# -*- coding: utf-8 -*- # pylint: disable-msg=W0612,E1101,W0141 import datetime import itertools from warnings import catch_warnings, simplefilter import numpy as np from numpy.random import randn import pytest import pytz from pandas.compat import ( StringIO, lrange, lzip, product as cart_product, range, u, zip) from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype import pandas as pd from pandas import DataFrame, Panel, Series, Timestamp, isna from pandas.core.index import Index, MultiIndex import pandas.util.testing as tm AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew', 'mad', 'std', 'var', 'sem'] class Base(object): def setup_method(self, method): index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two', 'three']], codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], names=['first', 'second']) self.frame = DataFrame(np.random.randn(10, 3), index=index, columns=Index(['A', 'B', 'C'], name='exp')) self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']], codes=[[0, 1, 2, 3]], names=['first']) # create test series object 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) s[3] = np.NaN self.series = s self.tdf = tm.makeTimeDataFrame(100) self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum() # use Int64Index, to make sure things work self.ymd.index.set_levels([lev.astype('i8') for lev in self.ymd.index.levels], inplace=True) self.ymd.index.set_names(['year', 'month', 'day'], inplace=True) class TestMultiLevel(Base): def test_append(self): a, b = self.frame[:5], self.frame[5:] result = a.append(b) tm.assert_frame_equal(result, self.frame) result = a['A'].append(b['A']) tm.assert_series_equal(result, self.frame['A']) def test_append_index(self): idx1 = Index([1.1, 1.2, 1.3]) idx2 = pd.date_range('2011-01-01', freq='D', periods=3, tz='Asia/Tokyo') idx3 = Index(['A', 'B', 'C']) midx_lv2 = MultiIndex.from_arrays([idx1, idx2]) midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3]) result = idx1.append(midx_lv2) # see gh-7112 tz = pytz.timezone('Asia/Tokyo') expected_tuples = [(1.1, tz.localize(datetime.datetime(2011, 1, 1))), (1.2, tz.localize(datetime.datetime(2011, 1, 2))), (1.3, tz.localize(datetime.datetime(2011, 1, 3)))] expected = Index([1.1, 1.2, 1.3] + expected_tuples) tm.assert_index_equal(result, expected) result = midx_lv2.append(idx1) expected = Index(expected_tuples + [1.1, 1.2, 1.3]) tm.assert_index_equal(result, expected) result = midx_lv2.append(midx_lv2) expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)]) tm.assert_index_equal(result, expected) result = midx_lv2.append(midx_lv3) tm.assert_index_equal(result, expected) result = midx_lv3.append(midx_lv2) expected = Index._simple_new( np.array([(1.1, tz.localize(datetime.datetime(2011, 1, 1)), 'A'), (1.2, tz.localize(datetime.datetime(2011, 1, 2)), 'B'), (1.3, tz.localize(datetime.datetime(2011, 1, 3)), 'C')] + expected_tuples), None) tm.assert_index_equal(result, expected) def test_dataframe_constructor(self): multi = DataFrame(np.random.randn(4, 4), index=[np.array(['a', 'a', 'b', 'b']), np.array(['x', 'y', 'x', 'y'])]) assert isinstance(multi.index, MultiIndex) assert not isinstance(multi.columns, MultiIndex) multi = DataFrame(np.random.randn(4, 4), columns=[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']]) assert isinstance(multi.columns, MultiIndex) def test_series_constructor(self): multi = Series(1., index=[np.array(['a', 'a', 'b', 'b']), np.array( ['x', 'y', 'x', 'y'])]) assert isinstance(multi.index, MultiIndex) multi = Series(1., index=[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']]) assert isinstance(multi.index, MultiIndex) multi = Series(lrange(4), index=[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']]) assert isinstance(multi.index, MultiIndex) def test_reindex_level(self): # axis=0 month_sums = self.ymd.sum(level='month') result = month_sums.reindex(self.ymd.index, level=1) expected = self.ymd.groupby(level='month').transform(np.sum) tm.assert_frame_equal(result, expected) # Series result = month_sums['A'].reindex(self.ymd.index, level=1) expected = self.ymd['A'].groupby(level='month').transform(np.sum) tm.assert_series_equal(result, expected, check_names=False) # axis=1 month_sums = self.ymd.T.sum(axis=1, level='month') result = month_sums.reindex(columns=self.ymd.index, level=1) expected = self.ymd.groupby(level='month').transform(np.sum).T tm.assert_frame_equal(result, expected) def test_binops_level(self): def _check_op(opname): op = getattr(DataFrame, opname) month_sums = self.ymd.sum(level='month') result = op(self.ymd, month_sums, level='month') broadcasted = self.ymd.groupby(level='month').transform(np.sum) expected = op(self.ymd, broadcasted) tm.assert_frame_equal(result, expected) # Series op = getattr(Series, opname) result = op(self.ymd['A'], month_sums['A'], level='month') broadcasted = self.ymd['A'].groupby(level='month').transform( np.sum) expected = op(self.ymd['A'], broadcasted) expected.name = 'A' tm.assert_series_equal(result, expected) _check_op('sub') _check_op('add') _check_op('mul') _check_op('div') def test_pickle(self): def _test_roundtrip(frame): unpickled = tm.round_trip_pickle(frame) tm.assert_frame_equal(frame, unpickled) _test_roundtrip(self.frame) _test_roundtrip(self.frame.T) _test_roundtrip(self.ymd) _test_roundtrip(self.ymd.T) def test_reindex(self): expected = self.frame.iloc[[0, 3]] reindexed = self.frame.loc[[('foo', 'one'), ('bar', 'one')]] tm.assert_frame_equal(reindexed, expected) with catch_warnings(record=True): simplefilter("ignore", DeprecationWarning) reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]] tm.assert_frame_equal(reindexed, expected) def test_reindex_preserve_levels(self): new_index = self.ymd.index[::10] chunk = self.ymd.reindex(new_index) assert chunk.index is new_index chunk = self.ymd.loc[new_index] assert chunk.index is new_index with catch_warnings(record=True): simplefilter("ignore", DeprecationWarning) chunk = self.ymd.ix[new_index] assert chunk.index is new_index ymdT = self.ymd.T chunk = ymdT.reindex(columns=new_index) assert chunk.columns is new_index chunk = ymdT.loc[:, new_index] assert chunk.columns is new_index def test_repr_to_string(self): repr(self.frame) repr(self.ymd) repr(self.frame.T) repr(self.ymd.T) buf = StringIO() self.frame.to_string(buf=buf) self.ymd.to_string(buf=buf) self.frame.T.to_string(buf=buf) self.ymd.T.to_string(buf=buf) def test_repr_name_coincide(self): index = MultiIndex.from_tuples([('a', 0, 'foo'), ('b', 1, 'bar')], names=['a', 'b', 'c']) df = DataFrame({'value': [0, 1]}, index=index) lines = repr(df).split('\n') assert lines[2].startswith('a 0 foo') 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) assert deleveled.index.name == self.ymd.index.name deleveled = self.series.reset_index() assert isinstance(deleveled, DataFrame) assert len(deleveled.columns) == len(self.series.index.levels) + 1 assert deleveled.index.name == self.series.index.name deleveled = self.series.reset_index(drop=True) assert isinstance(deleveled, Series) assert deleveled.index.name == self.series.index.name 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() with pytest.raises(TypeError, match='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']], codes=[[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 pytest.raises(IndexError, match="Too many levels"): self.frame.index._get_level_number(2) with pytest.raises(IndexError, match="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 left.index.is_unique is False 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']], codes=[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']], codes=[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']], codes=[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 pytest.raises(ValueError, match="level should contain"): unstacked.stack([0, 'month']) def test_stack_multiple_out_of_bounds(self): # nlevels == 3 unstacked = self.ymd.unstack(['year', 'month']) with pytest.raises(IndexError, match="Too many levels"): unstacked.stack([2, 3]) with pytest.raises(IndexError, match="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]] codes = [[0, 0, 1, 1], [0, 2, 0, 2]] index = MultiIndex(levels, codes) 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) @pytest.mark.slow def test_unstack_number_of_levels_larger_than_int32(self): # GH 20601 df = DataFrame(np.random.randn(2 ** 16, 2), index=[np.arange(2 ** 16), np.arange(2 ** 16)]) with pytest.raises(ValueError, match='int32 overflow'): df.unstack() 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, codes=[[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"]], codes=[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']], codes=[[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): simplefilter("ignore", FutureWarning) 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 pytest.raises(TypeError, match='hierarchical axis'): self.ymd.reorder_levels([1, 2], axis=1) with pytest.raises(IndexError, match='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_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') @pytest.mark.parametrize('op', AGG_FUNCTIONS) @pytest.mark.parametrize('level', [0, 1]) @pytest.mark.parametrize('skipna', [True, False]) @pytest.mark.parametrize('sort', [True, False]) def test_series_group_min_max(self, op, level, skipna, sort): # GH 17537 grouped = self.series.groupby(level=level, sort=sort) # skipna=True leftside = grouped.agg(lambda x: getattr(x, op)(skipna=skipna)) 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('op', AGG_FUNCTIONS) @pytest.mark.parametrize('level', [0, 1]) @pytest.mark.parametrize('axis', [0, 1]) @pytest.mark.parametrize('skipna', [True, False]) @pytest.mark.parametrize('sort', [True, False]) def test_frame_group_ops(self, op, level, axis, skipna, sort): # GH 17537 self.frame.iloc[1, [1, 2]] = np.nan self.frame.iloc[7, [0, 1]] = np.nan 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_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): codes = np.tile(np.arange(500), 2) level = np.arange(500) index = MultiIndex(levels=[level] * 8 + [[0, 1]], codes=[codes] * 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, codes=[np.arange(2).repeat(500)] + [codes] * 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, codes=([codes] * 4 + [np.arange(2).repeat(500)] + [codes] * 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_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]], codes=[[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]], codes=[[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_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_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 df.index.is_unique is False result = df.drop(ts, level='tstamp') expected = df.loc[idx != 4] tm.assert_frame_equal(result, expected) @pytest.mark.parametrize('box', [Series, DataFrame]) def test_drop_tz_aware_timestamp_across_dst(self, box): # GH 21761 start = Timestamp('2017-10-29', tz='Europe/Berlin') end = Timestamp('2017-10-29 04:00:00', tz='Europe/Berlin') index = pd.date_range(start, end, freq='15min') data = box(data=[1] * len(index), index=index) result = data.drop(start) expected_start = Timestamp('2017-10-29 00:15:00', tz='Europe/Berlin') expected_idx = pd.date_range(expected_start, end, freq='15min') expected = box(data=[1] * len(expected_idx), index=expected_idx) tm.assert_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])] codes = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)] index = MultiIndex(levels=levels, codes=codes) 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_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_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_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"]) index = list(df.index) index[0] = ("faz", "boo") df.index = index repr(df) # this travels an improper code path index[0] = ["faz", "boo"] df.index = index repr(df) def test_tuples_have_na(self): index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]], codes=[[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 pytest.raises(ValueError, match=(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 pytest.raises(ValueError, match=("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 pytest.raises(ValueError, match=("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), ) 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, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]) assert index.is_lexsorted() index = MultiIndex(levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]]) assert not index.is_lexsorted() index = MultiIndex(levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]]) assert not index.is_lexsorted() assert index.lexsort_depth == 0 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']], codes=[[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): # doc example df = DataFrame({'value': [1, 2, 3, 4]}, index=MultiIndex( levels=[['a', 'b'], ['bb', 'aa']], codes=[[0, 0, 1, 1], [0, 1, 0, 1]])) assert df.index.is_lexsorted() assert not df.index.is_monotonic # sort it expected = DataFrame({'value': [2, 1, 4, 3]}, index=MultiIndex( levels=[['a', 'b'], ['aa', 'bb']], codes=[[0, 0, 1, 1], [0, 1, 0, 1]])) result = df.sort_index() assert result.index.is_lexsorted() assert result.index.is_monotonic tm.assert_frame_equal(result, expected) # reconstruct result = df.sort_index().copy() 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): # 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)