2835 lines
105 KiB
Python
2835 lines
105 KiB
Python
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# -*- coding: utf-8 -*-
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# pylint: disable-msg=W0612,E1101,W0141
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from warnings import catch_warnings
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import datetime
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import itertools
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import pytest
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import pytz
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from numpy.random import randn
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import numpy as np
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from pandas.core.index import Index, MultiIndex
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from pandas import Panel, DataFrame, Series, notna, isna, Timestamp
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from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype
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import pandas.core.common as com
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import pandas.util.testing as tm
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from pandas.compat import (range, lrange, StringIO, lzip, u, product as
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cart_product, zip)
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import pandas as pd
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import pandas._libs.index as _index
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class Base(object):
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def setup_method(self, method):
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index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
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'three']],
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labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
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[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
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names=['first', 'second'])
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self.frame = DataFrame(np.random.randn(10, 3), index=index,
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columns=Index(['A', 'B', 'C'], name='exp'))
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self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
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labels=[[0, 1, 2, 3]], names=['first'])
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# create test series object
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arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
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['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
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tuples = lzip(*arrays)
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index = MultiIndex.from_tuples(tuples)
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s = Series(randn(8), index=index)
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s[3] = np.NaN
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self.series = s
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tm.N = 100
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self.tdf = tm.makeTimeDataFrame()
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self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
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lambda x: x.day]).sum()
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# use Int64Index, to make sure things work
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self.ymd.index.set_levels([lev.astype('i8')
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for lev in self.ymd.index.levels],
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inplace=True)
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self.ymd.index.set_names(['year', 'month', 'day'], inplace=True)
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class TestMultiLevel(Base):
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def test_append(self):
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a, b = self.frame[:5], self.frame[5:]
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result = a.append(b)
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tm.assert_frame_equal(result, self.frame)
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result = a['A'].append(b['A'])
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tm.assert_series_equal(result, self.frame['A'])
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def test_append_index(self):
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idx1 = Index([1.1, 1.2, 1.3])
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idx2 = pd.date_range('2011-01-01', freq='D', periods=3,
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tz='Asia/Tokyo')
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idx3 = Index(['A', 'B', 'C'])
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midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
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midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
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result = idx1.append(midx_lv2)
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# see gh-7112
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tz = pytz.timezone('Asia/Tokyo')
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expected_tuples = [(1.1, tz.localize(datetime.datetime(2011, 1, 1))),
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(1.2, tz.localize(datetime.datetime(2011, 1, 2))),
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(1.3, tz.localize(datetime.datetime(2011, 1, 3)))]
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expected = Index([1.1, 1.2, 1.3] + expected_tuples)
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tm.assert_index_equal(result, expected)
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result = midx_lv2.append(idx1)
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expected = Index(expected_tuples + [1.1, 1.2, 1.3])
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tm.assert_index_equal(result, expected)
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result = midx_lv2.append(midx_lv2)
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expected = MultiIndex.from_arrays([idx1.append(idx1),
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idx2.append(idx2)])
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tm.assert_index_equal(result, expected)
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result = midx_lv2.append(midx_lv3)
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tm.assert_index_equal(result, expected)
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result = midx_lv3.append(midx_lv2)
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expected = Index._simple_new(
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np.array([(1.1, tz.localize(datetime.datetime(2011, 1, 1)), 'A'),
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(1.2, tz.localize(datetime.datetime(2011, 1, 2)), 'B'),
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(1.3, tz.localize(datetime.datetime(2011, 1, 3)), 'C')] +
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expected_tuples), None)
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tm.assert_index_equal(result, expected)
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def test_dataframe_constructor(self):
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multi = DataFrame(np.random.randn(4, 4),
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index=[np.array(['a', 'a', 'b', 'b']),
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np.array(['x', 'y', 'x', 'y'])])
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assert isinstance(multi.index, MultiIndex)
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assert not isinstance(multi.columns, MultiIndex)
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multi = DataFrame(np.random.randn(4, 4),
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columns=[['a', 'a', 'b', 'b'],
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['x', 'y', 'x', 'y']])
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assert isinstance(multi.columns, MultiIndex)
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def test_series_constructor(self):
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multi = Series(1., index=[np.array(['a', 'a', 'b', 'b']), np.array(
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['x', 'y', 'x', 'y'])])
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assert isinstance(multi.index, MultiIndex)
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multi = Series(1., index=[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']])
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assert isinstance(multi.index, MultiIndex)
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multi = Series(lrange(4), index=[['a', 'a', 'b', 'b'],
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['x', 'y', 'x', 'y']])
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assert isinstance(multi.index, MultiIndex)
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def test_reindex_level(self):
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# axis=0
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month_sums = self.ymd.sum(level='month')
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result = month_sums.reindex(self.ymd.index, level=1)
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expected = self.ymd.groupby(level='month').transform(np.sum)
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tm.assert_frame_equal(result, expected)
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# Series
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result = month_sums['A'].reindex(self.ymd.index, level=1)
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expected = self.ymd['A'].groupby(level='month').transform(np.sum)
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tm.assert_series_equal(result, expected, check_names=False)
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# axis=1
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month_sums = self.ymd.T.sum(axis=1, level='month')
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result = month_sums.reindex(columns=self.ymd.index, level=1)
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expected = self.ymd.groupby(level='month').transform(np.sum).T
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tm.assert_frame_equal(result, expected)
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def test_binops_level(self):
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def _check_op(opname):
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op = getattr(DataFrame, opname)
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month_sums = self.ymd.sum(level='month')
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result = op(self.ymd, month_sums, level='month')
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broadcasted = self.ymd.groupby(level='month').transform(np.sum)
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expected = op(self.ymd, broadcasted)
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tm.assert_frame_equal(result, expected)
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# Series
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op = getattr(Series, opname)
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result = op(self.ymd['A'], month_sums['A'], level='month')
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broadcasted = self.ymd['A'].groupby(level='month').transform(
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np.sum)
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expected = op(self.ymd['A'], broadcasted)
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expected.name = 'A'
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tm.assert_series_equal(result, expected)
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_check_op('sub')
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_check_op('add')
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_check_op('mul')
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_check_op('div')
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def test_pickle(self):
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def _test_roundtrip(frame):
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unpickled = tm.round_trip_pickle(frame)
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tm.assert_frame_equal(frame, unpickled)
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_test_roundtrip(self.frame)
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_test_roundtrip(self.frame.T)
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_test_roundtrip(self.ymd)
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_test_roundtrip(self.ymd.T)
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def test_reindex(self):
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expected = self.frame.iloc[[0, 3]]
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reindexed = self.frame.loc[[('foo', 'one'), ('bar', 'one')]]
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tm.assert_frame_equal(reindexed, expected)
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with catch_warnings(record=True):
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reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
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tm.assert_frame_equal(reindexed, expected)
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def test_reindex_preserve_levels(self):
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new_index = self.ymd.index[::10]
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chunk = self.ymd.reindex(new_index)
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assert chunk.index is new_index
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chunk = self.ymd.loc[new_index]
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assert chunk.index is new_index
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with catch_warnings(record=True):
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chunk = self.ymd.ix[new_index]
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assert chunk.index is new_index
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ymdT = self.ymd.T
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chunk = ymdT.reindex(columns=new_index)
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assert chunk.columns is new_index
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chunk = ymdT.loc[:, new_index]
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assert chunk.columns is new_index
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def test_repr_to_string(self):
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repr(self.frame)
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repr(self.ymd)
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repr(self.frame.T)
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repr(self.ymd.T)
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buf = StringIO()
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self.frame.to_string(buf=buf)
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self.ymd.to_string(buf=buf)
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self.frame.T.to_string(buf=buf)
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self.ymd.T.to_string(buf=buf)
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def test_repr_name_coincide(self):
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index = MultiIndex.from_tuples([('a', 0, 'foo'), ('b', 1, 'bar')],
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names=['a', 'b', 'c'])
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df = DataFrame({'value': [0, 1]}, index=index)
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lines = repr(df).split('\n')
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assert lines[2].startswith('a 0 foo')
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def test_getitem_simple(self):
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df = self.frame.T
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col = df['foo', 'one']
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tm.assert_almost_equal(col.values, df.values[:, 0])
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with pytest.raises(KeyError):
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df[('foo', 'four')]
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with pytest.raises(KeyError):
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df['foobar']
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def test_series_getitem(self):
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s = self.ymd['A']
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result = s[2000, 3]
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# TODO(wesm): unused?
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# result2 = s.loc[2000, 3]
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expected = s.reindex(s.index[42:65])
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expected.index = expected.index.droplevel(0).droplevel(0)
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tm.assert_series_equal(result, expected)
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result = s[2000, 3, 10]
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expected = s[49]
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assert result == expected
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# fancy
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expected = s.reindex(s.index[49:51])
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result = s.loc[[(2000, 3, 10), (2000, 3, 13)]]
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tm.assert_series_equal(result, expected)
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with catch_warnings(record=True):
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result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
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tm.assert_series_equal(result, expected)
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# key error
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pytest.raises(KeyError, s.__getitem__, (2000, 3, 4))
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def test_series_getitem_corner(self):
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s = self.ymd['A']
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# don't segfault, GH #495
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# out of bounds access
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pytest.raises(IndexError, s.__getitem__, len(self.ymd))
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# generator
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result = s[(x > 0 for x in s)]
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expected = s[s > 0]
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tm.assert_series_equal(result, expected)
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def test_series_setitem(self):
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s = self.ymd['A']
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s[2000, 3] = np.nan
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assert isna(s.values[42:65]).all()
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assert notna(s.values[:42]).all()
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assert notna(s.values[65:]).all()
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s[2000, 3, 10] = np.nan
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assert isna(s[49])
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def test_series_slice_partial(self):
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pass
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def test_frame_getitem_setitem_boolean(self):
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df = self.frame.T.copy()
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values = df.values
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result = df[df > 0]
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expected = df.where(df > 0)
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tm.assert_frame_equal(result, expected)
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df[df > 0] = 5
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values[values > 0] = 5
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tm.assert_almost_equal(df.values, values)
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df[df == 5] = 0
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values[values == 5] = 0
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tm.assert_almost_equal(df.values, values)
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# a df that needs alignment first
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df[df[:-1] < 0] = 2
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np.putmask(values[:-1], values[:-1] < 0, 2)
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tm.assert_almost_equal(df.values, values)
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with tm.assert_raises_regex(TypeError, 'boolean values only'):
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df[df * 0] = 2
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def test_frame_getitem_setitem_slice(self):
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# getitem
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result = self.frame.iloc[:4]
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expected = self.frame[:4]
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tm.assert_frame_equal(result, expected)
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# setitem
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cp = self.frame.copy()
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cp.iloc[:4] = 0
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assert (cp.values[:4] == 0).all()
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assert (cp.values[4:] != 0).all()
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def test_frame_getitem_setitem_multislice(self):
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levels = [['t1', 't2'], ['a', 'b', 'c']]
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labels = [[0, 0, 0, 1, 1], [0, 1, 2, 0, 1]]
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midx = MultiIndex(labels=labels, levels=levels, names=[None, 'id'])
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df = DataFrame({'value': [1, 2, 3, 7, 8]}, index=midx)
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result = df.loc[:, 'value']
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tm.assert_series_equal(df['value'], result)
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with catch_warnings(record=True):
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result = df.ix[:, 'value']
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tm.assert_series_equal(df['value'], result)
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result = df.loc[df.index[1:3], 'value']
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|
|
tm.assert_series_equal(df['value'][1:3], result)
|
||
|
|
|
||
|
|
result = df.loc[:, :]
|
||
|
|
tm.assert_frame_equal(df, result)
|
||
|
|
|
||
|
|
result = df
|
||
|
|
df.loc[:, 'value'] = 10
|
||
|
|
result['value'] = 10
|
||
|
|
tm.assert_frame_equal(df, result)
|
||
|
|
|
||
|
|
df.loc[:, :] = 10
|
||
|
|
tm.assert_frame_equal(df, result)
|
||
|
|
|
||
|
|
def test_frame_getitem_multicolumn_empty_level(self):
|
||
|
|
f = DataFrame({'a': ['1', '2', '3'], 'b': ['2', '3', '4']})
|
||
|
|
f.columns = [['level1 item1', 'level1 item2'], ['', 'level2 item2'],
|
||
|
|
['level3 item1', 'level3 item2']]
|
||
|
|
|
||
|
|
result = f['level1 item1']
|
||
|
|
expected = DataFrame([['1'], ['2'], ['3']], index=f.index,
|
||
|
|
columns=['level3 item1'])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_frame_setitem_multi_column(self):
|
||
|
|
df = DataFrame(randn(10, 4), columns=[['a', 'a', 'b', 'b'],
|
||
|
|
[0, 1, 0, 1]])
|
||
|
|
|
||
|
|
cp = df.copy()
|
||
|
|
cp['a'] = cp['b']
|
||
|
|
tm.assert_frame_equal(cp['a'], cp['b'])
|
||
|
|
|
||
|
|
# set with ndarray
|
||
|
|
cp = df.copy()
|
||
|
|
cp['a'] = cp['b'].values
|
||
|
|
tm.assert_frame_equal(cp['a'], cp['b'])
|
||
|
|
|
||
|
|
# ---------------------------------------
|
||
|
|
# #1803
|
||
|
|
columns = MultiIndex.from_tuples([('A', '1'), ('A', '2'), ('B', '1')])
|
||
|
|
df = DataFrame(index=[1, 3, 5], columns=columns)
|
||
|
|
|
||
|
|
# Works, but adds a column instead of updating the two existing ones
|
||
|
|
df['A'] = 0.0 # Doesn't work
|
||
|
|
assert (df['A'].values == 0).all()
|
||
|
|
|
||
|
|
# it broadcasts
|
||
|
|
df['B', '1'] = [1, 2, 3]
|
||
|
|
df['A'] = df['B', '1']
|
||
|
|
|
||
|
|
sliced_a1 = df['A', '1']
|
||
|
|
sliced_a2 = df['A', '2']
|
||
|
|
sliced_b1 = df['B', '1']
|
||
|
|
tm.assert_series_equal(sliced_a1, sliced_b1, check_names=False)
|
||
|
|
tm.assert_series_equal(sliced_a2, sliced_b1, check_names=False)
|
||
|
|
assert sliced_a1.name == ('A', '1')
|
||
|
|
assert sliced_a2.name == ('A', '2')
|
||
|
|
assert sliced_b1.name == ('B', '1')
|
||
|
|
|
||
|
|
def test_getitem_tuple_plus_slice(self):
|
||
|
|
# GH #671
|
||
|
|
df = DataFrame({'a': lrange(10),
|
||
|
|
'b': lrange(10),
|
||
|
|
'c': np.random.randn(10),
|
||
|
|
'd': np.random.randn(10)})
|
||
|
|
|
||
|
|
idf = df.set_index(['a', 'b'])
|
||
|
|
|
||
|
|
result = idf.loc[(0, 0), :]
|
||
|
|
expected = idf.loc[0, 0]
|
||
|
|
expected2 = idf.xs((0, 0))
|
||
|
|
with catch_warnings(record=True):
|
||
|
|
expected3 = idf.ix[0, 0]
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
tm.assert_series_equal(result, expected2)
|
||
|
|
tm.assert_series_equal(result, expected3)
|
||
|
|
|
||
|
|
def test_getitem_setitem_tuple_plus_columns(self):
|
||
|
|
# GH #1013
|
||
|
|
|
||
|
|
df = self.ymd[:5]
|
||
|
|
|
||
|
|
result = df.loc[(2000, 1, 6), ['A', 'B', 'C']]
|
||
|
|
expected = df.loc[2000, 1, 6][['A', 'B', 'C']]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_xs(self):
|
||
|
|
xs = self.frame.xs(('bar', 'two'))
|
||
|
|
xs2 = self.frame.loc[('bar', 'two')]
|
||
|
|
|
||
|
|
tm.assert_series_equal(xs, xs2)
|
||
|
|
tm.assert_almost_equal(xs.values, self.frame.values[4])
|
||
|
|
|
||
|
|
# GH 6574
|
||
|
|
# missing values in returned index should be preserrved
|
||
|
|
acc = [
|
||
|
|
('a', 'abcde', 1),
|
||
|
|
('b', 'bbcde', 2),
|
||
|
|
('y', 'yzcde', 25),
|
||
|
|
('z', 'xbcde', 24),
|
||
|
|
('z', None, 26),
|
||
|
|
('z', 'zbcde', 25),
|
||
|
|
('z', 'ybcde', 26),
|
||
|
|
]
|
||
|
|
df = DataFrame(acc,
|
||
|
|
columns=['a1', 'a2', 'cnt']).set_index(['a1', 'a2'])
|
||
|
|
expected = DataFrame({'cnt': [24, 26, 25, 26]}, index=Index(
|
||
|
|
['xbcde', np.nan, 'zbcde', 'ybcde'], name='a2'))
|
||
|
|
|
||
|
|
result = df.xs('z', level='a1')
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_xs_partial(self):
|
||
|
|
result = self.frame.xs('foo')
|
||
|
|
result2 = self.frame.loc['foo']
|
||
|
|
expected = self.frame.T['foo'].T
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
tm.assert_frame_equal(result, result2)
|
||
|
|
|
||
|
|
result = self.ymd.xs((2000, 4))
|
||
|
|
expected = self.ymd.loc[2000, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# ex from #1796
|
||
|
|
index = MultiIndex(levels=[['foo', 'bar'], ['one', 'two'], [-1, 1]],
|
||
|
|
labels=[[0, 0, 0, 0, 1, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 0, 0, 1, 1], [0, 1, 0, 1, 0, 1,
|
||
|
|
0, 1]])
|
||
|
|
df = DataFrame(np.random.randn(8, 4), index=index,
|
||
|
|
columns=list('abcd'))
|
||
|
|
|
||
|
|
result = df.xs(['foo', 'one'])
|
||
|
|
expected = df.loc['foo', 'one']
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_xs_level(self):
|
||
|
|
result = self.frame.xs('two', level='second')
|
||
|
|
expected = self.frame[self.frame.index.get_level_values(1) == 'two']
|
||
|
|
expected.index = expected.index.droplevel(1)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
index = MultiIndex.from_tuples([('x', 'y', 'z'), ('a', 'b', 'c'), (
|
||
|
|
'p', 'q', 'r')])
|
||
|
|
df = DataFrame(np.random.randn(3, 5), index=index)
|
||
|
|
result = df.xs('c', level=2)
|
||
|
|
expected = df[1:2]
|
||
|
|
expected.index = expected.index.droplevel(2)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# this is a copy in 0.14
|
||
|
|
result = self.frame.xs('two', level='second')
|
||
|
|
|
||
|
|
# setting this will give a SettingWithCopyError
|
||
|
|
# as we are trying to write a view
|
||
|
|
def f(x):
|
||
|
|
x[:] = 10
|
||
|
|
|
||
|
|
pytest.raises(com.SettingWithCopyError, f, result)
|
||
|
|
|
||
|
|
def test_xs_level_multiple(self):
|
||
|
|
from pandas import read_table
|
||
|
|
text = """ A B C D E
|
||
|
|
one two three four
|
||
|
|
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
|
||
|
|
a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744
|
||
|
|
x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838"""
|
||
|
|
|
||
|
|
df = read_table(StringIO(text), sep=r'\s+', engine='python')
|
||
|
|
|
||
|
|
result = df.xs(('a', 4), level=['one', 'four'])
|
||
|
|
expected = df.xs('a').xs(4, level='four')
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# this is a copy in 0.14
|
||
|
|
result = df.xs(('a', 4), level=['one', 'four'])
|
||
|
|
|
||
|
|
# setting this will give a SettingWithCopyError
|
||
|
|
# as we are trying to write a view
|
||
|
|
def f(x):
|
||
|
|
x[:] = 10
|
||
|
|
|
||
|
|
pytest.raises(com.SettingWithCopyError, f, result)
|
||
|
|
|
||
|
|
# GH2107
|
||
|
|
dates = lrange(20111201, 20111205)
|
||
|
|
ids = 'abcde'
|
||
|
|
idx = MultiIndex.from_tuples([x for x in cart_product(dates, ids)])
|
||
|
|
idx.names = ['date', 'secid']
|
||
|
|
df = DataFrame(np.random.randn(len(idx), 3), idx, ['X', 'Y', 'Z'])
|
||
|
|
|
||
|
|
rs = df.xs(20111201, level='date')
|
||
|
|
xp = df.loc[20111201, :]
|
||
|
|
tm.assert_frame_equal(rs, xp)
|
||
|
|
|
||
|
|
def test_xs_level0(self):
|
||
|
|
from pandas import read_table
|
||
|
|
text = """ A B C D E
|
||
|
|
one two three four
|
||
|
|
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
|
||
|
|
a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744
|
||
|
|
x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838"""
|
||
|
|
|
||
|
|
df = read_table(StringIO(text), sep=r'\s+', engine='python')
|
||
|
|
|
||
|
|
result = df.xs('a', level=0)
|
||
|
|
expected = df.xs('a')
|
||
|
|
assert len(result) == 2
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_xs_level_series(self):
|
||
|
|
s = self.frame['A']
|
||
|
|
result = s[:, 'two']
|
||
|
|
expected = self.frame.xs('two', level=1)['A']
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
s = self.ymd['A']
|
||
|
|
result = s[2000, 5]
|
||
|
|
expected = self.ymd.loc[2000, 5]['A']
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# not implementing this for now
|
||
|
|
|
||
|
|
pytest.raises(TypeError, s.__getitem__, (2000, slice(3, 4)))
|
||
|
|
|
||
|
|
# result = s[2000, 3:4]
|
||
|
|
# lv =s.index.get_level_values(1)
|
||
|
|
# expected = s[(lv == 3) | (lv == 4)]
|
||
|
|
# expected.index = expected.index.droplevel(0)
|
||
|
|
# tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# can do this though
|
||
|
|
|
||
|
|
def test_get_loc_single_level(self):
|
||
|
|
s = Series(np.random.randn(len(self.single_level)),
|
||
|
|
index=self.single_level)
|
||
|
|
for k in self.single_level.values:
|
||
|
|
s[k]
|
||
|
|
|
||
|
|
def test_getitem_toplevel(self):
|
||
|
|
df = self.frame.T
|
||
|
|
|
||
|
|
result = df['foo']
|
||
|
|
expected = df.reindex(columns=df.columns[:3])
|
||
|
|
expected.columns = expected.columns.droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df['bar']
|
||
|
|
result2 = df.loc[:, 'bar']
|
||
|
|
|
||
|
|
expected = df.reindex(columns=df.columns[3:5])
|
||
|
|
expected.columns = expected.columns.droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
tm.assert_frame_equal(result, result2)
|
||
|
|
|
||
|
|
def test_getitem_setitem_slice_integers(self):
|
||
|
|
index = MultiIndex(levels=[[0, 1, 2], [0, 2]],
|
||
|
|
labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])
|
||
|
|
|
||
|
|
frame = DataFrame(np.random.randn(len(index), 4), index=index,
|
||
|
|
columns=['a', 'b', 'c', 'd'])
|
||
|
|
res = frame.loc[1:2]
|
||
|
|
exp = frame.reindex(frame.index[2:])
|
||
|
|
tm.assert_frame_equal(res, exp)
|
||
|
|
|
||
|
|
frame.loc[1:2] = 7
|
||
|
|
assert (frame.loc[1:2] == 7).values.all()
|
||
|
|
|
||
|
|
series = Series(np.random.randn(len(index)), index=index)
|
||
|
|
|
||
|
|
res = series.loc[1:2]
|
||
|
|
exp = series.reindex(series.index[2:])
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
series.loc[1:2] = 7
|
||
|
|
assert (series.loc[1:2] == 7).values.all()
|
||
|
|
|
||
|
|
def test_getitem_int(self):
|
||
|
|
levels = [[0, 1], [0, 1, 2]]
|
||
|
|
labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
|
||
|
|
index = MultiIndex(levels=levels, labels=labels)
|
||
|
|
|
||
|
|
frame = DataFrame(np.random.randn(6, 2), index=index)
|
||
|
|
|
||
|
|
result = frame.loc[1]
|
||
|
|
expected = frame[-3:]
|
||
|
|
expected.index = expected.index.droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# raises exception
|
||
|
|
pytest.raises(KeyError, frame.loc.__getitem__, 3)
|
||
|
|
|
||
|
|
# however this will work
|
||
|
|
result = self.frame.iloc[2]
|
||
|
|
expected = self.frame.xs(self.frame.index[2])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_getitem_partial(self):
|
||
|
|
ymd = self.ymd.T
|
||
|
|
result = ymd[2000, 2]
|
||
|
|
|
||
|
|
expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
|
||
|
|
expected.columns = expected.columns.droplevel(0).droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_setitem_change_dtype(self):
|
||
|
|
dft = self.frame.T
|
||
|
|
s = dft['foo', 'two']
|
||
|
|
dft['foo', 'two'] = s > s.median()
|
||
|
|
tm.assert_series_equal(dft['foo', 'two'], s > s.median())
|
||
|
|
# assert isinstance(dft._data.blocks[1].items, MultiIndex)
|
||
|
|
|
||
|
|
reindexed = dft.reindex(columns=[('foo', 'two')])
|
||
|
|
tm.assert_series_equal(reindexed['foo', 'two'], s > s.median())
|
||
|
|
|
||
|
|
def test_frame_setitem_ix(self):
|
||
|
|
self.frame.loc[('bar', 'two'), 'B'] = 5
|
||
|
|
assert self.frame.loc[('bar', 'two'), 'B'] == 5
|
||
|
|
|
||
|
|
# with integer labels
|
||
|
|
df = self.frame.copy()
|
||
|
|
df.columns = lrange(3)
|
||
|
|
df.loc[('bar', 'two'), 1] = 7
|
||
|
|
assert df.loc[('bar', 'two'), 1] == 7
|
||
|
|
|
||
|
|
with catch_warnings(record=True):
|
||
|
|
df = self.frame.copy()
|
||
|
|
df.columns = lrange(3)
|
||
|
|
df.ix[('bar', 'two'), 1] = 7
|
||
|
|
assert df.loc[('bar', 'two'), 1] == 7
|
||
|
|
|
||
|
|
def test_fancy_slice_partial(self):
|
||
|
|
result = self.frame.loc['bar':'baz']
|
||
|
|
expected = self.frame[3:7]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.ymd.loc[(2000, 2):(2000, 4)]
|
||
|
|
lev = self.ymd.index.labels[1]
|
||
|
|
expected = self.ymd[(lev >= 1) & (lev <= 3)]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_getitem_partial_column_select(self):
|
||
|
|
idx = MultiIndex(labels=[[0, 0, 0], [0, 1, 1], [1, 0, 1]],
|
||
|
|
levels=[['a', 'b'], ['x', 'y'], ['p', 'q']])
|
||
|
|
df = DataFrame(np.random.rand(3, 2), index=idx)
|
||
|
|
|
||
|
|
result = df.loc[('a', 'y'), :]
|
||
|
|
expected = df.loc[('a', 'y')]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.loc[('a', 'y'), [1, 0]]
|
||
|
|
expected = df.loc[('a', 'y')][[1, 0]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
with catch_warnings(record=True):
|
||
|
|
result = df.ix[('a', 'y'), [1, 0]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
pytest.raises(KeyError, df.loc.__getitem__,
|
||
|
|
(('a', 'foo'), slice(None, None)))
|
||
|
|
|
||
|
|
def test_delevel_infer_dtype(self):
|
||
|
|
tuples = [tuple
|
||
|
|
for tuple in cart_product(
|
||
|
|
['foo', 'bar'], [10, 20], [1.0, 1.1])]
|
||
|
|
index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
|
||
|
|
df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
|
||
|
|
index=index)
|
||
|
|
deleveled = df.reset_index()
|
||
|
|
assert is_integer_dtype(deleveled['prm1'])
|
||
|
|
assert is_float_dtype(deleveled['prm2'])
|
||
|
|
|
||
|
|
def test_reset_index_with_drop(self):
|
||
|
|
deleveled = self.ymd.reset_index(drop=True)
|
||
|
|
assert len(deleveled.columns) == len(self.ymd.columns)
|
||
|
|
|
||
|
|
deleveled = self.series.reset_index()
|
||
|
|
assert isinstance(deleveled, DataFrame)
|
||
|
|
assert len(deleveled.columns) == len(self.series.index.levels) + 1
|
||
|
|
|
||
|
|
deleveled = self.series.reset_index(drop=True)
|
||
|
|
assert isinstance(deleveled, Series)
|
||
|
|
|
||
|
|
def test_count_level(self):
|
||
|
|
def _check_counts(frame, axis=0):
|
||
|
|
index = frame._get_axis(axis)
|
||
|
|
for i in range(index.nlevels):
|
||
|
|
result = frame.count(axis=axis, level=i)
|
||
|
|
expected = frame.groupby(axis=axis, level=i).count()
|
||
|
|
expected = expected.reindex_like(result).astype('i8')
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
self.frame.iloc[1, [1, 2]] = np.nan
|
||
|
|
self.frame.iloc[7, [0, 1]] = np.nan
|
||
|
|
self.ymd.iloc[1, [1, 2]] = np.nan
|
||
|
|
self.ymd.iloc[7, [0, 1]] = np.nan
|
||
|
|
|
||
|
|
_check_counts(self.frame)
|
||
|
|
_check_counts(self.ymd)
|
||
|
|
_check_counts(self.frame.T, axis=1)
|
||
|
|
_check_counts(self.ymd.T, axis=1)
|
||
|
|
|
||
|
|
# can't call with level on regular DataFrame
|
||
|
|
df = tm.makeTimeDataFrame()
|
||
|
|
tm.assert_raises_regex(
|
||
|
|
TypeError, 'hierarchical', df.count, level=0)
|
||
|
|
|
||
|
|
self.frame['D'] = 'foo'
|
||
|
|
result = self.frame.count(level=0, numeric_only=True)
|
||
|
|
tm.assert_index_equal(result.columns, Index(list('ABC'), name='exp'))
|
||
|
|
|
||
|
|
def test_count_level_series(self):
|
||
|
|
index = MultiIndex(levels=[['foo', 'bar', 'baz'], ['one', 'two',
|
||
|
|
'three', 'four']],
|
||
|
|
labels=[[0, 0, 0, 2, 2], [2, 0, 1, 1, 2]])
|
||
|
|
|
||
|
|
s = Series(np.random.randn(len(index)), index=index)
|
||
|
|
|
||
|
|
result = s.count(level=0)
|
||
|
|
expected = s.groupby(level=0).count()
|
||
|
|
tm.assert_series_equal(
|
||
|
|
result.astype('f8'), expected.reindex(result.index).fillna(0))
|
||
|
|
|
||
|
|
result = s.count(level=1)
|
||
|
|
expected = s.groupby(level=1).count()
|
||
|
|
tm.assert_series_equal(
|
||
|
|
result.astype('f8'), expected.reindex(result.index).fillna(0))
|
||
|
|
|
||
|
|
def test_count_level_corner(self):
|
||
|
|
s = self.frame['A'][:0]
|
||
|
|
result = s.count(level=0)
|
||
|
|
expected = Series(0, index=s.index.levels[0], name='A')
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
df = self.frame[:0]
|
||
|
|
result = df.count(level=0)
|
||
|
|
expected = DataFrame({}, index=s.index.levels[0],
|
||
|
|
columns=df.columns).fillna(0).astype(np.int64)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_get_level_number_out_of_bounds(self):
|
||
|
|
with tm.assert_raises_regex(IndexError, "Too many levels"):
|
||
|
|
self.frame.index._get_level_number(2)
|
||
|
|
with tm.assert_raises_regex(IndexError,
|
||
|
|
"not a valid level number"):
|
||
|
|
self.frame.index._get_level_number(-3)
|
||
|
|
|
||
|
|
def test_unstack(self):
|
||
|
|
# just check that it works for now
|
||
|
|
unstacked = self.ymd.unstack()
|
||
|
|
unstacked.unstack()
|
||
|
|
|
||
|
|
# test that ints work
|
||
|
|
self.ymd.astype(int).unstack()
|
||
|
|
|
||
|
|
# test that int32 work
|
||
|
|
self.ymd.astype(np.int32).unstack()
|
||
|
|
|
||
|
|
def test_unstack_multiple_no_empty_columns(self):
|
||
|
|
index = MultiIndex.from_tuples([(0, 'foo', 0), (0, 'bar', 0), (
|
||
|
|
1, 'baz', 1), (1, 'qux', 1)])
|
||
|
|
|
||
|
|
s = Series(np.random.randn(4), index=index)
|
||
|
|
|
||
|
|
unstacked = s.unstack([1, 2])
|
||
|
|
expected = unstacked.dropna(axis=1, how='all')
|
||
|
|
tm.assert_frame_equal(unstacked, expected)
|
||
|
|
|
||
|
|
def test_stack(self):
|
||
|
|
# regular roundtrip
|
||
|
|
unstacked = self.ymd.unstack()
|
||
|
|
restacked = unstacked.stack()
|
||
|
|
tm.assert_frame_equal(restacked, self.ymd)
|
||
|
|
|
||
|
|
unlexsorted = self.ymd.sort_index(level=2)
|
||
|
|
|
||
|
|
unstacked = unlexsorted.unstack(2)
|
||
|
|
restacked = unstacked.stack()
|
||
|
|
tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)
|
||
|
|
|
||
|
|
unlexsorted = unlexsorted[::-1]
|
||
|
|
unstacked = unlexsorted.unstack(1)
|
||
|
|
restacked = unstacked.stack().swaplevel(1, 2)
|
||
|
|
tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)
|
||
|
|
|
||
|
|
unlexsorted = unlexsorted.swaplevel(0, 1)
|
||
|
|
unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
|
||
|
|
restacked = unstacked.stack(0).swaplevel(1, 2)
|
||
|
|
tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)
|
||
|
|
|
||
|
|
# columns unsorted
|
||
|
|
unstacked = self.ymd.unstack()
|
||
|
|
unstacked = unstacked.sort_index(axis=1, ascending=False)
|
||
|
|
restacked = unstacked.stack()
|
||
|
|
tm.assert_frame_equal(restacked, self.ymd)
|
||
|
|
|
||
|
|
# more than 2 levels in the columns
|
||
|
|
unstacked = self.ymd.unstack(1).unstack(1)
|
||
|
|
|
||
|
|
result = unstacked.stack(1)
|
||
|
|
expected = self.ymd.unstack()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = unstacked.stack(2)
|
||
|
|
expected = self.ymd.unstack(1)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = unstacked.stack(0)
|
||
|
|
expected = self.ymd.stack().unstack(1).unstack(1)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# not all levels present in each echelon
|
||
|
|
unstacked = self.ymd.unstack(2).loc[:, ::3]
|
||
|
|
stacked = unstacked.stack().stack()
|
||
|
|
ymd_stacked = self.ymd.stack()
|
||
|
|
tm.assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))
|
||
|
|
|
||
|
|
# stack with negative number
|
||
|
|
result = self.ymd.unstack(0).stack(-2)
|
||
|
|
expected = self.ymd.unstack(0).stack(0)
|
||
|
|
|
||
|
|
# GH10417
|
||
|
|
def check(left, right):
|
||
|
|
tm.assert_series_equal(left, right)
|
||
|
|
assert not left.index.is_unique
|
||
|
|
li, ri = left.index, right.index
|
||
|
|
tm.assert_index_equal(li, ri)
|
||
|
|
|
||
|
|
df = DataFrame(np.arange(12).reshape(4, 3),
|
||
|
|
index=list('abab'),
|
||
|
|
columns=['1st', '2nd', '3rd'])
|
||
|
|
|
||
|
|
mi = MultiIndex(levels=[['a', 'b'], ['1st', '2nd', '3rd']],
|
||
|
|
labels=[np.tile(
|
||
|
|
np.arange(2).repeat(3), 2), np.tile(
|
||
|
|
np.arange(3), 4)])
|
||
|
|
|
||
|
|
left, right = df.stack(), Series(np.arange(12), index=mi)
|
||
|
|
check(left, right)
|
||
|
|
|
||
|
|
df.columns = ['1st', '2nd', '1st']
|
||
|
|
mi = MultiIndex(levels=[['a', 'b'], ['1st', '2nd']], labels=[np.tile(
|
||
|
|
np.arange(2).repeat(3), 2), np.tile(
|
||
|
|
[0, 1, 0], 4)])
|
||
|
|
|
||
|
|
left, right = df.stack(), Series(np.arange(12), index=mi)
|
||
|
|
check(left, right)
|
||
|
|
|
||
|
|
tpls = ('a', 2), ('b', 1), ('a', 1), ('b', 2)
|
||
|
|
df.index = MultiIndex.from_tuples(tpls)
|
||
|
|
mi = MultiIndex(levels=[['a', 'b'], [1, 2], ['1st', '2nd']],
|
||
|
|
labels=[np.tile(
|
||
|
|
np.arange(2).repeat(3), 2), np.repeat(
|
||
|
|
[1, 0, 1], [3, 6, 3]), np.tile(
|
||
|
|
[0, 1, 0], 4)])
|
||
|
|
|
||
|
|
left, right = df.stack(), Series(np.arange(12), index=mi)
|
||
|
|
check(left, right)
|
||
|
|
|
||
|
|
def test_unstack_odd_failure(self):
|
||
|
|
data = """day,time,smoker,sum,len
|
||
|
|
Fri,Dinner,No,8.25,3.
|
||
|
|
Fri,Dinner,Yes,27.03,9
|
||
|
|
Fri,Lunch,No,3.0,1
|
||
|
|
Fri,Lunch,Yes,13.68,6
|
||
|
|
Sat,Dinner,No,139.63,45
|
||
|
|
Sat,Dinner,Yes,120.77,42
|
||
|
|
Sun,Dinner,No,180.57,57
|
||
|
|
Sun,Dinner,Yes,66.82,19
|
||
|
|
Thur,Dinner,No,3.0,1
|
||
|
|
Thur,Lunch,No,117.32,44
|
||
|
|
Thur,Lunch,Yes,51.51,17"""
|
||
|
|
|
||
|
|
df = pd.read_csv(StringIO(data)).set_index(['day', 'time', 'smoker'])
|
||
|
|
|
||
|
|
# it works, #2100
|
||
|
|
result = df.unstack(2)
|
||
|
|
|
||
|
|
recons = result.stack()
|
||
|
|
tm.assert_frame_equal(recons, df)
|
||
|
|
|
||
|
|
def test_stack_mixed_dtype(self):
|
||
|
|
df = self.frame.T
|
||
|
|
df['foo', 'four'] = 'foo'
|
||
|
|
df = df.sort_index(level=1, axis=1)
|
||
|
|
|
||
|
|
stacked = df.stack()
|
||
|
|
result = df['foo'].stack().sort_index()
|
||
|
|
tm.assert_series_equal(stacked['foo'], result, check_names=False)
|
||
|
|
assert result.name is None
|
||
|
|
assert stacked['bar'].dtype == np.float_
|
||
|
|
|
||
|
|
def test_unstack_bug(self):
|
||
|
|
df = DataFrame({'state': ['naive', 'naive', 'naive', 'activ', 'activ',
|
||
|
|
'activ'],
|
||
|
|
'exp': ['a', 'b', 'b', 'b', 'a', 'a'],
|
||
|
|
'barcode': [1, 2, 3, 4, 1, 3],
|
||
|
|
'v': ['hi', 'hi', 'bye', 'bye', 'bye', 'peace'],
|
||
|
|
'extra': np.arange(6.)})
|
||
|
|
|
||
|
|
result = df.groupby(['state', 'exp', 'barcode', 'v']).apply(len)
|
||
|
|
|
||
|
|
unstacked = result.unstack()
|
||
|
|
restacked = unstacked.stack()
|
||
|
|
tm.assert_series_equal(
|
||
|
|
restacked, result.reindex(restacked.index).astype(float))
|
||
|
|
|
||
|
|
def test_stack_unstack_preserve_names(self):
|
||
|
|
unstacked = self.frame.unstack()
|
||
|
|
assert unstacked.index.name == 'first'
|
||
|
|
assert unstacked.columns.names == ['exp', 'second']
|
||
|
|
|
||
|
|
restacked = unstacked.stack()
|
||
|
|
assert restacked.index.names == self.frame.index.names
|
||
|
|
|
||
|
|
def test_unstack_level_name(self):
|
||
|
|
result = self.frame.unstack('second')
|
||
|
|
expected = self.frame.unstack(level=1)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_stack_level_name(self):
|
||
|
|
unstacked = self.frame.unstack('second')
|
||
|
|
result = unstacked.stack('exp')
|
||
|
|
expected = self.frame.unstack().stack(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.stack('exp')
|
||
|
|
expected = self.frame.stack()
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_stack_unstack_multiple(self):
|
||
|
|
unstacked = self.ymd.unstack(['year', 'month'])
|
||
|
|
expected = self.ymd.unstack('year').unstack('month')
|
||
|
|
tm.assert_frame_equal(unstacked, expected)
|
||
|
|
assert unstacked.columns.names == expected.columns.names
|
||
|
|
|
||
|
|
# series
|
||
|
|
s = self.ymd['A']
|
||
|
|
s_unstacked = s.unstack(['year', 'month'])
|
||
|
|
tm.assert_frame_equal(s_unstacked, expected['A'])
|
||
|
|
|
||
|
|
restacked = unstacked.stack(['year', 'month'])
|
||
|
|
restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
|
||
|
|
restacked = restacked.sort_index(level=0)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(restacked, self.ymd)
|
||
|
|
assert restacked.index.names == self.ymd.index.names
|
||
|
|
|
||
|
|
# GH #451
|
||
|
|
unstacked = self.ymd.unstack([1, 2])
|
||
|
|
expected = self.ymd.unstack(1).unstack(1).dropna(axis=1, how='all')
|
||
|
|
tm.assert_frame_equal(unstacked, expected)
|
||
|
|
|
||
|
|
unstacked = self.ymd.unstack([2, 1])
|
||
|
|
expected = self.ymd.unstack(2).unstack(1).dropna(axis=1, how='all')
|
||
|
|
tm.assert_frame_equal(unstacked, expected.loc[:, unstacked.columns])
|
||
|
|
|
||
|
|
def test_stack_names_and_numbers(self):
|
||
|
|
unstacked = self.ymd.unstack(['year', 'month'])
|
||
|
|
|
||
|
|
# Can't use mixture of names and numbers to stack
|
||
|
|
with tm.assert_raises_regex(ValueError, "level should contain"):
|
||
|
|
unstacked.stack([0, 'month'])
|
||
|
|
|
||
|
|
def test_stack_multiple_out_of_bounds(self):
|
||
|
|
# nlevels == 3
|
||
|
|
unstacked = self.ymd.unstack(['year', 'month'])
|
||
|
|
|
||
|
|
with tm.assert_raises_regex(IndexError, "Too many levels"):
|
||
|
|
unstacked.stack([2, 3])
|
||
|
|
with tm.assert_raises_regex(IndexError,
|
||
|
|
"not a valid level number"):
|
||
|
|
unstacked.stack([-4, -3])
|
||
|
|
|
||
|
|
def test_unstack_period_series(self):
|
||
|
|
# GH 4342
|
||
|
|
idx1 = pd.PeriodIndex(['2013-01', '2013-01', '2013-02', '2013-02',
|
||
|
|
'2013-03', '2013-03'], freq='M', name='period')
|
||
|
|
idx2 = Index(['A', 'B'] * 3, name='str')
|
||
|
|
value = [1, 2, 3, 4, 5, 6]
|
||
|
|
|
||
|
|
idx = MultiIndex.from_arrays([idx1, idx2])
|
||
|
|
s = Series(value, index=idx)
|
||
|
|
|
||
|
|
result1 = s.unstack()
|
||
|
|
result2 = s.unstack(level=1)
|
||
|
|
result3 = s.unstack(level=0)
|
||
|
|
|
||
|
|
e_idx = pd.PeriodIndex(
|
||
|
|
['2013-01', '2013-02', '2013-03'], freq='M', name='period')
|
||
|
|
expected = DataFrame({'A': [1, 3, 5], 'B': [2, 4, 6]}, index=e_idx,
|
||
|
|
columns=['A', 'B'])
|
||
|
|
expected.columns.name = 'str'
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result1, expected)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
tm.assert_frame_equal(result3, expected.T)
|
||
|
|
|
||
|
|
idx1 = pd.PeriodIndex(['2013-01', '2013-01', '2013-02', '2013-02',
|
||
|
|
'2013-03', '2013-03'], freq='M', name='period1')
|
||
|
|
|
||
|
|
idx2 = pd.PeriodIndex(['2013-12', '2013-11', '2013-10', '2013-09',
|
||
|
|
'2013-08', '2013-07'], freq='M', name='period2')
|
||
|
|
idx = MultiIndex.from_arrays([idx1, idx2])
|
||
|
|
s = Series(value, index=idx)
|
||
|
|
|
||
|
|
result1 = s.unstack()
|
||
|
|
result2 = s.unstack(level=1)
|
||
|
|
result3 = s.unstack(level=0)
|
||
|
|
|
||
|
|
e_idx = pd.PeriodIndex(
|
||
|
|
['2013-01', '2013-02', '2013-03'], freq='M', name='period1')
|
||
|
|
e_cols = pd.PeriodIndex(['2013-07', '2013-08', '2013-09', '2013-10',
|
||
|
|
'2013-11', '2013-12'],
|
||
|
|
freq='M', name='period2')
|
||
|
|
expected = DataFrame([[np.nan, np.nan, np.nan, np.nan, 2, 1],
|
||
|
|
[np.nan, np.nan, 4, 3, np.nan, np.nan],
|
||
|
|
[6, 5, np.nan, np.nan, np.nan, np.nan]],
|
||
|
|
index=e_idx, columns=e_cols)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result1, expected)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
tm.assert_frame_equal(result3, expected.T)
|
||
|
|
|
||
|
|
def test_unstack_period_frame(self):
|
||
|
|
# GH 4342
|
||
|
|
idx1 = pd.PeriodIndex(['2014-01', '2014-02', '2014-02', '2014-02',
|
||
|
|
'2014-01', '2014-01'],
|
||
|
|
freq='M', name='period1')
|
||
|
|
idx2 = pd.PeriodIndex(['2013-12', '2013-12', '2014-02', '2013-10',
|
||
|
|
'2013-10', '2014-02'],
|
||
|
|
freq='M', name='period2')
|
||
|
|
value = {'A': [1, 2, 3, 4, 5, 6], 'B': [6, 5, 4, 3, 2, 1]}
|
||
|
|
idx = MultiIndex.from_arrays([idx1, idx2])
|
||
|
|
df = DataFrame(value, index=idx)
|
||
|
|
|
||
|
|
result1 = df.unstack()
|
||
|
|
result2 = df.unstack(level=1)
|
||
|
|
result3 = df.unstack(level=0)
|
||
|
|
|
||
|
|
e_1 = pd.PeriodIndex(['2014-01', '2014-02'], freq='M', name='period1')
|
||
|
|
e_2 = pd.PeriodIndex(['2013-10', '2013-12', '2014-02', '2013-10',
|
||
|
|
'2013-12', '2014-02'], freq='M', name='period2')
|
||
|
|
e_cols = MultiIndex.from_arrays(['A A A B B B'.split(), e_2])
|
||
|
|
expected = DataFrame([[5, 1, 6, 2, 6, 1], [4, 2, 3, 3, 5, 4]],
|
||
|
|
index=e_1, columns=e_cols)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result1, expected)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
e_1 = pd.PeriodIndex(['2014-01', '2014-02', '2014-01',
|
||
|
|
'2014-02'], freq='M', name='period1')
|
||
|
|
e_2 = pd.PeriodIndex(
|
||
|
|
['2013-10', '2013-12', '2014-02'], freq='M', name='period2')
|
||
|
|
e_cols = MultiIndex.from_arrays(['A A B B'.split(), e_1])
|
||
|
|
expected = DataFrame([[5, 4, 2, 3], [1, 2, 6, 5], [6, 3, 1, 4]],
|
||
|
|
index=e_2, columns=e_cols)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result3, expected)
|
||
|
|
|
||
|
|
def test_stack_multiple_bug(self):
|
||
|
|
""" bug when some uniques are not present in the data #3170"""
|
||
|
|
id_col = ([1] * 3) + ([2] * 3)
|
||
|
|
name = (['a'] * 3) + (['b'] * 3)
|
||
|
|
date = pd.to_datetime(['2013-01-03', '2013-01-04', '2013-01-05'] * 2)
|
||
|
|
var1 = np.random.randint(0, 100, 6)
|
||
|
|
df = DataFrame(dict(ID=id_col, NAME=name, DATE=date, VAR1=var1))
|
||
|
|
|
||
|
|
multi = df.set_index(['DATE', 'ID'])
|
||
|
|
multi.columns.name = 'Params'
|
||
|
|
unst = multi.unstack('ID')
|
||
|
|
down = unst.resample('W-THU').mean()
|
||
|
|
|
||
|
|
rs = down.stack('ID')
|
||
|
|
xp = unst.loc[:, ['VAR1']].resample('W-THU').mean().stack('ID')
|
||
|
|
xp.columns.name = 'Params'
|
||
|
|
tm.assert_frame_equal(rs, xp)
|
||
|
|
|
||
|
|
def test_stack_dropna(self):
|
||
|
|
# GH #3997
|
||
|
|
df = DataFrame({'A': ['a1', 'a2'], 'B': ['b1', 'b2'], 'C': [1, 1]})
|
||
|
|
df = df.set_index(['A', 'B'])
|
||
|
|
|
||
|
|
stacked = df.unstack().stack(dropna=False)
|
||
|
|
assert len(stacked) > len(stacked.dropna())
|
||
|
|
|
||
|
|
stacked = df.unstack().stack(dropna=True)
|
||
|
|
tm.assert_frame_equal(stacked, stacked.dropna())
|
||
|
|
|
||
|
|
def test_unstack_multiple_hierarchical(self):
|
||
|
|
df = DataFrame(index=[[0, 0, 0, 0, 1, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 0, 0, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1
|
||
|
|
]],
|
||
|
|
columns=[[0, 0, 1, 1], [0, 1, 0, 1]])
|
||
|
|
|
||
|
|
df.index.names = ['a', 'b', 'c']
|
||
|
|
df.columns.names = ['d', 'e']
|
||
|
|
|
||
|
|
# it works!
|
||
|
|
df.unstack(['b', 'c'])
|
||
|
|
|
||
|
|
def test_groupby_transform(self):
|
||
|
|
s = self.frame['A']
|
||
|
|
grouper = s.index.get_level_values(0)
|
||
|
|
|
||
|
|
grouped = s.groupby(grouper)
|
||
|
|
|
||
|
|
applied = grouped.apply(lambda x: x * 2)
|
||
|
|
expected = grouped.transform(lambda x: x * 2)
|
||
|
|
result = applied.reindex(expected.index)
|
||
|
|
tm.assert_series_equal(result, expected, check_names=False)
|
||
|
|
|
||
|
|
def test_unstack_sparse_keyspace(self):
|
||
|
|
# memory problems with naive impl #2278
|
||
|
|
# Generate Long File & Test Pivot
|
||
|
|
NUM_ROWS = 1000
|
||
|
|
|
||
|
|
df = DataFrame({'A': np.random.randint(100, size=NUM_ROWS),
|
||
|
|
'B': np.random.randint(300, size=NUM_ROWS),
|
||
|
|
'C': np.random.randint(-7, 7, size=NUM_ROWS),
|
||
|
|
'D': np.random.randint(-19, 19, size=NUM_ROWS),
|
||
|
|
'E': np.random.randint(3000, size=NUM_ROWS),
|
||
|
|
'F': np.random.randn(NUM_ROWS)})
|
||
|
|
|
||
|
|
idf = df.set_index(['A', 'B', 'C', 'D', 'E'])
|
||
|
|
|
||
|
|
# it works! is sufficient
|
||
|
|
idf.unstack('E')
|
||
|
|
|
||
|
|
def test_unstack_unobserved_keys(self):
|
||
|
|
# related to #2278 refactoring
|
||
|
|
levels = [[0, 1], [0, 1, 2, 3]]
|
||
|
|
labels = [[0, 0, 1, 1], [0, 2, 0, 2]]
|
||
|
|
|
||
|
|
index = MultiIndex(levels, labels)
|
||
|
|
|
||
|
|
df = DataFrame(np.random.randn(4, 2), index=index)
|
||
|
|
|
||
|
|
result = df.unstack()
|
||
|
|
assert len(result.columns) == 4
|
||
|
|
|
||
|
|
recons = result.stack()
|
||
|
|
tm.assert_frame_equal(recons, df)
|
||
|
|
|
||
|
|
def test_stack_order_with_unsorted_levels(self):
|
||
|
|
# GH 16323
|
||
|
|
|
||
|
|
def manual_compare_stacked(df, df_stacked, lev0, lev1):
|
||
|
|
assert all(df.loc[row, col] ==
|
||
|
|
df_stacked.loc[(row, col[lev0]), col[lev1]]
|
||
|
|
for row in df.index for col in df.columns)
|
||
|
|
|
||
|
|
# deep check for 1-row case
|
||
|
|
for width in [2, 3]:
|
||
|
|
levels_poss = itertools.product(
|
||
|
|
itertools.permutations([0, 1, 2], width),
|
||
|
|
repeat=2)
|
||
|
|
|
||
|
|
for levels in levels_poss:
|
||
|
|
columns = MultiIndex(levels=levels,
|
||
|
|
labels=[[0, 0, 1, 1],
|
||
|
|
[0, 1, 0, 1]])
|
||
|
|
df = DataFrame(columns=columns, data=[range(4)])
|
||
|
|
for stack_lev in range(2):
|
||
|
|
df_stacked = df.stack(stack_lev)
|
||
|
|
manual_compare_stacked(df, df_stacked,
|
||
|
|
stack_lev, 1 - stack_lev)
|
||
|
|
|
||
|
|
# check multi-row case
|
||
|
|
mi = MultiIndex(levels=[["A", "C", "B"], ["B", "A", "C"]],
|
||
|
|
labels=[np.repeat(range(3), 3), np.tile(range(3), 3)])
|
||
|
|
df = DataFrame(columns=mi, index=range(5),
|
||
|
|
data=np.arange(5 * len(mi)).reshape(5, -1))
|
||
|
|
manual_compare_stacked(df, df.stack(0), 0, 1)
|
||
|
|
|
||
|
|
def test_groupby_corner(self):
|
||
|
|
midx = MultiIndex(levels=[['foo'], ['bar'], ['baz']],
|
||
|
|
labels=[[0], [0], [0]],
|
||
|
|
names=['one', 'two', 'three'])
|
||
|
|
df = DataFrame([np.random.rand(4)], columns=['a', 'b', 'c', 'd'],
|
||
|
|
index=midx)
|
||
|
|
# should work
|
||
|
|
df.groupby(level='three')
|
||
|
|
|
||
|
|
def test_groupby_level_no_obs(self):
|
||
|
|
# #1697
|
||
|
|
midx = MultiIndex.from_tuples([('f1', 's1'), ('f1', 's2'), (
|
||
|
|
'f2', 's1'), ('f2', 's2'), ('f3', 's1'), ('f3', 's2')])
|
||
|
|
df = DataFrame(
|
||
|
|
[[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], columns=midx)
|
||
|
|
df1 = df.loc(axis=1)[df.columns.map(
|
||
|
|
lambda u: u[0] in ['f2', 'f3'])]
|
||
|
|
|
||
|
|
grouped = df1.groupby(axis=1, level=0)
|
||
|
|
result = grouped.sum()
|
||
|
|
assert (result.columns == ['f2', 'f3']).all()
|
||
|
|
|
||
|
|
def test_join(self):
|
||
|
|
a = self.frame.loc[self.frame.index[:5], ['A']]
|
||
|
|
b = self.frame.loc[self.frame.index[2:], ['B', 'C']]
|
||
|
|
|
||
|
|
joined = a.join(b, how='outer').reindex(self.frame.index)
|
||
|
|
expected = self.frame.copy()
|
||
|
|
expected.values[np.isnan(joined.values)] = np.nan
|
||
|
|
|
||
|
|
assert not np.isnan(joined.values).all()
|
||
|
|
|
||
|
|
# TODO what should join do with names ?
|
||
|
|
tm.assert_frame_equal(joined, expected, check_names=False)
|
||
|
|
|
||
|
|
def test_swaplevel(self):
|
||
|
|
swapped = self.frame['A'].swaplevel()
|
||
|
|
swapped2 = self.frame['A'].swaplevel(0)
|
||
|
|
swapped3 = self.frame['A'].swaplevel(0, 1)
|
||
|
|
swapped4 = self.frame['A'].swaplevel('first', 'second')
|
||
|
|
assert not swapped.index.equals(self.frame.index)
|
||
|
|
tm.assert_series_equal(swapped, swapped2)
|
||
|
|
tm.assert_series_equal(swapped, swapped3)
|
||
|
|
tm.assert_series_equal(swapped, swapped4)
|
||
|
|
|
||
|
|
back = swapped.swaplevel()
|
||
|
|
back2 = swapped.swaplevel(0)
|
||
|
|
back3 = swapped.swaplevel(0, 1)
|
||
|
|
back4 = swapped.swaplevel('second', 'first')
|
||
|
|
assert back.index.equals(self.frame.index)
|
||
|
|
tm.assert_series_equal(back, back2)
|
||
|
|
tm.assert_series_equal(back, back3)
|
||
|
|
tm.assert_series_equal(back, back4)
|
||
|
|
|
||
|
|
ft = self.frame.T
|
||
|
|
swapped = ft.swaplevel('first', 'second', axis=1)
|
||
|
|
exp = self.frame.swaplevel('first', 'second').T
|
||
|
|
tm.assert_frame_equal(swapped, exp)
|
||
|
|
|
||
|
|
def test_swaplevel_panel(self):
|
||
|
|
with catch_warnings(record=True):
|
||
|
|
panel = Panel({'ItemA': self.frame, 'ItemB': self.frame * 2})
|
||
|
|
expected = panel.copy()
|
||
|
|
expected.major_axis = expected.major_axis.swaplevel(0, 1)
|
||
|
|
|
||
|
|
for result in (panel.swaplevel(axis='major'),
|
||
|
|
panel.swaplevel(0, axis='major'),
|
||
|
|
panel.swaplevel(0, 1, axis='major')):
|
||
|
|
tm.assert_panel_equal(result, expected)
|
||
|
|
|
||
|
|
def test_reorder_levels(self):
|
||
|
|
result = self.ymd.reorder_levels(['month', 'day', 'year'])
|
||
|
|
expected = self.ymd.swaplevel(0, 1).swaplevel(1, 2)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.ymd['A'].reorder_levels(['month', 'day', 'year'])
|
||
|
|
expected = self.ymd['A'].swaplevel(0, 1).swaplevel(1, 2)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.ymd.T.reorder_levels(['month', 'day', 'year'], axis=1)
|
||
|
|
expected = self.ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
with tm.assert_raises_regex(TypeError, 'hierarchical axis'):
|
||
|
|
self.ymd.reorder_levels([1, 2], axis=1)
|
||
|
|
|
||
|
|
with tm.assert_raises_regex(IndexError, 'Too many levels'):
|
||
|
|
self.ymd.index.reorder_levels([1, 2, 3])
|
||
|
|
|
||
|
|
def test_insert_index(self):
|
||
|
|
df = self.ymd[:5].T
|
||
|
|
df[2000, 1, 10] = df[2000, 1, 7]
|
||
|
|
assert isinstance(df.columns, MultiIndex)
|
||
|
|
assert (df[2000, 1, 10] == df[2000, 1, 7]).all()
|
||
|
|
|
||
|
|
def test_alignment(self):
|
||
|
|
x = Series(data=[1, 2, 3], index=MultiIndex.from_tuples([("A", 1), (
|
||
|
|
"A", 2), ("B", 3)]))
|
||
|
|
|
||
|
|
y = Series(data=[4, 5, 6], index=MultiIndex.from_tuples([("Z", 1), (
|
||
|
|
"Z", 2), ("B", 3)]))
|
||
|
|
|
||
|
|
res = x - y
|
||
|
|
exp_index = x.index.union(y.index)
|
||
|
|
exp = x.reindex(exp_index) - y.reindex(exp_index)
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
# hit non-monotonic code path
|
||
|
|
res = x[::-1] - y[::-1]
|
||
|
|
exp_index = x.index.union(y.index)
|
||
|
|
exp = x.reindex(exp_index) - y.reindex(exp_index)
|
||
|
|
tm.assert_series_equal(res, exp)
|
||
|
|
|
||
|
|
def test_frame_getitem_view(self):
|
||
|
|
df = self.frame.T.copy()
|
||
|
|
|
||
|
|
# this works because we are modifying the underlying array
|
||
|
|
# really a no-no
|
||
|
|
df['foo'].values[:] = 0
|
||
|
|
assert (df['foo'].values == 0).all()
|
||
|
|
|
||
|
|
# but not if it's mixed-type
|
||
|
|
df['foo', 'four'] = 'foo'
|
||
|
|
df = df.sort_index(level=0, axis=1)
|
||
|
|
|
||
|
|
# this will work, but will raise/warn as its chained assignment
|
||
|
|
def f():
|
||
|
|
df['foo']['one'] = 2
|
||
|
|
return df
|
||
|
|
|
||
|
|
pytest.raises(com.SettingWithCopyError, f)
|
||
|
|
|
||
|
|
try:
|
||
|
|
df = f()
|
||
|
|
except:
|
||
|
|
pass
|
||
|
|
assert (df['foo', 'one'] == 0).all()
|
||
|
|
|
||
|
|
def test_count(self):
|
||
|
|
frame = self.frame.copy()
|
||
|
|
frame.index.names = ['a', 'b']
|
||
|
|
|
||
|
|
result = frame.count(level='b')
|
||
|
|
expect = self.frame.count(level=1)
|
||
|
|
tm.assert_frame_equal(result, expect, check_names=False)
|
||
|
|
|
||
|
|
result = frame.count(level='a')
|
||
|
|
expect = self.frame.count(level=0)
|
||
|
|
tm.assert_frame_equal(result, expect, check_names=False)
|
||
|
|
|
||
|
|
series = self.series.copy()
|
||
|
|
series.index.names = ['a', 'b']
|
||
|
|
|
||
|
|
result = series.count(level='b')
|
||
|
|
expect = self.series.count(level=1)
|
||
|
|
tm.assert_series_equal(result, expect, check_names=False)
|
||
|
|
assert result.index.name == 'b'
|
||
|
|
|
||
|
|
result = series.count(level='a')
|
||
|
|
expect = self.series.count(level=0)
|
||
|
|
tm.assert_series_equal(result, expect, check_names=False)
|
||
|
|
assert result.index.name == 'a'
|
||
|
|
|
||
|
|
pytest.raises(KeyError, series.count, 'x')
|
||
|
|
pytest.raises(KeyError, frame.count, level='x')
|
||
|
|
|
||
|
|
AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew',
|
||
|
|
'mad', 'std', 'var', 'sem']
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('sort', [True, False])
|
||
|
|
def test_series_group_min_max(self, sort):
|
||
|
|
# GH 17537
|
||
|
|
for op, level, skipna in cart_product(self.AGG_FUNCTIONS, lrange(2),
|
||
|
|
[False, True]):
|
||
|
|
grouped = self.series.groupby(level=level, sort=sort)
|
||
|
|
aggf = lambda x: getattr(x, op)(skipna=skipna)
|
||
|
|
# skipna=True
|
||
|
|
leftside = grouped.agg(aggf)
|
||
|
|
rightside = getattr(self.series, op)(level=level, skipna=skipna)
|
||
|
|
if sort:
|
||
|
|
rightside = rightside.sort_index(level=level)
|
||
|
|
tm.assert_series_equal(leftside, rightside)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('sort', [True, False])
|
||
|
|
def test_frame_group_ops(self, sort):
|
||
|
|
# GH 17537
|
||
|
|
self.frame.iloc[1, [1, 2]] = np.nan
|
||
|
|
self.frame.iloc[7, [0, 1]] = np.nan
|
||
|
|
|
||
|
|
for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS,
|
||
|
|
lrange(2), lrange(2),
|
||
|
|
[False, True]):
|
||
|
|
|
||
|
|
if axis == 0:
|
||
|
|
frame = self.frame
|
||
|
|
else:
|
||
|
|
frame = self.frame.T
|
||
|
|
|
||
|
|
grouped = frame.groupby(level=level, axis=axis, sort=sort)
|
||
|
|
|
||
|
|
pieces = []
|
||
|
|
|
||
|
|
def aggf(x):
|
||
|
|
pieces.append(x)
|
||
|
|
return getattr(x, op)(skipna=skipna, axis=axis)
|
||
|
|
|
||
|
|
leftside = grouped.agg(aggf)
|
||
|
|
rightside = getattr(frame, op)(level=level, axis=axis,
|
||
|
|
skipna=skipna)
|
||
|
|
if sort:
|
||
|
|
rightside = rightside.sort_index(level=level, axis=axis)
|
||
|
|
frame = frame.sort_index(level=level, axis=axis)
|
||
|
|
|
||
|
|
# for good measure, groupby detail
|
||
|
|
level_index = frame._get_axis(axis).levels[level]
|
||
|
|
|
||
|
|
tm.assert_index_equal(leftside._get_axis(axis), level_index)
|
||
|
|
tm.assert_index_equal(rightside._get_axis(axis), level_index)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(leftside, rightside)
|
||
|
|
|
||
|
|
def test_stat_op_corner(self):
|
||
|
|
obj = Series([10.0], index=MultiIndex.from_tuples([(2, 3)]))
|
||
|
|
|
||
|
|
result = obj.sum(level=0)
|
||
|
|
expected = Series([10.0], index=[2])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_frame_any_all_group(self):
|
||
|
|
df = DataFrame(
|
||
|
|
{'data': [False, False, True, False, True, False, True]},
|
||
|
|
index=[
|
||
|
|
['one', 'one', 'two', 'one', 'two', 'two', 'two'],
|
||
|
|
[0, 1, 0, 2, 1, 2, 3]])
|
||
|
|
|
||
|
|
result = df.any(level=0)
|
||
|
|
ex = DataFrame({'data': [False, True]}, index=['one', 'two'])
|
||
|
|
tm.assert_frame_equal(result, ex)
|
||
|
|
|
||
|
|
result = df.all(level=0)
|
||
|
|
ex = DataFrame({'data': [False, False]}, index=['one', 'two'])
|
||
|
|
tm.assert_frame_equal(result, ex)
|
||
|
|
|
||
|
|
def test_std_var_pass_ddof(self):
|
||
|
|
index = MultiIndex.from_arrays([np.arange(5).repeat(10), np.tile(
|
||
|
|
np.arange(10), 5)])
|
||
|
|
df = DataFrame(np.random.randn(len(index), 5), index=index)
|
||
|
|
|
||
|
|
for meth in ['var', 'std']:
|
||
|
|
ddof = 4
|
||
|
|
alt = lambda x: getattr(x, meth)(ddof=ddof)
|
||
|
|
|
||
|
|
result = getattr(df[0], meth)(level=0, ddof=ddof)
|
||
|
|
expected = df[0].groupby(level=0).agg(alt)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = getattr(df, meth)(level=0, ddof=ddof)
|
||
|
|
expected = df.groupby(level=0).agg(alt)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_frame_series_agg_multiple_levels(self):
|
||
|
|
result = self.ymd.sum(level=['year', 'month'])
|
||
|
|
expected = self.ymd.groupby(level=['year', 'month']).sum()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.ymd['A'].sum(level=['year', 'month'])
|
||
|
|
expected = self.ymd['A'].groupby(level=['year', 'month']).sum()
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_groupby_multilevel(self):
|
||
|
|
result = self.ymd.groupby(level=[0, 1]).mean()
|
||
|
|
|
||
|
|
k1 = self.ymd.index.get_level_values(0)
|
||
|
|
k2 = self.ymd.index.get_level_values(1)
|
||
|
|
|
||
|
|
expected = self.ymd.groupby([k1, k2]).mean()
|
||
|
|
|
||
|
|
# TODO groupby with level_values drops names
|
||
|
|
tm.assert_frame_equal(result, expected, check_names=False)
|
||
|
|
assert result.index.names == self.ymd.index.names[:2]
|
||
|
|
|
||
|
|
result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
|
||
|
|
tm.assert_frame_equal(result, result2)
|
||
|
|
|
||
|
|
def test_groupby_multilevel_with_transform(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_multilevel_consolidate(self):
|
||
|
|
index = MultiIndex.from_tuples([('foo', 'one'), ('foo', 'two'), (
|
||
|
|
'bar', 'one'), ('bar', 'two')])
|
||
|
|
df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
|
||
|
|
df['Totals', ''] = df.sum(1)
|
||
|
|
df = df._consolidate()
|
||
|
|
|
||
|
|
def test_ix_preserve_names(self):
|
||
|
|
result = self.ymd.loc[2000]
|
||
|
|
result2 = self.ymd['A'].loc[2000]
|
||
|
|
assert result.index.names == self.ymd.index.names[1:]
|
||
|
|
assert result2.index.names == self.ymd.index.names[1:]
|
||
|
|
|
||
|
|
result = self.ymd.loc[2000, 2]
|
||
|
|
result2 = self.ymd['A'].loc[2000, 2]
|
||
|
|
assert result.index.name == self.ymd.index.names[2]
|
||
|
|
assert result2.index.name == self.ymd.index.names[2]
|
||
|
|
|
||
|
|
def test_partial_set(self):
|
||
|
|
# GH #397
|
||
|
|
df = self.ymd.copy()
|
||
|
|
exp = self.ymd.copy()
|
||
|
|
df.loc[2000, 4] = 0
|
||
|
|
exp.loc[2000, 4].values[:] = 0
|
||
|
|
tm.assert_frame_equal(df, exp)
|
||
|
|
|
||
|
|
df['A'].loc[2000, 4] = 1
|
||
|
|
exp['A'].loc[2000, 4].values[:] = 1
|
||
|
|
tm.assert_frame_equal(df, exp)
|
||
|
|
|
||
|
|
df.loc[2000] = 5
|
||
|
|
exp.loc[2000].values[:] = 5
|
||
|
|
tm.assert_frame_equal(df, exp)
|
||
|
|
|
||
|
|
# this works...for now
|
||
|
|
df['A'].iloc[14] = 5
|
||
|
|
assert df['A'][14] == 5
|
||
|
|
|
||
|
|
def test_unstack_preserve_types(self):
|
||
|
|
# GH #403
|
||
|
|
self.ymd['E'] = 'foo'
|
||
|
|
self.ymd['F'] = 2
|
||
|
|
|
||
|
|
unstacked = self.ymd.unstack('month')
|
||
|
|
assert unstacked['A', 1].dtype == np.float64
|
||
|
|
assert unstacked['E', 1].dtype == np.object_
|
||
|
|
assert unstacked['F', 1].dtype == np.float64
|
||
|
|
|
||
|
|
def test_unstack_group_index_overflow(self):
|
||
|
|
labels = np.tile(np.arange(500), 2)
|
||
|
|
level = np.arange(500)
|
||
|
|
|
||
|
|
index = MultiIndex(levels=[level] * 8 + [[0, 1]],
|
||
|
|
labels=[labels] * 8 + [np.arange(2).repeat(500)])
|
||
|
|
|
||
|
|
s = Series(np.arange(1000), index=index)
|
||
|
|
result = s.unstack()
|
||
|
|
assert result.shape == (500, 2)
|
||
|
|
|
||
|
|
# test roundtrip
|
||
|
|
stacked = result.stack()
|
||
|
|
tm.assert_series_equal(s, stacked.reindex(s.index))
|
||
|
|
|
||
|
|
# put it at beginning
|
||
|
|
index = MultiIndex(levels=[[0, 1]] + [level] * 8,
|
||
|
|
labels=[np.arange(2).repeat(500)] + [labels] * 8)
|
||
|
|
|
||
|
|
s = Series(np.arange(1000), index=index)
|
||
|
|
result = s.unstack(0)
|
||
|
|
assert result.shape == (500, 2)
|
||
|
|
|
||
|
|
# put it in middle
|
||
|
|
index = MultiIndex(levels=[level] * 4 + [[0, 1]] + [level] * 4,
|
||
|
|
labels=([labels] * 4 + [np.arange(2).repeat(500)] +
|
||
|
|
[labels] * 4))
|
||
|
|
|
||
|
|
s = Series(np.arange(1000), index=index)
|
||
|
|
result = s.unstack(4)
|
||
|
|
assert result.shape == (500, 2)
|
||
|
|
|
||
|
|
def test_pyint_engine(self):
|
||
|
|
# GH 18519 : when combinations of codes cannot be represented in 64
|
||
|
|
# bits, the index underlying the MultiIndex engine works with Python
|
||
|
|
# integers, rather than uint64.
|
||
|
|
N = 5
|
||
|
|
keys = [tuple(l) for l in [[0] * 10 * N,
|
||
|
|
[1] * 10 * N,
|
||
|
|
[2] * 10 * N,
|
||
|
|
[np.nan] * N + [2] * 9 * N,
|
||
|
|
[0] * N + [2] * 9 * N,
|
||
|
|
[np.nan] * N + [2] * 8 * N + [0] * N]]
|
||
|
|
# Each level contains 4 elements (including NaN), so it is represented
|
||
|
|
# in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a
|
||
|
|
# 64 bit engine and truncating the first levels, the fourth and fifth
|
||
|
|
# keys would collide; if truncating the last levels, the fifth and
|
||
|
|
# sixth; if rotating bits rather than shifting, the third and fifth.
|
||
|
|
|
||
|
|
for idx in range(len(keys)):
|
||
|
|
index = MultiIndex.from_tuples(keys)
|
||
|
|
assert index.get_loc(keys[idx]) == idx
|
||
|
|
|
||
|
|
expected = np.arange(idx + 1, dtype=np.intp)
|
||
|
|
result = index.get_indexer([keys[i] for i in expected])
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
# With missing key:
|
||
|
|
idces = range(len(keys))
|
||
|
|
expected = np.array([-1] + list(idces), dtype=np.intp)
|
||
|
|
missing = tuple([0, 1] * 5 * N)
|
||
|
|
result = index.get_indexer([missing] + [keys[i] for i in idces])
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
def test_getitem_lowerdim_corner(self):
|
||
|
|
pytest.raises(KeyError, self.frame.loc.__getitem__,
|
||
|
|
(('bar', 'three'), 'B'))
|
||
|
|
|
||
|
|
# in theory should be inserting in a sorted space????
|
||
|
|
self.frame.loc[('bar', 'three'), 'B'] = 0
|
||
|
|
assert self.frame.sort_index().loc[('bar', 'three'), 'B'] == 0
|
||
|
|
|
||
|
|
# ---------------------------------------------------------------------
|
||
|
|
# AMBIGUOUS CASES!
|
||
|
|
|
||
|
|
def test_partial_ix_missing(self):
|
||
|
|
pytest.skip("skipping for now")
|
||
|
|
|
||
|
|
result = self.ymd.loc[2000, 0]
|
||
|
|
expected = self.ymd.loc[2000]['A']
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# need to put in some work here
|
||
|
|
|
||
|
|
# self.ymd.loc[2000, 0] = 0
|
||
|
|
# assert (self.ymd.loc[2000]['A'] == 0).all()
|
||
|
|
|
||
|
|
# Pretty sure the second (and maybe even the first) is already wrong.
|
||
|
|
pytest.raises(Exception, self.ymd.loc.__getitem__, (2000, 6))
|
||
|
|
pytest.raises(Exception, self.ymd.loc.__getitem__, (2000, 6), 0)
|
||
|
|
|
||
|
|
# ---------------------------------------------------------------------
|
||
|
|
|
||
|
|
def test_to_html(self):
|
||
|
|
self.ymd.columns.name = 'foo'
|
||
|
|
self.ymd.to_html()
|
||
|
|
self.ymd.T.to_html()
|
||
|
|
|
||
|
|
def test_level_with_tuples(self):
|
||
|
|
index = MultiIndex(levels=[[('foo', 'bar', 0), ('foo', 'baz', 0), (
|
||
|
|
'foo', 'qux', 0)], [0, 1]],
|
||
|
|
labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])
|
||
|
|
|
||
|
|
series = Series(np.random.randn(6), index=index)
|
||
|
|
frame = DataFrame(np.random.randn(6, 4), index=index)
|
||
|
|
|
||
|
|
result = series[('foo', 'bar', 0)]
|
||
|
|
result2 = series.loc[('foo', 'bar', 0)]
|
||
|
|
expected = series[:2]
|
||
|
|
expected.index = expected.index.droplevel(0)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
tm.assert_series_equal(result2, expected)
|
||
|
|
|
||
|
|
pytest.raises(KeyError, series.__getitem__, (('foo', 'bar', 0), 2))
|
||
|
|
|
||
|
|
result = frame.loc[('foo', 'bar', 0)]
|
||
|
|
result2 = frame.xs(('foo', 'bar', 0))
|
||
|
|
expected = frame[:2]
|
||
|
|
expected.index = expected.index.droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
index = MultiIndex(levels=[[('foo', 'bar'), ('foo', 'baz'), (
|
||
|
|
'foo', 'qux')], [0, 1]],
|
||
|
|
labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])
|
||
|
|
|
||
|
|
series = Series(np.random.randn(6), index=index)
|
||
|
|
frame = DataFrame(np.random.randn(6, 4), index=index)
|
||
|
|
|
||
|
|
result = series[('foo', 'bar')]
|
||
|
|
result2 = series.loc[('foo', 'bar')]
|
||
|
|
expected = series[:2]
|
||
|
|
expected.index = expected.index.droplevel(0)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
tm.assert_series_equal(result2, expected)
|
||
|
|
|
||
|
|
result = frame.loc[('foo', 'bar')]
|
||
|
|
result2 = frame.xs(('foo', 'bar'))
|
||
|
|
expected = frame[:2]
|
||
|
|
expected.index = expected.index.droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
def test_int_series_slicing(self):
|
||
|
|
s = self.ymd['A']
|
||
|
|
result = s[5:]
|
||
|
|
expected = s.reindex(s.index[5:])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
exp = self.ymd['A'].copy()
|
||
|
|
s[5:] = 0
|
||
|
|
exp.values[5:] = 0
|
||
|
|
tm.assert_numpy_array_equal(s.values, exp.values)
|
||
|
|
|
||
|
|
result = self.ymd[5:]
|
||
|
|
expected = self.ymd.reindex(s.index[5:])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('unicode_strings', [True, False])
|
||
|
|
def test_mixed_depth_get(self, unicode_strings):
|
||
|
|
# If unicode_strings is True, the column labels in dataframe
|
||
|
|
# construction will use unicode strings in Python 2 (pull request
|
||
|
|
# #17099).
|
||
|
|
|
||
|
|
arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
|
||
|
|
['', 'OD', 'OD', 'result1', 'result2', 'result1'],
|
||
|
|
['', 'wx', 'wy', '', '', '']]
|
||
|
|
|
||
|
|
if unicode_strings:
|
||
|
|
arrays = [[u(s) for s in arr] for arr in arrays]
|
||
|
|
|
||
|
|
tuples = sorted(zip(*arrays))
|
||
|
|
index = MultiIndex.from_tuples(tuples)
|
||
|
|
df = DataFrame(np.random.randn(4, 6), columns=index)
|
||
|
|
|
||
|
|
result = df['a']
|
||
|
|
expected = df['a', '', ''].rename('a')
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = df['routine1', 'result1']
|
||
|
|
expected = df['routine1', 'result1', '']
|
||
|
|
expected = expected.rename(('routine1', 'result1'))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_mixed_depth_insert(self):
|
||
|
|
arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
|
||
|
|
['', 'OD', 'OD', 'result1', 'result2', 'result1'],
|
||
|
|
['', 'wx', 'wy', '', '', '']]
|
||
|
|
|
||
|
|
tuples = sorted(zip(*arrays))
|
||
|
|
index = MultiIndex.from_tuples(tuples)
|
||
|
|
df = DataFrame(randn(4, 6), columns=index)
|
||
|
|
|
||
|
|
result = df.copy()
|
||
|
|
expected = df.copy()
|
||
|
|
result['b'] = [1, 2, 3, 4]
|
||
|
|
expected['b', '', ''] = [1, 2, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_mixed_depth_drop(self):
|
||
|
|
arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
|
||
|
|
['', 'OD', 'OD', 'result1', 'result2', 'result1'],
|
||
|
|
['', 'wx', 'wy', '', '', '']]
|
||
|
|
|
||
|
|
tuples = sorted(zip(*arrays))
|
||
|
|
index = MultiIndex.from_tuples(tuples)
|
||
|
|
df = DataFrame(randn(4, 6), columns=index)
|
||
|
|
|
||
|
|
result = df.drop('a', axis=1)
|
||
|
|
expected = df.drop([('a', '', '')], axis=1)
|
||
|
|
tm.assert_frame_equal(expected, result)
|
||
|
|
|
||
|
|
result = df.drop(['top'], axis=1)
|
||
|
|
expected = df.drop([('top', 'OD', 'wx')], axis=1)
|
||
|
|
expected = expected.drop([('top', 'OD', 'wy')], axis=1)
|
||
|
|
tm.assert_frame_equal(expected, result)
|
||
|
|
|
||
|
|
result = df.drop(('top', 'OD', 'wx'), axis=1)
|
||
|
|
expected = df.drop([('top', 'OD', 'wx')], axis=1)
|
||
|
|
tm.assert_frame_equal(expected, result)
|
||
|
|
|
||
|
|
expected = df.drop([('top', 'OD', 'wy')], axis=1)
|
||
|
|
expected = df.drop('top', axis=1)
|
||
|
|
|
||
|
|
result = df.drop('result1', level=1, axis=1)
|
||
|
|
expected = df.drop([('routine1', 'result1', ''),
|
||
|
|
('routine2', 'result1', '')], axis=1)
|
||
|
|
tm.assert_frame_equal(expected, result)
|
||
|
|
|
||
|
|
def test_drop_nonunique(self):
|
||
|
|
df = DataFrame([["x-a", "x", "a", 1.5], ["x-a", "x", "a", 1.2],
|
||
|
|
["z-c", "z", "c", 3.1], ["x-a", "x", "a", 4.1],
|
||
|
|
["x-b", "x", "b", 5.1], ["x-b", "x", "b", 4.1],
|
||
|
|
["x-b", "x", "b", 2.2],
|
||
|
|
["y-a", "y", "a", 1.2], ["z-b", "z", "b", 2.1]],
|
||
|
|
columns=["var1", "var2", "var3", "var4"])
|
||
|
|
|
||
|
|
grp_size = df.groupby("var1").size()
|
||
|
|
drop_idx = grp_size.loc[grp_size == 1]
|
||
|
|
|
||
|
|
idf = df.set_index(["var1", "var2", "var3"])
|
||
|
|
|
||
|
|
# it works! #2101
|
||
|
|
result = idf.drop(drop_idx.index, level=0).reset_index()
|
||
|
|
expected = df[-df.var1.isin(drop_idx.index)]
|
||
|
|
|
||
|
|
result.index = expected.index
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_mixed_depth_pop(self):
|
||
|
|
arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
|
||
|
|
['', 'OD', 'OD', 'result1', 'result2', 'result1'],
|
||
|
|
['', 'wx', 'wy', '', '', '']]
|
||
|
|
|
||
|
|
tuples = sorted(zip(*arrays))
|
||
|
|
index = MultiIndex.from_tuples(tuples)
|
||
|
|
df = DataFrame(randn(4, 6), columns=index)
|
||
|
|
|
||
|
|
df1 = df.copy()
|
||
|
|
df2 = df.copy()
|
||
|
|
result = df1.pop('a')
|
||
|
|
expected = df2.pop(('a', '', ''))
|
||
|
|
tm.assert_series_equal(expected, result, check_names=False)
|
||
|
|
tm.assert_frame_equal(df1, df2)
|
||
|
|
assert result.name == 'a'
|
||
|
|
|
||
|
|
expected = df1['top']
|
||
|
|
df1 = df1.drop(['top'], axis=1)
|
||
|
|
result = df2.pop('top')
|
||
|
|
tm.assert_frame_equal(expected, result)
|
||
|
|
tm.assert_frame_equal(df1, df2)
|
||
|
|
|
||
|
|
def test_reindex_level_partial_selection(self):
|
||
|
|
result = self.frame.reindex(['foo', 'qux'], level=0)
|
||
|
|
expected = self.frame.iloc[[0, 1, 2, 7, 8, 9]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.T.reindex(['foo', 'qux'], axis=1, level=0)
|
||
|
|
tm.assert_frame_equal(result, expected.T)
|
||
|
|
|
||
|
|
result = self.frame.loc[['foo', 'qux']]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame['A'].loc[['foo', 'qux']]
|
||
|
|
tm.assert_series_equal(result, expected['A'])
|
||
|
|
|
||
|
|
result = self.frame.T.loc[:, ['foo', 'qux']]
|
||
|
|
tm.assert_frame_equal(result, expected.T)
|
||
|
|
|
||
|
|
def test_setitem_multiple_partial(self):
|
||
|
|
expected = self.frame.copy()
|
||
|
|
result = self.frame.copy()
|
||
|
|
result.loc[['foo', 'bar']] = 0
|
||
|
|
expected.loc['foo'] = 0
|
||
|
|
expected.loc['bar'] = 0
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
expected = self.frame.copy()
|
||
|
|
result = self.frame.copy()
|
||
|
|
result.loc['foo':'bar'] = 0
|
||
|
|
expected.loc['foo'] = 0
|
||
|
|
expected.loc['bar'] = 0
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
expected = self.frame['A'].copy()
|
||
|
|
result = self.frame['A'].copy()
|
||
|
|
result.loc[['foo', 'bar']] = 0
|
||
|
|
expected.loc['foo'] = 0
|
||
|
|
expected.loc['bar'] = 0
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = self.frame['A'].copy()
|
||
|
|
result = self.frame['A'].copy()
|
||
|
|
result.loc['foo':'bar'] = 0
|
||
|
|
expected.loc['foo'] = 0
|
||
|
|
expected.loc['bar'] = 0
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_drop_level(self):
|
||
|
|
result = self.frame.drop(['bar', 'qux'], level='first')
|
||
|
|
expected = self.frame.iloc[[0, 1, 2, 5, 6]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.drop(['two'], level='second')
|
||
|
|
expected = self.frame.iloc[[0, 2, 3, 6, 7, 9]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.T.drop(['bar', 'qux'], axis=1, level='first')
|
||
|
|
expected = self.frame.iloc[[0, 1, 2, 5, 6]].T
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.T.drop(['two'], axis=1, level='second')
|
||
|
|
expected = self.frame.iloc[[0, 2, 3, 6, 7, 9]].T
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_drop_level_nonunique_datetime(self):
|
||
|
|
# GH 12701
|
||
|
|
idx = Index([2, 3, 4, 4, 5], name='id')
|
||
|
|
idxdt = pd.to_datetime(['201603231400',
|
||
|
|
'201603231500',
|
||
|
|
'201603231600',
|
||
|
|
'201603231600',
|
||
|
|
'201603231700'])
|
||
|
|
df = DataFrame(np.arange(10).reshape(5, 2),
|
||
|
|
columns=list('ab'), index=idx)
|
||
|
|
df['tstamp'] = idxdt
|
||
|
|
df = df.set_index('tstamp', append=True)
|
||
|
|
ts = Timestamp('201603231600')
|
||
|
|
assert not df.index.is_unique
|
||
|
|
|
||
|
|
result = df.drop(ts, level='tstamp')
|
||
|
|
expected = df.loc[idx != 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_drop_preserve_names(self):
|
||
|
|
index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
|
||
|
|
[1, 2, 3, 1, 2, 3]],
|
||
|
|
names=['one', 'two'])
|
||
|
|
|
||
|
|
df = DataFrame(np.random.randn(6, 3), index=index)
|
||
|
|
|
||
|
|
result = df.drop([(0, 2)])
|
||
|
|
assert result.index.names == ('one', 'two')
|
||
|
|
|
||
|
|
def test_unicode_repr_issues(self):
|
||
|
|
levels = [Index([u('a/\u03c3'), u('b/\u03c3'), u('c/\u03c3')]),
|
||
|
|
Index([0, 1])]
|
||
|
|
labels = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)]
|
||
|
|
index = MultiIndex(levels=levels, labels=labels)
|
||
|
|
|
||
|
|
repr(index.levels)
|
||
|
|
|
||
|
|
# NumPy bug
|
||
|
|
# repr(index.get_level_values(1))
|
||
|
|
|
||
|
|
def test_unicode_repr_level_names(self):
|
||
|
|
index = MultiIndex.from_tuples([(0, 0), (1, 1)],
|
||
|
|
names=[u('\u0394'), 'i1'])
|
||
|
|
|
||
|
|
s = Series(lrange(2), index=index)
|
||
|
|
df = DataFrame(np.random.randn(2, 4), index=index)
|
||
|
|
repr(s)
|
||
|
|
repr(df)
|
||
|
|
|
||
|
|
def test_dataframe_insert_column_all_na(self):
|
||
|
|
# GH #1534
|
||
|
|
mix = MultiIndex.from_tuples([('1a', '2a'), ('1a', '2b'), ('1a', '2c')
|
||
|
|
])
|
||
|
|
df = DataFrame([[1, 2], [3, 4], [5, 6]], index=mix)
|
||
|
|
s = Series({(1, 1): 1, (1, 2): 2})
|
||
|
|
df['new'] = s
|
||
|
|
assert df['new'].isna().all()
|
||
|
|
|
||
|
|
def test_join_segfault(self):
|
||
|
|
# 1532
|
||
|
|
df1 = DataFrame({'a': [1, 1], 'b': [1, 2], 'x': [1, 2]})
|
||
|
|
df2 = DataFrame({'a': [2, 2], 'b': [1, 2], 'y': [1, 2]})
|
||
|
|
df1 = df1.set_index(['a', 'b'])
|
||
|
|
df2 = df2.set_index(['a', 'b'])
|
||
|
|
# it works!
|
||
|
|
for how in ['left', 'right', 'outer']:
|
||
|
|
df1.join(df2, how=how)
|
||
|
|
|
||
|
|
def test_set_column_scalar_with_ix(self):
|
||
|
|
subset = self.frame.index[[1, 4, 5]]
|
||
|
|
|
||
|
|
self.frame.loc[subset] = 99
|
||
|
|
assert (self.frame.loc[subset].values == 99).all()
|
||
|
|
|
||
|
|
col = self.frame['B']
|
||
|
|
col[subset] = 97
|
||
|
|
assert (self.frame.loc[subset, 'B'] == 97).all()
|
||
|
|
|
||
|
|
def test_frame_dict_constructor_empty_series(self):
|
||
|
|
s1 = Series([
|
||
|
|
1, 2, 3, 4
|
||
|
|
], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)]))
|
||
|
|
s2 = Series([
|
||
|
|
1, 2, 3, 4
|
||
|
|
], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)]))
|
||
|
|
s3 = Series()
|
||
|
|
|
||
|
|
# it works!
|
||
|
|
DataFrame({'foo': s1, 'bar': s2, 'baz': s3})
|
||
|
|
DataFrame.from_dict({'foo': s1, 'baz': s3, 'bar': s2})
|
||
|
|
|
||
|
|
def test_indexing_ambiguity_bug_1678(self):
|
||
|
|
columns = MultiIndex.from_tuples([('Ohio', 'Green'), ('Ohio', 'Red'), (
|
||
|
|
'Colorado', 'Green')])
|
||
|
|
index = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)
|
||
|
|
])
|
||
|
|
|
||
|
|
frame = DataFrame(np.arange(12).reshape((4, 3)), index=index,
|
||
|
|
columns=columns)
|
||
|
|
|
||
|
|
result = frame.iloc[:, 1]
|
||
|
|
exp = frame.loc[:, ('Ohio', 'Red')]
|
||
|
|
assert isinstance(result, Series)
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
def test_nonunique_assignment_1750(self):
|
||
|
|
df = DataFrame([[1, 1, "x", "X"], [1, 1, "y", "Y"], [1, 2, "z", "Z"]],
|
||
|
|
columns=list("ABCD"))
|
||
|
|
|
||
|
|
df = df.set_index(['A', 'B'])
|
||
|
|
ix = MultiIndex.from_tuples([(1, 1)])
|
||
|
|
|
||
|
|
df.loc[ix, "C"] = '_'
|
||
|
|
|
||
|
|
assert (df.xs((1, 1))['C'] == '_').all()
|
||
|
|
|
||
|
|
def test_indexing_over_hashtable_size_cutoff(self):
|
||
|
|
n = 10000
|
||
|
|
|
||
|
|
old_cutoff = _index._SIZE_CUTOFF
|
||
|
|
_index._SIZE_CUTOFF = 20000
|
||
|
|
|
||
|
|
s = Series(np.arange(n),
|
||
|
|
MultiIndex.from_arrays((["a"] * n, np.arange(n))))
|
||
|
|
|
||
|
|
# hai it works!
|
||
|
|
assert s[("a", 5)] == 5
|
||
|
|
assert s[("a", 6)] == 6
|
||
|
|
assert s[("a", 7)] == 7
|
||
|
|
|
||
|
|
_index._SIZE_CUTOFF = old_cutoff
|
||
|
|
|
||
|
|
def test_multiindex_na_repr(self):
|
||
|
|
# only an issue with long columns
|
||
|
|
|
||
|
|
from numpy import nan
|
||
|
|
df3 = DataFrame({
|
||
|
|
'A' * 30: {('A', 'A0006000', 'nuit'): 'A0006000'},
|
||
|
|
'B' * 30: {('A', 'A0006000', 'nuit'): nan},
|
||
|
|
'C' * 30: {('A', 'A0006000', 'nuit'): nan},
|
||
|
|
'D' * 30: {('A', 'A0006000', 'nuit'): nan},
|
||
|
|
'E' * 30: {('A', 'A0006000', 'nuit'): 'A'},
|
||
|
|
'F' * 30: {('A', 'A0006000', 'nuit'): nan},
|
||
|
|
})
|
||
|
|
|
||
|
|
idf = df3.set_index(['A' * 30, 'C' * 30])
|
||
|
|
repr(idf)
|
||
|
|
|
||
|
|
def test_assign_index_sequences(self):
|
||
|
|
# #2200
|
||
|
|
df = DataFrame({"a": [1, 2, 3],
|
||
|
|
"b": [4, 5, 6],
|
||
|
|
"c": [7, 8, 9]}).set_index(["a", "b"])
|
||
|
|
l = list(df.index)
|
||
|
|
l[0] = ("faz", "boo")
|
||
|
|
df.index = l
|
||
|
|
repr(df)
|
||
|
|
|
||
|
|
# this travels an improper code path
|
||
|
|
l[0] = ["faz", "boo"]
|
||
|
|
df.index = l
|
||
|
|
repr(df)
|
||
|
|
|
||
|
|
def test_tuples_have_na(self):
|
||
|
|
index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
|
||
|
|
labels=[[1, 1, 1, 1, -1, 0, 0, 0], [0, 1, 2, 3, 0,
|
||
|
|
1, 2, 3]])
|
||
|
|
|
||
|
|
assert isna(index[4][0])
|
||
|
|
assert isna(index.values[4][0])
|
||
|
|
|
||
|
|
def test_duplicate_groupby_issues(self):
|
||
|
|
idx_tp = [('600809', '20061231'), ('600809', '20070331'),
|
||
|
|
('600809', '20070630'), ('600809', '20070331')]
|
||
|
|
dt = ['demo', 'demo', 'demo', 'demo']
|
||
|
|
|
||
|
|
idx = MultiIndex.from_tuples(idx_tp, names=['STK_ID', 'RPT_Date'])
|
||
|
|
s = Series(dt, index=idx)
|
||
|
|
|
||
|
|
result = s.groupby(s.index).first()
|
||
|
|
assert len(result) == 3
|
||
|
|
|
||
|
|
def test_duplicate_mi(self):
|
||
|
|
# GH 4516
|
||
|
|
df = DataFrame([['foo', 'bar', 1.0, 1], ['foo', 'bar', 2.0, 2],
|
||
|
|
['bah', 'bam', 3.0, 3],
|
||
|
|
['bah', 'bam', 4.0, 4], ['foo', 'bar', 5.0, 5],
|
||
|
|
['bah', 'bam', 6.0, 6]],
|
||
|
|
columns=list('ABCD'))
|
||
|
|
df = df.set_index(['A', 'B'])
|
||
|
|
df = df.sort_index(level=0)
|
||
|
|
expected = DataFrame([['foo', 'bar', 1.0, 1], ['foo', 'bar', 2.0, 2],
|
||
|
|
['foo', 'bar', 5.0, 5]],
|
||
|
|
columns=list('ABCD')).set_index(['A', 'B'])
|
||
|
|
result = df.loc[('foo', 'bar')]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_duplicated_drop_duplicates(self):
|
||
|
|
# GH 4060
|
||
|
|
idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))
|
||
|
|
|
||
|
|
expected = np.array(
|
||
|
|
[False, False, False, True, False, False], dtype=bool)
|
||
|
|
duplicated = idx.duplicated()
|
||
|
|
tm.assert_numpy_array_equal(duplicated, expected)
|
||
|
|
assert duplicated.dtype == bool
|
||
|
|
expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
|
||
|
|
tm.assert_index_equal(idx.drop_duplicates(), expected)
|
||
|
|
|
||
|
|
expected = np.array([True, False, False, False, False, False])
|
||
|
|
duplicated = idx.duplicated(keep='last')
|
||
|
|
tm.assert_numpy_array_equal(duplicated, expected)
|
||
|
|
assert duplicated.dtype == bool
|
||
|
|
expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
|
||
|
|
tm.assert_index_equal(idx.drop_duplicates(keep='last'), expected)
|
||
|
|
|
||
|
|
expected = np.array([True, False, False, True, False, False])
|
||
|
|
duplicated = idx.duplicated(keep=False)
|
||
|
|
tm.assert_numpy_array_equal(duplicated, expected)
|
||
|
|
assert duplicated.dtype == bool
|
||
|
|
expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
|
||
|
|
tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
|
||
|
|
|
||
|
|
def test_multiindex_set_index(self):
|
||
|
|
# segfault in #3308
|
||
|
|
d = {'t1': [2, 2.5, 3], 't2': [4, 5, 6]}
|
||
|
|
df = DataFrame(d)
|
||
|
|
tuples = [(0, 1), (0, 2), (1, 2)]
|
||
|
|
df['tuples'] = tuples
|
||
|
|
|
||
|
|
index = MultiIndex.from_tuples(df['tuples'])
|
||
|
|
# it works!
|
||
|
|
df.set_index(index)
|
||
|
|
|
||
|
|
def test_datetimeindex(self):
|
||
|
|
idx1 = pd.DatetimeIndex(
|
||
|
|
['2013-04-01 9:00', '2013-04-02 9:00', '2013-04-03 9:00'
|
||
|
|
] * 2, tz='Asia/Tokyo')
|
||
|
|
idx2 = pd.date_range('2010/01/01', periods=6, freq='M',
|
||
|
|
tz='US/Eastern')
|
||
|
|
idx = MultiIndex.from_arrays([idx1, idx2])
|
||
|
|
|
||
|
|
expected1 = pd.DatetimeIndex(['2013-04-01 9:00', '2013-04-02 9:00',
|
||
|
|
'2013-04-03 9:00'], tz='Asia/Tokyo')
|
||
|
|
|
||
|
|
tm.assert_index_equal(idx.levels[0], expected1)
|
||
|
|
tm.assert_index_equal(idx.levels[1], idx2)
|
||
|
|
|
||
|
|
# from datetime combos
|
||
|
|
# GH 7888
|
||
|
|
date1 = datetime.date.today()
|
||
|
|
date2 = datetime.datetime.today()
|
||
|
|
date3 = Timestamp.today()
|
||
|
|
|
||
|
|
for d1, d2 in itertools.product(
|
||
|
|
[date1, date2, date3], [date1, date2, date3]):
|
||
|
|
index = MultiIndex.from_product([[d1], [d2]])
|
||
|
|
assert isinstance(index.levels[0], pd.DatetimeIndex)
|
||
|
|
assert isinstance(index.levels[1], pd.DatetimeIndex)
|
||
|
|
|
||
|
|
def test_constructor_with_tz(self):
|
||
|
|
|
||
|
|
index = pd.DatetimeIndex(['2013/01/01 09:00', '2013/01/02 09:00'],
|
||
|
|
name='dt1', tz='US/Pacific')
|
||
|
|
columns = pd.DatetimeIndex(['2014/01/01 09:00', '2014/01/02 09:00'],
|
||
|
|
name='dt2', tz='Asia/Tokyo')
|
||
|
|
|
||
|
|
result = MultiIndex.from_arrays([index, columns])
|
||
|
|
tm.assert_index_equal(result.levels[0], index)
|
||
|
|
tm.assert_index_equal(result.levels[1], columns)
|
||
|
|
|
||
|
|
result = MultiIndex.from_arrays([Series(index), Series(columns)])
|
||
|
|
tm.assert_index_equal(result.levels[0], index)
|
||
|
|
tm.assert_index_equal(result.levels[1], columns)
|
||
|
|
|
||
|
|
def test_set_index_datetime(self):
|
||
|
|
# GH 3950
|
||
|
|
df = DataFrame(
|
||
|
|
{'label': ['a', 'a', 'a', 'b', 'b', 'b'],
|
||
|
|
'datetime': ['2011-07-19 07:00:00', '2011-07-19 08:00:00',
|
||
|
|
'2011-07-19 09:00:00', '2011-07-19 07:00:00',
|
||
|
|
'2011-07-19 08:00:00', '2011-07-19 09:00:00'],
|
||
|
|
'value': range(6)})
|
||
|
|
df.index = pd.to_datetime(df.pop('datetime'), utc=True)
|
||
|
|
df.index = df.index.tz_convert('US/Pacific')
|
||
|
|
|
||
|
|
expected = pd.DatetimeIndex(['2011-07-19 07:00:00',
|
||
|
|
'2011-07-19 08:00:00',
|
||
|
|
'2011-07-19 09:00:00'], name='datetime')
|
||
|
|
expected = expected.tz_localize('UTC').tz_convert('US/Pacific')
|
||
|
|
|
||
|
|
df = df.set_index('label', append=True)
|
||
|
|
tm.assert_index_equal(df.index.levels[0], expected)
|
||
|
|
tm.assert_index_equal(df.index.levels[1],
|
||
|
|
Index(['a', 'b'], name='label'))
|
||
|
|
|
||
|
|
df = df.swaplevel(0, 1)
|
||
|
|
tm.assert_index_equal(df.index.levels[0],
|
||
|
|
Index(['a', 'b'], name='label'))
|
||
|
|
tm.assert_index_equal(df.index.levels[1], expected)
|
||
|
|
|
||
|
|
df = DataFrame(np.random.random(6))
|
||
|
|
idx1 = pd.DatetimeIndex(['2011-07-19 07:00:00', '2011-07-19 08:00:00',
|
||
|
|
'2011-07-19 09:00:00', '2011-07-19 07:00:00',
|
||
|
|
'2011-07-19 08:00:00', '2011-07-19 09:00:00'],
|
||
|
|
tz='US/Eastern')
|
||
|
|
idx2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-01 09:00',
|
||
|
|
'2012-04-01 09:00', '2012-04-02 09:00',
|
||
|
|
'2012-04-02 09:00', '2012-04-02 09:00'],
|
||
|
|
tz='US/Eastern')
|
||
|
|
idx3 = pd.date_range('2011-01-01 09:00', periods=6, tz='Asia/Tokyo')
|
||
|
|
|
||
|
|
df = df.set_index(idx1)
|
||
|
|
df = df.set_index(idx2, append=True)
|
||
|
|
df = df.set_index(idx3, append=True)
|
||
|
|
|
||
|
|
expected1 = pd.DatetimeIndex(['2011-07-19 07:00:00',
|
||
|
|
'2011-07-19 08:00:00',
|
||
|
|
'2011-07-19 09:00:00'], tz='US/Eastern')
|
||
|
|
expected2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-02 09:00'],
|
||
|
|
tz='US/Eastern')
|
||
|
|
|
||
|
|
tm.assert_index_equal(df.index.levels[0], expected1)
|
||
|
|
tm.assert_index_equal(df.index.levels[1], expected2)
|
||
|
|
tm.assert_index_equal(df.index.levels[2], idx3)
|
||
|
|
|
||
|
|
# GH 7092
|
||
|
|
tm.assert_index_equal(df.index.get_level_values(0), idx1)
|
||
|
|
tm.assert_index_equal(df.index.get_level_values(1), idx2)
|
||
|
|
tm.assert_index_equal(df.index.get_level_values(2), idx3)
|
||
|
|
|
||
|
|
def test_reset_index_datetime(self):
|
||
|
|
# GH 3950
|
||
|
|
for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern']:
|
||
|
|
idx1 = pd.date_range('1/1/2011', periods=5, freq='D', tz=tz,
|
||
|
|
name='idx1')
|
||
|
|
idx2 = Index(range(5), name='idx2', dtype='int64')
|
||
|
|
idx = MultiIndex.from_arrays([idx1, idx2])
|
||
|
|
df = DataFrame(
|
||
|
|
{'a': np.arange(5, dtype='int64'),
|
||
|
|
'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)
|
||
|
|
|
||
|
|
expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
|
||
|
|
datetime.datetime(2011, 1, 2),
|
||
|
|
datetime.datetime(2011, 1, 3),
|
||
|
|
datetime.datetime(2011, 1, 4),
|
||
|
|
datetime.datetime(2011, 1, 5)],
|
||
|
|
'idx2': np.arange(5, dtype='int64'),
|
||
|
|
'a': np.arange(5, dtype='int64'),
|
||
|
|
'b': ['A', 'B', 'C', 'D', 'E']},
|
||
|
|
columns=['idx1', 'idx2', 'a', 'b'])
|
||
|
|
expected['idx1'] = expected['idx1'].apply(
|
||
|
|
lambda d: Timestamp(d, tz=tz))
|
||
|
|
|
||
|
|
tm.assert_frame_equal(df.reset_index(), expected)
|
||
|
|
|
||
|
|
idx3 = pd.date_range('1/1/2012', periods=5, freq='MS',
|
||
|
|
tz='Europe/Paris', name='idx3')
|
||
|
|
idx = MultiIndex.from_arrays([idx1, idx2, idx3])
|
||
|
|
df = DataFrame(
|
||
|
|
{'a': np.arange(5, dtype='int64'),
|
||
|
|
'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)
|
||
|
|
|
||
|
|
expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
|
||
|
|
datetime.datetime(2011, 1, 2),
|
||
|
|
datetime.datetime(2011, 1, 3),
|
||
|
|
datetime.datetime(2011, 1, 4),
|
||
|
|
datetime.datetime(2011, 1, 5)],
|
||
|
|
'idx2': np.arange(5, dtype='int64'),
|
||
|
|
'idx3': [datetime.datetime(2012, 1, 1),
|
||
|
|
datetime.datetime(2012, 2, 1),
|
||
|
|
datetime.datetime(2012, 3, 1),
|
||
|
|
datetime.datetime(2012, 4, 1),
|
||
|
|
datetime.datetime(2012, 5, 1)],
|
||
|
|
'a': np.arange(5, dtype='int64'),
|
||
|
|
'b': ['A', 'B', 'C', 'D', 'E']},
|
||
|
|
columns=['idx1', 'idx2', 'idx3', 'a', 'b'])
|
||
|
|
expected['idx1'] = expected['idx1'].apply(
|
||
|
|
lambda d: Timestamp(d, tz=tz))
|
||
|
|
expected['idx3'] = expected['idx3'].apply(
|
||
|
|
lambda d: Timestamp(d, tz='Europe/Paris'))
|
||
|
|
tm.assert_frame_equal(df.reset_index(), expected)
|
||
|
|
|
||
|
|
# GH 7793
|
||
|
|
idx = MultiIndex.from_product([['a', 'b'], pd.date_range(
|
||
|
|
'20130101', periods=3, tz=tz)])
|
||
|
|
df = DataFrame(
|
||
|
|
np.arange(6, dtype='int64').reshape(
|
||
|
|
6, 1), columns=['a'], index=idx)
|
||
|
|
|
||
|
|
expected = DataFrame({'level_0': 'a a a b b b'.split(),
|
||
|
|
'level_1': [
|
||
|
|
datetime.datetime(2013, 1, 1),
|
||
|
|
datetime.datetime(2013, 1, 2),
|
||
|
|
datetime.datetime(2013, 1, 3)] * 2,
|
||
|
|
'a': np.arange(6, dtype='int64')},
|
||
|
|
columns=['level_0', 'level_1', 'a'])
|
||
|
|
expected['level_1'] = expected['level_1'].apply(
|
||
|
|
lambda d: Timestamp(d, freq='D', tz=tz))
|
||
|
|
tm.assert_frame_equal(df.reset_index(), expected)
|
||
|
|
|
||
|
|
def test_reset_index_period(self):
|
||
|
|
# GH 7746
|
||
|
|
idx = MultiIndex.from_product(
|
||
|
|
[pd.period_range('20130101', periods=3, freq='M'), list('abc')],
|
||
|
|
names=['month', 'feature'])
|
||
|
|
|
||
|
|
df = DataFrame(np.arange(9, dtype='int64').reshape(-1, 1),
|
||
|
|
index=idx, columns=['a'])
|
||
|
|
expected = DataFrame({
|
||
|
|
'month': ([pd.Period('2013-01', freq='M')] * 3 +
|
||
|
|
[pd.Period('2013-02', freq='M')] * 3 +
|
||
|
|
[pd.Period('2013-03', freq='M')] * 3),
|
||
|
|
'feature': ['a', 'b', 'c'] * 3,
|
||
|
|
'a': np.arange(9, dtype='int64')
|
||
|
|
}, columns=['month', 'feature', 'a'])
|
||
|
|
tm.assert_frame_equal(df.reset_index(), expected)
|
||
|
|
|
||
|
|
def test_reset_index_multiindex_columns(self):
|
||
|
|
levels = [['A', ''], ['B', 'b']]
|
||
|
|
df = DataFrame([[0, 2], [1, 3]],
|
||
|
|
columns=MultiIndex.from_tuples(levels))
|
||
|
|
result = df[['B']].rename_axis('A').reset_index()
|
||
|
|
tm.assert_frame_equal(result, df)
|
||
|
|
|
||
|
|
# gh-16120: already existing column
|
||
|
|
with tm.assert_raises_regex(ValueError,
|
||
|
|
(r"cannot insert \('A', ''\), "
|
||
|
|
"already exists")):
|
||
|
|
df.rename_axis('A').reset_index()
|
||
|
|
|
||
|
|
# gh-16164: multiindex (tuple) full key
|
||
|
|
result = df.set_index([('A', '')]).reset_index()
|
||
|
|
tm.assert_frame_equal(result, df)
|
||
|
|
|
||
|
|
# with additional (unnamed) index level
|
||
|
|
idx_col = DataFrame([[0], [1]],
|
||
|
|
columns=MultiIndex.from_tuples([('level_0', '')]))
|
||
|
|
expected = pd.concat([idx_col, df[[('B', 'b'), ('A', '')]]], axis=1)
|
||
|
|
result = df.set_index([('B', 'b')], append=True).reset_index()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# with index name which is a too long tuple...
|
||
|
|
with tm.assert_raises_regex(ValueError,
|
||
|
|
("Item must have length equal to number "
|
||
|
|
"of levels.")):
|
||
|
|
df.rename_axis([('C', 'c', 'i')]).reset_index()
|
||
|
|
|
||
|
|
# or too short...
|
||
|
|
levels = [['A', 'a', ''], ['B', 'b', 'i']]
|
||
|
|
df2 = DataFrame([[0, 2], [1, 3]],
|
||
|
|
columns=MultiIndex.from_tuples(levels))
|
||
|
|
idx_col = DataFrame([[0], [1]],
|
||
|
|
columns=MultiIndex.from_tuples([('C', 'c', 'ii')]))
|
||
|
|
expected = pd.concat([idx_col, df2], axis=1)
|
||
|
|
result = df2.rename_axis([('C', 'c')]).reset_index(col_fill='ii')
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# ... which is incompatible with col_fill=None
|
||
|
|
with tm.assert_raises_regex(ValueError,
|
||
|
|
("col_fill=None is incompatible with "
|
||
|
|
r"incomplete column name \('C', 'c'\)")):
|
||
|
|
df2.rename_axis([('C', 'c')]).reset_index(col_fill=None)
|
||
|
|
|
||
|
|
# with col_level != 0
|
||
|
|
result = df2.rename_axis([('c', 'ii')]).reset_index(col_level=1,
|
||
|
|
col_fill='C')
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_set_index_period(self):
|
||
|
|
# GH 6631
|
||
|
|
df = DataFrame(np.random.random(6))
|
||
|
|
idx1 = pd.period_range('2011-01-01', periods=3, freq='M')
|
||
|
|
idx1 = idx1.append(idx1)
|
||
|
|
idx2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')
|
||
|
|
idx2 = idx2.append(idx2).append(idx2)
|
||
|
|
idx3 = pd.period_range('2005', periods=6, freq='A')
|
||
|
|
|
||
|
|
df = df.set_index(idx1)
|
||
|
|
df = df.set_index(idx2, append=True)
|
||
|
|
df = df.set_index(idx3, append=True)
|
||
|
|
|
||
|
|
expected1 = pd.period_range('2011-01-01', periods=3, freq='M')
|
||
|
|
expected2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')
|
||
|
|
|
||
|
|
tm.assert_index_equal(df.index.levels[0], expected1)
|
||
|
|
tm.assert_index_equal(df.index.levels[1], expected2)
|
||
|
|
tm.assert_index_equal(df.index.levels[2], idx3)
|
||
|
|
|
||
|
|
tm.assert_index_equal(df.index.get_level_values(0), idx1)
|
||
|
|
tm.assert_index_equal(df.index.get_level_values(1), idx2)
|
||
|
|
tm.assert_index_equal(df.index.get_level_values(2), idx3)
|
||
|
|
|
||
|
|
def test_repeat(self):
|
||
|
|
# GH 9361
|
||
|
|
# fixed by # GH 7891
|
||
|
|
m_idx = MultiIndex.from_tuples([(1, 2), (3, 4), (5, 6), (7, 8)])
|
||
|
|
data = ['a', 'b', 'c', 'd']
|
||
|
|
m_df = Series(data, index=m_idx)
|
||
|
|
assert m_df.repeat(3).shape == (3 * len(data), )
|
||
|
|
|
||
|
|
def test_iloc_mi(self):
|
||
|
|
# GH 13797
|
||
|
|
# Test if iloc can handle integer locations in MultiIndexed DataFrame
|
||
|
|
|
||
|
|
data = [['str00', 'str01'], ['str10', 'str11'], ['str20', 'srt21'],
|
||
|
|
['str30', 'str31'], ['str40', 'str41']]
|
||
|
|
|
||
|
|
mi = MultiIndex.from_tuples(
|
||
|
|
[('CC', 'A'), ('CC', 'B'), ('CC', 'B'), ('BB', 'a'), ('BB', 'b')])
|
||
|
|
|
||
|
|
expected = DataFrame(data)
|
||
|
|
df_mi = DataFrame(data, index=mi)
|
||
|
|
|
||
|
|
result = DataFrame([[df_mi.iloc[r, c] for c in range(2)]
|
||
|
|
for r in range(5)])
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSorted(Base):
|
||
|
|
""" everything you wanted to test about sorting """
|
||
|
|
|
||
|
|
def test_sort_index_preserve_levels(self):
|
||
|
|
result = self.frame.sort_index()
|
||
|
|
assert result.index.names == self.frame.index.names
|
||
|
|
|
||
|
|
def test_sorting_repr_8017(self):
|
||
|
|
|
||
|
|
np.random.seed(0)
|
||
|
|
data = np.random.randn(3, 4)
|
||
|
|
|
||
|
|
for gen, extra in [([1., 3., 2., 5.], 4.), ([1, 3, 2, 5], 4),
|
||
|
|
([Timestamp('20130101'), Timestamp('20130103'),
|
||
|
|
Timestamp('20130102'), Timestamp('20130105')],
|
||
|
|
Timestamp('20130104')),
|
||
|
|
(['1one', '3one', '2one', '5one'], '4one')]:
|
||
|
|
columns = MultiIndex.from_tuples([('red', i) for i in gen])
|
||
|
|
df = DataFrame(data, index=list('def'), columns=columns)
|
||
|
|
df2 = pd.concat([df,
|
||
|
|
DataFrame('world', index=list('def'),
|
||
|
|
columns=MultiIndex.from_tuples(
|
||
|
|
[('red', extra)]))], axis=1)
|
||
|
|
|
||
|
|
# check that the repr is good
|
||
|
|
# make sure that we have a correct sparsified repr
|
||
|
|
# e.g. only 1 header of read
|
||
|
|
assert str(df2).splitlines()[0].split() == ['red']
|
||
|
|
|
||
|
|
# GH 8017
|
||
|
|
# sorting fails after columns added
|
||
|
|
|
||
|
|
# construct single-dtype then sort
|
||
|
|
result = df.copy().sort_index(axis=1)
|
||
|
|
expected = df.iloc[:, [0, 2, 1, 3]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df2.sort_index(axis=1)
|
||
|
|
expected = df2.iloc[:, [0, 2, 1, 4, 3]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# setitem then sort
|
||
|
|
result = df.copy()
|
||
|
|
result[('red', extra)] = 'world'
|
||
|
|
|
||
|
|
result = result.sort_index(axis=1)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_sort_index_level(self):
|
||
|
|
df = self.frame.copy()
|
||
|
|
df.index = np.arange(len(df))
|
||
|
|
|
||
|
|
# axis=1
|
||
|
|
|
||
|
|
# series
|
||
|
|
a_sorted = self.frame['A'].sort_index(level=0)
|
||
|
|
|
||
|
|
# preserve names
|
||
|
|
assert a_sorted.index.names == self.frame.index.names
|
||
|
|
|
||
|
|
# inplace
|
||
|
|
rs = self.frame.copy()
|
||
|
|
rs.sort_index(level=0, inplace=True)
|
||
|
|
tm.assert_frame_equal(rs, self.frame.sort_index(level=0))
|
||
|
|
|
||
|
|
def test_sort_index_level_large_cardinality(self):
|
||
|
|
|
||
|
|
# #2684 (int64)
|
||
|
|
index = MultiIndex.from_arrays([np.arange(4000)] * 3)
|
||
|
|
df = DataFrame(np.random.randn(4000), index=index, dtype=np.int64)
|
||
|
|
|
||
|
|
# it works!
|
||
|
|
result = df.sort_index(level=0)
|
||
|
|
assert result.index.lexsort_depth == 3
|
||
|
|
|
||
|
|
# #2684 (int32)
|
||
|
|
index = MultiIndex.from_arrays([np.arange(4000)] * 3)
|
||
|
|
df = DataFrame(np.random.randn(4000), index=index, dtype=np.int32)
|
||
|
|
|
||
|
|
# it works!
|
||
|
|
result = df.sort_index(level=0)
|
||
|
|
assert (result.dtypes.values == df.dtypes.values).all()
|
||
|
|
assert result.index.lexsort_depth == 3
|
||
|
|
|
||
|
|
def test_sort_index_level_by_name(self):
|
||
|
|
self.frame.index.names = ['first', 'second']
|
||
|
|
result = self.frame.sort_index(level='second')
|
||
|
|
expected = self.frame.sort_index(level=1)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_sort_index_level_mixed(self):
|
||
|
|
sorted_before = self.frame.sort_index(level=1)
|
||
|
|
|
||
|
|
df = self.frame.copy()
|
||
|
|
df['foo'] = 'bar'
|
||
|
|
sorted_after = df.sort_index(level=1)
|
||
|
|
tm.assert_frame_equal(sorted_before,
|
||
|
|
sorted_after.drop(['foo'], axis=1))
|
||
|
|
|
||
|
|
dft = self.frame.T
|
||
|
|
sorted_before = dft.sort_index(level=1, axis=1)
|
||
|
|
dft['foo', 'three'] = 'bar'
|
||
|
|
|
||
|
|
sorted_after = dft.sort_index(level=1, axis=1)
|
||
|
|
tm.assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
|
||
|
|
sorted_after.drop([('foo', 'three')], axis=1))
|
||
|
|
|
||
|
|
def test_is_lexsorted(self):
|
||
|
|
levels = [[0, 1], [0, 1, 2]]
|
||
|
|
|
||
|
|
index = MultiIndex(levels=levels,
|
||
|
|
labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
|
||
|
|
assert index.is_lexsorted()
|
||
|
|
|
||
|
|
index = MultiIndex(levels=levels,
|
||
|
|
labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
|
||
|
|
assert not index.is_lexsorted()
|
||
|
|
|
||
|
|
index = MultiIndex(levels=levels,
|
||
|
|
labels=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
|
||
|
|
assert not index.is_lexsorted()
|
||
|
|
assert index.lexsort_depth == 0
|
||
|
|
|
||
|
|
def test_getitem_multilevel_index_tuple_not_sorted(self):
|
||
|
|
index_columns = list("abc")
|
||
|
|
df = DataFrame([[0, 1, 0, "x"], [0, 0, 1, "y"]],
|
||
|
|
columns=index_columns + ["data"])
|
||
|
|
df = df.set_index(index_columns)
|
||
|
|
query_index = df.index[:1]
|
||
|
|
rs = df.loc[query_index, "data"]
|
||
|
|
|
||
|
|
xp_idx = MultiIndex.from_tuples([(0, 1, 0)], names=['a', 'b', 'c'])
|
||
|
|
xp = Series(['x'], index=xp_idx, name='data')
|
||
|
|
tm.assert_series_equal(rs, xp)
|
||
|
|
|
||
|
|
def test_getitem_slice_not_sorted(self):
|
||
|
|
df = self.frame.sort_index(level=1).T
|
||
|
|
|
||
|
|
# buglet with int typechecking
|
||
|
|
result = df.iloc[:, :np.int32(3)]
|
||
|
|
expected = df.reindex(columns=df.columns[:3])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_frame_getitem_not_sorted2(self):
|
||
|
|
# 13431
|
||
|
|
df = DataFrame({'col1': ['b', 'd', 'b', 'a'],
|
||
|
|
'col2': [3, 1, 1, 2],
|
||
|
|
'data': ['one', 'two', 'three', 'four']})
|
||
|
|
|
||
|
|
df2 = df.set_index(['col1', 'col2'])
|
||
|
|
df2_original = df2.copy()
|
||
|
|
|
||
|
|
df2.index.set_levels(['b', 'd', 'a'], level='col1', inplace=True)
|
||
|
|
df2.index.set_labels([0, 1, 0, 2], level='col1', inplace=True)
|
||
|
|
assert not df2.index.is_lexsorted()
|
||
|
|
assert not df2.index.is_monotonic
|
||
|
|
|
||
|
|
assert df2_original.index.equals(df2.index)
|
||
|
|
expected = df2.sort_index()
|
||
|
|
assert expected.index.is_lexsorted()
|
||
|
|
assert expected.index.is_monotonic
|
||
|
|
|
||
|
|
result = df2.sort_index(level=0)
|
||
|
|
assert result.index.is_lexsorted()
|
||
|
|
assert result.index.is_monotonic
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_frame_getitem_not_sorted(self):
|
||
|
|
df = self.frame.T
|
||
|
|
df['foo', 'four'] = 'foo'
|
||
|
|
|
||
|
|
arrays = [np.array(x) for x in zip(*df.columns.values)]
|
||
|
|
|
||
|
|
result = df['foo']
|
||
|
|
result2 = df.loc[:, 'foo']
|
||
|
|
expected = df.reindex(columns=df.columns[arrays[0] == 'foo'])
|
||
|
|
expected.columns = expected.columns.droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
df = df.T
|
||
|
|
result = df.xs('foo')
|
||
|
|
result2 = df.loc['foo']
|
||
|
|
expected = df.reindex(df.index[arrays[0] == 'foo'])
|
||
|
|
expected.index = expected.index.droplevel(0)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
def test_series_getitem_not_sorted(self):
|
||
|
|
arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
|
||
|
|
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
|
||
|
|
tuples = lzip(*arrays)
|
||
|
|
index = MultiIndex.from_tuples(tuples)
|
||
|
|
s = Series(randn(8), index=index)
|
||
|
|
|
||
|
|
arrays = [np.array(x) for x in zip(*index.values)]
|
||
|
|
|
||
|
|
result = s['qux']
|
||
|
|
result2 = s.loc['qux']
|
||
|
|
expected = s[arrays[0] == 'qux']
|
||
|
|
expected.index = expected.index.droplevel(0)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
tm.assert_series_equal(result2, expected)
|
||
|
|
|
||
|
|
def test_sort_index_and_reconstruction(self):
|
||
|
|
|
||
|
|
# 15622
|
||
|
|
# lexsortedness should be identical
|
||
|
|
# across MultiIndex consruction methods
|
||
|
|
|
||
|
|
df = DataFrame([[1, 1], [2, 2]], index=list('ab'))
|
||
|
|
expected = DataFrame([[1, 1], [2, 2], [1, 1], [2, 2]],
|
||
|
|
index=MultiIndex.from_tuples([(0.5, 'a'),
|
||
|
|
(0.5, 'b'),
|
||
|
|
(0.8, 'a'),
|
||
|
|
(0.8, 'b')]))
|
||
|
|
assert expected.index.is_lexsorted()
|
||
|
|
|
||
|
|
result = DataFrame(
|
||
|
|
[[1, 1], [2, 2], [1, 1], [2, 2]],
|
||
|
|
index=MultiIndex.from_product([[0.5, 0.8], list('ab')]))
|
||
|
|
result = result.sort_index()
|
||
|
|
assert result.index.is_lexsorted()
|
||
|
|
assert result.index.is_monotonic
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = DataFrame(
|
||
|
|
[[1, 1], [2, 2], [1, 1], [2, 2]],
|
||
|
|
index=MultiIndex(levels=[[0.5, 0.8], ['a', 'b']],
|
||
|
|
labels=[[0, 0, 1, 1], [0, 1, 0, 1]]))
|
||
|
|
result = result.sort_index()
|
||
|
|
assert result.index.is_lexsorted()
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
concatted = pd.concat([df, df], keys=[0.8, 0.5])
|
||
|
|
result = concatted.sort_index()
|
||
|
|
|
||
|
|
assert result.index.is_lexsorted()
|
||
|
|
assert result.index.is_monotonic
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# 14015
|
||
|
|
df = DataFrame([[1, 2], [6, 7]],
|
||
|
|
columns=MultiIndex.from_tuples(
|
||
|
|
[(0, '20160811 12:00:00'),
|
||
|
|
(0, '20160809 12:00:00')],
|
||
|
|
names=['l1', 'Date']))
|
||
|
|
|
||
|
|
df.columns.set_levels(pd.to_datetime(df.columns.levels[1]),
|
||
|
|
level=1,
|
||
|
|
inplace=True)
|
||
|
|
assert not df.columns.is_lexsorted()
|
||
|
|
assert not df.columns.is_monotonic
|
||
|
|
result = df.sort_index(axis=1)
|
||
|
|
assert result.columns.is_lexsorted()
|
||
|
|
assert result.columns.is_monotonic
|
||
|
|
result = df.sort_index(axis=1, level=1)
|
||
|
|
assert result.columns.is_lexsorted()
|
||
|
|
assert result.columns.is_monotonic
|
||
|
|
|
||
|
|
def test_sort_index_and_reconstruction_doc_example(self):
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|
# doc example
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|
df = DataFrame({'value': [1, 2, 3, 4]},
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|
index=MultiIndex(
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|
|
levels=[['a', 'b'], ['bb', 'aa']],
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||
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labels=[[0, 0, 1, 1], [0, 1, 0, 1]]))
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|
assert df.index.is_lexsorted()
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|
assert not df.index.is_monotonic
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|
|
|
||
|
|
# sort it
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|
|
expected = DataFrame({'value': [2, 1, 4, 3]},
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|
|
index=MultiIndex(
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||
|
|
levels=[['a', 'b'], ['aa', 'bb']],
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||
|
|
labels=[[0, 0, 1, 1], [0, 1, 0, 1]]))
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||
|
|
result = df.sort_index()
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||
|
|
assert result.index.is_lexsorted()
|
||
|
|
assert result.index.is_monotonic
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# reconstruct
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||
|
|
result = df.sort_index().copy()
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||
|
|
result.index = result.index._sort_levels_monotonic()
|
||
|
|
assert result.index.is_lexsorted()
|
||
|
|
assert result.index.is_monotonic
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_sort_index_reorder_on_ops(self):
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||
|
|
# 15687
|
||
|
|
df = DataFrame(
|
||
|
|
np.random.randn(8, 2),
|
||
|
|
index=MultiIndex.from_product(
|
||
|
|
[['a', 'b'], ['big', 'small'], ['red', 'blu']],
|
||
|
|
names=['letter', 'size', 'color']),
|
||
|
|
columns=['near', 'far'])
|
||
|
|
df = df.sort_index()
|
||
|
|
|
||
|
|
def my_func(group):
|
||
|
|
group.index = ['newz', 'newa']
|
||
|
|
return group
|
||
|
|
|
||
|
|
result = df.groupby(level=['letter', 'size']).apply(
|
||
|
|
my_func).sort_index()
|
||
|
|
expected = MultiIndex.from_product(
|
||
|
|
[['a', 'b'], ['big', 'small'], ['newa', 'newz']],
|
||
|
|
names=['letter', 'size', None])
|
||
|
|
|
||
|
|
tm.assert_index_equal(result.index, expected)
|
||
|
|
|
||
|
|
def test_sort_non_lexsorted(self):
|
||
|
|
# degenerate case where we sort but don't
|
||
|
|
# have a satisfying result :<
|
||
|
|
# GH 15797
|
||
|
|
idx = MultiIndex([['A', 'B', 'C'],
|
||
|
|
['c', 'b', 'a']],
|
||
|
|
[[0, 1, 2, 0, 1, 2],
|
||
|
|
[0, 2, 1, 1, 0, 2]])
|
||
|
|
|
||
|
|
df = DataFrame({'col': range(len(idx))},
|
||
|
|
index=idx,
|
||
|
|
dtype='int64')
|
||
|
|
assert df.index.is_lexsorted() is False
|
||
|
|
assert df.index.is_monotonic is False
|
||
|
|
|
||
|
|
sorted = df.sort_index()
|
||
|
|
assert sorted.index.is_lexsorted() is True
|
||
|
|
assert sorted.index.is_monotonic is True
|
||
|
|
|
||
|
|
expected = DataFrame(
|
||
|
|
{'col': [1, 4, 5, 2]},
|
||
|
|
index=MultiIndex.from_tuples([('B', 'a'), ('B', 'c'),
|
||
|
|
('C', 'a'), ('C', 'b')]),
|
||
|
|
dtype='int64')
|
||
|
|
result = sorted.loc[pd.IndexSlice['B':'C', 'a':'c'], :]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_sort_index_nan(self):
|
||
|
|
# GH 14784
|
||
|
|
# incorrect sorting w.r.t. nans
|
||
|
|
tuples = [[12, 13], [np.nan, np.nan], [np.nan, 3], [1, 2]]
|
||
|
|
mi = MultiIndex.from_tuples(tuples)
|
||
|
|
|
||
|
|
df = DataFrame(np.arange(16).reshape(4, 4),
|
||
|
|
index=mi, columns=list('ABCD'))
|
||
|
|
s = Series(np.arange(4), index=mi)
|
||
|
|
|
||
|
|
df2 = DataFrame({
|
||
|
|
'date': pd.to_datetime([
|
||
|
|
'20121002', '20121007', '20130130', '20130202', '20130305',
|
||
|
|
'20121002', '20121207', '20130130', '20130202', '20130305',
|
||
|
|
'20130202', '20130305'
|
||
|
|
]),
|
||
|
|
'user_id': [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
|
||
|
|
'whole_cost': [1790, np.nan, 280, 259, np.nan, 623, 90, 312,
|
||
|
|
np.nan, 301, 359, 801],
|
||
|
|
'cost': [12, 15, 10, 24, 39, 1, 0, np.nan, 45, 34, 1, 12]
|
||
|
|
}).set_index(['date', 'user_id'])
|
||
|
|
|
||
|
|
# sorting frame, default nan position is last
|
||
|
|
result = df.sort_index()
|
||
|
|
expected = df.iloc[[3, 0, 2, 1], :]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# sorting frame, nan position last
|
||
|
|
result = df.sort_index(na_position='last')
|
||
|
|
expected = df.iloc[[3, 0, 2, 1], :]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# sorting frame, nan position first
|
||
|
|
result = df.sort_index(na_position='first')
|
||
|
|
expected = df.iloc[[1, 2, 3, 0], :]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# sorting frame with removed rows
|
||
|
|
result = df2.dropna().sort_index()
|
||
|
|
expected = df2.sort_index().dropna()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# sorting series, default nan position is last
|
||
|
|
result = s.sort_index()
|
||
|
|
expected = s.iloc[[3, 0, 2, 1]]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# sorting series, nan position last
|
||
|
|
result = s.sort_index(na_position='last')
|
||
|
|
expected = s.iloc[[3, 0, 2, 1]]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# sorting series, nan position first
|
||
|
|
result = s.sort_index(na_position='first')
|
||
|
|
expected = s.iloc[[1, 2, 3, 0]]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_sort_ascending_list(self):
|
||
|
|
# GH: 16934
|
||
|
|
|
||
|
|
# Set up a Series with a three level MultiIndex
|
||
|
|
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
|
||
|
|
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'],
|
||
|
|
[4, 3, 2, 1, 4, 3, 2, 1]]
|
||
|
|
tuples = lzip(*arrays)
|
||
|
|
mi = MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
|
||
|
|
s = Series(range(8), index=mi)
|
||
|
|
|
||
|
|
# Sort with boolean ascending
|
||
|
|
result = s.sort_index(level=['third', 'first'], ascending=False)
|
||
|
|
expected = s.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# Sort with list of boolean ascending
|
||
|
|
result = s.sort_index(level=['third', 'first'],
|
||
|
|
ascending=[False, True])
|
||
|
|
expected = s.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
|
||
|
|
tm.assert_series_equal(result, expected)
|