""" Test cases for .boxplot method """
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import itertools
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import string
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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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MultiIndex,
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Series,
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date_range,
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plotting,
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timedelta_range,
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)
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import pandas._testing as tm
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from pandas.tests.plotting.common import (
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TestPlotBase,
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_check_plot_works,
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)
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from pandas.io.formats.printing import pprint_thing
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@td.skip_if_no_mpl
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class TestDataFramePlots(TestPlotBase):
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def test_stacked_boxplot_set_axis(self):
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# GH2980
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import matplotlib.pyplot as plt
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n = 80
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df = DataFrame(
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{
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"Clinical": np.random.choice([0, 1, 2, 3], n),
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"Confirmed": np.random.choice([0, 1, 2, 3], n),
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"Discarded": np.random.choice([0, 1, 2, 3], n),
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},
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index=np.arange(0, n),
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)
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ax = df.plot(kind="bar", stacked=True)
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assert [int(x.get_text()) for x in ax.get_xticklabels()] == df.index.to_list()
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ax.set_xticks(np.arange(0, 80, 10))
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plt.draw() # Update changes
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assert [int(x.get_text()) for x in ax.get_xticklabels()] == list(
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np.arange(0, 80, 10)
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)
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@pytest.mark.slow
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def test_boxplot_legacy1(self):
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df = DataFrame(
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np.random.randn(6, 4),
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index=list(string.ascii_letters[:6]),
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columns=["one", "two", "three", "four"],
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)
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df["indic"] = ["foo", "bar"] * 3
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df["indic2"] = ["foo", "bar", "foo"] * 2
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_check_plot_works(df.boxplot, return_type="dict")
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_check_plot_works(df.boxplot, column=["one", "two"], return_type="dict")
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# _check_plot_works adds an ax so catch warning. see GH #13188
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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_check_plot_works(df.boxplot, column=["one", "two"], by="indic")
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_check_plot_works(df.boxplot, column="one", by=["indic", "indic2"])
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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_check_plot_works(df.boxplot, by="indic")
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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_check_plot_works(df.boxplot, by=["indic", "indic2"])
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_check_plot_works(plotting._core.boxplot, data=df["one"], return_type="dict")
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_check_plot_works(df.boxplot, notch=1, return_type="dict")
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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_check_plot_works(df.boxplot, by="indic", notch=1)
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def test_boxplot_legacy2(self):
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df = DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
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df["X"] = Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
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df["Y"] = Series(["A"] * 10)
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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_check_plot_works(df.boxplot, by="X")
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# When ax is supplied and required number of axes is 1,
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# passed ax should be used:
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fig, ax = self.plt.subplots()
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axes = df.boxplot("Col1", by="X", ax=ax)
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ax_axes = ax.axes
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assert ax_axes is axes
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fig, ax = self.plt.subplots()
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axes = df.groupby("Y").boxplot(ax=ax, return_type="axes")
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ax_axes = ax.axes
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assert ax_axes is axes["A"]
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# Multiple columns with an ax argument should use same figure
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fig, ax = self.plt.subplots()
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with tm.assert_produces_warning(UserWarning):
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axes = df.boxplot(
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column=["Col1", "Col2"], by="X", ax=ax, return_type="axes"
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)
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assert axes["Col1"].get_figure() is fig
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# When by is None, check that all relevant lines are present in the
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# dict
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fig, ax = self.plt.subplots()
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d = df.boxplot(ax=ax, return_type="dict")
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lines = list(itertools.chain.from_iterable(d.values()))
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assert len(ax.get_lines()) == len(lines)
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def test_boxplot_return_type_none(self, hist_df):
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# GH 12216; return_type=None & by=None -> axes
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result = hist_df.boxplot()
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assert isinstance(result, self.plt.Axes)
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def test_boxplot_return_type_legacy(self):
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# API change in https://github.com/pandas-dev/pandas/pull/7096
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df = DataFrame(
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np.random.randn(6, 4),
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index=list(string.ascii_letters[:6]),
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columns=["one", "two", "three", "four"],
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)
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msg = "return_type must be {'axes', 'dict', 'both'}"
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with pytest.raises(ValueError, match=msg):
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df.boxplot(return_type="NOT_A_TYPE")
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result = df.boxplot()
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self._check_box_return_type(result, "axes")
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with tm.assert_produces_warning(False):
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result = df.boxplot(return_type="dict")
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self._check_box_return_type(result, "dict")
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with tm.assert_produces_warning(False):
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result = df.boxplot(return_type="axes")
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self._check_box_return_type(result, "axes")
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with tm.assert_produces_warning(False):
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result = df.boxplot(return_type="both")
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self._check_box_return_type(result, "both")
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def test_boxplot_axis_limits(self, hist_df):
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def _check_ax_limits(col, ax):
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y_min, y_max = ax.get_ylim()
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assert y_min <= col.min()
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assert y_max >= col.max()
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df = hist_df.copy()
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df["age"] = np.random.randint(1, 20, df.shape[0])
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# One full row
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height_ax, weight_ax = df.boxplot(["height", "weight"], by="category")
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_check_ax_limits(df["height"], height_ax)
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_check_ax_limits(df["weight"], weight_ax)
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assert weight_ax._sharey == height_ax
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# Two rows, one partial
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p = df.boxplot(["height", "weight", "age"], by="category")
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height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0]
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dummy_ax = p[1, 1]
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_check_ax_limits(df["height"], height_ax)
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_check_ax_limits(df["weight"], weight_ax)
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_check_ax_limits(df["age"], age_ax)
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assert weight_ax._sharey == height_ax
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assert age_ax._sharey == height_ax
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assert dummy_ax._sharey is None
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def test_boxplot_empty_column(self):
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df = DataFrame(np.random.randn(20, 4))
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df.loc[:, 0] = np.nan
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_check_plot_works(df.boxplot, return_type="axes")
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def test_figsize(self):
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df = DataFrame(np.random.rand(10, 5), columns=["A", "B", "C", "D", "E"])
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result = df.boxplot(return_type="axes", figsize=(12, 8))
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assert result.figure.bbox_inches.width == 12
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assert result.figure.bbox_inches.height == 8
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def test_fontsize(self):
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df = DataFrame({"a": [1, 2, 3, 4, 5, 6]})
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self._check_ticks_props(
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df.boxplot("a", fontsize=16), xlabelsize=16, ylabelsize=16
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)
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def test_boxplot_numeric_data(self):
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# GH 22799
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df = DataFrame(
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{
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"a": date_range("2012-01-01", periods=100),
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"b": np.random.randn(100),
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"c": np.random.randn(100) + 2,
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"d": date_range("2012-01-01", periods=100).astype(str),
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"e": date_range("2012-01-01", periods=100, tz="UTC"),
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"f": timedelta_range("1 days", periods=100),
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}
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)
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ax = df.plot(kind="box")
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assert [x.get_text() for x in ax.get_xticklabels()] == ["b", "c"]
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@pytest.mark.parametrize(
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"colors_kwd, expected",
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[
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(
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{"boxes": "r", "whiskers": "b", "medians": "g", "caps": "c"},
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{"boxes": "r", "whiskers": "b", "medians": "g", "caps": "c"},
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),
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({"boxes": "r"}, {"boxes": "r"}),
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("r", {"boxes": "r", "whiskers": "r", "medians": "r", "caps": "r"}),
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],
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)
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def test_color_kwd(self, colors_kwd, expected):
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# GH: 26214
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df = DataFrame(np.random.rand(10, 2))
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result = df.boxplot(color=colors_kwd, return_type="dict")
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for k, v in expected.items():
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assert result[k][0].get_color() == v
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@pytest.mark.parametrize(
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"scheme,expected",
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[
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(
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"dark_background",
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{
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"boxes": "#8dd3c7",
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"whiskers": "#8dd3c7",
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"medians": "#bfbbd9",
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"caps": "#8dd3c7",
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},
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),
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(
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"default",
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{
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"boxes": "#1f77b4",
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"whiskers": "#1f77b4",
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"medians": "#2ca02c",
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"caps": "#1f77b4",
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},
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),
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],
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)
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def test_colors_in_theme(self, scheme, expected):
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# GH: 40769
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df = DataFrame(np.random.rand(10, 2))
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import matplotlib.pyplot as plt
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plt.style.use(scheme)
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result = df.plot.box(return_type="dict")
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for k, v in expected.items():
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assert result[k][0].get_color() == v
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@pytest.mark.parametrize(
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"dict_colors, msg",
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[({"boxes": "r", "invalid_key": "r"}, "invalid key 'invalid_key'")],
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)
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def test_color_kwd_errors(self, dict_colors, msg):
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# GH: 26214
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df = DataFrame(np.random.rand(10, 2))
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with pytest.raises(ValueError, match=msg):
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df.boxplot(color=dict_colors, return_type="dict")
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@pytest.mark.parametrize(
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"props, expected",
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[
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("boxprops", "boxes"),
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("whiskerprops", "whiskers"),
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("capprops", "caps"),
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("medianprops", "medians"),
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],
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)
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def test_specified_props_kwd(self, props, expected):
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# GH 30346
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df = DataFrame({k: np.random.random(100) for k in "ABC"})
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kwd = {props: {"color": "C1"}}
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result = df.boxplot(return_type="dict", **kwd)
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assert result[expected][0].get_color() == "C1"
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@pytest.mark.parametrize("vert", [True, False])
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def test_plot_xlabel_ylabel(self, vert):
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df = DataFrame(
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{
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"a": np.random.randn(100),
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"b": np.random.randn(100),
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"group": np.random.choice(["group1", "group2"], 100),
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}
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)
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xlabel, ylabel = "x", "y"
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ax = df.plot(kind="box", vert=vert, xlabel=xlabel, ylabel=ylabel)
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assert ax.get_xlabel() == xlabel
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assert ax.get_ylabel() == ylabel
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@pytest.mark.parametrize("vert", [True, False])
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def test_boxplot_xlabel_ylabel(self, vert):
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df = DataFrame(
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{
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"a": np.random.randn(100),
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"b": np.random.randn(100),
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"group": np.random.choice(["group1", "group2"], 100),
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}
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)
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xlabel, ylabel = "x", "y"
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ax = df.boxplot(vert=vert, xlabel=xlabel, ylabel=ylabel)
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assert ax.get_xlabel() == xlabel
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assert ax.get_ylabel() == ylabel
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@pytest.mark.parametrize("vert", [True, False])
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def test_boxplot_group_xlabel_ylabel(self, vert):
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df = DataFrame(
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{
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"a": np.random.randn(100),
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"b": np.random.randn(100),
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"group": np.random.choice(["group1", "group2"], 100),
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}
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)
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xlabel, ylabel = "x", "y"
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ax = df.boxplot(by="group", vert=vert, xlabel=xlabel, ylabel=ylabel)
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for subplot in ax:
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assert subplot.get_xlabel() == xlabel
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assert subplot.get_ylabel() == ylabel
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self.plt.close()
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ax = df.boxplot(by="group", vert=vert)
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for subplot in ax:
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target_label = subplot.get_xlabel() if vert else subplot.get_ylabel()
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assert target_label == pprint_thing(["group"])
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self.plt.close()
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@td.skip_if_no_mpl
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class TestDataFrameGroupByPlots(TestPlotBase):
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def test_boxplot_legacy1(self, hist_df):
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grouped = hist_df.groupby(by="gender")
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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axes = _check_plot_works(grouped.boxplot, return_type="axes")
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self._check_axes_shape(list(axes.values), axes_num=2, layout=(1, 2))
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axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
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self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
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@pytest.mark.slow
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def test_boxplot_legacy2(self):
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tuples = zip(string.ascii_letters[:10], range(10))
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df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
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grouped = df.groupby(level=1)
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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axes = _check_plot_works(grouped.boxplot, return_type="axes")
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self._check_axes_shape(list(axes.values), axes_num=10, layout=(4, 3))
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axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
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self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
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def test_boxplot_legacy3(self):
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tuples = zip(string.ascii_letters[:10], range(10))
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df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
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grouped = df.unstack(level=1).groupby(level=0, axis=1)
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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axes = _check_plot_works(grouped.boxplot, return_type="axes")
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self._check_axes_shape(list(axes.values), axes_num=3, layout=(2, 2))
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axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
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self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
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def test_grouped_plot_fignums(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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gender = np.random.RandomState(42).choice(["male", "female"], size=n)
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df = DataFrame({"height": height, "weight": weight, "gender": gender})
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gb = df.groupby("gender")
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res = gb.plot()
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assert len(self.plt.get_fignums()) == 2
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assert len(res) == 2
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tm.close()
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res = gb.boxplot(return_type="axes")
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assert len(self.plt.get_fignums()) == 1
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assert len(res) == 2
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tm.close()
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# now works with GH 5610 as gender is excluded
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res = df.groupby("gender").hist()
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tm.close()
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@pytest.mark.slow
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def test_grouped_box_return_type(self, hist_df):
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df = hist_df
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# old style: return_type=None
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result = df.boxplot(by="gender")
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assert isinstance(result, np.ndarray)
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self._check_box_return_type(
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result, None, expected_keys=["height", "weight", "category"]
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)
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# now for groupby
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result = df.groupby("gender").boxplot(return_type="dict")
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self._check_box_return_type(result, "dict", expected_keys=["Male", "Female"])
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columns2 = "X B C D A G Y N Q O".split()
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df2 = DataFrame(np.random.randn(50, 10), columns=columns2)
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categories2 = "A B C D E F G H I J".split()
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df2["category"] = categories2 * 5
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for t in ["dict", "axes", "both"]:
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returned = df.groupby("classroom").boxplot(return_type=t)
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self._check_box_return_type(returned, t, expected_keys=["A", "B", "C"])
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returned = df.boxplot(by="classroom", return_type=t)
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self._check_box_return_type(
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returned, t, expected_keys=["height", "weight", "category"]
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)
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returned = df2.groupby("category").boxplot(return_type=t)
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self._check_box_return_type(returned, t, expected_keys=categories2)
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returned = df2.boxplot(by="category", return_type=t)
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self._check_box_return_type(returned, t, expected_keys=columns2)
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@pytest.mark.slow
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def test_grouped_box_layout(self, hist_df):
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df = hist_df
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msg = "Layout of 1x1 must be larger than required size 2"
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with pytest.raises(ValueError, match=msg):
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df.boxplot(column=["weight", "height"], by=df.gender, layout=(1, 1))
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msg = "The 'layout' keyword is not supported when 'by' is None"
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with pytest.raises(ValueError, match=msg):
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df.boxplot(
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column=["height", "weight", "category"],
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layout=(2, 1),
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return_type="dict",
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)
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msg = "At least one dimension of layout must be positive"
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with pytest.raises(ValueError, match=msg):
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df.boxplot(column=["weight", "height"], by=df.gender, layout=(-1, -1))
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# _check_plot_works adds an ax so catch warning. see GH #13188
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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box = _check_plot_works(
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df.groupby("gender").boxplot, column="height", return_type="dict"
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2))
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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box = _check_plot_works(
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df.groupby("category").boxplot, column="height", return_type="dict"
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))
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|
# GH 6769
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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box = _check_plot_works(
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df.groupby("classroom").boxplot, column="height", return_type="dict"
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
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|
# GH 5897
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axes = df.boxplot(
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column=["height", "weight", "category"], by="gender", return_type="axes"
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
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for ax in [axes["height"]]:
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self._check_visible(ax.get_xticklabels(), visible=False)
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self._check_visible([ax.xaxis.get_label()], visible=False)
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for ax in [axes["weight"], axes["category"]]:
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self._check_visible(ax.get_xticklabels())
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self._check_visible([ax.xaxis.get_label()])
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|
box = df.groupby("classroom").boxplot(
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column=["height", "weight", "category"], return_type="dict"
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
|
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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box = _check_plot_works(
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df.groupby("category").boxplot,
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column="height",
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layout=(3, 2),
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return_type="dict",
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
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with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
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box = _check_plot_works(
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df.groupby("category").boxplot,
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column="height",
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layout=(3, -1),
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return_type="dict",
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
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box = df.boxplot(
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column=["height", "weight", "category"], by="gender", layout=(4, 1)
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1))
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box = df.boxplot(
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column=["height", "weight", "category"], by="gender", layout=(-1, 1)
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1))
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box = df.groupby("classroom").boxplot(
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column=["height", "weight", "category"], layout=(1, 4), return_type="dict"
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4))
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box = df.groupby("classroom").boxplot( # noqa
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column=["height", "weight", "category"], layout=(1, -1), return_type="dict"
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)
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self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3))
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|
@pytest.mark.slow
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def test_grouped_box_multiple_axes(self, hist_df):
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# GH 6970, GH 7069
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df = hist_df
|
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# check warning to ignore sharex / sharey
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# this check should be done in the first function which
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# passes multiple axes to plot, hist or boxplot
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# location should be changed if other test is added
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# which has earlier alphabetical order
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with tm.assert_produces_warning(UserWarning):
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fig, axes = self.plt.subplots(2, 2)
|
df.groupby("category").boxplot(column="height", return_type="axes", ax=axes)
|
self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))
|
|
fig, axes = self.plt.subplots(2, 3)
|
with tm.assert_produces_warning(UserWarning):
|
returned = df.boxplot(
|
column=["height", "weight", "category"],
|
by="gender",
|
return_type="axes",
|
ax=axes[0],
|
)
|
returned = np.array(list(returned.values))
|
self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
|
tm.assert_numpy_array_equal(returned, axes[0])
|
assert returned[0].figure is fig
|
|
# draw on second row
|
with tm.assert_produces_warning(UserWarning):
|
returned = df.groupby("classroom").boxplot(
|
column=["height", "weight", "category"], return_type="axes", ax=axes[1]
|
)
|
returned = np.array(list(returned.values))
|
self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
|
tm.assert_numpy_array_equal(returned, axes[1])
|
assert returned[0].figure is fig
|
|
msg = "The number of passed axes must be 3, the same as the output plot"
|
with pytest.raises(ValueError, match=msg):
|
fig, axes = self.plt.subplots(2, 3)
|
# pass different number of axes from required
|
with tm.assert_produces_warning(UserWarning):
|
axes = df.groupby("classroom").boxplot(ax=axes)
|
|
def test_fontsize(self):
|
df = DataFrame({"a": [1, 2, 3, 4, 5, 6], "b": [0, 0, 0, 1, 1, 1]})
|
self._check_ticks_props(
|
df.boxplot("a", by="b", fontsize=16), xlabelsize=16, ylabelsize=16
|
)
|
|
@pytest.mark.parametrize(
|
"col, expected_xticklabel",
|
[
|
("v", ["(a, v)", "(b, v)", "(c, v)", "(d, v)", "(e, v)"]),
|
(["v"], ["(a, v)", "(b, v)", "(c, v)", "(d, v)", "(e, v)"]),
|
("v1", ["(a, v1)", "(b, v1)", "(c, v1)", "(d, v1)", "(e, v1)"]),
|
(
|
["v", "v1"],
|
[
|
"(a, v)",
|
"(a, v1)",
|
"(b, v)",
|
"(b, v1)",
|
"(c, v)",
|
"(c, v1)",
|
"(d, v)",
|
"(d, v1)",
|
"(e, v)",
|
"(e, v1)",
|
],
|
),
|
(
|
None,
|
[
|
"(a, v)",
|
"(a, v1)",
|
"(b, v)",
|
"(b, v1)",
|
"(c, v)",
|
"(c, v1)",
|
"(d, v)",
|
"(d, v1)",
|
"(e, v)",
|
"(e, v1)",
|
],
|
),
|
],
|
)
|
def test_groupby_boxplot_subplots_false(self, col, expected_xticklabel):
|
# GH 16748
|
df = DataFrame(
|
{
|
"cat": np.random.choice(list("abcde"), 100),
|
"v": np.random.rand(100),
|
"v1": np.random.rand(100),
|
}
|
)
|
grouped = df.groupby("cat")
|
|
axes = _check_plot_works(
|
grouped.boxplot, subplots=False, column=col, return_type="axes"
|
)
|
|
result_xticklabel = [x.get_text() for x in axes.get_xticklabels()]
|
assert expected_xticklabel == result_xticklabel
|
|
def test_groupby_boxplot_object(self, hist_df):
|
# GH 43480
|
df = hist_df.astype("object")
|
grouped = df.groupby("gender")
|
msg = "boxplot method requires numerical columns, nothing to plot"
|
with pytest.raises(ValueError, match=msg):
|
_check_plot_works(grouped.boxplot, subplots=False)
|
|
def test_boxplot_multiindex_column(self):
|
# GH 16748
|
arrays = [
|
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
|
["one", "two", "one", "two", "one", "two", "one", "two"],
|
]
|
tuples = list(zip(*arrays))
|
index = MultiIndex.from_tuples(tuples, names=["first", "second"])
|
df = DataFrame(np.random.randn(3, 8), index=["A", "B", "C"], columns=index)
|
|
col = [("bar", "one"), ("bar", "two")]
|
axes = _check_plot_works(df.boxplot, column=col, return_type="axes")
|
|
expected_xticklabel = ["(bar, one)", "(bar, two)"]
|
result_xticklabel = [x.get_text() for x in axes.get_xticklabels()]
|
assert expected_xticklabel == result_xticklabel
|
