DOC: Suppress warnings in visualization.rst

Author: sinhrks

doc/source/visualization.rst

@@ -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();
@@ -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:
@@ -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
 
@@ -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:

pandas/tests/test_graphics.py

@@ -5,6 +5,7 @@
 import itertools
 import os
 import string
+import warnings
 from distutils.version import LooseVersion
 
 from datetime import datetime, date
@@ -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)
@@ -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')
+            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, ))
 
@@ -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))
@@ -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')
@@ -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
@@ -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])
@@ -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

pandas/tests/test_groupby.py

@@ -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)
@@ -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('_')])