CLN: Remove unnecessary usages of pd. in tests

Author: jschendel

pandas/tests/indexing/test_chaining_and_caching.py

@@ -318,9 +318,9 @@ def random_text(nobs=100):
     def test_setting_with_copy_bug(self):
 
         # operating on a copy
-        df = pd.DataFrame({'a': list(range(4)),
-                           'b': list('ab..'),
-                           'c': ['a', 'b', np.nan, 'd']})
+        df = DataFrame({'a': list(range(4)),
+                        'b': list('ab..'),
+                        'c': ['a', 'b', np.nan, 'd']})
         mask = pd.isna(df.c)
 
         def f():

pandas/tests/indexing/test_datetime.py

@@ -11,8 +11,8 @@ def test_setitem_with_datetime_tz(self):
         # support .loc with alignment and tz-aware DatetimeIndex
         mask = np.array([True, False, True, False])
 
-        idx = pd.date_range('20010101', periods=4, tz='UTC')
-        df = pd.DataFrame({'a': np.arange(4)}, index=idx).astype('float64')
+        idx = date_range('20010101', periods=4, tz='UTC')
+        df = DataFrame({'a': np.arange(4)}, index=idx).astype('float64')
 
         result = df.copy()
         result.loc[mask, :] = df.loc[mask, :]
@@ -22,8 +22,8 @@ def test_setitem_with_datetime_tz(self):
         result.loc[mask] = df.loc[mask]
         tm.assert_frame_equal(result, df)
 
-        idx = pd.date_range('20010101', periods=4)
-        df = pd.DataFrame({'a': np.arange(4)}, index=idx).astype('float64')
+        idx = date_range('20010101', periods=4)
+        df = DataFrame({'a': np.arange(4)}, index=idx).astype('float64')
 
         result = df.copy()
         result.loc[mask, :] = df.loc[mask, :]
@@ -127,10 +127,9 @@ def test_indexing_with_datetimeindex_tz(self):
 
         # GH 12050
         # indexing on a series with a datetimeindex with tz
-        index = pd.date_range('2015-01-01', periods=2, tz='utc')
+        index = date_range('2015-01-01', periods=2, tz='utc')
 
-        ser = pd.Series(range(2), index=index,
-                        dtype='int64')
+        ser = Series(range(2), index=index, dtype='int64')
 
         # list-like indexing
 
@@ -141,7 +140,7 @@ def test_indexing_with_datetimeindex_tz(self):
             # setitem
             result = ser.copy()
             result[sel] = 1
-            expected = pd.Series(1, index=index)
+            expected = Series(1, index=index)
             tm.assert_series_equal(result, expected)
 
             # .loc getitem
@@ -150,7 +149,7 @@ def test_indexing_with_datetimeindex_tz(self):
             # .loc setitem
             result = ser.copy()
             result.loc[sel] = 1
-            expected = pd.Series(1, index=index)
+            expected = Series(1, index=index)
             tm.assert_series_equal(result, expected)
 
         # single element indexing
@@ -161,7 +160,7 @@ def test_indexing_with_datetimeindex_tz(self):
         # setitem
         result = ser.copy()
         result[index[1]] = 5
-        expected = pd.Series([0, 5], index=index)
+        expected = Series([0, 5], index=index)
         tm.assert_series_equal(result, expected)
 
         # .loc getitem
@@ -170,16 +169,15 @@ def test_indexing_with_datetimeindex_tz(self):
         # .loc setitem
         result = ser.copy()
         result.loc[index[1]] = 5
-        expected = pd.Series([0, 5], index=index)
+        expected = Series([0, 5], index=index)
         tm.assert_series_equal(result, expected)
 
     def test_partial_setting_with_datetimelike_dtype(self):
 
         # GH9478
         # a datetimeindex alignment issue with partial setting
-        df = pd.DataFrame(np.arange(6.).reshape(3, 2), columns=list('AB'),
-                          index=pd.date_range('1/1/2000', periods=3,
-                                              freq='1H'))
+        df = DataFrame(np.arange(6.).reshape(3, 2), columns=list('AB'),
+                       index=date_range('1/1/2000', periods=3, freq='1H'))
         expected = df.copy()
         expected['C'] = [expected.index[0]] + [pd.NaT, pd.NaT]
 
@@ -196,7 +194,7 @@ def test_loc_setitem_datetime(self):
         for conv in [lambda x: x, lambda x: x.to_datetime64(),
                      lambda x: x.to_pydatetime(), lambda x: np.datetime64(x)]:
 
-            df = pd.DataFrame()
+            df = DataFrame()
             df.loc[conv(dt1), 'one'] = 100
             df.loc[conv(dt2), 'one'] = 200
 

pandas/tests/indexing/test_iloc.py

@@ -173,7 +173,7 @@ def test_iloc_getitem_neg_int_can_reach_first_index(self):
         tm.assert_series_equal(result, expected)
 
         # check the length 1 Series case highlighted in GH10547
-        expected = pd.Series(['a'], index=['A'])
+        expected = Series(['a'], index=['A'])
         result = expected.iloc[[-1]]
         tm.assert_series_equal(result, expected)
 
@@ -285,9 +285,7 @@ def test_iloc_setitem(self):
     def test_iloc_setitem_int_multiindex_series(
             self, data, indexes, values, expected_k):
         # GH17148
-        df = pd.DataFrame(
-            data=data,
-            columns=['i', 'j', 'k'])
+        df = DataFrame(data=data, columns=['i', 'j', 'k'])
         df = df.set_index(['i', 'j'])
 
         series = df.k.copy()
@@ -597,13 +595,13 @@ def test_iloc_non_unique_indexing(self):
         idx = np.array(lrange(30)) * 99
         expected = df.iloc[idx]
 
-        df3 = pd.concat([df, 2 * df, 3 * df])
+        df3 = concat([df, 2 * df, 3 * df])
         result = df3.iloc[idx]
 
         tm.assert_frame_equal(result, expected)
 
         df2 = DataFrame({'A': [0.1] * 1000, 'B': [1] * 1000})
-        df2 = pd.concat([df2, 2 * df2, 3 * df2])
+        df2 = concat([df2, 2 * df2, 3 * df2])
 
         sidx = df2.index.to_series()
         expected = df2.iloc[idx[idx <= sidx.max()]]
@@ -615,8 +613,7 @@ def test_iloc_non_unique_indexing(self):
             new_list.append(s * 3)
 
         expected = DataFrame(new_list)
-        expected = pd.concat([expected, DataFrame(index=idx[idx > sidx.max()])
-                              ])
+        expected = concat([expected, DataFrame(index=idx[idx > sidx.max()])])
         with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
             result = df2.loc[idx]
         tm.assert_frame_equal(result, expected, check_index_type=False)

pandas/tests/indexing/test_indexing.py

@@ -70,7 +70,7 @@ def test_inf_upcast(self):
         # np.inf should cause an index to convert to float
 
         # Test with np.inf in rows
-        df = pd.DataFrame(columns=[0])
+        df = DataFrame(columns=[0])
         df.loc[1] = 1
         df.loc[2] = 2
         df.loc[np.inf] = 3
@@ -83,7 +83,7 @@ def test_inf_upcast(self):
         tm.assert_index_equal(result, expected)
 
         # Test with np.inf in columns
-        df = pd.DataFrame()
+        df = DataFrame()
         df.loc[0, 0] = 1
         df.loc[1, 1] = 2
         df.loc[0, np.inf] = 3
@@ -274,8 +274,8 @@ def test_indexing_mixed_frame_bug(self):
 
     def test_multitype_list_index_access(self):
         # GH 10610
-        df = pd.DataFrame(np.random.random((10, 5)),
-                          columns=["a"] + [20, 21, 22, 23])
+        df = DataFrame(np.random.random((10, 5)),
+                       columns=["a"] + [20, 21, 22, 23])
 
         with pytest.raises(KeyError):
             df[[22, 26, -8]]
@@ -469,16 +469,15 @@ def test_string_slice(self):
         # GH 14424
         # string indexing against datetimelike with object
         # dtype should properly raises KeyError
-        df = pd.DataFrame([1], pd.Index([pd.Timestamp('2011-01-01')],
-                                        dtype=object))
+        df = DataFrame([1], Index([pd.Timestamp('2011-01-01')], dtype=object))
         assert df.index.is_all_dates
         with pytest.raises(KeyError):
             df['2011']
 
         with pytest.raises(KeyError):
             df.loc['2011', 0]
 
-        df = pd.DataFrame()
+        df = DataFrame()
         assert not df.index.is_all_dates
         with pytest.raises(KeyError):
             df['2011']
@@ -571,7 +570,7 @@ def test_astype_assignment_with_dups(self):
 
         # GH 4686
         # assignment with dups that has a dtype change
-        cols = pd.MultiIndex.from_tuples([('A', '1'), ('B', '1'), ('A', '2')])
+        cols = MultiIndex.from_tuples([('A', '1'), ('B', '1'), ('A', '2')])
         df = DataFrame(np.arange(3).reshape((1, 3)),
                        columns=cols, dtype=object)
         index = df.index.copy()
@@ -584,23 +583,23 @@ def test_astype_assignment_with_dups(self):
         # expected = Series({'float64': 2, 'object': 1}).sort_index()
 
     @pytest.mark.parametrize("index,val", [
-        (pd.Index([0, 1, 2]), 2),
-        (pd.Index([0, 1, '2']), '2'),
-        (pd.Index([0, 1, 2, np.inf, 4]), 4),
-        (pd.Index([0, 1, 2, np.nan, 4]), 4),
-        (pd.Index([0, 1, 2, np.inf]), np.inf),
-        (pd.Index([0, 1, 2, np.nan]), np.nan),
+        (Index([0, 1, 2]), 2),
+        (Index([0, 1, '2']), '2'),
+        (Index([0, 1, 2, np.inf, 4]), 4),
+        (Index([0, 1, 2, np.nan, 4]), 4),
+        (Index([0, 1, 2, np.inf]), np.inf),
+        (Index([0, 1, 2, np.nan]), np.nan),
     ])
     def test_index_contains(self, index, val):
         assert val in index
 
     @pytest.mark.parametrize("index,val", [
-        (pd.Index([0, 1, 2]), '2'),
-        (pd.Index([0, 1, '2']), 2),
-        (pd.Index([0, 1, 2, np.inf]), 4),
-        (pd.Index([0, 1, 2, np.nan]), 4),
-        (pd.Index([0, 1, 2, np.inf]), np.nan),
-        (pd.Index([0, 1, 2, np.nan]), np.inf),
+        (Index([0, 1, 2]), '2'),
+        (Index([0, 1, '2']), 2),
+        (Index([0, 1, 2, np.inf]), 4),
+        (Index([0, 1, 2, np.nan]), 4),
+        (Index([0, 1, 2, np.inf]), np.nan),
+        (Index([0, 1, 2, np.nan]), np.inf),
         # Checking if np.inf in Int64Index should not cause an OverflowError
         # Related to GH 16957
         (pd.Int64Index([0, 1, 2]), np.inf),
@@ -705,7 +704,7 @@ def test_float_index_non_scalar_assignment(self):
         tm.assert_frame_equal(df, df2)
 
     def test_float_index_at_iat(self):
-        s = pd.Series([1, 2, 3], index=[0.1, 0.2, 0.3])
+        s = Series([1, 2, 3], index=[0.1, 0.2, 0.3])
         for el, item in s.iteritems():
             assert s.at[el] == item
         for i in range(len(s)):
@@ -744,7 +743,7 @@ def run_tests(df, rhs, right):
 
         xs = np.arange(20).reshape(5, 4)
         cols = ['jim', 'joe', 'jolie', 'joline']
-        df = pd.DataFrame(xs, columns=cols, index=list('abcde'))
+        df = DataFrame(xs, columns=cols, index=list('abcde'))
 
         # right hand side; permute the indices and multiplpy by -2
         rhs = -2 * df.iloc[3:0:-1, 2:0:-1]
@@ -795,9 +794,9 @@ def test_slice_with_zero_step_raises(self):
                                    lambda: s.ix[::0])
 
     def test_indexing_assignment_dict_already_exists(self):
-        df = pd.DataFrame({'x': [1, 2, 6],
-                           'y': [2, 2, 8],
-                           'z': [-5, 0, 5]}).set_index('z')
+        df = DataFrame({'x': [1, 2, 6],
+                        'y': [2, 2, 8],
+                        'z': [-5, 0, 5]}).set_index('z')
         expected = df.copy()
         rhs = dict(x=9, y=99)
         df.loc[5] = rhs
@@ -819,15 +818,15 @@ def test_range_in_series_indexing(self):
         # range can cause an indexing error
         # GH 11652
         for x in [5, 999999, 1000000]:
-            s = pd.Series(index=range(x))
+            s = Series(index=range(x))
             s.loc[range(1)] = 42
             tm.assert_series_equal(s.loc[range(1)], Series(42.0, index=[0]))
 
             s.loc[range(2)] = 43
             tm.assert_series_equal(s.loc[range(2)], Series(43.0, index=[0, 1]))
 
     def test_non_reducing_slice(self):
-        df = pd.DataFrame([[0, 1], [2, 3]])
+        df = DataFrame([[0, 1], [2, 3]])
 
         slices = [
             # pd.IndexSlice[:, :],
@@ -841,23 +840,23 @@ def test_non_reducing_slice(self):
             slice(None, None, None),
             [0, 1],
             np.array([0, 1]),
-            pd.Series([0, 1])
+            Series([0, 1])
         ]
         for slice_ in slices:
             tslice_ = _non_reducing_slice(slice_)
             assert isinstance(df.loc[tslice_], DataFrame)
 
     def test_list_slice(self):
         # like dataframe getitem
-        slices = [['A'], pd.Series(['A']), np.array(['A'])]
-        df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['A', 'B'])
+        slices = [['A'], Series(['A']), np.array(['A'])]
+        df = DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['A', 'B'])
         expected = pd.IndexSlice[:, ['A']]
         for subset in slices:
             result = _non_reducing_slice(subset)
             tm.assert_frame_equal(df.loc[result], df.loc[expected])
 
     def test_maybe_numeric_slice(self):
-        df = pd.DataFrame({'A': [1, 2], 'B': ['c', 'd'], 'C': [True, False]})
+        df = DataFrame({'A': [1, 2], 'B': ['c', 'd'], 'C': [True, False]})
         result = _maybe_numeric_slice(df, slice_=None)
         expected = pd.IndexSlice[:, ['A']]
         assert result == expected
@@ -870,20 +869,19 @@ def test_maybe_numeric_slice(self):
 
     def test_partial_boolean_frame_indexing(self):
         # GH 17170
-        df = pd.DataFrame(np.arange(9.).reshape(3, 3),
-                          index=list('abc'),
-                          columns=list('ABC'))
-        index_df = pd.DataFrame(1, index=list('ab'), columns=list('AB'))
+        df = DataFrame(np.arange(9.).reshape(3, 3),
+                       index=list('abc'), columns=list('ABC'))
+        index_df = DataFrame(1, index=list('ab'), columns=list('AB'))
         result = df[index_df.notnull()]
-        expected = pd.DataFrame(np.array([[0., 1., np.nan],
-                                          [3., 4., np.nan],
-                                          [np.nan] * 3]),
-                                index=list('abc'),
-                                columns=list('ABC'))
+        expected = DataFrame(np.array([[0., 1., np.nan],
+                                       [3., 4., np.nan],
+                                       [np.nan] * 3]),
+                             index=list('abc'),
+                             columns=list('ABC'))
         tm.assert_frame_equal(result, expected)
 
     def test_no_reference_cycle(self):
-        df = pd.DataFrame({'a': [0, 1], 'b': [2, 3]})
+        df = DataFrame({'a': [0, 1], 'b': [2, 3]})
         for name in ('loc', 'iloc', 'at', 'iat'):
             getattr(df, name)
         with catch_warnings(record=True):

pandas/tests/indexing/test_indexing_slow.py

@@ -70,7 +70,7 @@ def loop(mi, df, keys):
             keys += list(map(lambda t: t[:-1], vals[::n // m]))
 
             # covers both unique index and non-unique index
-            df = pd.DataFrame(vals, columns=cols)
+            df = DataFrame(vals, columns=cols)
             a, b = pd.concat([df, df]), df.drop_duplicates(subset=cols[:-1])
 
             for frame in a, b:

pandas/tests/indexing/test_interval.py

@@ -179,7 +179,7 @@ def test_non_unique(self):
 
         idx = IntervalIndex.from_tuples([(1, 3), (3, 7)])
 
-        s = pd.Series(range(len(idx)), index=idx)
+        s = Series(range(len(idx)), index=idx)
 
         result = s.loc[Interval(1, 3)]
         assert result == 0