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| import numpy as np
| import pytest
|
| from pandas import (
| DataFrame,
| IndexSlice,
| Series,
| )
|
| pytest.importorskip("matplotlib")
| pytest.importorskip("jinja2")
|
| import matplotlib as mpl
|
| from pandas.io.formats.style import Styler
|
|
| @pytest.fixture
| def df():
| return DataFrame([[1, 2], [2, 4]], columns=["A", "B"])
|
|
| @pytest.fixture
| def styler(df):
| return Styler(df, uuid_len=0)
|
|
| @pytest.fixture
| def df_blank():
| return DataFrame([[0, 0], [0, 0]], columns=["A", "B"], index=["X", "Y"])
|
|
| @pytest.fixture
| def styler_blank(df_blank):
| return Styler(df_blank, uuid_len=0)
|
|
| @pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
| def test_function_gradient(styler, f):
| for c_map in [None, "YlOrRd"]:
| result = getattr(styler, f)(cmap=c_map)._compute().ctx
| assert all("#" in x[0][1] for x in result.values())
| assert result[(0, 0)] == result[(0, 1)]
| assert result[(1, 0)] == result[(1, 1)]
|
|
| @pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
| def test_background_gradient_color(styler, f):
| result = getattr(styler, f)(subset=IndexSlice[1, "A"])._compute().ctx
| if f == "background_gradient":
| assert result[(1, 0)] == [("background-color", "#fff7fb"), ("color", "#000000")]
| elif f == "text_gradient":
| assert result[(1, 0)] == [("color", "#fff7fb")]
|
|
| @pytest.mark.parametrize(
| "axis, expected",
| [
| (0, ["low", "low", "high", "high"]),
| (1, ["low", "high", "low", "high"]),
| (None, ["low", "mid", "mid", "high"]),
| ],
| )
| @pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
| def test_background_gradient_axis(styler, axis, expected, f):
| if f == "background_gradient":
| colors = {
| "low": [("background-color", "#f7fbff"), ("color", "#000000")],
| "mid": [("background-color", "#abd0e6"), ("color", "#000000")],
| "high": [("background-color", "#08306b"), ("color", "#f1f1f1")],
| }
| elif f == "text_gradient":
| colors = {
| "low": [("color", "#f7fbff")],
| "mid": [("color", "#abd0e6")],
| "high": [("color", "#08306b")],
| }
| result = getattr(styler, f)(cmap="Blues", axis=axis)._compute().ctx
| for i, cell in enumerate([(0, 0), (0, 1), (1, 0), (1, 1)]):
| assert result[cell] == colors[expected[i]]
|
|
| @pytest.mark.parametrize(
| "cmap, expected",
| [
| (
| "PuBu",
| {
| (4, 5): [("background-color", "#86b0d3"), ("color", "#000000")],
| (4, 6): [("background-color", "#83afd3"), ("color", "#f1f1f1")],
| },
| ),
| (
| "YlOrRd",
| {
| (4, 8): [("background-color", "#fd913e"), ("color", "#000000")],
| (4, 9): [("background-color", "#fd8f3d"), ("color", "#f1f1f1")],
| },
| ),
| (
| None,
| {
| (7, 0): [("background-color", "#48c16e"), ("color", "#f1f1f1")],
| (7, 1): [("background-color", "#4cc26c"), ("color", "#000000")],
| },
| ),
| ],
| )
| def test_text_color_threshold(cmap, expected):
| # GH 39888
| df = DataFrame(np.arange(100).reshape(10, 10))
| result = df.style.background_gradient(cmap=cmap, axis=None)._compute().ctx
| for k in expected.keys():
| assert result[k] == expected[k]
|
|
| def test_background_gradient_vmin_vmax():
| # GH 12145
| df = DataFrame(range(5))
| ctx = df.style.background_gradient(vmin=1, vmax=3)._compute().ctx
| assert ctx[(0, 0)] == ctx[(1, 0)]
| assert ctx[(4, 0)] == ctx[(3, 0)]
|
|
| def test_background_gradient_int64():
| # GH 28869
| df1 = Series(range(3)).to_frame()
| df2 = Series(range(3), dtype="Int64").to_frame()
| ctx1 = df1.style.background_gradient()._compute().ctx
| ctx2 = df2.style.background_gradient()._compute().ctx
| assert ctx2[(0, 0)] == ctx1[(0, 0)]
| assert ctx2[(1, 0)] == ctx1[(1, 0)]
| assert ctx2[(2, 0)] == ctx1[(2, 0)]
|
|
| @pytest.mark.parametrize(
| "axis, gmap, expected",
| [
| (
| 0,
| [1, 2],
| {
| (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
| (1, 0): [("background-color", "#023858"), ("color", "#f1f1f1")],
| (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")],
| (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
| },
| ),
| (
| 1,
| [1, 2],
| {
| (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
| (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
| (0, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
| (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
| },
| ),
| (
| None,
| np.array([[2, 1], [1, 2]]),
| {
| (0, 0): [("background-color", "#023858"), ("color", "#f1f1f1")],
| (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
| (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")],
| (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
| },
| ),
| ],
| )
| def test_background_gradient_gmap_array(styler_blank, axis, gmap, expected):
| # tests when gmap is given as a sequence and converted to ndarray
| result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute().ctx
| assert result == expected
|
|
| @pytest.mark.parametrize(
| "gmap, axis", [([1, 2, 3], 0), ([1, 2], 1), (np.array([[1, 2], [1, 2]]), None)]
| )
| def test_background_gradient_gmap_array_raises(gmap, axis):
| # test when gmap as converted ndarray is bad shape
| df = DataFrame([[0, 0, 0], [0, 0, 0]])
| msg = "supplied 'gmap' is not correct shape"
| with pytest.raises(ValueError, match=msg):
| df.style.background_gradient(gmap=gmap, axis=axis)._compute()
|
|
| @pytest.mark.parametrize(
| "gmap",
| [
| DataFrame( # reverse the columns
| [[2, 1], [1, 2]], columns=["B", "A"], index=["X", "Y"]
| ),
| DataFrame( # reverse the index
| [[2, 1], [1, 2]], columns=["A", "B"], index=["Y", "X"]
| ),
| DataFrame( # reverse the index and columns
| [[1, 2], [2, 1]], columns=["B", "A"], index=["Y", "X"]
| ),
| DataFrame( # add unnecessary columns
| [[1, 2, 3], [2, 1, 3]], columns=["A", "B", "C"], index=["X", "Y"]
| ),
| DataFrame( # add unnecessary index
| [[1, 2], [2, 1], [3, 3]], columns=["A", "B"], index=["X", "Y", "Z"]
| ),
| ],
| )
| @pytest.mark.parametrize(
| "subset, exp_gmap", # exp_gmap is underlying map DataFrame should conform to
| [
| (None, [[1, 2], [2, 1]]),
| (["A"], [[1], [2]]), # slice only column "A" in data and gmap
| (["B", "A"], [[2, 1], [1, 2]]), # reverse the columns in data
| (IndexSlice["X", :], [[1, 2]]), # slice only index "X" in data and gmap
| (IndexSlice[["Y", "X"], :], [[2, 1], [1, 2]]), # reverse the index in data
| ],
| )
| def test_background_gradient_gmap_dataframe_align(styler_blank, gmap, subset, exp_gmap):
| # test gmap given as DataFrame that it aligns to the data including subset
| expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap, subset=subset)
| result = styler_blank.background_gradient(axis=None, gmap=gmap, subset=subset)
| assert expected._compute().ctx == result._compute().ctx
|
|
| @pytest.mark.parametrize(
| "gmap, axis, exp_gmap",
| [
| (Series([2, 1], index=["Y", "X"]), 0, [[1, 1], [2, 2]]), # revrse the index
| (Series([2, 1], index=["B", "A"]), 1, [[1, 2], [1, 2]]), # revrse the cols
| (Series([1, 2, 3], index=["X", "Y", "Z"]), 0, [[1, 1], [2, 2]]), # add idx
| (Series([1, 2, 3], index=["A", "B", "C"]), 1, [[1, 2], [1, 2]]), # add col
| ],
| )
| def test_background_gradient_gmap_series_align(styler_blank, gmap, axis, exp_gmap):
| # test gmap given as Series that it aligns to the data including subset
| expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap)._compute()
| result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute()
| assert expected.ctx == result.ctx
|
|
| @pytest.mark.parametrize(
| "gmap, axis",
| [
| (DataFrame([[1, 2], [2, 1]], columns=["A", "B"], index=["X", "Y"]), 1),
| (DataFrame([[1, 2], [2, 1]], columns=["A", "B"], index=["X", "Y"]), 0),
| ],
| )
| def test_background_gradient_gmap_wrong_dataframe(styler_blank, gmap, axis):
| # test giving a gmap in DataFrame but with wrong axis
| msg = "'gmap' is a DataFrame but underlying data for operations is a Series"
| with pytest.raises(ValueError, match=msg):
| styler_blank.background_gradient(gmap=gmap, axis=axis)._compute()
|
|
| def test_background_gradient_gmap_wrong_series(styler_blank):
| # test giving a gmap in Series form but with wrong axis
| msg = "'gmap' is a Series but underlying data for operations is a DataFrame"
| gmap = Series([1, 2], index=["X", "Y"])
| with pytest.raises(ValueError, match=msg):
| styler_blank.background_gradient(gmap=gmap, axis=None)._compute()
|
|
| def test_background_gradient_nullable_dtypes():
| # GH 50712
| df1 = DataFrame([[1], [0], [np.nan]], dtype=float)
| df2 = DataFrame([[1], [0], [None]], dtype="Int64")
|
| ctx1 = df1.style.background_gradient()._compute().ctx
| ctx2 = df2.style.background_gradient()._compute().ctx
| assert ctx1 == ctx2
|
|
| @pytest.mark.parametrize(
| "cmap",
| ["PuBu", mpl.colormaps["PuBu"]],
| )
| def test_bar_colormap(cmap):
| data = DataFrame([[1, 2], [3, 4]])
| ctx = data.style.bar(cmap=cmap, axis=None)._compute().ctx
| pubu_colors = {
| (0, 0): "#d0d1e6",
| (1, 0): "#056faf",
| (0, 1): "#73a9cf",
| (1, 1): "#023858",
| }
| for k, v in pubu_colors.items():
| assert v in ctx[k][1][1]
|
|
| def test_bar_color_raises(df):
| msg = "`color` must be string or list or tuple of 2 strings"
| with pytest.raises(ValueError, match=msg):
| df.style.bar(color={"a", "b"}).to_html()
| with pytest.raises(ValueError, match=msg):
| df.style.bar(color=["a", "b", "c"]).to_html()
|
| msg = "`color` and `cmap` cannot both be given"
| with pytest.raises(ValueError, match=msg):
| df.style.bar(color="something", cmap="something else").to_html()
|
|
| @pytest.mark.parametrize(
| "plot_method",
| ["scatter", "hexbin"],
| )
| def test_pass_colormap_instance(df, plot_method):
| # https://github.com/pandas-dev/pandas/issues/49374
| cmap = mpl.colors.ListedColormap([[1, 1, 1], [0, 0, 0]])
| df["c"] = df.A + df.B
| kwargs = dict(x="A", y="B", c="c", colormap=cmap)
| if plot_method == "hexbin":
| kwargs["C"] = kwargs.pop("c")
| getattr(df.plot, plot_method)(**kwargs)
|
|
