ -29,6 +29,10 @@ from decimal import Decimal import operator import os +from typing import ( + Callable, + Literal, +) from dateutil.tz import ( tzlocal, @@ -90,7 +94,7 @@ # pytest -def pytest_addoption(parser): +def pytest_addoption(parser) -> None: parser.addoption("--skip-slow", action="store_true", help="skip slow tests") parser.addoption("--skip-network", action="store_true", help="skip network tests") parser.addoption("--skip-db", action="store_true", help="skip db tests") @@ -231,7 +235,7 @@ def pytest_collection_modifyitems(items, config): @pytest.fixture -def add_doctest_imports(doctest_namespace): +def add_doctest_imports(doctest_namespace) -> None: """ Make `np` and `pd` names available for doctests. """ @@ -243,7 +247,7 @@ def add_doctest_imports(doctest_namespace): # Autouse fixtures # ---------------------------------------------------------------- @pytest.fixture(autouse=True) -def configure_tests(): +def configure_tests() -> None: """ Configure settings for all tests and test modules. """ @@ -530,7 +534,7 @@ def multiindex_year_month_day_dataframe_random_data(): @pytest.fixture -def lexsorted_two_level_string_multiindex(): +def lexsorted_two_level_string_multiindex() -> MultiIndex: """ 2-level MultiIndex, lexsorted, with string names. """ @@ -542,7 +546,9 @@ def lexsorted_two_level_string_multiindex(): @pytest.fixture -def multiindex_dataframe_random_data(lexsorted_two_level_string_multiindex): +def multiindex_dataframe_random_data( + lexsorted_two_level_string_multiindex, +) -> DataFrame: """DataFrame with 2 level MultiIndex with random data""" index = lexsorted_two_level_string_multiindex return DataFrame( @@ -715,7 +721,7 @@ def index_with_missing(request): # Series' # ---------------------------------------------------------------- @pytest.fixture -def string_series(): +def string_series() -> Series: """ Fixture for Series of floats with Index of unique strings """ @@ -725,7 +731,7 @@ def string_series(): @pytest.fixture -def object_series(): +def object_series() -> Series: """ Fixture for Series of dtype object with Index of unique strings """ @@ -735,7 +741,7 @@ def object_series(): @pytest.fixture -def datetime_series(): +def datetime_series() -> Series: """ Fixture for Series of floats with DatetimeIndex """ @@ -758,7 +764,7 @@ def _create_series(index): @pytest.fixture -def series_with_simple_index(index): +def series_with_simple_index(index) -> Series: """ Fixture for tests on series with changing types of indices. """ @@ -766,7 +772,7 @@ def series_with_simple_index(index): @pytest.fixture -def series_with_multilevel_index(): +def series_with_multilevel_index() -> Series: """ Fixture with a Series with a 2-level MultiIndex. """ @@ -804,7 +810,7 @@ def index_or_series_obj(request): # DataFrames # ---------------------------------------------------------------- @pytest.fixture -def int_frame(): +def int_frame() -> DataFrame: """ Fixture for DataFrame of ints with index of unique strings @@ -833,7 +839,7 @@ def int_frame(): @pytest.fixture -def datetime_frame(): +def datetime_frame() -> DataFrame: """ Fixture for DataFrame of floats with DatetimeIndex @@ -862,7 +868,7 @@ def datetime_frame(): @pytest.fixture -def float_frame(): +def float_frame() -> DataFrame: """ Fixture for DataFrame of floats with index of unique strings @@ -891,7 +897,7 @@ def float_frame(): @pytest.fixture -def mixed_type_frame(): +def mixed_type_frame() -> DataFrame: """ Fixture for DataFrame of float/int/string columns with RangeIndex Columns are ['a', 'b', 'c', 'float32', 'int32']. @@ -909,7 +915,7 @@ def mixed_type_frame(): @pytest.fixture -def rand_series_with_duplicate_datetimeindex(): +def rand_series_with_duplicate_datetimeindex() -> Series: """ Fixture for Series with a DatetimeIndex that has duplicates. """ @@ -1151,7 +1157,7 @@ def strict_data_files(pytestconfig): @pytest.fixture -def datapath(strict_data_files): +def datapath(strict_data_files: str) -> Callable[..., str]: """ Get the path to a data file. @@ -1186,7 +1192,7 @@ def deco(*args): @pytest.fixture -def iris(datapath): +def iris(datapath) -> DataFrame: """ The iris dataset as a DataFrame. """ @@ -1376,7 +1382,7 @@ def timedelta64_dtype(request): @pytest.fixture -def fixed_now_ts(): +def fixed_now_ts() -> Timestamp: """ Fixture emits fixed Timestamp.now() """ @@ -1845,7 +1851,7 @@ def using_array_manager(): @pytest.fixture -def using_copy_on_write(): +def using_copy_on_write() -> Literal[False]: """ Fixture to check if Copy-on-Write is enabled. """ diff --git a/pandas/io/common.py b/pandas/io/common.py index ef8b466145daa..d911499aa848e 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -821,7 +821,10 @@ def get_handle( # XZ Compression elif compression == "xz": - handle = get_lzma_file()(handle, ioargs.mode) + # error: Argument 1 to "LZMAFile" has incompatible type "Union[str, + # BaseBuffer]"; expected "Optional[Union[Union[str, bytes, PathLike[str], + # PathLike[bytes]], IO[bytes]]]" + handle = get_lzma_file()(handle, ioargs.mode) # type: ignore[arg-type] # Zstd Compression elif compression == "zstd": diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py index bfdf3eb6992ca..6350f402ac0e5 100644 --- a/pandas/tests/api/test_api.py +++ b/pandas/tests/api/test_api.py @@ -16,7 +16,9 @@ def check(self, namespace, expected, ignored=None): # ignored ones # compare vs the expected - result = sorted(f for f in dir(namespace) if not f.startswith("__")) + result = sorted( + f for f in dir(namespace) if not f.startswith("__") and f != "annotations" + ) if ignored is not None: result = sorted(set(result) - set(ignored)) diff --git a/pandas/tests/util/test_assert_attr_equal.py b/pandas/tests/util/test_assert_attr_equal.py index 115ef58e085cc..bbbb0bf2172b1 100644 --- a/pandas/tests/util/test_assert_attr_equal.py +++ b/pandas/tests/util/test_assert_attr_equal.py @@ -10,7 +10,7 @@ def test_assert_attr_equal(nulls_fixture): obj = SimpleNamespace() obj.na_value = nulls_fixture - assert tm.assert_attr_equal("na_value", obj, obj) + tm.assert_attr_equal("na_value", obj, obj) def test_assert_attr_equal_different_nulls(nulls_fixture, nulls_fixture2): @@ -21,13 +21,13 @@ def test_assert_attr_equal_different_nulls(nulls_fixture, nulls_fixture2): obj2.na_value = nulls_fixture2 if nulls_fixture is nulls_fixture2: - assert tm.assert_attr_equal("na_value", obj, obj2) + tm.assert_attr_equal("na_value", obj, obj2) elif is_float(nulls_fixture) and is_float(nulls_fixture2): # we consider float("nan") and np.float64("nan") to be equivalent - assert tm.assert_attr_equal("na_value", obj, obj2) + tm.assert_attr_equal("na_value", obj, obj2) elif type(nulls_fixture) is type(nulls_fixture2): # e.g. Decimal("NaN") - assert tm.assert_attr_equal("na_value", obj, obj2) + tm.assert_attr_equal("na_value", obj, obj2) else: with pytest.raises(AssertionError, match='"na_value" are different'): tm.assert_attr_equal("na_value", obj, obj2) diff --git a/pyright_reportGeneralTypeIssues.json b/pyright_reportGeneralTypeIssues.json index 541ae4b198166..98da481a6d80f 100644 --- a/pyright_reportGeneralTypeIssues.json +++ b/pyright_reportGeneralTypeIssues.json @@ -16,6 +16,7 @@ "pandas/util/version", # and all files that currently don't pass "pandas/_config/config.py", + "pandas/_testing/__init__.py", "pandas/core/algorithms.py", "pandas/core/apply.py", "pandas/core/array_algos/take.py",
After this most of the low hanging return annotations that `pyright --createstub pandas` finds are in pandas/core (there are still plenty of more tricky return annotations). xref #47521
https://api.github.com/repos/pandas-dev/pandas/pulls/47558
2022-07-01T01:41:45Z
2022-07-03T14:22:54Z
2022-07-03T14:22:53Z
2022-09-21T15:28:31Z
ENH: RangeIndex._shallow_copy can return RangeIndex
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index c70acc0a0b18c..6f7e150965583 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -277,7 +277,7 @@ Other enhancements - Allow reading compressed SAS files with :func:`read_sas` (e.g., ``.sas7bdat.gz`` files) - :meth:`DatetimeIndex.astype` now supports casting timezone-naive indexes to ``datetime64[s]``, ``datetime64[ms]``, and ``datetime64[us]``, and timezone-aware indexes to the corresponding ``datetime64[unit, tzname]`` dtypes (:issue:`47579`) - :class:`Series` reducers (e.g. ``min``, ``max``, ``sum``, ``mean``) will now successfully operate when the dtype is numeric and ``numeric_only=True`` is provided; previously this would raise a ``NotImplementedError`` (:issue:`47500`) -- +- :meth:`RangeIndex.union` now can return a :class:`RangeIndex` instead of a :class:`Int64Index` if the resulting values are equally spaced (:issue:`47557`, :issue:`43885`) .. --------------------------------------------------------------------------- .. _whatsnew_150.notable_bug_fixes: @@ -1008,7 +1008,7 @@ Reshaping - Bug in :func:`concat` with identical key leads to error when indexing :class:`MultiIndex` (:issue:`46519`) - Bug in :meth:`DataFrame.join` with a list when using suffixes to join DataFrames with duplicate column names (:issue:`46396`) - Bug in :meth:`DataFrame.pivot_table` with ``sort=False`` results in sorted index (:issue:`17041`) -- +- Bug in :meth:`concat` when ``axis=1`` and ``sort=False`` where the resulting Index was a :class:`Int64Index` instead of a :class:`RangeIndex` (:issue:`46675`) Sparse ^^^^^^ diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py index 2d6d121a089c0..12a995c7de99a 100644 --- a/pandas/core/indexes/range.py +++ b/pandas/core/indexes/range.py @@ -20,6 +20,7 @@ index as libindex, lib, ) +from pandas._libs.algos import unique_deltas from pandas._libs.lib import no_default from pandas._typing import ( Dtype, @@ -436,7 +437,15 @@ def _shallow_copy(self, values, name: Hashable = no_default): if values.dtype.kind == "f": return Float64Index(values, name=name) - return Int64Index._simple_new(values, name=name) + # GH 46675 & 43885: If values is equally spaced, return a + # more memory-compact RangeIndex instead of Int64Index + unique_diffs = unique_deltas(values) + if len(unique_diffs) == 1 and unique_diffs[0] != 0: + diff = unique_diffs[0] + new_range = range(values[0], values[-1] + diff, diff) + return type(self)._simple_new(new_range, name=name) + else: + return Int64Index._simple_new(values, name=name) def _view(self: RangeIndex) -> RangeIndex: result = type(self)._simple_new(self._range, name=self._name) @@ -638,6 +647,17 @@ def _extended_gcd(self, a: int, b: int) -> tuple[int, int, int]: old_t, t = t, old_t - quotient * t return old_r, old_s, old_t + def _range_in_self(self, other: range) -> bool: + """Check if other range is contained in self""" + # https://stackoverflow.com/a/32481015 + if not other: + return True + if not self._range: + return False + if len(other) > 1 and other.step % self._range.step: + return False + return other.start in self._range and other[-1] in self._range + def _union(self, other: Index, sort): """ Form the union of two Index objects and sorts if possible @@ -647,10 +667,12 @@ def _union(self, other: Index, sort): other : Index or array-like sort : False or None, default None - Whether to sort resulting index. ``sort=None`` returns a - monotonically increasing ``RangeIndex`` if possible or a sorted - ``Int64Index`` if not. ``sort=False`` always returns an - unsorted ``Int64Index`` + Whether to sort (monotonically increasing) the resulting index. + ``sort=None`` returns a ``RangeIndex`` if possible or a sorted + ``Int64Index`` if not. + ``sort=False`` can return a ``RangeIndex`` if self is monotonically + increasing and other is fully contained in self. Otherwise, returns + an unsorted ``Int64Index`` .. versionadded:: 0.25.0 @@ -658,53 +680,58 @@ def _union(self, other: Index, sort): ------- union : Index """ - if isinstance(other, RangeIndex) and sort is None: - start_s, step_s = self.start, self.step - end_s = self.start + self.step * (len(self) - 1) - start_o, step_o = other.start, other.step - end_o = other.start + other.step * (len(other) - 1) - if self.step < 0: - start_s, step_s, end_s = end_s, -step_s, start_s - if other.step < 0: - start_o, step_o, end_o = end_o, -step_o, start_o - if len(self) == 1 and len(other) == 1: - step_s = step_o = abs(self.start - other.start) - elif len(self) == 1: - step_s = step_o - elif len(other) == 1: - step_o = step_s - start_r = min(start_s, start_o) - end_r = max(end_s, end_o) - if step_o == step_s: - if ( - (start_s - start_o) % step_s == 0 - and (start_s - end_o) <= step_s - and (start_o - end_s) <= step_s - ): - return type(self)(start_r, end_r + step_s, step_s) - if ( - (step_s % 2 == 0) - and (abs(start_s - start_o) == step_s / 2) - and (abs(end_s - end_o) == step_s / 2) - ): - # e.g. range(0, 10, 2) and range(1, 11, 2) - # but not range(0, 20, 4) and range(1, 21, 4) GH#44019 - return type(self)(start_r, end_r + step_s / 2, step_s / 2) - - elif step_o % step_s == 0: - if ( - (start_o - start_s) % step_s == 0 - and (start_o + step_s >= start_s) - and (end_o - step_s <= end_s) - ): - return type(self)(start_r, end_r + step_s, step_s) - elif step_s % step_o == 0: - if ( - (start_s - start_o) % step_o == 0 - and (start_s + step_o >= start_o) - and (end_s - step_o <= end_o) - ): - return type(self)(start_r, end_r + step_o, step_o) + if isinstance(other, RangeIndex): + if sort is None or ( + sort is False and self.step > 0 and self._range_in_self(other._range) + ): + # GH 47557: Can still return a RangeIndex + # if other range in self and sort=False + start_s, step_s = self.start, self.step + end_s = self.start + self.step * (len(self) - 1) + start_o, step_o = other.start, other.step + end_o = other.start + other.step * (len(other) - 1) + if self.step < 0: + start_s, step_s, end_s = end_s, -step_s, start_s + if other.step < 0: + start_o, step_o, end_o = end_o, -step_o, start_o + if len(self) == 1 and len(other) == 1: + step_s = step_o = abs(self.start - other.start) + elif len(self) == 1: + step_s = step_o + elif len(other) == 1: + step_o = step_s + start_r = min(start_s, start_o) + end_r = max(end_s, end_o) + if step_o == step_s: + if ( + (start_s - start_o) % step_s == 0 + and (start_s - end_o) <= step_s + and (start_o - end_s) <= step_s + ): + return type(self)(start_r, end_r + step_s, step_s) + if ( + (step_s % 2 == 0) + and (abs(start_s - start_o) == step_s / 2) + and (abs(end_s - end_o) == step_s / 2) + ): + # e.g. range(0, 10, 2) and range(1, 11, 2) + # but not range(0, 20, 4) and range(1, 21, 4) GH#44019 + return type(self)(start_r, end_r + step_s / 2, step_s / 2) + + elif step_o % step_s == 0: + if ( + (start_o - start_s) % step_s == 0 + and (start_o + step_s >= start_s) + and (end_o - step_s <= end_s) + ): + return type(self)(start_r, end_r + step_s, step_s) + elif step_s % step_o == 0: + if ( + (start_s - start_o) % step_o == 0 + and (start_s + step_o >= start_o) + and (end_s - step_o <= end_o) + ): + return type(self)(start_r, end_r + step_o, step_o) return super()._union(other, sort=sort) diff --git a/pandas/tests/indexes/ranges/test_setops.py b/pandas/tests/indexes/ranges/test_setops.py index 2942010af2720..71bd2f5590b8f 100644 --- a/pandas/tests/indexes/ranges/test_setops.py +++ b/pandas/tests/indexes/ranges/test_setops.py @@ -145,8 +145,9 @@ def test_union_noncomparable(self, sort): expected = Index(np.concatenate((other, index))) tm.assert_index_equal(result, expected) - @pytest.fixture( - params=[ + @pytest.mark.parametrize( + "idx1, idx2, expected_sorted, expected_notsorted", + [ ( RangeIndex(0, 10, 1), RangeIndex(0, 10, 1), @@ -157,13 +158,13 @@ def test_union_noncomparable(self, sort): RangeIndex(0, 10, 1), RangeIndex(5, 20, 1), RangeIndex(0, 20, 1), - Int64Index(range(20)), + RangeIndex(0, 20, 1), ), ( RangeIndex(0, 10, 1), RangeIndex(10, 20, 1), RangeIndex(0, 20, 1), - Int64Index(range(20)), + RangeIndex(0, 20, 1), ), ( RangeIndex(0, -10, -1), @@ -175,7 +176,7 @@ def test_union_noncomparable(self, sort): RangeIndex(0, -10, -1), RangeIndex(-10, -20, -1), RangeIndex(-19, 1, 1), - Int64Index(range(0, -20, -1)), + RangeIndex(0, -20, -1), ), ( RangeIndex(0, 10, 2), @@ -205,7 +206,7 @@ def test_union_noncomparable(self, sort): RangeIndex(0, 100, 5), RangeIndex(0, 100, 20), RangeIndex(0, 100, 5), - Int64Index(range(0, 100, 5)), + RangeIndex(0, 100, 5), ), ( RangeIndex(0, -100, -5), @@ -230,7 +231,7 @@ def test_union_noncomparable(self, sort): RangeIndex(0, 100, 2), RangeIndex(100, 150, 200), RangeIndex(0, 102, 2), - Int64Index(range(0, 102, 2)), + RangeIndex(0, 102, 2), ), ( RangeIndex(0, -100, -2), @@ -242,13 +243,13 @@ def test_union_noncomparable(self, sort): RangeIndex(0, -100, -1), RangeIndex(0, -50, -3), RangeIndex(-99, 1, 1), - Int64Index(list(range(0, -100, -1))), + RangeIndex(0, -100, -1), ), ( RangeIndex(0, 1, 1), RangeIndex(5, 6, 10), RangeIndex(0, 6, 5), - Int64Index([0, 5]), + RangeIndex(0, 10, 5), ), ( RangeIndex(0, 10, 5), @@ -274,16 +275,17 @@ def test_union_noncomparable(self, sort): Int64Index([1, 5, 6]), Int64Index([1, 5, 6]), ), - ] + # GH 43885 + ( + RangeIndex(0, 10), + RangeIndex(0, 5), + RangeIndex(0, 10), + RangeIndex(0, 10), + ), + ], + ids=lambda x: repr(x) if isinstance(x, RangeIndex) else x, ) - def unions(self, request): - """Inputs and expected outputs for RangeIndex.union tests""" - return request.param - - def test_union_sorted(self, unions): - - idx1, idx2, expected_sorted, expected_notsorted = unions - + def test_union_sorted(self, idx1, idx2, expected_sorted, expected_notsorted): res1 = idx1.union(idx2, sort=None) tm.assert_index_equal(res1, expected_sorted, exact=True) diff --git a/pandas/tests/reshape/concat/test_index.py b/pandas/tests/reshape/concat/test_index.py index 5796c47884db7..9993700fd0737 100644 --- a/pandas/tests/reshape/concat/test_index.py +++ b/pandas/tests/reshape/concat/test_index.py @@ -432,3 +432,25 @@ def test_concat_index_find_common(self, dtype): [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="Int32") ) tm.assert_frame_equal(result, expected) + + def test_concat_axis_1_sort_false_rangeindex(self): + # GH 46675 + s1 = Series(["a", "b", "c"]) + s2 = Series(["a", "b"]) + s3 = Series(["a", "b", "c", "d"]) + s4 = Series([], dtype=object) + result = concat( + [s1, s2, s3, s4], sort=False, join="outer", ignore_index=False, axis=1 + ) + expected = DataFrame( + [ + ["a"] * 3 + [np.nan], + ["b"] * 3 + [np.nan], + ["c", np.nan] * 2, + [np.nan] * 2 + ["d"] + [np.nan], + ], + dtype=object, + ) + tm.assert_frame_equal( + result, expected, check_index_type=True, check_column_type=True + )
- [x] closes #46675 (Replace xxxx with the Github issue number) - [x] closes #43885 - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added an entry in the latest `doc/source/whatsnew/v1.5.0.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47557
2022-07-01T00:17:47Z
2022-07-08T23:58:32Z
2022-07-08T23:58:31Z
2022-07-09T00:12:05Z
REF: share some constructor code
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py index d3c4815af5759..00dc8b2a4a13b 100644 --- a/pandas/core/arrays/datetimelike.py +++ b/pandas/core/arrays/datetimelike.py @@ -94,6 +94,10 @@ DatetimeTZDtype, ExtensionDtype, ) +from pandas.core.dtypes.generic import ( + ABCCategorical, + ABCMultiIndex, +) from pandas.core.dtypes.missing import ( is_valid_na_for_dtype, isna, @@ -114,6 +118,8 @@ NDArrayBackedExtensionArray, ravel_compat, ) +from pandas.core.arrays.base import ExtensionArray +from pandas.core.arrays.integer import IntegerArray import pandas.core.common as com from pandas.core.construction import ( array as pd_array, @@ -2024,6 +2030,36 @@ def factorize( # type:ignore[override] # Shared Constructor Helpers +def ensure_arraylike_for_datetimelike(data, copy: bool, cls_name: str): + if not hasattr(data, "dtype"): + # e.g. list, tuple + if np.ndim(data) == 0: + # i.e. generator + data = list(data) + data = np.asarray(data) + copy = False + elif isinstance(data, ABCMultiIndex): + raise TypeError(f"Cannot create a {cls_name} from a MultiIndex.") + else: + data = extract_array(data, extract_numpy=True) + + if isinstance(data, IntegerArray): + data = data.to_numpy("int64", na_value=iNaT) + copy = False + elif not isinstance(data, (np.ndarray, ExtensionArray)): + # GH#24539 e.g. xarray, dask object + data = np.asarray(data) + + elif isinstance(data, ABCCategorical): + # GH#18664 preserve tz in going DTI->Categorical->DTI + # TODO: cases where we need to do another pass through maybe_convert_dtype, + # e.g. the categories are timedelta64s + data = data.categories.take(data.codes, fill_value=NaT)._values + copy = False + + return data, copy + + def validate_periods(periods): """ If a `periods` argument is passed to the Datetime/Timedelta Array/Index diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py index c6d0625de00fa..e3a1d1d0705c4 100644 --- a/pandas/core/arrays/datetimes.py +++ b/pandas/core/arrays/datetimes.py @@ -53,7 +53,6 @@ DT64NS_DTYPE, INT64_DTYPE, is_bool_dtype, - is_categorical_dtype, is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64_ns_dtype, @@ -69,13 +68,9 @@ pandas_dtype, ) from pandas.core.dtypes.dtypes import DatetimeTZDtype -from pandas.core.dtypes.generic import ABCMultiIndex from pandas.core.dtypes.missing import isna -from pandas.core.arrays import ( - ExtensionArray, - datetimelike as dtl, -) +from pandas.core.arrays import datetimelike as dtl from pandas.core.arrays._ranges import generate_regular_range from pandas.core.arrays.integer import IntegerArray import pandas.core.common as com @@ -2064,23 +2059,9 @@ def _sequence_to_dt64ns( # if dtype has an embedded tz, capture it tz = validate_tz_from_dtype(dtype, tz) - if not hasattr(data, "dtype"): - # e.g. list, tuple - if np.ndim(data) == 0: - # i.e. generator - data = list(data) - data = np.asarray(data) - copy = False - elif isinstance(data, ABCMultiIndex): - raise TypeError("Cannot create a DatetimeArray from a MultiIndex.") - else: - data = extract_array(data, extract_numpy=True) - - if isinstance(data, IntegerArray): - data = data.to_numpy("int64", na_value=iNaT) - elif not isinstance(data, (np.ndarray, ExtensionArray)): - # GH#24539 e.g. xarray, dask object - data = np.asarray(data) + data, copy = dtl.ensure_arraylike_for_datetimelike( + data, copy, cls_name="DatetimeArray" + ) if isinstance(data, DatetimeArray): inferred_freq = data.freq @@ -2320,13 +2301,6 @@ def maybe_convert_dtype(data, copy: bool, tz: tzinfo | None = None): "Passing PeriodDtype data is invalid. Use `data.to_timestamp()` instead" ) - elif is_categorical_dtype(data.dtype): - # GH#18664 preserve tz in going DTI->Categorical->DTI - # TODO: cases where we need to do another pass through this func, - # e.g. the categories are timedelta64s - data = data.categories.take(data.codes, fill_value=NaT)._values - copy = False - elif is_extension_array_dtype(data.dtype) and not is_datetime64tz_dtype(data.dtype): # TODO: We have no tests for these data = np.array(data, dtype=np.object_) diff --git a/pandas/core/arrays/timedeltas.py b/pandas/core/arrays/timedeltas.py index b2dabc5276ba6..74bd345fe4e18 100644 --- a/pandas/core/arrays/timedeltas.py +++ b/pandas/core/arrays/timedeltas.py @@ -51,15 +51,10 @@ is_timedelta64_dtype, pandas_dtype, ) -from pandas.core.dtypes.generic import ( - ABCCategorical, - ABCMultiIndex, -) from pandas.core.dtypes.missing import isna from pandas.core import nanops from pandas.core.arrays import ( - ExtensionArray, IntegerArray, datetimelike as dtl, ) @@ -936,26 +931,9 @@ def sequence_to_td64ns( if unit is not None: unit = parse_timedelta_unit(unit) - # Unwrap whatever we have into a np.ndarray - if not hasattr(data, "dtype"): - # e.g. list, tuple - if np.ndim(data) == 0: - # i.e. generator - data = list(data) - data = np.array(data, copy=False) - elif isinstance(data, ABCMultiIndex): - raise TypeError("Cannot create a TimedeltaArray from a MultiIndex.") - else: - data = extract_array(data, extract_numpy=True) - - if isinstance(data, IntegerArray): - data = data.to_numpy("int64", na_value=iNaT) - elif not isinstance(data, (np.ndarray, ExtensionArray)): - # GH#24539 e.g. xarray, dask object - data = np.asarray(data) - elif isinstance(data, ABCCategorical): - data = data.categories.take(data.codes, fill_value=NaT)._values - copy = False + data, copy = dtl.ensure_arraylike_for_datetimelike( + data, copy, cls_name="TimedeltaArray" + ) if isinstance(data, TimedeltaArray): inferred_freq = data.freq
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47555
2022-06-30T21:52:37Z
2022-07-01T02:04:29Z
2022-07-01T02:04:29Z
2022-07-01T14:29:33Z
REF: Reduce duplicative methods between XML parser classes
diff --git a/pandas/io/xml.py b/pandas/io/xml.py index 83cc01752682f..95fac0c739895 100644 --- a/pandas/io/xml.py +++ b/pandas/io/xml.py @@ -7,6 +7,7 @@ import io from typing import ( Any, + Callable, Sequence, ) @@ -177,7 +178,7 @@ def parse_data(self) -> list[dict[str, str | None]]: raise AbstractMethodError(self) - def _parse_nodes(self) -> list[dict[str, str | None]]: + def _parse_nodes(self, elems: list[Any]) -> list[dict[str, str | None]]: """ Parse xml nodes. @@ -197,102 +198,6 @@ def _parse_nodes(self) -> list[dict[str, str | None]]: will have optional keys filled with None values. """ - raise AbstractMethodError(self) - - def _iterparse_nodes(self) -> list[dict[str, str | None]]: - """ - Iterparse xml nodes. - - This method will read in local disk, decompressed XML files for elements - and underlying descendants using iterparse, a method to iterate through - an XML tree without holding entire XML tree in memory. - - Raises - ------ - TypeError - * If `iterparse` is not a dict or its dict value is not list-like. - ParserError - * If `path_or_buffer` is not a physical, decompressed file on disk. - * If no data is returned from selected items in `iterparse`. - - Notes - ----- - Namespace URIs will be removed from return node values. Also, - elements with missing children or attributes in submitted list - will have optional keys filled with None values. - """ - - raise AbstractMethodError(self) - - def _validate_path(self) -> None: - """ - Validate xpath. - - This method checks for syntax, evaluation, or empty nodes return. - - Raises - ------ - SyntaxError - * If xpah is not supported or issues with namespaces. - - ValueError - * If xpah does not return any nodes. - """ - - raise AbstractMethodError(self) - - def _validate_names(self) -> None: - """ - Validate names. - - This method will check if names is a list-like and aligns - with length of parse nodes. - - Raises - ------ - ValueError - * If value is not a list and less then length of nodes. - """ - raise AbstractMethodError(self) - - def _parse_doc(self, raw_doc) -> bytes: - """ - Build tree from path_or_buffer. - - This method will parse XML object into tree - either from string/bytes or file location. - """ - raise AbstractMethodError(self) - - -class _EtreeFrameParser(_XMLFrameParser): - """ - Internal class to parse XML into DataFrames with the Python - standard library XML module: `xml.etree.ElementTree`. - """ - - def parse_data(self) -> list[dict[str, str | None]]: - from xml.etree.ElementTree import XML - - if self.stylesheet is not None: - raise ValueError( - "To use stylesheet, you need lxml installed and selected as parser." - ) - - if self.iterparse is None: - self.xml_doc = XML(self._parse_doc(self.path_or_buffer)) - self._validate_path() - - self._validate_names() - - xml_dicts: list[dict[str, str | None]] = ( - self._parse_nodes() if self.iterparse is None else self._iterparse_nodes() - ) - - return xml_dicts - - def _parse_nodes(self) -> list[dict[str, str | None]]: - elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces) dicts: list[dict[str, str | None]] if self.elems_only and self.attrs_only: @@ -375,8 +280,28 @@ def _parse_nodes(self) -> list[dict[str, str | None]]: return dicts - def _iterparse_nodes(self) -> list[dict[str, str | None]]: - from xml.etree.ElementTree import iterparse + def _iterparse_nodes(self, iterparse: Callable) -> list[dict[str, str | None]]: + """ + Iterparse xml nodes. + + This method will read in local disk, decompressed XML files for elements + and underlying descendants using iterparse, a method to iterate through + an XML tree without holding entire XML tree in memory. + + Raises + ------ + TypeError + * If `iterparse` is not a dict or its dict value is not list-like. + ParserError + * If `path_or_buffer` is not a physical, decompressed file on disk. + * If no data is returned from selected items in `iterparse`. + + Notes + ----- + Namespace URIs will be removed from return node values. Also, + elements with missing children or attributes in submitted list + will have optional keys filled with None values. + """ dicts: list[dict[str, str | None]] = [] row: dict[str, str | None] | None = None @@ -433,7 +358,13 @@ def _iterparse_nodes(self) -> list[dict[str, str | None]]: if curr_elem == row_node and row is not None: dicts.append(row) row = None + elem.clear() + if hasattr(elem, "getprevious"): + while ( + elem.getprevious() is not None and elem.getparent() is not None + ): + del elem.getparent()[0] if dicts == []: raise ParserError("No result from selected items in iterparse.") @@ -446,6 +377,81 @@ def _iterparse_nodes(self) -> list[dict[str, str | None]]: return dicts + def _validate_path(self) -> None: + """ + Validate xpath. + + This method checks for syntax, evaluation, or empty nodes return. + + Raises + ------ + SyntaxError + * If xpah is not supported or issues with namespaces. + + ValueError + * If xpah does not return any nodes. + """ + + raise AbstractMethodError(self) + + def _validate_names(self) -> None: + """ + Validate names. + + This method will check if names is a list-like and aligns + with length of parse nodes. + + Raises + ------ + ValueError + * If value is not a list and less then length of nodes. + """ + raise AbstractMethodError(self) + + def _parse_doc( + self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str] + ) -> bytes: + """ + Build tree from path_or_buffer. + + This method will parse XML object into tree + either from string/bytes or file location. + """ + raise AbstractMethodError(self) + + +class _EtreeFrameParser(_XMLFrameParser): + """ + Internal class to parse XML into DataFrames with the Python + standard library XML module: `xml.etree.ElementTree`. + """ + + def parse_data(self) -> list[dict[str, str | None]]: + from xml.etree.ElementTree import ( + XML, + iterparse, + ) + + if self.stylesheet is not None: + raise ValueError( + "To use stylesheet, you need lxml installed and selected as parser." + ) + + if self.iterparse is None: + self.xml_doc = XML(self._parse_doc(self.path_or_buffer)) + self._validate_path() + elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces) + + self._validate_names() + + xml_dicts: list[dict[str, str | None]] = ( + self._parse_nodes(elems) + if self.iterparse is None + else self._iterparse_nodes(iterparse) + ) + + return xml_dicts + def _validate_path(self) -> None: """ Notes @@ -495,7 +501,9 @@ def _validate_names(self) -> None: f"{type(self.names).__name__} is not a valid type for names" ) - def _parse_doc(self, raw_doc) -> bytes: + def _parse_doc( + self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str] + ) -> bytes: from xml.etree.ElementTree import ( XMLParser, parse, @@ -531,7 +539,10 @@ def parse_data(self) -> list[dict[str, str | None]]: validate xpath, names, optionally parse and run XSLT, and parse original or transformed XML and return specific nodes. """ - from lxml.etree import XML + from lxml.etree import ( + XML, + iterparse, + ) if self.iterparse is None: self.xml_doc = XML(self._parse_doc(self.path_or_buffer)) @@ -541,172 +552,18 @@ def parse_data(self) -> list[dict[str, str | None]]: self.xml_doc = XML(self._transform_doc()) self._validate_path() + elems = self.xml_doc.xpath(self.xpath, namespaces=self.namespaces) self._validate_names() xml_dicts: list[dict[str, str | None]] = ( - self._parse_nodes() if self.iterparse is None else self._iterparse_nodes() + self._parse_nodes(elems) + if self.iterparse is None + else self._iterparse_nodes(iterparse) ) return xml_dicts - def _parse_nodes(self) -> list[dict[str, str | None]]: - elems = self.xml_doc.xpath(self.xpath, namespaces=self.namespaces) - dicts: list[dict[str, str | None]] - - if self.elems_only and self.attrs_only: - raise ValueError("Either element or attributes can be parsed not both.") - - elif self.elems_only: - if self.names: - dicts = [ - { - **( - {el.tag: el.text.strip()} - if el.text and not el.text.isspace() - else {} - ), - **{ - nm: ch.text.strip() if ch.text else None - for nm, ch in zip(self.names, el.xpath("*")) - }, - } - for el in elems - ] - else: - dicts = [ - { - ch.tag: ch.text.strip() if ch.text else None - for ch in el.xpath("*") - } - for el in elems - ] - - elif self.attrs_only: - dicts = [el.attrib for el in elems] - - else: - if self.names: - dicts = [ - { - **el.attrib, - **( - {el.tag: el.text.strip()} - if el.text and not el.text.isspace() - else {} - ), - **{ - nm: ch.text.strip() if ch.text else None - for nm, ch in zip(self.names, el.xpath("*")) - }, - } - for el in elems - ] - else: - dicts = [ - { - **el.attrib, - **( - {el.tag: el.text.strip()} - if el.text and not el.text.isspace() - else {} - ), - **{ - ch.tag: ch.text.strip() if ch.text else None - for ch in el.xpath("*") - }, - } - for el in elems - ] - - if self.namespaces or "}" in list(dicts[0].keys())[0]: - dicts = [ - {k.split("}")[1] if "}" in k else k: v for k, v in d.items()} - for d in dicts - ] - - keys = list(dict.fromkeys([k for d in dicts for k in d.keys()])) - dicts = [{k: d[k] if k in d.keys() else None for k in keys} for d in dicts] - - if self.names: - dicts = [{nm: v for nm, v in zip(self.names, d.values())} for d in dicts] - - return dicts - - def _iterparse_nodes(self) -> list[dict[str, str | None]]: - from lxml.etree import iterparse - - dicts: list[dict[str, str | None]] = [] - row: dict[str, str | None] | None = None - - if not isinstance(self.iterparse, dict): - raise TypeError( - f"{type(self.iterparse).__name__} is not a valid type for iterparse" - ) - - row_node = next(iter(self.iterparse.keys())) if self.iterparse else "" - if not is_list_like(self.iterparse[row_node]): - raise TypeError( - f"{type(self.iterparse[row_node])} is not a valid type " - "for value in iterparse" - ) - - if ( - not isinstance(self.path_or_buffer, str) - or is_url(self.path_or_buffer) - or is_fsspec_url(self.path_or_buffer) - or self.path_or_buffer.startswith(("<?xml", "<")) - or infer_compression(self.path_or_buffer, "infer") is not None - ): - raise ParserError( - "iterparse is designed for large XML files that are fully extracted on " - "local disk and not as compressed files or online sources." - ) - - for event, elem in iterparse(self.path_or_buffer, events=("start", "end")): - curr_elem = elem.tag.split("}")[1] if "}" in elem.tag else elem.tag - - if event == "start": - if curr_elem == row_node: - row = {} - - if row is not None: - if self.names: - for col, nm in zip(self.iterparse[row_node], self.names): - if curr_elem == col: - elem_val = elem.text.strip() if elem.text else None - if elem_val not in row.values() and nm not in row: - row[nm] = elem_val - if col in elem.attrib: - if elem.attrib[col] not in row.values() and nm not in row: - row[nm] = elem.attrib[col] - else: - for col in self.iterparse[row_node]: - if curr_elem == col: - row[col] = elem.text.strip() if elem.text else None - if col in elem.attrib: - row[col] = elem.attrib[col] - - if event == "end": - if curr_elem == row_node and row is not None: - dicts.append(row) - row = None - - elem.clear() - while elem.getprevious() is not None and elem.getparent() is not None: - del elem.getparent()[0] - - if dicts == []: - raise ParserError("No result from selected items in iterparse.") - - keys = list(dict.fromkeys([k for d in dicts for k in d.keys()])) - dicts = [{k: d[k] if k in d.keys() else None for k in keys} for d in dicts] - - if self.names: - dicts = [{nm: v for nm, v in zip(self.names, d.values())} for d in dicts] - - return dicts - def _validate_path(self) -> None: msg = ( @@ -748,7 +605,9 @@ def _validate_names(self) -> None: f"{type(self.names).__name__} is not a valid type for names" ) - def _parse_doc(self, raw_doc) -> bytes: + def _parse_doc( + self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str] + ) -> bytes: from lxml.etree import ( XMLParser, fromstring,
- [X] Relates to #40131 - [X] [Tests passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests). - [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) _(unnecessary)_ - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file _(unnecessary)_
https://api.github.com/repos/pandas-dev/pandas/pulls/47553
2022-06-30T03:11:47Z
2022-07-01T17:23:12Z
2022-07-01T17:23:12Z
2022-07-01T18:59:29Z
ENH/TST: Add BaseMethodsTests tests for ArrowExtensionArray
diff --git a/pandas/core/base.py b/pandas/core/base.py index 3d18194d14bec..33dbc292660d0 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -983,10 +983,12 @@ def unique(self): if not isinstance(values, np.ndarray): result: ArrayLike = values.unique() - if self.dtype.kind in ["m", "M"] and isinstance(self, ABCSeries): - # GH#31182 Series._values returns EA, unpack for backward-compat - if getattr(self.dtype, "tz", None) is None: - result = np.asarray(result) + if ( + isinstance(self.dtype, np.dtype) and self.dtype.kind in ["m", "M"] + ) and isinstance(self, ABCSeries): + # GH#31182 Series._values returns EA + # unpack numpy datetime for backward-compat + result = np.asarray(result) else: result = unique1d(values) diff --git a/pandas/tests/extension/base/methods.py b/pandas/tests/extension/base/methods.py index 6e9130b18e94f..838c9f5b8a35f 100644 --- a/pandas/tests/extension/base/methods.py +++ b/pandas/tests/extension/base/methods.py @@ -5,6 +5,7 @@ import pytest from pandas.core.dtypes.common import is_bool_dtype +from pandas.core.dtypes.missing import na_value_for_dtype import pandas as pd import pandas._testing as tm @@ -49,8 +50,7 @@ def test_value_counts_with_normalize(self, data): else: expected = pd.Series(0.0, index=result.index) expected[result > 0] = 1 / len(values) - - if isinstance(data.dtype, pd.core.dtypes.dtypes.BaseMaskedDtype): + if na_value_for_dtype(data.dtype) is pd.NA: # TODO(GH#44692): avoid special-casing expected = expected.astype("Float64") diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py index ac7b2f73f202c..861cc44310751 100644 --- a/pandas/tests/extension/test_arrow.py +++ b/pandas/tests/extension/test_arrow.py @@ -153,6 +153,32 @@ def data_for_grouping(dtype): return pd.array([B, B, None, None, A, A, B, C], dtype=dtype) +@pytest.fixture +def data_for_sorting(data_for_grouping): + """ + Length-3 array with a known sort order. + + This should be three items [B, C, A] with + A < B < C + """ + return type(data_for_grouping)._from_sequence( + [data_for_grouping[0], data_for_grouping[7], data_for_grouping[4]] + ) + + +@pytest.fixture +def data_missing_for_sorting(data_for_grouping): + """ + Length-3 array with a known sort order. + + This should be three items [B, NA, A] with + A < B and NA missing. + """ + return type(data_for_grouping)._from_sequence( + [data_for_grouping[0], data_for_grouping[2], data_for_grouping[4]] + ) + + @pytest.fixture def na_value(): """The scalar missing value for this type. Default 'None'""" @@ -654,7 +680,7 @@ def test_setitem_loc_scalar_single(self, data, using_array_manager, request): if pa_version_under2p0 and tz not in (None, "UTC"): request.node.add_marker( pytest.mark.xfail( - reason=(f"Not supported by pyarrow < 2.0 with timestamp type {tz}") + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" ) ) elif using_array_manager and pa.types.is_duration(data.dtype.pyarrow_dtype): @@ -988,6 +1014,287 @@ def test_EA_types(self, engine, data, request): super().test_EA_types(engine, data) +class TestBaseMethods(base.BaseMethodsTests): + @pytest.mark.parametrize("dropna", [True, False]) + def test_value_counts(self, all_data, dropna, request): + pa_dtype = all_data.dtype.pyarrow_dtype + if pa.types.is_date(pa_dtype) or ( + pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None + ): + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError, + reason="GH 34986", + ) + ) + elif pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"value_count has no kernel for {pa_dtype}", + ) + ) + super().test_value_counts(all_data, dropna) + + def test_value_counts_with_normalize(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + if pa.types.is_date(pa_dtype) or ( + pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None + ): + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError, + reason="GH 34986", + ) + ) + elif pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"value_count has no pyarrow kernel for {pa_dtype}", + ) + ) + super().test_value_counts_with_normalize(data) + + def test_argmin_argmax( + self, data_for_sorting, data_missing_for_sorting, na_value, request + ): + pa_dtype = data_for_sorting.dtype.pyarrow_dtype + if pa.types.is_boolean(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"{pa_dtype} only has 2 unique possible values", + ) + ) + super().test_argmin_argmax(data_for_sorting, data_missing_for_sorting, na_value) + + @pytest.mark.parametrize("ascending", [True, False]) + def test_sort_values(self, data_for_sorting, ascending, sort_by_key, request): + pa_dtype = data_for_sorting.dtype.pyarrow_dtype + if pa.types.is_duration(pa_dtype) and not ascending and not pa_version_under2p0: + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=( + f"unique has no pyarrow kernel " + f"for {pa_dtype} when ascending={ascending}" + ), + ) + ) + super().test_sort_values(data_for_sorting, ascending, sort_by_key) + + @pytest.mark.parametrize("ascending", [True, False]) + def test_sort_values_frame(self, data_for_sorting, ascending, request): + pa_dtype = data_for_sorting.dtype.pyarrow_dtype + if pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=( + f"dictionary_encode has no pyarrow kernel " + f"for {pa_dtype} when ascending={ascending}" + ), + ) + ) + super().test_sort_values_frame(data_for_sorting, ascending) + + @pytest.mark.parametrize("box", [pd.Series, lambda x: x]) + @pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique]) + def test_unique(self, data, box, method, request): + pa_dtype = data.dtype.pyarrow_dtype + if pa.types.is_duration(pa_dtype) and not pa_version_under2p0: + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"unique has no pyarrow kernel for {pa_dtype}.", + ) + ) + super().test_unique(data, box, method) + + @pytest.mark.parametrize("na_sentinel", [-1, -2]) + def test_factorize(self, data_for_grouping, na_sentinel, request): + pa_dtype = data_for_grouping.dtype.pyarrow_dtype + if pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"dictionary_encode has no pyarrow kernel for {pa_dtype}", + ) + ) + elif pa.types.is_boolean(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"{pa_dtype} only has 2 unique possible values", + ) + ) + super().test_factorize(data_for_grouping, na_sentinel) + + @pytest.mark.parametrize("na_sentinel", [-1, -2]) + def test_factorize_equivalence(self, data_for_grouping, na_sentinel, request): + pa_dtype = data_for_grouping.dtype.pyarrow_dtype + if pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"dictionary_encode has no pyarrow kernel for {pa_dtype}", + ) + ) + super().test_factorize_equivalence(data_for_grouping, na_sentinel) + + def test_factorize_empty(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + if pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"dictionary_encode has no pyarrow kernel for {pa_dtype}", + ) + ) + super().test_factorize_empty(data) + + def test_fillna_copy_frame(self, data_missing, request, using_array_manager): + pa_dtype = data_missing.dtype.pyarrow_dtype + if using_array_manager and pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Checking ndim when using arraymanager with {pa_dtype}" + ) + ) + super().test_fillna_copy_frame(data_missing) + + def test_fillna_copy_series(self, data_missing, request, using_array_manager): + pa_dtype = data_missing.dtype.pyarrow_dtype + if using_array_manager and pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Checking ndim when using arraymanager with {pa_dtype}" + ) + ) + super().test_fillna_copy_series(data_missing) + + def test_shift_fill_value(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + tz = getattr(pa_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_shift_fill_value(data) + + @pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]]) + def test_repeat(self, data, repeats, as_series, use_numpy, request): + pa_dtype = data.dtype.pyarrow_dtype + tz = getattr(pa_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC") and repeats != 0: + request.node.add_marker( + pytest.mark.xfail( + reason=( + f"Not supported by pyarrow < 2.0 with " + f"timestamp type {tz} when repeats={repeats}" + ) + ) + ) + super().test_repeat(data, repeats, as_series, use_numpy) + + def test_insert(self, data, request): + pa_dtype = data.dtype.pyarrow_dtype + tz = getattr(pa_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_insert(data) + + def test_combine_first(self, data, request, using_array_manager): + pa_dtype = data.dtype.pyarrow_dtype + tz = getattr(pa_dtype, "tz", None) + if using_array_manager and pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Checking ndim when using arraymanager with {pa_dtype}" + ) + ) + elif pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" + ) + ) + super().test_combine_first(data) + + @pytest.mark.parametrize("frame", [True, False]) + @pytest.mark.parametrize( + "periods, indices", + [(-2, [2, 3, 4, -1, -1]), (0, [0, 1, 2, 3, 4]), (2, [-1, -1, 0, 1, 2])], + ) + def test_container_shift( + self, data, frame, periods, indices, request, using_array_manager + ): + pa_dtype = data.dtype.pyarrow_dtype + if ( + using_array_manager + and pa.types.is_duration(pa_dtype) + and periods in (-2, 2) + ): + request.node.add_marker( + pytest.mark.xfail( + reason=( + f"Checking ndim when using arraymanager with " + f"{pa_dtype} and periods={periods}" + ) + ) + ) + super().test_container_shift(data, frame, periods, indices) + + @pytest.mark.xfail( + reason="result dtype pyarrow[bool] better than expected dtype object" + ) + def test_combine_le(self, data_repeated): + super().test_combine_le(data_repeated) + + def test_combine_add(self, data_repeated, request): + pa_dtype = next(data_repeated(1)).dtype.pyarrow_dtype + if pa.types.is_temporal(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=TypeError, + reason=f"{pa_dtype} cannot be added to {pa_dtype}", + ) + ) + super().test_combine_add(data_repeated) + + def test_searchsorted(self, data_for_sorting, as_series, request): + pa_dtype = data_for_sorting.dtype.pyarrow_dtype + if pa.types.is_boolean(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"{pa_dtype} only has 2 unique possible values", + ) + ) + super().test_searchsorted(data_for_sorting, as_series) + + def test_where_series(self, data, na_value, as_frame, request, using_array_manager): + pa_dtype = data.dtype.pyarrow_dtype + if using_array_manager and pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Checking ndim when using arraymanager with {pa_dtype}" + ) + ) + elif pa.types.is_temporal(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"Unsupported cast from double to {pa_dtype}", + ) + ) + super().test_where_series(data, na_value, as_frame) + + def test_arrowdtype_construct_from_string_type_with_unsupported_parameters(): with pytest.raises(NotImplementedError, match="Passing pyarrow type"): ArrowDtype.construct_from_string("timestamp[s, tz=UTC][pyarrow]") diff --git a/pandas/tests/extension/test_string.py b/pandas/tests/extension/test_string.py index 8a8bdee90e467..6cea21b6672d8 100644 --- a/pandas/tests/extension/test_string.py +++ b/pandas/tests/extension/test_string.py @@ -167,9 +167,7 @@ def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna): class TestMethods(base.BaseMethodsTests): - @pytest.mark.xfail(reason="returns nullable: GH 44692") - def test_value_counts_with_normalize(self, data): - super().test_value_counts_with_normalize(data) + pass class TestCasting(base.BaseCastingTests):
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
https://api.github.com/repos/pandas-dev/pandas/pulls/47552
2022-06-30T00:09:54Z
2022-07-05T19:59:18Z
2022-07-05T19:59:18Z
2022-07-05T19:59:28Z
ASV: Non-unique DataFrame index
diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py index 2c0e2e6ca442a..69e3d166943a8 100644 --- a/asv_bench/benchmarks/indexing.py +++ b/asv_bench/benchmarks/indexing.py @@ -157,25 +157,39 @@ def time_boolean_rows_boolean(self): class DataFrameNumericIndexing: - def setup(self): + + params = [ + (Int64Index, UInt64Index, Float64Index), + ("unique_monotonic_inc", "nonunique_monotonic_inc"), + ] + param_names = ["index_dtype", "index_structure"] + + def setup(self, index, index_structure): + N = 10**5 + indices = { + "unique_monotonic_inc": index(range(N)), + "nonunique_monotonic_inc": index( + list(range(55)) + [54] + list(range(55, N - 1)) + ), + } self.idx_dupe = np.array(range(30)) * 99 - self.df = DataFrame(np.random.randn(100000, 5)) + self.df = DataFrame(np.random.randn(N, 5), index=indices[index_structure]) self.df_dup = concat([self.df, 2 * self.df, 3 * self.df]) - self.bool_indexer = [True] * 50000 + [False] * 50000 + self.bool_indexer = [True] * (N // 2) + [False] * (N - N // 2) - def time_iloc_dups(self): + def time_iloc_dups(self, index, index_structure): self.df_dup.iloc[self.idx_dupe] - def time_loc_dups(self): + def time_loc_dups(self, index, index_structure): self.df_dup.loc[self.idx_dupe] - def time_iloc(self): + def time_iloc(self, index, index_structure): self.df.iloc[:100, 0] - def time_loc(self): + def time_loc(self, index, index_structure): self.df.loc[:100, 0] - def time_bool_indexer(self): + def time_bool_indexer(self, index, index_structure): self.df[self.bool_indexer]
- [x] closes #26064 - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). I added an asv benchmark for non-unique indices in DataFrames - based on the benchmark of indices in Series.
https://api.github.com/repos/pandas-dev/pandas/pulls/47551
2022-06-29T20:49:03Z
2022-06-30T16:18:32Z
2022-06-30T16:18:32Z
2022-06-30T17:01:58Z
REGR: fix eval with inplace=True to correctly update column values inplace
diff --git a/doc/source/whatsnew/v1.4.4.rst b/doc/source/whatsnew/v1.4.4.rst index 57b8fdee5888a..1e7ed256c05ef 100644 --- a/doc/source/whatsnew/v1.4.4.rst +++ b/doc/source/whatsnew/v1.4.4.rst @@ -19,6 +19,7 @@ Fixed regressions - Fixed regression in :meth:`DataFrame.loc` not updating the cache correctly after values were set (:issue:`47867`) - Fixed regression in :meth:`DataFrame.loc` not aligning index in some cases when setting a :class:`DataFrame` (:issue:`47578`) - Fixed regression in setting ``None`` or non-string value into a ``string``-dtype Series using a mask (:issue:`47628`) +- Fixed regression in :meth:`DataFrame.eval` creating a copy when updating inplace (:issue:`47449`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/computation/eval.py b/pandas/core/computation/eval.py index fa0ef46b850d1..0a9bb6a4bb6fa 100644 --- a/pandas/core/computation/eval.py +++ b/pandas/core/computation/eval.py @@ -19,6 +19,7 @@ ) from pandas.core.computation.parsing import tokenize_string from pandas.core.computation.scope import ensure_scope +from pandas.core.generic import NDFrame from pandas.io.formats.printing import pprint_thing @@ -387,7 +388,10 @@ def eval( try: with warnings.catch_warnings(record=True): # TODO: Filter the warnings we actually care about here. - target[assigner] = ret + if inplace and isinstance(target, NDFrame): + target.loc[:, assigner] = ret + else: + target[assigner] = ret except (TypeError, IndexError) as err: raise ValueError("Cannot assign expression output to target") from err diff --git a/pandas/tests/computation/test_eval.py b/pandas/tests/computation/test_eval.py index 672a87edbad66..b3b80e8e5db65 100644 --- a/pandas/tests/computation/test_eval.py +++ b/pandas/tests/computation/test_eval.py @@ -1912,6 +1912,26 @@ def test_eval_no_support_column_name(request, column): tm.assert_frame_equal(result, expected) +@td.skip_array_manager_not_yet_implemented +def test_set_inplace(using_copy_on_write): + # https://github.com/pandas-dev/pandas/issues/47449 + # Ensure we don't only update the DataFrame inplace, but also the actual + # column values, such that references to this column also get updated + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result_view = df[:] + ser = df["A"] + df.eval("A = B + C", inplace=True) + expected = DataFrame({"A": [11, 13, 15], "B": [4, 5, 6], "C": [7, 8, 9]}) + tm.assert_frame_equal(df, expected) + if not using_copy_on_write: + tm.assert_series_equal(ser, expected["A"]) + tm.assert_series_equal(result_view["A"], expected["A"]) + else: + expected = Series([1, 2, 3], name="A") + tm.assert_series_equal(ser, expected) + tm.assert_series_equal(result_view["A"], expected) + + class TestValidate: @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0]) def test_validate_bool_args(self, value):
- [x] closes #47449 - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. The bad commit that causes this issue (see https://github.com/pandas-dev/pandas/pull/43406) is the rootcause for multiple others, so am not sure if we want to tackle the overall problem bit by bit or if there is a generic solution to all of them.
https://api.github.com/repos/pandas-dev/pandas/pulls/47550
2022-06-29T16:32:14Z
2022-08-19T10:21:24Z
2022-08-19T10:21:24Z
2022-08-19T13:16:50Z
REGR: NotImplementedError: Prefix not defined when slicing offset with loc
diff --git a/doc/source/whatsnew/v1.4.4.rst b/doc/source/whatsnew/v1.4.4.rst index 927841d42b543..05ebe424a8ea9 100644 --- a/doc/source/whatsnew/v1.4.4.rst +++ b/doc/source/whatsnew/v1.4.4.rst @@ -22,6 +22,7 @@ Fixed regressions - Fixed regression in :meth:`DataFrame.loc` not updating the cache correctly after values were set (:issue:`47867`) - Fixed regression in :meth:`DataFrame.loc` not aligning index in some cases when setting a :class:`DataFrame` (:issue:`47578`) - Fixed regression in :meth:`DataFrame.loc` setting a length-1 array like value to a single value in the DataFrame (:issue:`46268`) +- Fixed regression when slicing with :meth:`DataFrame.loc` with :class:`DateOffset`-index (:issue:`46671`) - Fixed regression in setting ``None`` or non-string value into a ``string``-dtype Series using a mask (:issue:`47628`) - Fixed regression using custom Index subclasses (for example, used in xarray) with :meth:`~DataFrame.reset_index` or :meth:`Index.insert` (:issue:`47071`) - Fixed regression in :meth:`DatetimeIndex.intersection` when the :class:`DatetimeIndex` has dates crossing daylight savings time (:issue:`46702`) diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py index 84955d5137383..6867ef936d45e 100644 --- a/pandas/core/indexes/datetimelike.py +++ b/pandas/core/indexes/datetimelike.py @@ -223,7 +223,12 @@ def _parsed_string_to_bounds(self, reso: Resolution, parsed): def _parse_with_reso(self, label: str): # overridden by TimedeltaIndex - parsed, reso_str = parsing.parse_time_string(label, self.freq) + try: + if self.freq is None or hasattr(self.freq, "rule_code"): + freq = self.freq + except NotImplementedError: + freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None)) + parsed, reso_str = parsing.parse_time_string(label, freq) reso = Resolution.from_attrname(reso_str) return parsed, reso diff --git a/pandas/tests/frame/indexing/test_getitem.py b/pandas/tests/frame/indexing/test_getitem.py index a98fa52e1009d..f5c85bd98d8ad 100644 --- a/pandas/tests/frame/indexing/test_getitem.py +++ b/pandas/tests/frame/indexing/test_getitem.py @@ -8,12 +8,14 @@ CategoricalDtype, CategoricalIndex, DataFrame, + DateOffset, DatetimeIndex, Index, MultiIndex, Series, Timestamp, concat, + date_range, get_dummies, period_range, ) @@ -172,6 +174,47 @@ def test_getitem_iloc_two_dimensional_generator(self): expected = Series([5, 6], name="b", index=[1, 2]) tm.assert_series_equal(result, expected) + def test_getitem_iloc_dateoffset_days(self): + # GH 46671 + df = DataFrame( + list(range(10)), + index=date_range("01-01-2022", periods=10, freq=DateOffset(days=1)), + ) + result = df.loc["2022-01-01":"2022-01-03"] + expected = DataFrame( + [0, 1, 2], + index=DatetimeIndex( + ["2022-01-01", "2022-01-02", "2022-01-03"], + dtype="datetime64[ns]", + freq=DateOffset(days=1), + ), + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + list(range(10)), + index=date_range( + "01-01-2022", periods=10, freq=DateOffset(days=1, hours=2) + ), + ) + result = df.loc["2022-01-01":"2022-01-03"] + expected = DataFrame( + [0, 1, 2], + index=DatetimeIndex( + ["2022-01-01 00:00:00", "2022-01-02 02:00:00", "2022-01-03 04:00:00"], + dtype="datetime64[ns]", + freq=DateOffset(days=1, hours=2), + ), + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + list(range(10)), + index=date_range("01-01-2022", periods=10, freq=DateOffset(minutes=3)), + ) + result = df.loc["2022-01-01":"2022-01-03"] + tm.assert_frame_equal(result, df) + class TestGetitemCallable: def test_getitem_callable(self, float_frame):
- [x] closes #46671 - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/v1.4.4.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47547
2022-06-29T09:09:29Z
2022-08-23T05:06:32Z
2022-08-23T05:06:32Z
2022-08-30T14:10:13Z
DOC: Convert URL example to static code block
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst index 4c5d189e1bba3..7d1aa76613d33 100644 --- a/doc/source/user_guide/io.rst +++ b/doc/source/user_guide/io.rst @@ -2559,16 +2559,29 @@ Let's look at a few examples. Read a URL with no options: -.. ipython:: python +.. code-block:: ipython - url = "https://www.fdic.gov/resources/resolutions/bank-failures/failed-bank-list" - dfs = pd.read_html(url) - dfs + In [320]: "https://www.fdic.gov/resources/resolutions/bank-failures/failed-bank-list" + In [321]: pd.read_html(url) + Out[321]: + [ Bank NameBank CityCity StateSt ... Acquiring InstitutionAI Closing DateClosing FundFund + 0 Almena State Bank Almena KS ... Equity Bank October 23, 2020 10538 + 1 First City Bank of Florida Fort Walton Beach FL ... United Fidelity Bank, fsb October 16, 2020 10537 + 2 The First State Bank Barboursville WV ... MVB Bank, Inc. April 3, 2020 10536 + 3 Ericson State Bank Ericson NE ... Farmers and Merchants Bank February 14, 2020 10535 + 4 City National Bank of New Jersey Newark NJ ... Industrial Bank November 1, 2019 10534 + .. ... ... ... ... ... ... ... + 558 Superior Bank, FSB Hinsdale IL ... Superior Federal, FSB July 27, 2001 6004 + 559 Malta National Bank Malta OH ... North Valley Bank May 3, 2001 4648 + 560 First Alliance Bank & Trust Co. Manchester NH ... Southern New Hampshire Bank & Trust February 2, 2001 4647 + 561 National State Bank of Metropolis Metropolis IL ... Banterra Bank of Marion December 14, 2000 4646 + 562 Bank of Honolulu Honolulu HI ... Bank of the Orient October 13, 2000 4645 + + [563 rows x 7 columns]] .. note:: - The data from the above URL changes every Monday so the resulting data above - and the data below may be slightly different. + The data from the above URL changes every Monday so the resulting data above may be slightly different. Read in the content of the file from the above URL and pass it to ``read_html`` as a string:
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). Avoids flaky doc build failure if URL is not available: https://github.com/pandas-dev/pandas/runs/7100909800?check_suite_focus=true
https://api.github.com/repos/pandas-dev/pandas/pulls/47540
2022-06-28T23:19:38Z
2022-06-30T20:49:55Z
2022-06-30T20:49:55Z
2022-06-30T20:49:58Z
BUG: PeriodArray.__sub__(Period) overflows
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 452b4259d4460..40f13726b90b8 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -904,6 +904,7 @@ Period - Bug in constructing a :class:`Period` from a :class:`Timestamp` or ``np.datetime64`` object with non-zero nanoseconds and ``freq="ns"`` incorrectly truncating the nanoseconds (:issue:`46811`) - Bug in adding ``np.timedelta64("NaT", "ns")`` to a :class:`Period` with a timedelta-like freq incorrectly raising ``IncompatibleFrequency`` instead of returning ``NaT`` (:issue:`47196`) - Bug in adding an array of integers to an array with :class:`PeriodDtype` giving incorrect results when ``dtype.freq.n > 1`` (:issue:`47209`) +- Bug in subtracting a :class:`Period` from an array with :class:`PeriodDtype` returning incorrect results instead of raising ``OverflowError`` when the operation overflows (:issue:`47538`) - Plotting diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py index 5e65b124ae0f4..d3c4815af5759 100644 --- a/pandas/core/arrays/datetimelike.py +++ b/pandas/core/arrays/datetimelike.py @@ -1182,8 +1182,9 @@ def _sub_period(self, other: Period) -> npt.NDArray[np.object_]: # If the operation is well-defined, we return an object-dtype ndarray # of DateOffsets. Null entries are filled with pd.NaT self._check_compatible_with(other) - asi8 = self.asi8 - new_i8_data = asi8 - other.ordinal # TODO: checked_add_with_arr + new_i8_data = checked_add_with_arr( + self.asi8, -other.ordinal, arr_mask=self._isnan + ) new_data = np.array([self.freq.base * x for x in new_i8_data]) if self._hasna: diff --git a/pandas/tests/arrays/test_period.py b/pandas/tests/arrays/test_period.py index de0e766e4a2aa..a4b442ff526e9 100644 --- a/pandas/tests/arrays/test_period.py +++ b/pandas/tests/arrays/test_period.py @@ -115,6 +115,20 @@ def test_sub_period(): arr - other +def test_sub_period_overflow(): + # GH#47538 + dti = pd.date_range("1677-09-22", periods=2, freq="D") + pi = dti.to_period("ns") + + per = pd.Period._from_ordinal(10**14, pi.freq) + + with pytest.raises(OverflowError, match="Overflow in int64 addition"): + pi - per + + with pytest.raises(OverflowError, match="Overflow in int64 addition"): + per - pi + + # ---------------------------------------------------------------------------- # Methods
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47538
2022-06-28T21:51:05Z
2022-06-28T23:22:04Z
2022-06-28T23:22:04Z
2022-06-28T23:32:47Z
ENH: DatetimeIndex.snap support non-nano
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py index 4bc01b8f4ddb0..aacc2a2225743 100644 --- a/pandas/core/indexes/datetimes.py +++ b/pandas/core/indexes/datetimes.py @@ -44,7 +44,6 @@ from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.common import ( - DT64NS_DTYPE, is_datetime64_dtype, is_datetime64tz_dtype, is_scalar, @@ -552,7 +551,7 @@ def snap(self, freq="S") -> DatetimeIndex: # Superdumb, punting on any optimizing freq = to_offset(freq) - snapped = np.empty(len(self), dtype=DT64NS_DTYPE) + dta = self._data.copy() for i, v in enumerate(self): s = v @@ -563,9 +562,8 @@ def snap(self, freq="S") -> DatetimeIndex: s = t0 else: s = t1 - snapped[i] = s + dta[i] = s - dta = DatetimeArray(snapped, dtype=self.dtype) return DatetimeIndex._simple_new(dta, name=self.name) # -------------------------------------------------------------------- diff --git a/pandas/tests/indexes/datetimes/methods/test_snap.py b/pandas/tests/indexes/datetimes/methods/test_snap.py index e591441c4f148..a94d00d919082 100644 --- a/pandas/tests/indexes/datetimes/methods/test_snap.py +++ b/pandas/tests/indexes/datetimes/methods/test_snap.py @@ -7,10 +7,30 @@ import pandas._testing as tm +def astype_non_nano(dti_nano, unit): + # TODO(2.0): remove once DTI/DTA.astype supports non-nano + if unit == "ns": + return dti_nano + + dta_nano = dti_nano._data + arr_nano = dta_nano._ndarray + + arr = arr_nano.astype(f"M8[{unit}]") + if dti_nano.tz is None: + dtype = arr.dtype + else: + dtype = type(dti_nano.dtype)(tz=dti_nano.tz, unit=unit) + dta = type(dta_nano)._simple_new(arr, dtype=dtype) + dti = DatetimeIndex(dta, name=dti_nano.name) + assert dti.dtype == dtype + return dti + + @pytest.mark.filterwarnings("ignore::DeprecationWarning") @pytest.mark.parametrize("tz", [None, "Asia/Shanghai", "Europe/Berlin"]) @pytest.mark.parametrize("name", [None, "my_dti"]) -def test_dti_snap(name, tz): +@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"]) +def test_dti_snap(name, tz, unit): dti = DatetimeIndex( [ "1/1/2002", @@ -25,10 +45,12 @@ def test_dti_snap(name, tz): tz=tz, freq="D", ) + dti = astype_non_nano(dti, unit) result = dti.snap(freq="W-MON") expected = date_range("12/31/2001", "1/7/2002", name=name, tz=tz, freq="w-mon") expected = expected.repeat([3, 4]) + expected = astype_non_nano(expected, unit) tm.assert_index_equal(result, expected) assert result.tz == expected.tz assert result.freq is None @@ -38,6 +60,7 @@ def test_dti_snap(name, tz): expected = date_range("1/1/2002", "1/7/2002", name=name, tz=tz, freq="b") expected = expected.repeat([1, 1, 1, 2, 2]) + expected = astype_non_nano(expected, unit) tm.assert_index_equal(result, expected) assert result.tz == expected.tz assert result.freq is None
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47537
2022-06-28T21:44:51Z
2022-06-29T17:12:13Z
2022-06-29T17:12:13Z
2022-06-29T17:22:46Z
ENH/TST: Add BaseParsinngTests tests for ArrowExtensionArray
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py index 1c4187f2aafd2..dfb58f0edd127 100644 --- a/pandas/core/arrays/arrow/array.py +++ b/pandas/core/arrays/arrow/array.py @@ -63,6 +63,23 @@ ArrowExtensionArrayT = TypeVar("ArrowExtensionArrayT", bound="ArrowExtensionArray") +def to_pyarrow_type( + dtype: ArrowDtype | pa.DataType | Dtype | None, +) -> pa.DataType | None: + """ + Convert dtype to a pyarrow type instance. + """ + if isinstance(dtype, ArrowDtype): + pa_dtype = dtype.pyarrow_dtype + elif isinstance(dtype, pa.DataType): + pa_dtype = dtype + elif dtype: + pa_dtype = pa.from_numpy_dtype(dtype) + else: + pa_dtype = None + return pa_dtype + + class ArrowExtensionArray(OpsMixin, ExtensionArray): """ Base class for ExtensionArray backed by Arrow ChunkedArray. @@ -89,13 +106,7 @@ def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy=False): """ Construct a new ExtensionArray from a sequence of scalars. """ - if isinstance(dtype, ArrowDtype): - pa_dtype = dtype.pyarrow_dtype - elif dtype: - pa_dtype = pa.from_numpy_dtype(dtype) - else: - pa_dtype = None - + pa_dtype = to_pyarrow_type(dtype) if isinstance(scalars, cls): data = scalars._data if pa_dtype: @@ -113,7 +124,40 @@ def _from_sequence_of_strings( """ Construct a new ExtensionArray from a sequence of strings. """ - return cls._from_sequence(strings, dtype=dtype, copy=copy) + pa_type = to_pyarrow_type(dtype) + if pa.types.is_timestamp(pa_type): + from pandas.core.tools.datetimes import to_datetime + + scalars = to_datetime(strings, errors="raise") + elif pa.types.is_date(pa_type): + from pandas.core.tools.datetimes import to_datetime + + scalars = to_datetime(strings, errors="raise").date + elif pa.types.is_duration(pa_type): + from pandas.core.tools.timedeltas import to_timedelta + + scalars = to_timedelta(strings, errors="raise") + elif pa.types.is_time(pa_type): + from pandas.core.tools.times import to_time + + # "coerce" to allow "null times" (None) to not raise + scalars = to_time(strings, errors="coerce") + elif pa.types.is_boolean(pa_type): + from pandas.core.arrays import BooleanArray + + scalars = BooleanArray._from_sequence_of_strings(strings).to_numpy() + elif ( + pa.types.is_integer(pa_type) + or pa.types.is_floating(pa_type) + or pa.types.is_decimal(pa_type) + ): + from pandas.core.tools.numeric import to_numeric + + scalars = to_numeric(strings, errors="raise") + else: + # Let pyarrow try to infer or raise + scalars = strings + return cls._from_sequence(scalars, dtype=pa_type, copy=copy) def __getitem__(self, item: PositionalIndexer): """Select a subset of self. diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py index 06ce6901effc7..5e2006811f6eb 100644 --- a/pandas/tests/extension/test_arrow.py +++ b/pandas/tests/extension/test_arrow.py @@ -712,7 +712,7 @@ def test_setitem_loc_iloc_slice(self, data, using_array_manager, request): if pa_version_under2p0 and tz not in (None, "UTC"): request.node.add_marker( pytest.mark.xfail( - reason=(f"Not supported by pyarrow < 2.0 with timestamp type {tz}") + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" ) ) elif using_array_manager and pa.types.is_duration(data.dtype.pyarrow_dtype): @@ -728,7 +728,7 @@ def test_setitem_slice_array(self, data, request): if pa_version_under2p0 and tz not in (None, "UTC"): request.node.add_marker( pytest.mark.xfail( - reason=(f"Not supported by pyarrow < 2.0 with timestamp type {tz}") + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" ) ) super().test_setitem_slice_array(data) @@ -742,7 +742,7 @@ def test_setitem_with_expansion_dataframe_column( if pa_version_under2p0 and tz not in (None, "UTC") and not is_null_slice: request.node.add_marker( pytest.mark.xfail( - reason=(f"Not supported by pyarrow < 2.0 with timestamp type {tz}") + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" ) ) elif ( @@ -780,7 +780,7 @@ def test_setitem_frame_2d_values(self, data, using_array_manager, request): if pa_version_under2p0 and tz not in (None, "UTC"): request.node.add_marker( pytest.mark.xfail( - reason=(f"Not supported by pyarrow < 2.0 with timestamp type {tz}") + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}" ) ) elif using_array_manager and pa.types.is_duration(data.dtype.pyarrow_dtype): @@ -796,6 +796,24 @@ def test_setitem_preserves_views(self, data): super().test_setitem_preserves_views(data) +class TestBaseParsing(base.BaseParsingTests): + @pytest.mark.parametrize("engine", ["c", "python"]) + def test_EA_types(self, engine, data, request): + pa_dtype = data.dtype.pyarrow_dtype + if pa.types.is_boolean(pa_dtype): + request.node.add_marker( + pytest.mark.xfail(raises=TypeError, reason="GH 47534") + ) + elif pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is not None: + request.node.add_marker( + pytest.mark.xfail( + raises=NotImplementedError, + reason=f"Parameterized types with tz={pa_dtype.tz} not supported.", + ) + ) + super().test_EA_types(engine, data) + + def test_arrowdtype_construct_from_string_type_with_unsupported_parameters(): with pytest.raises(NotImplementedError, match="Passing pyarrow type"): ArrowDtype.construct_from_string("timestamp[s, tz=UTC][pyarrow]")
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
https://api.github.com/repos/pandas-dev/pandas/pulls/47536
2022-06-28T21:42:48Z
2022-06-29T16:04:05Z
2022-06-29T16:04:04Z
2022-06-29T17:02:39Z
ENH: DatetimeIndex.indexer_between_time support non-nano
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py index 4bc01b8f4ddb0..50233a9c7cd78 100644 --- a/pandas/core/indexes/datetimes.py +++ b/pandas/core/indexes/datetimes.py @@ -26,9 +26,11 @@ ) from pandas._libs.tslibs import ( Resolution, + periods_per_day, timezones, to_offset, ) +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit from pandas._libs.tslibs.offsets import prefix_mapping from pandas._typing import ( Dtype, @@ -448,7 +450,7 @@ def _maybe_utc_convert(self, other: Index) -> tuple[DatetimeIndex, Index]: # -------------------------------------------------------------------- - def _get_time_micros(self) -> np.ndarray: + def _get_time_micros(self) -> npt.NDArray[np.int64]: """ Return the number of microseconds since midnight. @@ -458,8 +460,20 @@ def _get_time_micros(self) -> np.ndarray: """ values = self._data._local_timestamps() - nanos = values % (24 * 3600 * 1_000_000_000) - micros = nanos // 1000 + reso = self._data._reso + ppd = periods_per_day(reso) + + frac = values % ppd + if reso == NpyDatetimeUnit.NPY_FR_ns.value: + micros = frac // 1000 + elif reso == NpyDatetimeUnit.NPY_FR_us.value: + micros = frac + elif reso == NpyDatetimeUnit.NPY_FR_ms.value: + micros = frac * 1000 + elif reso == NpyDatetimeUnit.NPY_FR_s.value: + micros = frac * 1_000_000 + else: # pragma: no cover + raise NotImplementedError(reso) micros[self._isnan] = -1 return micros diff --git a/pandas/tests/indexes/datetimes/test_indexing.py b/pandas/tests/indexes/datetimes/test_indexing.py index b8f72a8c1f988..a203fee5b3a61 100644 --- a/pandas/tests/indexes/datetimes/test_indexing.py +++ b/pandas/tests/indexes/datetimes/test_indexing.py @@ -777,3 +777,32 @@ def test_indexer_between_time(self): msg = r"Cannot convert arg \[datetime\.datetime\(2010, 1, 2, 1, 0\)\] to a time" with pytest.raises(ValueError, match=msg): rng.indexer_between_time(datetime(2010, 1, 2, 1), datetime(2010, 1, 2, 5)) + + @pytest.mark.parametrize("unit", ["us", "ms", "s"]) + def test_indexer_between_time_non_nano(self, unit): + # For simple cases like this, the non-nano indexer_between_time + # should match the nano result + + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + arr_nano = rng._data._ndarray + + arr = arr_nano.astype(f"M8[{unit}]") + + dta = type(rng._data)._simple_new(arr, dtype=arr.dtype) + dti = DatetimeIndex(dta) + assert dti.dtype == arr.dtype + + tic = time(1, 25) + toc = time(2, 29) + + result = dti.indexer_between_time(tic, toc) + expected = rng.indexer_between_time(tic, toc) + tm.assert_numpy_array_equal(result, expected) + + # case with non-zero micros in arguments + tic = time(1, 25, 0, 45678) + toc = time(2, 29, 0, 1234) + + result = dti.indexer_between_time(tic, toc) + expected = rng.indexer_between_time(tic, toc) + tm.assert_numpy_array_equal(result, expected)
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47535
2022-06-28T20:45:41Z
2022-06-28T23:11:33Z
2022-06-28T23:11:33Z
2022-06-28T23:32:35Z
WEB: Update to `jupyterlite==0.1.0b10`
diff --git a/environment.yml b/environment.yml index 0a6055d80c071..eb4d53e116927 100644 --- a/environment.yml +++ b/environment.yml @@ -127,4 +127,4 @@ dependencies: # build the interactive terminal - jupyterlab >=3.4,<4 - pip: - - jupyterlite==0.1.0b9 + - jupyterlite==0.1.0b10 diff --git a/requirements-dev.txt b/requirements-dev.txt index f5dfeb8e7ff30..ff410c59b43dd 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -97,5 +97,5 @@ feedparser pyyaml requests jupyterlab >=3.4,<4 -jupyterlite==0.1.0b9 +jupyterlite==0.1.0b10 setuptools>=51.0.0
Update to the latest `jupyterlite` release for the interactive terminal on the website. The latest release adds support for caching assets and offline mode, which should help speed things up a little bit, at least when the page is reloaded. See the release notes for more information: https://github.com/jupyterlite/jupyterlite/releases/tag/v0.1.0b10 - [x] This should help with #47530 - [x] ~[Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature~ - [x] ~All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).~ - [x] ~Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.~ - [x] ~Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.~
https://api.github.com/repos/pandas-dev/pandas/pulls/47532
2022-06-28T16:10:38Z
2022-07-21T16:00:43Z
2022-07-21T16:00:43Z
2022-07-22T08:54:56Z
WEB: Add Telegram to follow us and footer
diff --git a/web/pandas/_templates/layout.html b/web/pandas/_templates/layout.html index 52e06a9bec55b..67876d88a2d1a 100644 --- a/web/pandas/_templates/layout.html +++ b/web/pandas/_templates/layout.html @@ -69,6 +69,11 @@ </main> <footer class="container pt-4 pt-md-5 border-top"> <ul class="list-inline social-buttons float-end"> + <li class="list-inline-item"> + <a href="https://t.me/s/pandas_dev"> + <i class="fab fa-telegram"></i> + </a> + </li> <li class="list-inline-item"> <a href="https://twitter.com/pandas_dev/"> <i class="fab fa-twitter"></i> diff --git a/web/pandas/index.html b/web/pandas/index.html index 930f6caa59cb9..98f91f8d0a359 100644 --- a/web/pandas/index.html +++ b/web/pandas/index.html @@ -72,9 +72,19 @@ <h4>Latest version: {{ releases[0].name }}</h4> {% endif %} <h4>Follow us</h4> <div class="text-center"> - <p> - <a href="https://twitter.com/pandas_dev?ref_src=twsrc%5Etfw" class="twitter-follow-button" data-show-count="false">Follow @pandas_dev</a><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> - </p> + <ul class="list-inline social-buttons"> + <li class="list-inline-item"> + <a href="https://t.me/s/pandas_dev"> + <i class="follow-us-button fab fa-telegram"></i> + </a> + </li> + <li class="list-inline-item"> + <a href="https://twitter.com/pandas_dev/"> + <i class="follow-us-button fab fa-twitter"></i> + </a> + </li> + </ul> + </div> <h4>Get the book</h4> <p class="book"> diff --git a/web/pandas/static/css/pandas.css b/web/pandas/static/css/pandas.css index 67955dd35587c..d5112dd220355 100644 --- a/web/pandas/static/css/pandas.css +++ b/web/pandas/static/css/pandas.css @@ -32,6 +32,13 @@ ol ol, ol ul, ul ol, ul ul { .pink { color: #e70488; } +.follow-us-button { + font-size: 2.4rem !important; + color: #0d6efd !important; +} +.follow-us-button:hover { + color: #0b5ed7 !important; +} .fab { font-size: 1.2rem; color: #666;
Since we've been posting way more in Telegram than in Twitter, probably worth adding it to the website. Adding buttons here in the web footer, next to the other icons, and in the home page, in the `Follow us` section (I changed the twitter banner to a button, which I think looks better with more than one item). This is what's linked for the Telegram icons: https://t.me/s/pandas_dev
https://api.github.com/repos/pandas-dev/pandas/pulls/47531
2022-06-28T14:45:24Z
2022-06-28T20:15:48Z
2022-06-28T20:15:48Z
2022-06-30T09:51:28Z
WARN: Clarify datetime warning when can't parse in accordance with dayfirst
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 56b09348189ee..2fed373a5c79b 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -727,6 +727,7 @@ Other Deprecations - Deprecated the ``display.column_space`` global configuration option (:issue:`7576`) - Deprecated the argument ``na_sentinel`` in :func:`factorize`, :meth:`Index.factorize`, and :meth:`.ExtensionArray.factorize`; pass ``use_na_sentinel=True`` instead to use the sentinel ``-1`` for NaN values and ``use_na_sentinel=False`` instead of ``na_sentinel=None`` to encode NaN values (:issue:`46910`) - Deprecated :meth:`DataFrameGroupBy.transform` not aligning the result when the UDF returned DataFrame (:issue:`45648`) +- Clarified warning from :func:`to_datetime` when delimited dates can't be parsed in accordance to specified ``dayfirst`` argument (:issue:`46210`) .. --------------------------------------------------------------------------- diff --git a/pandas/_libs/tslibs/parsing.pyx b/pandas/_libs/tslibs/parsing.pyx index 8b42ed195957b..5cb11436f6f45 100644 --- a/pandas/_libs/tslibs/parsing.pyx +++ b/pandas/_libs/tslibs/parsing.pyx @@ -85,8 +85,9 @@ _DEFAULT_DATETIME = datetime(1, 1, 1).replace(hour=0, minute=0, second=0, microsecond=0) PARSING_WARNING_MSG = ( - "Parsing '{date_string}' in {format} format. Provide format " - "or specify infer_datetime_format=True for consistent parsing." + "Parsing dates in {format} format when dayfirst={dayfirst} was specified. " + "This may lead to inconsistently parsed dates! Specify a format " + "to ensure consistent parsing." ) cdef: @@ -185,16 +186,16 @@ cdef inline object _parse_delimited_date(str date_string, bint dayfirst): if dayfirst and not swapped_day_and_month: warnings.warn( PARSING_WARNING_MSG.format( - date_string=date_string, - format='MM/DD/YYYY' + format='MM/DD/YYYY', + dayfirst='True', ), stacklevel=4, ) elif not dayfirst and swapped_day_and_month: warnings.warn( PARSING_WARNING_MSG.format( - date_string=date_string, - format='DD/MM/YYYY' + format='DD/MM/YYYY', + dayfirst='False (the default)', ), stacklevel=4, ) diff --git a/pandas/tests/io/parser/test_parse_dates.py b/pandas/tests/io/parser/test_parse_dates.py index 449d5a954613b..d05961b702c51 100644 --- a/pandas/tests/io/parser/test_parse_dates.py +++ b/pandas/tests/io/parser/test_parse_dates.py @@ -1677,9 +1677,7 @@ def test_parse_delimited_date_swap_with_warning( ): parser = all_parsers expected = DataFrame({0: [expected]}, dtype="datetime64[ns]") - warning_msg = ( - "Provide format or specify infer_datetime_format=True for consistent parsing" - ) + warning_msg = "Specify a format to ensure consistent parsing" with tm.assert_produces_warning(UserWarning, match=warning_msg): result = parser.read_csv( StringIO(date_string), header=None, dayfirst=dayfirst, parse_dates=[0] @@ -1687,6 +1685,17 @@ def test_parse_delimited_date_swap_with_warning( tm.assert_frame_equal(result, expected) +def test_parse_multiple_delimited_dates_with_swap_warnings(): + # GH46210 + warning_msg = "Specify a format to ensure consistent parsing" + with tm.assert_produces_warning(UserWarning, match=warning_msg) as record: + pd.to_datetime(["01/01/2000", "31/05/2000", "31/05/2001", "01/02/2000"]) + assert len({str(warning.message) for warning in record}) == 1 + # Using set(record) as repetitions of the same warning are suppressed + # https://docs.python.org/3/library/warnings.html + # and here we care to check that the warning is only shows once to users. + + def _helper_hypothesis_delimited_date(call, date_string, **kwargs): msg, result = None, None try: @@ -1848,12 +1857,14 @@ def test_parse_dates_and_keep_orgin_column(all_parsers): def test_dayfirst_warnings(): # GH 12585 warning_msg_day_first = ( - "Parsing '31/12/2014' in DD/MM/YYYY format. Provide " - "format or specify infer_datetime_format=True for consistent parsing." + r"Parsing dates in DD/MM/YYYY format when dayfirst=False \(the default\) was " + r"specified. This may lead to inconsistently parsed dates! Specify a format " + r"to ensure consistent parsing." ) warning_msg_month_first = ( - "Parsing '03/30/2011' in MM/DD/YYYY format. Provide " - "format or specify infer_datetime_format=True for consistent parsing." + "Parsing dates in MM/DD/YYYY format when dayfirst=True was " + "specified. This may lead to inconsistently parsed dates! Specify a format " + "to ensure consistent parsing." ) # CASE 1: valid input diff --git a/pandas/tests/io/xml/test_xml_dtypes.py b/pandas/tests/io/xml/test_xml_dtypes.py index 6aa4ddfac7628..5629830767c3c 100644 --- a/pandas/tests/io/xml/test_xml_dtypes.py +++ b/pandas/tests/io/xml/test_xml_dtypes.py @@ -457,7 +457,7 @@ def test_day_first_parse_dates(parser): ) with tm.assert_produces_warning( - UserWarning, match="Parsing '31/12/2020' in DD/MM/YYYY format" + UserWarning, match="Parsing dates in DD/MM/YYYY format" ): df_result = read_xml(xml, parse_dates=["date"], parser=parser) df_iter = read_xml_iterparse( diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py index 4c34b0c0aec0a..f712b4a24e5e5 100644 --- a/pandas/tests/tools/test_to_datetime.py +++ b/pandas/tests/tools/test_to_datetime.py @@ -1964,8 +1964,9 @@ def test_dayfirst(self, cache): def test_dayfirst_warnings_valid_input(self): # GH 12585 warning_msg_day_first = ( - "Parsing '31/12/2014' in DD/MM/YYYY format. Provide " - "format or specify infer_datetime_format=True for consistent parsing." + r"Parsing dates in DD/MM/YYYY format when dayfirst=False \(the default\) " + "was specified. This may lead to inconsistently parsed dates! Specify a " + "format to ensure consistent parsing." ) # CASE 1: valid input @@ -2001,12 +2002,14 @@ def test_dayfirst_warnings_invalid_input(self): # cannot consistently process with single format # warnings *always* raised warning_msg_day_first = ( - "Parsing '31/12/2014' in DD/MM/YYYY format. Provide " - "format or specify infer_datetime_format=True for consistent parsing." + r"Parsing dates in DD/MM/YYYY format when dayfirst=False \(the default\) " + "was specified. This may lead to inconsistently parsed dates! Specify a " + "format to ensure consistent parsing." ) warning_msg_month_first = ( - "Parsing '03/30/2011' in MM/DD/YYYY format. Provide " - "format or specify infer_datetime_format=True for consistent parsing." + r"Parsing dates in MM/DD/YYYY format when dayfirst=True " + "was specified. This may lead to inconsistently parsed dates! Specify a " + "format to ensure consistent parsing." ) arr = ["31/12/2014", "03/30/2011"]
- [x] closes #46210 (Replace xxxx with the Github issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. (also, typing swapped_day_and_month whilst I'm here as I hadn't done that when introducing the original warning message)
https://api.github.com/repos/pandas-dev/pandas/pulls/47528
2022-06-28T12:10:29Z
2022-06-28T21:44:13Z
2022-06-28T21:44:12Z
2022-06-28T21:44:19Z
TYP: more return annotations for io/*
diff --git a/pandas/io/common.py b/pandas/io/common.py index d692f26ab0576..ef8b466145daa 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -925,7 +925,7 @@ class _BufferedWriter(BytesIO, ABC): # type: ignore[misc] """ @abstractmethod - def write_to_buffer(self): + def write_to_buffer(self) -> None: ... def close(self) -> None: diff --git a/pandas/io/date_converters.py b/pandas/io/date_converters.py index 077524fbee465..85e92da8c2a54 100644 --- a/pandas/io/date_converters.py +++ b/pandas/io/date_converters.py @@ -1,13 +1,16 @@ """This module is designed for community supported date conversion functions""" +from __future__ import annotations + import warnings import numpy as np from pandas._libs.tslibs import parsing +from pandas._typing import npt from pandas.util._exceptions import find_stack_level -def parse_date_time(date_col, time_col): +def parse_date_time(date_col, time_col) -> npt.NDArray[np.object_]: """ Parse columns with dates and times into a single datetime column. @@ -26,7 +29,7 @@ def parse_date_time(date_col, time_col): return parsing.try_parse_date_and_time(date_col, time_col) -def parse_date_fields(year_col, month_col, day_col): +def parse_date_fields(year_col, month_col, day_col) -> npt.NDArray[np.object_]: """ Parse columns with years, months and days into a single date column. @@ -48,7 +51,9 @@ def parse_date_fields(year_col, month_col, day_col): return parsing.try_parse_year_month_day(year_col, month_col, day_col) -def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col, second_col): +def parse_all_fields( + year_col, month_col, day_col, hour_col, minute_col, second_col +) -> npt.NDArray[np.object_]: """ Parse columns with datetime information into a single datetime column. @@ -78,7 +83,7 @@ def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col, second_ ) -def generic_parser(parse_func, *cols): +def generic_parser(parse_func, *cols) -> np.ndarray: """ Use dateparser to parse columns with data information into a single datetime column. diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py index 24b881bda4805..58bb4b1a8f849 100644 --- a/pandas/io/excel/_base.py +++ b/pandas/io/excel/_base.py @@ -1069,7 +1069,7 @@ class ExcelWriter(metaclass=abc.ABCMeta): _supported_extensions: tuple[str, ...] def __new__( - cls, + cls: type[ExcelWriter], path: FilePath | WriteExcelBuffer | ExcelWriter, engine: str | None = None, date_format: str | None = None, @@ -1079,7 +1079,7 @@ def __new__( if_sheet_exists: Literal["error", "new", "replace", "overlay"] | None = None, engine_kwargs: dict | None = None, **kwargs, - ): + ) -> ExcelWriter: if kwargs: if engine_kwargs is not None: raise ValueError("Cannot use both engine_kwargs and **kwargs") @@ -1325,7 +1325,7 @@ def cur_sheet(self): return self._cur_sheet @property - def handles(self): + def handles(self) -> IOHandles[bytes]: """ Handles to Excel sheets. @@ -1344,7 +1344,7 @@ def path(self): self._deprecate("path") return self._path - def __fspath__(self): + def __fspath__(self) -> str: return getattr(self._handles.handle, "name", "") def _get_sheet_name(self, sheet_name: str | None) -> str: @@ -1402,10 +1402,10 @@ def check_extension(cls, ext: str) -> Literal[True]: return True # Allow use as a contextmanager - def __enter__(self): + def __enter__(self) -> ExcelWriter: return self - def __exit__(self, exc_type, exc_value, traceback): + def __exit__(self, exc_type, exc_value, traceback) -> None: self.close() def close(self) -> None: @@ -1699,13 +1699,13 @@ def close(self) -> None: """close io if necessary""" self._reader.close() - def __enter__(self): + def __enter__(self) -> ExcelFile: return self - def __exit__(self, exc_type, exc_value, traceback): + def __exit__(self, exc_type, exc_value, traceback) -> None: self.close() - def __del__(self): + def __del__(self) -> None: # Ensure we don't leak file descriptors, but put in try/except in case # attributes are already deleted try: diff --git a/pandas/io/excel/_openpyxl.py b/pandas/io/excel/_openpyxl.py index 87cc07d3fd21d..c3cd3fbe9e853 100644 --- a/pandas/io/excel/_openpyxl.py +++ b/pandas/io/excel/_openpyxl.py @@ -33,6 +33,7 @@ if TYPE_CHECKING: from openpyxl.descriptors.serialisable import Serialisable + from openpyxl.workbook import Workbook class OpenpyxlWriter(ExcelWriter): @@ -79,7 +80,7 @@ def __init__( self.book.remove(self.book.worksheets[0]) @property - def book(self): + def book(self) -> Workbook: """ Book instance of class openpyxl.workbook.Workbook. diff --git a/pandas/io/feather_format.py b/pandas/io/feather_format.py index 9813b91419060..4ecd5b7604088 100644 --- a/pandas/io/feather_format.py +++ b/pandas/io/feather_format.py @@ -31,7 +31,7 @@ def to_feather( path: FilePath | WriteBuffer[bytes], storage_options: StorageOptions = None, **kwargs, -): +) -> None: """ Write a DataFrame to the binary Feather format. diff --git a/pandas/io/parsers/base_parser.py b/pandas/io/parsers/base_parser.py index cdd24a1194a45..531fa5400f466 100644 --- a/pandas/io/parsers/base_parser.py +++ b/pandas/io/parsers/base_parser.py @@ -220,7 +220,7 @@ def _validate_parse_dates_presence(self, columns: Sequence[Hashable]) -> Iterabl for col in cols_needed ] - def close(self): + def close(self) -> None: pass @final diff --git a/pandas/io/parsers/python_parser.py b/pandas/io/parsers/python_parser.py index ec953c9df036c..3e897f9b1334e 100644 --- a/pandas/io/parsers/python_parser.py +++ b/pandas/io/parsers/python_parser.py @@ -308,7 +308,11 @@ def _exclude_implicit_index( }, names # legacy - def get_chunk(self, size=None): + def get_chunk( + self, size: int | None = None + ) -> tuple[ + Index | None, Sequence[Hashable] | MultiIndex, Mapping[Hashable, ArrayLike] + ]: if size is None: # error: "PythonParser" has no attribute "chunksize" size = self.chunksize # type: ignore[attr-defined] diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py index e811ace78f1f5..4b14b99f91f3f 100644 --- a/pandas/io/parsers/readers.py +++ b/pandas/io/parsers/readers.py @@ -493,7 +493,22 @@ class _DeprecationConfig(NamedTuple): } -def validate_integer(name, val, min_val=0): +@overload +def validate_integer(name, val: None, min_val=...) -> None: + ... + + +@overload +def validate_integer(name, val: int | float, min_val=...) -> int: + ... + + +@overload +def validate_integer(name, val: int | None, min_val=...) -> int | None: + ... + + +def validate_integer(name, val: int | float | None, min_val=0) -> int | None: """ Checks whether the 'name' parameter for parsing is either an integer OR float that can SAFELY be cast to an integer @@ -509,17 +524,18 @@ def validate_integer(name, val, min_val=0): min_val : int Minimum allowed value (val < min_val will result in a ValueError) """ - msg = f"'{name:s}' must be an integer >={min_val:d}" + if val is None: + return val - if val is not None: - if is_float(val): - if int(val) != val: - raise ValueError(msg) - val = int(val) - elif not (is_integer(val) and val >= min_val): + msg = f"'{name:s}' must be an integer >={min_val:d}" + if is_float(val): + if int(val) != val: raise ValueError(msg) + val = int(val) + elif not (is_integer(val) and val >= min_val): + raise ValueError(msg) - return val + return int(val) def _validate_names(names: Sequence[Hashable] | None) -> None: @@ -1784,7 +1800,7 @@ def __exit__(self, exc_type, exc_value, traceback) -> None: self.close() -def TextParser(*args, **kwds): +def TextParser(*args, **kwds) -> TextFileReader: """ Converts lists of lists/tuples into DataFrames with proper type inference and optional (e.g. string to datetime) conversion. Also enables iterating diff --git a/pandas/io/sas/sas_xport.py b/pandas/io/sas/sas_xport.py index 500e88eb0ef76..a2e217767d1d4 100644 --- a/pandas/io/sas/sas_xport.py +++ b/pandas/io/sas/sas_xport.py @@ -396,7 +396,7 @@ def _read_header(self): dtype = np.dtype(dtypel) self._dtype = dtype - def __next__(self): + def __next__(self) -> pd.DataFrame: return self.read(nrows=self._chunksize or 1) def _record_count(self) -> int: @@ -434,7 +434,7 @@ def _record_count(self) -> int: return (total_records_length - tail_pad) // self.record_length - def get_chunk(self, size=None): + def get_chunk(self, size=None) -> pd.DataFrame: """ Reads lines from Xport file and returns as dataframe diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 701642ad2cfe2..24290b8370ed2 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -14,6 +14,7 @@ from functools import partial import re from typing import ( + TYPE_CHECKING, Any, Iterator, Sequence, @@ -50,6 +51,9 @@ import pandas.core.common as com from pandas.core.tools.datetimes import to_datetime +if TYPE_CHECKING: + from sqlalchemy import Table + class DatabaseError(OSError): pass @@ -277,7 +281,9 @@ def read_sql_table( if not pandas_sql.has_table(table_name): raise ValueError(f"Table {table_name} not found") - table = pandas_sql.read_table( + # error: Item "SQLiteDatabase" of "Union[SQLDatabase, SQLiteDatabase]" + # has no attribute "read_table" + table = pandas_sql.read_table( # type: ignore[union-attr] table_name, index_col=index_col, coerce_float=coerce_float, @@ -701,7 +707,7 @@ def to_sql( ) -def has_table(table_name: str, con, schema: str | None = None): +def has_table(table_name: str, con, schema: str | None = None) -> bool: """ Check if DataBase has named table. @@ -728,7 +734,7 @@ def has_table(table_name: str, con, schema: str | None = None): table_exists = has_table -def pandasSQL_builder(con, schema: str | None = None): +def pandasSQL_builder(con, schema: str | None = None) -> SQLDatabase | SQLiteDatabase: """ Convenience function to return the correct PandasSQL subclass based on the provided parameters. @@ -806,7 +812,7 @@ def __init__( def exists(self): return self.pd_sql.has_table(self.name, self.schema) - def sql_schema(self): + def sql_schema(self) -> str: from sqlalchemy.schema import CreateTable return str(CreateTable(self.table).compile(self.pd_sql.connectable)) @@ -816,7 +822,7 @@ def _execute_create(self): self.table = self.table.to_metadata(self.pd_sql.meta) self.table.create(bind=self.pd_sql.connectable) - def create(self): + def create(self) -> None: if self.exists(): if self.if_exists == "fail": raise ValueError(f"Table '{self.name}' already exists.") @@ -862,7 +868,7 @@ def _execute_insert_multi(self, conn, keys: list[str], data_iter) -> int: result = conn.execute(stmt) return result.rowcount - def insert_data(self): + def insert_data(self) -> tuple[list[str], list[np.ndarray]]: if self.index is not None: temp = self.frame.copy() temp.index.names = self.index @@ -875,7 +881,9 @@ def insert_data(self): column_names = list(map(str, temp.columns)) ncols = len(column_names) - data_list = [None] * ncols + # this just pre-allocates the list: None's will be replaced with ndarrays + # error: List item 0 has incompatible type "None"; expected "ndarray" + data_list: list[np.ndarray] = [None] * ncols # type: ignore[list-item] for i, (_, ser) in enumerate(temp.items()): vals = ser._values @@ -894,9 +902,7 @@ def insert_data(self): mask = isna(d) d[mask] = None - # error: No overload variant of "__setitem__" of "list" matches - # argument types "int", "ndarray" - data_list[i] = d # type: ignore[call-overload] + data_list[i] = d return column_names, data_list @@ -974,7 +980,13 @@ def _query_iterator( yield self.frame - def read(self, coerce_float=True, parse_dates=None, columns=None, chunksize=None): + def read( + self, + coerce_float=True, + parse_dates=None, + columns=None, + chunksize=None, + ) -> DataFrame | Iterator[DataFrame]: from sqlalchemy import select if columns is not None and len(columns) > 0: @@ -1398,7 +1410,7 @@ def read_table( columns=None, schema: str | None = None, chunksize: int | None = None, - ): + ) -> DataFrame | Iterator[DataFrame]: """ Read SQL database table into a DataFrame. @@ -1487,13 +1499,13 @@ def _query_iterator( def read_query( self, sql: str, - index_col: str | None = None, + index_col: str | Sequence[str] | None = None, coerce_float: bool = True, parse_dates=None, params=None, chunksize: int | None = None, dtype: DtypeArg | None = None, - ): + ) -> DataFrame | Iterator[DataFrame]: """ Read SQL query into a DataFrame. @@ -1620,7 +1632,7 @@ def check_case_sensitive( self, name, schema, - ): + ) -> None: """ Checks table name for issues with case-sensitivity. Method is called after data is inserted. @@ -1741,7 +1753,7 @@ def has_table(self, name: str, schema: str | None = None): insp = inspect(self.connectable) return insp.has_table(name, schema or self.meta.schema) - def get_table(self, table_name: str, schema: str | None = None): + def get_table(self, table_name: str, schema: str | None = None) -> Table: from sqlalchemy import ( Numeric, Table, @@ -1756,7 +1768,7 @@ def get_table(self, table_name: str, schema: str | None = None): column.type.asdecimal = False return tbl - def drop_table(self, table_name: str, schema: str | None = None): + def drop_table(self, table_name: str, schema: str | None = None) -> None: schema = schema or self.meta.schema if self.has_table(table_name, schema): self.meta.reflect(bind=self.connectable, only=[table_name], schema=schema) @@ -1839,7 +1851,7 @@ def __init__(self, *args, **kwargs) -> None: sqlite3.register_adapter(time, lambda _: _.strftime("%H:%M:%S.%f")) super().__init__(*args, **kwargs) - def sql_schema(self): + def sql_schema(self) -> str: return str(";\n".join(self.table)) def _execute_create(self): @@ -1847,7 +1859,7 @@ def _execute_create(self): for stmt in self.table: conn.execute(stmt) - def insert_statement(self, *, num_rows: int): + def insert_statement(self, *, num_rows: int) -> str: names = list(map(str, self.frame.columns)) wld = "?" # wildcard char escape = _get_valid_sqlite_name @@ -2049,7 +2061,7 @@ def read_query( parse_dates=None, chunksize: int | None = None, dtype: DtypeArg | None = None, - ): + ) -> DataFrame | Iterator[DataFrame]: args = _convert_params(sql, params) cursor = self.execute(*args) @@ -2164,17 +2176,17 @@ def to_sql( table.create() return table.insert(chunksize, method) - def has_table(self, name: str, schema: str | None = None): + def has_table(self, name: str, schema: str | None = None) -> bool: wld = "?" query = f"SELECT name FROM sqlite_master WHERE type='table' AND name={wld};" return len(self.execute(query, [name]).fetchall()) > 0 - def get_table(self, table_name: str, schema: str | None = None): + def get_table(self, table_name: str, schema: str | None = None) -> None: return None # not supported in fallback mode - def drop_table(self, name: str, schema: str | None = None): + def drop_table(self, name: str, schema: str | None = None) -> None: drop_sql = f"DROP TABLE {_get_valid_sqlite_name(name)}" self.execute(drop_sql) @@ -2205,7 +2217,7 @@ def get_schema( con=None, dtype: DtypeArg | None = None, schema: str | None = None, -): +) -> str: """ Get the SQL db table schema for the given frame.
null
https://api.github.com/repos/pandas-dev/pandas/pulls/47524
2022-06-28T02:29:04Z
2022-06-28T17:06:59Z
2022-06-28T17:06:59Z
2022-06-28T17:07:07Z
CI: Bump Mac/Windows build timeout to 120 minutes
diff --git a/.github/workflows/macos-windows.yml b/.github/workflows/macos-windows.yml index 7052b770e2586..cf9a59400bc92 100644 --- a/.github/workflows/macos-windows.yml +++ b/.github/workflows/macos-windows.yml @@ -23,7 +23,7 @@ jobs: defaults: run: shell: bash -el {0} - timeout-minutes: 90 + timeout-minutes: 120 strategy: matrix: os: [macos-latest, windows-latest]
Needed since Windows builds are running with 1 worker #47443
https://api.github.com/repos/pandas-dev/pandas/pulls/47523
2022-06-28T00:10:25Z
2022-07-08T22:18:45Z
2022-07-08T22:18:45Z
2022-07-08T22:18:48Z
ENH: TimedeltaArray add/sub with NaT preserve reso
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py index d354d28d0f46f..5e65b124ae0f4 100644 --- a/pandas/core/arrays/datetimelike.py +++ b/pandas/core/arrays/datetimelike.py @@ -1087,7 +1087,7 @@ def _cmp_method(self, other, op): __rdivmod__ = make_invalid_op("__rdivmod__") @final - def _add_datetimelike_scalar(self, other): + def _add_datetimelike_scalar(self, other) -> DatetimeArray: if not is_timedelta64_dtype(self.dtype): raise TypeError( f"cannot add {type(self).__name__} and {type(other).__name__}" @@ -1102,16 +1102,12 @@ def _add_datetimelike_scalar(self, other): if other is NaT: # In this case we specifically interpret NaT as a datetime, not # the timedelta interpretation we would get by returning self + NaT - result = self.asi8.view("m8[ms]") + NaT.to_datetime64() - return DatetimeArray(result) + result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]") + # Preserve our resolution + return DatetimeArray._simple_new(result, dtype=result.dtype) i8 = self.asi8 - # Incompatible types in assignment (expression has type "ndarray[Any, - # dtype[signedinteger[_64Bit]]]", variable has type - # "ndarray[Any, dtype[datetime64]]") - result = checked_add_with_arr( # type: ignore[assignment] - i8, other.value, arr_mask=self._isnan - ) + result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan) dtype = DatetimeTZDtype(tz=other.tz) if other.tz else DT64NS_DTYPE return DatetimeArray(result, dtype=dtype, freq=self.freq) @@ -1275,12 +1271,14 @@ def _add_nat(self): raise TypeError( f"Cannot add {type(self).__name__} and {type(NaT).__name__}" ) + self = cast("TimedeltaArray | DatetimeArray", self) # GH#19124 pd.NaT is treated like a timedelta for both timedelta # and datetime dtypes result = np.empty(self.shape, dtype=np.int64) result.fill(iNaT) - return type(self)(result, dtype=self.dtype, freq=None) + result = result.view(self._ndarray.dtype) # preserve reso + return type(self)._simple_new(result, dtype=self.dtype, freq=None) @final def _sub_nat(self): @@ -1905,6 +1903,13 @@ class TimelikeOps(DatetimeLikeArrayMixin): def _reso(self) -> int: return get_unit_from_dtype(self._ndarray.dtype) + @cache_readonly + def _unit(self) -> str: + # e.g. "ns", "us", "ms" + # error: Argument 1 to "dtype_to_unit" has incompatible type + # "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]" + return dtype_to_unit(self.dtype) # type: ignore[arg-type] + def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs): if ( ufunc in [np.isnan, np.isinf, np.isfinite] @@ -2105,3 +2110,21 @@ def maybe_infer_freq(freq): freq_infer = True freq = None return freq, freq_infer + + +def dtype_to_unit(dtype: DatetimeTZDtype | np.dtype) -> str: + """ + Return the unit str corresponding to the dtype's resolution. + + Parameters + ---------- + dtype : DatetimeTZDtype or np.dtype + If np.dtype, we assume it is a datetime64 dtype. + + Returns + ------- + str + """ + if isinstance(dtype, DatetimeTZDtype): + return dtype.unit + return np.datetime_data(dtype)[0] diff --git a/pandas/tests/arrays/test_timedeltas.py b/pandas/tests/arrays/test_timedeltas.py index 5983c2f644949..36acb8f0fe389 100644 --- a/pandas/tests/arrays/test_timedeltas.py +++ b/pandas/tests/arrays/test_timedeltas.py @@ -6,7 +6,10 @@ import pandas as pd from pandas import Timedelta import pandas._testing as tm -from pandas.core.arrays import TimedeltaArray +from pandas.core.arrays import ( + DatetimeArray, + TimedeltaArray, +) class TestNonNano: @@ -25,6 +28,11 @@ def reso(self, unit): else: raise NotImplementedError(unit) + @pytest.fixture + def tda(self, unit): + arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]") + return TimedeltaArray._simple_new(arr, dtype=arr.dtype) + def test_non_nano(self, unit, reso): arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]") tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype) @@ -33,39 +41,55 @@ def test_non_nano(self, unit, reso): assert tda[0]._reso == reso @pytest.mark.parametrize("field", TimedeltaArray._field_ops) - def test_fields(self, unit, field): - arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]") - tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype) - - as_nano = arr.astype("m8[ns]") + def test_fields(self, tda, field): + as_nano = tda._ndarray.astype("m8[ns]") tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype) result = getattr(tda, field) expected = getattr(tda_nano, field) tm.assert_numpy_array_equal(result, expected) - def test_to_pytimedelta(self, unit): - arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]") - tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype) - - as_nano = arr.astype("m8[ns]") + def test_to_pytimedelta(self, tda): + as_nano = tda._ndarray.astype("m8[ns]") tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype) result = tda.to_pytimedelta() expected = tda_nano.to_pytimedelta() tm.assert_numpy_array_equal(result, expected) - def test_total_seconds(self, unit): - arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]") - tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype) - - as_nano = arr.astype("m8[ns]") + def test_total_seconds(self, unit, tda): + as_nano = tda._ndarray.astype("m8[ns]") tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype) result = tda.total_seconds() expected = tda_nano.total_seconds() tm.assert_numpy_array_equal(result, expected) + @pytest.mark.parametrize( + "nat", [np.datetime64("NaT", "ns"), np.datetime64("NaT", "us")] + ) + def test_add_nat_datetimelike_scalar(self, nat, tda): + result = tda + nat + assert isinstance(result, DatetimeArray) + assert result._reso == tda._reso + assert result.isna().all() + + result = nat + tda + assert isinstance(result, DatetimeArray) + assert result._reso == tda._reso + assert result.isna().all() + + def test_add_pdnat(self, tda): + result = tda + pd.NaT + assert isinstance(result, TimedeltaArray) + assert result._reso == tda._reso + assert result.isna().all() + + result = pd.NaT + tda + assert isinstance(result, TimedeltaArray) + assert result._reso == tda._reso + assert result.isna().all() + class TestTimedeltaArray: @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47522
2022-06-27T23:52:32Z
2022-06-28T18:38:44Z
2022-06-28T18:38:44Z
2022-06-28T20:42:30Z
added link to advanced indexing guide in .loc docstring
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 09fa08339bd43..22b03b3b2e47e 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -521,6 +521,9 @@ def loc(self) -> _LocIndexer: sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1 + + Please see the :ref:`user guide<advanced.advanced_hierarchical>` + for more details and explanations of advanced indexing. """ return _LocIndexer("loc", self)
closes #46620 Not sure if the online documentation also needs to change with this docstring. if it does, how can I change that?
https://api.github.com/repos/pandas-dev/pandas/pulls/47520
2022-06-27T18:44:34Z
2022-06-28T00:08:03Z
2022-06-28T00:08:02Z
2022-06-28T00:08:11Z
PERF: use c-division in nancorr
diff --git a/pandas/_libs/algos.pyx b/pandas/_libs/algos.pyx index 29f9a22c9b36e..c05d6a300ccf0 100644 --- a/pandas/_libs/algos.pyx +++ b/pandas/_libs/algos.pyx @@ -324,6 +324,7 @@ def kth_smallest(numeric_t[::1] arr, Py_ssize_t k) -> numeric_t: @cython.boundscheck(False) @cython.wraparound(False) +@cython.cdivision(True) def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None): cdef: Py_ssize_t i, j, xi, yi, N, K @@ -356,8 +357,8 @@ def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None): nobs += 1 dx = vx - meanx dy = vy - meany - meanx += 1 / nobs * dx - meany += 1 / nobs * dy + meanx += 1. / nobs * dx + meany += 1. / nobs * dy ssqdmx += (vx - meanx) * dx ssqdmy += (vy - meany) * dy covxy += (vx - meanx) * dy
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. There's three divisions in this function that can safely be done at the C-level: meanx += 1 / nobs * dx meany += 1 / nobs * dy because `nobs` is at least `1` when we get here (it starts at `0`, `nobs+=1` is called a few lines above, and `nobs` can't decrease), and result[xi, yi] = result[yi, xi] = covxy / divisor because `divisor` is checked to not be equal to `0` in the preceding line. This makes a noticeable difference, example: ``` arr1 = np.random.randn(100, 100) ``` On main: ``` %%timeit nancorr(arr1) 5.39 ms ± 21.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ``` On this branch: ``` %%timeit nancorr(arr1) 3.86 ms ± 58.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ``` I didn't find much difference in nancorr_spearman, I think because that one calls the Python function rank_1d within the loop, so that might be a bigger bottleneck there
https://api.github.com/repos/pandas-dev/pandas/pulls/47518
2022-06-27T15:11:57Z
2022-07-03T15:02:13Z
2022-07-03T15:02:13Z
2022-07-03T15:02:17Z
DOC: Fix a typo in documentation for styler
diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb index 58187b3052819..43021fcbc13fb 100644 --- a/doc/source/user_guide/style.ipynb +++ b/doc/source/user_guide/style.ipynb @@ -151,7 +151,7 @@ "\n", "### Formatting Values\n", "\n", - "Before adding styles it is useful to show that the [Styler][styler] can distinguish the *display* value from the *actual* value, in both datavlaues and index or columns headers. To control the display value, the text is printed in each cell as string, and we can use the [.format()][formatfunc] and [.format_index()][formatfuncindex] methods to manipulate this according to a [format spec string][format] or a callable that takes a single value and returns a string. It is possible to define this for the whole table, or index, or for individual columns, or MultiIndex levels. \n", + "Before adding styles it is useful to show that the [Styler][styler] can distinguish the *display* value from the *actual* value, in both datavalues and index or columns headers. To control the display value, the text is printed in each cell as string, and we can use the [.format()][formatfunc] and [.format_index()][formatfuncindex] methods to manipulate this according to a [format spec string][format] or a callable that takes a single value and returns a string. It is possible to define this for the whole table, or index, or for individual columns, or MultiIndex levels. \n", "\n", "Additionally, the format function has a **precision** argument to specifically help formatting floats, as well as **decimal** and **thousands** separators to support other locales, an **na_rep** argument to display missing data, and an **escape** argument to help displaying safe-HTML or safe-LaTeX. The default formatter is configured to adopt pandas' `styler.format.precision` option, controllable using `with pd.option_context('format.precision', 2):` \n", "\n",
datavlaues => datavalues
https://api.github.com/repos/pandas-dev/pandas/pulls/47517
2022-06-27T07:09:17Z
2022-06-27T16:28:11Z
2022-06-27T16:28:11Z
2022-06-27T16:28:18Z
TYP: Return annotations for io/{formats,json}
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 932805463d1c3..6c0a747b16745 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1008,7 +1008,7 @@ def _repr_fits_horizontal_(self, ignore_width: bool = False) -> bool: # used by repr_html under IPython notebook or scripts ignore terminal # dims - if ignore_width or not console.in_interactive_session(): + if ignore_width or width is None or not console.in_interactive_session(): return True if get_option("display.width") is not None or console.in_ipython_frontend(): diff --git a/pandas/io/formats/console.py b/pandas/io/formats/console.py index bdd2b3d6e4c6a..2a6cbe0762903 100644 --- a/pandas/io/formats/console.py +++ b/pandas/io/formats/console.py @@ -1,11 +1,12 @@ """ Internal module for console introspection """ +from __future__ import annotations from shutil import get_terminal_size -def get_console_size(): +def get_console_size() -> tuple[int | None, int | None]: """ Return console size as tuple = (width, height). @@ -43,14 +44,14 @@ def get_console_size(): # Note if the User sets width/Height to None (auto-detection) # and we're in a script (non-inter), this will return (None,None) # caller needs to deal. - return (display_width or terminal_width, display_height or terminal_height) + return display_width or terminal_width, display_height or terminal_height # ---------------------------------------------------------------------- # Detect our environment -def in_interactive_session(): +def in_interactive_session() -> bool: """ Check if we're running in an interactive shell. @@ -75,7 +76,7 @@ def check_main(): return check_main() -def in_ipython_frontend(): +def in_ipython_frontend() -> bool: """ Check if we're inside an IPython zmq frontend. diff --git a/pandas/io/formats/css.py b/pandas/io/formats/css.py index 5335887785881..a6d2645590dde 100644 --- a/pandas/io/formats/css.py +++ b/pandas/io/formats/css.py @@ -7,6 +7,7 @@ from typing import ( Callable, Generator, + Iterator, ) import warnings @@ -369,7 +370,7 @@ def atomize(self, declarations) -> Generator[tuple[str, str], None, None]: expand_margin = _side_expander("margin-{:s}") expand_padding = _side_expander("padding-{:s}") - def parse(self, declarations_str: str): + def parse(self, declarations_str: str) -> Iterator[tuple[str, str]]: """ Generates (prop, value) pairs from declarations. diff --git a/pandas/io/formats/excel.py b/pandas/io/formats/excel.py index effb0652aa2d3..8478b72d97a5e 100644 --- a/pandas/io/formats/excel.py +++ b/pandas/io/formats/excel.py @@ -197,7 +197,7 @@ def build_xlstyle(self, props: Mapping[str, str]) -> dict[str, dict[str, str]]: # TODO: handle cell width and height: needs support in pandas.io.excel - def remove_none(d: dict[str, str]) -> None: + def remove_none(d: dict[str, str | None]) -> None: """Remove key where value is None, through nested dicts""" for k, v in list(d.items()): if v is None: @@ -528,7 +528,7 @@ def __init__( self.inf_rep = inf_rep @property - def header_style(self): + def header_style(self) -> dict[str, dict[str, str | bool]]: return { "font": {"bold": True}, "borders": { @@ -850,7 +850,7 @@ def write( freeze_panes=None, engine=None, storage_options: StorageOptions = None, - ): + ) -> None: """ writer : path-like, file-like, or ExcelWriter object File path or existing ExcelWriter diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py index cf5e35f6ddcd1..08deafce3af98 100644 --- a/pandas/io/formats/format.py +++ b/pandas/io/formats/format.py @@ -22,6 +22,7 @@ Callable, Hashable, Iterable, + Iterator, List, Mapping, Sequence, @@ -1203,12 +1204,15 @@ def save_to_buffer( with get_buffer(buf, encoding=encoding) as f: f.write(string) if buf is None: - return f.getvalue() + # error: "WriteBuffer[str]" has no attribute "getvalue" + return f.getvalue() # type: ignore[attr-defined] return None @contextmanager -def get_buffer(buf: FilePath | WriteBuffer[str] | None, encoding: str | None = None): +def get_buffer( + buf: FilePath | WriteBuffer[str] | None, encoding: str | None = None +) -> Iterator[WriteBuffer[str]] | Iterator[StringIO]: """ Context manager to open, yield and close buffer for filenames or Path-like objects, otherwise yield buf unchanged. diff --git a/pandas/io/formats/html.py b/pandas/io/formats/html.py index dfd95b96c68e8..b6494682d308d 100644 --- a/pandas/io/formats/html.py +++ b/pandas/io/formats/html.py @@ -89,7 +89,7 @@ def render(self) -> list[str]: return self.elements @property - def should_show_dimensions(self): + def should_show_dimensions(self) -> bool: return self.fmt.should_show_dimensions @property diff --git a/pandas/io/formats/info.py b/pandas/io/formats/info.py index c0bdf37e5273a..07ec50a2cd6a8 100644 --- a/pandas/io/formats/info.py +++ b/pandas/io/formats/info.py @@ -566,7 +566,7 @@ def dtypes(self) -> Iterable[Dtype]: return [self.data.dtypes] @property - def dtype_counts(self): + def dtype_counts(self) -> Mapping[str, int]: from pandas.core.frame import DataFrame return _get_dataframe_dtype_counts(DataFrame(self.data)) @@ -1087,7 +1087,7 @@ def _fill_non_empty_info(self) -> None: if self.display_memory_usage: self.add_memory_usage_line() - def add_series_name_line(self): + def add_series_name_line(self) -> None: self._lines.append(f"Series name: {self.data.name}") @property diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py index 24669e84443a6..10b607da45ca8 100644 --- a/pandas/io/formats/style.py +++ b/pandas/io/formats/style.py @@ -12,6 +12,7 @@ Callable, Hashable, Sequence, + overload, ) import warnings @@ -591,6 +592,52 @@ def to_excel( engine=engine, ) + @overload + def to_latex( + self, + buf: FilePath | WriteBuffer[str], + *, + column_format: str | None = ..., + position: str | None = ..., + position_float: str | None = ..., + hrules: bool | None = ..., + clines: str | None = ..., + label: str | None = ..., + caption: str | tuple | None = ..., + sparse_index: bool | None = ..., + sparse_columns: bool | None = ..., + multirow_align: str | None = ..., + multicol_align: str | None = ..., + siunitx: bool = ..., + environment: str | None = ..., + encoding: str | None = ..., + convert_css: bool = ..., + ) -> None: + ... + + @overload + def to_latex( + self, + buf: None = ..., + *, + column_format: str | None = ..., + position: str | None = ..., + position_float: str | None = ..., + hrules: bool | None = ..., + clines: str | None = ..., + label: str | None = ..., + caption: str | tuple | None = ..., + sparse_index: bool | None = ..., + sparse_columns: bool | None = ..., + multirow_align: str | None = ..., + multicol_align: str | None = ..., + siunitx: bool = ..., + environment: str | None = ..., + encoding: str | None = ..., + convert_css: bool = ..., + ) -> str: + ... + def to_latex( self, buf: FilePath | WriteBuffer[str] | None = None, @@ -610,7 +657,7 @@ def to_latex( environment: str | None = None, encoding: str | None = None, convert_css: bool = False, - ): + ) -> str | None: r""" Write Styler to a file, buffer or string in LaTeX format. @@ -1161,6 +1208,46 @@ def to_latex( ) return save_to_buffer(latex, buf=buf, encoding=encoding) + @overload + def to_html( + self, + buf: FilePath | WriteBuffer[str], + *, + table_uuid: str | None = ..., + table_attributes: str | None = ..., + sparse_index: bool | None = ..., + sparse_columns: bool | None = ..., + bold_headers: bool = ..., + caption: str | None = ..., + max_rows: int | None = ..., + max_columns: int | None = ..., + encoding: str | None = ..., + doctype_html: bool = ..., + exclude_styles: bool = ..., + **kwargs, + ) -> None: + ... + + @overload + def to_html( + self, + buf: None = ..., + *, + table_uuid: str | None = ..., + table_attributes: str | None = ..., + sparse_index: bool | None = ..., + sparse_columns: bool | None = ..., + bold_headers: bool = ..., + caption: str | None = ..., + max_rows: int | None = ..., + max_columns: int | None = ..., + encoding: str | None = ..., + doctype_html: bool = ..., + exclude_styles: bool = ..., + **kwargs, + ) -> str: + ... + @Substitution(buf=buf, encoding=encoding) def to_html( self, @@ -1178,7 +1265,7 @@ def to_html( doctype_html: bool = False, exclude_styles: bool = False, **kwargs, - ): + ) -> str | None: """ Write Styler to a file, buffer or string in HTML-CSS format. @@ -1292,10 +1379,38 @@ def to_html( html, buf=buf, encoding=(encoding if buf is not None else None) ) + @overload + def to_string( + self, + buf: FilePath | WriteBuffer[str], + *, + encoding=..., + sparse_index: bool | None = ..., + sparse_columns: bool | None = ..., + max_rows: int | None = ..., + max_columns: int | None = ..., + delimiter: str = ..., + ) -> None: + ... + + @overload + def to_string( + self, + buf: None = ..., + *, + encoding=..., + sparse_index: bool | None = ..., + sparse_columns: bool | None = ..., + max_rows: int | None = ..., + max_columns: int | None = ..., + delimiter: str = ..., + ) -> str: + ... + @Substitution(buf=buf, encoding=encoding) def to_string( self, - buf=None, + buf: FilePath | WriteBuffer[str] | None = None, *, encoding=None, sparse_index: bool | None = None, @@ -1303,7 +1418,7 @@ def to_string( max_rows: int | None = None, max_columns: int | None = None, delimiter: str = " ", - ): + ) -> str | None: """ Write Styler to a file, buffer or string in text format. diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py index fbea7a71202eb..7260346faa307 100644 --- a/pandas/io/json/_json.py +++ b/pandas/io/json/_json.py @@ -11,7 +11,11 @@ from typing import ( Any, Callable, + Generic, + Literal, Mapping, + TypeVar, + overload, ) import numpy as np @@ -21,9 +25,12 @@ from pandas._typing import ( CompressionOptions, DtypeArg, + FilePath, IndexLabel, JSONSerializable, + ReadBuffer, StorageOptions, + WriteBuffer, ) from pandas.errors import AbstractMethodError from pandas.util._decorators import ( @@ -66,13 +73,53 @@ ) from pandas.io.parsers.readers import validate_integer +FrameSeriesStrT = TypeVar("FrameSeriesStrT", bound=Literal["frame", "series"]) + loads = json.loads dumps = json.dumps # interface to/from +@overload +def to_json( + path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes], + obj: NDFrame, + orient: str | None = ..., + date_format: str = ..., + double_precision: int = ..., + force_ascii: bool = ..., + date_unit: str = ..., + default_handler: Callable[[Any], JSONSerializable] | None = ..., + lines: bool = ..., + compression: CompressionOptions = ..., + index: bool = ..., + indent: int = ..., + storage_options: StorageOptions = ..., +) -> None: + ... + + +@overload def to_json( - path_or_buf, + path_or_buf: None, + obj: NDFrame, + orient: str | None = ..., + date_format: str = ..., + double_precision: int = ..., + force_ascii: bool = ..., + date_unit: str = ..., + default_handler: Callable[[Any], JSONSerializable] | None = ..., + lines: bool = ..., + compression: CompressionOptions = ..., + index: bool = ..., + indent: int = ..., + storage_options: StorageOptions = ..., +) -> str: + ... + + +def to_json( + path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes] | None, obj: NDFrame, orient: str | None = None, date_format: str = "epoch", @@ -85,7 +132,7 @@ def to_json( index: bool = True, indent: int = 0, storage_options: StorageOptions = None, -): +) -> str | None: if not index and orient not in ["split", "table"]: raise ValueError( @@ -131,6 +178,7 @@ def to_json( handles.handle.write(s) else: return s + return None class Writer(ABC): @@ -168,7 +216,7 @@ def __init__( def _format_axes(self): raise AbstractMethodError(self) - def write(self): + def write(self) -> str: iso_dates = self.date_format == "iso" return dumps( self.obj_to_write, @@ -313,6 +361,101 @@ def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]: return {"schema": self.schema, "data": self.obj} +@overload +def read_json( + path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes], + *, + orient=..., + typ: Literal["frame"] = ..., + dtype: DtypeArg | None = ..., + convert_axes=..., + convert_dates=..., + keep_default_dates: bool = ..., + numpy: bool = ..., + precise_float: bool = ..., + date_unit=..., + encoding=..., + encoding_errors: str | None = ..., + lines: bool = ..., + chunksize: int, + compression: CompressionOptions = ..., + nrows: int | None = ..., + storage_options: StorageOptions = ..., +) -> JsonReader[Literal["frame"]]: + ... + + +@overload +def read_json( + path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes], + *, + orient=..., + typ: Literal["series"], + dtype: DtypeArg | None = ..., + convert_axes=..., + convert_dates=..., + keep_default_dates: bool = ..., + numpy: bool = ..., + precise_float: bool = ..., + date_unit=..., + encoding=..., + encoding_errors: str | None = ..., + lines: bool = ..., + chunksize: int, + compression: CompressionOptions = ..., + nrows: int | None = ..., + storage_options: StorageOptions = ..., +) -> JsonReader[Literal["series"]]: + ... + + +@overload +def read_json( + path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes], + *, + orient=..., + typ: Literal["series"], + dtype: DtypeArg | None = ..., + convert_axes=..., + convert_dates=..., + keep_default_dates: bool = ..., + numpy: bool = ..., + precise_float: bool = ..., + date_unit=..., + encoding=..., + encoding_errors: str | None = ..., + lines: bool = ..., + chunksize: None = ..., + compression: CompressionOptions = ..., + nrows: int | None = ..., + storage_options: StorageOptions = ..., +) -> Series: + ... + + +@overload +def read_json( + path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes], + orient=..., + typ: Literal["frame"] = ..., + dtype: DtypeArg | None = ..., + convert_axes=..., + convert_dates=..., + keep_default_dates: bool = ..., + numpy: bool = ..., + precise_float: bool = ..., + date_unit=..., + encoding=..., + encoding_errors: str | None = ..., + lines: bool = ..., + chunksize: None = ..., + compression: CompressionOptions = ..., + nrows: int | None = ..., + storage_options: StorageOptions = ..., +) -> DataFrame: + ... + + @doc( storage_options=_shared_docs["storage_options"], decompression_options=_shared_docs["decompression_options"] % "path_or_buf", @@ -322,9 +465,9 @@ def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]: version="2.0", allowed_args=["path_or_buf"], stacklevel=3 ) def read_json( - path_or_buf=None, + path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes], orient=None, - typ="frame", + typ: Literal["frame", "series"] = "frame", dtype: DtypeArg | None = None, convert_axes=None, convert_dates=True, @@ -339,7 +482,7 @@ def read_json( compression: CompressionOptions = "infer", nrows: int | None = None, storage_options: StorageOptions = None, -): +) -> DataFrame | Series | JsonReader: """ Convert a JSON string to pandas object. @@ -613,7 +756,7 @@ def read_json( return json_reader.read() -class JsonReader(abc.Iterator): +class JsonReader(abc.Iterator, Generic[FrameSeriesStrT]): """ JsonReader provides an interface for reading in a JSON file. @@ -626,7 +769,7 @@ def __init__( self, filepath_or_buffer, orient, - typ, + typ: FrameSeriesStrT, dtype, convert_axes, convert_dates, @@ -739,10 +882,23 @@ def _combine_lines(self, lines) -> str: f'[{",".join([line for line in (line.strip() for line in lines) if line])}]' ) - def read(self): + @overload + def read(self: JsonReader[Literal["frame"]]) -> DataFrame: + ... + + @overload + def read(self: JsonReader[Literal["series"]]) -> Series: + ... + + @overload + def read(self: JsonReader[Literal["frame", "series"]]) -> DataFrame | Series: + ... + + def read(self) -> DataFrame | Series: """ Read the whole JSON input into a pandas object. """ + obj: DataFrame | Series if self.lines: if self.chunksize: obj = concat(self) @@ -759,7 +915,7 @@ def read(self): self.close() return obj - def _get_object_parser(self, json): + def _get_object_parser(self, json) -> DataFrame | Series: """ Parses a json document into a pandas object. """ @@ -786,7 +942,7 @@ def _get_object_parser(self, json): return obj - def close(self): + def close(self) -> None: """ If we opened a stream earlier, in _get_data_from_filepath, we should close it. @@ -796,7 +952,22 @@ def close(self): if self.handles is not None: self.handles.close() - def __next__(self): + def __iter__(self: JsonReader[FrameSeriesStrT]) -> JsonReader[FrameSeriesStrT]: + return self + + @overload + def __next__(self: JsonReader[Literal["frame"]]) -> DataFrame: + ... + + @overload + def __next__(self: JsonReader[Literal["series"]]) -> Series: + ... + + @overload + def __next__(self: JsonReader[Literal["frame", "series"]]) -> DataFrame | Series: + ... + + def __next__(self) -> DataFrame | Series: if self.nrows: if self.nrows_seen >= self.nrows: self.close() @@ -816,10 +987,10 @@ def __next__(self): self.close() raise StopIteration - def __enter__(self): + def __enter__(self) -> JsonReader[FrameSeriesStrT]: return self - def __exit__(self, exc_type, exc_value, traceback): + def __exit__(self, exc_type, exc_value, traceback) -> None: self.close() @@ -875,7 +1046,7 @@ def __init__( self.keep_default_dates = keep_default_dates self.obj: DataFrame | Series | None = None - def check_keys_split(self, decoded): + def check_keys_split(self, decoded) -> None: """ Checks that dict has only the appropriate keys for orient='split'. """ diff --git a/pandas/io/json/_table_schema.py b/pandas/io/json/_table_schema.py index c630f0d7613e0..44c5ce0e5ee83 100644 --- a/pandas/io/json/_table_schema.py +++ b/pandas/io/json/_table_schema.py @@ -115,8 +115,9 @@ def set_default_names(data): return data -def convert_pandas_type_to_json_field(arr): +def convert_pandas_type_to_json_field(arr) -> dict[str, JSONSerializable]: dtype = arr.dtype + name: JSONSerializable if arr.name is None: name = "values" else: @@ -141,7 +142,7 @@ def convert_pandas_type_to_json_field(arr): return field -def convert_json_field_to_pandas_type(field): +def convert_json_field_to_pandas_type(field) -> str | CategoricalDtype: """ Converts a JSON field descriptor into its corresponding NumPy / pandas type
null
https://api.github.com/repos/pandas-dev/pandas/pulls/47516
2022-06-27T01:19:53Z
2022-06-27T22:01:22Z
2022-06-27T22:01:21Z
2022-09-21T15:28:31Z
ENH/TST: Add BaseGroupbyTests tests for ArrowExtensionArray
diff --git a/pandas/core/dtypes/missing.py b/pandas/core/dtypes/missing.py index 37b42ad66c027..e809e761ebbb2 100644 --- a/pandas/core/dtypes/missing.py +++ b/pandas/core/dtypes/missing.py @@ -41,6 +41,7 @@ ) from pandas.core.dtypes.dtypes import ( CategoricalDtype, + DatetimeTZDtype, ExtensionDtype, IntervalDtype, PeriodDtype, @@ -754,10 +755,14 @@ def isna_all(arr: ArrayLike) -> bool: chunk_len = max(total_len // 40, 1000) dtype = arr.dtype - if dtype.kind == "f": + if dtype.kind == "f" and isinstance(dtype, np.dtype): checker = nan_checker - elif dtype.kind in ["m", "M"] or dtype.type is Period: + elif ( + (isinstance(dtype, np.dtype) and dtype.kind in ["m", "M"]) + or isinstance(dtype, DatetimeTZDtype) + or dtype.type is Period + ): # error: Incompatible types in assignment (expression has type # "Callable[[Any], Any]", variable has type "ufunc") checker = lambda x: np.asarray(x.view("i8")) == iNaT # type: ignore[assignment] diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py index 5e2006811f6eb..ac7b2f73f202c 100644 --- a/pandas/tests/extension/test_arrow.py +++ b/pandas/tests/extension/test_arrow.py @@ -106,6 +106,53 @@ def all_data(request, data, data_missing): return data_missing +@pytest.fixture +def data_for_grouping(dtype): + """ + Data for factorization, grouping, and unique tests. + + Expected to be like [B, B, NA, NA, A, A, B, C] + + Where A < B < C and NA is missing + """ + pa_dtype = dtype.pyarrow_dtype + if pa.types.is_boolean(pa_dtype): + A = False + B = True + C = True + elif pa.types.is_floating(pa_dtype): + A = -1.1 + B = 0.0 + C = 1.1 + elif pa.types.is_signed_integer(pa_dtype): + A = -1 + B = 0 + C = 1 + elif pa.types.is_unsigned_integer(pa_dtype): + A = 0 + B = 1 + C = 10 + elif pa.types.is_date(pa_dtype): + A = date(1999, 12, 31) + B = date(2010, 1, 1) + C = date(2022, 1, 1) + elif pa.types.is_timestamp(pa_dtype): + A = datetime(1999, 1, 1, 1, 1, 1, 1) + B = datetime(2020, 1, 1) + C = datetime(2020, 1, 1, 1) + elif pa.types.is_duration(pa_dtype): + A = timedelta(-1) + B = timedelta(0) + C = timedelta(1, 4) + elif pa.types.is_time(pa_dtype): + A = time(0, 0) + B = time(0, 12) + C = time(12, 12) + else: + raise NotImplementedError + return pd.array([B, B, None, None, A, A, B, C], dtype=dtype) + + @pytest.fixture def na_value(): """The scalar missing value for this type. Default 'None'""" @@ -219,6 +266,133 @@ def test_loc_iloc_frame_single_dtype(self, request, using_array_manager, data): super().test_loc_iloc_frame_single_dtype(data) +class TestBaseGroupby(base.BaseGroupbyTests): + def test_groupby_agg_extension(self, data_for_grouping, request): + tz = getattr(data_for_grouping.dtype.pyarrow_dtype, "tz", None) + if pa_version_under2p0 and tz not in (None, "UTC"): + request.node.add_marker( + pytest.mark.xfail( + reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}." + ) + ) + super().test_groupby_agg_extension(data_for_grouping) + + def test_groupby_extension_no_sort(self, data_for_grouping, request): + pa_dtype = data_for_grouping.dtype.pyarrow_dtype + if pa.types.is_boolean(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"{pa_dtype} only has 2 unique possible values", + ) + ) + elif pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"pyarrow doesn't support factorizing {pa_dtype}", + ) + ) + elif pa.types.is_date(pa_dtype) or ( + pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None + ): + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError, + reason="GH 34986", + ) + ) + super().test_groupby_extension_no_sort(data_for_grouping) + + def test_groupby_extension_transform(self, data_for_grouping, request): + pa_dtype = data_for_grouping.dtype.pyarrow_dtype + if pa.types.is_boolean(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason=f"{pa_dtype} only has 2 unique possible values", + ) + ) + elif pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"pyarrow doesn't support factorizing {pa_dtype}", + ) + ) + super().test_groupby_extension_transform(data_for_grouping) + + def test_groupby_extension_apply( + self, data_for_grouping, groupby_apply_op, request + ): + pa_dtype = data_for_grouping.dtype.pyarrow_dtype + # Is there a better way to get the "series" ID for groupby_apply_op? + is_series = "series" in request.node.nodeid + is_object = "object" in request.node.nodeid + if pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"pyarrow doesn't support factorizing {pa_dtype}", + ) + ) + elif pa.types.is_date(pa_dtype) or ( + pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None + ): + if is_object: + request.node.add_marker( + pytest.mark.xfail( + raises=TypeError, + reason="GH 47514: _concat_datetime expects axis arg.", + ) + ) + elif not is_series: + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError, + reason="GH 34986", + ) + ) + super().test_groupby_extension_apply(data_for_grouping, groupby_apply_op) + + def test_in_numeric_groupby(self, data_for_grouping, request): + pa_dtype = data_for_grouping.dtype.pyarrow_dtype + if pa.types.is_integer(pa_dtype) or pa.types.is_floating(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + reason="ArrowExtensionArray doesn't support .sum() yet.", + ) + ) + super().test_in_numeric_groupby(data_for_grouping) + + @pytest.mark.parametrize("as_index", [True, False]) + def test_groupby_extension_agg(self, as_index, data_for_grouping, request): + pa_dtype = data_for_grouping.dtype.pyarrow_dtype + if pa.types.is_boolean(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=ValueError, + reason=f"{pa_dtype} only has 2 unique possible values", + ) + ) + elif pa.types.is_duration(pa_dtype): + request.node.add_marker( + pytest.mark.xfail( + raises=pa.ArrowNotImplementedError, + reason=f"pyarrow doesn't support factorizing {pa_dtype}", + ) + ) + elif as_index is True and ( + pa.types.is_date(pa_dtype) + or (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None) + ): + request.node.add_marker( + pytest.mark.xfail( + raises=AttributeError, + reason="GH 34986", + ) + ) + super().test_groupby_extension_agg(as_index, data_for_grouping) + + class TestBaseDtype(base.BaseDtypeTests): def test_construct_from_string_own_name(self, dtype, request): pa_dtype = dtype.pyarrow_dtype @@ -736,8 +910,8 @@ def test_setitem_slice_array(self, data, request): def test_setitem_with_expansion_dataframe_column( self, data, full_indexer, using_array_manager, request ): - # Is there a way to get the full_indexer id "null_slice"? - is_null_slice = full_indexer(pd.Series(dtype=object)) == slice(None) + # Is there a better way to get the full_indexer id "null_slice"? + is_null_slice = "null_slice" in request.node.nodeid tz = getattr(data.dtype.pyarrow_dtype, "tz", None) if pa_version_under2p0 and tz not in (None, "UTC") and not is_null_slice: request.node.add_marker(
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
https://api.github.com/repos/pandas-dev/pandas/pulls/47515
2022-06-27T00:23:49Z
2022-07-03T14:24:31Z
2022-07-03T14:24:31Z
2022-07-05T16:45:44Z
"Backport PR #47387 on branch 1.4.x (TST,WARN: read_csv raises warning at wrong stacklevel, but _assert_raised_with_correct_stacklevel doesn't fail )"
diff --git a/doc/source/whatsnew/v1.4.4.rst b/doc/source/whatsnew/v1.4.4.rst index b462f0c6a8ffe..ef9537200bccd 100644 --- a/doc/source/whatsnew/v1.4.4.rst +++ b/doc/source/whatsnew/v1.4.4.rst @@ -23,7 +23,7 @@ Fixed regressions Bug fixes ~~~~~~~~~ -- +- The :class:`errors.FutureWarning` raised when passing arguments (other than ``filepath_or_buffer``) as positional in :func:`read_csv` is now raised at the correct stacklevel (:issue:`47385`) - .. --------------------------------------------------------------------------- diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py index 7480874fa7b23..3e792786b863a 100644 --- a/pandas/io/parsers/readers.py +++ b/pandas/io/parsers/readers.py @@ -581,9 +581,7 @@ def _read( return parser.read(nrows) -@deprecate_nonkeyword_arguments( - version=None, allowed_args=["filepath_or_buffer"], stacklevel=3 -) +@deprecate_nonkeyword_arguments(version=None, allowed_args=["filepath_or_buffer"]) @Appender( _doc_read_csv_and_table.format( func_name="read_csv", diff --git a/pandas/tests/io/parser/common/test_common_basic.py b/pandas/tests/io/parser/common/test_common_basic.py index 69b087eff8a20..4c9606a873402 100644 --- a/pandas/tests/io/parser/common/test_common_basic.py +++ b/pandas/tests/io/parser/common/test_common_basic.py @@ -806,8 +806,7 @@ def test_read_csv_posargs_deprecation(all_parsers): "In a future version of pandas all arguments of read_csv " "except for the argument 'filepath_or_buffer' will be keyword-only" ) - with tm.assert_produces_warning(FutureWarning, match=msg): - parser.read_csv(f, " ") + parser.read_csv_check_warnings(FutureWarning, msg, f, " ") @pytest.mark.parametrize("delimiter", [",", "\t"])
Backport PR #47387
https://api.github.com/repos/pandas-dev/pandas/pulls/47513
2022-06-26T19:16:54Z
2022-07-02T17:04:03Z
2022-07-02T17:04:03Z
2022-07-02T17:04:13Z
TYP: some return annotations in pytables.py
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index c20ce0c847b61..b96fa4a57f188 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -19,9 +19,11 @@ Any, Callable, Hashable, + Iterator, Literal, Sequence, cast, + overload, ) import warnings @@ -592,7 +594,7 @@ def __init__( self._filters = None self.open(mode=mode, **kwargs) - def __fspath__(self): + def __fspath__(self) -> str: return self._path @property @@ -603,16 +605,16 @@ def root(self): return self._handle.root @property - def filename(self): + def filename(self) -> str: return self._path def __getitem__(self, key: str): return self.get(key) - def __setitem__(self, key: str, value): + def __setitem__(self, key: str, value) -> None: self.put(key, value) - def __delitem__(self, key: str): + def __delitem__(self, key: str) -> None: return self.remove(key) def __getattr__(self, name: str): @@ -644,10 +646,10 @@ def __repr__(self) -> str: pstr = pprint_thing(self._path) return f"{type(self)}\nFile path: {pstr}\n" - def __enter__(self): + def __enter__(self) -> HDFStore: return self - def __exit__(self, exc_type, exc_value, traceback): + def __exit__(self, exc_type, exc_value, traceback) -> None: self.close() def keys(self, include: str = "pandas") -> list[str]: @@ -684,7 +686,7 @@ def keys(self, include: str = "pandas") -> list[str]: f"`include` should be either 'pandas' or 'native' but is '{include}'" ) - def __iter__(self): + def __iter__(self) -> Iterator[str]: return iter(self.keys()) def items(self): @@ -706,7 +708,7 @@ def iteritems(self): ) yield from self.items() - def open(self, mode: str = "a", **kwargs): + def open(self, mode: str = "a", **kwargs) -> None: """ Open the file in the specified mode @@ -751,7 +753,7 @@ def open(self, mode: str = "a", **kwargs): self._handle = tables.open_file(self._path, self._mode, **kwargs) - def close(self): + def close(self) -> None: """ Close the PyTables file handle """ @@ -768,7 +770,7 @@ def is_open(self) -> bool: return False return bool(self._handle.isopen) - def flush(self, fsync: bool = False): + def flush(self, fsync: bool = False) -> None: """ Force all buffered modifications to be written to disk. @@ -1096,7 +1098,7 @@ def put( errors: str = "strict", track_times: bool = True, dropna: bool = False, - ): + ) -> None: """ Store object in HDFStore. @@ -1152,7 +1154,7 @@ def put( dropna=dropna, ) - def remove(self, key: str, where=None, start=None, stop=None): + def remove(self, key: str, where=None, start=None, stop=None) -> None: """ Remove pandas object partially by specifying the where condition @@ -1228,7 +1230,7 @@ def append( data_columns: Literal[True] | list[str] | None = None, encoding=None, errors: str = "strict", - ): + ) -> None: """ Append to Table in file. Node must already exist and be Table format. @@ -1305,7 +1307,7 @@ def append_to_multiple( axes=None, dropna=False, **kwargs, - ): + ) -> None: """ Append to multiple tables @@ -1399,7 +1401,7 @@ def create_table_index( columns=None, optlevel: int | None = None, kind: str | None = None, - ): + ) -> None: """ Create a pytables index on the table. @@ -1545,7 +1547,7 @@ def copy( complevel: int | None = None, fletcher32: bool = False, overwrite=True, - ): + ) -> HDFStore: """ Copy the existing store to a new file, updating in place. @@ -1933,7 +1935,7 @@ def __iter__(self): self.close() - def close(self): + def close(self) -> None: if self.auto_close: self.store.close() @@ -2037,7 +2039,7 @@ def itemsize(self) -> int: def kind_attr(self) -> str: return f"{self.name}_kind" - def set_pos(self, pos: int): + def set_pos(self, pos: int) -> None: """set the position of this column in the Table""" self.pos = pos if pos is not None and self.typ is not None: @@ -2072,7 +2074,9 @@ def is_indexed(self) -> bool: return False return getattr(self.table.cols, self.cname).is_indexed - def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): + def convert( + self, values: np.ndarray, nan_rep, encoding: str, errors: str + ) -> tuple[np.ndarray, np.ndarray] | tuple[DatetimeIndex, DatetimeIndex]: """ Convert the data from this selection to the appropriate pandas type. """ @@ -2140,7 +2144,7 @@ def cvalues(self): def __iter__(self): return iter(self.values) - def maybe_set_size(self, min_itemsize=None): + def maybe_set_size(self, min_itemsize=None) -> None: """ maybe set a string col itemsize: min_itemsize can be an integer or a dict with this columns name @@ -2153,10 +2157,10 @@ def maybe_set_size(self, min_itemsize=None): if min_itemsize is not None and self.typ.itemsize < min_itemsize: self.typ = _tables().StringCol(itemsize=min_itemsize, pos=self.pos) - def validate_names(self): + def validate_names(self) -> None: pass - def validate_and_set(self, handler: AppendableTable, append: bool): + def validate_and_set(self, handler: AppendableTable, append: bool) -> None: self.table = handler.table self.validate_col() self.validate_attr(append) @@ -2183,7 +2187,7 @@ def validate_col(self, itemsize=None): return None - def validate_attr(self, append: bool): + def validate_attr(self, append: bool) -> None: # check for backwards incompatibility if append: existing_kind = getattr(self.attrs, self.kind_attr, None) @@ -2192,7 +2196,7 @@ def validate_attr(self, append: bool): f"incompatible kind in col [{existing_kind} - {self.kind}]" ) - def update_info(self, info): + def update_info(self, info) -> None: """ set/update the info for this indexable with the key/value if there is a conflict raise/warn as needed @@ -2225,17 +2229,17 @@ def update_info(self, info): if value is not None or existing_value is not None: idx[key] = value - def set_info(self, info): + def set_info(self, info) -> None: """set my state from the passed info""" idx = info.get(self.name) if idx is not None: self.__dict__.update(idx) - def set_attr(self): + def set_attr(self) -> None: """set the kind for this column""" setattr(self.attrs, self.kind_attr, self.kind) - def validate_metadata(self, handler: AppendableTable): + def validate_metadata(self, handler: AppendableTable) -> None: """validate that kind=category does not change the categories""" if self.meta == "category": new_metadata = self.metadata @@ -2250,7 +2254,7 @@ def validate_metadata(self, handler: AppendableTable): "different categories to the existing" ) - def write_metadata(self, handler: AppendableTable): + def write_metadata(self, handler: AppendableTable) -> None: """set the meta data""" if self.metadata is not None: handler.write_metadata(self.cname, self.metadata) @@ -2263,7 +2267,13 @@ class GenericIndexCol(IndexCol): def is_indexed(self) -> bool: return False - def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): + # error: Return type "Tuple[Int64Index, Int64Index]" of "convert" + # incompatible with return type "Union[Tuple[ndarray[Any, Any], + # ndarray[Any, Any]], Tuple[DatetimeIndex, DatetimeIndex]]" in + # supertype "IndexCol" + def convert( # type: ignore[override] + self, values: np.ndarray, nan_rep, encoding: str, errors: str + ) -> tuple[Int64Index, Int64Index]: """ Convert the data from this selection to the appropriate pandas type. @@ -2276,12 +2286,10 @@ def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): """ assert isinstance(values, np.ndarray), type(values) - # error: Incompatible types in assignment (expression has type - # "Int64Index", variable has type "ndarray") - values = Int64Index(np.arange(len(values))) # type: ignore[assignment] - return values, values + index = Int64Index(np.arange(len(values))) + return index, index - def set_attr(self): + def set_attr(self) -> None: pass @@ -2362,7 +2370,7 @@ def __eq__(self, other: Any) -> bool: for a in ["name", "cname", "dtype", "pos"] ) - def set_data(self, data: ArrayLike): + def set_data(self, data: ArrayLike) -> None: assert data is not None assert self.dtype is None @@ -2448,7 +2456,7 @@ def cvalues(self): """return my cython values""" return self.data - def validate_attr(self, append): + def validate_attr(self, append) -> None: """validate that we have the same order as the existing & same dtype""" if append: existing_fields = getattr(self.attrs, self.kind_attr, None) @@ -2562,7 +2570,7 @@ def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): return self.values, converted - def set_attr(self): + def set_attr(self) -> None: """set the data for this column""" setattr(self.attrs, self.kind_attr, self.values) setattr(self.attrs, self.meta_attr, self.meta) @@ -2575,7 +2583,7 @@ class DataIndexableCol(DataCol): is_data_indexable = True - def validate_names(self): + def validate_names(self) -> None: if not Index(self.values).is_object(): # TODO: should the message here be more specifically non-str? raise ValueError("cannot have non-object label DataIndexableCol") @@ -2672,12 +2680,12 @@ def __repr__(self) -> str: return f"{self.pandas_type:12.12} (shape->{s})" return self.pandas_type - def set_object_info(self): + def set_object_info(self) -> None: """set my pandas type & version""" self.attrs.pandas_type = str(self.pandas_kind) self.attrs.pandas_version = str(_version) - def copy(self): + def copy(self) -> Fixed: new_self = copy.copy(self) return new_self @@ -2709,11 +2717,11 @@ def _fletcher32(self) -> bool: def attrs(self): return self.group._v_attrs - def set_attrs(self): + def set_attrs(self) -> None: """set our object attributes""" pass - def get_attrs(self): + def get_attrs(self) -> None: """get our object attributes""" pass @@ -2730,17 +2738,17 @@ def is_exists(self) -> bool: def nrows(self): return getattr(self.storable, "nrows", None) - def validate(self, other): + def validate(self, other) -> Literal[True] | None: """validate against an existing storable""" if other is None: - return + return None return True - def validate_version(self, where=None): + def validate_version(self, where=None) -> None: """are we trying to operate on an old version?""" - return True + pass - def infer_axes(self): + def infer_axes(self) -> bool: """ infer the axes of my storer return a boolean indicating if we have a valid storer or not @@ -2767,7 +2775,9 @@ def write(self, **kwargs): "cannot write on an abstract storer: subclasses should implement" ) - def delete(self, where=None, start: int | None = None, stop: int | None = None): + def delete( + self, where=None, start: int | None = None, stop: int | None = None + ) -> None: """ support fully deleting the node in its entirety (only) - where specification must be None @@ -2842,7 +2852,7 @@ def f(values, freq=None, tz=None): return factory, kwargs - def validate_read(self, columns, where): + def validate_read(self, columns, where) -> None: """ raise if any keywords are passed which are not-None """ @@ -2861,12 +2871,12 @@ def validate_read(self, columns, where): def is_exists(self) -> bool: return True - def set_attrs(self): + def set_attrs(self) -> None: """set our object attributes""" self.attrs.encoding = self.encoding self.attrs.errors = self.errors - def get_attrs(self): + def get_attrs(self) -> None: """retrieve our attributes""" self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None)) self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict")) @@ -2924,7 +2934,7 @@ def read_index( else: # pragma: no cover raise TypeError(f"unrecognized index variety: {variety}") - def write_index(self, key: str, index: Index): + def write_index(self, key: str, index: Index) -> None: if isinstance(index, MultiIndex): setattr(self.attrs, f"{key}_variety", "multi") self.write_multi_index(key, index) @@ -2947,7 +2957,7 @@ def write_index(self, key: str, index: Index): if isinstance(index, DatetimeIndex) and index.tz is not None: node._v_attrs.tz = _get_tz(index.tz) - def write_multi_index(self, key: str, index: MultiIndex): + def write_multi_index(self, key: str, index: MultiIndex) -> None: setattr(self.attrs, f"{key}_nlevels", index.nlevels) for i, (lev, level_codes, name) in enumerate( @@ -3033,7 +3043,7 @@ def read_index_node( return index - def write_array_empty(self, key: str, value: ArrayLike): + def write_array_empty(self, key: str, value: ArrayLike) -> None: """write a 0-len array""" # ugly hack for length 0 axes arr = np.empty((1,) * value.ndim) @@ -3152,7 +3162,7 @@ def read( columns=None, start: int | None = None, stop: int | None = None, - ): + ) -> Series: self.validate_read(columns, where) index = self.read_index("index", start=start, stop=stop) values = self.read_array("values", start=start, stop=stop) @@ -3203,7 +3213,7 @@ def read( columns=None, start: int | None = None, stop: int | None = None, - ): + ) -> DataFrame: # start, stop applied to rows, so 0th axis only self.validate_read(columns, where) select_axis = self.obj_type()._get_block_manager_axis(0) @@ -3352,7 +3362,7 @@ def __getitem__(self, c: str): return a return None - def validate(self, other): + def validate(self, other) -> None: """validate against an existing table""" if other is None: return @@ -3449,7 +3459,7 @@ def is_transposed(self) -> bool: return False @property - def data_orientation(self): + def data_orientation(self) -> tuple[int, ...]: """return a tuple of my permutated axes, non_indexable at the front""" return tuple( itertools.chain( @@ -3488,7 +3498,7 @@ def _get_metadata_path(self, key: str) -> str: group = self.group._v_pathname return f"{group}/meta/{key}/meta" - def write_metadata(self, key: str, values: np.ndarray): + def write_metadata(self, key: str, values: np.ndarray) -> None: """ Write out a metadata array to the key as a fixed-format Series. @@ -3512,7 +3522,7 @@ def read_metadata(self, key: str): return self.parent.select(self._get_metadata_path(key)) return None - def set_attrs(self): + def set_attrs(self) -> None: """set our table type & indexables""" self.attrs.table_type = str(self.table_type) self.attrs.index_cols = self.index_cols() @@ -3525,7 +3535,7 @@ def set_attrs(self): self.attrs.levels = self.levels self.attrs.info = self.info - def get_attrs(self): + def get_attrs(self) -> None: """retrieve our attributes""" self.non_index_axes = getattr(self.attrs, "non_index_axes", None) or [] self.data_columns = getattr(self.attrs, "data_columns", None) or [] @@ -3537,14 +3547,14 @@ def get_attrs(self): self.index_axes = [a for a in self.indexables if a.is_an_indexable] self.values_axes = [a for a in self.indexables if not a.is_an_indexable] - def validate_version(self, where=None): + def validate_version(self, where=None) -> None: """are we trying to operate on an old version?""" if where is not None: if self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1: ws = incompatibility_doc % ".".join([str(x) for x in self.version]) warnings.warn(ws, IncompatibilityWarning) - def validate_min_itemsize(self, min_itemsize): + def validate_min_itemsize(self, min_itemsize) -> None: """ validate the min_itemsize doesn't contain items that are not in the axes this needs data_columns to be defined @@ -3642,7 +3652,9 @@ def f(i, c): return _indexables - def create_index(self, columns=None, optlevel=None, kind: str | None = None): + def create_index( + self, columns=None, optlevel=None, kind: str | None = None + ) -> None: """ Create a pytables index on the specified columns. @@ -4100,7 +4112,7 @@ def get_blk_items(mgr): return blocks, blk_items - def process_axes(self, obj, selection: Selection, columns=None): + def process_axes(self, obj, selection: Selection, columns=None) -> DataFrame: """process axes filters""" # make a copy to avoid side effects if columns is not None: @@ -4354,7 +4366,7 @@ def write( # add the rows table.write_data(chunksize, dropna=dropna) - def write_data(self, chunksize: int | None, dropna: bool = False): + def write_data(self, chunksize: int | None, dropna: bool = False) -> None: """ we form the data into a 2-d including indexes,values,mask write chunk-by-chunk """ @@ -4419,7 +4431,7 @@ def write_data_chunk( indexes: list[np.ndarray], mask: npt.NDArray[np.bool_] | None, values: list[np.ndarray], - ): + ) -> None: """ Parameters ---------- @@ -4701,7 +4713,7 @@ def pandas_type(self) -> str: def storable(self): return getattr(self.group, "table", None) or self.group - def get_attrs(self): + def get_attrs(self) -> None: """retrieve our attributes""" self.non_index_axes = [] self.nan_rep = None @@ -4823,10 +4835,20 @@ def _get_tz(tz: tzinfo) -> str | tzinfo: return zone +@overload +def _set_tz( + values: np.ndarray | Index, tz: str | tzinfo, coerce: bool = False +) -> DatetimeIndex: + ... + + +@overload +def _set_tz(values: np.ndarray | Index, tz: None, coerce: bool = False) -> np.ndarray: + ... + + def _set_tz( - values: np.ndarray | Index, - tz: str | tzinfo | None, - coerce: bool = False, + values: np.ndarray | Index, tz: str | tzinfo | None, coerce: bool = False ) -> np.ndarray | DatetimeIndex: """ coerce the values to a DatetimeIndex if tz is set
Mostly the low-hanging fruit in pytables.py
https://api.github.com/repos/pandas-dev/pandas/pulls/47512
2022-06-26T16:39:44Z
2022-06-26T19:23:30Z
2022-06-26T19:23:30Z
2022-09-21T15:28:31Z
TYP: Missing return annotations in util/tseries/plotting
diff --git a/pandas/plotting/_misc.py b/pandas/plotting/_misc.py index 6e7b53e4c5ae4..0e82a0fc924fb 100644 --- a/pandas/plotting/_misc.py +++ b/pandas/plotting/_misc.py @@ -1,7 +1,18 @@ +from __future__ import annotations + from contextlib import contextmanager +from typing import ( + TYPE_CHECKING, + Iterator, +) from pandas.plotting._core import _get_plot_backend +if TYPE_CHECKING: + from matplotlib.axes import Axes + from matplotlib.figure import Figure + import numpy as np + def table(ax, data, rowLabels=None, colLabels=None, **kwargs): """ @@ -27,7 +38,7 @@ def table(ax, data, rowLabels=None, colLabels=None, **kwargs): ) -def register(): +def register() -> None: """ Register pandas formatters and converters with matplotlib. @@ -49,7 +60,7 @@ def register(): plot_backend.register() -def deregister(): +def deregister() -> None: """ Remove pandas formatters and converters. @@ -81,7 +92,7 @@ def scatter_matrix( hist_kwds=None, range_padding=0.05, **kwargs, -): +) -> np.ndarray: """ Draw a matrix of scatter plots. @@ -156,7 +167,7 @@ def scatter_matrix( ) -def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds): +def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds) -> Axes: """ Plot a multidimensional dataset in 2D. @@ -239,7 +250,7 @@ def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds): def andrews_curves( frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwargs -): +) -> Axes: """ Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data. @@ -297,7 +308,7 @@ def andrews_curves( ) -def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds): +def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds) -> Figure: """ Bootstrap plot on mean, median and mid-range statistics. @@ -364,7 +375,7 @@ def parallel_coordinates( axvlines_kwds=None, sort_labels=False, **kwargs, -): +) -> Axes: """ Parallel coordinates plotting. @@ -430,7 +441,7 @@ def parallel_coordinates( ) -def lag_plot(series, lag=1, ax=None, **kwds): +def lag_plot(series, lag=1, ax=None, **kwds) -> Axes: """ Lag plot for time series. @@ -474,7 +485,7 @@ def lag_plot(series, lag=1, ax=None, **kwds): return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds) -def autocorrelation_plot(series, ax=None, **kwargs): +def autocorrelation_plot(series, ax=None, **kwargs) -> Axes: """ Autocorrelation plot for time series. @@ -531,21 +542,21 @@ def __getitem__(self, key): raise ValueError(f"{key} is not a valid pandas plotting option") return super().__getitem__(key) - def __setitem__(self, key, value): + def __setitem__(self, key, value) -> None: key = self._get_canonical_key(key) - return super().__setitem__(key, value) + super().__setitem__(key, value) - def __delitem__(self, key): + def __delitem__(self, key) -> None: key = self._get_canonical_key(key) if key in self._DEFAULT_KEYS: raise ValueError(f"Cannot remove default parameter {key}") - return super().__delitem__(key) + super().__delitem__(key) def __contains__(self, key) -> bool: key = self._get_canonical_key(key) return super().__contains__(key) - def reset(self): + def reset(self) -> None: """ Reset the option store to its initial state @@ -560,7 +571,7 @@ def _get_canonical_key(self, key): return self._ALIASES.get(key, key) @contextmanager - def use(self, key, value): + def use(self, key, value) -> Iterator[_Options]: """ Temporarily set a parameter value using the with statement. Aliasing allowed. diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index c541003f1160c..7ad68a812c2e2 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -314,12 +314,12 @@ def get_freq(self) -> str | None: return _maybe_add_count("N", delta) @cache_readonly - def day_deltas(self): + def day_deltas(self) -> list[int]: ppd = periods_per_day(self._reso) return [x / ppd for x in self.deltas] @cache_readonly - def hour_deltas(self): + def hour_deltas(self) -> list[int]: pph = periods_per_day(self._reso) // 24 return [x / pph for x in self.deltas] @@ -328,10 +328,10 @@ def fields(self) -> np.ndarray: # structured array of fields return build_field_sarray(self.i8values, reso=self._reso) @cache_readonly - def rep_stamp(self): + def rep_stamp(self) -> Timestamp: return Timestamp(self.i8values[0]) - def month_position_check(self): + def month_position_check(self) -> str | None: return month_position_check(self.fields, self.index.dayofweek) @cache_readonly @@ -394,7 +394,11 @@ def _get_annual_rule(self) -> str | None: return None pos_check = self.month_position_check() - return {"cs": "AS", "bs": "BAS", "ce": "A", "be": "BA"}.get(pos_check) + # error: Argument 1 to "get" of "dict" has incompatible type + # "Optional[str]"; expected "str" + return {"cs": "AS", "bs": "BAS", "ce": "A", "be": "BA"}.get( + pos_check # type: ignore[arg-type] + ) def _get_quarterly_rule(self) -> str | None: if len(self.mdiffs) > 1: @@ -404,13 +408,21 @@ def _get_quarterly_rule(self) -> str | None: return None pos_check = self.month_position_check() - return {"cs": "QS", "bs": "BQS", "ce": "Q", "be": "BQ"}.get(pos_check) + # error: Argument 1 to "get" of "dict" has incompatible type + # "Optional[str]"; expected "str" + return {"cs": "QS", "bs": "BQS", "ce": "Q", "be": "BQ"}.get( + pos_check # type: ignore[arg-type] + ) def _get_monthly_rule(self) -> str | None: if len(self.mdiffs) > 1: return None pos_check = self.month_position_check() - return {"cs": "MS", "bs": "BMS", "ce": "M", "be": "BM"}.get(pos_check) + # error: Argument 1 to "get" of "dict" has incompatible type + # "Optional[str]"; expected "str" + return {"cs": "MS", "bs": "BMS", "ce": "M", "be": "BM"}.get( + pos_check # type: ignore[arg-type] + ) def _is_business_daily(self) -> bool: # quick check: cannot be business daily diff --git a/pandas/util/_exceptions.py b/pandas/util/_exceptions.py index ef467f096e963..c718451fbf621 100644 --- a/pandas/util/_exceptions.py +++ b/pandas/util/_exceptions.py @@ -3,10 +3,11 @@ import contextlib import inspect import os +from typing import Iterator @contextlib.contextmanager -def rewrite_exception(old_name: str, new_name: str): +def rewrite_exception(old_name: str, new_name: str) -> Iterator[None]: """ Rewrite the message of an exception. """ diff --git a/pandas/util/_test_decorators.py b/pandas/util/_test_decorators.py index bbcf984e68b4b..4a4f27f6c7906 100644 --- a/pandas/util/_test_decorators.py +++ b/pandas/util/_test_decorators.py @@ -27,7 +27,10 @@ def test_foo(): from contextlib import contextmanager import locale -from typing import Callable +from typing import ( + Callable, + Iterator, +) import warnings import numpy as np @@ -253,7 +256,7 @@ def check_file_leaks(func) -> Callable: @contextmanager -def file_leak_context(): +def file_leak_context() -> Iterator[None]: """ ContextManager analogue to check_file_leaks. """ @@ -290,7 +293,7 @@ def async_mark(): return async_mark -def mark_array_manager_not_yet_implemented(request): +def mark_array_manager_not_yet_implemented(request) -> None: mark = pytest.mark.xfail(reason="Not yet implemented for ArrayManager") request.node.add_marker(mark) diff --git a/pandas/util/_validators.py b/pandas/util/_validators.py index 8e3de9404fbee..caa191dc78493 100644 --- a/pandas/util/_validators.py +++ b/pandas/util/_validators.py @@ -7,6 +7,8 @@ from typing import ( Iterable, Sequence, + TypeVar, + overload, ) import warnings @@ -19,6 +21,9 @@ is_integer, ) +BoolishT = TypeVar("BoolishT", bool, int) +BoolishNoneT = TypeVar("BoolishNoneT", bool, int, None) + def _check_arg_length(fname, args, max_fname_arg_count, compat_args): """ @@ -78,7 +83,7 @@ def _check_for_default_values(fname, arg_val_dict, compat_args): ) -def validate_args(fname, args, max_fname_arg_count, compat_args): +def validate_args(fname, args, max_fname_arg_count, compat_args) -> None: """ Checks whether the length of the `*args` argument passed into a function has at most `len(compat_args)` arguments and whether or not all of these @@ -132,7 +137,7 @@ def _check_for_invalid_keys(fname, kwargs, compat_args): raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'") -def validate_kwargs(fname, kwargs, compat_args): +def validate_kwargs(fname, kwargs, compat_args) -> None: """ Checks whether parameters passed to the **kwargs argument in a function `fname` are valid parameters as specified in `*compat_args` @@ -159,7 +164,9 @@ def validate_kwargs(fname, kwargs, compat_args): _check_for_default_values(fname, kwds, compat_args) -def validate_args_and_kwargs(fname, args, kwargs, max_fname_arg_count, compat_args): +def validate_args_and_kwargs( + fname, args, kwargs, max_fname_arg_count, compat_args +) -> None: """ Checks whether parameters passed to the *args and **kwargs argument in a function `fname` are valid parameters as specified in `*compat_args` @@ -215,7 +222,9 @@ def validate_args_and_kwargs(fname, args, kwargs, max_fname_arg_count, compat_ar validate_kwargs(fname, kwargs, compat_args) -def validate_bool_kwarg(value, arg_name, none_allowed=True, int_allowed=False): +def validate_bool_kwarg( + value: BoolishNoneT, arg_name, none_allowed=True, int_allowed=False +) -> BoolishNoneT: """ Ensure that argument passed in arg_name can be interpreted as boolean. @@ -424,12 +433,22 @@ def validate_percentile(q: float | Iterable[float]) -> np.ndarray: return q_arr +@overload +def validate_ascending(ascending: BoolishT) -> BoolishT: + ... + + +@overload +def validate_ascending(ascending: Sequence[BoolishT]) -> list[BoolishT]: + ... + + def validate_ascending( - ascending: bool | int | Sequence[bool | int] = True, -): + ascending: bool | int | Sequence[BoolishT], +) -> bool | int | list[BoolishT]: """Validate ``ascending`` kwargs for ``sort_index`` method.""" kwargs = {"none_allowed": False, "int_allowed": True} - if not isinstance(ascending, (list, tuple)): + if not isinstance(ascending, Sequence): return validate_bool_kwarg(ascending, "ascending", **kwargs) return [validate_bool_kwarg(item, "ascending", **kwargs) for item in ascending]
Found with ```sh pyright --createstub pandas grep -R "# -> " typings/pandas/ ``` Pyright can infer many of the missing return annotations :)
https://api.github.com/repos/pandas-dev/pandas/pulls/47510
2022-06-26T02:16:40Z
2022-06-27T16:50:04Z
2022-06-27T16:50:04Z
2022-09-21T15:29:48Z
TYP: misc
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 6c0a747b16745..a8c33a144378e 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -5545,7 +5545,7 @@ def replace( inplace: bool = False, limit=None, regex: bool = False, - method: str | lib.NoDefault = lib.no_default, + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default, ): return super().replace( to_replace=to_replace, diff --git a/pandas/core/generic.py b/pandas/core/generic.py index a10cfe1c615fa..34db1ab813dd1 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -6608,7 +6608,7 @@ def replace( inplace: bool_t = False, limit: int | None = None, regex=False, - method=lib.no_default, + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default, ): if not ( is_scalar(to_replace) diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py index 15d06ef3bc8e5..4da39318579eb 100644 --- a/pandas/core/indexes/multi.py +++ b/pandas/core/indexes/multi.py @@ -2643,7 +2643,10 @@ def _get_indexer_level_0(self, target) -> npt.NDArray[np.intp]: return ci.get_indexer_for(target) def get_slice_bound( - self, label: Hashable | Sequence[Hashable], side: str, kind=lib.no_default + self, + label: Hashable | Sequence[Hashable], + side: Literal["left", "right"], + kind=lib.no_default, ) -> int: """ For an ordered MultiIndex, compute slice bound @@ -2758,7 +2761,7 @@ def slice_locs( # happens in get_slice_bound method), but it adds meaningful doc. return super().slice_locs(start, end, step) - def _partial_tup_index(self, tup: tuple, side="left"): + def _partial_tup_index(self, tup: tuple, side: Literal["left", "right"] = "left"): if len(tup) > self._lexsort_depth: raise UnsortedIndexError( f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth " diff --git a/pandas/core/series.py b/pandas/core/series.py index 06815348fe4b9..cdebb67e1b6dc 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -5204,7 +5204,7 @@ def replace( inplace=False, limit=None, regex=False, - method: str | lib.NoDefault = lib.no_default, + method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default, ): return super().replace( to_replace=to_replace, diff --git a/pandas/core/shared_docs.py b/pandas/core/shared_docs.py index 3a8a95865d10e..09ee254633b22 100644 --- a/pandas/core/shared_docs.py +++ b/pandas/core/shared_docs.py @@ -541,7 +541,7 @@ string. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case `to_replace` must be ``None``. - method : {{'pad', 'ffill', 'bfill', `None`}} + method : {{'pad', 'ffill', 'bfill'}} The method to use when for replacement, when `to_replace` is a scalar, list or tuple and `value` is ``None``. diff --git a/pandas/core/window/expanding.py b/pandas/core/window/expanding.py index d1a8b70b34462..dcdcbc0483d59 100644 --- a/pandas/core/window/expanding.py +++ b/pandas/core/window/expanding.py @@ -9,6 +9,7 @@ from pandas._typing import ( Axis, + QuantileInterpolation, WindowingRankType, ) @@ -651,7 +652,7 @@ def kurt(self, numeric_only: bool = False, **kwargs): def quantile( self, quantile: float, - interpolation: str = "linear", + interpolation: QuantileInterpolation = "linear", numeric_only: bool = False, **kwargs, ): diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py index b45f43adbe952..6e47c46cc7203 100644 --- a/pandas/core/window/rolling.py +++ b/pandas/core/window/rolling.py @@ -29,6 +29,7 @@ ArrayLike, Axis, NDFrameT, + QuantileInterpolation, WindowingRankType, ) from pandas.compat._optional import import_optional_dependency @@ -1658,7 +1659,7 @@ def kurt(self, numeric_only: bool = False, **kwargs): def quantile( self, quantile: float, - interpolation: str = "linear", + interpolation: QuantileInterpolation = "linear", numeric_only: bool = False, **kwargs, ): @@ -2553,7 +2554,7 @@ def kurt(self, numeric_only: bool = False, **kwargs): def quantile( self, quantile: float, - interpolation: str = "linear", + interpolation: QuantileInterpolation = "linear", numeric_only: bool = False, **kwargs, ):
null
https://api.github.com/repos/pandas-dev/pandas/pulls/47509
2022-06-25T22:50:22Z
2022-06-28T17:11:20Z
2022-06-28T17:11:20Z
2022-09-21T15:28:31Z
REGR: concat materializing index even if sort is not necessary
diff --git a/doc/source/whatsnew/v1.4.4.rst b/doc/source/whatsnew/v1.4.4.rst index b462f0c6a8ffe..0f3d7a55fd6d7 100644 --- a/doc/source/whatsnew/v1.4.4.rst +++ b/doc/source/whatsnew/v1.4.4.rst @@ -14,7 +14,7 @@ including other versions of pandas. Fixed regressions ~~~~~~~~~~~~~~~~~ -- +- Fixed regression in :func:`concat` materializing :class:`Index` during sorting even if :class:`Index` was already sorted (:issue:`47501`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/indexes/api.py b/pandas/core/indexes/api.py index 634187a317baf..71ecb358cf575 100644 --- a/pandas/core/indexes/api.py +++ b/pandas/core/indexes/api.py @@ -157,7 +157,7 @@ def _get_combined_index( index = union_indexes(indexes, sort=False) index = ensure_index(index) - if sort: + if sort and not index.is_monotonic_increasing: index = safe_sort_index(index) # GH 29879 if copy: diff --git a/pandas/tests/reshape/concat/test_index.py b/pandas/tests/reshape/concat/test_index.py index b20e4bcc2256b..74a3e93c32ebe 100644 --- a/pandas/tests/reshape/concat/test_index.py +++ b/pandas/tests/reshape/concat/test_index.py @@ -387,3 +387,14 @@ def test_concat_with_levels_with_none_keys(self, levels): msg = "levels supported only when keys is not None" with pytest.raises(ValueError, match=msg): concat([df1, df2], levels=levels) + + def test_concat_range_index_result(self): + # GH#47501 + df1 = DataFrame({"a": [1, 2]}) + df2 = DataFrame({"b": [1, 2]}) + + result = concat([df1, df2], sort=True, axis=1) + expected = DataFrame({"a": [1, 2], "b": [1, 2]}) + tm.assert_frame_equal(result, expected) + expected_index = pd.RangeIndex(0, 2) + tm.assert_index_equal(result.index, expected_index, exact=True)
- [x] closes #47501 (Replace xxxx with the Github issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47508
2022-06-25T21:20:02Z
2022-06-30T20:28:18Z
2022-06-30T20:28:18Z
2022-07-02T16:38:20Z
TYP: _item_cache and _ixs
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b4a278185b01b..932805463d1c3 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3558,16 +3558,16 @@ def T(self) -> DataFrame: # ---------------------------------------------------------------------- # Indexing Methods - def _ixs(self, i: int, axis: int = 0): + def _ixs(self, i: int, axis: int = 0) -> Series: """ Parameters ---------- i : int axis : int - Notes - ----- - If slice passed, the resulting data will be a view. + Returns + ------- + Series """ # irow if axis == 0: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 589ea6e67d926..a10cfe1c615fa 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2014,7 +2014,7 @@ def __getstate__(self) -> dict[str, Any]: } @final - def __setstate__(self, state): + def __setstate__(self, state) -> None: if isinstance(state, BlockManager): self._mgr = state elif isinstance(state, dict): @@ -2047,7 +2047,7 @@ def __setstate__(self, state): elif len(state) == 2: raise NotImplementedError("Pre-0.12 pickles are no longer supported") - self._item_cache = {} + self._item_cache: dict[Hashable, Series] = {} # ---------------------------------------------------------------------- # Rendering Methods diff --git a/pandas/core/series.py b/pandas/core/series.py index 6ebee74810305..06815348fe4b9 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -932,7 +932,7 @@ def _take_with_is_copy(self, indices, axis=0) -> Series: """ return self.take(indices=indices, axis=axis) - def _ixs(self, i: int, axis: int = 0): + def _ixs(self, i: int, axis: int = 0) -> Any: """ Return the i-th value or values in the Series by location.
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. Noticed these in #47497 I have a question about the `_ixs` docstring in `frame.py` - it says that if a slice is passed, then the result is a view. However, the arguments are annotated to be `int` only. Is the docstring correct, or is the type annotation too tight? I presume it's the former, since I'm not able to pass a slice: ```python >>> df = pd.DataFrame({'a': [1,2, 3], 'b': [4,5,6]}) >>> df a b 0 1 4 1 2 5 2 3 6 >>> df._ixs(0, axis=1) 0 1 1 2 2 3 Name: a, dtype: int64 >>> df._ixs(slice(0, 2, None), axis=1) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/megorelli/pandas-dev/pandas/core/frame.py", line 3588, in _ixs col_mgr = self._mgr.iget(i) File "/home/megorelli/pandas-dev/pandas/core/internals/managers.py", line 988, in iget block = self.blocks[self.blknos[i]] TypeError: only integer scalar arrays can be converted to a scalar index ``` Also, typing `_ixs` as that's what's called within `get_item_cache`. For `Series._ixs`, I've annotated the return type as `Any`, as that's what the return type of `Series.pop` is.
https://api.github.com/repos/pandas-dev/pandas/pulls/47506
2022-06-25T17:41:06Z
2022-06-27T16:47:45Z
2022-06-27T16:47:45Z
2022-06-27T16:47:51Z
BUG: read_xml iterparse doesn't handle multiple toplevel elements with lxml parser
diff --git a/pandas/io/xml.py b/pandas/io/xml.py index 78fbeaad09300..9398e995c81ce 100644 --- a/pandas/io/xml.py +++ b/pandas/io/xml.py @@ -693,7 +693,7 @@ def _iterparse_nodes(self) -> list[dict[str, str | None]]: row = None elem.clear() - while elem.getprevious() is not None: + while elem.getprevious() is not None and elem.getparent() is not None: del elem.getparent()[0] if dicts == []: diff --git a/pandas/tests/io/xml/test_xml.py b/pandas/tests/io/xml/test_xml.py index eb2230bbf7fd5..b89adf85d8e26 100644 --- a/pandas/tests/io/xml/test_xml.py +++ b/pandas/tests/io/xml/test_xml.py @@ -1271,7 +1271,7 @@ def test_wrong_dict_value(datapath, parser): read_xml(filename, parser=parser, iterparse={"book": "category"}) -def test_bad_xml(datapath, parser): +def test_bad_xml(parser): bad_xml = """\ <?xml version='1.0' encoding='utf-8'?> <row> @@ -1312,6 +1312,113 @@ def test_bad_xml(datapath, parser): ) +def test_comment(parser): + xml = """\ +<!-- comment before root --> +<shapes> + <!-- comment within root --> + <shape> + <name>circle</name> + <type>2D</type> + </shape> + <shape> + <name>sphere</name> + <type>3D</type> + <!-- comment within child --> + </shape> + <!-- comment within root --> +</shapes> +<!-- comment after root -->""" + + df_xpath = read_xml(xml, xpath=".//shape", parser=parser) + + df_iter = read_xml_iterparse( + xml, parser=parser, iterparse={"shape": ["name", "type"]} + ) + + df_expected = DataFrame( + { + "name": ["circle", "sphere"], + "type": ["2D", "3D"], + } + ) + + tm.assert_frame_equal(df_xpath, df_expected) + tm.assert_frame_equal(df_iter, df_expected) + + +def test_dtd(parser): + xml = """\ +<?xml version="1.0" encoding="UTF-8"?> +<!DOCTYPE non-profits [ + <!ELEMENT shapes (shape*) > + <!ELEMENT shape ( name, type )> + <!ELEMENT name (#PCDATA)> +]> +<shapes> + <shape> + <name>circle</name> + <type>2D</type> + </shape> + <shape> + <name>sphere</name> + <type>3D</type> + </shape> +</shapes>""" + + df_xpath = read_xml(xml, xpath=".//shape", parser=parser) + + df_iter = read_xml_iterparse( + xml, parser=parser, iterparse={"shape": ["name", "type"]} + ) + + df_expected = DataFrame( + { + "name": ["circle", "sphere"], + "type": ["2D", "3D"], + } + ) + + tm.assert_frame_equal(df_xpath, df_expected) + tm.assert_frame_equal(df_iter, df_expected) + + +def test_processing_instruction(parser): + xml = """\ +<?xml version="1.0" encoding="UTF-8"?> +<?xml-stylesheet type="text/xsl" href="style.xsl"?> +<?display table-view?> +<?sort alpha-ascending?> +<?textinfo whitespace is allowed ?> +<?elementnames <shape>, <name>, <type> ?> +<shapes> + <shape> + <name>circle</name> + <type>2D</type> + </shape> + <shape> + <name>sphere</name> + <type>3D</type> + </shape> +</shapes>""" + + df_xpath = read_xml(xml, xpath=".//shape", parser=parser) + + df_iter = read_xml_iterparse( + xml, parser=parser, iterparse={"shape": ["name", "type"]} + ) + + df_expected = DataFrame( + { + "name": ["circle", "sphere"], + "type": ["2D", "3D"], + } + ) + + tm.assert_frame_equal(df_xpath, df_expected) + tm.assert_frame_equal(df_iter, df_expected) + + def test_no_result(datapath, parser): filename = datapath("io", "data", "xml", "books.xml") with pytest.raises(
- [X] closes #47422 - [X] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. *(not necessary for unreleased new 1.5 feature)*
https://api.github.com/repos/pandas-dev/pandas/pulls/47504
2022-06-25T03:54:20Z
2022-06-27T22:26:49Z
2022-06-27T22:26:49Z
2022-06-28T22:45:56Z
DOC: Resolve links in whatsnew
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 7e943bb5832e6..a931838fa4910 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -232,7 +232,7 @@ Other enhancements ^^^^^^^^^^^^^^^^^^ - :meth:`Series.map` now raises when ``arg`` is dict but ``na_action`` is not either ``None`` or ``'ignore'`` (:issue:`46588`) - :meth:`MultiIndex.to_frame` now supports the argument ``allow_duplicates`` and raises on duplicate labels if it is missing or False (:issue:`45245`) -- :class:`StringArray` now accepts array-likes containing nan-likes (``None``, ``np.nan``) for the ``values`` parameter in its constructor in addition to strings and :attr:`pandas.NA`. (:issue:`40839`) +- :class:`.StringArray` now accepts array-likes containing nan-likes (``None``, ``np.nan``) for the ``values`` parameter in its constructor in addition to strings and :attr:`pandas.NA`. (:issue:`40839`) - Improved the rendering of ``categories`` in :class:`CategoricalIndex` (:issue:`45218`) - :meth:`DataFrame.plot` will now allow the ``subplots`` parameter to be a list of iterables specifying column groups, so that columns may be grouped together in the same subplot (:issue:`29688`). - :meth:`to_numeric` now preserves float64 arrays when downcasting would generate values not representable in float32 (:issue:`43693`) @@ -243,17 +243,17 @@ Other enhancements - Implemented a ``bool``-dtype :class:`Index`, passing a bool-dtype array-like to ``pd.Index`` will now retain ``bool`` dtype instead of casting to ``object`` (:issue:`45061`) - Implemented a complex-dtype :class:`Index`, passing a complex-dtype array-like to ``pd.Index`` will now retain complex dtype instead of casting to ``object`` (:issue:`45845`) - :class:`Series` and :class:`DataFrame` with ``IntegerDtype`` now supports bitwise operations (:issue:`34463`) -- Add ``milliseconds`` field support for :class:`~pandas.DateOffset` (:issue:`43371`) +- Add ``milliseconds`` field support for :class:`.DateOffset` (:issue:`43371`) - :meth:`DataFrame.reset_index` now accepts a ``names`` argument which renames the index names (:issue:`6878`) -- :meth:`pd.concat` now raises when ``levels`` is given but ``keys`` is None (:issue:`46653`) -- :meth:`pd.concat` now raises when ``levels`` contains duplicate values (:issue:`46653`) +- :func:`concat` now raises when ``levels`` is given but ``keys`` is None (:issue:`46653`) +- :func:`concat` now raises when ``levels`` contains duplicate values (:issue:`46653`) - Added ``numeric_only`` argument to :meth:`DataFrame.corr`, :meth:`DataFrame.corrwith`, :meth:`DataFrame.cov`, :meth:`DataFrame.idxmin`, :meth:`DataFrame.idxmax`, :meth:`.DataFrameGroupBy.idxmin`, :meth:`.DataFrameGroupBy.idxmax`, :meth:`.GroupBy.var`, :meth:`.GroupBy.std`, :meth:`.GroupBy.sem`, and :meth:`.DataFrameGroupBy.quantile` (:issue:`46560`) - A :class:`errors.PerformanceWarning` is now thrown when using ``string[pyarrow]`` dtype with methods that don't dispatch to ``pyarrow.compute`` methods (:issue:`42613`, :issue:`46725`) - Added ``validate`` argument to :meth:`DataFrame.join` (:issue:`46622`) - A :class:`errors.PerformanceWarning` is now thrown when using ``string[pyarrow]`` dtype with methods that don't dispatch to ``pyarrow.compute`` methods (:issue:`42613`) - Added ``numeric_only`` argument to :meth:`Resampler.sum`, :meth:`Resampler.prod`, :meth:`Resampler.min`, :meth:`Resampler.max`, :meth:`Resampler.first`, and :meth:`Resampler.last` (:issue:`46442`) - ``times`` argument in :class:`.ExponentialMovingWindow` now accepts ``np.timedelta64`` (:issue:`47003`) -- :class:`DataError`, :class:`SpecificationError`, :class:`SettingWithCopyError`, :class:`SettingWithCopyWarning`, :class:`NumExprClobberingError`, :class:`UndefinedVariableError`, and :class:`IndexingError` are now exposed in ``pandas.errors`` (:issue:`27656`) +- :class:`.DataError`, :class:`.SpecificationError`, :class:`.SettingWithCopyError`, :class:`.SettingWithCopyWarning`, :class:`.NumExprClobberingError`, :class:`.UndefinedVariableError`, and :class:`.IndexingError` are now exposed in ``pandas.errors`` (:issue:`27656`) - Added ``check_like`` argument to :func:`testing.assert_series_equal` (:issue:`47247`) - Allow reading compressed SAS files with :func:`read_sas` (e.g., ``.sas7bdat.gz`` files) @@ -702,7 +702,7 @@ Other Deprecations - Deprecated treating float-dtype data as wall-times when passed with a timezone to :class:`Series` or :class:`DatetimeIndex` (:issue:`45573`) - Deprecated the behavior of :meth:`Series.fillna` and :meth:`DataFrame.fillna` with ``timedelta64[ns]`` dtype and incompatible fill value; in a future version this will cast to a common dtype (usually object) instead of raising, matching the behavior of other dtypes (:issue:`45746`) - Deprecated the ``warn`` parameter in :func:`infer_freq` (:issue:`45947`) -- Deprecated allowing non-keyword arguments in :meth:`ExtensionArray.argsort` (:issue:`46134`) +- Deprecated allowing non-keyword arguments in :meth:`.ExtensionArray.argsort` (:issue:`46134`) - Deprecated treating all-bool ``object``-dtype columns as bool-like in :meth:`DataFrame.any` and :meth:`DataFrame.all` with ``bool_only=True``, explicitly cast to bool instead (:issue:`46188`) - Deprecated behavior of method :meth:`DataFrame.quantile`, attribute ``numeric_only`` will default False. Including datetime/timedelta columns in the result (:issue:`7308`). - Deprecated :attr:`Timedelta.freq` and :attr:`Timedelta.is_populated` (:issue:`46430`) @@ -716,8 +716,8 @@ Other Deprecations - Deprecated the ``closed`` argument in :class:`Interval` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) - Deprecated the ``closed`` argument in :class:`IntervalIndex` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) - Deprecated the ``closed`` argument in :class:`IntervalDtype` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) -- Deprecated the ``closed`` argument in :class:`IntervalArray` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) -- Deprecated the ``closed`` argument in :class:`intervaltree` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) +- Deprecated the ``closed`` argument in :class:`.IntervalArray` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) +- Deprecated the ``closed`` argument in :class:`IntervalTree` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) - Deprecated the ``closed`` argument in :class:`ArrowInterval` in favor of ``inclusive`` argument; In a future version passing ``closed`` will raise (:issue:`40245`) - Deprecated allowing ``unit="M"`` or ``unit="Y"`` in :class:`Timestamp` constructor with a non-round float value (:issue:`47267`) - Deprecated the ``display.column_space`` global configuration option (:issue:`7576`) @@ -756,8 +756,8 @@ Bug fixes Categorical ^^^^^^^^^^^ -- Bug in :meth:`Categorical.view` not accepting integer dtypes (:issue:`25464`) -- Bug in :meth:`CategoricalIndex.union` when the index's categories are integer-dtype and the index contains ``NaN`` values incorrectly raising instead of casting to ``float64`` (:issue:`45362`) +- Bug in :meth:`.Categorical.view` not accepting integer dtypes (:issue:`25464`) +- Bug in :meth:`.CategoricalIndex.union` when the index's categories are integer-dtype and the index contains ``NaN`` values incorrectly raising instead of casting to ``float64`` (:issue:`45362`) - Datetimelike @@ -770,7 +770,7 @@ Datetimelike - Bug in :meth:`DatetimeIndex.tz_localize` localizing to UTC failing to make a copy of the underlying data (:issue:`46460`) - Bug in :meth:`DatetimeIndex.resolution` incorrectly returning "day" instead of "nanosecond" for nanosecond-resolution indexes (:issue:`46903`) - Bug in :class:`Timestamp` with an integer or float value and ``unit="Y"`` or ``unit="M"`` giving slightly-wrong results (:issue:`47266`) -- Bug in :class:`DatetimeArray` construction when passed another :class:`DatetimeArray` and ``freq=None`` incorrectly inferring the freq from the given array (:issue:`47296`) +- Bug in :class:`.DatetimeArray` construction when passed another :class:`.DatetimeArray` and ``freq=None`` incorrectly inferring the freq from the given array (:issue:`47296`) - Timedelta @@ -889,7 +889,7 @@ I/O Period ^^^^^^ -- Bug in subtraction of :class:`Period` from :class:`PeriodArray` returning wrong results (:issue:`45999`) +- Bug in subtraction of :class:`Period` from :class:`.PeriodArray` returning wrong results (:issue:`45999`) - Bug in :meth:`Period.strftime` and :meth:`PeriodIndex.strftime`, directives ``%l`` and ``%u`` were giving wrong results (:issue:`46252`) - Bug in inferring an incorrect ``freq`` when passing a string to :class:`Period` microseconds that are a multiple of 1000 (:issue:`46811`) - Bug in constructing a :class:`Period` from a :class:`Timestamp` or ``np.datetime64`` object with non-zero nanoseconds and ``freq="ns"`` incorrectly truncating the nanoseconds (:issue:`46811`) @@ -914,17 +914,17 @@ Groupby/resample/rolling - Bug in :meth:`.DataFrameGroupBy.size` and :meth:`.DataFrameGroupBy.transform` with ``func="size"`` produced incorrect results when ``axis=1`` (:issue:`45715`) - Bug in :meth:`.ExponentialMovingWindow.mean` with ``axis=1`` and ``engine='numba'`` when the :class:`DataFrame` has more columns than rows (:issue:`46086`) - Bug when using ``engine="numba"`` would return the same jitted function when modifying ``engine_kwargs`` (:issue:`46086`) -- Bug in :meth:`.DataFrameGroupby.transform` fails when ``axis=1`` and ``func`` is ``"first"`` or ``"last"`` (:issue:`45986`) -- Bug in :meth:`DataFrameGroupby.cumsum` with ``skipna=False`` giving incorrect results (:issue:`46216`) +- Bug in :meth:`.DataFrameGroupBy.transform` fails when ``axis=1`` and ``func`` is ``"first"`` or ``"last"`` (:issue:`45986`) +- Bug in :meth:`DataFrameGroupBy.cumsum` with ``skipna=False`` giving incorrect results (:issue:`46216`) - Bug in :meth:`.GroupBy.cumsum` with ``timedelta64[ns]`` dtype failing to recognize ``NaT`` as a null value (:issue:`46216`) -- Bug in :meth:`GroupBy.cummin` and :meth:`GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`) -- Bug in :meth:`GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`) -- Bug in :meth:`GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`) +- Bug in :meth:`.GroupBy.cummin` and :meth:`.GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`) +- Bug in :meth:`.GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`) +- Bug in :meth:`.GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`) - Bug in :meth:`.GroupBy.apply` would fail when ``func`` was a string and args or kwargs were supplied (:issue:`46479`) - Bug in :meth:`SeriesGroupBy.apply` would incorrectly name its result when there was a unique group (:issue:`46369`) -- Bug in :meth:`Rolling.sum` and :meth:`Rolling.mean` would give incorrect result with window of same values (:issue:`42064`, :issue:`46431`) -- Bug in :meth:`Rolling.var` and :meth:`Rolling.std` would give non-zero result with window of same values (:issue:`42064`) -- Bug in :meth:`Rolling.skew` and :meth:`Rolling.kurt` would give NaN with window of same values (:issue:`30993`) +- Bug in :meth:`.Rolling.sum` and :meth:`.Rolling.mean` would give incorrect result with window of same values (:issue:`42064`, :issue:`46431`) +- Bug in :meth:`.Rolling.var` and :meth:`.Rolling.std` would give non-zero result with window of same values (:issue:`42064`) +- Bug in :meth:`.Rolling.skew` and :meth:`.Rolling.kurt` would give NaN with window of same values (:issue:`30993`) - Bug in :meth:`.Rolling.var` would segfault calculating weighted variance when window size was larger than data size (:issue:`46760`) - Bug in :meth:`Grouper.__repr__` where ``dropna`` was not included. Now it is (:issue:`46754`) - Bug in :meth:`DataFrame.rolling` gives ValueError when center=True, axis=1 and win_type is specified (:issue:`46135`) @@ -958,8 +958,8 @@ Styler ^^^^^^ - Bug when attempting to apply styling functions to an empty DataFrame subset (:issue:`45313`) - Bug in :class:`CSSToExcelConverter` leading to ``TypeError`` when border color provided without border style for ``xlsxwriter`` engine (:issue:`42276`) -- Bug in :meth:`Styler.set_sticky` leading to white text on white background in dark mode (:issue:`46984`) -- Bug in :meth:`Styler.to_latex` causing ``UnboundLocalError`` when ``clines="all;data"`` and the ``DataFrame`` has no rows. (:issue:`47203`) +- Bug in :meth:`.Styler.set_sticky` leading to white text on white background in dark mode (:issue:`46984`) +- Bug in :meth:`.Styler.to_latex` causing ``UnboundLocalError`` when ``clines="all;data"`` and the ``DataFrame`` has no rows. (:issue:`47203`) Metadata ^^^^^^^^
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. Only added `.` in places where it would get the link to resolve.
https://api.github.com/repos/pandas-dev/pandas/pulls/47499
2022-06-24T19:24:15Z
2022-06-24T20:57:26Z
2022-06-24T20:57:26Z
2022-06-24T21:31:27Z
DOC: Minor fixes in whatsnew 1.5.0
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 35594fbc8fb8a..b8d8f318611fc 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -477,6 +477,9 @@ Deprecations .. _whatsnew_150.deprecations.int_slicing_series: +Label-based integer slicing on a Series with an Int64Index or RangeIndex +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + In a future version, integer slicing on a :class:`Series` with a :class:`Int64Index` or :class:`RangeIndex` will be treated as *label-based*, not positional. This will make the behavior consistent with other :meth:`Series.__getitem__` and :meth:`Series.__setitem__` behaviors (:issue:`45162`). For example: @@ -830,7 +833,7 @@ Indexing - Bug when setting a value too large for a :class:`Series` dtype failing to coerce to a common type (:issue:`26049`, :issue:`32878`) - Bug in :meth:`loc.__setitem__` treating ``range`` keys as positional instead of label-based (:issue:`45479`) - Bug in :meth:`Series.__setitem__` when setting ``boolean`` dtype values containing ``NA`` incorrectly raising instead of casting to ``boolean`` dtype (:issue:`45462`) -- Bug in :meth:`Series.__setitem__` where setting :attr:`NA` into a numeric-dtpye :class:`Series` would incorrectly upcast to object-dtype rather than treating the value as ``np.nan`` (:issue:`44199`) +- Bug in :meth:`Series.__setitem__` where setting :attr:`NA` into a numeric-dtype :class:`Series` would incorrectly upcast to object-dtype rather than treating the value as ``np.nan`` (:issue:`44199`) - Bug in :meth:`Series.__setitem__` with ``datetime64[ns]`` dtype, an all-``False`` boolean mask, and an incompatible value incorrectly casting to ``object`` instead of retaining ``datetime64[ns]`` dtype (:issue:`45967`) - Bug in :meth:`Index.__getitem__` raising ``ValueError`` when indexer is from boolean dtype with ``NA`` (:issue:`45806`) - Bug in :meth:`Series.mask` with ``inplace=True`` or setting values with a boolean mask with small integer dtypes incorrectly raising (:issue:`45750`) @@ -936,7 +939,7 @@ Reshaping - Bug in :func:`concat` between a :class:`Series` with integer dtype and another with :class:`CategoricalDtype` with integer categories and containing ``NaN`` values casting to object dtype instead of ``float64`` (:issue:`45359`) - Bug in :func:`get_dummies` that selected object and categorical dtypes but not string (:issue:`44965`) - Bug in :meth:`DataFrame.align` when aligning a :class:`MultiIndex` to a :class:`Series` with another :class:`MultiIndex` (:issue:`46001`) -- Bug in concanenation with ``IntegerDtype``, or ``FloatingDtype`` arrays where the resulting dtype did not mirror the behavior of the non-nullable dtypes (:issue:`46379`) +- Bug in concatenation with ``IntegerDtype``, or ``FloatingDtype`` arrays where the resulting dtype did not mirror the behavior of the non-nullable dtypes (:issue:`46379`) - Bug in :func:`concat` not sorting the column names when ``None`` is included (:issue:`47331`) - Bug in :func:`concat` with identical key leads to error when indexing :class:`MultiIndex` (:issue:`46519`) - Bug in :meth:`DataFrame.join` with a list when using suffixes to join DataFrames with duplicate column names (:issue:`46396`)
- [ ] closes #xxxx (Replace xxxx with the Github issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/47498
2022-06-24T18:21:11Z
2022-06-24T20:55:52Z
2022-06-24T20:55:52Z
2022-06-24T21:30:42Z
BUG: read_excel not forward filling index columns when no names are given
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 6fc1ec9c6ff90..a86436fac3487 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -934,6 +934,7 @@ I/O - Bug in :func:`read_parquet` when ``engine="fastparquet"`` where the file was not closed on error (:issue:`46555`) - :meth:`to_html` now excludes the ``border`` attribute from ``<table>`` elements when ``border`` keyword is set to ``False``. - Bug in :func:`read_sas` with certain types of compressed SAS7BDAT files (:issue:`35545`) +- Bug in :func:`read_excel` not forward filling :class:`MultiIndex` when no names were given (:issue:`47487`) - Bug in :func:`read_sas` returned ``None`` rather than an empty DataFrame for SAS7BDAT files with zero rows (:issue:`18198`) - Bug in :class:`StataWriter` where value labels were always written with default encoding (:issue:`46750`) - Bug in :class:`StataWriterUTF8` where some valid characters were removed from variable names (:issue:`47276`) diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py index 0c4268626099a..a0abddc82e6c8 100644 --- a/pandas/io/excel/_base.py +++ b/pandas/io/excel/_base.py @@ -788,9 +788,27 @@ def parse( # If there is a MultiIndex header and an index then there is also # a row containing just the index name(s) - has_index_names = ( - is_list_header and not is_len_one_list_header and index_col is not None - ) + has_index_names = False + if is_list_header and not is_len_one_list_header and index_col is not None: + + index_col_list: Sequence[int] + if isinstance(index_col, int): + index_col_list = [index_col] + else: + assert isinstance(index_col, Sequence) + index_col_list = index_col + + # We have to handle mi without names. If any of the entries in the data + # columns are not empty, this is a regular row + assert isinstance(header, Sequence) + if len(header) < len(data): + potential_index_names = data[len(header)] + potential_data = [ + x + for i, x in enumerate(potential_index_names) + if not control_row[i] and i not in index_col_list + ] + has_index_names = all(x == "" or x is None for x in potential_data) if is_list_like(index_col): # Forward fill values for MultiIndex index. diff --git a/pandas/tests/io/data/excel/multiindex_no_index_names.xlsx b/pandas/tests/io/data/excel/multiindex_no_index_names.xlsx new file mode 100755 index 0000000000000..3913ffce5befb Binary files /dev/null and b/pandas/tests/io/data/excel/multiindex_no_index_names.xlsx differ diff --git a/pandas/tests/io/excel/test_openpyxl.py b/pandas/tests/io/excel/test_openpyxl.py index ea9a45cf829f2..3b122c8572751 100644 --- a/pandas/tests/io/excel/test_openpyxl.py +++ b/pandas/tests/io/excel/test_openpyxl.py @@ -396,3 +396,17 @@ def test_ints_spelled_with_decimals(datapath, ext): result = pd.read_excel(path) expected = DataFrame(range(2, 12), columns=[1]) tm.assert_frame_equal(result, expected) + + +def test_read_multiindex_header_no_index_names(datapath, ext): + # GH#47487 + path = datapath("io", "data", "excel", f"multiindex_no_index_names{ext}") + result = pd.read_excel(path, index_col=[0, 1, 2], header=[0, 1, 2]) + expected = DataFrame( + [[np.nan, "x", "x", "x"], ["x", np.nan, np.nan, np.nan]], + columns=pd.MultiIndex.from_tuples( + [("X", "Y", "A1"), ("X", "Y", "A2"), ("XX", "YY", "B1"), ("XX", "YY", "B2")] + ), + index=pd.MultiIndex.from_tuples([("A", "AA", "AAA"), ("A", "BB", "BBB")]), + ) + tm.assert_frame_equal(result, expected)
- [x] closes #47487 (Replace xxxx with the Github issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. The prevoious has_index_name routine was too simple. This version is aligned with the TextReader version that is used a few lines below
https://api.github.com/repos/pandas-dev/pandas/pulls/47496
2022-06-24T13:05:35Z
2022-07-03T15:27:34Z
2022-07-03T15:27:34Z
2022-07-03T17:10:01Z
BUG: read_csv ignoring non existing header rows for python engine
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index 35594fbc8fb8a..5629ebf604697 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -873,6 +873,7 @@ I/O - Bug in :func:`read_csv` interpreting second row as :class:`Index` names even when ``index_col=False`` (:issue:`46569`) - Bug in :func:`read_parquet` when ``engine="pyarrow"`` which caused partial write to disk when column of unsupported datatype was passed (:issue:`44914`) - Bug in :func:`DataFrame.to_excel` and :class:`ExcelWriter` would raise when writing an empty DataFrame to a ``.ods`` file (:issue:`45793`) +- Bug in :func:`read_csv` ignoring non-existing header row for ``engine="python"`` (:issue:`47400`) - Bug in :func:`read_excel` raising uncontrolled ``IndexError`` when ``header`` references non-existing rows (:issue:`43143`) - Bug in :func:`read_html` where elements surrounding ``<br>`` were joined without a space between them (:issue:`29528`) - Bug in :func:`read_csv` when data is longer than header leading to issues with callables in ``usecols`` expecting strings (:issue:`46997`) diff --git a/pandas/io/parsers/python_parser.py b/pandas/io/parsers/python_parser.py index 2bf66923ddd60..ec953c9df036c 100644 --- a/pandas/io/parsers/python_parser.py +++ b/pandas/io/parsers/python_parser.py @@ -379,10 +379,16 @@ def _infer_columns( line = self._next_line() except StopIteration as err: - if self.line_pos < hr: + if 0 < self.line_pos <= hr and ( + not have_mi_columns or hr != header[-1] + ): + # If no rows we want to raise a different message and if + # we have mi columns, the last line is not part of the header + joi = list(map(str, header[:-1] if have_mi_columns else header)) + msg = f"[{','.join(joi)}], len of {len(joi)}, " raise ValueError( - f"Passed header={hr} but only {self.line_pos + 1} lines in " - "file" + f"Passed header={msg}" + f"but only {self.line_pos} lines in file" ) from err # We have an empty file, so check diff --git a/pandas/tests/io/parser/test_header.py b/pandas/tests/io/parser/test_header.py index 3fc23525df89e..4ded70db8bae7 100644 --- a/pandas/tests/io/parser/test_header.py +++ b/pandas/tests/io/parser/test_header.py @@ -666,3 +666,15 @@ def test_header_none_and_on_bad_lines_skip(all_parsers): ) expected = DataFrame({"a": ["x", "z"], "b": [1, 3]}) tm.assert_frame_equal(result, expected) + + +@skip_pyarrow +def test_header_missing_rows(all_parsers): + # GH#47400 + parser = all_parsers + data = """a,b +1,2 +""" + msg = r"Passed header=\[0,1,2\], len of 3, but only 2 lines in file" + with pytest.raises(ValueError, match=msg): + parser.read_csv(StringIO(data), header=[0, 1, 2])
- [x] closes #47400 (Replace xxxx with the Github issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. Initially, I thought this would be a case for deprecation, but this was simply a bug and was supposed to work before. Also, I aligned the error message of the python parser with the c parser
https://api.github.com/repos/pandas-dev/pandas/pulls/47493
2022-06-24T10:21:23Z
2022-06-24T21:10:11Z
2022-06-24T21:10:11Z
2022-06-25T14:23:34Z
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
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
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: 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
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
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
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&mdash; _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
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
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
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
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
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. ![2015-11-23 10 53 47](https://cloud.githubusercontent.com/assets/1696302/11329349/58e78d80-91de-11e5-82e6-6c90b80d93fb.png) The fix simply exclude `linear-gradient` if its width is 0. After the fix (same environment) ![2015-11-23 10 54 18](https://cloud.githubusercontent.com/assets/1696302/11329369/9d37dce2-91de-11e5-9636-29d2b2a843cd.png)
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
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
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">&#182;</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">&#182;</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&nbsp;[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">&#39;A&#39;</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">&#39;BCDE&#39;</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&nbsp;[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&nbsp;[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">&#39;</span><span class="se">\n</span><span class="s">&#39;</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>[&apos;&apos;, - &apos; &lt;style type=&quot;text/css&quot; &gt;&apos;, - &apos; &apos;, - &apos; &apos;, - &apos; #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {&apos;, - &apos; &apos;, - &apos; background-color: red;&apos;, - &apos; &apos;, - &apos; }&apos;, - &apos; &apos;]</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&nbsp;[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">&quot;&quot;&quot;</span> -<span class="sd"> Takes a scalar and returns a string with</span> -<span class="sd"> the css property `&#39;color: red&#39;` for negative</span> -<span class="sd"> strings, black otherwise.</span> -<span class="sd"> &quot;&quot;&quot;</span> - <span class="n">color</span> <span class="o">=</span> <span class="s">&#39;red&#39;</span> <span class="k">if</span> <span class="n">val</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s">&#39;black&#39;</span> - <span class="k">return</span> <span class="s">&#39;color: %s&#39;</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&nbsp;[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>&lt;style&gt;</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&nbsp;[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">&#39;&#39;&#39;</span> -<span class="sd"> highlight the maximum in a Series yellow.</span> -<span class="sd"> &#39;&#39;&#39;</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">&#39;background-color: yellow&#39;</span> <span class="k">if</span> <span class="n">v</span> <span class="k">else</span> <span class="s">&#39;&#39;</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&nbsp;[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"> - 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- - <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"> - 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- -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&nbsp;[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">&#39;yellow&#39;</span><span class="p">):</span> - <span class="sd">&#39;&#39;&#39;</span> -<span class="sd"> highlight the maximum in a Series or DataFrame</span> -<span class="sd"> &#39;&#39;&#39;</span> - <span class="n">attr</span> <span class="o">=</span> <span class="s">&#39;background-color: {}&#39;</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">&#39;&#39;</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">&#39;&#39;</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&nbsp;[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">&#39;darkorange&#39;</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">&#182;</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">&#182;</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&nbsp;[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">&#39;B&#39;</span><span class="p">,</span> <span class="s">&#39;C&#39;</span><span class="p">,</span> <span class="s">&#39;D&#39;</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&nbsp;[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">&#39;B&#39;</span><span class="p">,</span> <span class="s">&#39;D&#39;</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 { - 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- 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">&#182;</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&nbsp;[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">&#39;red&#39;</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&nbsp;[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">&quot;green&quot;</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; - 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- 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&nbsp;[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">&#39;viridis&#39;</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"> - 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- 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&nbsp;[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">&#39;viridis&#39;</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">&#39;red&#39;</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&nbsp;[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">&#39;A&#39;</span><span class="p">,</span> <span class="s">&#39;B&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;#d65f5f&#39;</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%); - 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- - <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col2" class="data row0 col2"> - - 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> - - <tr> - - <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row1"> - - 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"> - - 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"> - 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- 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&nbsp;[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; - - } - - #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2 { - - background-color: yellow; - - } - - #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1 { - - background-color: yellow; - - } - - #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 { - - background-color: yellow; - - } - - #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0 { - - background-color: yellow; - - } - - </style> - - <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 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col0" class="data row0 col0"> - - 1.0 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col1" class="data row0 col1"> - - 1.329212 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col2" class="data row0 col2"> - - nan - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col3" class="data row0 col3"> - - -0.31628 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col4" class="data row0 col4"> - - -0.99081 - - - </tr> - - <tr> - - <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row1"> - - 1 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col0" class="data row1 col0"> - - 2.0 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col1" class="data row1 col1"> - - -1.070816 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col2" class="data row1 col2"> - - -1.438713 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col3" class="data row1 col3"> - - 0.564417 - - - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col4" class="data row1 col4"> - - 0.295722 - - - </tr> - - <tr> - - <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row2"> - - 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"> - 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- - <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col4" class="data row5 col4"> - - -0.063328 - - - </tr> - - <tr> - - <th id="T_35620402_8d9b_11e5_8913_a45e60bd97fb" class="row_heading level4 row6"> - - 6 - - - <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&nbsp;[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&nbsp;[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">&#39;background-color&#39;</span><span class="p">:</span> <span class="s">&#39;black&#39;</span><span class="p">,</span> - <span class="s">&#39;color&#39;</span><span class="p">:</span> <span class="s">&#39;lawngreen&#39;</span><span class="p">,</span> - <span class="s">&#39;border-color&#39;</span><span class="p">:</span> <span class="s">&#39;white&#39;</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; - 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- 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; - 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- 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 { - 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- -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"> - 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- -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">&#182;</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&nbsp;[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"> - 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- -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">&#182;</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">&#182;</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&nbsp;[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">&#39;display.precision&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span> - 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- -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"> - 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- - <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"> - 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- - <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&nbsp;[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>\ - 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- </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">&#182;</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&nbsp;[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">&#39;Colormaps, with a caption.&#39;</span><span class="p">)</span>\ - 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- } - - </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">&#182;</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&nbsp;[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">&quot;#ffff99&quot;</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">&quot;tr:hover&quot;</span><span class="p">,</span> - <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&quot;background-color&quot;</span><span class="p">,</span> <span class="s">&quot;%s&quot;</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">&quot;th&quot;</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&quot;font-size&quot;</span><span class="p">,</span> <span class="s">&quot;150%&quot;</span><span class="p">),</span> - <span class="p">(</span><span class="s">&quot;text-align&quot;</span><span class="p">,</span> <span class="s">&quot;center&quot;</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">&quot;caption&quot;</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&quot;caption-side&quot;</span><span class="p">,</span> <span class="s">&quot;bottom&quot;</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">&quot;Hover to highlight.&quot;</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"> - 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- - <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">&#182;</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">&#182;</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">&#182;</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&nbsp;[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> - -</div> -</div> -</div> - 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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> - - </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&nbsp;[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">&quot;th&quot;</span><span class="p">,</span> - <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&quot;font-size&quot;</span><span class="p">,</span> <span class="s">&quot;4pt&quot;</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">&quot;td&quot;</span><span class="p">,</span> - <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&#39;padding&#39;</span><span class="p">,</span> <span class="s">&quot;0em 0em&quot;</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">&quot;th:hover&quot;</span><span class="p">,</span> - <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&quot;font-size&quot;</span><span class="p">,</span> <span class="s">&quot;12pt&quot;</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">&quot;tr:hover td:hover&quot;</span><span class="p">,</span> - <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&#39;max-width&#39;</span><span class="p">,</span> <span class="s">&#39;200px&#39;</span><span class="p">),</span> - <span class="p">(</span><span class="s">&#39;font-size&#39;</span><span class="p">,</span> <span class="s">&#39;12pt&#39;</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&nbsp;[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">&#39;max-width&#39;</span><span class="p">:</span> <span class="s">&#39;80px&#39;</span><span class="p">,</span> <span class="s">&#39;font-size&#39;</span><span class="p">:</span> <span class="s">&#39;1pt&#39;</span><span class="p">})</span>\ - <span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">&quot;Hover to magify&quot;</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 { - - font-size: 4pt; - - } - - #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb td { - - padding: 0em 0em; - - } - - #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th:hover { - - font-size: 12pt; - - } - - #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb tr:hover td:hover { - - max-width: 200px; - - font-size: 12pt; - - } - - - #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0 { - - background-color: #ecf2f8; - - max-width: 80px; - - font-size: 1pt; - - } - - #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1 { - - background-color: #b8cce5; - - max-width: 80px; - 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- -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">&#182;</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">&#182;</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">&#182;</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">&quot;&quot;&quot;...&quot;&quot;&quot;</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
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
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}/*! 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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 } - } - }); - </script> - <!-- End of mathjax configuration --></head> -<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">&#182;</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&nbsp;[1]:</div> +<div class="prompt input_prompt">In&nbsp;[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&nbsp;[2]:</div> +<div class="prompt input_prompt">In&nbsp;[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&nbsp;[3]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;</span><span class="se">\n</span><span class="s">&#39;</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 &apos; &lt;style type=&quot;text/css&quot; &gt;&apos;, &apos; &apos;, &apos; &apos;, - &apos; #T_e7c1f51a_8bd5_11e5_803e_a45e60bd97fbrow0_col2 {&apos;, + &apos; #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {&apos;, &apos; &apos;, &apos; background-color: red;&apos;, &apos; &apos;, @@ -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&nbsp;[4]:</div> +<div class="prompt input_prompt">In&nbsp;[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&nbsp;[5]:</div> +<div class="prompt input_prompt">In&nbsp;[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&nbsp;[6]:</div> +<div class="prompt input_prompt">In&nbsp;[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&nbsp;[7]:</div> +<div class="prompt input_prompt">In&nbsp;[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&nbsp;[8]:</div> +<div class="prompt input_prompt">In&nbsp;[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"> + <td id="T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0" class="data row0 col0"> 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"> -0.31628 - <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&nbsp;[9]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;yellow&#39;</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&nbsp;[10]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;darkorange&#39;</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&nbsp;[11]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;B&#39;</span><span class="p">,</span> <span class="s">&#39;C&#39;</span><span class="p">,</span> <span class="s">&#39;D&#39;</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"> -0.00204 - <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&nbsp;[12]:</div> +<div class="prompt input_prompt">In&nbsp;[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 - <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col2" class="data row0 col2"> + <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&nbsp;[13]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;red&#39;</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 - <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col1" class="data row0 col1"> + <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 - <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col0" class="data row8 col0"> + <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 - <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col2" class="data row8 col2"> + <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col2" class="data row8 col2"> -1.325963 - <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col3" class="data row8 col3"> + <td id="T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col3" class="data row8 col3"> 1.428984 - <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col4" class="data row8 col4"> + <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 - <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col0" class="data row9 col0"> + <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&nbsp;[14]:</div> +<div class="prompt input_prompt">In&nbsp;[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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#T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col0 { + #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0 { 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> - <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row0"> + <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row0"> 0 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col0" class="data row0 col0"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0" class="data row0 col0"> 1.0 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col1" class="data row0 col1"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1" class="data row0 col1"> 1.329212 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col2" class="data row0 col2"> + <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"> -0.31628 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col4" class="data row0 col4"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4" class="data row0 col4"> -0.99081 @@ -5058,32 +4763,32 @@ <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 row1"> + <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row1"> 1 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col0" class="data row1 col0"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0" class="data row1 col0"> 2.0 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col1" class="data row1 col1"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1" class="data row1 col1"> -1.070816 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col2" class="data row1 col2"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2" class="data row1 col2"> -1.438713 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col3" class="data row1 col3"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3" class="data row1 col3"> 0.564417 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col4" class="data row1 col4"> + <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> - <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row2"> + <th id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fb" class="row_heading level4 row2"> 2 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col0" class="data row2 col0"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0" class="data row2 col0"> 3.0 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col1" class="data row2 col1"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1" class="data row2 col1"> -1.626404 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col2" class="data row2 col2"> + <td id="T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2" class="data row2 col2"> 0.219565 - <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col3" class="data row2 col3"> + <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&nbsp;[15]:</div> +<div class="prompt input_prompt">In&nbsp;[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 - <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0" class="data row1 col0"> + <td id="T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0" class="data row1 col0"> 2.0 - <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1" class="data row1 col1"> + <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 - <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4" class="data row1 col4"> + <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&nbsp;[16]:</div> +<div class="prompt input_prompt">In&nbsp;[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 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0" class="data row1 col0"> + <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"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1" class="data row2 col1"> -1.626404 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2" class="data row2 col2"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2" class="data row2 col2"> 0.219565 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3" class="data row2 col3"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3" class="data row2 col3"> 0.678805 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4" class="data row2 col4"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4" class="data row2 col4"> 1.889273 @@ -6121,32 +5826,32 @@ <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 row3"> + <th id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fb" class="row_heading level4 row3"> 3 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0" class="data row3 col0"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0" class="data row3 col0"> 4.0 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1" class="data row3 col1"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1" class="data row3 col1"> 0.961538 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2" class="data row3 col2"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2" class="data row3 col2"> 0.104011 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3" class="data row3 col3"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3" class="data row3 col3"> -0.481165 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4" class="data row3 col4"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4" class="data row3 col4"> 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 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0" class="data row4 col0"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0" class="data row4 col0"> 5.0 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1" class="data row4 col1"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1" class="data row4 col1"> 1.453425 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2" class="data row4 col2"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2" class="data row4 col2"> 1.057737 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3" class="data row4 col3"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3" class="data row4 col3"> 0.165562 - <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4" class="data row4 col4"> + <td id="T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4" class="data row4 col4"> 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&nbsp;[17]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;A&#39;</span><span class="p">,</span> <span class="s">&#39;B&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;#d65f5f&#39;</span><span class="p">)</span> @@ -6224,14 +5929,14 @@ <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[17]:</div> +<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_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col0 { + #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; @@ -6251,7 +5956,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col0 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0 { width: 10em; @@ -6261,7 +5966,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1 { width: 10em; @@ -6271,7 +5976,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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; @@ -6291,7 +5996,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0 { width: 10em; @@ -6301,7 +6006,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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; @@ -6321,7 +6026,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1 { width: 10em; @@ -6331,7 +6036,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0 { width: 10em; @@ -6341,7 +6046,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1 { width: 10em; @@ -6351,7 +6056,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0 { width: 10em; @@ -6361,7 +6066,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1 { width: 10em; @@ -6371,7 +6076,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0 { width: 10em; @@ -6381,7 +6086,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1 { + #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1 { width: 10em; @@ -6391,7 +6096,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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> @@ -6459,32 +6164,32 @@ <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 row0"> + <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row0"> 0 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col0" class="data row0 col0"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0" class="data row0 col0"> 1.0 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1" class="data row0 col1"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1" class="data row0 col1"> 1.329212 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col2" class="data row0 col2"> + <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 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col4" class="data row0 col4"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col4" class="data row0 col4"> -0.99081 @@ -6493,32 +6198,32 @@ <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 row1"> + <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row1"> 1 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col0" class="data row1 col0"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0" class="data row1 col0"> 2.0 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1" class="data row1 col1"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1" class="data row1 col1"> -1.070816 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col2" class="data row1 col2"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col2" class="data row1 col2"> -1.438713 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col3" class="data row1 col3"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col3" class="data row1 col3"> 0.564417 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col4" class="data row1 col4"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col4" class="data row1 col4"> 0.295722 @@ -6527,32 +6232,32 @@ <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 row2"> + <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row2"> 2 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col0" class="data row2 col0"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0" class="data row2 col0"> 3.0 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col1" class="data row2 col1"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1" class="data row2 col1"> -1.626404 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col2" class="data row2 col2"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col2" class="data row2 col2"> 0.219565 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col3" class="data row2 col3"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col3" class="data row2 col3"> 0.678805 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col4" class="data row2 col4"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col4" class="data row2 col4"> 1.889273 @@ -6561,32 +6266,32 @@ <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 row3"> + <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row3"> 3 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0" class="data row3 col0"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0" class="data row3 col0"> 4.0 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1" class="data row3 col1"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1" class="data row3 col1"> 0.961538 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col2" class="data row3 col2"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col2" class="data row3 col2"> 0.104011 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col3" class="data row3 col3"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col3" class="data row3 col3"> -0.481165 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col4" class="data row3 col4"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col4" class="data row3 col4"> 0.850229 @@ -6595,32 +6300,32 @@ <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 row4"> + <th id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb" class="row_heading level4 row4"> 4 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0" class="data row4 col0"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0" class="data row4 col0"> 5.0 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1" class="data row4 col1"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1" class="data row4 col1"> 1.453425 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col2" class="data row4 col2"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col2" class="data row4 col2"> 1.057737 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col3" class="data row4 col3"> + <td id="T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col3" class="data row4 col3"> 0.165562 - <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col4" class="data row4 col4"> + <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&nbsp;[18]:</div> +<div class="prompt input_prompt">In&nbsp;[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"> -0.99081 @@ -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 - <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col1" class="data row6 col1"> + <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col1" class="data row6 col1"> 0.121668 - <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col2" class="data row6 col2"> + <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2" class="data row6 col2"> 1.207603 - <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col3" class="data row6 col3"> + <td id="T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col3" class="data row6 col3"> -0.00204 - <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&nbsp;[19]:</div> +<div class="prompt input_prompt">In&nbsp;[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 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col0" class="data row0 col0"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col0" class="data row0 col0"> 1.0 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col1" class="data row0 col1"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col1" class="data row0 col1"> 1.329212 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col2" class="data row0 col2"> + <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"> -0.31628 - <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"> -0.99081 @@ -7363,32 +7068,32 @@ <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 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 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col1" class="data row6 col1"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col1" class="data row6 col1"> 0.121668 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col2" class="data row6 col2"> + <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"> -0.00204 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col4" class="data row6 col4"> + <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 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col0" class="data row7 col0"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col0" class="data row7 col0"> 8.0 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col1" class="data row7 col1"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col1" class="data row7 col1"> 0.354493 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col2" class="data row7 col2"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col2" class="data row7 col2"> 1.037528 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col3" class="data row7 col3"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col3" class="data row7 col3"> -0.385684 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col4" class="data row7 col4"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col4" class="data row7 col4"> 0.519818 @@ -7601,32 +7306,32 @@ <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 row8"> + <th id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fb" class="row_heading level4 row8"> 8 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col0" class="data row8 col0"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col0" class="data row8 col0"> 9.0 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col1" class="data row8 col1"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col1" class="data row8 col1"> 1.686583 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col2" class="data row8 col2"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col2" class="data row8 col2"> -1.325963 - <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 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col4" class="data row8 col4"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4" class="data row8 col4"> -2.089354 @@ -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 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col0" class="data row9 col0"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col0" class="data row9 col0"> 10.0 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col1" class="data row9 col1"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col1" class="data row9 col1"> -0.12982 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col2" class="data row9 col2"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col2" class="data row9 col2"> 0.631523 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col3" class="data row9 col3"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3" class="data row9 col3"> -0.586538 - <td id="T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col4" class="data row9 col4"> + <td id="T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col4" class="data row9 col4"> 0.29072 @@ -7690,7 +7395,7 @@ <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&nbsp;[20]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;background-color&#39;</span><span class="p">:</span> <span class="s">&#39;black&#39;</span><span class="p">,</span> @@ -7706,14 +7411,14 @@ <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[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; @@ -7723,7 +7428,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col1 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1 { color: lawngreen; @@ -7733,7 +7438,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2 { color: lawngreen; @@ -7743,7 +7448,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col3 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3 { color: lawngreen; @@ -7753,7 +7458,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col4 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4 { color: lawngreen; @@ -7763,7 +7468,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col0 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0 { color: lawngreen; @@ -7773,7 +7478,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col1 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1 { color: lawngreen; @@ -7783,7 +7488,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2 { color: lawngreen; @@ -7793,7 +7498,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3 { color: lawngreen; @@ -7803,7 +7508,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col4 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4 { color: lawngreen; @@ -7813,7 +7518,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col0 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0 { color: lawngreen; @@ -7823,7 +7528,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col1 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1 { color: lawngreen; @@ -7833,7 +7538,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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; @@ -7853,7 +7558,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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 } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col0 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0 { color: lawngreen; @@ -7873,7 +7578,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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; @@ -7993,7 +7698,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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 } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0 { color: lawngreen; @@ -8023,7 +7728,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1 { color: lawngreen; @@ -8033,7 +7738,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #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 } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0 { color: lawngreen; @@ -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 { color: lawngreen; @@ -8113,7 +7818,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0 { color: lawngreen; @@ -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 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4 { color: lawngreen; @@ -8163,7 +7868,7 @@ <h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styl } - #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col0 { + #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0 { color: lawngreen; @@ -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&nbsp;[21]:</div> +<div class="prompt input_prompt">In&nbsp;[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" > - 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#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 - <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col2" class="data row0 col2"> + <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"> -0.31628 - <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"> -1.070816 - <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"> -1.626404 - <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"> -0.481165 - <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"> -0.00204 - <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 - <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col0" class="data row7 col0"> + <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col0" class="data row7 col0"> 8.0 - <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col1" class="data row7 col1"> + <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col1" class="data row7 col1"> 0.354493 - <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col2" class="data row7 col2"> + <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 - <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col4" class="data row7 col4"> + <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 - <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col0" class="data row8 col0"> + <td id="T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0" class="data row8 col0"> 9.0 - <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col1" class="data row8 col1"> + <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&nbsp;[22]:</div> +<div class="prompt input_prompt">In&nbsp;[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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<td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col0" class="data row2 col0"> + <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col0" class="data row2 col0"> -3.0 - <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col1" class="data row2 col1"> + <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col1" class="data row2 col1"> 1.626404 - <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col2" class="data row2 col2"> + <td id="T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col2" class="data row2 col2"> -0.219565 - <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">&#182;</a>< </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> -<div class="prompt input_prompt">In&nbsp;[23]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#39;display.precision&#39;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</a>< </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> -<div class="prompt input_prompt">In&nbsp;[24]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#182;</a>< <div class="output"> -<div class="output_area"><div class="prompt output_prompt">Out[24]:</div> +<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" > - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0 { color: black; @@ -10902,7 +10617,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1 { color: black; @@ -10910,7 +10625,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2 { color: black; @@ -10918,7 +10633,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3 { color: red; @@ -10926,7 +10641,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4 { color: red; @@ -10934,7 +10649,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0 { color: black; @@ -10942,7 +10657,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col1 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1 { color: red; @@ -10950,7 +10665,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2 { color: red; @@ -10958,7 +10673,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3 { color: black; @@ -10966,7 +10681,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4 { color: black; @@ -10974,7 +10689,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0 { color: black; @@ -10982,7 +10697,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1 { color: red; @@ -10990,7 +10705,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2 { color: black; @@ -10998,7 +10713,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3 { color: black; @@ -11006,7 +10721,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4 { color: black; @@ -11014,7 +10729,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0 { color: black; @@ -11022,7 +10737,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0 { color: black; @@ -11062,7 +10777,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</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">&#182;</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">&#182;</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">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4 { color: black; @@ -11094,7 +10809,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0 { color: black; @@ -11102,7 +10817,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</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">&#182;</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">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3 { color: black; @@ -11126,7 +10841,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4 { color: red; @@ -11134,7 +10849,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0 { color: black; @@ -11142,7 +10857,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</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">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2 { color: black; @@ -11158,7 +10873,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3 { color: red; @@ -11166,7 +10881,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4 { color: black; @@ -11174,7 +10889,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0 { color: black; @@ -11182,7 +10897,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 { color: black; @@ -11190,7 +10905,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2 { color: black; @@ -11198,7 +10913,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3 { color: red; @@ -11206,7 +10921,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4 { color: black; @@ -11214,7 +10929,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</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">&#182;</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">&#182;</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">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3 { color: black; @@ -11246,7 +10961,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4 { color: red; @@ -11254,7 +10969,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0 { color: black; @@ -11262,7 +10977,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</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">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2 { color: black; @@ -11278,7 +10993,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3 { color: red; @@ -11286,7 +11001,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</a>< } - #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4 { + #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4 { color: black; @@ -11296,7 +11011,7 @@ <h2 id="Precision">Precision<a class="anchor-link" href="#Precision">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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 - 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<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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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"> + <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3" class="data row7 col3"> -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">&#182;</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">&#182;</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"> + <td id="T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3" class="data row9 col3"> -0.586538 - <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4" class="data row9 col4"> + <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&nbsp;[26]:</div> +<div class="prompt input_prompt">In&nbsp;[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> @@ -12454,19 +12169,19 @@ <h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">&# <div class="output"> -<div class="output_area"><div class="prompt output_prompt">Out[26]:</div> +<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> @@ -12511,32 +12226,32 @@ <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 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"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col0" class="data row0 col0"> 1.0 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col1" class="data row0 col1"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col1" class="data row0 col1"> 1.329212 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col2" class="data row0 col2"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col2" class="data row0 col2"> 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"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col4" class="data row0 col4"> -0.99081 @@ -12545,32 +12260,32 @@ <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 row1"> + <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row1"> 1 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col0" class="data row1 col0"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col0" class="data row1 col0"> 2.0 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col1" class="data row1 col1"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col1" class="data row1 col1"> -1.070816 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col2" class="data row1 col2"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col2" class="data row1 col2"> -1.438713 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col3" class="data row1 col3"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col3" class="data row1 col3"> 0.564417 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col4" class="data row1 col4"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col4" class="data row1 col4"> 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"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col0" class="data row2 col0"> 3.0 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col1" class="data row2 col1"> + <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 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col3" class="data row2 col3"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col3" class="data row2 col3"> 0.678805 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col4" class="data row2 col4"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col4" class="data row2 col4"> 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"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col2" class="data row3 col2"> 0.104011 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col3" class="data row3 col3"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col3" class="data row3 col3"> -0.481165 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col4" class="data row3 col4"> + <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 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col3" class="data row4 col3"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col3" class="data row4 col3"> 0.165562 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col4" class="data row4 col4"> + <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"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col3" class="data row5 col3"> 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> - <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row6"> + <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 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col1" class="data row6 col1"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col1" class="data row6 col1"> 0.121668 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col2" class="data row6 col2"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col2" class="data row6 col2"> 1.207603 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col3" class="data row6 col3"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col3" class="data row6 col3"> -0.00204 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col4" class="data row6 col4"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col4" class="data row6 col4"> 1.627796 @@ -12749,32 +12464,32 @@ <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 row7"> + <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"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col0" class="data row7 col0"> 8.0 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col1" class="data row7 col1"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col1" class="data row7 col1"> 0.354493 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col2" class="data row7 col2"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col2" class="data row7 col2"> 1.037528 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col3" class="data row7 col3"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col3" class="data row7 col3"> -0.385684 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col4" class="data row7 col4"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col4" class="data row7 col4"> 0.519818 @@ -12783,32 +12498,32 @@ <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 row8"> + <th id="T_359bea52_8d9b_11e5_be48_a45e60bd97fb" class="row_heading level4 row8"> 8 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col0" class="data row8 col0"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col0" class="data row8 col0"> 9.0 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col1" class="data row8 col1"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col1" class="data row8 col1"> 1.686583 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col2" class="data row8 col2"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col2" class="data row8 col2"> -1.325963 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col3" class="data row8 col3"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col3" class="data row8 col3"> 1.428984 - <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col4" class="data row8 col4"> + <td id="T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col4" class="data row8 col4"> -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">&#182;</a>< </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> -<div class="prompt input_prompt">In&nbsp;[27]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#182;</a>< <style type="text/css" > - 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#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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</a>< </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> -<div class="prompt input_prompt">In&nbsp;[28]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&quot;th&quot;</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&quot;font-size&quot;</span><span class="p">,</span> <span class="s">&quot;4pt&quot;</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">&quot;td&quot;</span><span class="p">,</span> + <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&#39;padding&#39;</span><span class="p">,</span> <span class="s">&quot;0em 0em&quot;</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">&quot;th:hover&quot;</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&quot;font-size&quot;</span><span class="p">,</span> <span class="s">&quot;12pt&quot;</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">&quot;tr:hover td:hover&quot;</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">&quot;tr:hover td:hover&quot;</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">&#39;max-width&#39;</span><span class="p">,</span> <span class="s">&#39;200px&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s">&#39;font-size&#39;</span><span class="p">,</span> <span class="s">&#39;12pt&#39;</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">&#182;</a>< </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> -<div class="prompt input_prompt">In&nbsp;[29]:</div> +<div class="prompt input_prompt">In&nbsp;[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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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">&#182;</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"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14" class="data row0 col14"> 0.08 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col15" class="data row0 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15" class="data row0 col15"> -1.25 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col16" class="data row0 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16" class="data row0 col16"> 1.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col17" class="data row0 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17" class="data row0 col17"> -1.05 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col18" class="data row0 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18" class="data row0 col18"> 1.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col19" class="data row0 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19" class="data row0 col19"> -0.42 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col20" class="data row0 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20" class="data row0 col20"> 2.29 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col21" class="data row0 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21" class="data row0 col21"> -2.59 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col22" class="data row0 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22" class="data row0 col22"> 2.82 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col23" class="data row0 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23" class="data row0 col23"> 0.68 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col24" class="data row0 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24" class="data row0 col24"> -1.58 @@ -18901,132 +18624,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row1"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row1"> 1 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col0" class="data row1 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0" class="data row1 col0"> -1.75 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col1" class="data row1 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1" class="data row1 col1"> 1.56 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col2" class="data row1 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2" class="data row1 col2"> -1.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col3" class="data row1 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3" class="data row1 col3"> -1.1 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col4" class="data row1 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4" class="data row1 col4"> 1.03 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col5" class="data row1 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5" class="data row1 col5"> 0.0 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col6" class="data row1 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6" class="data row1 col6"> -2.46 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col7" class="data row1 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7" class="data row1 col7"> 3.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col8" class="data row1 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8" class="data row1 col8"> -1.66 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col9" class="data row1 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9" class="data row1 col9"> 1.27 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col10" class="data row1 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10" class="data row1 col10"> -0.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col11" class="data row1 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11" class="data row1 col11"> -0.02 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col12" class="data row1 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12" class="data row1 col12"> 1.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col13" class="data row1 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13" class="data row1 col13"> -1.09 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col14" class="data row1 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14" class="data row1 col14"> -1.86 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col15" class="data row1 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15" class="data row1 col15"> -1.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col16" class="data row1 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16" class="data row1 col16"> -0.68 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col17" class="data row1 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17" class="data row1 col17"> -0.81 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col18" class="data row1 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18" class="data row1 col18"> 0.35 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col19" class="data row1 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19" class="data row1 col19"> -0.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col20" class="data row1 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20" class="data row1 col20"> 1.79 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col21" class="data row1 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21" class="data row1 col21"> -2.82 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col22" class="data row1 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22" class="data row1 col22"> 2.26 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col23" class="data row1 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23" class="data row1 col23"> 0.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col24" class="data row1 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24" class="data row1 col24"> 0.44 @@ -19035,132 +18758,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row2"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row2"> 2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col0" class="data row2 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0" class="data row2 col0"> -0.65 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col1" class="data row2 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1" class="data row2 col1"> 3.22 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col2" class="data row2 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2" class="data row2 col2"> -1.76 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col3" class="data row2 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3" class="data row2 col3"> 0.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col4" class="data row2 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4" class="data row2 col4"> 2.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col5" class="data row2 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5" class="data row2 col5"> -0.37 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col6" class="data row2 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6" class="data row2 col6"> -3.0 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col7" class="data row2 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7" class="data row2 col7"> 3.73 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col8" class="data row2 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8" class="data row2 col8"> -1.87 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col9" class="data row2 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9" class="data row2 col9"> 2.46 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col10" class="data row2 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10" class="data row2 col10"> 0.21 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col11" class="data row2 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11" class="data row2 col11"> -0.24 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col12" class="data row2 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12" class="data row2 col12"> -0.1 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col13" class="data row2 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13" class="data row2 col13"> -0.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col14" class="data row2 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14" class="data row2 col14"> -3.02 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col15" class="data row2 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15" class="data row2 col15"> -0.82 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col16" class="data row2 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16" class="data row2 col16"> -0.21 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col17" class="data row2 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17" class="data row2 col17"> -0.23 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col18" class="data row2 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18" class="data row2 col18"> 0.86 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col19" class="data row2 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19" class="data row2 col19"> -0.68 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col20" class="data row2 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col20" class="data row2 col20"> 1.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col21" class="data row2 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col21" class="data row2 col21"> -4.89 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col22" class="data row2 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col22" class="data row2 col22"> 3.03 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col23" class="data row2 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col23" class="data row2 col23"> 1.91 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col24" class="data row2 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col24" class="data row2 col24"> 0.61 @@ -19169,132 +18892,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row3"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row3"> 3 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col0" class="data row3 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col0" class="data row3 col0"> -1.62 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col1" class="data row3 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col1" class="data row3 col1"> 3.71 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col2" class="data row3 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col2" class="data row3 col2"> -2.31 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col3" class="data row3 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col3" class="data row3 col3"> 0.43 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col4" class="data row3 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col4" class="data row3 col4"> 4.17 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col5" class="data row3 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col5" class="data row3 col5"> -0.43 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col6" class="data row3 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col6" class="data row3 col6"> -3.86 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col7" class="data row3 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7" class="data row3 col7"> 4.16 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col8" class="data row3 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8" class="data row3 col8"> -2.15 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col9" class="data row3 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9" class="data row3 col9"> 1.08 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col10" class="data row3 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10" class="data row3 col10"> 0.12 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col11" class="data row3 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11" class="data row3 col11"> 0.6 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col12" class="data row3 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12" class="data row3 col12"> -0.89 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col13" class="data row3 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13" class="data row3 col13"> 0.27 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col14" class="data row3 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14" class="data row3 col14"> -3.67 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col15" class="data row3 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15" class="data row3 col15"> -2.71 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col16" class="data row3 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16" class="data row3 col16"> -0.31 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col17" class="data row3 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17" class="data row3 col17"> -1.59 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col18" class="data row3 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18" class="data row3 col18"> 1.35 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col19" class="data row3 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19" class="data row3 col19"> -1.83 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col20" class="data row3 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20" class="data row3 col20"> 0.91 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col21" class="data row3 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21" class="data row3 col21"> -5.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col22" class="data row3 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22" class="data row3 col22"> 2.81 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col23" class="data row3 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23" class="data row3 col23"> 2.11 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col24" class="data row3 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24" class="data row3 col24"> 0.28 @@ -19303,132 +19026,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row4"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row4"> 4 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col0" class="data row4 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0" class="data row4 col0"> -3.35 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col1" class="data row4 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1" class="data row4 col1"> 4.48 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col2" class="data row4 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2" class="data row4 col2"> -1.86 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col3" class="data row4 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3" class="data row4 col3"> -1.7 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col4" class="data row4 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4" class="data row4 col4"> 5.19 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col5" class="data row4 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5" class="data row4 col5"> -1.02 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col6" class="data row4 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6" class="data row4 col6"> -3.81 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col7" class="data row4 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7" class="data row4 col7"> 4.72 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col8" class="data row4 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8" class="data row4 col8"> -0.72 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col9" class="data row4 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9" class="data row4 col9"> 1.08 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col10" class="data row4 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10" class="data row4 col10"> -0.18 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col11" class="data row4 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11" class="data row4 col11"> 0.83 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col12" class="data row4 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12" class="data row4 col12"> -0.22 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col13" class="data row4 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13" class="data row4 col13"> -1.08 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col14" class="data row4 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14" class="data row4 col14"> -4.27 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col15" class="data row4 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15" class="data row4 col15"> -2.88 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col16" class="data row4 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16" class="data row4 col16"> -0.97 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col17" class="data row4 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17" class="data row4 col17"> -1.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col18" class="data row4 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18" class="data row4 col18"> 1.53 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col19" class="data row4 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19" class="data row4 col19"> -1.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col20" class="data row4 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20" class="data row4 col20"> 2.21 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col21" class="data row4 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21" class="data row4 col21"> -6.34 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col22" class="data row4 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22" class="data row4 col22"> 3.34 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col23" class="data row4 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23" class="data row4 col23"> 2.49 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col24" class="data row4 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24" class="data row4 col24"> 2.09 @@ -19437,132 +19160,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row5"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row5"> 5 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col0" class="data row5 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0" class="data row5 col0"> -0.84 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col1" class="data row5 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1" class="data row5 col1"> 4.23 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col2" class="data row5 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2" class="data row5 col2"> -1.65 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col3" class="data row5 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3" class="data row5 col3"> -2.0 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col4" class="data row5 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4" class="data row5 col4"> 5.34 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col5" class="data row5 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5" class="data row5 col5"> -0.99 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col6" class="data row5 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6" class="data row5 col6"> -4.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col7" class="data row5 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7" class="data row5 col7"> 3.94 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col8" class="data row5 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8" class="data row5 col8"> -1.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col9" class="data row5 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9" class="data row5 col9"> -0.94 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col10" class="data row5 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10" class="data row5 col10"> 1.24 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col11" class="data row5 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11" class="data row5 col11"> 0.09 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col12" class="data row5 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12" class="data row5 col12"> -1.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col13" class="data row5 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13" class="data row5 col13"> -0.11 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col14" class="data row5 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14" class="data row5 col14"> -4.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col15" class="data row5 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15" class="data row5 col15"> -0.85 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col16" class="data row5 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16" class="data row5 col16"> -2.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col17" class="data row5 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17" class="data row5 col17"> -1.35 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col18" class="data row5 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18" class="data row5 col18"> 0.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col19" class="data row5 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19" class="data row5 col19"> -1.63 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col20" class="data row5 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20" class="data row5 col20"> 1.54 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col21" class="data row5 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21" class="data row5 col21"> -6.51 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col22" class="data row5 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22" class="data row5 col22"> 2.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col23" class="data row5 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23" class="data row5 col23"> 2.14 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col24" class="data row5 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24" class="data row5 col24"> 3.77 @@ -19571,132 +19294,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row6"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row6"> 6 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col0" class="data row6 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0" class="data row6 col0"> -0.74 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col1" class="data row6 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1" class="data row6 col1"> 5.35 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col2" class="data row6 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2" class="data row6 col2"> -2.11 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col3" class="data row6 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3" class="data row6 col3"> -1.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col4" class="data row6 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4" class="data row6 col4"> 4.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col5" class="data row6 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5" class="data row6 col5"> -1.85 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col6" class="data row6 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6" class="data row6 col6"> -3.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col7" class="data row6 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7" class="data row6 col7"> 3.76 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col8" class="data row6 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8" class="data row6 col8"> -3.22 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col9" class="data row6 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9" class="data row6 col9"> -1.23 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col10" class="data row6 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10" class="data row6 col10"> 0.34 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col11" class="data row6 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11" class="data row6 col11"> 0.57 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col12" class="data row6 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12" class="data row6 col12"> -1.82 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col13" class="data row6 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13" class="data row6 col13"> 0.54 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col14" class="data row6 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14" class="data row6 col14"> -4.43 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col15" class="data row6 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15" class="data row6 col15"> -1.83 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col16" class="data row6 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16" class="data row6 col16"> -4.03 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col17" class="data row6 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17" class="data row6 col17"> -2.62 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col18" class="data row6 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18" class="data row6 col18"> -0.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col19" class="data row6 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19" class="data row6 col19"> -4.68 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col20" class="data row6 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20" class="data row6 col20"> 1.93 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col21" class="data row6 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21" class="data row6 col21"> -8.46 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col22" class="data row6 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22" class="data row6 col22"> 3.34 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col23" class="data row6 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23" class="data row6 col23"> 2.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col24" class="data row6 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24" class="data row6 col24"> 5.81 @@ -19705,132 +19428,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row7"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row7"> 7 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col0" class="data row7 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0" class="data row7 col0"> -0.44 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col1" class="data row7 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1" class="data row7 col1"> 4.69 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col2" class="data row7 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2" class="data row7 col2"> -2.3 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col3" class="data row7 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3" class="data row7 col3"> -0.21 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col4" class="data row7 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4" class="data row7 col4"> 5.93 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col5" class="data row7 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5" class="data row7 col5"> -2.63 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col6" class="data row7 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6" class="data row7 col6"> -1.83 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col7" class="data row7 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7" class="data row7 col7"> 5.46 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col8" class="data row7 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8" class="data row7 col8"> -4.5 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col9" class="data row7 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9" class="data row7 col9"> -3.16 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col10" class="data row7 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10" class="data row7 col10"> -1.73 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col11" class="data row7 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11" class="data row7 col11"> 0.18 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col12" class="data row7 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12" class="data row7 col12"> 0.11 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col13" class="data row7 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13" class="data row7 col13"> 0.04 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col14" class="data row7 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14" class="data row7 col14"> -5.99 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col15" class="data row7 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15" class="data row7 col15"> -0.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col16" class="data row7 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16" class="data row7 col16"> -6.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col17" class="data row7 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17" class="data row7 col17"> -3.89 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col18" class="data row7 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18" class="data row7 col18"> 0.71 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col19" class="data row7 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19" class="data row7 col19"> -3.95 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col20" class="data row7 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20" class="data row7 col20"> 0.67 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col21" class="data row7 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21" class="data row7 col21"> -7.26 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col22" class="data row7 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22" class="data row7 col22"> 2.97 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col23" class="data row7 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23" class="data row7 col23"> 3.39 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col24" class="data row7 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24" class="data row7 col24"> 6.66 @@ -19839,132 +19562,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row8"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row8"> 8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col0" class="data row8 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0" class="data row8 col0"> 0.92 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col1" class="data row8 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1" class="data row8 col1"> 5.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col2" class="data row8 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2" class="data row8 col2"> -3.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col3" class="data row8 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3" class="data row8 col3"> -0.65 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col4" class="data row8 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4" class="data row8 col4"> 5.99 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col5" class="data row8 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5" class="data row8 col5"> -3.19 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col6" class="data row8 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6" class="data row8 col6"> -1.83 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col7" class="data row8 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7" class="data row8 col7"> 5.63 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col8" class="data row8 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8" class="data row8 col8"> -3.53 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col9" class="data row8 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9" class="data row8 col9"> -1.3 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col10" class="data row8 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10" class="data row8 col10"> -1.61 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col11" class="data row8 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11" class="data row8 col11"> 0.82 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col12" class="data row8 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12" class="data row8 col12"> -2.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col13" class="data row8 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13" class="data row8 col13"> -0.4 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col14" class="data row8 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14" class="data row8 col14"> -6.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col15" class="data row8 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15" class="data row8 col15"> -0.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col16" class="data row8 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16" class="data row8 col16"> -6.6 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col17" class="data row8 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17" class="data row8 col17"> -3.48 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col18" class="data row8 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18" class="data row8 col18"> -0.04 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col19" class="data row8 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19" class="data row8 col19"> -4.6 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col20" class="data row8 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20" class="data row8 col20"> 0.51 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col21" class="data row8 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21" class="data row8 col21"> -5.85 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col22" class="data row8 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22" class="data row8 col22"> 3.23 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col23" class="data row8 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23" class="data row8 col23"> 2.4 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col24" class="data row8 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24" class="data row8 col24"> 5.08 @@ -19973,132 +19696,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row9"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row9"> 9 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col0" class="data row9 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0" class="data row9 col0"> 0.38 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col1" class="data row9 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1" class="data row9 col1"> 5.54 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col2" class="data row9 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2" class="data row9 col2"> -4.49 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col3" class="data row9 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3" class="data row9 col3"> -0.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col4" class="data row9 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4" class="data row9 col4"> 7.05 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col5" class="data row9 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5" class="data row9 col5"> -2.64 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col6" class="data row9 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6" class="data row9 col6"> -0.44 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col7" class="data row9 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7" class="data row9 col7"> 5.35 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col8" class="data row9 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8" class="data row9 col8"> -1.96 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col9" class="data row9 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9" class="data row9 col9"> -0.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col10" class="data row9 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10" class="data row9 col10"> -0.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col11" class="data row9 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11" class="data row9 col11"> 0.26 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col12" class="data row9 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12" class="data row9 col12"> -3.37 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col13" class="data row9 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13" class="data row9 col13"> -0.82 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col14" class="data row9 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14" class="data row9 col14"> -6.05 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col15" class="data row9 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15" class="data row9 col15"> -2.61 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col16" class="data row9 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16" class="data row9 col16"> -8.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col17" class="data row9 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17" class="data row9 col17"> -4.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col18" class="data row9 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18" class="data row9 col18"> 0.41 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col19" class="data row9 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19" class="data row9 col19"> -4.71 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col20" class="data row9 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20" class="data row9 col20"> 1.89 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col21" class="data row9 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21" class="data row9 col21"> -6.93 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col22" class="data row9 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22" class="data row9 col22"> 2.14 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col23" class="data row9 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23" class="data row9 col23"> 3.0 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col24" class="data row9 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24" class="data row9 col24"> 5.16 @@ -20107,132 +19830,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row10"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row10"> 10 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col0" class="data row10 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0" class="data row10 col0"> 2.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col1" class="data row10 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1" class="data row10 col1"> 5.84 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col2" class="data row10 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2" class="data row10 col2"> -3.9 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col3" class="data row10 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3" class="data row10 col3"> -0.98 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col4" class="data row10 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4" class="data row10 col4"> 7.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col5" class="data row10 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5" class="data row10 col5"> -2.49 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col6" class="data row10 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6" class="data row10 col6"> -0.59 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col7" class="data row10 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7" class="data row10 col7"> 5.59 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col8" class="data row10 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8" class="data row10 col8"> -2.22 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col9" class="data row10 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9" class="data row10 col9"> -0.71 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col10" class="data row10 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10" class="data row10 col10"> -0.46 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col11" class="data row10 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11" class="data row10 col11"> 1.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col12" class="data row10 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12" class="data row10 col12"> -2.79 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col13" class="data row10 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13" class="data row10 col13"> 0.48 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col14" class="data row10 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14" class="data row10 col14"> -5.97 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col15" class="data row10 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15" class="data row10 col15"> -3.44 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col16" class="data row10 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16" class="data row10 col16"> -7.77 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col17" class="data row10 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17" class="data row10 col17"> -5.49 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col18" class="data row10 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18" class="data row10 col18"> -0.7 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col19" class="data row10 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19" class="data row10 col19"> -4.61 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col20" class="data row10 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20" class="data row10 col20"> -0.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col21" class="data row10 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21" class="data row10 col21"> -7.72 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col22" class="data row10 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22" class="data row10 col22"> 1.54 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col23" class="data row10 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23" class="data row10 col23"> 5.02 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col24" class="data row10 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24" class="data row10 col24"> 5.81 @@ -20241,132 +19964,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row11"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row11"> 11 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col0" class="data row11 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0" class="data row11 col0"> 1.86 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col1" class="data row11 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1" class="data row11 col1"> 4.47 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col2" class="data row11 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2" class="data row11 col2"> -2.17 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col3" class="data row11 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3" class="data row11 col3"> -1.38 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col4" class="data row11 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4" class="data row11 col4"> 5.9 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col5" class="data row11 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5" class="data row11 col5"> -0.49 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col6" class="data row11 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6" class="data row11 col6"> 0.02 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col7" class="data row11 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7" class="data row11 col7"> 5.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col8" class="data row11 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8" class="data row11 col8"> -1.04 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col9" class="data row11 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9" class="data row11 col9"> -0.6 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col10" class="data row11 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10" class="data row11 col10"> 0.49 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col11" class="data row11 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11" class="data row11 col11"> 1.96 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col12" class="data row11 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12" class="data row11 col12"> -1.47 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col13" class="data row11 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13" class="data row11 col13"> 1.88 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col14" class="data row11 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14" class="data row11 col14"> -5.92 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col15" class="data row11 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15" class="data row11 col15"> -4.55 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col16" class="data row11 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16" class="data row11 col16"> -8.15 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col17" class="data row11 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17" class="data row11 col17"> -3.42 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col18" class="data row11 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18" class="data row11 col18"> -2.24 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col19" class="data row11 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19" class="data row11 col19"> -4.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col20" class="data row11 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20" class="data row11 col20"> -1.17 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col21" class="data row11 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21" class="data row11 col21"> -7.9 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col22" class="data row11 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22" class="data row11 col22"> 1.36 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col23" class="data row11 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23" class="data row11 col23"> 5.31 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col24" class="data row11 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24" class="data row11 col24"> 5.83 @@ -20375,132 +20098,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row12"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row12"> 12 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col0" class="data row12 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0" class="data row12 col0"> 3.19 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col1" class="data row12 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1" class="data row12 col1"> 4.22 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col2" class="data row12 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2" class="data row12 col2"> -3.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col3" class="data row12 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3" class="data row12 col3"> -2.27 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col4" class="data row12 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4" class="data row12 col4"> 5.93 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col5" class="data row12 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5" class="data row12 col5"> -2.64 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col6" class="data row12 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6" class="data row12 col6"> 0.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col7" class="data row12 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7" class="data row12 col7"> 6.72 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col8" class="data row12 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8" class="data row12 col8"> -2.84 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col9" class="data row12 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9" class="data row12 col9"> -0.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col10" class="data row12 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10" class="data row12 col10"> 1.89 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col11" class="data row12 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11" class="data row12 col11"> 2.63 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col12" class="data row12 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12" class="data row12 col12"> -1.53 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col13" class="data row12 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13" class="data row12 col13"> 0.75 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col14" class="data row12 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14" class="data row12 col14"> -5.27 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col15" class="data row12 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15" class="data row12 col15"> -4.53 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col16" class="data row12 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16" class="data row12 col16"> -7.57 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col17" class="data row12 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17" class="data row12 col17"> -2.85 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col18" class="data row12 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18" class="data row12 col18"> -2.17 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col19" class="data row12 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19" class="data row12 col19"> -4.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col20" class="data row12 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20" class="data row12 col20"> -1.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col21" class="data row12 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21" class="data row12 col21"> -8.99 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col22" class="data row12 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22" class="data row12 col22"> 2.11 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col23" class="data row12 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23" class="data row12 col23"> 6.42 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col24" class="data row12 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24" class="data row12 col24"> 5.6 @@ -20509,132 +20232,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row13"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row13"> 13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col0" class="data row13 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0" class="data row13 col0"> 2.31 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col1" class="data row13 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1" class="data row13 col1"> 4.45 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col2" class="data row13 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2" class="data row13 col2"> -3.87 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col3" class="data row13 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3" class="data row13 col3"> -2.05 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col4" class="data row13 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4" class="data row13 col4"> 6.76 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col5" class="data row13 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5" class="data row13 col5"> -3.25 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col6" class="data row13 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6" class="data row13 col6"> -2.17 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col7" class="data row13 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7" class="data row13 col7"> 7.99 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col8" class="data row13 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8" class="data row13 col8"> -2.56 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col9" class="data row13 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9" class="data row13 col9"> -0.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col10" class="data row13 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10" class="data row13 col10"> 0.71 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col11" class="data row13 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11" class="data row13 col11"> 2.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col12" class="data row13 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12" class="data row13 col12"> -0.16 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col13" class="data row13 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13" class="data row13 col13"> -0.46 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col14" class="data row13 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14" class="data row13 col14"> -5.1 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col15" class="data row13 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15" class="data row13 col15"> -3.79 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col16" class="data row13 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16" class="data row13 col16"> -7.58 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col17" class="data row13 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17" class="data row13 col17"> -4.0 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col18" class="data row13 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18" class="data row13 col18"> 0.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col19" class="data row13 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19" class="data row13 col19"> -3.67 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col20" class="data row13 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20" class="data row13 col20"> -1.05 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col21" class="data row13 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21" class="data row13 col21"> -8.71 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col22" class="data row13 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22" class="data row13 col22"> 2.47 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col23" class="data row13 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23" class="data row13 col23"> 5.87 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col24" class="data row13 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24" class="data row13 col24"> 6.71 @@ -20643,132 +20366,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row14"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row14"> 14 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col0" class="data row14 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0" class="data row14 col0"> 3.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col1" class="data row14 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1" class="data row14 col1"> 4.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col2" class="data row14 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2" class="data row14 col2"> -3.88 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col3" class="data row14 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3" class="data row14 col3"> -1.58 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col4" class="data row14 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4" class="data row14 col4"> 6.22 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col5" class="data row14 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5" class="data row14 col5"> -3.23 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col6" class="data row14 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6" class="data row14 col6"> -1.46 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col7" class="data row14 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7" class="data row14 col7"> 5.57 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col8" class="data row14 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8" class="data row14 col8"> -2.93 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col9" class="data row14 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9" class="data row14 col9"> -0.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col10" class="data row14 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10" class="data row14 col10"> -0.97 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col11" class="data row14 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11" class="data row14 col11"> 1.72 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col12" class="data row14 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12" class="data row14 col12"> 3.61 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col13" class="data row14 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13" class="data row14 col13"> 0.29 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col14" class="data row14 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14" class="data row14 col14"> -4.21 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col15" class="data row14 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15" class="data row14 col15"> -4.1 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col16" class="data row14 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16" class="data row14 col16"> -6.68 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col17" class="data row14 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17" class="data row14 col17"> -4.5 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col18" class="data row14 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18" class="data row14 col18"> -2.19 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col19" class="data row14 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19" class="data row14 col19"> -2.43 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col20" class="data row14 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20" class="data row14 col20"> -1.64 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col21" class="data row14 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21" class="data row14 col21"> -9.36 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col22" class="data row14 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22" class="data row14 col22"> 3.36 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col23" class="data row14 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23" class="data row14 col23"> 6.11 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col24" class="data row14 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24" class="data row14 col24"> 7.53 @@ -20777,132 +20500,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row15"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row15"> 15 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col0" class="data row15 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0" class="data row15 col0"> 5.64 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col1" class="data row15 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1" class="data row15 col1"> 5.31 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col2" class="data row15 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2" class="data row15 col2"> -3.98 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col3" class="data row15 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3" class="data row15 col3"> -2.26 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col4" class="data row15 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4" class="data row15 col4"> 5.91 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col5" class="data row15 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5" class="data row15 col5"> -3.3 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col6" class="data row15 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6" class="data row15 col6"> -1.03 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col7" class="data row15 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7" class="data row15 col7"> 5.68 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col8" class="data row15 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8" class="data row15 col8"> -3.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col9" class="data row15 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9" class="data row15 col9"> -0.33 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col10" class="data row15 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10" class="data row15 col10"> -1.16 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col11" class="data row15 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11" class="data row15 col11"> 2.19 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col12" class="data row15 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12" class="data row15 col12"> 4.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col13" class="data row15 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13" class="data row15 col13"> 1.01 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col14" class="data row15 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14" class="data row15 col14"> -3.22 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col15" class="data row15 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15" class="data row15 col15"> -4.31 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col16" class="data row15 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16" class="data row15 col16"> -5.74 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col17" class="data row15 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17" class="data row15 col17"> -4.44 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col18" class="data row15 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18" class="data row15 col18"> -2.3 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col19" class="data row15 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19" class="data row15 col19"> -1.36 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col20" class="data row15 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20" class="data row15 col20"> -1.2 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col21" class="data row15 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21" class="data row15 col21"> -11.27 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col22" class="data row15 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22" class="data row15 col22"> 2.59 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col23" class="data row15 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23" class="data row15 col23"> 6.69 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col24" class="data row15 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24" class="data row15 col24"> 5.91 @@ -20911,132 +20634,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row16"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row16"> 16 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col0" class="data row16 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0" class="data row16 col0"> 4.08 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col1" class="data row16 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1" class="data row16 col1"> 4.34 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col2" class="data row16 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2" class="data row16 col2"> -2.44 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col3" class="data row16 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3" class="data row16 col3"> -3.3 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col4" class="data row16 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4" class="data row16 col4"> 6.04 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col5" class="data row16 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5" class="data row16 col5"> -2.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col6" class="data row16 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6" class="data row16 col6"> -0.47 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col7" class="data row16 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7" class="data row16 col7"> 5.28 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col8" class="data row16 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8" class="data row16 col8"> -4.84 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col9" class="data row16 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9" class="data row16 col9"> 1.58 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col10" class="data row16 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10" class="data row16 col10"> 0.23 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col11" class="data row16 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11" class="data row16 col11"> 0.1 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col12" class="data row16 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12" class="data row16 col12"> 5.79 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col13" class="data row16 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13" class="data row16 col13"> 1.8 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col14" class="data row16 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14" class="data row16 col14"> -3.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col15" class="data row16 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15" class="data row16 col15"> -3.85 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col16" class="data row16 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16" class="data row16 col16"> -5.53 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col17" class="data row16 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17" class="data row16 col17"> -2.97 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col18" class="data row16 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18" class="data row16 col18"> -2.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col19" class="data row16 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19" class="data row16 col19"> -1.15 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col20" class="data row16 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20" class="data row16 col20"> -0.56 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col21" class="data row16 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21" class="data row16 col21"> -13.13 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col22" class="data row16 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22" class="data row16 col22"> 2.07 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col23" class="data row16 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23" class="data row16 col23"> 6.16 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col24" class="data row16 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24" class="data row16 col24"> 4.94 @@ -21045,132 +20768,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row17"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row17"> 17 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col0" class="data row17 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0" class="data row17 col0"> 5.64 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col1" class="data row17 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1" class="data row17 col1"> 4.57 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col2" class="data row17 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2" class="data row17 col2"> -3.53 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col3" class="data row17 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3" class="data row17 col3"> -3.76 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col4" class="data row17 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4" class="data row17 col4"> 6.58 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col5" class="data row17 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5" class="data row17 col5"> -2.58 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col6" class="data row17 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6" class="data row17 col6"> -0.75 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col7" class="data row17 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7" class="data row17 col7"> 6.58 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col8" class="data row17 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8" class="data row17 col8"> -4.78 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col9" class="data row17 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9" class="data row17 col9"> 3.63 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col10" class="data row17 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10" class="data row17 col10"> -0.29 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col11" class="data row17 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11" class="data row17 col11"> 0.56 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col12" class="data row17 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12" class="data row17 col12"> 5.76 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col13" class="data row17 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13" class="data row17 col13"> 2.05 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col14" class="data row17 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14" class="data row17 col14"> -2.27 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col15" class="data row17 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15" class="data row17 col15"> -2.31 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col16" class="data row17 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16" class="data row17 col16"> -4.95 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col17" class="data row17 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17" class="data row17 col17"> -3.16 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col18" class="data row17 col18"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18" class="data row17 col18"> -3.06 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col19" class="data row17 col19"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19" class="data row17 col19"> -2.43 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col20" class="data row17 col20"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20" class="data row17 col20"> 0.84 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col21" class="data row17 col21"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21" class="data row17 col21"> -12.57 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col22" class="data row17 col22"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22" class="data row17 col22"> 3.56 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col23" class="data row17 col23"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23" class="data row17 col23"> 7.36 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col24" class="data row17 col24"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24" class="data row17 col24"> 4.7 @@ -21179,132 +20902,132 @@ <h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">&#182;</a>< <tr> - <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row18"> + <th id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb" class="row_heading level24 row18"> 18 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col0" class="data row18 col0"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0" class="data row18 col0"> 5.99 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col1" class="data row18 col1"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1" class="data row18 col1"> 5.82 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col2" class="data row18 col2"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2" class="data row18 col2"> -2.85 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col3" class="data row18 col3"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3" class="data row18 col3"> -4.15 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col4" class="data row18 col4"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4" class="data row18 col4"> 7.12 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col5" class="data row18 col5"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5" class="data row18 col5"> -3.32 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col6" class="data row18 col6"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6" class="data row18 col6"> -1.21 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col7" class="data row18 col7"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7" class="data row18 col7"> 7.93 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col8" class="data row18 col8"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8" class="data row18 col8"> -4.85 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col9" class="data row18 col9"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9" class="data row18 col9"> 1.44 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col10" class="data row18 col10"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10" class="data row18 col10"> -0.63 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col11" class="data row18 col11"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11" class="data row18 col11"> 0.35 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col12" class="data row18 col12"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12" class="data row18 col12"> 7.47 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col13" class="data row18 col13"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13" class="data row18 col13"> 0.87 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col14" class="data row18 col14"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14" class="data row18 col14"> -1.52 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col15" class="data row18 col15"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15" class="data row18 col15"> -2.09 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col16" class="data row18 col16"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16" class="data row18 col16"> -4.23 - <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col17" class="data row18 col17"> + <td id="T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17" class="data row18 col17"> -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">&#182;</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", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -289,32 +287,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -323,32 +321,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -357,32 +355,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -391,32 +389,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -425,32 +423,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -462,10 +460,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x10fc7a9b0>" + "<pandas.core.style.Styler at 0x111c2c320>" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -497,7 +495,7 @@ " ' <style type=\"text/css\" >',\n", " ' ',\n", " ' ',\n", - " ' #T_e7c1f51a_8bd5_11e5_803e_a45e60bd97fbrow0_col2 {',\n", + " ' #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {',\n", " ' ',\n", " ' background-color: red;',\n", " ' ',\n", @@ -505,7 +503,7 @@ " ' ']" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -532,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -558,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -570,301 +568,301 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #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", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -872,7 +870,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\">\n", + " <table id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -898,32 +896,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <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", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -932,32 +930,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -966,32 +964,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <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", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -1000,32 +998,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <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", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -1034,32 +1032,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -1068,32 +1066,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -1102,32 +1100,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <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", " \n", " 1.627796\n", " \n", @@ -1136,32 +1134,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <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", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <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", " \n", " 0.519818\n", " \n", @@ -1170,32 +1168,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <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", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <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", " -2.089354\n", " \n", @@ -1204,32 +1202,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", - " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <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", " \n", " 0.631523\n", " \n", " \n", - " <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", " \n", " -0.586538\n", " \n", " \n", - " <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", " 0.29072\n", " \n", @@ -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": { "collapsed": false }, @@ -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", " \n", " }\n", " \n", - " #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col2 {\n", + " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #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", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #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", " \n", "\n", " <thead>\n", @@ -1360,32 +1358,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -1394,32 +1392,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <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", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <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", " \n", " 0.295722\n", " \n", @@ -1428,32 +1426,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <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", " 3.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -1462,32 +1460,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <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", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -1496,32 +1494,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -1530,32 +1528,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <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", " 6.0\n", " \n", " \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", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -1564,32 +1562,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -1598,32 +1596,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -1632,32 +1630,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -1666,32 +1664,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -1703,10 +1701,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x11335eb70>" + "<pandas.core.style.Styler at 0x111c35160>" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1724,7 +1722,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -1736,7 +1734,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", @@ -1744,7 +1742,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", @@ -1752,7 +1750,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", @@ -1760,7 +1758,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", @@ -1768,7 +1766,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", @@ -1776,7 +1774,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", @@ -1784,7 +1782,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", @@ -1792,7 +1790,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", @@ -1800,7 +1798,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", @@ -1808,7 +1806,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", @@ -1816,7 +1814,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", @@ -1824,7 +1822,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", @@ -1832,7 +1830,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", @@ -1840,7 +1838,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", @@ -1848,7 +1846,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", @@ -1856,7 +1854,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", @@ -1864,7 +1862,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", @@ -1872,7 +1870,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", @@ -1880,7 +1878,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", @@ -1888,7 +1886,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", @@ -1896,7 +1894,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", @@ -1904,7 +1902,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", @@ -1912,7 +1910,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", @@ -1920,7 +1918,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", @@ -1928,7 +1926,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", @@ -1936,7 +1934,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", @@ -1944,7 +1942,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", @@ -1952,7 +1950,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", @@ -1960,7 +1958,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -1968,7 +1966,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", @@ -1976,7 +1974,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", @@ -1984,7 +1982,7 @@ " \n", " }\n", " \n", - " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col1 {\n", + " #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", " }\n", " \n", - " #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", " \n", - " #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", " }\n", " \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", " }\n", " \n", - " #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", " }\n", " \n", - " #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", " }\n", " \n", - " #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", " }\n", " \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", " }\n", " \n", - " #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", " <thead>\n", @@ -2164,32 +2162,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <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", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <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", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -2198,32 +2196,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <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", " 2.0\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <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", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -2232,32 +2230,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <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", " 3.0\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <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", " \n", " 1.889273\n", " \n", @@ -2266,32 +2264,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " 3\n", " \n", " \n", - " <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", " 4.0\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -2300,32 +2298,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <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", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -2334,32 +2332,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <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", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <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", " \n", " -0.063328\n", " \n", @@ -2368,32 +2366,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " 6\n", " \n", " \n", - " <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", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <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", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -2402,32 +2400,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -2436,32 +2434,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <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", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -2470,32 +2468,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <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", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -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 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -2571,7 +2569,7 @@ " <style type=\"text/css\" >\n", " \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", " <thead>\n", @@ -2605,32 +2603,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -2639,32 +2637,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -2673,32 +2671,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -2707,32 +2705,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -2741,32 +2739,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -2775,32 +2773,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -2809,32 +2807,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -2843,32 +2841,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -2877,32 +2875,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -2911,32 +2909,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -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": { "collapsed": false }, @@ -3011,19 +3009,19 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col2 {\n", + " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col1 {\n", + " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col3 {\n", + " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", @@ -3031,7 +3029,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\">\n", + " <table id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -3057,32 +3055,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -3091,32 +3089,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -3125,32 +3123,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -3159,32 +3157,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -3193,32 +3191,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -3227,32 +3225,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -3261,32 +3259,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -3295,32 +3293,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -3329,32 +3327,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -3363,32 +3361,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -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 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -3433,49 +3431,49 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -3483,7 +3481,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\">\n", + " <table id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -3509,32 +3507,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -3543,32 +3541,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -3577,32 +3575,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -3611,32 +3609,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -3645,32 +3643,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -3679,32 +3677,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -3713,32 +3711,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -3747,32 +3745,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -3781,32 +3779,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -3815,32 +3813,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -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": { "collapsed": false }, @@ -3906,7 +3904,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2 {\n", + " #T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2 {\n", " \n", " background-color: red;\n", " \n", @@ -3914,7 +3912,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\">\n", + " <table id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -3940,32 +3938,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -3974,32 +3972,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <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", " \n", " 0.295722\n", " \n", @@ -4008,32 +4006,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <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", " \n", " 1.889273\n", " \n", @@ -4042,32 +4040,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -4076,32 +4074,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <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", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -4110,32 +4108,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -4144,32 +4142,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -4178,32 +4176,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -4212,32 +4210,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <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", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <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", " \n", " -1.325963\n", " \n", " \n", - " <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", " \n", " 1.428984\n", " \n", " \n", - " <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", " \n", " -2.089354\n", " \n", @@ -4246,32 +4244,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <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", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -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": { "collapsed": false }, @@ -4316,301 +4314,301 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0 {\n", " \n", " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1 {\n", " \n", " background-color: #188d18;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2 {\n", " \n", " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3 {\n", " \n", " background-color: #c7eec7;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4 {\n", " \n", " background-color: #a6dca6;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0 {\n", " \n", " background-color: #ccf1cc;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1 {\n", " \n", " background-color: #c0eac0;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2 {\n", " \n", " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3 {\n", " \n", " background-color: #62b662;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4 {\n", " \n", " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0 {\n", " \n", " background-color: #b3e3b3;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1 {\n", " \n", " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2 {\n", " \n", " background-color: #56af56;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3 {\n", " \n", " background-color: #56af56;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0 {\n", " \n", " background-color: #99d599;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1 {\n", " \n", " background-color: #329c32;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2 {\n", " \n", " background-color: #5fb55f;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3 {\n", " \n", " background-color: #daf9da;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4 {\n", " \n", " background-color: #3ba13b;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0 {\n", " \n", " background-color: #80c780;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1 {\n", " \n", " background-color: #108910;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2 {\n", " \n", " background-color: #0d870d;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3 {\n", " \n", " background-color: #90d090;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4 {\n", " \n", " background-color: #4fac4f;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0 {\n", " \n", " background-color: #66b866;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1 {\n", " \n", " background-color: #d2f4d2;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2 {\n", " \n", " background-color: #389f38;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3 {\n", " \n", " background-color: #048204;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4 {\n", " \n", " background-color: #70be70;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0 {\n", " \n", " background-color: #4daa4d;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1 {\n", " \n", " background-color: #6cbc6c;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3 {\n", " \n", " background-color: #a3daa3;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4 {\n", " \n", " background-color: #0e880e;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0 {\n", " \n", " background-color: #329c32;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1 {\n", " \n", " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2 {\n", " \n", " background-color: #0e880e;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3 {\n", " \n", " background-color: #cff3cf;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4 {\n", " \n", " background-color: #4fac4f;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0 {\n", " \n", " background-color: #198e19;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2 {\n", " \n", " background-color: #dcfadc;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4 {\n", " \n", " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col0 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col1 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1 {\n", " \n", " background-color: #7ec67e;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2 {\n", " \n", " background-color: #319b31;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3 {\n", " \n", " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col4 {\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4 {\n", " \n", " background-color: #5cb35c;\n", " \n", @@ -4618,7 +4616,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\">\n", + " <table id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -4644,32 +4642,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -4678,32 +4676,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -4712,32 +4710,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -4746,32 +4744,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -4780,32 +4778,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -4814,32 +4812,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -4848,32 +4846,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -4882,32 +4880,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -4916,32 +4914,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -4950,32 +4948,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -4987,10 +4985,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x11335e048>" + "<pandas.core.style.Styler at 0x111c35828>" ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -5013,7 +5011,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -5025,151 +5023,151 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0 {\n", " \n", " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1 {\n", " \n", " background-color: #e5e419;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2 {\n", " \n", " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3 {\n", " \n", " background-color: #46327e;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4 {\n", " \n", " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0 {\n", " \n", " background-color: #3b528b;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1 {\n", " \n", " background-color: #433e85;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2 {\n", " \n", " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3 {\n", " \n", " background-color: #bddf26;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4 {\n", " \n", " background-color: #25838e;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0 {\n", " \n", " background-color: #21918c;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1 {\n", " \n", " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2 {\n", " \n", " background-color: #35b779;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3 {\n", " \n", " background-color: #fde725;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: #fde725;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0 {\n", " \n", " background-color: #5ec962;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1 {\n", " \n", " background-color: #95d840;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2 {\n", " \n", " background-color: #26ad81;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3 {\n", " \n", " background-color: #440154;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4 {\n", " \n", " background-color: #2cb17e;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0 {\n", " \n", " background-color: #fde725;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1 {\n", " \n", " background-color: #fde725;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2 {\n", " \n", " background-color: #fde725;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3 {\n", " \n", " background-color: #1f9e89;\n", " \n", " }\n", " \n", - " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4 {\n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4 {\n", " \n", " background-color: #1f958b;\n", " \n", @@ -5177,7 +5175,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\">\n", + " <table id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -5203,32 +5201,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -5237,32 +5235,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -5271,32 +5269,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -5305,32 +5303,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -5339,32 +5337,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -5376,10 +5374,10 @@ " " ], "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 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -5403,7 +5401,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0 {\n", " \n", " background-color: #31688e;\n", " \n", @@ -5411,7 +5409,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1 {\n", " \n", " background-color: #efe51c;\n", " \n", @@ -5419,7 +5417,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2 {\n", " \n", " background-color: #440154;\n", " \n", @@ -5427,7 +5425,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3 {\n", " \n", " background-color: #277f8e;\n", " \n", @@ -5435,7 +5433,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4 {\n", " \n", " background-color: #31688e;\n", " \n", @@ -5443,7 +5441,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0 {\n", " \n", " background-color: #21918c;\n", " \n", @@ -5451,7 +5449,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1 {\n", " \n", " background-color: #25858e;\n", " \n", @@ -5459,7 +5457,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2 {\n", " \n", " background-color: #31688e;\n", " \n", @@ -5467,7 +5465,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3 {\n", " \n", " background-color: #d5e21a;\n", " \n", @@ -5475,7 +5473,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4 {\n", " \n", " background-color: #29af7f;\n", " \n", @@ -5483,7 +5481,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0 {\n", " \n", " background-color: #35b779;\n", " \n", @@ -5491,7 +5489,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1 {\n", " \n", " background-color: #31688e;\n", " \n", @@ -5499,7 +5497,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2 {\n", " \n", " background-color: #6ccd5a;\n", " \n", @@ -5507,7 +5505,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3 {\n", " \n", " background-color: #fde725;\n", " \n", @@ -5515,7 +5513,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: #fde725;\n", " \n", @@ -5523,7 +5521,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0 {\n", " \n", " background-color: #90d743;\n", " \n", @@ -5531,7 +5529,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1 {\n", " \n", " background-color: #b8de29;\n", " \n", @@ -5539,7 +5537,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2 {\n", " \n", " background-color: #5ac864;\n", " \n", @@ -5547,7 +5545,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3 {\n", " \n", " background-color: #31688e;\n", " \n", @@ -5555,7 +5553,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4 {\n", " \n", " background-color: #63cb5f;\n", " \n", @@ -5563,7 +5561,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0 {\n", " \n", " background-color: #fde725;\n", " \n", @@ -5571,7 +5569,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1 {\n", " \n", " background-color: #fde725;\n", " \n", @@ -5579,7 +5577,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2 {\n", " \n", " background-color: #fde725;\n", " \n", @@ -5587,7 +5585,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3 {\n", " \n", " background-color: #46c06f;\n", " \n", @@ -5595,7 +5593,7 @@ " \n", " }\n", " \n", - " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4 {\n", " \n", " background-color: #3bbb75;\n", " \n", @@ -5605,7 +5603,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\">\n", + " <table id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -5631,32 +5629,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -5665,32 +5663,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -5699,32 +5697,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -5733,32 +5731,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -5767,32 +5765,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -5804,10 +5802,10 @@ " " ], "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 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -5841,7 +5839,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5851,7 +5849,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -5861,7 +5859,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5871,7 +5869,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -5881,7 +5879,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5891,7 +5889,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -5901,7 +5899,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5911,7 +5909,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -5921,7 +5919,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5931,7 +5929,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -5941,7 +5939,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5951,7 +5949,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -5961,7 +5959,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5971,7 +5969,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -5981,7 +5979,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -5991,7 +5989,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -6001,7 +5999,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -6011,7 +6009,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -6021,7 +6019,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0 {\n", " \n", " width: 10em;\n", " \n", @@ -6031,7 +6029,7 @@ " \n", " }\n", " \n", - " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1 {\n", " \n", " width: 10em;\n", " \n", @@ -6043,7 +6041,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\">\n", + " <table id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -6069,32 +6067,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -6103,32 +6101,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -6137,32 +6135,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -6171,32 +6169,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -6205,32 +6203,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -6239,32 +6237,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -6273,32 +6271,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -6307,32 +6305,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -6341,32 +6339,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -6375,32 +6373,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -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": { "collapsed": false }, @@ -6445,31 +6443,31 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col4 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col2 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col1 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col3 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col0 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: yellow;\n", " \n", @@ -6477,7 +6475,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\">\n", + " <table id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -6503,32 +6501,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -6537,32 +6535,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -6571,32 +6569,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -6605,32 +6603,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -6639,32 +6637,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -6673,32 +6671,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -6707,32 +6705,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -6741,32 +6739,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <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", " 8.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -6775,32 +6773,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <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", " 9.0\n", " \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", " \n", " 1.686583\n", " \n", " \n", - " <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", " \n", " -1.325963\n", " \n", " \n", - " <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", " \n", " 1.428984\n", " \n", " \n", - " <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", " \n", " -2.089354\n", " \n", @@ -6809,32 +6807,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \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", " \n", @@ -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", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col2 {\n", + " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col1 {\n", + " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col4 {\n", + " #T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #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", - " <table id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\">\n", + " <table id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -6930,32 +6928,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -6964,32 +6962,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -6998,32 +6996,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -7032,32 +7030,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -7066,32 +7064,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -7100,32 +7098,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -7134,32 +7132,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \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", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <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", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -7168,32 +7166,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 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", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -7202,32 +7200,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <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", " \n", " 1.428984\n", " \n", " \n", - " <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", " \n", " -2.089354\n", " \n", @@ -7236,32 +7234,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " 9\n", " \n", " \n", - " <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", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <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", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -7273,10 +7271,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x11335e390>" + "<pandas.core.style.Styler at 0x111c35d30>" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -7294,7 +7292,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -7306,7 +7304,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7316,7 +7314,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7326,7 +7324,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7336,7 +7334,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7346,7 +7344,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7356,7 +7354,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7366,7 +7364,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7376,7 +7374,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7386,7 +7384,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7396,7 +7394,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7406,7 +7404,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7416,7 +7414,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7426,7 +7424,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7436,7 +7434,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7446,7 +7444,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7456,7 +7454,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7466,7 +7464,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7476,7 +7474,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7486,7 +7484,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7496,7 +7494,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7506,7 +7504,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7516,7 +7514,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7526,7 +7524,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7536,7 +7534,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7546,7 +7544,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7556,7 +7554,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7566,7 +7564,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7576,7 +7574,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7586,7 +7584,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7596,7 +7594,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7606,7 +7604,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7616,7 +7614,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7626,7 +7624,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7636,7 +7634,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7646,7 +7644,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7656,7 +7654,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7666,7 +7664,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7676,7 +7674,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7686,7 +7684,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7696,7 +7694,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7706,7 +7704,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7716,7 +7714,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7726,7 +7724,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7736,7 +7734,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7746,7 +7744,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7756,7 +7754,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7766,7 +7764,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7776,7 +7774,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7786,7 +7784,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7796,7 +7794,7 @@ " \n", " }\n", " \n", - " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4 {\n", " \n", " color: lawngreen;\n", " \n", @@ -7808,7 +7806,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\">\n", + " <table id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -7834,32 +7832,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 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", " \n", " -0.31628\n", " \n", " \n", - " <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", " \n", " -0.99081\n", " \n", @@ -7868,32 +7866,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <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", " -1.626404\n", " \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", " 0.219565\n", " \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", " -0.481165\n", " \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", " \n", - " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \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", " -1.336936\n", " \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", " \n", " 0.29072\n", " \n", @@ -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 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col0 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - 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" #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col0 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col1 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col2 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col3 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col4 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col0 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col1 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col2 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col3 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col4 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -8521,7 +8519,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\">\n", + " <table id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -8547,32 +8545,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " 0\n", " \n", " \n", - " <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", " \n", " 1.0\n", " \n", " \n", - " <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", " \n", " 1.329212\n", " \n", " \n", - " <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", " \n", " nan\n", " \n", " \n", - " <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", " \n", " -0.31628\n", " \n", " \n", - " <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", " \n", " -0.99081\n", " \n", @@ -8581,32 +8579,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " 1\n", " \n", " \n", - " <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", " \n", " 2.0\n", " \n", " \n", - " <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", " \n", " -1.070816\n", " \n", " \n", - " <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", " \n", " -1.438713\n", " \n", " \n", - " <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", " \n", " 0.564417\n", " \n", " \n", - " <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", " \n", " 0.295722\n", " \n", @@ -8615,32 +8613,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -8649,32 +8647,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -8683,32 +8681,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -8717,32 +8715,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -8751,32 +8749,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -8785,32 +8783,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -8819,32 +8817,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -8853,32 +8851,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -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 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -8918,301 +8916,301 @@ " <style type=\"text/css\" >\n", " \n", " \n", - 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" #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col2 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col3 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col4 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col0 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col1 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col2 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col3 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col4 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4 {\n", " \n", " color: red;\n", " \n", @@ -9220,7 +9218,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\">\n", + " <table id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -9246,32 +9244,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " -1.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " -1.329212\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " 0.31628\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " 0.99081\n", " \n", @@ -9280,32 +9278,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " -2.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " 1.070816\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " 1.438713\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " -0.564417\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " -0.295722\n", " \n", @@ -9314,32 +9312,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " -3.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " 1.626404\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " -0.219565\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " -0.678805\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " -1.889273\n", " \n", @@ -9348,32 +9346,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " -4.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " -0.961538\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " -0.104011\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " 0.481165\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " -0.850229\n", " \n", @@ -9382,32 +9380,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " -5.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " -1.453425\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " -1.057737\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " -0.165562\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " -0.515018\n", " \n", @@ -9416,32 +9414,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " -6.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " 1.336936\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " -0.562861\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " -1.392855\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " 0.063328\n", " \n", @@ -9450,32 +9448,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " -7.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " -0.121668\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " -1.207603\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " 0.00204\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " -1.627796\n", " \n", @@ -9484,32 +9482,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " -8.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " -0.354493\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " -1.037528\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " 0.385684\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " -0.519818\n", " \n", @@ -9518,32 +9516,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " -9.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " -1.686583\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " 1.325963\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " -1.428984\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " 2.089354\n", " \n", @@ -9552,32 +9550,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " -10.0\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " 0.12982\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " -0.631523\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " 0.586538\n", " \n", " \n", - " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " -0.29072\n", " \n", @@ -9589,10 +9587,10 @@ " " ], "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 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -9652,7 +9657,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", @@ -9660,7 +9665,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", @@ -9668,7 +9673,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", @@ -9676,7 +9681,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", @@ -9684,7 +9689,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", @@ -9692,7 +9697,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", @@ -9700,7 +9705,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", @@ -9708,7 +9713,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", @@ -9716,7 +9721,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", @@ -9724,7 +9729,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", @@ -9732,7 +9737,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", @@ -9740,7 +9745,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", @@ -9748,7 +9753,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", @@ -9756,7 +9761,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", @@ -9764,7 +9769,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", @@ -9772,7 +9777,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", @@ -9780,7 +9785,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", @@ -9788,7 +9793,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", @@ -9796,7 +9801,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", @@ -9804,7 +9809,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", @@ -9812,7 +9817,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", @@ -9820,7 +9825,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", @@ -9828,7 +9833,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", @@ -9836,7 +9841,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", @@ -9844,7 +9849,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", @@ -9852,7 +9857,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", @@ -9860,7 +9865,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", @@ -9868,7 +9873,7 @@ " \n", " }\n", " \n", - " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", @@ -9876,7 +9881,7 @@ " \n", " }\n", " \n", - " #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", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", @@ -10476,7 +10481,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", @@ -10484,7 +10489,7 @@ " \n", " }\n", " \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 @@ " \n", " }\n", " \n", - " #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 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", @@ -10508,7 +10513,7 @@ " \n", " }\n", " \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 @@ " \n", " }\n", " \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", " \n", - " #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 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", @@ -10548,7 +10553,7 @@ " \n", " }\n", " \n", - " #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 @@ " \n", " }\n", " \n", - " #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 @@ " \n", " }\n", " \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 @@ " \n", " }\n", " \n", - " #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 @@ " \n", " }\n", " \n", - " #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 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", @@ -10620,7 +10625,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", @@ -10628,7 +10633,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", @@ -10636,7 +10641,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", @@ -10644,7 +10649,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", @@ -10652,7 +10657,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", @@ -10660,7 +10665,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", @@ -10668,7 +10673,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", @@ -10676,7 +10681,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", @@ -10684,7 +10689,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -10692,7 +10697,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", @@ -10700,7 +10705,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", @@ -10708,7 +10713,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", @@ -10716,7 +10721,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", @@ -10724,7 +10729,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", @@ -10732,7 +10737,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", @@ -10740,7 +10745,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", @@ -10748,7 +10753,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", @@ -10756,7 +10761,7 @@ " \n", " }\n", " \n", - " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", @@ -10764,7 +10769,7 @@ " \n", " }\n", " \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", " }\n", " \n", - 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" #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1 {\n", " \n", " background-color: #329c32;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2 {\n", " \n", " background-color: #5fb55f;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3 {\n", " \n", " background-color: #daf9da;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4 {\n", " \n", " background-color: #3ba13b;\n", " \n", " }\n", " \n", - " #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", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2 {\n", " \n", " background-color: #0d870d;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3 {\n", " \n", " background-color: #90d090;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4 {\n", " \n", " background-color: #4fac4f;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0 {\n", " \n", " background-color: #66b866;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1 {\n", " \n", " background-color: #d2f4d2;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2 {\n", " \n", " background-color: #389f38;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3 {\n", " \n", " background-color: #048204;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4 {\n", " \n", " background-color: #70be70;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0 {\n", " \n", " background-color: #4daa4d;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1 {\n", " \n", " background-color: #6cbc6c;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3 {\n", " \n", " background-color: #a3daa3;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4 {\n", " \n", " background-color: #0e880e;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0 {\n", " \n", " background-color: #329c32;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1 {\n", " \n", " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2 {\n", " \n", " background-color: #0e880e;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3 {\n", " \n", " background-color: #cff3cf;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4 {\n", " \n", " background-color: #4fac4f;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0 {\n", " \n", " background-color: #198e19;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2 {\n", " \n", " background-color: #dcfadc;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #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", " \n", " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1 {\n", " \n", " background-color: #7ec67e;\n", " \n", " }\n", " \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", " }\n", " \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", " 0\n", " \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", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -11645,32 +11650,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -11679,32 +11684,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -11713,32 +11718,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -11747,32 +11752,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -11781,32 +11786,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -11815,32 +11820,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -11849,32 +11854,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -11883,32 +11888,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <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", " 9.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <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", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <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", " \n", " -2.089354\n", " \n", @@ -11917,32 +11922,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <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", " \n", " 0.29072\n", " \n", @@ -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", " <style type=\"text/css\" >\n", " \n", - " #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb tr:hover {\n", + " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb tr:hover {\n", " \n", " background-color: #ffff99;\n", " \n", " }\n", " \n", - " #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb th {\n", + " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb th {\n", " \n", " font-size: 150%;\n", " \n", @@ -12010,7 +12015,7 @@ " \n", " }\n", " \n", - " #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb caption {\n", + " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb caption {\n", " \n", " caption-side: bottom;\n", " \n", @@ -12019,7 +12024,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\">\n", + " <table id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\">\n", " \n", " <caption>Hover to highlight.</caption>\n", " \n", @@ -12047,32 +12052,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -12081,32 +12086,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -12115,32 +12120,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 2\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", " \n", " 0.219565\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", " \n", " 0.678805\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", " \n", " 1.889273\n", " \n", @@ -12149,32 +12154,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", " \n", " 3\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", " \n", " 4.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", " \n", " 0.961538\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", " \n", " 0.104011\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", " \n", " -0.481165\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", " \n", " 0.850229\n", " \n", @@ -12183,32 +12188,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", " \n", " 4\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", " \n", " 1.057737\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -12217,32 +12222,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -12251,32 +12256,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -12285,32 +12290,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", " \n", " 0.519818\n", " \n", @@ -12319,32 +12324,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", " \n", " 9.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", " \n", " 1.686583\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", " \n", " -1.325963\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", " \n", " 1.428984\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", " \n", " -2.089354\n", " \n", @@ -12353,32 +12358,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", " \n", " 9\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", " \n", " 10.0\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", " \n", " 0.631523\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", " \n", " -0.586538\n", " \n", " \n", - " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", " \n", " 0.29072\n", " \n", @@ -12390,10 +12395,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x11338e4a8>" + "<pandas.core.style.Styler at 0x114c42710>" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -12470,7 +12475,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -12482,301 +12487,301 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1 {\n", " \n", " background-color: #779894;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3 {\n", " \n", " background-color: #809f9b;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4 {\n", " \n", " background-color: #aec2bf;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col0 {\n", " \n", " background-color: #799995;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col1 {\n", " \n", " background-color: #8ba7a3;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col2 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3 {\n", " \n", " background-color: #dfe8e7;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4 {\n", " \n", " background-color: #d7e2e0;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0 {\n", " \n", " background-color: #9cb5b1;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2 {\n", " \n", " background-color: #cedbd9;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3 {\n", " \n", " background-color: #cedbd9;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0 {\n", " \n", " background-color: #c1d1cf;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1 {\n", " \n", " background-color: #9cb5b1;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2 {\n", " \n", " background-color: #dce5e4;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3 {\n", " \n", " background-color: #668b86;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4 {\n", " \n", " background-color: #a9bebb;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0 {\n", " \n", " background-color: #e5eceb;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1 {\n", " \n", " background-color: #6c908b;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2 {\n", " \n", " background-color: #678c87;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3 {\n", " \n", " background-color: #cedbd9;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4 {\n", " \n", " background-color: #c5d4d2;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0 {\n", " \n", " background-color: #e5eceb;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1 {\n", " \n", " background-color: #71948f;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2 {\n", " \n", " background-color: #a4bbb8;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3 {\n", " \n", " background-color: #5a827d;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4 {\n", " \n", " background-color: #f2f2f2;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0 {\n", " \n", " background-color: #c1d1cf;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1 {\n", " \n", " background-color: #edf3f2;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3 {\n", " \n", " background-color: #b3c6c4;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4 {\n", " \n", " background-color: #698e89;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0 {\n", " \n", " background-color: #9cb5b1;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1 {\n", " \n", " background-color: #d7e2e0;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2 {\n", " \n", " background-color: #698e89;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3 {\n", " \n", " background-color: #759792;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4 {\n", " \n", " background-color: #c5d4d2;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0 {\n", " \n", " background-color: #799995;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2 {\n", " \n", " background-color: #628882;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1 {\n", " \n", " background-color: #e7eeed;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2 {\n", " \n", " background-color: #9bb4b0;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3 {\n", " \n", " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4 {\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4 {\n", " \n", " background-color: #d7e2e0;\n", " \n", @@ -12784,7 +12789,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\">\n", + " <table id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -12810,32 +12815,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", " \n", " 0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", " \n", " 1.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", " \n", " 1.329212\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", " \n", " nan\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", " \n", " -0.31628\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", " \n", " -0.99081\n", " \n", @@ -12844,32 +12849,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", " \n", " 1\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", " \n", " 2.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", " \n", " -1.070816\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", " \n", " -1.438713\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", " \n", " 0.564417\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", " \n", " 0.295722\n", " \n", @@ -12878,32 +12883,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", " \n", " 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", " \n", " 3.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", " \n", " -1.626404\n", " \n", " \n", - " <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", " \n", " 0.219565\n", " \n", " \n", - " <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", " \n", " 0.678805\n", " \n", " \n", - " <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", " \n", @@ -12912,32 +12917,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " 0.961538\n", " \n", " \n", - " <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", " \n", " 0.104011\n", " \n", " \n", - " <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", " \n", " -0.481165\n", " \n", " \n", - " <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", " \n", " 0.850229\n", " \n", @@ -12946,32 +12951,32 @@ " \n", " <tr>\n", " \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", " \n", " 4\n", " \n", " \n", - " <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", " \n", " 5.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", " \n", " 1.453425\n", " \n", " \n", - " <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", " \n", " 1.057737\n", " \n", " \n", - " <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", " \n", " 0.165562\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", " \n", " 0.515018\n", " \n", @@ -12980,32 +12985,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", " \n", " 5\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", " \n", " 6.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", " \n", " -1.336936\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", " \n", " 0.562861\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", " \n", " 1.392855\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", " \n", " -0.063328\n", " \n", @@ -13014,32 +13019,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", " \n", " 6\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", " \n", " 7.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", " \n", " 0.121668\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", " \n", " 1.207603\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", " \n", " -0.00204\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", " \n", " 1.627796\n", " \n", @@ -13048,32 +13053,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", " \n", " 7\n", " \n", " \n", - " <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", " \n", " 8.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", " \n", " 0.354493\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", " \n", " 1.037528\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", " \n", " -0.385684\n", " \n", " \n", - " <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", " \n", " 0.519818\n", " \n", @@ -13082,32 +13087,32 @@ " \n", " <tr>\n", " \n", - " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", " \n", " 8\n", " \n", " \n", - " <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", " \n", " 9.0\n", " \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", " \n", " 1.686583\n", " \n", " \n", - " <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", " \n", " -1.325963\n", " \n", " \n", - " <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", " \n", " 1.428984\n", " \n", " \n", - " <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", " \n", " -2.089354\n", " \n", @@ -13116,32 +13121,32 @@ " \n", " <tr>\n", " \n", - " <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", " \n", " 9\n", " \n", " \n", - " <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", " 10.0\n", " \n", " \n", - " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", " \n", " -0.12982\n", " \n", " \n", - " <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", " \n", " 0.631523\n", " \n", " \n", - " <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", " \n", " -0.586538\n", " \n", " \n", - " <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", " 0.29072\n", " \n", @@ -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", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0 {\n", " \n", " background-color: #ecf2f8;\n", " \n", @@ -13233,7 +13246,7 @@ " \n", " }\n", " \n", - " #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", " \n", - " #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", " \n", @@ -13263,7 +13276,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4 {\n", " \n", " background-color: #eeaab7;\n", " \n", @@ -13273,7 +13286,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5 {\n", " \n", " background-color: #f8dce2;\n", " \n", @@ -13283,7 +13296,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6 {\n", " \n", " background-color: #f2c1cb;\n", " \n", @@ -13293,7 +13306,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7 {\n", " \n", " background-color: #83a6d2;\n", " \n", @@ -13303,7 +13316,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8 {\n", " \n", " background-color: #de5f79;\n", " \n", @@ -13313,7 +13326,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9 {\n", " \n", " background-color: #c3d4e9;\n", " \n", @@ -13323,7 +13336,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10 {\n", " \n", " background-color: #eeacba;\n", " \n", @@ -13333,7 +13346,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11 {\n", " \n", " background-color: #f7dae0;\n", " \n", @@ -13343,7 +13356,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12 {\n", " \n", " background-color: #6f98ca;\n", " \n", @@ -13353,7 +13366,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13 {\n", " \n", " background-color: #e890a1;\n", " \n", @@ -13363,7 +13376,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -13373,7 +13386,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15 {\n", " \n", " background-color: #ea96a7;\n", " \n", @@ -13383,7 +13396,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16 {\n", " \n", " background-color: #adc4e1;\n", " \n", @@ -13393,7 +13406,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17 {\n", " \n", " background-color: #eca3b1;\n", " \n", @@ -13403,7 +13416,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18 {\n", " \n", " background-color: #b7cbe5;\n", " \n", @@ -13413,7 +13426,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19 {\n", " \n", " background-color: #f5ced6;\n", " \n", @@ -13423,7 +13436,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20 {\n", " \n", " background-color: #6590c7;\n", " \n", @@ -13433,7 +13446,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -13443,7 +13456,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -13453,7 +13466,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23 {\n", " \n", " background-color: #cfddee;\n", " \n", @@ -13463,7 +13476,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24 {\n", " \n", " background-color: #e58094;\n", " \n", @@ -13473,7 +13486,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0 {\n", " \n", " background-color: #e4798e;\n", " \n", @@ -13483,7 +13496,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1 {\n", " \n", " background-color: #aec5e1;\n", " \n", @@ -13493,7 +13506,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2 {\n", " \n", " background-color: #eb9ead;\n", " \n", @@ -13503,7 +13516,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3 {\n", " \n", " background-color: #ec9faf;\n", " \n", @@ -13513,7 +13526,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4 {\n", " \n", " background-color: #cbdaec;\n", " \n", @@ -13523,7 +13536,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5 {\n", " \n", " background-color: #f9e0e5;\n", " \n", @@ -13533,7 +13546,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6 {\n", " \n", " background-color: #db4f6b;\n", " \n", @@ -13543,7 +13556,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -13553,7 +13566,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8 {\n", " \n", " background-color: #e57f93;\n", " \n", @@ -13563,7 +13576,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9 {\n", " \n", " background-color: #bdd0e7;\n", " \n", @@ -13573,7 +13586,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10 {\n", " \n", " background-color: #f3c2cc;\n", " \n", @@ -13583,7 +13596,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11 {\n", " \n", " background-color: #f8dfe4;\n", " \n", @@ -13593,7 +13606,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12 {\n", " \n", " background-color: #b0c6e2;\n", " \n", @@ -13603,7 +13616,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13 {\n", " \n", " background-color: #ec9faf;\n", " \n", @@ -13613,7 +13626,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14 {\n", " \n", " background-color: #e27389;\n", " \n", @@ -13623,7 +13636,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15 {\n", " \n", " background-color: #eb9dac;\n", " \n", @@ -13633,7 +13646,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16 {\n", " \n", " background-color: #f1b8c3;\n", " \n", @@ -13643,7 +13656,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17 {\n", " \n", " background-color: #efb1be;\n", " \n", @@ -13653,7 +13666,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -13663,7 +13676,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19 {\n", " \n", " background-color: #f8dce2;\n", " \n", @@ -13673,7 +13686,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20 {\n", " \n", " background-color: #a0bbdc;\n", " \n", @@ -13683,7 +13696,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -13693,7 +13706,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22 {\n", " \n", " background-color: #86a8d3;\n", " \n", @@ -13703,7 +13716,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23 {\n", " \n", " background-color: #d9e4f1;\n", " \n", @@ -13713,7 +13726,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24 {\n", " \n", " background-color: #ecf2f8;\n", " \n", @@ -13723,7 +13736,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -13733,7 +13746,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1 {\n", " \n", " background-color: #5887c2;\n", " \n", @@ -13743,7 +13756,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2 {\n", " \n", " background-color: #f2bfca;\n", " \n", @@ -13753,7 +13766,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3 {\n", " \n", " background-color: #c7d7eb;\n", " \n", @@ -13763,7 +13776,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: #82a5d1;\n", " \n", @@ -13773,7 +13786,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5 {\n", " \n", " background-color: #ebf1f8;\n", " \n", @@ -13783,7 +13796,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6 {\n", " \n", " background-color: #e78a9d;\n", " \n", @@ -13793,7 +13806,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -13803,7 +13816,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8 {\n", " \n", " background-color: #f1bbc6;\n", " \n", @@ -13813,7 +13826,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9 {\n", " \n", " background-color: #779dcd;\n", " \n", @@ -13823,7 +13836,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10 {\n", " \n", " background-color: #d4e0ef;\n", " \n", @@ -13833,7 +13846,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11 {\n", " \n", " background-color: #e6edf6;\n", " \n", @@ -13843,7 +13856,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12 {\n", " \n", " background-color: #e0e9f4;\n", " \n", @@ -13853,7 +13866,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13 {\n", " \n", " background-color: #fbeaed;\n", " \n", @@ -13863,7 +13876,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14 {\n", " \n", " background-color: #e78a9d;\n", " \n", @@ -13873,7 +13886,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15 {\n", " \n", " background-color: #fae7eb;\n", " \n", @@ -13883,7 +13896,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16 {\n", " \n", " background-color: #e6edf6;\n", " \n", @@ -13893,7 +13906,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17 {\n", " \n", " background-color: #e6edf6;\n", " \n", @@ -13903,7 +13916,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18 {\n", " \n", " background-color: #b9cde6;\n", " \n", @@ -13913,7 +13926,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -13923,7 +13936,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col20 {\n", " \n", " background-color: #a0bbdc;\n", " \n", @@ -13933,7 +13946,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -13943,7 +13956,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col22 {\n", " \n", " background-color: #618ec5;\n", " \n", @@ -13953,7 +13966,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col23 {\n", " \n", " background-color: #8faed6;\n", " \n", @@ -13963,7 +13976,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col24 {\n", " \n", " background-color: #c3d4e9;\n", " \n", @@ -13973,7 +13986,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col0 {\n", " \n", " background-color: #f6d5db;\n", " \n", @@ -13983,7 +13996,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col1 {\n", " \n", " background-color: #5384c0;\n", " \n", @@ -13993,7 +14006,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col2 {\n", " \n", " background-color: #f1bbc6;\n", " \n", @@ -14003,7 +14016,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col3 {\n", " \n", " background-color: #c7d7eb;\n", " \n", @@ -14013,7 +14026,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -14023,7 +14036,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col5 {\n", " \n", " background-color: #e7eef6;\n", " \n", @@ -14033,7 +14046,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col6 {\n", " \n", " background-color: #e58195;\n", " \n", @@ -14043,7 +14056,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -14053,7 +14066,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8 {\n", " \n", " background-color: #f2c1cb;\n", " \n", @@ -14063,7 +14076,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9 {\n", " \n", " background-color: #b1c7e2;\n", " \n", @@ -14073,7 +14086,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10 {\n", " \n", " background-color: #d4e0ef;\n", " \n", @@ -14083,7 +14096,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11 {\n", " \n", " background-color: #c1d3e8;\n", " \n", @@ -14093,7 +14106,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -14103,7 +14116,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13 {\n", " \n", " background-color: #cedced;\n", " \n", @@ -14113,7 +14126,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14 {\n", " \n", " background-color: #e7899c;\n", " \n", @@ -14123,7 +14136,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15 {\n", " \n", " background-color: #eeacba;\n", " \n", @@ -14133,7 +14146,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16 {\n", " \n", " background-color: #e2eaf4;\n", " \n", @@ -14143,7 +14156,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17 {\n", " \n", " background-color: #f7d6dd;\n", " \n", @@ -14153,7 +14166,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18 {\n", " \n", " background-color: #a8c0df;\n", " \n", @@ -14163,7 +14176,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19 {\n", " \n", " background-color: #f5ccd4;\n", " \n", @@ -14173,7 +14186,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20 {\n", " \n", " background-color: #b7cbe5;\n", " \n", @@ -14183,7 +14196,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -14193,7 +14206,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22 {\n", " \n", " background-color: #739acc;\n", " \n", @@ -14203,7 +14216,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23 {\n", " \n", " background-color: #8dadd5;\n", " \n", @@ -14213,7 +14226,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24 {\n", " \n", " background-color: #cddbed;\n", " \n", @@ -14223,7 +14236,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0 {\n", " \n", " background-color: #ea9aaa;\n", " \n", @@ -14233,7 +14246,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1 {\n", " \n", " background-color: #5887c2;\n", " \n", @@ -14243,7 +14256,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2 {\n", " \n", " background-color: #f4c9d2;\n", " \n", @@ -14253,7 +14266,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3 {\n", " \n", " background-color: #f5ced6;\n", " \n", @@ -14263,7 +14276,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -14273,7 +14286,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5 {\n", " \n", " background-color: #fae4e9;\n", " \n", @@ -14283,7 +14296,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6 {\n", " \n", " background-color: #e78c9e;\n", " \n", @@ -14293,7 +14306,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7 {\n", " \n", " background-color: #5182bf;\n", " \n", @@ -14303,7 +14316,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -14313,7 +14326,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9 {\n", " \n", " background-color: #c1d3e8;\n", " \n", @@ -14323,7 +14336,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10 {\n", " \n", " background-color: #e8eff7;\n", " \n", @@ -14333,7 +14346,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11 {\n", " \n", " background-color: #c9d8eb;\n", " \n", @@ -14343,7 +14356,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12 {\n", " \n", " background-color: #eaf0f7;\n", " \n", @@ -14353,7 +14366,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13 {\n", " \n", " background-color: #f9e2e6;\n", " \n", @@ -14363,7 +14376,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14 {\n", " \n", " background-color: #e47c91;\n", " \n", @@ -14373,7 +14386,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15 {\n", " \n", " background-color: #eda8b6;\n", " \n", @@ -14383,7 +14396,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16 {\n", " \n", " background-color: #fae6ea;\n", " \n", @@ -14393,7 +14406,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17 {\n", " \n", " background-color: #f5ccd4;\n", " \n", @@ -14403,7 +14416,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18 {\n", " \n", " background-color: #b3c9e3;\n", " \n", @@ -14413,7 +14426,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19 {\n", " \n", " background-color: #f4cad3;\n", " \n", @@ -14423,7 +14436,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20 {\n", " \n", " background-color: #9fbadc;\n", " \n", @@ -14433,7 +14446,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -14443,7 +14456,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22 {\n", " \n", " background-color: #7da2cf;\n", " \n", @@ -14453,7 +14466,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23 {\n", " \n", " background-color: #95b3d8;\n", " \n", @@ -14463,7 +14476,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24 {\n", " \n", " background-color: #a2bcdd;\n", " \n", @@ -14473,7 +14486,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0 {\n", " \n", " background-color: #fbeaed;\n", " \n", @@ -14483,7 +14496,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1 {\n", " \n", " background-color: #6490c6;\n", " \n", @@ -14493,7 +14506,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2 {\n", " \n", " background-color: #f6d1d8;\n", " \n", @@ -14503,7 +14516,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3 {\n", " \n", " background-color: #f3c6cf;\n", " \n", @@ -14513,7 +14526,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -14523,7 +14536,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5 {\n", " \n", " background-color: #fae6ea;\n", " \n", @@ -14533,7 +14546,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6 {\n", " \n", " background-color: #e68598;\n", " \n", @@ -14543,7 +14556,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7 {\n", " \n", " background-color: #6d96ca;\n", " \n", @@ -14553,7 +14566,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8 {\n", " \n", " background-color: #f9e3e7;\n", " \n", @@ -14563,7 +14576,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9 {\n", " \n", " background-color: #fae7eb;\n", " \n", @@ -14573,7 +14586,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10 {\n", " \n", " background-color: #bdd0e7;\n", " \n", @@ -14583,7 +14596,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11 {\n", " \n", " background-color: #e0e9f4;\n", " \n", @@ -14593,7 +14606,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12 {\n", " \n", " background-color: #f5ced6;\n", " \n", @@ -14603,7 +14616,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13 {\n", " \n", " background-color: #e6edf6;\n", " \n", @@ -14613,7 +14626,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14 {\n", " \n", " background-color: #e47a90;\n", " \n", @@ -14623,7 +14636,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15 {\n", " \n", " background-color: #fbeaed;\n", " \n", @@ -14633,7 +14646,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16 {\n", " \n", " background-color: #f3c5ce;\n", " \n", @@ -14643,7 +14656,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17 {\n", " \n", " background-color: #f7dae0;\n", " \n", @@ -14653,7 +14666,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18 {\n", " \n", " background-color: #cbdaec;\n", " \n", @@ -14663,7 +14676,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19 {\n", " \n", " background-color: #f6d2d9;\n", " \n", @@ -14673,7 +14686,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20 {\n", " \n", " background-color: #b5cae4;\n", " \n", @@ -14683,7 +14696,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -14693,7 +14706,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22 {\n", " \n", " background-color: #8faed6;\n", " \n", @@ -14703,7 +14716,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23 {\n", " \n", " background-color: #a3bddd;\n", " \n", @@ -14713,7 +14726,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24 {\n", " \n", " background-color: #7199cb;\n", " \n", @@ -14723,7 +14736,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0 {\n", " \n", " background-color: #e6edf6;\n", " \n", @@ -14733,7 +14746,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1 {\n", " \n", " background-color: #4e80be;\n", " \n", @@ -14743,7 +14756,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: #f8dee3;\n", " \n", @@ -14753,7 +14766,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -14763,7 +14776,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4 {\n", " \n", " background-color: #6a94c9;\n", " \n", @@ -14773,7 +14786,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5 {\n", " \n", " background-color: #fae4e9;\n", " \n", @@ -14783,7 +14796,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6 {\n", " \n", " background-color: #f3c2cc;\n", " \n", @@ -14793,7 +14806,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7 {\n", " \n", " background-color: #759ccd;\n", " \n", @@ -14803,7 +14816,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8 {\n", " \n", " background-color: #f2c1cb;\n", " \n", @@ -14813,7 +14826,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -14823,7 +14836,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10 {\n", " \n", " background-color: #cbdaec;\n", " \n", @@ -14833,7 +14846,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11 {\n", " \n", " background-color: #c5d5ea;\n", " \n", @@ -14843,7 +14856,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12 {\n", " \n", " background-color: #fae6ea;\n", " \n", @@ -14853,7 +14866,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13 {\n", " \n", " background-color: #c6d6ea;\n", " \n", @@ -14863,7 +14876,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14 {\n", " \n", " background-color: #eca3b1;\n", " \n", @@ -14873,7 +14886,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15 {\n", " \n", " background-color: #fae4e9;\n", " \n", @@ -14883,7 +14896,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16 {\n", " \n", " background-color: #eeacba;\n", " \n", @@ -14893,7 +14906,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17 {\n", " \n", " background-color: #f6d1d8;\n", " \n", @@ -14903,7 +14916,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18 {\n", " \n", " background-color: #d8e3f1;\n", " \n", @@ -14913,7 +14926,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19 {\n", " \n", " background-color: #eb9bab;\n", " \n", @@ -14923,7 +14936,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20 {\n", " \n", " background-color: #a3bddd;\n", " \n", @@ -14933,7 +14946,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -14943,7 +14956,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22 {\n", " \n", " background-color: #81a4d1;\n", " \n", @@ -14953,7 +14966,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23 {\n", " \n", " background-color: #95b3d8;\n", " \n", @@ -14963,7 +14976,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -14973,7 +14986,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -14983,7 +14996,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1 {\n", " \n", " background-color: #759ccd;\n", " \n", @@ -14993,7 +15006,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2 {\n", " \n", " background-color: #f2bdc8;\n", " \n", @@ -15003,7 +15016,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15013,7 +15026,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4 {\n", " \n", " background-color: #5686c1;\n", " \n", @@ -15023,7 +15036,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5 {\n", " \n", " background-color: #f0b5c1;\n", " \n", @@ -15033,7 +15046,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6 {\n", " \n", " background-color: #f4c9d2;\n", " \n", @@ -15043,7 +15056,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7 {\n", " \n", " background-color: #628fc6;\n", " \n", @@ -15053,7 +15066,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8 {\n", " \n", " background-color: #e68396;\n", " \n", @@ -15063,7 +15076,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9 {\n", " \n", " background-color: #eda7b5;\n", " \n", @@ -15073,7 +15086,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10 {\n", " \n", " background-color: #f5ccd4;\n", " \n", @@ -15083,7 +15096,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11 {\n", " \n", " background-color: #e8eff7;\n", " \n", @@ -15093,7 +15106,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12 {\n", " \n", " background-color: #eaf0f7;\n", " \n", @@ -15103,7 +15116,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13 {\n", " \n", " background-color: #ebf1f8;\n", " \n", @@ -15113,7 +15126,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14 {\n", " \n", " background-color: #de5c76;\n", " \n", @@ -15123,7 +15136,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15133,7 +15146,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16 {\n", " \n", " background-color: #dd5671;\n", " \n", @@ -15143,7 +15156,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17 {\n", " \n", " background-color: #e993a4;\n", " \n", @@ -15153,7 +15166,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18 {\n", " \n", " background-color: #dae5f2;\n", " \n", @@ -15163,7 +15176,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19 {\n", " \n", " background-color: #e991a3;\n", " \n", @@ -15173,7 +15186,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20 {\n", " \n", " background-color: #dce6f2;\n", " \n", @@ -15183,7 +15196,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -15193,7 +15206,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22 {\n", " \n", " background-color: #a0bbdc;\n", " \n", @@ -15203,7 +15216,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23 {\n", " \n", " background-color: #96b4d9;\n", " \n", @@ -15213,7 +15226,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -15223,7 +15236,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0 {\n", " \n", " background-color: #d3dfef;\n", " \n", @@ -15233,7 +15246,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: #487cbc;\n", " \n", @@ -15243,7 +15256,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2 {\n", " \n", " background-color: #ea9aaa;\n", " \n", @@ -15253,7 +15266,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: #fae7eb;\n", " \n", @@ -15263,7 +15276,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -15273,7 +15286,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5 {\n", " \n", " background-color: #eb9ead;\n", " \n", @@ -15283,7 +15296,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6 {\n", " \n", " background-color: #f3c5ce;\n", " \n", @@ -15293,7 +15306,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7 {\n", " \n", " background-color: #4d7fbe;\n", " \n", @@ -15303,7 +15316,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8 {\n", " \n", " background-color: #e994a5;\n", " \n", @@ -15313,7 +15326,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9 {\n", " \n", " background-color: #f6d5db;\n", " \n", @@ -15323,7 +15336,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10 {\n", " \n", " background-color: #f5ccd4;\n", " \n", @@ -15333,7 +15346,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11 {\n", " \n", " background-color: #d5e1f0;\n", " \n", @@ -15343,7 +15356,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12 {\n", " \n", " background-color: #f0b4c0;\n", " \n", @@ -15353,7 +15366,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15363,7 +15376,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14 {\n", " \n", " background-color: #da4966;\n", " \n", @@ -15373,7 +15386,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15383,7 +15396,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -15393,7 +15406,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17 {\n", " \n", " background-color: #ea96a7;\n", " \n", @@ -15403,7 +15416,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18 {\n", " \n", " background-color: #ecf2f8;\n", " \n", @@ -15413,7 +15426,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19 {\n", " \n", " background-color: #e3748a;\n", " \n", @@ -15423,7 +15436,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20 {\n", " \n", " background-color: #dde7f3;\n", " \n", @@ -15433,7 +15446,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21 {\n", " \n", " background-color: #dc516d;\n", " \n", @@ -15443,7 +15456,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22 {\n", " \n", " background-color: #91b0d7;\n", " \n", @@ -15453,7 +15466,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23 {\n", " \n", " background-color: #a8c0df;\n", " \n", @@ -15463,7 +15476,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24 {\n", " \n", " background-color: #5c8ac4;\n", " \n", @@ -15473,7 +15486,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: #dce6f2;\n", " \n", @@ -15483,7 +15496,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1 {\n", " \n", " background-color: #6590c7;\n", " \n", @@ -15493,7 +15506,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2 {\n", " \n", " background-color: #ea99a9;\n", " \n", @@ -15503,7 +15516,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15513,7 +15526,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -15523,7 +15536,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5 {\n", " \n", " background-color: #f3c5ce;\n", " \n", @@ -15533,7 +15546,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15543,7 +15556,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7 {\n", " \n", " background-color: #6a94c9;\n", " \n", @@ -15553,7 +15566,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8 {\n", " \n", " background-color: #f6d5db;\n", " \n", @@ -15563,7 +15576,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9 {\n", " \n", " background-color: #ebf1f8;\n", " \n", @@ -15573,7 +15586,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15583,7 +15596,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11 {\n", " \n", " background-color: #dfe8f3;\n", " \n", @@ -15593,7 +15606,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12 {\n", " \n", " background-color: #efb2bf;\n", " \n", @@ -15603,7 +15616,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15613,7 +15626,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14 {\n", " \n", " background-color: #e27389;\n", " \n", @@ -15623,7 +15636,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15 {\n", " \n", " background-color: #f3c5ce;\n", " \n", @@ -15633,7 +15646,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -15643,7 +15656,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17 {\n", " \n", " background-color: #ea9aaa;\n", " \n", @@ -15653,7 +15666,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18 {\n", " \n", " background-color: #dae5f2;\n", " \n", @@ -15663,7 +15676,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19 {\n", " \n", " background-color: #e993a4;\n", " \n", @@ -15673,7 +15686,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20 {\n", " \n", " background-color: #b9cde6;\n", " \n", @@ -15683,7 +15696,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21 {\n", " \n", " background-color: #de5f79;\n", " \n", @@ -15693,7 +15706,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22 {\n", " \n", " background-color: #b3c9e3;\n", " \n", @@ -15703,7 +15716,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23 {\n", " \n", " background-color: #9fbadc;\n", " \n", @@ -15713,7 +15726,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24 {\n", " \n", " background-color: #6f98ca;\n", " \n", @@ -15723,7 +15736,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0 {\n", " \n", " background-color: #c6d6ea;\n", " \n", @@ -15733,7 +15746,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1 {\n", " \n", " background-color: #6f98ca;\n", " \n", @@ -15743,7 +15756,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2 {\n", " \n", " background-color: #ea96a7;\n", " \n", @@ -15753,7 +15766,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3 {\n", " \n", " background-color: #f7dae0;\n", " \n", @@ -15763,7 +15776,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -15773,7 +15786,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5 {\n", " \n", " background-color: #f0b7c2;\n", " \n", @@ -15783,7 +15796,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6 {\n", " \n", " background-color: #fae4e9;\n", " \n", @@ -15793,7 +15806,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7 {\n", " \n", " background-color: #759ccd;\n", " \n", @@ -15803,7 +15816,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8 {\n", " \n", " background-color: #f2bdc8;\n", " \n", @@ -15813,7 +15826,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9 {\n", " \n", " background-color: #f9e2e6;\n", " \n", @@ -15823,7 +15836,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10 {\n", " \n", " background-color: #fae7eb;\n", " \n", @@ -15833,7 +15846,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11 {\n", " \n", " background-color: #cbdaec;\n", " \n", @@ -15843,7 +15856,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12 {\n", " \n", " background-color: #efb1be;\n", " \n", @@ -15853,7 +15866,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13 {\n", " \n", " background-color: #eaf0f7;\n", " \n", @@ -15863,7 +15876,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14 {\n", " \n", " background-color: #e0657d;\n", " \n", @@ -15873,7 +15886,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15 {\n", " \n", " background-color: #eca1b0;\n", " \n", @@ -15883,7 +15896,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -15893,7 +15906,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17 {\n", " \n", " background-color: #e27087;\n", " \n", @@ -15903,7 +15916,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18 {\n", " \n", " background-color: #f9e2e6;\n", " \n", @@ -15913,7 +15926,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19 {\n", " \n", " background-color: #e68699;\n", " \n", @@ -15923,7 +15936,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20 {\n", " \n", " background-color: #fae6ea;\n", " \n", @@ -15933,7 +15946,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -15943,7 +15956,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22 {\n", " \n", " background-color: #d1deee;\n", " \n", @@ -15953,7 +15966,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23 {\n", " \n", " background-color: #82a5d1;\n", " \n", @@ -15963,7 +15976,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24 {\n", " \n", " background-color: #7099cb;\n", " \n", @@ -15973,7 +15986,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0 {\n", " \n", " background-color: #a9c1e0;\n", " \n", @@ -15983,7 +15996,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1 {\n", " \n", " background-color: #6892c8;\n", " \n", @@ -15993,7 +16006,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2 {\n", " \n", " background-color: #f7d6dd;\n", " \n", @@ -16003,7 +16016,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16013,7 +16026,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -16023,7 +16036,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5 {\n", " \n", " background-color: #e4ecf5;\n", " \n", @@ -16033,7 +16046,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6 {\n", " \n", " background-color: #d8e3f1;\n", " \n", @@ -16043,7 +16056,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7 {\n", " \n", " background-color: #477bbc;\n", " \n", @@ -16053,7 +16066,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16063,7 +16076,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9 {\n", " \n", " background-color: #e7eef6;\n", " \n", @@ -16073,7 +16086,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10 {\n", " \n", " background-color: #cbdaec;\n", " \n", @@ -16083,7 +16096,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11 {\n", " \n", " background-color: #a6bfde;\n", " \n", @@ -16093,7 +16106,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12 {\n", " \n", " background-color: #fae8ec;\n", " \n", @@ -16103,7 +16116,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13 {\n", " \n", " background-color: #a9c1e0;\n", " \n", @@ -16113,7 +16126,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14 {\n", " \n", " background-color: #e3748a;\n", " \n", @@ -16123,7 +16136,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15 {\n", " \n", " background-color: #ea99a9;\n", " \n", @@ -16133,7 +16146,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -16143,7 +16156,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17 {\n", " \n", " background-color: #f0b7c2;\n", " \n", @@ -16153,7 +16166,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18 {\n", " \n", " background-color: #f6d5db;\n", " \n", @@ -16163,7 +16176,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19 {\n", " \n", " background-color: #eb9ead;\n", " \n", @@ -16173,7 +16186,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16183,7 +16196,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21 {\n", " \n", " background-color: #d8415f;\n", " \n", @@ -16193,7 +16206,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22 {\n", " \n", " background-color: #b5cae4;\n", " \n", @@ -16203,7 +16216,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23 {\n", " \n", " background-color: #5182bf;\n", " \n", @@ -16213,7 +16226,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24 {\n", " \n", " background-color: #457abb;\n", " \n", @@ -16223,7 +16236,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0 {\n", " \n", " background-color: #92b1d7;\n", " \n", @@ -16233,7 +16246,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1 {\n", " \n", " background-color: #7ba0cf;\n", " \n", @@ -16243,7 +16256,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2 {\n", " \n", " background-color: #f3c5ce;\n", " \n", @@ -16253,7 +16266,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3 {\n", " \n", " background-color: #f7d7de;\n", " \n", @@ -16263,7 +16276,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4 {\n", " \n", " background-color: #5485c1;\n", " \n", @@ -16273,7 +16286,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5 {\n", " \n", " background-color: #f5cfd7;\n", " \n", @@ -16283,7 +16296,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6 {\n", " \n", " background-color: #d4e0ef;\n", " \n", @@ -16293,7 +16306,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -16303,7 +16316,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8 {\n", " \n", " background-color: #f4cad3;\n", " \n", @@ -16313,7 +16326,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9 {\n", " \n", " background-color: #dfe8f3;\n", " \n", @@ -16323,7 +16336,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10 {\n", " \n", " background-color: #b0c6e2;\n", " \n", @@ -16333,7 +16346,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11 {\n", " \n", " background-color: #9fbadc;\n", " \n", @@ -16343,7 +16356,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12 {\n", " \n", " background-color: #fae8ec;\n", " \n", @@ -16353,7 +16366,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13 {\n", " \n", " background-color: #cad9ec;\n", " \n", @@ -16363,7 +16376,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14 {\n", " \n", " background-color: #e991a3;\n", " \n", @@ -16373,7 +16386,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15 {\n", " \n", " background-color: #eca3b1;\n", " \n", @@ -16383,7 +16396,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16 {\n", " \n", " background-color: #de5c76;\n", " \n", @@ -16393,7 +16406,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17 {\n", " \n", " background-color: #f4cad3;\n", " \n", @@ -16403,7 +16416,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18 {\n", " \n", " background-color: #f7dae0;\n", " \n", @@ -16413,7 +16426,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19 {\n", " \n", " background-color: #eb9dac;\n", " \n", @@ -16423,7 +16436,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16433,7 +16446,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -16443,7 +16456,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22 {\n", " \n", " background-color: #acc3e1;\n", " \n", @@ -16453,7 +16466,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23 {\n", " \n", " background-color: #497dbd;\n", " \n", @@ -16463,7 +16476,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24 {\n", " \n", " background-color: #5c8ac4;\n", " \n", @@ -16473,7 +16486,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0 {\n", " \n", " background-color: #bccfe7;\n", " \n", @@ -16483,7 +16496,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1 {\n", " \n", " background-color: #8faed6;\n", " \n", @@ -16493,7 +16506,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2 {\n", " \n", " background-color: #eda6b4;\n", " \n", @@ -16503,7 +16516,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3 {\n", " \n", " background-color: #f5ced6;\n", " \n", @@ -16513,7 +16526,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4 {\n", " \n", " background-color: #5c8ac4;\n", " \n", @@ -16523,7 +16536,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5 {\n", " \n", " background-color: #efb2bf;\n", " \n", @@ -16533,7 +16546,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6 {\n", " \n", " background-color: #f4cad3;\n", " \n", @@ -16543,7 +16556,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -16553,7 +16566,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8 {\n", " \n", " background-color: #f3c2cc;\n", " \n", @@ -16563,7 +16576,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9 {\n", " \n", " background-color: #fae8ec;\n", " \n", @@ -16573,7 +16586,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10 {\n", " \n", " background-color: #dde7f3;\n", " \n", @@ -16583,7 +16596,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11 {\n", " \n", " background-color: #bbcee6;\n", " \n", @@ -16593,7 +16606,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16603,7 +16616,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16613,7 +16626,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14 {\n", " \n", " background-color: #e78a9d;\n", " \n", @@ -16623,7 +16636,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15 {\n", " \n", " background-color: #eda7b5;\n", " \n", @@ -16633,7 +16646,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16 {\n", " \n", " background-color: #dc546f;\n", " \n", @@ -16643,7 +16656,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17 {\n", " \n", " background-color: #eca3b1;\n", " \n", @@ -16653,7 +16666,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18 {\n", " \n", " background-color: #e6edf6;\n", " \n", @@ -16663,7 +16676,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19 {\n", " \n", " background-color: #eeaab7;\n", " \n", @@ -16673,7 +16686,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20 {\n", " \n", " background-color: #f9e3e7;\n", " \n", @@ -16683,7 +16696,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -16693,7 +16706,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22 {\n", " \n", " background-color: #b8cce5;\n", " \n", @@ -16703,7 +16716,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23 {\n", " \n", " background-color: #7099cb;\n", " \n", @@ -16713,7 +16726,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24 {\n", " \n", " background-color: #5e8bc4;\n", " \n", @@ -16723,7 +16736,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0 {\n", " \n", " background-color: #91b0d7;\n", " \n", @@ -16733,7 +16746,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1 {\n", " \n", " background-color: #86a8d3;\n", " \n", @@ -16743,7 +16756,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2 {\n", " \n", " background-color: #efb2bf;\n", " \n", @@ -16753,7 +16766,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3 {\n", " \n", " background-color: #f9e3e7;\n", " \n", @@ -16763,7 +16776,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4 {\n", " \n", " background-color: #5e8bc4;\n", " \n", @@ -16773,7 +16786,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5 {\n", " \n", " background-color: #f2bfca;\n", " \n", @@ -16783,7 +16796,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6 {\n", " \n", " background-color: #fae6ea;\n", " \n", @@ -16793,7 +16806,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7 {\n", " \n", " background-color: #6b95c9;\n", " \n", @@ -16803,7 +16816,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8 {\n", " \n", " background-color: #f3c6cf;\n", " \n", @@ -16813,7 +16826,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9 {\n", " \n", " background-color: #e8eff7;\n", " \n", @@ -16823,7 +16836,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16833,7 +16846,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11 {\n", " \n", " background-color: #bdd0e7;\n", " \n", @@ -16843,7 +16856,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12 {\n", " \n", " background-color: #95b3d8;\n", " \n", @@ -16853,7 +16866,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13 {\n", " \n", " background-color: #dae5f2;\n", " \n", @@ -16863,7 +16876,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14 {\n", " \n", " background-color: #eeabb8;\n", " \n", @@ -16873,7 +16886,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15 {\n", " \n", " background-color: #eeacba;\n", " \n", @@ -16883,7 +16896,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16 {\n", " \n", " background-color: #e3748a;\n", " \n", @@ -16893,7 +16906,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17 {\n", " \n", " background-color: #eca4b3;\n", " \n", @@ -16903,7 +16916,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18 {\n", " \n", " background-color: #f7d6dd;\n", " \n", @@ -16913,7 +16926,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19 {\n", " \n", " background-color: #f6d2d9;\n", " \n", @@ -16923,7 +16936,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20 {\n", " \n", " background-color: #f9e3e7;\n", " \n", @@ -16933,7 +16946,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -16943,7 +16956,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22 {\n", " \n", " background-color: #9bb7da;\n", " \n", @@ -16953,7 +16966,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23 {\n", " \n", " background-color: #618ec5;\n", " \n", @@ -16963,7 +16976,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -16973,7 +16986,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0 {\n", " \n", " background-color: #5787c2;\n", " \n", @@ -16983,7 +16996,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1 {\n", " \n", " background-color: #5e8bc4;\n", " \n", @@ -16993,7 +17006,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2 {\n", " \n", " background-color: #f5cfd7;\n", " \n", @@ -17003,7 +17016,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17013,7 +17026,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4 {\n", " \n", " background-color: #5384c0;\n", " \n", @@ -17023,7 +17036,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5 {\n", " \n", " background-color: #f8dee3;\n", " \n", @@ -17033,7 +17046,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6 {\n", " \n", " background-color: #dce6f2;\n", " \n", @@ -17043,7 +17056,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7 {\n", " \n", " background-color: #5787c2;\n", " \n", @@ -17053,7 +17066,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8 {\n", " \n", " background-color: #f9e3e7;\n", " \n", @@ -17063,7 +17076,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9 {\n", " \n", " background-color: #cedced;\n", " \n", @@ -17073,7 +17086,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10 {\n", " \n", " background-color: #dde7f3;\n", " \n", @@ -17083,7 +17096,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11 {\n", " \n", " background-color: #9cb8db;\n", " \n", @@ -17093,7 +17106,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12 {\n", " \n", " background-color: #749bcc;\n", " \n", @@ -17103,7 +17116,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13 {\n", " \n", " background-color: #b2c8e3;\n", " \n", @@ -17113,7 +17126,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14 {\n", " \n", " background-color: #f8dfe4;\n", " \n", @@ -17123,7 +17136,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15 {\n", " \n", " background-color: #f4c9d2;\n", " \n", @@ -17133,7 +17146,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16 {\n", " \n", " background-color: #eeabb8;\n", " \n", @@ -17143,7 +17156,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17 {\n", " \n", " background-color: #f3c6cf;\n", " \n", @@ -17153,7 +17166,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17163,7 +17176,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19 {\n", " \n", " background-color: #e2eaf4;\n", " \n", @@ -17173,7 +17186,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20 {\n", " \n", " background-color: #dfe8f3;\n", " \n", @@ -17183,7 +17196,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -17193,7 +17206,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22 {\n", " \n", " background-color: #94b2d8;\n", " \n", @@ -17203,7 +17216,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -17213,7 +17226,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24 {\n", " \n", " background-color: #5384c0;\n", " \n", @@ -17223,7 +17236,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0 {\n", " \n", " background-color: #6993c8;\n", " \n", @@ -17233,7 +17246,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1 {\n", " \n", " background-color: #6590c7;\n", " \n", @@ -17243,7 +17256,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2 {\n", " \n", " background-color: #e2eaf4;\n", " \n", @@ -17253,7 +17266,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17263,7 +17276,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4 {\n", " \n", " background-color: #457abb;\n", " \n", @@ -17273,7 +17286,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5 {\n", " \n", " background-color: #e3ebf5;\n", " \n", @@ -17283,7 +17296,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6 {\n", " \n", " background-color: #bdd0e7;\n", " \n", @@ -17293,7 +17306,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7 {\n", " \n", " background-color: #5384c0;\n", " \n", @@ -17303,7 +17316,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8 {\n", " \n", " background-color: #f7d7de;\n", " \n", @@ -17313,7 +17326,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9 {\n", " \n", " background-color: #96b4d9;\n", " \n", @@ -17323,7 +17336,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10 {\n", " \n", " background-color: #b0c6e2;\n", " \n", @@ -17333,7 +17346,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11 {\n", " \n", " background-color: #b2c8e3;\n", " \n", @@ -17343,7 +17356,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12 {\n", " \n", " background-color: #4a7ebd;\n", " \n", @@ -17353,7 +17366,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13 {\n", " \n", " background-color: #92b1d7;\n", " \n", @@ -17363,7 +17376,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17373,7 +17386,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17383,7 +17396,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16 {\n", " \n", " background-color: #f4cad3;\n", " \n", @@ -17393,7 +17406,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17 {\n", " \n", " background-color: #ebf1f8;\n", " \n", @@ -17403,7 +17416,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18 {\n", " \n", " background-color: #dce6f2;\n", " \n", @@ -17413,7 +17426,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19 {\n", " \n", " background-color: #c9d8eb;\n", " \n", @@ -17423,7 +17436,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20 {\n", " \n", " background-color: #bfd1e8;\n", " \n", @@ -17433,7 +17446,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -17443,7 +17456,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22 {\n", " \n", " background-color: #8faed6;\n", " \n", @@ -17453,7 +17466,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -17463,7 +17476,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24 {\n", " \n", " background-color: #5a88c3;\n", " \n", @@ -17473,7 +17486,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0 {\n", " \n", " background-color: #628fc6;\n", " \n", @@ -17483,7 +17496,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1 {\n", " \n", " background-color: #749bcc;\n", " \n", @@ -17493,7 +17506,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2 {\n", " \n", " background-color: #f9e2e6;\n", " \n", @@ -17503,7 +17516,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3 {\n", " \n", " background-color: #f8dee3;\n", " \n", @@ -17513,7 +17526,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4 {\n", " \n", " background-color: #5182bf;\n", " \n", @@ -17523,7 +17536,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17533,7 +17546,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6 {\n", " \n", " background-color: #d4e0ef;\n", " \n", @@ -17543,7 +17556,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7 {\n", " \n", " background-color: #5182bf;\n", " \n", @@ -17553,7 +17566,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8 {\n", " \n", " background-color: #f4cad3;\n", " \n", @@ -17563,7 +17576,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9 {\n", " \n", " background-color: #85a7d2;\n", " \n", @@ -17573,7 +17586,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10 {\n", " \n", " background-color: #cbdaec;\n", " \n", @@ -17583,7 +17596,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11 {\n", " \n", " background-color: #bccfe7;\n", " \n", @@ -17593,7 +17606,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12 {\n", " \n", " background-color: #5f8cc5;\n", " \n", @@ -17603,7 +17616,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13 {\n", " \n", " background-color: #a2bcdd;\n", " \n", @@ -17613,7 +17626,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17623,7 +17636,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17633,7 +17646,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16 {\n", " \n", " background-color: #f3c6cf;\n", " \n", @@ -17643,7 +17656,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17 {\n", " \n", " background-color: #fae7eb;\n", " \n", @@ -17653,7 +17666,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18 {\n", " \n", " background-color: #fbeaed;\n", " \n", @@ -17663,7 +17676,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17673,7 +17686,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20 {\n", " \n", " background-color: #b7cbe5;\n", " \n", @@ -17683,7 +17696,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -17693,7 +17706,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22 {\n", " \n", " background-color: #86a8d3;\n", " \n", @@ -17703,7 +17716,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -17713,7 +17726,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24 {\n", " \n", " background-color: #739acc;\n", " \n", @@ -17723,7 +17736,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0 {\n", " \n", " background-color: #6a94c9;\n", " \n", @@ -17733,7 +17746,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1 {\n", " \n", " background-color: #6d96ca;\n", " \n", @@ -17743,7 +17756,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2 {\n", " \n", " background-color: #f4c9d2;\n", " \n", @@ -17753,7 +17766,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3 {\n", " \n", " background-color: #eeaebb;\n", " \n", @@ -17763,7 +17776,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4 {\n", " \n", " background-color: #5384c0;\n", " \n", @@ -17773,7 +17786,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5 {\n", " \n", " background-color: #f2bfca;\n", " \n", @@ -17783,7 +17796,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17793,7 +17806,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -17803,7 +17816,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8 {\n", " \n", " background-color: #ec9faf;\n", " \n", @@ -17813,7 +17826,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9 {\n", " \n", " background-color: #c6d6ea;\n", " \n", @@ -17823,7 +17836,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -17833,7 +17846,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11 {\n", " \n", " background-color: #dae5f2;\n", " \n", @@ -17843,7 +17856,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12 {\n", " \n", " background-color: #4c7ebd;\n", " \n", @@ -17853,7 +17866,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13 {\n", " \n", " background-color: #d1deee;\n", " \n", @@ -17863,7 +17876,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14 {\n", " \n", " background-color: #fae6ea;\n", " \n", @@ -17873,7 +17886,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15 {\n", " \n", " background-color: #f7d9df;\n", " \n", @@ -17883,7 +17896,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16 {\n", " \n", " background-color: #eeacba;\n", " \n", @@ -17893,7 +17906,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17 {\n", " \n", " background-color: #f6d1d8;\n", " \n", @@ -17903,7 +17916,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18 {\n", " \n", " background-color: #f6d2d9;\n", " \n", @@ -17913,7 +17926,7 @@ " \n", " }\n", " \n", - " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19 {\n", " \n", " background-color: #f4c9d2;\n", " \n", @@ -17923,7 +17936,7 @@ " \n", " }\n", " \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" > + + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow0_col0 { + + background-color: #31688e; + + background-color: #31688e; + + background-color: #31688e; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow0_col1 { + + background-color: #89d548; + + background-color: #89d548; + + background-color: #89d548; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow0_col2 { + + background-color: #3aba76; + + background-color: #3aba76; + + background-color: #3aba76; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow0_col3 { + + background-color: #31688e; + + background-color: #31688e; + + background-color: #31688e; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow0_col4 { + + background-color: #24878e; + + background-color: #24878e; + + background-color: #24878e; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow1_col0 { + + background-color: #fde725; + + background-color: #fde725; + + background-color: #fde725; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow1_col1 { + + background-color: #2c738e; + + background-color: #2c738e; + + background-color: #2c738e; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow1_col2 { + + background-color: #1f9f88; + + background-color: #1f9f88; + + background-color: #1f9f88; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow1_col3 { + + background-color: #fde725; + + background-color: #fde725; + + background-color: #fde725; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow1_col4 { + + background-color: #297a8e; + + background-color: #297a8e; + + background-color: #297a8e; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow2_col0 { + + background-color: #27808e; + + background-color: #27808e; + + background-color: #27808e; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow2_col1 { + + background-color: #3bbb75; + + background-color: #3bbb75; + + background-color: #3bbb75; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow2_col2 { + + background-color: #b2dd2d; + + background-color: #b2dd2d; + + background-color: #b2dd2d; + + } + + #T_8d7c2fec_8d9e_11e5_8e2c_a45e60bd97fbrow2_col3 { + + background-color: #1f9a8a; + + background-color: #1f9a8a; + + background-color: #1f9a8a; + + } 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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
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
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