Python pandas.util.testing.RNGContext() Examples

def test_grouped_plot_fignums(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)
        df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
        gb = df.groupby('gender')

        res = gb.plot()
        assert len(self.plt.get_fignums()) == 2
        assert len(res) == 2
        tm.close()

        res = gb.boxplot(return_type='axes')
        assert len(self.plt.get_fignums()) == 1
        assert len(res) == 2
        tm.close()

        # now works with GH 5610 as gender is excluded
        res = df.groupby('gender').hist()
        tm.close() 

def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in plotting._core._common_kinds:
                if not _ok_for_gaussian_kde(kind):
                    continue
                with pytest.raises(TypeError):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ['area']
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in kinds:
                with pytest.raises(TypeError):
                    df.plot(kind=kind) 

def test_grouped_plot_fignums(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)
        df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
        gb = df.groupby('gender')

        res = gb.plot()
        assert len(self.plt.get_fignums()) == 2
        assert len(res) == 2
        tm.close()

        res = gb.boxplot(return_type='axes')
        assert len(self.plt.get_fignums()) == 1
        assert len(res) == 2
        tm.close()

        # now works with GH 5610 as gender is excluded
        res = df.groupby('gender').hist()
        tm.close() 

def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in plotting._core._common_kinds:
                if not _ok_for_gaussian_kde(kind):
                    continue
                with pytest.raises(TypeError):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ['area']
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in kinds:
                with pytest.raises(TypeError):
                    df.plot(kind=kind) 

def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in plotting._core._common_kinds:
                if not _ok_for_gaussian_kde(kind):
                    continue
                with pytest.raises(TypeError):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ['area']
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in kinds:
                with pytest.raises(TypeError):
                    df.plot(kind=kind) 

def test_grouped_plot_fignums(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)
        df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
        gb = df.groupby('gender')

        res = gb.plot()
        assert len(self.plt.get_fignums()) == 2
        assert len(res) == 2
        tm.close()

        res = gb.boxplot(return_type='axes')
        assert len(self.plt.get_fignums()) == 1
        assert len(res) == 2
        tm.close()

        # now works with GH 5610 as gender is excluded
        res = df.groupby('gender').hist()
        tm.close() 

def test_grouped_plot_fignums(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)
        df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
        gb = df.groupby('gender')

        res = gb.plot()
        assert len(self.plt.get_fignums()) == 2
        assert len(res) == 2
        tm.close()

        res = gb.boxplot(return_type='axes')
        assert len(self.plt.get_fignums()) == 1
        assert len(res) == 2
        tm.close()

        # now works with GH 5610 as gender is excluded
        res = df.groupby('gender').hist()
        tm.close() 

def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in plotting._core._common_kinds:
                if not _ok_for_gaussian_kde(kind):
                    continue
                with pytest.raises(TypeError):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ['area']
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in kinds:
                with pytest.raises(TypeError):
                    df.plot(kind=kind) 

def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in plotting._core._common_kinds:
                if not _ok_for_gaussian_kde(kind):
                    continue
                with pytest.raises(TypeError):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ['area']
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in kinds:
                with pytest.raises(TypeError):
                    df.plot(kind=kind) 

def test_grouped_plot_fignums(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)
        df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
        gb = df.groupby('gender')

        res = gb.plot()
        assert len(self.plt.get_fignums()) == 2
        assert len(res) == 2
        tm.close()

        res = gb.boxplot(return_type='axes')
        assert len(self.plt.get_fignums()) == 1
        assert len(res) == 2
        tm.close()

        # now works with GH 5610 as gender is excluded
        res = df.groupby('gender').hist()
        tm.close() 

def test_grouped_plot_fignums(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)
        df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
        gb = df.groupby('gender')

        res = gb.plot()
        assert len(self.plt.get_fignums()) == 2
        assert len(res) == 2
        tm.close()

        res = gb.boxplot(return_type='axes')
        assert len(self.plt.get_fignums()) == 1
        assert len(res) == 2
        tm.close()

        # now works with GH 5610 as gender is excluded
        res = df.groupby('gender').hist()
        tm.close() 

def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in plotting._core._common_kinds:
                if not _ok_for_gaussian_kde(kind):
                    continue
                with pytest.raises(TypeError):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ['area']
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in kinds:
                with pytest.raises(TypeError):
                    df.plot(kind=kind) 

def test_line_area_stacked(self):
        with tm.RNGContext(42):
            df = DataFrame(rand(6, 4), columns=['w', 'x', 'y', 'z'])
            neg_df = -df
            # each column has either positive or negative value
            sep_df = DataFrame({'w': rand(6),
                                'x': rand(6),
                                'y': -rand(6),
                                'z': -rand(6)})
            # each column has positive-negative mixed value
            mixed_df = DataFrame(randn(6, 4),
                                 index=list(string.ascii_letters[:6]),
                                 columns=['w', 'x', 'y', 'z'])

            for kind in ['line', 'area']:
                ax1 = _check_plot_works(df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(neg_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(neg_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(sep_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(sep_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines[:2], ax2.lines[:2])
                self._compare_stacked_y_cood(ax1.lines[2:], ax2.lines[2:])

                _check_plot_works(mixed_df.plot, stacked=False)
                with pytest.raises(ValueError):
                    mixed_df.plot(stacked=True)

                _check_plot_works(df.plot, kind=kind, logx=True, stacked=True) 

def test_rng_context():
    import numpy as np

    expected0 = 1.764052345967664
    expected1 = 1.6243453636632417

    with tm.RNGContext(0):
        with tm.RNGContext(1):
            assert np.random.randn() == expected1
        assert np.random.randn() == expected0 

def test_series_groupby_plotting_nominally_works(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)

        weight.groupby(gender).plot()
        tm.close()
        height.groupby(gender).hist()
        tm.close()
        # Regression test for GH8733
        height.groupby(gender).plot(alpha=0.5)
        tm.close() 

def test_scatter_matrix_axis(self):
        scatter_matrix = plotting.scatter_matrix

        with tm.RNGContext(42):
            df = DataFrame(randn(100, 3))

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()

        # GH 5662
        expected = ['-2', '0', '2']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)

        df[0] = ((df[0] - 2) / 3)

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
        expected = ['-1.0', '-0.5', '0.0']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) 

def test_grouped_hist_legacy2(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender_int = np.random.choice([0, 1], size=n)
        df_int = DataFrame({'height': height, 'weight': weight,
                            'gender': gender_int})
        gb = df_int.groupby('gender')
        axes = gb.hist()
        assert len(axes) == 2
        assert len(self.plt.get_fignums()) == 2
        tm.close() 

def test_line_area_stacked(self):
        with tm.RNGContext(42):
            df = DataFrame(rand(6, 4), columns=['w', 'x', 'y', 'z'])
            neg_df = -df
            # each column has either positive or negative value
            sep_df = DataFrame({'w': rand(6),
                                'x': rand(6),
                                'y': -rand(6),
                                'z': -rand(6)})
            # each column has positive-negative mixed value
            mixed_df = DataFrame(randn(6, 4),
                                 index=list(string.ascii_letters[:6]),
                                 columns=['w', 'x', 'y', 'z'])

            for kind in ['line', 'area']:
                ax1 = _check_plot_works(df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(neg_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(neg_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(sep_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(sep_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines[:2], ax2.lines[:2])
                self._compare_stacked_y_cood(ax1.lines[2:], ax2.lines[2:])

                _check_plot_works(mixed_df.plot, stacked=False)
                with pytest.raises(ValueError):
                    mixed_df.plot(stacked=True)

                _check_plot_works(df.plot, kind=kind, logx=True, stacked=True) 

def setup_method(self, method):

        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
        self.mpl_ge_2_1_0 = plotting._compat._mpl_ge_2_1_0()
        self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()
        self.mpl_ge_2_2_2 = plotting._compat._mpl_ge_2_2_2()
        self.mpl_ge_3_0_0 = plotting._compat._mpl_ge_3_0_0()

        self.bp_n_objects = 7
        self.polycollection_factor = 2
        self.default_figsize = (6.4, 4.8)
        self.default_tick_position = 'left'

        n = 100
        with tm.RNGContext(42):
            gender = np.random.choice(['Male', 'Female'], size=n)
            classroom = np.random.choice(['A', 'B', 'C'], size=n)

            self.hist_df = DataFrame({'gender': gender,
                                      'classroom': classroom,
                                      'height': random.normal(66, 4, size=n),
                                      'weight': random.normal(161, 32, size=n),
                                      'category': random.randint(4, size=n)})

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame({"A": np.random.uniform(size=20),
                                    "B": np.random.uniform(size=20),
                                    "C": np.arange(20) + np.random.uniform(
                                        size=20)}) 

def test_series_groupby_plotting_nominally_works(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)

        weight.groupby(gender).plot()
        tm.close()
        height.groupby(gender).hist()
        tm.close()
        # Regression test for GH8733
        height.groupby(gender).plot(alpha=0.5)
        tm.close() 

def test_scatter_matrix_axis(self):
        scatter_matrix = plotting.scatter_matrix

        with tm.RNGContext(42):
            df = DataFrame(randn(100, 3))

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()

        # GH 5662
        if self.mpl_ge_2_0_0:
            expected = ['-2', '0', '2']
        else:
            expected = ['-2', '-1', '0', '1', '2']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)

        df[0] = ((df[0] - 2) / 3)

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
        if self.mpl_ge_2_0_0:
            expected = ['-1.0', '-0.5', '0.0']
        else:
            expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) 

def test_grouped_hist_legacy2(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender_int = np.random.choice([0, 1], size=n)
        df_int = DataFrame({'height': height, 'weight': weight,
                            'gender': gender_int})
        gb = df_int.groupby('gender')
        axes = gb.hist()
        assert len(axes) == 2
        assert len(self.plt.get_fignums()) == 2
        tm.close() 

def test_line_area_stacked(self):
        with tm.RNGContext(42):
            df = DataFrame(rand(6, 4), columns=['w', 'x', 'y', 'z'])
            neg_df = -df
            # each column has either positive or negative value
            sep_df = DataFrame({'w': rand(6),
                                'x': rand(6),
                                'y': -rand(6),
                                'z': -rand(6)})
            # each column has positive-negative mixed value
            mixed_df = DataFrame(randn(6, 4),
                                 index=list(string.ascii_letters[:6]),
                                 columns=['w', 'x', 'y', 'z'])

            for kind in ['line', 'area']:
                ax1 = _check_plot_works(df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(neg_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(neg_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(sep_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(sep_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines[:2], ax2.lines[:2])
                self._compare_stacked_y_cood(ax1.lines[2:], ax2.lines[2:])

                _check_plot_works(mixed_df.plot, stacked=False)
                with pytest.raises(ValueError):
                    mixed_df.plot(stacked=True)

                _check_plot_works(df.plot, kind=kind, logx=True, stacked=True) 

def test_series_groupby_plotting_nominally_works(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)

        weight.groupby(gender).plot()
        tm.close()
        height.groupby(gender).hist()
        tm.close()
        # Regression test for GH8733
        height.groupby(gender).plot(alpha=0.5)
        tm.close() 

def test_scatter_matrix_axis(self):
        tm._skip_if_no_scipy()
        scatter_matrix = plotting.scatter_matrix

        with tm.RNGContext(42):
            df = DataFrame(randn(100, 3))

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()

        # GH 5662
        if self.mpl_ge_2_0_0:
            expected = ['-2', '0', '2']
        else:
            expected = ['-2', '-1', '0', '1', '2']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)

        df[0] = ((df[0] - 2) / 3)

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
        if self.mpl_ge_2_0_0:
            expected = ['-1.0', '-0.5', '0.0']
        else:
            expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) 

def test_grouped_hist_legacy2(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender_int = np.random.choice([0, 1], size=n)
        df_int = DataFrame({'height': height, 'weight': weight,
                            'gender': gender_int})
        gb = df_int.groupby('gender')
        axes = gb.hist()
        assert len(axes) == 2
        assert len(self.plt.get_fignums()) == 2
        tm.close() 

def test_line_area_stacked(self):
        with tm.RNGContext(42):
            df = DataFrame(rand(6, 4), columns=['w', 'x', 'y', 'z'])
            neg_df = -df
            # each column has either positive or negative value
            sep_df = DataFrame({'w': rand(6),
                                'x': rand(6),
                                'y': -rand(6),
                                'z': -rand(6)})
            # each column has positive-negative mixed value
            mixed_df = DataFrame(randn(6, 4),
                                 index=list(string.ascii_letters[:6]),
                                 columns=['w', 'x', 'y', 'z'])

            for kind in ['line', 'area']:
                ax1 = _check_plot_works(df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(neg_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(neg_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(sep_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(sep_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines[:2], ax2.lines[:2])
                self._compare_stacked_y_cood(ax1.lines[2:], ax2.lines[2:])

                _check_plot_works(mixed_df.plot, stacked=False)
                with pytest.raises(ValueError):
                    mixed_df.plot(stacked=True)

                _check_plot_works(df.plot, kind=kind, logx=True, stacked=True) 

def setup_method(self, method):

        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
        self.mpl_ge_2_1_0 = plotting._compat._mpl_ge_2_1_0()
        self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()
        self.mpl_ge_2_2_2 = plotting._compat._mpl_ge_2_2_2()
        self.mpl_ge_3_0_0 = plotting._compat._mpl_ge_3_0_0()

        self.bp_n_objects = 7
        self.polycollection_factor = 2
        self.default_figsize = (6.4, 4.8)
        self.default_tick_position = 'left'

        n = 100
        with tm.RNGContext(42):
            gender = np.random.choice(['Male', 'Female'], size=n)
            classroom = np.random.choice(['A', 'B', 'C'], size=n)

            self.hist_df = DataFrame({'gender': gender,
                                      'classroom': classroom,
                                      'height': random.normal(66, 4, size=n),
                                      'weight': random.normal(161, 32, size=n),
                                      'category': random.randint(4, size=n)})

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame({"A": np.random.uniform(size=20),
                                    "B": np.random.uniform(size=20),
                                    "C": np.arange(20) + np.random.uniform(
                                        size=20)}) 

def test_series_groupby_plotting_nominally_works(self):
        n = 10
        weight = Series(np.random.normal(166, 20, size=n))
        height = Series(np.random.normal(60, 10, size=n))
        with tm.RNGContext(42):
            gender = np.random.choice(['male', 'female'], size=n)

        weight.groupby(gender).plot()
        tm.close()
        height.groupby(gender).hist()
        tm.close()
        # Regression test for GH8733
        height.groupby(gender).plot(alpha=0.5)
        tm.close() 

def test_rng_context():
    import numpy as np

    expected0 = 1.764052345967664
    expected1 = 1.6243453636632417

    with tm.RNGContext(0):
        with tm.RNGContext(1):
            assert np.random.randn() == expected1
        assert np.random.randn() == expected0