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DOC: Suppress warnings in visualization.rst #8877

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Feb 12, 2015
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DOC: Suppress warnings in visualization.rst #8877

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18 changes: 9 additions & 9 deletions doc/source/visualization.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -387,7 +387,6 @@ The existing interface ``DataFrame.boxplot`` to plot boxplot still can be used.
np.random.seed(123456)

.. ipython:: python
:okwarning:

df = DataFrame(rand(10,5))
plt.figure();
Expand Down Expand Up @@ -1271,7 +1270,7 @@ or columns needed, given the other.
.. ipython:: python

@savefig frame_plot_subplots_layout.png
df.plot(subplots=True, layout=(3, 2), figsize=(6, 6), sharex=False);
df.plot(subplots=True, layout=(2, 3), figsize=(6, 6), sharex=False);

.. ipython:: python
:suppress:
Expand All @@ -1282,22 +1281,23 @@ The above example is identical to using

.. ipython:: python

df.plot(subplots=True, layout=(3, -1), figsize=(6, 6), sharex=False);
df.plot(subplots=True, layout=(2, -1), figsize=(6, 6), sharex=False);

.. ipython:: python
:suppress:

plt.close('all')

The required number of columns (2) is inferred from the number of series to plot
and the given number of rows (3).
The required number of columns (3) is inferred from the number of series to plot
and the given number of rows (2).

Also, you can pass multiple axes created beforehand as list-like via ``ax`` keyword.
This allows to use more complicated layout.
The passed axes must be the same number as the subplots being drawn.

When multiple axes are passed via ``ax`` keyword, ``layout``, ``sharex`` and ``sharey`` keywords are ignored.
These must be configured when creating axes.
When multiple axes are passed via ``ax`` keyword, ``layout``, ``sharex`` and ``sharey`` keywords
don't affect to the output. You should explicitly pass ``sharex=False`` and ``sharey=False``,
otherwise you will see a warning.

.. ipython:: python

Expand All @@ -1306,9 +1306,9 @@ These must be configured when creating axes.
target1 = [axes[0][0], axes[1][1], axes[2][2], axes[3][3]]
target2 = [axes[3][0], axes[2][1], axes[1][2], axes[0][3]]

df.plot(subplots=True, ax=target1, legend=False);
df.plot(subplots=True, ax=target1, legend=False, sharex=False, sharey=False);
@savefig frame_plot_subplots_multi_ax.png
(-df).plot(subplots=True, ax=target2, legend=False);
(-df).plot(subplots=True, ax=target2, legend=False, sharex=False, sharey=False);

.. ipython:: python
:suppress:
Expand Down
121 changes: 100 additions & 21 deletions pandas/tests/test_graphics.py
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Expand Up @@ -5,6 +5,7 @@
import itertools
import os
import string
import warnings
from distutils.version import LooseVersion

from datetime import datetime, date
Expand Down Expand Up @@ -1297,12 +1298,12 @@ def test_subplots_multiple_axes(self):
df = DataFrame(np.random.rand(10, 3),
index=list(string.ascii_letters[:10]))

returned = df.plot(subplots=True, ax=axes[0])
returned = df.plot(subplots=True, ax=axes[0], sharex=False, sharey=False)
self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
self.assertEqual(returned.shape, (3, ))
self.assertIs(returned[0].figure, fig)
# draw on second row
returned = df.plot(subplots=True, ax=axes[1])
returned = df.plot(subplots=True, ax=axes[1], sharex=False, sharey=False)
self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
self.assertEqual(returned.shape, (3, ))
self.assertIs(returned[0].figure, fig)
Expand All @@ -1319,26 +1320,32 @@ def test_subplots_multiple_axes(self):
# (show warning is tested in
# TestDataFrameGroupByPlots.test_grouped_box_multiple_axes
fig, axes = self.plt.subplots(2, 2)
df = DataFrame(np.random.rand(10, 4),
index=list(string.ascii_letters[:10]))

returned = df.plot(subplots=True, ax=axes, layout=(2, 1))
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
self.assertEqual(returned.shape, (4, ))

returned = df.plot(subplots=True, ax=axes, layout=(2, -1))
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
self.assertEqual(returned.shape, (4, ))

returned = df.plot(subplots=True, ax=axes, layout=(-1, 2))
with warnings.catch_warnings():
warnings.simplefilter('ignore')
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Why is the 'catch warnings' needed here? I thought that explicitly stating sharex=False, sharey=False (as you do in this test) is enough to suppress the warning? (that is what you added in the docs?)

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Required, because the test specifies layout keyword which can't be effective when ax keyword is passed. I understand these tests was added in #8297, and intend to test layout is ignored properly.

@TomAugspurger ?

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ah, that makes sense. Maybe explicitely test that the warning is triggered instead of ignoring it?

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@sinhrks that sounds about right, but I could have added both the axes and layout kws without realizing that the layout would be ignored.

df = DataFrame(np.random.rand(10, 4),
index=list(string.ascii_letters[:10]))

returned = df.plot(subplots=True, ax=axes, layout=(2, 1),
sharex=False, sharey=False)
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
self.assertEqual(returned.shape, (4, ))

returned = df.plot(subplots=True, ax=axes, layout=(2, -1),
sharex=False, sharey=False)
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
self.assertEqual(returned.shape, (4, ))

returned = df.plot(subplots=True, ax=axes, layout=(-1, 2),
sharex=False, sharey=False)
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
self.assertEqual(returned.shape, (4, ))

# single column
fig, axes = self.plt.subplots(1, 1)
df = DataFrame(np.random.rand(10, 1),
index=list(string.ascii_letters[:10]))
axes = df.plot(subplots=True, ax=[axes])

axes = df.plot(subplots=True, ax=[axes], sharex=False, sharey=False)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
self.assertEqual(axes.shape, (1, ))

Expand Down Expand Up @@ -3122,13 +3129,14 @@ class TestDataFrameGroupByPlots(TestPlotBase):
@slow
def test_boxplot(self):
grouped = self.hist_df.groupby(by='gender')
axes = _check_plot_works(grouped.boxplot, return_type='axes')
with warnings.catch_warnings():
warnings.simplefilter('ignore')
axes = _check_plot_works(grouped.boxplot, return_type='axes')
self._check_axes_shape(list(axes.values()), axes_num=2, layout=(1, 2))

axes = _check_plot_works(grouped.boxplot, subplots=False,
return_type='axes')
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

tuples = lzip(string.ascii_letters[:10], range(10))
df = DataFrame(np.random.rand(10, 3),
index=MultiIndex.from_tuples(tuples))
Expand All @@ -3149,6 +3157,30 @@ def test_boxplot(self):
return_type='axes')
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

@slow
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 = tm.choice(['male', 'female'], size=n)
df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
gb = df.groupby('gender')

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

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

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

def test_series_plot_color_kwargs(self):
# GH1890
ax = Series(np.arange(12) + 1).plot(color='green')
Expand Down Expand Up @@ -3219,6 +3251,21 @@ def test_grouped_hist(self):
with tm.assert_produces_warning(FutureWarning):
df.hist(by='C', figsize='default')

@slow
def test_grouped_hist2(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 = tm.choice([0, 1], size=n)
df_int = DataFrame({'height': height, 'weight': weight,
'gender': gender_int})
gb = df_int.groupby('gender')
axes = gb.hist()
self.assertEqual(len(axes), 2)
self.assertEqual(len(self.plt.get_fignums()), 2)
tm.close()

@slow
def test_grouped_box_return_type(self):
df = self.hist_df
Expand Down Expand Up @@ -3334,15 +3381,21 @@ def test_grouped_box_multiple_axes(self):
self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))

fig, axes = self.plt.subplots(2, 3)
returned = df.boxplot(column=['height', 'weight', 'category'], by='gender',
return_type='axes', ax=axes[0])
with warnings.catch_warnings():
warnings.simplefilter('ignore')
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))
self.assert_numpy_array_equal(returned, axes[0])
self.assertIs(returned[0].figure, fig)

# draw on second row
returned = df.groupby('classroom').boxplot(column=['height', 'weight', 'category'],
return_type='axes', ax=axes[1])
with warnings.catch_warnings():
warnings.simplefilter('ignore')
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))
self.assert_numpy_array_equal(returned, axes[1])
Expand Down Expand Up @@ -3469,6 +3522,32 @@ def test_invalid_colormap(self):
with tm.assertRaises(ValueError):
df.plot(colormap='invalid_colormap')

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 = tm.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_plotting_with_float_index_works(self):
# GH 7025
df = DataFrame({'def': [1,1,1,2,2,2,3,3,3],
'val': np.random.randn(9)},
index=[1.0,2.0,3.0,1.0,2.0,3.0,1.0,2.0,3.0])

df.groupby('def')['val'].plot()
tm.close()
df.groupby('def')['val'].apply(lambda x: x.plot())
tm.close()


def assert_is_valid_plot_return_object(objs):
import matplotlib.pyplot as plt
Expand Down
87 changes: 0 additions & 87 deletions pandas/tests/test_groupby.py
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Expand Up @@ -35,11 +35,6 @@
import pandas as pd
from numpy.testing import assert_equal

def _skip_if_mpl_not_installed():
try:
import matplotlib.pyplot as plt
except ImportError:
raise nose.SkipTest("matplotlib not installed")

def commonSetUp(self):
self.dateRange = bdate_range('1/1/2005', periods=250)
Expand Down Expand Up @@ -4653,88 +4648,6 @@ def test_groupby_blacklist(self):
with tm.assertRaisesRegexp(AttributeError, msg):
getattr(gb, bl)

def test_series_groupby_plotting_nominally_works(self):
_skip_if_mpl_not_installed()

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 = tm.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_plotting_with_float_index_works(self):
_skip_if_mpl_not_installed()

# GH 7025
df = DataFrame({'def': [1,1,1,2,2,2,3,3,3],
'val': np.random.randn(9)},
index=[1.0,2.0,3.0,1.0,2.0,3.0,1.0,2.0,3.0])

df.groupby('def')['val'].plot()
tm.close()
df.groupby('def')['val'].apply(lambda x: x.plot())
tm.close()

@slow
def test_frame_groupby_plot_boxplot(self):
_skip_if_mpl_not_installed()

import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.use('Agg')
tm.close()

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 = tm.choice(['male', 'female'], size=n)
df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
gb = df.groupby('gender')

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

res = gb.boxplot()
self.assertEqual(len(plt.get_fignums()), 1)
self.assertEqual(len(res), 2)
tm.close()

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

@slow
def test_frame_groupby_hist(self):
_skip_if_mpl_not_installed()
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.use('Agg')
tm.close()

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 = tm.choice([0, 1], size=n)
df_int = DataFrame({'height': height, 'weight': weight,
'gender': gender_int})
gb = df_int.groupby('gender')
axes = gb.hist()
self.assertEqual(len(axes), 2)
self.assertEqual(len(plt.get_fignums()), 2)
tm.close()

def test_tab_completion(self):
grp = self.mframe.groupby(level='second')
results = set([v for v in dir(grp) if not v.startswith('_')])
Expand Down