757 lines
27 KiB
Python
757 lines
27 KiB
Python
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# coding=utf-8
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# pylint: disable-msg=E1101,W0612
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from collections import OrderedDict
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import pydoc
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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 Index, Series, DataFrame, date_range
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from pandas.core.indexes.datetimes import Timestamp
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from pandas.compat import range, lzip, isidentifier, string_types
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from pandas import (compat, Categorical, period_range, timedelta_range,
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DatetimeIndex, PeriodIndex, TimedeltaIndex)
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import pandas.io.formats.printing as printing
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from pandas.util.testing import (assert_series_equal,
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ensure_clean)
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import pandas.util.testing as tm
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from .common import TestData
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class SharedWithSparse(object):
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"""
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A collection of tests Series and SparseSeries can share.
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In generic tests on this class, use ``self._assert_series_equal()``
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which is implemented in sub-classes.
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"""
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def _assert_series_equal(self, left, right):
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"""Dispatch to series class dependent assertion"""
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raise NotImplementedError
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def test_scalarop_preserve_name(self):
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result = self.ts * 2
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assert result.name == self.ts.name
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def test_copy_name(self):
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result = self.ts.copy()
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assert result.name == self.ts.name
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def test_copy_index_name_checking(self):
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# don't want to be able to modify the index stored elsewhere after
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# making a copy
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self.ts.index.name = None
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assert self.ts.index.name is None
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assert self.ts is self.ts
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cp = self.ts.copy()
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cp.index.name = 'foo'
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printing.pprint_thing(self.ts.index.name)
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assert self.ts.index.name is None
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def test_append_preserve_name(self):
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result = self.ts[:5].append(self.ts[5:])
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assert result.name == self.ts.name
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def test_binop_maybe_preserve_name(self):
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# names match, preserve
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result = self.ts * self.ts
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assert result.name == self.ts.name
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result = self.ts.mul(self.ts)
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assert result.name == self.ts.name
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result = self.ts * self.ts[:-2]
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assert result.name == self.ts.name
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# names don't match, don't preserve
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cp = self.ts.copy()
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cp.name = 'something else'
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result = self.ts + cp
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assert result.name is None
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result = self.ts.add(cp)
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assert result.name is None
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ops = ['add', 'sub', 'mul', 'div', 'truediv', 'floordiv', 'mod', 'pow']
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ops = ops + ['r' + op for op in ops]
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for op in ops:
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# names match, preserve
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s = self.ts.copy()
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result = getattr(s, op)(s)
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assert result.name == self.ts.name
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# names don't match, don't preserve
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cp = self.ts.copy()
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cp.name = 'changed'
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result = getattr(s, op)(cp)
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assert result.name is None
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def test_combine_first_name(self):
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result = self.ts.combine_first(self.ts[:5])
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assert result.name == self.ts.name
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def test_getitem_preserve_name(self):
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result = self.ts[self.ts > 0]
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assert result.name == self.ts.name
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result = self.ts[[0, 2, 4]]
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assert result.name == self.ts.name
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result = self.ts[5:10]
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assert result.name == self.ts.name
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def test_pickle(self):
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unp_series = self._pickle_roundtrip(self.series)
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unp_ts = self._pickle_roundtrip(self.ts)
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assert_series_equal(unp_series, self.series)
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assert_series_equal(unp_ts, self.ts)
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def _pickle_roundtrip(self, obj):
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with ensure_clean() as path:
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obj.to_pickle(path)
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unpickled = pd.read_pickle(path)
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return unpickled
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def test_argsort_preserve_name(self):
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result = self.ts.argsort()
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assert result.name == self.ts.name
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def test_sort_index_name(self):
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result = self.ts.sort_index(ascending=False)
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assert result.name == self.ts.name
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def test_to_sparse_pass_name(self):
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result = self.ts.to_sparse()
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assert result.name == self.ts.name
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def test_constructor_dict(self):
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d = {'a': 0., 'b': 1., 'c': 2.}
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result = self.series_klass(d)
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expected = self.series_klass(d, index=sorted(d.keys()))
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self._assert_series_equal(result, expected)
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result = self.series_klass(d, index=['b', 'c', 'd', 'a'])
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expected = self.series_klass([1, 2, np.nan, 0],
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index=['b', 'c', 'd', 'a'])
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self._assert_series_equal(result, expected)
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def test_constructor_subclass_dict(self):
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data = tm.TestSubDict((x, 10.0 * x) for x in range(10))
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series = self.series_klass(data)
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expected = self.series_klass(dict(compat.iteritems(data)))
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self._assert_series_equal(series, expected)
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def test_constructor_ordereddict(self):
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# GH3283
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data = OrderedDict(
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('col%s' % i, np.random.random()) for i in range(12))
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series = self.series_klass(data)
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expected = self.series_klass(list(data.values()), list(data.keys()))
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self._assert_series_equal(series, expected)
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# Test with subclass
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class A(OrderedDict):
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pass
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series = self.series_klass(A(data))
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self._assert_series_equal(series, expected)
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def test_constructor_dict_multiindex(self):
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d = {('a', 'a'): 0., ('b', 'a'): 1., ('b', 'c'): 2.}
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_d = sorted(d.items())
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result = self.series_klass(d)
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expected = self.series_klass(
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[x[1] for x in _d],
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index=pd.MultiIndex.from_tuples([x[0] for x in _d]))
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self._assert_series_equal(result, expected)
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d['z'] = 111.
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_d.insert(0, ('z', d['z']))
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result = self.series_klass(d)
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expected = self.series_klass([x[1] for x in _d],
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index=pd.Index([x[0] for x in _d],
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tupleize_cols=False))
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result = result.reindex(index=expected.index)
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self._assert_series_equal(result, expected)
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def test_constructor_dict_timedelta_index(self):
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# GH #12169 : Resample category data with timedelta index
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# construct Series from dict as data and TimedeltaIndex as index
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# will result NaN in result Series data
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expected = self.series_klass(
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data=['A', 'B', 'C'],
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index=pd.to_timedelta([0, 10, 20], unit='s')
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)
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result = self.series_klass(
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data={pd.to_timedelta(0, unit='s'): 'A',
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pd.to_timedelta(10, unit='s'): 'B',
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pd.to_timedelta(20, unit='s'): 'C'},
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index=pd.to_timedelta([0, 10, 20], unit='s')
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)
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self._assert_series_equal(result, expected)
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def test_from_array_deprecated(self):
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with tm.assert_produces_warning(FutureWarning):
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self.series_klass.from_array([1, 2, 3])
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class TestSeriesMisc(TestData, SharedWithSparse):
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series_klass = Series
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# SharedWithSparse tests use generic, series_klass-agnostic assertion
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_assert_series_equal = staticmethod(tm.assert_series_equal)
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def test_tab_completion(self):
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# GH 9910
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s = Series(list('abcd'))
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# Series of str values should have .str but not .dt/.cat in __dir__
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assert 'str' in dir(s)
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assert 'dt' not in dir(s)
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assert 'cat' not in dir(s)
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# similarly for .dt
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s = Series(date_range('1/1/2015', periods=5))
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assert 'dt' in dir(s)
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assert 'str' not in dir(s)
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assert 'cat' not in dir(s)
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# Similarly for .cat, but with the twist that str and dt should be
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# there if the categories are of that type first cat and str.
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s = Series(list('abbcd'), dtype="category")
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assert 'cat' in dir(s)
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assert 'str' in dir(s) # as it is a string categorical
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assert 'dt' not in dir(s)
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# similar to cat and str
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s = Series(date_range('1/1/2015', periods=5)).astype("category")
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assert 'cat' in dir(s)
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assert 'str' not in dir(s)
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assert 'dt' in dir(s) # as it is a datetime categorical
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def test_tab_completion_with_categorical(self):
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# test the tab completion display
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ok_for_cat = ['categories', 'codes', 'ordered', 'set_categories',
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'add_categories', 'remove_categories',
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'rename_categories', 'reorder_categories',
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'remove_unused_categories', 'as_ordered', 'as_unordered']
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def get_dir(s):
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results = [r for r in s.cat.__dir__() if not r.startswith('_')]
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return list(sorted(set(results)))
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s = Series(list('aabbcde')).astype('category')
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results = get_dir(s)
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tm.assert_almost_equal(results, list(sorted(set(ok_for_cat))))
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@pytest.mark.parametrize("index", [
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tm.makeUnicodeIndex(10),
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tm.makeStringIndex(10),
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tm.makeCategoricalIndex(10),
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Index(['foo', 'bar', 'baz'] * 2),
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tm.makeDateIndex(10),
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tm.makePeriodIndex(10),
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tm.makeTimedeltaIndex(10),
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tm.makeIntIndex(10),
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tm.makeUIntIndex(10),
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tm.makeIntIndex(10),
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tm.makeFloatIndex(10),
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Index([True, False]),
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Index(['a{}'.format(i) for i in range(101)]),
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pd.MultiIndex.from_tuples(lzip('ABCD', 'EFGH')),
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pd.MultiIndex.from_tuples(lzip([0, 1, 2, 3], 'EFGH')), ])
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def test_index_tab_completion(self, index):
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# dir contains string-like values of the Index.
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s = pd.Series(index=index)
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dir_s = dir(s)
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for i, x in enumerate(s.index.unique(level=0)):
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if i < 100:
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assert (not isinstance(x, string_types) or
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not isidentifier(x) or x in dir_s)
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else:
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assert x not in dir_s
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def test_not_hashable(self):
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s_empty = Series()
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s = Series([1])
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pytest.raises(TypeError, hash, s_empty)
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pytest.raises(TypeError, hash, s)
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def test_contains(self):
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tm.assert_contains_all(self.ts.index, self.ts)
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def test_iter(self):
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for i, val in enumerate(self.series):
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assert val == self.series[i]
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for i, val in enumerate(self.ts):
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assert val == self.ts[i]
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def test_keys(self):
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# HACK: By doing this in two stages, we avoid 2to3 wrapping the call
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# to .keys() in a list()
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getkeys = self.ts.keys
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assert getkeys() is self.ts.index
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def test_values(self):
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tm.assert_almost_equal(self.ts.values, self.ts, check_dtype=False)
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def test_iteritems(self):
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for idx, val in compat.iteritems(self.series):
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assert val == self.series[idx]
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for idx, val in compat.iteritems(self.ts):
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assert val == self.ts[idx]
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# assert is lazy (genrators don't define reverse, lists do)
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assert not hasattr(self.series.iteritems(), 'reverse')
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def test_items(self):
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for idx, val in self.series.items():
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assert val == self.series[idx]
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for idx, val in self.ts.items():
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assert val == self.ts[idx]
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# assert is lazy (genrators don't define reverse, lists do)
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assert not hasattr(self.series.items(), 'reverse')
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def test_raise_on_info(self):
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s = Series(np.random.randn(10))
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with pytest.raises(AttributeError):
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s.info()
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def test_copy(self):
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for deep in [None, False, True]:
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s = Series(np.arange(10), dtype='float64')
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# default deep is True
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if deep is None:
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s2 = s.copy()
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else:
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s2 = s.copy(deep=deep)
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s2[::2] = np.NaN
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if deep is None or deep is True:
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# Did not modify original Series
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assert np.isnan(s2[0])
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assert not np.isnan(s[0])
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else:
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# we DID modify the original Series
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assert np.isnan(s2[0])
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assert np.isnan(s[0])
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# GH 11794
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# copy of tz-aware
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expected = Series([Timestamp('2012/01/01', tz='UTC')])
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expected2 = Series([Timestamp('1999/01/01', tz='UTC')])
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for deep in [None, False, True]:
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s = Series([Timestamp('2012/01/01', tz='UTC')])
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if deep is None:
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s2 = s.copy()
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else:
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s2 = s.copy(deep=deep)
|
||
|
|
|
||
|
|
s2[0] = pd.Timestamp('1999/01/01', tz='UTC')
|
||
|
|
|
||
|
|
# default deep is True
|
||
|
|
if deep is None or deep is True:
|
||
|
|
# Did not modify original Series
|
||
|
|
assert_series_equal(s2, expected2)
|
||
|
|
assert_series_equal(s, expected)
|
||
|
|
else:
|
||
|
|
# we DID modify the original Series
|
||
|
|
assert_series_equal(s2, expected2)
|
||
|
|
assert_series_equal(s, expected2)
|
||
|
|
|
||
|
|
def test_axis_alias(self):
|
||
|
|
s = Series([1, 2, np.nan])
|
||
|
|
assert_series_equal(s.dropna(axis='rows'), s.dropna(axis='index'))
|
||
|
|
assert s.dropna().sum('rows') == 3
|
||
|
|
assert s._get_axis_number('rows') == 0
|
||
|
|
assert s._get_axis_name('rows') == 'index'
|
||
|
|
|
||
|
|
def test_class_axis(self):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/18147
|
||
|
|
# no exception and no empty docstring
|
||
|
|
assert pydoc.getdoc(Series.index)
|
||
|
|
|
||
|
|
def test_numpy_unique(self):
|
||
|
|
# it works!
|
||
|
|
np.unique(self.ts)
|
||
|
|
|
||
|
|
def test_ndarray_compat(self):
|
||
|
|
|
||
|
|
# test numpy compat with Series as sub-class of NDFrame
|
||
|
|
tsdf = DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C'],
|
||
|
|
index=date_range('1/1/2000', periods=1000))
|
||
|
|
|
||
|
|
def f(x):
|
||
|
|
return x[x.idxmax()]
|
||
|
|
|
||
|
|
result = tsdf.apply(f)
|
||
|
|
expected = tsdf.max()
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# .item()
|
||
|
|
s = Series([1])
|
||
|
|
result = s.item()
|
||
|
|
assert result == 1
|
||
|
|
assert s.item() == s.iloc[0]
|
||
|
|
|
||
|
|
# using an ndarray like function
|
||
|
|
s = Series(np.random.randn(10))
|
||
|
|
result = Series(np.ones_like(s))
|
||
|
|
expected = Series(1, index=range(10), dtype='float64')
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# ravel
|
||
|
|
s = Series(np.random.randn(10))
|
||
|
|
tm.assert_almost_equal(s.ravel(order='F'), s.values.ravel(order='F'))
|
||
|
|
|
||
|
|
# compress
|
||
|
|
# GH 6658
|
||
|
|
s = Series([0, 1., -1], index=list('abc'))
|
||
|
|
result = np.compress(s > 0, s)
|
||
|
|
tm.assert_series_equal(result, Series([1.], index=['b']))
|
||
|
|
|
||
|
|
result = np.compress(s < -1, s)
|
||
|
|
# result empty Index(dtype=object) as the same as original
|
||
|
|
exp = Series([], dtype='float64', index=Index([], dtype='object'))
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
s = Series([0, 1., -1], index=[.1, .2, .3])
|
||
|
|
result = np.compress(s > 0, s)
|
||
|
|
tm.assert_series_equal(result, Series([1.], index=[.2]))
|
||
|
|
|
||
|
|
result = np.compress(s < -1, s)
|
||
|
|
# result empty Float64Index as the same as original
|
||
|
|
exp = Series([], dtype='float64', index=Index([], dtype='float64'))
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
def test_str_attribute(self):
|
||
|
|
# GH9068
|
||
|
|
methods = ['strip', 'rstrip', 'lstrip']
|
||
|
|
s = Series([' jack', 'jill ', ' jesse ', 'frank'])
|
||
|
|
for method in methods:
|
||
|
|
expected = Series([getattr(str, method)(x) for x in s.values])
|
||
|
|
assert_series_equal(getattr(Series.str, method)(s.str), expected)
|
||
|
|
|
||
|
|
# str accessor only valid with string values
|
||
|
|
s = Series(range(5))
|
||
|
|
with tm.assert_raises_regex(AttributeError,
|
||
|
|
'only use .str accessor'):
|
||
|
|
s.str.repeat(2)
|
||
|
|
|
||
|
|
def test_empty_method(self):
|
||
|
|
s_empty = pd.Series()
|
||
|
|
assert s_empty.empty
|
||
|
|
|
||
|
|
for full_series in [pd.Series([1]), pd.Series(index=[1])]:
|
||
|
|
assert not full_series.empty
|
||
|
|
|
||
|
|
def test_tab_complete_warning(self, ip):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/16409
|
||
|
|
pytest.importorskip('IPython', minversion="6.0.0")
|
||
|
|
from IPython.core.completer import provisionalcompleter
|
||
|
|
|
||
|
|
code = "import pandas as pd; s = pd.Series()"
|
||
|
|
ip.run_code(code)
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
with provisionalcompleter('ignore'):
|
||
|
|
list(ip.Completer.completions('s.', 1))
|
||
|
|
|
||
|
|
|
||
|
|
class TestCategoricalSeries(object):
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"method",
|
||
|
|
[
|
||
|
|
lambda x: x.cat.set_categories([1, 2, 3]),
|
||
|
|
lambda x: x.cat.reorder_categories([2, 3, 1], ordered=True),
|
||
|
|
lambda x: x.cat.rename_categories([1, 2, 3]),
|
||
|
|
lambda x: x.cat.remove_unused_categories(),
|
||
|
|
lambda x: x.cat.remove_categories([2]),
|
||
|
|
lambda x: x.cat.add_categories([4]),
|
||
|
|
lambda x: x.cat.as_ordered(),
|
||
|
|
lambda x: x.cat.as_unordered(),
|
||
|
|
])
|
||
|
|
def test_getname_categorical_accessor(self, method):
|
||
|
|
# GH 17509
|
||
|
|
s = Series([1, 2, 3], name='A').astype('category')
|
||
|
|
expected = 'A'
|
||
|
|
result = method(s).name
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_cat_accessor(self):
|
||
|
|
s = Series(Categorical(["a", "b", np.nan, "a"]))
|
||
|
|
tm.assert_index_equal(s.cat.categories, Index(["a", "b"]))
|
||
|
|
assert not s.cat.ordered, False
|
||
|
|
|
||
|
|
exp = Categorical(["a", "b", np.nan, "a"], categories=["b", "a"])
|
||
|
|
s.cat.set_categories(["b", "a"], inplace=True)
|
||
|
|
tm.assert_categorical_equal(s.values, exp)
|
||
|
|
|
||
|
|
res = s.cat.set_categories(["b", "a"])
|
||
|
|
tm.assert_categorical_equal(res.values, exp)
|
||
|
|
|
||
|
|
s[:] = "a"
|
||
|
|
s = s.cat.remove_unused_categories()
|
||
|
|
tm.assert_index_equal(s.cat.categories, Index(["a"]))
|
||
|
|
|
||
|
|
def test_cat_accessor_api(self):
|
||
|
|
# GH 9322
|
||
|
|
from pandas.core.arrays.categorical import CategoricalAccessor
|
||
|
|
assert Series.cat is CategoricalAccessor
|
||
|
|
s = Series(list('aabbcde')).astype('category')
|
||
|
|
assert isinstance(s.cat, CategoricalAccessor)
|
||
|
|
|
||
|
|
invalid = Series([1])
|
||
|
|
with tm.assert_raises_regex(AttributeError,
|
||
|
|
"only use .cat accessor"):
|
||
|
|
invalid.cat
|
||
|
|
assert not hasattr(invalid, 'cat')
|
||
|
|
|
||
|
|
def test_cat_accessor_no_new_attributes(self):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/10673
|
||
|
|
c = Series(list('aabbcde')).astype('category')
|
||
|
|
with tm.assert_raises_regex(AttributeError,
|
||
|
|
"You cannot add any new attribute"):
|
||
|
|
c.cat.xlabel = "a"
|
||
|
|
|
||
|
|
def test_categorical_delegations(self):
|
||
|
|
|
||
|
|
# invalid accessor
|
||
|
|
pytest.raises(AttributeError, lambda: Series([1, 2, 3]).cat)
|
||
|
|
tm.assert_raises_regex(
|
||
|
|
AttributeError,
|
||
|
|
r"Can only use .cat accessor with a 'category' dtype",
|
||
|
|
lambda: Series([1, 2, 3]).cat)
|
||
|
|
pytest.raises(AttributeError, lambda: Series(['a', 'b', 'c']).cat)
|
||
|
|
pytest.raises(AttributeError, lambda: Series(np.arange(5.)).cat)
|
||
|
|
pytest.raises(AttributeError,
|
||
|
|
lambda: Series([Timestamp('20130101')]).cat)
|
||
|
|
|
||
|
|
# Series should delegate calls to '.categories', '.codes', '.ordered'
|
||
|
|
# and the methods '.set_categories()' 'drop_unused_categories()' to the
|
||
|
|
# categorical# -*- coding: utf-8 -*-
|
||
|
|
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
|
||
|
|
exp_categories = Index(["a", "b", "c"])
|
||
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
||
|
|
s.cat.categories = [1, 2, 3]
|
||
|
|
exp_categories = Index([1, 2, 3])
|
||
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
||
|
|
|
||
|
|
exp_codes = Series([0, 1, 2, 0], dtype='int8')
|
||
|
|
tm.assert_series_equal(s.cat.codes, exp_codes)
|
||
|
|
|
||
|
|
assert s.cat.ordered
|
||
|
|
s = s.cat.as_unordered()
|
||
|
|
assert not s.cat.ordered
|
||
|
|
s.cat.as_ordered(inplace=True)
|
||
|
|
assert s.cat.ordered
|
||
|
|
|
||
|
|
# reorder
|
||
|
|
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
|
||
|
|
exp_categories = Index(["c", "b", "a"])
|
||
|
|
exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
|
||
|
|
s = s.cat.set_categories(["c", "b", "a"])
|
||
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
||
|
|
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
|
||
|
|
tm.assert_numpy_array_equal(s.__array__(), exp_values)
|
||
|
|
|
||
|
|
# remove unused categories
|
||
|
|
s = Series(Categorical(["a", "b", "b", "a"], categories=["a", "b", "c"
|
||
|
|
]))
|
||
|
|
exp_categories = Index(["a", "b"])
|
||
|
|
exp_values = np.array(["a", "b", "b", "a"], dtype=np.object_)
|
||
|
|
s = s.cat.remove_unused_categories()
|
||
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
||
|
|
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
|
||
|
|
tm.assert_numpy_array_equal(s.__array__(), exp_values)
|
||
|
|
|
||
|
|
# This method is likely to be confused, so test that it raises an error
|
||
|
|
# on wrong inputs:
|
||
|
|
def f():
|
||
|
|
s.set_categories([4, 3, 2, 1])
|
||
|
|
|
||
|
|
pytest.raises(Exception, f)
|
||
|
|
# right: s.cat.set_categories([4,3,2,1])
|
||
|
|
|
||
|
|
# GH18862 (let Series.cat.rename_categories take callables)
|
||
|
|
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
|
||
|
|
result = s.cat.rename_categories(lambda x: x.upper())
|
||
|
|
expected = Series(Categorical(["A", "B", "C", "A"],
|
||
|
|
categories=["A", "B", "C"],
|
||
|
|
ordered=True))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_str_accessor_api_for_categorical(self):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/10661
|
||
|
|
from pandas.core.strings import StringMethods
|
||
|
|
s = Series(list('aabb'))
|
||
|
|
s = s + " " + s
|
||
|
|
c = s.astype('category')
|
||
|
|
assert isinstance(c.str, StringMethods)
|
||
|
|
|
||
|
|
# str functions, which need special arguments
|
||
|
|
special_func_defs = [
|
||
|
|
('cat', (list("zyxw"),), {"sep": ","}),
|
||
|
|
('center', (10,), {}),
|
||
|
|
('contains', ("a",), {}),
|
||
|
|
('count', ("a",), {}),
|
||
|
|
('decode', ("UTF-8",), {}),
|
||
|
|
('encode', ("UTF-8",), {}),
|
||
|
|
('endswith', ("a",), {}),
|
||
|
|
('extract', ("([a-z]*) ",), {"expand": False}),
|
||
|
|
('extract', ("([a-z]*) ",), {"expand": True}),
|
||
|
|
('extractall', ("([a-z]*) ",), {}),
|
||
|
|
('find', ("a",), {}),
|
||
|
|
('findall', ("a",), {}),
|
||
|
|
('index', (" ",), {}),
|
||
|
|
('ljust', (10,), {}),
|
||
|
|
('match', ("a"), {}), # deprecated...
|
||
|
|
('normalize', ("NFC",), {}),
|
||
|
|
('pad', (10,), {}),
|
||
|
|
('partition', (" ",), {"expand": False}), # not default
|
||
|
|
('partition', (" ",), {"expand": True}), # default
|
||
|
|
('repeat', (3,), {}),
|
||
|
|
('replace', ("a", "z"), {}),
|
||
|
|
('rfind', ("a",), {}),
|
||
|
|
('rindex', (" ",), {}),
|
||
|
|
('rjust', (10,), {}),
|
||
|
|
('rpartition', (" ",), {"expand": False}), # not default
|
||
|
|
('rpartition', (" ",), {"expand": True}), # default
|
||
|
|
('slice', (0, 1), {}),
|
||
|
|
('slice_replace', (0, 1, "z"), {}),
|
||
|
|
('split', (" ",), {"expand": False}), # default
|
||
|
|
('split', (" ",), {"expand": True}), # not default
|
||
|
|
('startswith', ("a",), {}),
|
||
|
|
('wrap', (2,), {}),
|
||
|
|
('zfill', (10,), {})
|
||
|
|
]
|
||
|
|
_special_func_names = [f[0] for f in special_func_defs]
|
||
|
|
|
||
|
|
# * get, join: they need a individual elements of type lists, but
|
||
|
|
# we can't make a categorical with lists as individual categories.
|
||
|
|
# -> `s.str.split(" ").astype("category")` will error!
|
||
|
|
# * `translate` has different interfaces for py2 vs. py3
|
||
|
|
_ignore_names = ["get", "join", "translate"]
|
||
|
|
|
||
|
|
str_func_names = [f for f in dir(s.str) if not (
|
||
|
|
f.startswith("_") or
|
||
|
|
f in _special_func_names or
|
||
|
|
f in _ignore_names)]
|
||
|
|
|
||
|
|
func_defs = [(f, (), {}) for f in str_func_names]
|
||
|
|
func_defs.extend(special_func_defs)
|
||
|
|
|
||
|
|
for func, args, kwargs in func_defs:
|
||
|
|
res = getattr(c.str, func)(*args, **kwargs)
|
||
|
|
exp = getattr(s.str, func)(*args, **kwargs)
|
||
|
|
|
||
|
|
if isinstance(res, DataFrame):
|
||
|
|
tm.assert_frame_equal(res, exp)
|
||
|
|
else:
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
invalid = Series([1, 2, 3]).astype('category')
|
||
|
|
with tm.assert_raises_regex(AttributeError,
|
||
|
|
"Can only use .str "
|
||
|
|
"accessor with string"):
|
||
|
|
invalid.str
|
||
|
|
assert not hasattr(invalid, 'str')
|
||
|
|
|
||
|
|
def test_dt_accessor_api_for_categorical(self):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/10661
|
||
|
|
from pandas.core.indexes.accessors import Properties
|
||
|
|
|
||
|
|
s_dr = Series(date_range('1/1/2015', periods=5, tz="MET"))
|
||
|
|
c_dr = s_dr.astype("category")
|
||
|
|
|
||
|
|
s_pr = Series(period_range('1/1/2015', freq='D', periods=5))
|
||
|
|
c_pr = s_pr.astype("category")
|
||
|
|
|
||
|
|
s_tdr = Series(timedelta_range('1 days', '10 days'))
|
||
|
|
c_tdr = s_tdr.astype("category")
|
||
|
|
|
||
|
|
# only testing field (like .day)
|
||
|
|
# and bool (is_month_start)
|
||
|
|
get_ops = lambda x: x._datetimelike_ops
|
||
|
|
|
||
|
|
test_data = [
|
||
|
|
("Datetime", get_ops(DatetimeIndex), s_dr, c_dr),
|
||
|
|
("Period", get_ops(PeriodIndex), s_pr, c_pr),
|
||
|
|
("Timedelta", get_ops(TimedeltaIndex), s_tdr, c_tdr)]
|
||
|
|
|
||
|
|
assert isinstance(c_dr.dt, Properties)
|
||
|
|
|
||
|
|
special_func_defs = [
|
||
|
|
('strftime', ("%Y-%m-%d",), {}),
|
||
|
|
('tz_convert', ("EST",), {}),
|
||
|
|
('round', ("D",), {}),
|
||
|
|
('floor', ("D",), {}),
|
||
|
|
('ceil', ("D",), {}),
|
||
|
|
('asfreq', ("D",), {}),
|
||
|
|
# ('tz_localize', ("UTC",), {}),
|
||
|
|
]
|
||
|
|
_special_func_names = [f[0] for f in special_func_defs]
|
||
|
|
|
||
|
|
# the series is already localized
|
||
|
|
_ignore_names = ['tz_localize', 'components']
|
||
|
|
|
||
|
|
for name, attr_names, s, c in test_data:
|
||
|
|
func_names = [f
|
||
|
|
for f in dir(s.dt)
|
||
|
|
if not (f.startswith("_") or f in attr_names or f in
|
||
|
|
_special_func_names or f in _ignore_names)]
|
||
|
|
|
||
|
|
func_defs = [(f, (), {}) for f in func_names]
|
||
|
|
for f_def in special_func_defs:
|
||
|
|
if f_def[0] in dir(s.dt):
|
||
|
|
func_defs.append(f_def)
|
||
|
|
|
||
|
|
for func, args, kwargs in func_defs:
|
||
|
|
res = getattr(c.dt, func)(*args, **kwargs)
|
||
|
|
exp = getattr(s.dt, func)(*args, **kwargs)
|
||
|
|
|
||
|
|
if isinstance(res, DataFrame):
|
||
|
|
tm.assert_frame_equal(res, exp)
|
||
|
|
elif isinstance(res, Series):
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
else:
|
||
|
|
tm.assert_almost_equal(res, exp)
|
||
|
|
|
||
|
|
for attr in attr_names:
|
||
|
|
try:
|
||
|
|
res = getattr(c.dt, attr)
|
||
|
|
exp = getattr(s.dt, attr)
|
||
|
|
except Exception as e:
|
||
|
|
print(name, attr)
|
||
|
|
raise e
|
||
|
|
|
||
|
|
if isinstance(res, DataFrame):
|
||
|
|
tm.assert_frame_equal(res, exp)
|
||
|
|
elif isinstance(res, Series):
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
else:
|
||
|
|
tm.assert_almost_equal(res, exp)
|
||
|
|
|
||
|
|
invalid = Series([1, 2, 3]).astype('category')
|
||
|
|
with tm.assert_raises_regex(
|
||
|
|
AttributeError, "Can only use .dt accessor with datetimelike"):
|
||
|
|
invalid.dt
|
||
|
|
assert not hasattr(invalid, 'str')
|