def test_non_reducing_multi_slice_on_multiindex(self, slice_):
# GH 33562
cols = pd.MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]])
idxs = pd.MultiIndex.from_product([["U", "V"], ["W", "X"], ["Y", "Z"]])
df = DataFrame(np.arange(64).reshape(8, 8), columns=cols, index=idxs)
expected = df.loc[slice_]
result = df.loc[_non_reducing_slice(slice_)]
tm.assert_frame_equal(result, expected)
def test_list_slice(self, box):
# like dataframe getitem
subset = box(["A"])
df = DataFrame({"A": [1, 2], "B": [3, 4]}, index=["A", "B"])
expected = pd.IndexSlice[:, ["A"]]
result = _non_reducing_slice(subset)
tm.assert_frame_equal(df.loc[result], df.loc[expected])
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 = DataFrame(dic, index=[0, 1])
idx = pd.IndexSlice
slice_ = idx[:, idx["b", "d"]]
tslice_ = _non_reducing_slice(slice_)
result = df.loc[tslice_]
expected = DataFrame({("b", "d"): [4, 1]})
tm.assert_frame_equal(result, expected)
def absolute_background_gradient(
self,
cmap="YlOrRd",
low=0,
high=0,
axis=0,
subset=None,
text_color_threshold=0.408,
vmin: Optional[float] = 0,
vmax: Optional[float] = None,
):
"""
Color the background in a gradient style,
based on the magnitude (absolute value).
The background color is determined according
to the data in each column (optionally row).
Requires matplotlib.
Parameters
----------
cmap : str or colormap
Matplotlib colormap.
low : float
Compress the range by the low.
high : float
Compress the range by the high.
axis : {0 or 'index', 1 or 'columns', None}, default 0
Apply to each column (``axis=0`` or ``'index'``), to each row
(``axis=1`` or ``'columns'``), or to the entire DataFrame at once
with ``axis=None``.
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.
vmin : float, default 0
Minimum data value that corresponds to colormap minimum value.
When None, the minimum value of the data will be used.
vmax : float, optional
Maximum data value that corresponds to colormap maximum value.
When None (default): the maximum value of the data will be used.
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._absolute_background_gradient,
cmap=cmap,
subset=subset,
axis=axis,
low=low,
high=high,
text_color_threshold=text_color_threshold,
vmin=vmin,
vmax=vmax,
)
return self
def test_non_reducing_slice(self, slc):
df = DataFrame([[0, 1], [2, 3]])
tslice_ = _non_reducing_slice(slc)
assert isinstance(df.loc[tslice_], DataFrame)