TST: split up tests/indexing/test_indexing a bit

pandas/tests/indexing/common.py

@@ -0,0 +1,5 @@
+""" common utilities """
+
+
+def _mklbl(prefix, n):
+    return ["%s%s" % (prefix, i) for i in range(n)]

pandas/tests/indexing/test_chaining_and_caching.py

@@ -0,0 +1,358 @@
+import numpy as np
+import pandas as pd
+from pandas.core import common as com
+from pandas import (compat, DataFrame, option_context,
+                    Series, MultiIndex, date_range, Timestamp)
+from pandas.util import testing as tm
+
+
+class TestCaching(tm.TestCase):
+
+    def test_slice_consolidate_invalidate_item_cache(self):
+
+        # this is chained assignment, but will 'work'
+        with option_context('chained_assignment', None):
+
+            # #3970
+            df = DataFrame({"aa": compat.lrange(5), "bb": [2.2] * 5})
+
+            # Creates a second float block
+            df["cc"] = 0.0
+
+            # caches a reference to the 'bb' series
+            df["bb"]
+
+            # repr machinery triggers consolidation
+            repr(df)
+
+            # Assignment to wrong series
+            df['bb'].iloc[0] = 0.17
+            df._clear_item_cache()
+            self.assertAlmostEqual(df['bb'][0], 0.17)
+
+    def test_setitem_cache_updating(self):
+        # GH 5424
+        cont = ['one', 'two', 'three', 'four', 'five', 'six', 'seven']
+
+        for do_ref in [False, False]:
+            df = DataFrame({'a': cont,
+                            "b": cont[3:] + cont[:3],
+                            'c': np.arange(7)})
+
+            # ref the cache
+            if do_ref:
+                df.ix[0, "c"]
+
+            # set it
+            df.ix[7, 'c'] = 1
+
+            self.assertEqual(df.ix[0, 'c'], 0.0)
+            self.assertEqual(df.ix[7, 'c'], 1.0)
+
+        # GH 7084
+        # not updating cache on series setting with slices
+        expected = DataFrame({'A': [600, 600, 600]},
+                             index=date_range('5/7/2014', '5/9/2014'))
+        out = DataFrame({'A': [0, 0, 0]},
+                        index=date_range('5/7/2014', '5/9/2014'))
+        df = DataFrame({'C': ['A', 'A', 'A'], 'D': [100, 200, 300]})
+
+        # loop through df to update out
+        six = Timestamp('5/7/2014')
+        eix = Timestamp('5/9/2014')
+        for ix, row in df.iterrows():
+            out.loc[six:eix, row['C']] = out.loc[six:eix, row['C']] + row['D']
+
+        tm.assert_frame_equal(out, expected)
+        tm.assert_series_equal(out['A'], expected['A'])
+
+        # try via a chain indexing
+        # this actually works
+        out = DataFrame({'A': [0, 0, 0]},
+                        index=date_range('5/7/2014', '5/9/2014'))
+        for ix, row in df.iterrows():
+            v = out[row['C']][six:eix] + row['D']
+            out[row['C']][six:eix] = v
+
+        tm.assert_frame_equal(out, expected)
+        tm.assert_series_equal(out['A'], expected['A'])
+
+        out = DataFrame({'A': [0, 0, 0]},
+                        index=date_range('5/7/2014', '5/9/2014'))
+        for ix, row in df.iterrows():
+            out.loc[six:eix, row['C']] += row['D']
+
+        tm.assert_frame_equal(out, expected)
+        tm.assert_series_equal(out['A'], expected['A'])
+
+
+class TestChaining(tm.TestCase):
+
+    def test_setitem_chained_setfault(self):
+
+        # GH6026
+        # setfaults under numpy 1.7.1 (ok on 1.8)
+        data = ['right', 'left', 'left', 'left', 'right', 'left', 'timeout']
+        mdata = ['right', 'left', 'left', 'left', 'right', 'left', 'none']
+
+        df = DataFrame({'response': np.array(data)})
+        mask = df.response == 'timeout'
+        df.response[mask] = 'none'
+        tm.assert_frame_equal(df, DataFrame({'response': mdata}))
+
+        recarray = np.rec.fromarrays([data], names=['response'])
+        df = DataFrame(recarray)
+        mask = df.response == 'timeout'
+        df.response[mask] = 'none'
+        tm.assert_frame_equal(df, DataFrame({'response': mdata}))
+
+        df = DataFrame({'response': data, 'response1': data})
+        mask = df.response == 'timeout'
+        df.response[mask] = 'none'
+        tm.assert_frame_equal(df, DataFrame({'response': mdata,
+                                             'response1': data}))
+
+        # GH 6056
+        expected = DataFrame(dict(A=[np.nan, 'bar', 'bah', 'foo', 'bar']))
+        df = DataFrame(dict(A=np.array(['foo', 'bar', 'bah', 'foo', 'bar'])))
+        df['A'].iloc[0] = np.nan
+        result = df.head()
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(dict(A=np.array(['foo', 'bar', 'bah', 'foo', 'bar'])))
+        df.A.iloc[0] = np.nan
+        result = df.head()
+        tm.assert_frame_equal(result, expected)
+
+    def test_detect_chained_assignment(self):
+
+        pd.set_option('chained_assignment', 'raise')
+
+        # work with the chain
+        expected = DataFrame([[-5, 1], [-6, 3]], columns=list('AB'))
+        df = DataFrame(np.arange(4).reshape(2, 2),
+                       columns=list('AB'), dtype='int64')
+        self.assertIsNone(df.is_copy)
+        df['A'][0] = -5
+        df['A'][1] = -6
+        tm.assert_frame_equal(df, expected)
+
+        # test with the chaining
+        df = DataFrame({'A': Series(range(2), dtype='int64'),
+                        'B': np.array(np.arange(2, 4), dtype=np.float64)})
+        self.assertIsNone(df.is_copy)
+
+        def f():
+            df['A'][0] = -5
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        def f():
+            df['A'][1] = np.nan
+
+        self.assertRaises(com.SettingWithCopyError, f)
+        self.assertIsNone(df['A'].is_copy)
+
+        # using a copy (the chain), fails
+        df = DataFrame({'A': Series(range(2), dtype='int64'),
+                        'B': np.array(np.arange(2, 4), dtype=np.float64)})
+
+        def f():
+            df.loc[0]['A'] = -5
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        # doc example
+        df = DataFrame({'a': ['one', 'one', 'two', 'three',
+                              'two', 'one', 'six'],
+                        'c': Series(range(7), dtype='int64')})
+        self.assertIsNone(df.is_copy)
+        expected = DataFrame({'a': ['one', 'one', 'two', 'three',
+                                    'two', 'one', 'six'],
+                              'c': [42, 42, 2, 3, 4, 42, 6]})
+
+        def f():
+            indexer = df.a.str.startswith('o')
+            df[indexer]['c'] = 42
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        expected = DataFrame({'A': [111, 'bbb', 'ccc'], 'B': [1, 2, 3]})
+        df = DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]})
+
+        def f():
+            df['A'][0] = 111
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        def f():
+            df.loc[0]['A'] = 111
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        df.loc[0, 'A'] = 111
+        tm.assert_frame_equal(df, expected)
+
+        # make sure that is_copy is picked up reconstruction
+        # GH5475
+        df = DataFrame({"A": [1, 2]})
+        self.assertIsNone(df.is_copy)
+        with tm.ensure_clean('__tmp__pickle') as path:
+            df.to_pickle(path)
+            df2 = pd.read_pickle(path)
+            df2["B"] = df2["A"]
+            df2["B"] = df2["A"]
+
+        # a suprious raise as we are setting the entire column here
+        # GH5597
+        from string import ascii_letters as letters
+
+        def random_text(nobs=100):
+            df = []
+            for i in range(nobs):
+                idx = np.random.randint(len(letters), size=2)
+                idx.sort()
+                df.append([letters[idx[0]:idx[1]]])
+
+            return DataFrame(df, columns=['letters'])
+
+        df = random_text(100000)
+
+        # always a copy
+        x = df.iloc[[0, 1, 2]]
+        self.assertIsNotNone(x.is_copy)
+        x = df.iloc[[0, 1, 2, 4]]
+        self.assertIsNotNone(x.is_copy)
+
+        # explicity copy
+        indexer = df.letters.apply(lambda x: len(x) > 10)
+        df = df.ix[indexer].copy()
+        self.assertIsNone(df.is_copy)
+        df['letters'] = df['letters'].apply(str.lower)
+
+        # implicity take
+        df = random_text(100000)
+        indexer = df.letters.apply(lambda x: len(x) > 10)
+        df = df.ix[indexer]
+        self.assertIsNotNone(df.is_copy)
+        df['letters'] = df['letters'].apply(str.lower)
+
+        # implicity take 2
+        df = random_text(100000)
+        indexer = df.letters.apply(lambda x: len(x) > 10)
+        df = df.ix[indexer]
+        self.assertIsNotNone(df.is_copy)
+        df.loc[:, 'letters'] = df['letters'].apply(str.lower)
+
+        # should be ok even though it's a copy!
+        self.assertIsNone(df.is_copy)
+        df['letters'] = df['letters'].apply(str.lower)
+        self.assertIsNone(df.is_copy)
+
+        df = random_text(100000)
+        indexer = df.letters.apply(lambda x: len(x) > 10)
+        df.ix[indexer, 'letters'] = df.ix[indexer, 'letters'].apply(str.lower)
+
+        # an identical take, so no copy
+        df = DataFrame({'a': [1]}).dropna()
+        self.assertIsNone(df.is_copy)
+        df['a'] += 1
+
+        # inplace ops
+        # original from:
+        # http://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug
+        a = [12, 23]
+        b = [123, None]
+        c = [1234, 2345]
+        d = [12345, 23456]
+        tuples = [('eyes', 'left'), ('eyes', 'right'), ('ears', 'left'),
+                  ('ears', 'right')]
+        events = {('eyes', 'left'): a,
+                  ('eyes', 'right'): b,
+                  ('ears', 'left'): c,
+                  ('ears', 'right'): d}
+        multiind = MultiIndex.from_tuples(tuples, names=['part', 'side'])
+        zed = DataFrame(events, index=['a', 'b'], columns=multiind)
+
+        def f():
+            zed['eyes']['right'].fillna(value=555, inplace=True)
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        df = DataFrame(np.random.randn(10, 4))
+        s = df.iloc[:, 0].sort_values()
+        tm.assert_series_equal(s, df.iloc[:, 0].sort_values())
+        tm.assert_series_equal(s, df[0].sort_values())
+
+        # false positives GH6025
+        df = DataFrame({'column1': ['a', 'a', 'a'], 'column2': [4, 8, 9]})
+        str(df)
+        df['column1'] = df['column1'] + 'b'
+        str(df)
+        df = df[df['column2'] != 8]
+        str(df)
+        df['column1'] = df['column1'] + 'c'
+        str(df)
+
+        # from SO:
+        # http://stackoverflow.com/questions/24054495/potential-bug-setting-value-for-undefined-column-using-iloc
+        df = DataFrame(np.arange(0, 9), columns=['count'])
+        df['group'] = 'b'
+
+        def f():
+            df.iloc[0:5]['group'] = 'a'
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        # mixed type setting
+        # same dtype & changing dtype
+        df = DataFrame(dict(A=date_range('20130101', periods=5),
+                            B=np.random.randn(5),
+                            C=np.arange(5, dtype='int64'),
+                            D=list('abcde')))
+
+        def f():
+            df.ix[2]['D'] = 'foo'
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        def f():
+            df.ix[2]['C'] = 'foo'
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        def f():
+            df['C'][2] = 'foo'
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+    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']})
+        mask = pd.isnull(df.c)
+
+        def f():
+            df[['c']][mask] = df[['b']][mask]
+
+        self.assertRaises(com.SettingWithCopyError, f)
+
+        # invalid warning as we are returning a new object
+        # GH 8730
+        df1 = DataFrame({'x': Series(['a', 'b', 'c']),
+                         'y': Series(['d', 'e', 'f'])})
+        df2 = df1[['x']]
+
+        # this should not raise
+        df2['y'] = ['g', 'h', 'i']
+
+    def test_detect_chained_assignment_warnings(self):
+
+        # warnings
+        with option_context('chained_assignment', 'warn'):
+            df = DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]})
+            with tm.assert_produces_warning(
+                    expected_warning=com.SettingWithCopyWarning):
+                df.loc[0]['A'] = 111