Added tests for all implementation

pandas/tests/groupby/test_groupby.py

@@ -2117,25 +2117,33 @@ def interweave(list_obj):
  exp = DataFrame({'key': keys, 'val': _exp_vals})
  assert_frame_equal(result, exp)
 
+ @pytest.mark.parametrize("agg_func", ['any', 'all'])
  @pytest.mark.parametrize("skipna", [True, False])
- @pytest.mark.parametrize("vals,exp", [
- (['foo', 'bar', 'baz'], True), (['foo', '', ''], True),
- (['', '', ''], False), ([1, 2, 3], True), ([1, 0, 0], True),
- ([0, 0, 0], False), ([1., 2., 3.], True), ([1., 0., 0.], True),
- ([0., 0., 0.], False), ([True, True, True], True),
- ([True, False, False], True), ([False, False, False], False),
- ([np.nan, np.nan, np.nan], False)
+ @pytest.mark.parametrize("vals", [
+ ['foo', 'bar', 'baz'], ['foo', '', ''], ['', '', ''],
+ [1, 2, 3], [1, 0, 0], [0, 0, 0],
+ [1., 2., 3.], [1., 0., 0.], [0., 0., 0.],
+ [True, True, True], [True, False, False], [False, False, False],
+ [np.nan, np.nan, np.nan]
  ])
- def test_groupby_any(self, skipna, vals, exp):
+ def test_groupby_bool_aggs(self, agg_func, skipna, vals):
  df = DataFrame({'key': ['a'] * 3 + ['b'] * 3, 'val': vals * 2})
 
- # edge case for missing data with skipna=False
- if not(skipna) and all(isna(vals)):
- exp = True
+ if compat.PY3:
+ import builtins as bltins
+ else:
+ import __builtins__ as bltins
+
+ # Figure out expectation using Python builtin
+ exp = getattr(bltins, agg_func)(vals)
+
+ # edge case for missing data with skipna and 'any'
+ if skipna and all(isna(vals)) and agg_func=='any':
+ exp = False
 
  exp_df = DataFrame([exp] * 2, columns=['val'], index=pd.Index(
  ['a', 'b'], name='key'))
- result = df.groupby('key').any(skipna=skipna)
+ result = getattr(df.groupby('key'), agg_func)(skipna=skipna)
  assert_frame_equal(result, exp_df)
 
  def test_dont_clobber_name_column(self):