Python indexing._non_reducing_slice方法代碼示例

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def test_non_reducing_slice_on_multiindex(self):
        # GH 19861
        dic = {
            ('a', 'd'): [1, 4],
            ('a', 'c'): [2, 3],
            ('b', 'c'): [3, 2],
            ('b', 'd'): [4, 1]
        }
        df = pd.DataFrame(dic, index=[0, 1])
        idx = pd.IndexSlice
        slice_ = idx[:, idx['b', 'd']]
        tslice_ = _non_reducing_slice(slice_)

        result = df.loc[tslice_]
        expected = pd.DataFrame({('b', 'd'): [4, 1]})
        tm.assert_frame_equal(result, expected) 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def hide_columns(self, subset):
        """
        Hide columns from rendering.

        .. versionadded:: 0.23.0

        Parameters
        ----------
        subset : IndexSlice
            An argument to ``DataFrame.loc`` that identifies which columns
            are hidden.

        Returns
        -------
        self : Styler
        """
        subset = _non_reducing_slice(subset)
        hidden_df = self.data.loc[subset]
        self.hidden_columns = self.columns.get_indexer_for(hidden_df.columns)
        return self

    # -----------------------------------------------------------------------
    # A collection of "builtin" styles
    # ----------------------------------------------------------------------- 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def hide_columns(self, subset):
        """
        Hide columns from rendering.

        .. versionadded:: 0.23.0

        Parameters
        ----------
        subset: IndexSlice
            An argument to ``DataFrame.loc`` that identifies which columns
            are hidden.

        Returns
        -------
        self : Styler
        """
        subset = _non_reducing_slice(subset)
        hidden_df = self.data.loc[subset]
        self.hidden_columns = self.columns.get_indexer_for(hidden_df.columns)
        return self

    # -----------------------------------------------------------------------
    # A collection of "builtin" styles
    # ----------------------------------------------------------------------- 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def test_non_reducing_slice(self):
        df = pd.DataFrame([[0, 1], [2, 3]])

        slices = [
            # pd.IndexSlice[:, :],
            pd.IndexSlice[:, 1],
            pd.IndexSlice[1, :],
            pd.IndexSlice[[1], [1]],
            pd.IndexSlice[1, [1]],
            pd.IndexSlice[[1], 1],
            pd.IndexSlice[1],
            pd.IndexSlice[1, 1],
            slice(None, None, None),
            [0, 1],
            np.array([0, 1]),
            pd.Series([0, 1])
        ]
        for slice_ in slices:
            tslice_ = _non_reducing_slice(slice_)
            assert isinstance(df.loc[tslice_], DataFrame) 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def _apply(self, func, axis=0, subset=None, **kwargs):
        subset = slice(None) if subset is None else subset
        subset = _non_reducing_slice(subset)
        data = self.data.loc[subset]
        if axis is not None:
            result = data.apply(func, axis=axis,
                                result_type='expand', **kwargs)
            result.columns = data.columns
        else:
            result = func(data, **kwargs)
            if not isinstance(result, pd.DataFrame):
                raise TypeError(
                    "Function {func!r} must return a DataFrame when "
                    "passed to `Styler.apply` with axis=None"
                    .format(func=func))
            if not (result.index.equals(data.index) and
                    result.columns.equals(data.columns)):
                msg = ('Result of {func!r} must have identical index and '
                       'columns as the input'.format(func=func))
                raise ValueError(msg)

        result_shape = result.shape
        expected_shape = self.data.loc[subset].shape
        if result_shape != expected_shape:
            msg = ("Function {func!r} returned the wrong shape.\n"
                   "Result has shape: {res}\n"
                   "Expected shape:   {expect}".format(func=func,
                                                       res=result.shape,
                                                       expect=expected_shape))
            raise ValueError(msg)
        self._update_ctx(result)
        return self 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def _applymap(self, func, subset=None, **kwargs):
        func = partial(func, **kwargs)  # applymap doesn't take kwargs?
        if subset is None:
            subset = pd.IndexSlice[:]
        subset = _non_reducing_slice(subset)
        result = self.data.loc[subset].applymap(func)
        self._update_ctx(result)
        return self 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
                            subset=None):
        """
        Color the background in a gradient according to
        the data in each column (optionally row).
        Requires matplotlib.

        Parameters
        ----------
        cmap: str or colormap
            matplotlib colormap
        low, high: float
            compress the range by these values.
        axis: int or str
            1 or 'columns' for columnwise, 0 or 'index' for rowwise
        subset: IndexSlice
            a valid slice for ``data`` to limit the style application to

        Returns
        -------
        self : Styler

        Notes
        -----
        Tune ``low`` and ``high`` to keep the text legible by
        not using the entire range of the color map. These extend
        the range of the data by ``low * (x.max() - x.min())``
        and ``high * (x.max() - x.min())`` before normalizing.
        """
        subset = _maybe_numeric_slice(self.data, subset)
        subset = _non_reducing_slice(subset)
        self.apply(self._background_gradient, cmap=cmap, subset=subset,
                   axis=axis, low=low, high=high)
        return self 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def _apply(self, func, axis=0, subset=None, **kwargs):
        subset = slice(None) if subset is None else subset
        subset = _non_reducing_slice(subset)
        data = self.data.loc[subset]
        if axis is not None:
            result = data.apply(func, axis=axis, **kwargs)
        else:
            result = func(data, **kwargs)
            if not isinstance(result, pd.DataFrame):
                raise TypeError(
                    "Function {func!r} must return a DataFrame when "
                    "passed to `Styler.apply` with axis=None"
                    .format(func=func))
            if not (result.index.equals(data.index) and
                    result.columns.equals(data.columns)):
                msg = ('Result of {func!r} must have identical index and '
                       'columns as the input'.format(func=func))
                raise ValueError(msg)

        result_shape = result.shape
        expected_shape = self.data.loc[subset].shape
        if result_shape != expected_shape:
            msg = ("Function {func!r} returned the wrong shape.\n"
                   "Result has shape: {res}\n"
                   "Expected shape:   {expect}".format(func=func,
                                                       res=result.shape,
                                                       expect=expected_shape))
            raise ValueError(msg)
        self._update_ctx(result)
        return self 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
                            subset=None):
        """
        Color the background in a gradient according to
        the data in each column (optionally row).
        Requires matplotlib.

        .. versionadded:: 0.17.1

        Parameters
        ----------
        cmap: str or colormap
            matplotlib colormap
        low, high: float
            compress the range by these values.
        axis: int or str
            1 or 'columns' for columnwise, 0 or 'index' for rowwise
        subset: IndexSlice
            a valid slice for ``data`` to limit the style application to

        Returns
        -------
        self : Styler

        Notes
        -----
        Tune ``low`` and ``high`` to keep the text legible by
        not using the entire range of the color map. These extend
        the range of the data by ``low * (x.max() - x.min())``
        and ``high * (x.max() - x.min())`` before normalizing.
        """
        subset = _maybe_numeric_slice(self.data, subset)
        subset = _non_reducing_slice(subset)
        self.apply(self._background_gradient, cmap=cmap, subset=subset,
                   axis=axis, low=low, high=high)
        return self 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def _highlight_handler(self, subset=None, color='yellow', axis=None,
                           max_=True):
        subset = _non_reducing_slice(_maybe_numeric_slice(self.data, subset))
        self.apply(self._highlight_extrema, color=color, axis=axis,
                   subset=subset, max_=max_)
        return self 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def test_list_slice(self):
        # like dataframe getitem
        slices = [['A'], pd.Series(['A']), np.array(['A'])]
        df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['A', 'B'])
        expected = pd.IndexSlice[:, ['A']]
        for subset in slices:
            result = _non_reducing_slice(subset)
            tm.assert_frame_equal(df.loc[result], df.loc[expected]) 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
                            subset=None, text_color_threshold=0.408):
        """
        Color the background in a gradient according to
        the data in each column (optionally row).

        Requires matplotlib.

        Parameters
        ----------
        cmap : str or colormap
            matplotlib colormap
        low, high : float
            compress the range by these values.
        axis : int or str
            1 or 'columns' for columnwise, 0 or 'index' for rowwise
        subset : IndexSlice
            a valid slice for ``data`` to limit the style application to
        text_color_threshold : float or int
            luminance threshold for determining text color. Facilitates text
            visibility across varying background colors. From 0 to 1.
            0 = all text is dark colored, 1 = all text is light colored.

            .. versionadded:: 0.24.0

        Returns
        -------
        self : Styler

        Raises
        ------
        ValueError
            If ``text_color_threshold`` is not a value from 0 to 1.

        Notes
        -----
        Set ``text_color_threshold`` or tune ``low`` and ``high`` to keep the
        text legible by not using the entire range of the color map. The range
        of the data is extended by ``low * (x.max() - x.min())`` and ``high *
        (x.max() - x.min())`` before normalizing.
        """
        subset = _maybe_numeric_slice(self.data, subset)
        subset = _non_reducing_slice(subset)
        self.apply(self._background_gradient, cmap=cmap, subset=subset,
                   axis=axis, low=low, high=high,
                   text_color_threshold=text_color_threshold)
        return self 

# 需要導入模塊: from pandas.core import indexing [as 別名]
# 或者: from pandas.core.indexing import _non_reducing_slice [as 別名]
def bar(self, subset=None, axis=0, color='#d65f5f', width=100,
            align='left'):
        """
        Color the background ``color`` proptional to the values in each column.
        Excludes non-numeric data by default.

        Parameters
        ----------
        subset: IndexSlice, default None
            a valid slice for ``data`` to limit the style application to
        axis: int
        color: str or 2-tuple/list
            If a str is passed, the color is the same for both
            negative and positive numbers. If 2-tuple/list is used, the
            first element is the color_negative and the second is the
            color_positive (eg: ['#d65f5f', '#5fba7d'])
        width: float
            A number between 0 or 100. The largest value will cover ``width``
            percent of the cell's width
        align : {'left', 'zero',' mid'}, default 'left'
            - 'left' : the min value starts at the left of the cell
            - 'zero' : a value of zero is located at the center of the cell
            - 'mid' : the center of the cell is at (max-min)/2, or
              if values are all negative (positive) the zero is aligned
              at the right (left) of the cell

              .. versionadded:: 0.20.0

        Returns
        -------
        self : Styler
        """
        subset = _maybe_numeric_slice(self.data, subset)
        subset = _non_reducing_slice(subset)

        base = 'width: 10em; height: 80%;'

        if not(is_list_like(color)):
            color = [color, color]
        elif len(color) == 1:
            color = [color[0], color[0]]
        elif len(color) > 2:
            msg = ("Must pass `color` as string or a list-like"
                   " of length 2: [`color_negative`, `color_positive`]\n"
                   "(eg: color=['#d65f5f', '#5fba7d'])")
            raise ValueError(msg)

        if align == 'left':
            self.apply(self._bar_left, subset=subset, axis=axis, color=color,
                       width=width, base=base)
        elif align == 'zero':
            self.apply(self._bar_center_zero, subset=subset, axis=axis,
                       color=color, width=width, base=base)
        elif align == 'mid':
            self.apply(self._bar_center_mid, subset=subset, axis=axis,
                       color=color, width=width, base=base)
        else:
            msg = ("`align` must be one of {'left', 'zero',' mid'}")
            raise ValueError(msg)

        return self