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)
# 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_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_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_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.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_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()
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):
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_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)
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)
