Merge pull request #7162 from bwignall/issue7131_assert

CLN: Python 3.4 deprecation of assert_

Author: jreback

pandas/tests/test_base.py

@@ -221,7 +221,7 @@ def test_value_counts_unique_nunique(self):
 
             if isinstance(o, DatetimeIndex):
                 # DatetimeIndex.unique returns DatetimeIndex
-                self.assert_(o.unique().equals(klass(values)))
+                self.assertTrue(o.unique().equals(klass(values)))
             else:
                 self.assert_numpy_array_equal(o.unique(), values)
 
@@ -262,9 +262,9 @@ def test_value_counts_unique_nunique(self):
                 self.assert_numpy_array_equal(result[1:], values[2:])
 
                 if isinstance(o, DatetimeIndex):
-                    self.assert_(result[0] is pd.NaT)
+                    self.assertTrue(result[0] is pd.NaT)
                 else:
-                    self.assert_(pd.isnull(result[0]))
+                    self.assertTrue(pd.isnull(result[0]))
 
                 if isinstance(o, DatetimeIndex):
                     self.assertEqual(o.nunique(), 9)
@@ -351,7 +351,7 @@ def test_value_counts_inferred(self):
                                 dtype='datetime64[ns]')
             if isinstance(s, DatetimeIndex):
                 expected = DatetimeIndex(expected)
-                self.assert_(s.unique().equals(expected))
+                self.assertTrue(s.unique().equals(expected))
             else:
                 self.assert_numpy_array_equal(s.unique(), expected)
 

pandas/tests/test_frame.py

@@ -5089,7 +5089,7 @@ def _check_unaligned_frame(meth, op, df, other):
 
         # DataFrame
         self.assert_(df.eq(df).values.all())
-        self.assert_(not df.ne(df).values.any())
+        self.assertFalse(df.ne(df).values.any())
         for op in ['eq', 'ne', 'gt', 'lt', 'ge', 'le']:
             f = getattr(df, op)
             o = getattr(operator, op)
@@ -5149,17 +5149,17 @@ def _test_seq(df, idx_ser, col_ser):
         # NA
         df.ix[0, 0] = np.nan
         rs = df.eq(df)
-        self.assert_(not rs.ix[0, 0])
+        self.assertFalse(rs.ix[0, 0])
         rs = df.ne(df)
         self.assert_(rs.ix[0, 0])
         rs = df.gt(df)
-        self.assert_(not rs.ix[0, 0])
+        self.assertFalse(rs.ix[0, 0])
         rs = df.lt(df)
-        self.assert_(not rs.ix[0, 0])
+        self.assertFalse(rs.ix[0, 0])
         rs = df.ge(df)
-        self.assert_(not rs.ix[0, 0])
+        self.assertFalse(rs.ix[0, 0])
         rs = df.le(df)
-        self.assert_(not rs.ix[0, 0])
+        self.assertFalse(rs.ix[0, 0])
 
 
 
@@ -5169,14 +5169,14 @@ def _test_seq(df, idx_ser, col_ser):
         df = DataFrame({'a': arr})
         df2 = DataFrame({'a': arr2})
         rs = df.gt(df2)
-        self.assert_(not rs.values.any())
+        self.assertFalse(rs.values.any())
         rs = df.ne(df2)
         self.assert_(rs.values.all())
 
         arr3 = np.array([2j, np.nan, None])
         df3 = DataFrame({'a': arr3})
         rs = df3.gt(2j)
-        self.assert_(not rs.values.any())
+        self.assertFalse(rs.values.any())
 
         # corner, dtype=object
         df1 = DataFrame({'col': ['foo', np.nan, 'bar']})
@@ -8400,7 +8400,7 @@ def test_truncate_copy(self):
         index = self.tsframe.index
         truncated = self.tsframe.truncate(index[5], index[10])
         truncated.values[:] = 5.
-        self.assert_(not (self.tsframe.values[5:11] == 5).any())
+        self.assertFalse((self.tsframe.values[5:11] == 5).any())
 
     def test_xs(self):
         idx = self.frame.index[5]
@@ -10499,13 +10499,13 @@ def test_clip(self):
         median = self.frame.median().median()
 
         capped = self.frame.clip_upper(median)
-        self.assert_(not (capped.values > median).any())
+        self.assertFalse((capped.values > median).any())
 
         floored = self.frame.clip_lower(median)
-        self.assert_(not (floored.values < median).any())
+        self.assertFalse((floored.values < median).any())
 
         double = self.frame.clip(upper=median, lower=median)
-        self.assert_(not (double.values != median).any())
+        self.assertFalse((double.values != median).any())
 
     def test_dataframe_clip(self):
 
@@ -10536,7 +10536,7 @@ def test_get_X_columns(self):
                                       ['a', 'b', 'e'])
 
     def test_is_mixed_type(self):
-        self.assert_(not self.frame._is_mixed_type)
+        self.assertFalse(self.frame._is_mixed_type)
         self.assert_(self.mixed_frame._is_mixed_type)
 
     def test_get_numeric_data(self):
@@ -11787,7 +11787,7 @@ def test_constructor_frame_copy(self):
         cop = DataFrame(self.frame, copy=True)
         cop['A'] = 5
         self.assert_((cop['A'] == 5).all())
-        self.assert_(not (self.frame['A'] == 5).all())
+        self.assertFalse((self.frame['A'] == 5).all())
 
     def test_constructor_ndarray_copy(self):
         df = DataFrame(self.frame.values)
@@ -11797,15 +11797,15 @@ def test_constructor_ndarray_copy(self):
 
         df = DataFrame(self.frame.values, copy=True)
         self.frame.values[6] = 6
-        self.assert_(not (df.values[6] == 6).all())
+        self.assertFalse((df.values[6] == 6).all())
 
     def test_constructor_series_copy(self):
         series = self.frame._series
 
         df = DataFrame({'A': series['A']})
         df['A'][:] = 5
 
-        self.assert_(not (series['A'] == 5).all())
+        self.assertFalse((series['A'] == 5).all())
 
     def test_constructor_compound_dtypes(self):
         # GH 5191
@@ -11938,7 +11938,7 @@ def test_consolidate_inplace(self):
 
     def test_as_matrix_consolidate(self):
         self.frame['E'] = 7.
-        self.assert_(not self.frame._data.is_consolidated())
+        self.assertFalse(self.frame._data.is_consolidated())
         _ = self.frame.as_matrix()
         self.assert_(self.frame._data.is_consolidated())
 
@@ -12365,8 +12365,8 @@ def __nonzero__(self):
             r0 = getattr(all_na, name)(axis=0)
             r1 = getattr(all_na, name)(axis=1)
             if name == 'any':
-                self.assert_(not r0.any())
-                self.assert_(not r1.any())
+                self.assertFalse(r0.any())
+                self.assertFalse(r1.any())
             else:
                 self.assert_(r0.all())
                 self.assert_(r1.all())

pandas/tests/test_generic.py

@@ -1148,15 +1148,15 @@ def test_equals(self):
         self.assert_(s1.equals(s2))
 
         s1[1] = 99
-        self.assert_(not s1.equals(s2))
+        self.assertFalse(s1.equals(s2))
 
         # NaNs compare as equal
         s1 = pd.Series([1, np.nan, 3, np.nan], index=[0, 2, 1, 3])
         s2 = s1.copy()
         self.assert_(s1.equals(s2))
 
         s2[0] = 9.9
-        self.assert_(not s1.equals(s2))
+        self.assertFalse(s1.equals(s2))
 
         idx = MultiIndex.from_tuples([(0, 'a'), (1, 'b'), (2, 'c')])
         s1 = Series([1, 2, np.nan], index=idx)
@@ -1179,22 +1179,22 @@ def test_equals(self):
         self.assert_(df1['diff'].equals(df2['diff']))
         self.assert_(df1['bool'].equals(df2['bool']))
         self.assert_(df1.equals(df2))
-        self.assert_(not df1.equals(object))
+        self.assertFalse(df1.equals(object))
 
         # different dtype
         different = df1.copy()
         different['floats'] = different['floats'].astype('float32')
-        self.assert_(not df1.equals(different))
+        self.assertFalse(df1.equals(different))
 
         # different index
         different_index = -index
         different = df2.set_index(different_index)
-        self.assert_(not df1.equals(different))
+        self.assertFalse(df1.equals(different))
 
         # different columns
         different = df2.copy()
         different.columns = df2.columns[::-1]
-        self.assert_(not df1.equals(different))
+        self.assertFalse(df1.equals(different))
 
         # DatetimeIndex
         index = pd.date_range('2000-1-1', periods=10, freq='T')
@@ -1208,7 +1208,7 @@ def test_equals(self):
         self.assert_(df3.equals(df2))
 
         df2 = df1.set_index(['floats'], append=True)
-        self.assert_(not df3.equals(df2))
+        self.assertFalse(df3.equals(df2))
 
         # NaN in index
         df3 = df1.set_index(['floats'], append=True)

pandas/tests/test_graphics.py

@@ -413,7 +413,7 @@ def test_ts_area_lim(self):
         ax = self.ts.plot(kind='area', stacked=False)
         xmin, xmax = ax.get_xlim()
         lines = ax.get_lines()
-        self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0])       
+        self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0])
         self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1])
 
     def test_line_area_nan_series(self):
@@ -1023,13 +1023,13 @@ def test_area_lim(self):
             xmin, xmax = ax.get_xlim()
             ymin, ymax = ax.get_ylim()
             lines = ax.get_lines()
-            self.assertEqual(xmin, lines[0].get_data()[0][0])        
-            self.assertEqual(xmax, lines[0].get_data()[0][-1]) 
-            self.assertEqual(ymin, 0) 
+            self.assertEqual(xmin, lines[0].get_data()[0][0])
+            self.assertEqual(xmax, lines[0].get_data()[0][-1])
+            self.assertEqual(ymin, 0)
 
             ax = _check_plot_works(neg_df.plot, kind='area', stacked=stacked)
             ymin, ymax = ax.get_ylim()
-            self.assertEqual(ymax, 0) 
+            self.assertEqual(ymax, 0)
 
     @slow
     def test_bar_colors(self):
@@ -1471,7 +1471,7 @@ def test_hist(self):
         df = DataFrame(randn(100, 3))
         _check_plot_works(df.hist)
         axes = df.hist(grid=False)
-        self.assert_(not axes[1, 1].get_visible())
+        self.assertFalse(axes[1, 1].get_visible())
 
         df = DataFrame(randn(100, 1))
         _check_plot_works(df.hist)
@@ -1576,7 +1576,7 @@ def scat2(x, y, by=None, ax=None, figsize=None):
     def test_andrews_curves(self):
         from pandas.tools.plotting import andrews_curves
         from matplotlib import cm
-        
+
         df = self.iris
 
         _check_plot_works(andrews_curves, df, 'Name')
@@ -1601,15 +1601,15 @@ def test_andrews_curves(self):
         ax = andrews_curves(df, 'Name', color=colors)
         handles, labels = ax.get_legend_handles_labels()
         self._check_colors(handles, linecolors=colors)
-        
+
         with tm.assert_produces_warning(FutureWarning):
             andrews_curves(data=df, class_column='Name')
 
     @slow
     def test_parallel_coordinates(self):
         from pandas.tools.plotting import parallel_coordinates
         from matplotlib import cm
-        
+
         df = self.iris
 
         _check_plot_works(parallel_coordinates, df, 'Name')
@@ -1634,7 +1634,7 @@ def test_parallel_coordinates(self):
         ax = parallel_coordinates(df, 'Name', color=colors)
         handles, labels = ax.get_legend_handles_labels()
         self._check_colors(handles, linecolors=colors)
-        
+
         with tm.assert_produces_warning(FutureWarning):
             parallel_coordinates(data=df, class_column='Name')
         with tm.assert_produces_warning(FutureWarning):
@@ -1845,7 +1845,7 @@ def test_area_colors(self):
         poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
         self._check_colors(poly, facecolors=rgba_colors)
         tm.close()
-        
+
         ax = df.plot(kind='area', colormap=cm.jet)
         rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
         self._check_colors(ax.get_lines(), linecolors=rgba_colors)
@@ -1920,7 +1920,7 @@ def test_hexbin_basic(self):
         # hexbin should have 2 axes in the figure, 1 for plotting and another is colorbar
         self.assertEqual(len(axes[0].figure.axes), 2)
         # return value is single axes
-        self._check_axes_shape(axes, axes_num=1, layout=(1, )) 
+        self._check_axes_shape(axes, axes_num=1, layout=(1, ))
 
 
     @slow
@@ -2081,7 +2081,7 @@ def test_errorbar_with_partial_columns(self):
         df_err = DataFrame(d_err)
         for err in [d_err, df_err]:
             ax = _check_plot_works(df.plot, yerr=err)
-            self._check_has_errorbars(ax, xerr=0, yerr=1)            
+            self._check_has_errorbars(ax, xerr=0, yerr=1)
 
     @slow
     def test_errorbar_timeseries(self):