def test_grouped_hist(self):
import matplotlib.pyplot as plt
df = DataFrame(np.random.randn(500, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 4, 500)
axes = plotting.grouped_hist(df.A, by=df.C)
self.assert_(len(axes.ravel()) == 4)
plt.close('all')
axes = df.hist(by=df.C)
self.assert_(axes.ndim == 2)
self.assert_(len(axes.ravel()) == 4)
for ax in axes.ravel():
self.assert_(len(ax.patches) > 0)
plt.close('all')
# make sure kwargs to hist are handled
axes = plotting.grouped_hist(df.A, by=df.C, normed=True,
cumulative=True, bins=4)
# height of last bin (index 5) must be 1.0
for ax in axes.ravel():
height = ax.get_children()[5].get_height()
self.assertAlmostEqual(height, 1.0)
plt.close('all')
axes = plotting.grouped_hist(df.A, by=df.C, log=True)
# scale of y must be 'log'
for ax in axes.ravel():
self.assert_(ax.get_yscale() == 'log')
plt.close('all')
# propagate attr exception from matplotlib.Axes.hist
self.assertRaises(AttributeError, plotting.grouped_hist, df.A,
by=df.C, foo='bar')
def test_grouped_hist(self):
import matplotlib.pyplot as plt
df = DataFrame(np.random.randn(500, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 4, 500)
axes = plotting.grouped_hist(df.A, by=df.C)
self.assertTrue(len(axes.ravel()) == 4)
plt.close('all')
axes = df.hist(by=df.C)
self.assertTrue(axes.ndim == 2)
self.assertTrue(len(axes.ravel()) == 4)
for ax in axes.ravel():
self.assertTrue(len(ax.patches) > 0)
plt.close('all')
# make sure kwargs to hist are handled
axes = plotting.grouped_hist(df.A, by=df.C, normed=True,
cumulative=True, bins=4)
# height of last bin (index 5) must be 1.0
for ax in axes.ravel():
height = ax.get_children()[5].get_height()
self.assertAlmostEqual(height, 1.0)
plt.close('all')
axes = plotting.grouped_hist(df.A, by=df.C, log=True)
# scale of y must be 'log'
for ax in axes.ravel():
self.assertTrue(ax.get_yscale() == 'log')
plt.close('all')
# propagate attr exception from matplotlib.Axes.hist
self.assertRaises(AttributeError, plotting.grouped_hist, df.A,
by=df.C, foo='bar')
def test_grouped_hist(self):
import matplotlib.pyplot as plt
df = DataFrame(randn(500, 2), columns=["A", "B"])
df["C"] = np.random.randint(0, 4, 500)
axes = plotting.grouped_hist(df.A, by=df.C)
self.assertEqual(len(axes.ravel()), 4)
tm.close()
axes = df.hist(by=df.C)
self.assertEqual(axes.ndim, 2)
self.assertEqual(len(axes.ravel()), 4)
for ax in axes.ravel():
self.assert_(len(ax.patches) > 0)
tm.close()
# make sure kwargs to hist are handled
axes = plotting.grouped_hist(df.A, by=df.C, normed=True, cumulative=True, bins=4)
# height of last bin (index 5) must be 1.0
for ax in axes.ravel():
height = ax.get_children()[5].get_height()
self.assertAlmostEqual(height, 1.0)
tm.close()
axes = plotting.grouped_hist(df.A, by=df.C, log=True)
# scale of y must be 'log'
for ax in axes.ravel():
self.assertEqual(ax.get_yscale(), "log")
tm.close()
# propagate attr exception from matplotlib.Axes.hist
with tm.assertRaises(AttributeError):
plotting.grouped_hist(df.A, by=df.C, foo="bar")
def test_grouped_hist_legacy(self):
from matplotlib.patches import Rectangle
df = DataFrame(randn(500, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 4, 500)
df['D'] = ['X'] * 500
axes = plotting.grouped_hist(df.A, by=df.C)
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
tm.close()
axes = df.hist(by=df.C)
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
tm.close()
# group by a key with single value
axes = df.hist(by='D', rot=30)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
self._check_ticks_props(axes, xrot=30)
tm.close()
# make sure kwargs to hist are handled
xf, yf = 20, 18
xrot, yrot = 30, 40
axes = plotting.grouped_hist(df.A,
by=df.C,
normed=True,
cumulative=True,
bins=4,
xlabelsize=xf,
xrot=xrot,
ylabelsize=yf,
yrot=yrot)
# height of last bin (index 5) must be 1.0
for ax in axes.ravel():
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
height = rects[-1].get_height()
self.assertAlmostEqual(height, 1.0)
self._check_ticks_props(axes,
xlabelsize=xf,
xrot=xrot,
ylabelsize=yf,
yrot=yrot)
tm.close()
axes = plotting.grouped_hist(df.A, by=df.C, log=True)
# scale of y must be 'log'
self._check_ax_scales(axes, yaxis='log')
tm.close()
# propagate attr exception from matplotlib.Axes.hist
with tm.assertRaises(AttributeError):
plotting.grouped_hist(df.A, by=df.C, foo='bar')
with tm.assert_produces_warning(FutureWarning):
df.hist(by='C', figsize='default')
def test_grouped_hist_legacy(self):
from matplotlib.patches import Rectangle
df = DataFrame(randn(500, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 4, 500)
df['D'] = ['X'] * 500
axes = plotting.grouped_hist(df.A, by=df.C)
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
tm.close()
axes = df.hist(by=df.C)
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
tm.close()
# group by a key with single value
axes = df.hist(by='D', rot=30)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
self._check_ticks_props(axes, xrot=30)
tm.close()
# make sure kwargs to hist are handled
xf, yf = 20, 18
xrot, yrot = 30, 40
axes = plotting.grouped_hist(df.A, by=df.C, normed=True,
cumulative=True, bins=4,
xlabelsize=xf, xrot=xrot,
ylabelsize=yf, yrot=yrot)
# height of last bin (index 5) must be 1.0
for ax in axes.ravel():
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
height = rects[-1].get_height()
self.assertAlmostEqual(height, 1.0)
self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot,
ylabelsize=yf, yrot=yrot)
tm.close()
axes = plotting.grouped_hist(df.A, by=df.C, log=True)
# scale of y must be 'log'
self._check_ax_scales(axes, yaxis='log')
tm.close()
# propagate attr exception from matplotlib.Axes.hist
with tm.assertRaises(AttributeError):
plotting.grouped_hist(df.A, by=df.C, foo='bar')
with tm.assert_produces_warning(FutureWarning):
df.hist(by='C', figsize='default')
def test_grouped_hist(self):
import matplotlib.pyplot as plt
df = DataFrame(np.random.randn(500, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 4, 500)
axes = plotting.grouped_hist(df.A, by=df.C)
self.assert_(len(axes.ravel()) == 4)
plt.close('all')
axes = df.hist(by=df.C)
self.assert_(axes.ndim == 2)
self.assert_(len(axes.ravel()) == 4)
for ax in axes.ravel():
self.assert_(len(ax.patches) > 0)
def test_grouped_hist(self):
import matplotlib.pyplot as plt
df = DataFrame(np.random.randn(500, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 4, 500)
axes = plotting.grouped_hist(df.A, by=df.C)
self.assert_(len(axes.ravel()) == 4)
plt.close('all')
axes = df.hist(by=df.C)
self.assert_(axes.ndim == 2)
self.assert_(len(axes.ravel()) == 4)
for ax in axes.ravel():
self.assert_(len(ax.patches) > 0)
def test_grouped_hist(self):
df = DataFrame(np.random.randn(50, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 3, 50)
axes = plotting.grouped_hist(df.A, by=df.C)
self.assert_(len(axes.ravel()) == 4)
def test_grouped_hist(self):
df = DataFrame(np.random.randn(50, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 3, 50)
axes = plotting.grouped_hist(df.A, by=df.C)
self.assert_(len(axes.ravel()) == 4)
