271 lines
9.5 KiB
Python
271 lines
9.5 KiB
Python
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# -*- coding: utf-8 -*-
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# pylint: disable-msg=E1101,W0612
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from operator import methodcaller
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from copy import deepcopy
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from distutils.version import LooseVersion
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import pytest
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import numpy as np
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import pandas as pd
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from pandas import Series, DataFrame, date_range, MultiIndex
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from pandas.compat import range
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from pandas.util.testing import (assert_series_equal,
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assert_frame_equal,
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assert_almost_equal)
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import pandas.util.testing as tm
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import pandas.util._test_decorators as td
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from .test_generic import Generic
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try:
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import xarray
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_XARRAY_INSTALLED = True
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except ImportError:
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_XARRAY_INSTALLED = False
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class TestDataFrame(Generic):
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_typ = DataFrame
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_comparator = lambda self, x, y: assert_frame_equal(x, y)
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def test_rename_mi(self):
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df = DataFrame([
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11, 21, 31
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], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]))
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df.rename(str.lower)
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def test_set_axis_name(self):
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df = pd.DataFrame([[1, 2], [3, 4]])
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funcs = ['_set_axis_name', 'rename_axis']
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for func in funcs:
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result = methodcaller(func, 'foo')(df)
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assert df.index.name is None
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assert result.index.name == 'foo'
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result = methodcaller(func, 'cols', axis=1)(df)
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assert df.columns.name is None
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assert result.columns.name == 'cols'
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def test_set_axis_name_mi(self):
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df = DataFrame(
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np.empty((3, 3)),
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index=MultiIndex.from_tuples([("A", x) for x in list('aBc')]),
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columns=MultiIndex.from_tuples([('C', x) for x in list('xyz')])
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)
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level_names = ['L1', 'L2']
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funcs = ['_set_axis_name', 'rename_axis']
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for func in funcs:
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result = methodcaller(func, level_names)(df)
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assert result.index.names == level_names
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assert result.columns.names == [None, None]
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result = methodcaller(func, level_names, axis=1)(df)
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assert result.columns.names == ["L1", "L2"]
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assert result.index.names == [None, None]
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def test_nonzero_single_element(self):
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# allow single item via bool method
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df = DataFrame([[True]])
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assert df.bool()
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df = DataFrame([[False]])
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assert not df.bool()
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df = DataFrame([[False, False]])
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pytest.raises(ValueError, lambda: df.bool())
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pytest.raises(ValueError, lambda: bool(df))
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def test_get_numeric_data_preserve_dtype(self):
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# get the numeric data
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o = DataFrame({'A': [1, '2', 3.]})
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result = o._get_numeric_data()
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expected = DataFrame(index=[0, 1, 2], dtype=object)
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self._compare(result, expected)
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def test_metadata_propagation_indiv(self):
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# groupby
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df = DataFrame(
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{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
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'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
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'C': np.random.randn(8),
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'D': np.random.randn(8)})
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result = df.groupby('A').sum()
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self.check_metadata(df, result)
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# resample
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df = DataFrame(np.random.randn(1000, 2),
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index=date_range('20130101', periods=1000, freq='s'))
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result = df.resample('1T')
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self.check_metadata(df, result)
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# merging with override
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# GH 6923
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_metadata = DataFrame._metadata
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_finalize = DataFrame.__finalize__
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np.random.seed(10)
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df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
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df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
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DataFrame._metadata = ['filename']
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df1.filename = 'fname1.csv'
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df2.filename = 'fname2.csv'
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def finalize(self, other, method=None, **kwargs):
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for name in self._metadata:
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if method == 'merge':
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left, right = other.left, other.right
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value = getattr(left, name, '') + '|' + getattr(right,
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name, '')
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object.__setattr__(self, name, value)
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else:
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object.__setattr__(self, name, getattr(other, name, ''))
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return self
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DataFrame.__finalize__ = finalize
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result = df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
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assert result.filename == 'fname1.csv|fname2.csv'
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# concat
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# GH 6927
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DataFrame._metadata = ['filename']
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df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
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df1.filename = 'foo'
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def finalize(self, other, method=None, **kwargs):
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for name in self._metadata:
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if method == 'concat':
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value = '+'.join([getattr(
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o, name) for o in other.objs if getattr(o, name, None)
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])
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object.__setattr__(self, name, value)
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else:
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object.__setattr__(self, name, getattr(other, name, None))
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return self
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DataFrame.__finalize__ = finalize
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result = pd.concat([df1, df1])
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assert result.filename == 'foo+foo'
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# reset
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DataFrame._metadata = _metadata
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DataFrame.__finalize__ = _finalize
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def test_set_attribute(self):
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# Test for consistent setattr behavior when an attribute and a column
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# have the same name (Issue #8994)
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df = DataFrame({'x': [1, 2, 3]})
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df.y = 2
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df['y'] = [2, 4, 6]
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df.y = 5
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assert df.y == 5
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assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
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@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
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LooseVersion(xarray.__version__) <
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LooseVersion('0.10.0'),
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reason='xarray >= 0.10.0 required')
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@pytest.mark.parametrize(
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"index", ['FloatIndex', 'IntIndex',
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'StringIndex', 'UnicodeIndex',
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'DateIndex', 'PeriodIndex',
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'CategoricalIndex', 'TimedeltaIndex'])
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def test_to_xarray_index_types(self, index):
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from xarray import Dataset
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index = getattr(tm, 'make{}'.format(index))
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df = DataFrame({'a': list('abc'),
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'b': list(range(1, 4)),
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'c': np.arange(3, 6).astype('u1'),
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'd': np.arange(4.0, 7.0, dtype='float64'),
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'e': [True, False, True],
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'f': pd.Categorical(list('abc')),
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'g': pd.date_range('20130101', periods=3),
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'h': pd.date_range('20130101',
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periods=3,
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tz='US/Eastern')}
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)
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df.index = index(3)
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df.index.name = 'foo'
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df.columns.name = 'bar'
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result = df.to_xarray()
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assert result.dims['foo'] == 3
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assert len(result.coords) == 1
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assert len(result.data_vars) == 8
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assert_almost_equal(list(result.coords.keys()), ['foo'])
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assert isinstance(result, Dataset)
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# idempotency
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# categoricals are not preserved
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# datetimes w/tz are not preserved
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# column names are lost
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expected = df.copy()
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expected['f'] = expected['f'].astype(object)
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expected['h'] = expected['h'].astype('datetime64[ns]')
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expected.columns.name = None
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assert_frame_equal(result.to_dataframe(), expected,
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check_index_type=False, check_categorical=False)
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@td.skip_if_no('xarray', min_version='0.7.0')
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def test_to_xarray(self):
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from xarray import Dataset
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df = DataFrame({'a': list('abc'),
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'b': list(range(1, 4)),
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'c': np.arange(3, 6).astype('u1'),
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'd': np.arange(4.0, 7.0, dtype='float64'),
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'e': [True, False, True],
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'f': pd.Categorical(list('abc')),
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'g': pd.date_range('20130101', periods=3),
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'h': pd.date_range('20130101',
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periods=3,
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tz='US/Eastern')}
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)
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df.index.name = 'foo'
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result = df[0:0].to_xarray()
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assert result.dims['foo'] == 0
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assert isinstance(result, Dataset)
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# available in 0.7.1
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# MultiIndex
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df.index = pd.MultiIndex.from_product([['a'], range(3)],
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names=['one', 'two'])
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result = df.to_xarray()
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assert result.dims['one'] == 1
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assert result.dims['two'] == 3
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assert len(result.coords) == 2
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assert len(result.data_vars) == 8
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assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
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assert isinstance(result, Dataset)
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result = result.to_dataframe()
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expected = df.copy()
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expected['f'] = expected['f'].astype(object)
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expected['h'] = expected['h'].astype('datetime64[ns]')
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expected.columns.name = None
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assert_frame_equal(result,
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expected,
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check_index_type=False)
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def test_deepcopy_empty(self):
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# This test covers empty frame copying with non-empty column sets
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# as reported in issue GH15370
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empty_frame = DataFrame(data=[], index=[], columns=['A'])
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empty_frame_copy = deepcopy(empty_frame)
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self._compare(empty_frame_copy, empty_frame)
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