def test_kde_missing_vals(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() s = Series(np.random.uniform(size=50)) s[0] = np.nan axes = _check_plot_works(s.plot.kde) # gh-14821: check if the values have any missing values assert any(~np.isnan(axes.lines[0].get_xdata()))
def test_kde_missing_vals(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() s = Series(np.random.uniform(size=50)) s[0] = np.nan axes = _check_plot_works(s.plot.kde) # check if the values have any missing values # GH14821 self.assertTrue(any(~np.isnan(axes.lines[0].get_xdata())), msg="Missing Values not dropped")
def test_kde_missing_vals(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() s = Series(np.random.uniform(size=50)) s[0] = np.nan axes = _check_plot_works(s.plot.kde) # check if the values have any missing values # GH14821 self.assertTrue(any(~np.isnan(axes.lines[0].get_xdata())), msg='Missing Values not dropped')
def test_kde_kwargs(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() from numpy import linspace _check_plot_works(self.ts.plot.kde, bw_method=0.5, ind=linspace(-100, 100, 20)) _check_plot_works(self.ts.plot.density, bw_method=0.5, ind=linspace(-100, 100, 20)) ax = self.ts.plot.kde(logy=True, bw_method=0.5, ind=linspace(-100, 100, 20)) self._check_ax_scales(ax, yaxis="log") self._check_text_labels(ax.yaxis.get_label(), "Density")
def test_secondary_kde(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() ser = Series(np.random.randn(10)) fig, ax = self.plt.subplots() ax = ser.plot(secondary_y=True, kind='density', ax=ax) assert hasattr(ax, 'left_ax') assert not hasattr(ax, 'right_ax') axes = fig.get_axes() assert axes[1].get_yaxis().get_ticks_position() == 'right'
def test_kde_missing_vals(self): _skip_if_no_scipy_gaussian_kde() if not self.mpl_ge_1_5_0: pytest.skip("mpl is not supported") s = Series(np.random.uniform(size=50)) s[0] = np.nan axes = _check_plot_works(s.plot.kde) # gh-14821: check if the values have any missing values assert any(~np.isnan(axes.lines[0].get_xdata()))
def test_kde_kwargs(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() from numpy import linspace _check_plot_works(self.ts.plot.kde, bw_method=.5, ind=linspace(-100, 100, 20)) _check_plot_works(self.ts.plot.density, bw_method=.5, ind=linspace(-100, 100, 20)) ax = self.ts.plot.kde(logy=True, bw_method=.5, ind=linspace(-100, 100, 20)) self._check_ax_scales(ax, yaxis='log') self._check_text_labels(ax.yaxis.get_label(), 'Density')
def test_secondary_kde(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() import matplotlib.pyplot as plt # noqa ser = Series(np.random.randn(10)) ax = ser.plot(secondary_y=True, kind='density') self.assertTrue(hasattr(ax, 'left_ax')) self.assertFalse(hasattr(ax, 'right_ax')) fig = ax.get_figure() axes = fig.get_axes() self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right')
def test_hist_kde_color(self): ax = self.ts.plot.hist(logy=True, bins=10, color='b') self._check_ax_scales(ax, yaxis='log') self.assertEqual(len(ax.patches), 10) self._check_colors(ax.patches, facecolors=['b'] * 10) tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() ax = self.ts.plot.kde(logy=True, color='r') self._check_ax_scales(ax, yaxis='log') lines = ax.get_lines() self.assertEqual(len(lines), 1) self._check_colors(lines, ['r'])
def test_hist_kde_color(self): _, ax = self.plt.subplots() ax = self.ts.plot.hist(logy=True, bins=10, color='b', ax=ax) self._check_ax_scales(ax, yaxis='log') assert len(ax.patches) == 10 self._check_colors(ax.patches, facecolors=['b'] * 10) _skip_if_no_scipy_gaussian_kde() _, ax = self.plt.subplots() ax = self.ts.plot.kde(logy=True, color='r', ax=ax) self._check_ax_scales(ax, yaxis='log') lines = ax.get_lines() assert len(lines) == 1 self._check_colors(lines, ['r'])
def test_kde_kwargs(self): _skip_if_no_scipy_gaussian_kde() sample_points = np.linspace(-100, 100, 20) _check_plot_works(self.ts.plot.kde, bw_method='scott', ind=20) _check_plot_works(self.ts.plot.kde, bw_method=None, ind=20) _check_plot_works(self.ts.plot.kde, bw_method=None, ind=np.int(20)) _check_plot_works(self.ts.plot.kde, bw_method=.5, ind=sample_points) _check_plot_works(self.ts.plot.density, bw_method=.5, ind=sample_points) _, ax = self.plt.subplots() ax = self.ts.plot.kde(logy=True, bw_method=.5, ind=sample_points, ax=ax) self._check_ax_scales(ax, yaxis='log') self._check_text_labels(ax.yaxis.get_label(), 'Density')
def test_kde_kwargs(self): _skip_if_no_scipy_gaussian_kde() if not self.mpl_ge_1_5_0: pytest.skip("mpl is not supported") from numpy import linspace _check_plot_works(self.ts.plot.kde, bw_method=.5, ind=linspace(-100, 100, 20)) _check_plot_works(self.ts.plot.density, bw_method=.5, ind=linspace(-100, 100, 20)) _, ax = self.plt.subplots() ax = self.ts.plot.kde(logy=True, bw_method=.5, ind=linspace(-100, 100, 20), ax=ax) self._check_ax_scales(ax, yaxis='log') self._check_text_labels(ax.yaxis.get_label(), 'Density')
def test_kde_kwargs(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() if not self.mpl_ge_1_5_0: pytest.skip("mpl is not supported") from numpy import linspace _check_plot_works(self.ts.plot.kde, bw_method=.5, ind=linspace(-100, 100, 20)) _check_plot_works(self.ts.plot.density, bw_method=.5, ind=linspace(-100, 100, 20)) _, ax = self.plt.subplots() ax = self.ts.plot.kde(logy=True, bw_method=.5, ind=linspace(-100, 100, 20), ax=ax) self._check_ax_scales(ax, yaxis='log') self._check_text_labels(ax.yaxis.get_label(), 'Density')
def test_hist_kde(self): ax = self.ts.plot.hist(logy=True) self._check_ax_scales(ax, yaxis='log') xlabels = ax.get_xticklabels() # ticks are values, thus ticklabels are blank self._check_text_labels(xlabels, [''] * len(xlabels)) ylabels = ax.get_yticklabels() self._check_text_labels(ylabels, [''] * len(ylabels)) tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() _check_plot_works(self.ts.plot.kde) _check_plot_works(self.ts.plot.density) ax = self.ts.plot.kde(logy=True) self._check_ax_scales(ax, yaxis='log') xlabels = ax.get_xticklabels() self._check_text_labels(xlabels, [''] * len(xlabels)) ylabels = ax.get_yticklabels() self._check_text_labels(ylabels, [''] * len(ylabels))
def test_hist_kde(self): if not self.mpl_ge_1_5_0: pytest.skip("mpl is not supported") _, ax = self.plt.subplots() ax = self.ts.plot.hist(logy=True, ax=ax) self._check_ax_scales(ax, yaxis='log') xlabels = ax.get_xticklabels() # ticks are values, thus ticklabels are blank self._check_text_labels(xlabels, [''] * len(xlabels)) ylabels = ax.get_yticklabels() self._check_text_labels(ylabels, [''] * len(ylabels)) _skip_if_no_scipy_gaussian_kde() _check_plot_works(self.ts.plot.kde) _check_plot_works(self.ts.plot.density) _, ax = self.plt.subplots() ax = self.ts.plot.kde(logy=True, ax=ax) self._check_ax_scales(ax, yaxis='log') xlabels = ax.get_xticklabels() self._check_text_labels(xlabels, [''] * len(xlabels)) ylabels = ax.get_yticklabels() self._check_text_labels(ylabels, [''] * len(ylabels))
def test_kde_missing_vals(self): tm._skip_if_no_scipy() _skip_if_no_scipy_gaussian_kde() s = Series(np.random.uniform(size=50)) s[0] = np.nan _check_plot_works(s.plot.kde)