338 lines
12 KiB
Python
338 lines
12 KiB
Python
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""" test indexing with ix """
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import pytest
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from warnings import catch_warnings
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import numpy as np
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import pandas as pd
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from pandas.core.dtypes.common import is_scalar
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from pandas.compat import lrange
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from pandas import Series, DataFrame, option_context, MultiIndex
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from pandas.util import testing as tm
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from pandas.errors import PerformanceWarning
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class TestIX(object):
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def test_ix_deprecation(self):
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# GH 15114
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df = DataFrame({'A': [1, 2, 3]})
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with tm.assert_produces_warning(DeprecationWarning,
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check_stacklevel=False):
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df.ix[1, 'A']
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def test_ix_loc_setitem_consistency(self):
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# GH 5771
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# loc with slice and series
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s = Series(0, index=[4, 5, 6])
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s.loc[4:5] += 1
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expected = Series([1, 1, 0], index=[4, 5, 6])
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tm.assert_series_equal(s, expected)
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# GH 5928
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# chained indexing assignment
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df = DataFrame({'a': [0, 1, 2]})
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expected = df.copy()
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with catch_warnings(record=True):
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expected.ix[[0, 1, 2], 'a'] = -expected.ix[[0, 1, 2], 'a']
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with catch_warnings(record=True):
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df['a'].ix[[0, 1, 2]] = -df['a'].ix[[0, 1, 2]]
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tm.assert_frame_equal(df, expected)
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df = DataFrame({'a': [0, 1, 2], 'b': [0, 1, 2]})
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with catch_warnings(record=True):
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df['a'].ix[[0, 1, 2]] = -df['a'].ix[[0, 1, 2]].astype(
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'float64') + 0.5
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expected = DataFrame({'a': [0.5, -0.5, -1.5], 'b': [0, 1, 2]})
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tm.assert_frame_equal(df, expected)
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# GH 8607
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# ix setitem consistency
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df = DataFrame({'delta': [1174, 904, 161],
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'elapsed': [7673, 9277, 1470],
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'timestamp': [1413840976, 1413842580, 1413760580]})
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expected = DataFrame({'delta': [1174, 904, 161],
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'elapsed': [7673, 9277, 1470],
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'timestamp': pd.to_datetime(
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[1413840976, 1413842580, 1413760580],
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unit='s')
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})
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df2 = df.copy()
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df2['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
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tm.assert_frame_equal(df2, expected)
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df2 = df.copy()
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df2.loc[:, 'timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
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tm.assert_frame_equal(df2, expected)
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df2 = df.copy()
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with catch_warnings(record=True):
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df2.ix[:, 2] = pd.to_datetime(df['timestamp'], unit='s')
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tm.assert_frame_equal(df2, expected)
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def test_ix_loc_consistency(self):
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# GH 8613
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# some edge cases where ix/loc should return the same
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# this is not an exhaustive case
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def compare(result, expected):
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if is_scalar(expected):
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assert result == expected
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else:
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assert expected.equals(result)
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# failure cases for .loc, but these work for .ix
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df = DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
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for key in [slice(1, 3), tuple([slice(0, 2), slice(0, 2)]),
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tuple([slice(0, 2), df.columns[0:2]])]:
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for index in [tm.makeStringIndex, tm.makeUnicodeIndex,
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tm.makeDateIndex, tm.makePeriodIndex,
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tm.makeTimedeltaIndex]:
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df.index = index(len(df.index))
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with catch_warnings(record=True):
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df.ix[key]
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pytest.raises(TypeError, lambda: df.loc[key])
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df = DataFrame(np.random.randn(5, 4), columns=list('ABCD'),
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index=pd.date_range('2012-01-01', periods=5))
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for key in ['2012-01-03',
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'2012-01-31',
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slice('2012-01-03', '2012-01-03'),
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slice('2012-01-03', '2012-01-04'),
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slice('2012-01-03', '2012-01-06', 2),
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slice('2012-01-03', '2012-01-31'),
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tuple([[True, True, True, False, True]]), ]:
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# getitem
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# if the expected raises, then compare the exceptions
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try:
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with catch_warnings(record=True):
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expected = df.ix[key]
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except KeyError:
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pytest.raises(KeyError, lambda: df.loc[key])
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continue
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result = df.loc[key]
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compare(result, expected)
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# setitem
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df1 = df.copy()
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df2 = df.copy()
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with catch_warnings(record=True):
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df1.ix[key] = 10
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df2.loc[key] = 10
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compare(df2, df1)
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# edge cases
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s = Series([1, 2, 3, 4], index=list('abde'))
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result1 = s['a':'c']
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with catch_warnings(record=True):
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result2 = s.ix['a':'c']
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result3 = s.loc['a':'c']
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tm.assert_series_equal(result1, result2)
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tm.assert_series_equal(result1, result3)
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# now work rather than raising KeyError
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s = Series(range(5), [-2, -1, 1, 2, 3])
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with catch_warnings(record=True):
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result1 = s.ix[-10:3]
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result2 = s.loc[-10:3]
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tm.assert_series_equal(result1, result2)
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with catch_warnings(record=True):
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result1 = s.ix[0:3]
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result2 = s.loc[0:3]
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tm.assert_series_equal(result1, result2)
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def test_ix_weird_slicing(self):
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# http://stackoverflow.com/q/17056560/1240268
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df = DataFrame({'one': [1, 2, 3, np.nan, np.nan],
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'two': [1, 2, 3, 4, 5]})
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df.loc[df['one'] > 1, 'two'] = -df['two']
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expected = DataFrame({'one': {0: 1.0,
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1: 2.0,
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2: 3.0,
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3: np.nan,
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4: np.nan},
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'two': {0: 1,
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1: -2,
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2: -3,
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3: 4,
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4: 5}})
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tm.assert_frame_equal(df, expected)
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def test_ix_general(self):
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# ix general issues
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# GH 2817
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data = {'amount': {0: 700, 1: 600, 2: 222, 3: 333, 4: 444},
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'col': {0: 3.5, 1: 3.5, 2: 4.0, 3: 4.0, 4: 4.0},
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'year': {0: 2012, 1: 2011, 2: 2012, 3: 2012, 4: 2012}}
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df = DataFrame(data).set_index(keys=['col', 'year'])
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key = 4.0, 2012
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# emits a PerformanceWarning, ok
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with tm.assert_produces_warning(PerformanceWarning):
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tm.assert_frame_equal(df.loc[key], df.iloc[2:])
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# this is ok
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df.sort_index(inplace=True)
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res = df.loc[key]
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# col has float dtype, result should be Float64Index
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index = MultiIndex.from_arrays([[4.] * 3, [2012] * 3],
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names=['col', 'year'])
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expected = DataFrame({'amount': [222, 333, 444]}, index=index)
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tm.assert_frame_equal(res, expected)
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def test_ix_assign_column_mixed(self):
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# GH #1142
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df = DataFrame(tm.getSeriesData())
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df['foo'] = 'bar'
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orig = df.loc[:, 'B'].copy()
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df.loc[:, 'B'] = df.loc[:, 'B'] + 1
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tm.assert_series_equal(df.B, orig + 1)
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# GH 3668, mixed frame with series value
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df = DataFrame({'x': lrange(10), 'y': lrange(10, 20), 'z': 'bar'})
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expected = df.copy()
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for i in range(5):
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indexer = i * 2
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v = 1000 + i * 200
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expected.loc[indexer, 'y'] = v
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assert expected.loc[indexer, 'y'] == v
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df.loc[df.x % 2 == 0, 'y'] = df.loc[df.x % 2 == 0, 'y'] * 100
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tm.assert_frame_equal(df, expected)
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# GH 4508, making sure consistency of assignments
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df = DataFrame({'a': [1, 2, 3], 'b': [0, 1, 2]})
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df.loc[[0, 2, ], 'b'] = [100, -100]
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expected = DataFrame({'a': [1, 2, 3], 'b': [100, 1, -100]})
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tm.assert_frame_equal(df, expected)
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df = DataFrame({'a': lrange(4)})
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df['b'] = np.nan
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df.loc[[1, 3], 'b'] = [100, -100]
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expected = DataFrame({'a': [0, 1, 2, 3],
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'b': [np.nan, 100, np.nan, -100]})
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tm.assert_frame_equal(df, expected)
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# ok, but chained assignments are dangerous
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# if we turn off chained assignment it will work
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with option_context('chained_assignment', None):
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df = DataFrame({'a': lrange(4)})
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df['b'] = np.nan
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df['b'].loc[[1, 3]] = [100, -100]
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tm.assert_frame_equal(df, expected)
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def test_ix_get_set_consistency(self):
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# GH 4544
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# ix/loc get/set not consistent when
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# a mixed int/string index
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df = DataFrame(np.arange(16).reshape((4, 4)),
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columns=['a', 'b', 8, 'c'],
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index=['e', 7, 'f', 'g'])
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with catch_warnings(record=True):
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assert df.ix['e', 8] == 2
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assert df.loc['e', 8] == 2
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with catch_warnings(record=True):
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df.ix['e', 8] = 42
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assert df.ix['e', 8] == 42
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assert df.loc['e', 8] == 42
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df.loc['e', 8] = 45
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with catch_warnings(record=True):
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assert df.ix['e', 8] == 45
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assert df.loc['e', 8] == 45
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def test_ix_slicing_strings(self):
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# see gh-3836
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data = {'Classification':
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['SA EQUITY CFD', 'bbb', 'SA EQUITY', 'SA SSF', 'aaa'],
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'Random': [1, 2, 3, 4, 5],
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'X': ['correct', 'wrong', 'correct', 'correct', 'wrong']}
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df = DataFrame(data)
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x = df[~df.Classification.isin(['SA EQUITY CFD', 'SA EQUITY', 'SA SSF'
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])]
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with catch_warnings(record=True):
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df.ix[x.index, 'X'] = df['Classification']
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expected = DataFrame({'Classification': {0: 'SA EQUITY CFD',
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1: 'bbb',
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2: 'SA EQUITY',
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3: 'SA SSF',
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4: 'aaa'},
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'Random': {0: 1,
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1: 2,
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2: 3,
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3: 4,
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4: 5},
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'X': {0: 'correct',
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1: 'bbb',
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2: 'correct',
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3: 'correct',
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4: 'aaa'}}) # bug was 4: 'bbb'
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tm.assert_frame_equal(df, expected)
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def test_ix_setitem_out_of_bounds_axis_0(self):
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df = DataFrame(
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np.random.randn(2, 5), index=["row%s" % i for i in range(2)],
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columns=["col%s" % i for i in range(5)])
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with catch_warnings(record=True):
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pytest.raises(ValueError, df.ix.__setitem__, (2, 0), 100)
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def test_ix_setitem_out_of_bounds_axis_1(self):
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df = DataFrame(
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np.random.randn(5, 2), index=["row%s" % i for i in range(5)],
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columns=["col%s" % i for i in range(2)])
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with catch_warnings(record=True):
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pytest.raises(ValueError, df.ix.__setitem__, (0, 2), 100)
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def test_ix_empty_list_indexer_is_ok(self):
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with catch_warnings(record=True):
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from pandas.util.testing import makeCustomDataframe as mkdf
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df = mkdf(5, 2)
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# vertical empty
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tm.assert_frame_equal(df.ix[:, []], df.iloc[:, :0],
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check_index_type=True,
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check_column_type=True)
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# horizontal empty
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tm.assert_frame_equal(df.ix[[], :], df.iloc[:0, :],
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check_index_type=True,
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check_column_type=True)
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# horizontal empty
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tm.assert_frame_equal(df.ix[[]], df.iloc[:0, :],
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check_index_type=True,
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check_column_type=True)
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def test_ix_duplicate_returns_series(self):
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df = DataFrame(np.random.randn(3, 3), index=[0.1, 0.2, 0.2],
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columns=list('abc'))
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with catch_warnings(record=True):
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r = df.ix[0.2, 'a']
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e = df.loc[0.2, 'a']
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tm.assert_series_equal(r, e)
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