pruned venvs
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d3m1g0d
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import numpy as np
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import pytest
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from pandas import DataFrame, NaT, compat, date_range
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import pandas.util.testing as tm
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@pytest.fixture
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def float_frame():
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"""
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Fixture for DataFrame of floats with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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"""
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return DataFrame(tm.getSeriesData())
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@pytest.fixture
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def float_frame_with_na():
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"""
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Fixture for DataFrame of floats with index of unique strings
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Columns are ['A', 'B', 'C', 'D']; some entries are missing
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"""
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df = DataFrame(tm.getSeriesData())
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# set some NAs
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df.loc[5:10] = np.nan
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df.loc[15:20, -2:] = np.nan
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return df
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@pytest.fixture
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def float_frame2():
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"""
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Fixture for DataFrame of floats with index of unique strings
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Columns are ['D', 'C', 'B', 'A']
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"""
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return DataFrame(tm.getSeriesData(), columns=['D', 'C', 'B', 'A'])
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@pytest.fixture
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def bool_frame_with_na():
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"""
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Fixture for DataFrame of booleans with index of unique strings
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Columns are ['A', 'B', 'C', 'D']; some entries are missing
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"""
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df = DataFrame(tm.getSeriesData()) > 0
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df = df.astype(object)
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# set some NAs
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df.loc[5:10] = np.nan
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df.loc[15:20, -2:] = np.nan
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return df
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@pytest.fixture
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def int_frame():
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"""
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Fixture for DataFrame of ints with index of unique strings
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Columns are ['A', 'B', 'C', 'D']
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"""
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df = DataFrame({k: v.astype(int)
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for k, v in compat.iteritems(tm.getSeriesData())})
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# force these all to int64 to avoid platform testing issues
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return DataFrame({c: s for c, s in compat.iteritems(df)}, dtype=np.int64)
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@pytest.fixture
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def datetime_frame():
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"""
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Fixture for DataFrame of floats with DatetimeIndex
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Columns are ['A', 'B', 'C', 'D']
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"""
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return DataFrame(tm.getTimeSeriesData())
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@pytest.fixture
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def float_string_frame():
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"""
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Fixture for DataFrame of floats and strings with index of unique strings
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Columns are ['A', 'B', 'C', 'D', 'foo'].
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"""
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df = DataFrame(tm.getSeriesData())
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df['foo'] = 'bar'
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return df
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@pytest.fixture
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def mixed_float_frame():
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"""
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Fixture for DataFrame of different float types with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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"""
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df = DataFrame(tm.getSeriesData())
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df.A = df.A.astype('float32')
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df.B = df.B.astype('float32')
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df.C = df.C.astype('float16')
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df.D = df.D.astype('float64')
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return df
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@pytest.fixture
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def mixed_float_frame2():
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"""
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Fixture for DataFrame of different float types with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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"""
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df = DataFrame(tm.getSeriesData())
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df.D = df.D.astype('float32')
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df.C = df.C.astype('float32')
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df.B = df.B.astype('float16')
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df.D = df.D.astype('float64')
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return df
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@pytest.fixture
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def mixed_int_frame():
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"""
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Fixture for DataFrame of different int types with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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"""
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df = DataFrame({k: v.astype(int)
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for k, v in compat.iteritems(tm.getSeriesData())})
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df.A = df.A.astype('int32')
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df.B = np.ones(len(df.B), dtype='uint64')
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df.C = df.C.astype('uint8')
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df.D = df.C.astype('int64')
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return df
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@pytest.fixture
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def mixed_type_frame():
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"""
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Fixture for DataFrame of float/int/string columns with RangeIndex
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Columns are ['a', 'b', 'c', 'float32', 'int32'].
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"""
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return DataFrame({'a': 1., 'b': 2, 'c': 'foo',
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'float32': np.array([1.] * 10, dtype='float32'),
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'int32': np.array([1] * 10, dtype='int32')},
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index=np.arange(10))
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@pytest.fixture
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def timezone_frame():
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"""
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Fixture for DataFrame of date_range Series with different time zones
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Columns are ['A', 'B', 'C']; some entries are missing
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"""
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df = DataFrame({'A': date_range('20130101', periods=3),
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'B': date_range('20130101', periods=3,
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tz='US/Eastern'),
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'C': date_range('20130101', periods=3,
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tz='CET')})
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df.iloc[1, 1] = NaT
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df.iloc[1, 2] = NaT
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return df
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@pytest.fixture
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def empty_frame():
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"""
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Fixture for empty DataFrame
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"""
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return DataFrame({})
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@pytest.fixture
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def datetime_series():
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"""
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Fixture for Series of floats with DatetimeIndex
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"""
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return tm.makeTimeSeries(nper=30)
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@pytest.fixture
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def datetime_series_short():
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"""
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Fixture for Series of floats with DatetimeIndex
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"""
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return tm.makeTimeSeries(nper=30)[5:]
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@pytest.fixture
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def simple_frame():
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"""
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Fixture for simple 3x3 DataFrame
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Columns are ['one', 'two', 'three'], index is ['a', 'b', 'c'].
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"""
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arr = np.array([[1., 2., 3.],
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[4., 5., 6.],
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[7., 8., 9.]])
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return DataFrame(arr, columns=['one', 'two', 'three'],
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index=['a', 'b', 'c'])
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@pytest.fixture
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def frame_of_index_cols():
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"""
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Fixture for DataFrame of columns that can be used for indexing
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Columns are ['A', 'B', 'C', 'D', 'E', ('tuple', 'as', 'label')];
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'A' & 'B' contain duplicates (but are jointly unique), the rest are unique.
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"""
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df = DataFrame({'A': ['foo', 'foo', 'foo', 'bar', 'bar'],
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'B': ['one', 'two', 'three', 'one', 'two'],
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'C': ['a', 'b', 'c', 'd', 'e'],
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'D': np.random.randn(5),
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'E': np.random.randn(5),
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('tuple', 'as', 'label'): np.random.randn(5)})
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return df
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