# -*- coding: utf-8 -*- import pytest from warnings import catch_warnings import numpy as np from pandas import (Series, DataFrame, Index, Float64Index, Int64Index, RangeIndex) from pandas.util.testing import assert_series_equal, assert_almost_equal import pandas.util.testing as tm class TestFloatIndexers(object): def check(self, result, original, indexer, getitem): """ comparator for results we need to take care if we are indexing on a Series or a frame """ if isinstance(original, Series): expected = original.iloc[indexer] else: if getitem: expected = original.iloc[:, indexer] else: expected = original.iloc[indexer] assert_almost_equal(result, expected) def test_scalar_error(self): # GH 4892 # float_indexers should raise exceptions # on appropriate Index types & accessors # this duplicates the code below # but is spefically testing for the error # message for index in [tm.makeStringIndex, tm.makeUnicodeIndex, tm.makeCategoricalIndex, tm.makeDateIndex, tm.makeTimedeltaIndex, tm.makePeriodIndex, tm.makeIntIndex, tm.makeRangeIndex]: i = index(5) s = Series(np.arange(len(i)), index=i) def f(): s.iloc[3.0] tm.assert_raises_regex(TypeError, 'cannot do positional indexing', f) def f(): s.iloc[3.0] = 0 pytest.raises(TypeError, f) def test_scalar_non_numeric(self): # GH 4892 # float_indexers should raise exceptions # on appropriate Index types & accessors for index in [tm.makeStringIndex, tm.makeUnicodeIndex, tm.makeCategoricalIndex, tm.makeDateIndex, tm.makeTimedeltaIndex, tm.makePeriodIndex]: i = index(5) for s in [Series( np.arange(len(i)), index=i), DataFrame( np.random.randn( len(i), len(i)), index=i, columns=i)]: # getting for idxr, getitem in [(lambda x: x.ix, False), (lambda x: x.iloc, False), (lambda x: x, True)]: def f(): with catch_warnings(record=True): idxr(s)[3.0] # gettitem on a DataFrame is a KeyError as it is indexing # via labels on the columns if getitem and isinstance(s, DataFrame): error = KeyError else: error = TypeError pytest.raises(error, f) # label based can be a TypeError or KeyError def f(): s.loc[3.0] if s.index.inferred_type in ['string', 'unicode', 'mixed']: error = KeyError else: error = TypeError pytest.raises(error, f) # contains assert 3.0 not in s # setting with a float fails with iloc def f(): s.iloc[3.0] = 0 pytest.raises(TypeError, f) # setting with an indexer if s.index.inferred_type in ['categorical']: # Value or Type Error pass elif s.index.inferred_type in ['datetime64', 'timedelta64', 'period']: # these should prob work # and are inconsisten between series/dataframe ATM # for idxr in [lambda x: x.ix, # lambda x: x]: # s2 = s.copy() # def f(): # idxr(s2)[3.0] = 0 # pytest.raises(TypeError, f) pass else: s2 = s.copy() s2.loc[3.0] = 10 assert s2.index.is_object() for idxr in [lambda x: x.ix, lambda x: x]: s2 = s.copy() with catch_warnings(record=True): idxr(s2)[3.0] = 0 assert s2.index.is_object() # fallsback to position selection, series only s = Series(np.arange(len(i)), index=i) s[3] pytest.raises(TypeError, lambda: s[3.0]) def test_scalar_with_mixed(self): s2 = Series([1, 2, 3], index=['a', 'b', 'c']) s3 = Series([1, 2, 3], index=['a', 'b', 1.5]) # lookup in a pure string index # with an invalid indexer for idxr in [lambda x: x.ix, lambda x: x, lambda x: x.iloc]: def f(): with catch_warnings(record=True): idxr(s2)[1.0] pytest.raises(TypeError, f) pytest.raises(KeyError, lambda: s2.loc[1.0]) result = s2.loc['b'] expected = 2 assert result == expected # mixed index so we have label # indexing for idxr in [lambda x: x]: def f(): idxr(s3)[1.0] pytest.raises(TypeError, f) result = idxr(s3)[1] expected = 2 assert result == expected # mixed index so we have label # indexing for idxr in [lambda x: x.ix]: with catch_warnings(record=True): def f(): idxr(s3)[1.0] pytest.raises(TypeError, f) result = idxr(s3)[1] expected = 2 assert result == expected pytest.raises(TypeError, lambda: s3.iloc[1.0]) pytest.raises(KeyError, lambda: s3.loc[1.0]) result = s3.loc[1.5] expected = 3 assert result == expected def test_scalar_integer(self): # test how scalar float indexers work on int indexes # integer index for i in [Int64Index(range(5)), RangeIndex(5)]: for s in [Series(np.arange(len(i))), DataFrame(np.random.randn(len(i), len(i)), index=i, columns=i)]: # coerce to equal int for idxr, getitem in [(lambda x: x.ix, False), (lambda x: x.loc, False), (lambda x: x, True)]: with catch_warnings(record=True): result = idxr(s)[3.0] self.check(result, s, 3, getitem) # coerce to equal int for idxr, getitem in [(lambda x: x.ix, False), (lambda x: x.loc, False), (lambda x: x, True)]: if isinstance(s, Series): def compare(x, y): assert x == y expected = 100 else: compare = tm.assert_series_equal if getitem: expected = Series(100, index=range(len(s)), name=3) else: expected = Series(100., index=range(len(s)), name=3) s2 = s.copy() with catch_warnings(record=True): idxr(s2)[3.0] = 100 result = idxr(s2)[3.0] compare(result, expected) result = idxr(s2)[3] compare(result, expected) # contains # coerce to equal int assert 3.0 in s def test_scalar_float(self): # scalar float indexers work on a float index index = Index(np.arange(5.)) for s in [Series(np.arange(len(index)), index=index), DataFrame(np.random.randn(len(index), len(index)), index=index, columns=index)]: # assert all operations except for iloc are ok indexer = index[3] for idxr, getitem in [(lambda x: x.ix, False), (lambda x: x.loc, False), (lambda x: x, True)]: # getting with catch_warnings(record=True): result = idxr(s)[indexer] self.check(result, s, 3, getitem) # setting s2 = s.copy() def f(): with catch_warnings(record=True): idxr(s2)[indexer] = expected with catch_warnings(record=True): result = idxr(s2)[indexer] self.check(result, s, 3, getitem) # random integer is a KeyError with catch_warnings(record=True): pytest.raises(KeyError, lambda: idxr(s)[3.5]) # contains assert 3.0 in s # iloc succeeds with an integer expected = s.iloc[3] s2 = s.copy() s2.iloc[3] = expected result = s2.iloc[3] self.check(result, s, 3, False) # iloc raises with a float pytest.raises(TypeError, lambda: s.iloc[3.0]) def g(): s2.iloc[3.0] = 0 pytest.raises(TypeError, g) def test_slice_non_numeric(self): # GH 4892 # float_indexers should raise exceptions # on appropriate Index types & accessors for index in [tm.makeStringIndex, tm.makeUnicodeIndex, tm.makeDateIndex, tm.makeTimedeltaIndex, tm.makePeriodIndex]: index = index(5) for s in [Series(range(5), index=index), DataFrame(np.random.randn(5, 2), index=index)]: # getitem for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]: def f(): s.iloc[l] pytest.raises(TypeError, f) for idxr in [lambda x: x.ix, lambda x: x.loc, lambda x: x.iloc, lambda x: x]: def f(): with catch_warnings(record=True): idxr(s)[l] pytest.raises(TypeError, f) # setitem for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]: def f(): s.iloc[l] = 0 pytest.raises(TypeError, f) for idxr in [lambda x: x.ix, lambda x: x.loc, lambda x: x.iloc, lambda x: x]: def f(): with catch_warnings(record=True): idxr(s)[l] = 0 pytest.raises(TypeError, f) def test_slice_integer(self): # same as above, but for Integer based indexes # these coerce to a like integer # oob indicates if we are out of bounds # of positional indexing for index, oob in [(Int64Index(range(5)), False), (RangeIndex(5), False), (Int64Index(range(5)) + 10, True)]: # s is an in-range index s = Series(range(5), index=index) # getitem for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]: for idxr in [lambda x: x.loc, lambda x: x.ix]: with catch_warnings(record=True): result = idxr(s)[l] # these are all label indexing # except getitem which is positional # empty if oob: indexer = slice(0, 0) else: indexer = slice(3, 5) self.check(result, s, indexer, False) # positional indexing def f(): s[l] pytest.raises(TypeError, f) # getitem out-of-bounds for l in [slice(-6, 6), slice(-6.0, 6.0)]: for idxr in [lambda x: x.loc, lambda x: x.ix]: with catch_warnings(record=True): result = idxr(s)[l] # these are all label indexing # except getitem which is positional # empty if oob: indexer = slice(0, 0) else: indexer = slice(-6, 6) self.check(result, s, indexer, False) # positional indexing def f(): s[slice(-6.0, 6.0)] pytest.raises(TypeError, f) # getitem odd floats for l, res1 in [(slice(2.5, 4), slice(3, 5)), (slice(2, 3.5), slice(2, 4)), (slice(2.5, 3.5), slice(3, 4))]: for idxr in [lambda x: x.loc, lambda x: x.ix]: with catch_warnings(record=True): result = idxr(s)[l] if oob: res = slice(0, 0) else: res = res1 self.check(result, s, res, False) # positional indexing def f(): s[l] pytest.raises(TypeError, f) # setitem for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]: for idxr in [lambda x: x.loc, lambda x: x.ix]: sc = s.copy() with catch_warnings(record=True): idxr(sc)[l] = 0 result = idxr(sc)[l].values.ravel() assert (result == 0).all() # positional indexing def f(): s[l] = 0 pytest.raises(TypeError, f) def test_integer_positional_indexing(self): """ make sure that we are raising on positional indexing w.r.t. an integer index """ s = Series(range(2, 6), index=range(2, 6)) result = s[2:4] expected = s.iloc[2:4] assert_series_equal(result, expected) for idxr in [lambda x: x, lambda x: x.iloc]: for l in [slice(2, 4.0), slice(2.0, 4), slice(2.0, 4.0)]: def f(): idxr(s)[l] pytest.raises(TypeError, f) def test_slice_integer_frame_getitem(self): # similar to above, but on the getitem dim (of a DataFrame) for index in [Int64Index(range(5)), RangeIndex(5)]: s = DataFrame(np.random.randn(5, 2), index=index) def f(idxr): # getitem for l in [slice(0.0, 1), slice(0, 1.0), slice(0.0, 1.0)]: result = idxr(s)[l] indexer = slice(0, 2) self.check(result, s, indexer, False) # positional indexing def f(): s[l] pytest.raises(TypeError, f) # getitem out-of-bounds for l in [slice(-10, 10), slice(-10.0, 10.0)]: result = idxr(s)[l] self.check(result, s, slice(-10, 10), True) # positional indexing def f(): s[slice(-10.0, 10.0)] pytest.raises(TypeError, f) # getitem odd floats for l, res in [(slice(0.5, 1), slice(1, 2)), (slice(0, 0.5), slice(0, 1)), (slice(0.5, 1.5), slice(1, 2))]: result = idxr(s)[l] self.check(result, s, res, False) # positional indexing def f(): s[l] pytest.raises(TypeError, f) # setitem for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]: sc = s.copy() idxr(sc)[l] = 0 result = idxr(sc)[l].values.ravel() assert (result == 0).all() # positional indexing def f(): s[l] = 0 pytest.raises(TypeError, f) f(lambda x: x.loc) with catch_warnings(record=True): f(lambda x: x.ix) def test_slice_float(self): # same as above, but for floats index = Index(np.arange(5.)) + 0.1 for s in [Series(range(5), index=index), DataFrame(np.random.randn(5, 2), index=index)]: for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]: expected = s.iloc[3:4] for idxr in [lambda x: x.ix, lambda x: x.loc, lambda x: x]: # getitem with catch_warnings(record=True): result = idxr(s)[l] if isinstance(s, Series): tm.assert_series_equal(result, expected) else: tm.assert_frame_equal(result, expected) # setitem s2 = s.copy() with catch_warnings(record=True): idxr(s2)[l] = 0 result = idxr(s2)[l].values.ravel() assert (result == 0).all() def test_floating_index_doc_example(self): index = Index([1.5, 2, 3, 4.5, 5]) s = Series(range(5), index=index) assert s[3] == 2 assert s.loc[3] == 2 assert s.loc[3] == 2 assert s.iloc[3] == 3 def test_floating_misc(self): # related 236 # scalar/slicing of a float index s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64) # label based slicing result1 = s[1.0:3.0] result2 = s.loc[1.0:3.0] result3 = s.loc[1.0:3.0] assert_series_equal(result1, result2) assert_series_equal(result1, result3) # exact indexing when found result1 = s[5.0] result2 = s.loc[5.0] result3 = s.loc[5.0] assert result1 == result2 assert result1 == result3 result1 = s[5] result2 = s.loc[5] result3 = s.loc[5] assert result1 == result2 assert result1 == result3 assert s[5.0] == s[5] # value not found (and no fallbacking at all) # scalar integers pytest.raises(KeyError, lambda: s.loc[4]) pytest.raises(KeyError, lambda: s.loc[4]) pytest.raises(KeyError, lambda: s[4]) # fancy floats/integers create the correct entry (as nan) # fancy tests expected = Series([2, 0], index=Float64Index([5.0, 0.0])) for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float assert_series_equal(s[fancy_idx], expected) assert_series_equal(s.loc[fancy_idx], expected) assert_series_equal(s.loc[fancy_idx], expected) expected = Series([2, 0], index=Index([5, 0], dtype='int64')) for fancy_idx in [[5, 0], np.array([5, 0])]: # int assert_series_equal(s[fancy_idx], expected) assert_series_equal(s.loc[fancy_idx], expected) assert_series_equal(s.loc[fancy_idx], expected) # all should return the same as we are slicing 'the same' result1 = s.loc[2:5] result2 = s.loc[2.0:5.0] result3 = s.loc[2.0:5] result4 = s.loc[2.1:5] assert_series_equal(result1, result2) assert_series_equal(result1, result3) assert_series_equal(result1, result4) # previously this did fallback indexing result1 = s[2:5] result2 = s[2.0:5.0] result3 = s[2.0:5] result4 = s[2.1:5] assert_series_equal(result1, result2) assert_series_equal(result1, result3) assert_series_equal(result1, result4) result1 = s.loc[2:5] result2 = s.loc[2.0:5.0] result3 = s.loc[2.0:5] result4 = s.loc[2.1:5] assert_series_equal(result1, result2) assert_series_equal(result1, result3) assert_series_equal(result1, result4) # combined test result1 = s.loc[2:5] result2 = s.loc[2:5] result3 = s[2:5] assert_series_equal(result1, result2) assert_series_equal(result1, result3) # list selection result1 = s[[0.0, 5, 10]] result2 = s.loc[[0.0, 5, 10]] result3 = s.loc[[0.0, 5, 10]] result4 = s.iloc[[0, 2, 4]] assert_series_equal(result1, result2) assert_series_equal(result1, result3) assert_series_equal(result1, result4) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result1 = s[[1.6, 5, 10]] with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result2 = s.loc[[1.6, 5, 10]] with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result3 = s.loc[[1.6, 5, 10]] assert_series_equal(result1, result2) assert_series_equal(result1, result3) assert_series_equal(result1, Series( [np.nan, 2, 4], index=[1.6, 5, 10])) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result1 = s[[0, 1, 2]] with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result2 = s.loc[[0, 1, 2]] with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): result3 = s.loc[[0, 1, 2]] assert_series_equal(result1, result2) assert_series_equal(result1, result3) assert_series_equal(result1, Series( [0.0, np.nan, np.nan], index=[0, 1, 2])) result1 = s.loc[[2.5, 5]] result2 = s.loc[[2.5, 5]] assert_series_equal(result1, result2) assert_series_equal(result1, Series([1, 2], index=[2.5, 5.0])) result1 = s[[2.5]] result2 = s.loc[[2.5]] result3 = s.loc[[2.5]] assert_series_equal(result1, result2) assert_series_equal(result1, result3) assert_series_equal(result1, Series([1], index=[2.5])) def test_floating_tuples(self): # see gh-13509 s = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.1, 0.2], name='foo') result = s[0.0] assert result == (1, 1) expected = Series([(1, 1), (2, 2)], index=[0.0, 0.0], name='foo') s = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.0, 0.2], name='foo') result = s[0.0] tm.assert_series_equal(result, expected) def test_float64index_slicing_bug(self): # GH 5557, related to slicing a float index ser = {256: 2321.0, 1: 78.0, 2: 2716.0, 3: 0.0, 4: 369.0, 5: 0.0, 6: 269.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 3536.0, 11: 0.0, 12: 24.0, 13: 0.0, 14: 931.0, 15: 0.0, 16: 101.0, 17: 78.0, 18: 9643.0, 19: 0.0, 20: 0.0, 21: 0.0, 22: 63761.0, 23: 0.0, 24: 446.0, 25: 0.0, 26: 34773.0, 27: 0.0, 28: 729.0, 29: 78.0, 30: 0.0, 31: 0.0, 32: 3374.0, 33: 0.0, 34: 1391.0, 35: 0.0, 36: 361.0, 37: 0.0, 38: 61808.0, 39: 0.0, 40: 0.0, 41: 0.0, 42: 6677.0, 43: 0.0, 44: 802.0, 45: 0.0, 46: 2691.0, 47: 0.0, 48: 3582.0, 49: 0.0, 50: 734.0, 51: 0.0, 52: 627.0, 53: 70.0, 54: 2584.0, 55: 0.0, 56: 324.0, 57: 0.0, 58: 605.0, 59: 0.0, 60: 0.0, 61: 0.0, 62: 3989.0, 63: 10.0, 64: 42.0, 65: 0.0, 66: 904.0, 67: 0.0, 68: 88.0, 69: 70.0, 70: 8172.0, 71: 0.0, 72: 0.0, 73: 0.0, 74: 64902.0, 75: 0.0, 76: 347.0, 77: 0.0, 78: 36605.0, 79: 0.0, 80: 379.0, 81: 70.0, 82: 0.0, 83: 0.0, 84: 3001.0, 85: 0.0, 86: 1630.0, 87: 7.0, 88: 364.0, 89: 0.0, 90: 67404.0, 91: 9.0, 92: 0.0, 93: 0.0, 94: 7685.0, 95: 0.0, 96: 1017.0, 97: 0.0, 98: 2831.0, 99: 0.0, 100: 2963.0, 101: 0.0, 102: 854.0, 103: 0.0, 104: 0.0, 105: 0.0, 106: 0.0, 107: 0.0, 108: 0.0, 109: 0.0, 110: 0.0, 111: 0.0, 112: 0.0, 113: 0.0, 114: 0.0, 115: 0.0, 116: 0.0, 117: 0.0, 118: 0.0, 119: 0.0, 120: 0.0, 121: 0.0, 122: 0.0, 123: 0.0, 124: 0.0, 125: 0.0, 126: 67744.0, 127: 22.0, 128: 264.0, 129: 0.0, 260: 197.0, 268: 0.0, 265: 0.0, 269: 0.0, 261: 0.0, 266: 1198.0, 267: 0.0, 262: 2629.0, 258: 775.0, 257: 0.0, 263: 0.0, 259: 0.0, 264: 163.0, 250: 10326.0, 251: 0.0, 252: 1228.0, 253: 0.0, 254: 2769.0, 255: 0.0} # smoke test for the repr s = Series(ser) result = s.value_counts() str(result)