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120 lines
4.4 KiB
120 lines
4.4 KiB
""" Test cases for GroupBy.plot """
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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from pandas import (
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DataFrame,
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Index,
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Series,
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)
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import pandas._testing as tm
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from pandas.tests.plotting.common import TestPlotBase
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pytestmark = pytest.mark.slow
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@td.skip_if_no_mpl
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class TestDataFrameGroupByPlots(TestPlotBase):
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def test_series_groupby_plotting_nominally_works(self):
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n = 10
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weight = Series(np.random.normal(166, 20, size=n))
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height = Series(np.random.normal(60, 10, size=n))
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with tm.RNGContext(42):
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gender = np.random.choice(["male", "female"], size=n)
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weight.groupby(gender).plot()
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tm.close()
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height.groupby(gender).hist()
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tm.close()
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# Regression test for GH8733
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height.groupby(gender).plot(alpha=0.5)
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tm.close()
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def test_plotting_with_float_index_works(self):
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# GH 7025
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df = DataFrame(
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{"def": [1, 1, 1, 2, 2, 2, 3, 3, 3], "val": np.random.randn(9)},
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index=[1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
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)
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df.groupby("def")["val"].plot()
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tm.close()
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df.groupby("def")["val"].apply(lambda x: x.plot())
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tm.close()
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def test_hist_single_row(self):
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# GH10214
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bins = np.arange(80, 100 + 2, 1)
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df = DataFrame({"Name": ["AAA", "BBB"], "ByCol": [1, 2], "Mark": [85, 89]})
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df["Mark"].hist(by=df["ByCol"], bins=bins)
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df = DataFrame({"Name": ["AAA"], "ByCol": [1], "Mark": [85]})
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df["Mark"].hist(by=df["ByCol"], bins=bins)
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def test_plot_submethod_works(self):
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df = DataFrame({"x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 2, 1], "z": list("ababa")})
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df.groupby("z").plot.scatter("x", "y")
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tm.close()
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df.groupby("z")["x"].plot.line()
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tm.close()
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def test_plot_kwargs(self):
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df = DataFrame({"x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 2, 1], "z": list("ababa")})
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res = df.groupby("z").plot(kind="scatter", x="x", y="y")
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# check that a scatter plot is effectively plotted: the axes should
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# contain a PathCollection from the scatter plot (GH11805)
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assert len(res["a"].collections) == 1
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res = df.groupby("z").plot.scatter(x="x", y="y")
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assert len(res["a"].collections) == 1
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@pytest.mark.parametrize("column, expected_axes_num", [(None, 2), ("b", 1)])
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def test_groupby_hist_frame_with_legend(self, column, expected_axes_num):
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# GH 6279 - DataFrameGroupBy histogram can have a legend
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expected_layout = (1, expected_axes_num)
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expected_labels = column or [["a"], ["b"]]
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index = Index(15 * ["1"] + 15 * ["2"], name="c")
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df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
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g = df.groupby("c")
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for axes in g.hist(legend=True, column=column):
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self._check_axes_shape(
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axes, axes_num=expected_axes_num, layout=expected_layout
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)
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for ax, expected_label in zip(axes[0], expected_labels):
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self._check_legend_labels(ax, expected_label)
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@pytest.mark.parametrize("column", [None, "b"])
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def test_groupby_hist_frame_with_legend_raises(self, column):
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# GH 6279 - DataFrameGroupBy histogram with legend and label raises
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index = Index(15 * ["1"] + 15 * ["2"], name="c")
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df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
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g = df.groupby("c")
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with pytest.raises(ValueError, match="Cannot use both legend and label"):
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g.hist(legend=True, column=column, label="d")
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def test_groupby_hist_series_with_legend(self):
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# GH 6279 - SeriesGroupBy histogram can have a legend
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index = Index(15 * ["1"] + 15 * ["2"], name="c")
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df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
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g = df.groupby("c")
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for ax in g["a"].hist(legend=True):
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self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
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self._check_legend_labels(ax, ["1", "2"])
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def test_groupby_hist_series_with_legend_raises(self):
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# GH 6279 - SeriesGroupBy histogram with legend and label raises
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index = Index(15 * ["1"] + 15 * ["2"], name="c")
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df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
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g = df.groupby("c")
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with pytest.raises(ValueError, match="Cannot use both legend and label"):
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g.hist(legend=True, label="d")
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