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Version:
0.21.1 ▾
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from warnings import catch_warnings
import pytest
import numpy as np
import pandas as pd
from pandas import (Panel, Series, MultiIndex, DataFrame,
Timestamp, Index, date_range)
from pandas.util import testing as tm
from pandas.errors import PerformanceWarning, UnsortedIndexError
from pandas.tests.indexing.common import _mklbl
class TestMultiIndexBasic(object):
def test_iloc_getitem_multiindex2(self):
# TODO(wesm): fix this
pytest.skip('this test was being suppressed, '
'needs to be fixed')
arr = np.random.randn(3, 3)
df = DataFrame(arr, columns=[[2, 2, 4], [6, 8, 10]],
index=[[4, 4, 8], [8, 10, 12]])
rs = df.iloc[2]
xp = Series(arr[2], index=df.columns)
tm.assert_series_equal(rs, xp)
rs = df.iloc[:, 2]
xp = Series(arr[:, 2], index=df.index)
tm.assert_series_equal(rs, xp)
rs = df.iloc[2, 2]
xp = df.values[2, 2]
assert rs == xp
# for multiple items
# GH 5528
rs = df.iloc[[0, 1]]
xp = df.xs(4, drop_level=False)
tm.assert_frame_equal(rs, xp)
tup = zip(*[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']])
index = MultiIndex.from_tuples(tup)
df = DataFrame(np.random.randn(4, 4), index=index)
rs = df.iloc[[2, 3]]
xp = df.xs('b', drop_level=False)
tm.assert_frame_equal(rs, xp)
def test_setitem_multiindex(self):
with catch_warnings(record=True):
for index_fn in ('ix', 'loc'):
def assert_equal(a, b):
assert a == b
def check(target, indexers, value, compare_fn, expected=None):
fn = getattr(target, index_fn)
fn.__setitem__(indexers, value)
result = fn.__getitem__(indexers)
if expected is None:
expected = value
compare_fn(result, expected)
# GH7190
index = pd.MultiIndex.from_product([np.arange(0, 100),
np.arange(0, 80)],
names=['time', 'firm'])
t, n = 0, 2
df = DataFrame(np.nan, columns=['A', 'w', 'l', 'a', 'x',
'X', 'd', 'profit'],
index=index)
check(target=df, indexers=((t, n), 'X'), value=0,
compare_fn=assert_equal)
df = DataFrame(-999, columns=['A', 'w', 'l', 'a', 'x',
'X', 'd', 'profit'],
index=index)
check(target=df, indexers=((t, n), 'X'), value=1,
compare_fn=assert_equal)
df = DataFrame(columns=['A', 'w', 'l', 'a', 'x',
'X', 'd', 'profit'],
index=index)
check(target=df, indexers=((t, n), 'X'), value=2,
compare_fn=assert_equal)
# gh-7218: assigning with 0-dim arrays
df = DataFrame(-999, columns=['A', 'w', 'l', 'a', 'x',
'X', 'd', 'profit'],
index=index)
check(target=df,
indexers=((t, n), 'X'),
value=np.array(3),
compare_fn=assert_equal,
expected=3, )
# GH5206
df = pd.DataFrame(np.arange(25).reshape(5, 5),
columns='A,B,C,D,E'.split(','), dtype=float)
df['F'] = 99
row_selection = df['A'] % 2 == 0
col_selection = ['B', 'C']
with catch_warnings(record=True):
df.ix[row_selection, col_selection] = df['F']
output = pd.DataFrame(99., index=[0, 2, 4], columns=['B', 'C'])
with catch_warnings(record=True):
tm.assert_frame_equal(df.ix[row_selection, col_selection],
output)
check(target=df,
indexers=(row_selection, col_selection),
value=df['F'],
compare_fn=tm.assert_frame_equal,
expected=output, )
# GH11372
idx = pd.MultiIndex.from_product([
['A', 'B', 'C'],
pd.date_range('2015-01-01', '2015-04-01', freq='MS')])
cols = pd.MultiIndex.from_product([
['foo', 'bar'],
pd.date_range('2016-01-01', '2016-02-01', freq='MS')])
df = pd.DataFrame(np.random.random((12, 4)),
index=idx, columns=cols)
subidx = pd.MultiIndex.from_tuples(
[('A', pd.Timestamp('2015-01-01')),
('A', pd.Timestamp('2015-02-01'))])
subcols = pd.MultiIndex.from_tuples(
[('foo', pd.Timestamp('2016-01-01')),
('foo', pd.Timestamp('2016-02-01'))])
vals = pd.DataFrame(np.random.random((2, 2)),
index=subidx, columns=subcols)
check(target=df,
indexers=(subidx, subcols),
value=vals,
compare_fn=tm.assert_frame_equal, )
# set all columns
vals = pd.DataFrame(
np.random.random((2, 4)), index=subidx, columns=cols)
check(target=df,
indexers=(subidx, slice(None, None, None)),
value=vals,
compare_fn=tm.assert_frame_equal, )
# identity
copy = df.copy()
check(target=df, indexers=(df.index, df.columns), value=df,
compare_fn=tm.assert_frame_equal, expected=copy)
def test_loc_getitem_series(self):
# GH14730
# passing a series as a key with a MultiIndex
index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
x = Series(index=index, data=range(9), dtype=np.float64)
y = Series([1, 3])
expected = Series(
data=[0, 1, 2, 6, 7, 8],
index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]),
dtype=np.float64)
result = x.loc[y]
tm.assert_series_equal(result, expected)
result = x.loc[[1, 3]]
tm.assert_series_equal(result, expected)
# GH15424
y1 = Series([1, 3], index=[1, 2])
result = x.loc[y1]
tm.assert_series_equal(result, expected)
empty = Series(data=[], dtype=np.float64)
expected = Series([], index=MultiIndex(
levels=index.levels, labels=[[], []], dtype=np.float64))
result = x.loc[empty]
tm.assert_series_equal(result, expected)
def test_loc_getitem_array(self):
# GH15434
# passing an array as a key with a MultiIndex
index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
x = Series(index=index, data=range(9), dtype=np.float64)
y = np.array([1, 3])
expected = Series(
data=[0, 1, 2, 6, 7, 8],
index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]),
dtype=np.float64)
result = x.loc[y]
tm.assert_series_equal(result, expected)
# empty array:
empty = np.array([])
expected = Series([], index=MultiIndex(
levels=index.levels, labels=[[], []], dtype=np.float64))
result = x.loc[empty]
tm.assert_series_equal(result, expected)
# 0-dim array (scalar):
scalar = np.int64(1)
expected = Series(
data=[0, 1, 2],
index=['A', 'B', 'C'],
dtype=np.float64)
result = x.loc[scalar]
tm.assert_series_equal(result, expected)
def test_iloc_getitem_multiindex(self):
mi_labels = DataFrame(np.random.randn(4, 3),
columns=[['i', 'i', 'j'], ['A', 'A', 'B']],
index=[['i', 'i', 'j', 'k'],
['X', 'X', 'Y', 'Y']])
mi_int = DataFrame(np.random.randn(3, 3),
columns=[[2, 2, 4], [6, 8, 10]],
index=[[4, 4, 8], [8, 10, 12]])
# the first row
rs = mi_int.iloc[0]
with catch_warnings(record=True):
xp = mi_int.ix[4].ix[8]
tm.assert_series_equal(rs, xp, check_names=False)
assert rs.name == (4, 8)
assert xp.name == 8
# 2nd (last) columns
rs = mi_int.iloc[:, 2]
with catch_warnings(record=True):
xp = mi_int.ix[:, 2]
tm.assert_series_equal(rs, xp)
# corner column
rs = mi_int.iloc[2, 2]
with catch_warnings(record=True):
xp = mi_int.ix[:, 2].ix[2]
assert rs == xp
# this is basically regular indexing
rs = mi_labels.iloc[2, 2]
with catch_warnings(record=True):
xp = mi_labels.ix['j'].ix[:, 'j'].ix[0, 0]
assert rs == xp
def test_loc_multiindex(self):
mi_labels = DataFrame(np.random.randn(3, 3),
columns=[['i', 'i', 'j'], ['A', 'A', 'B']],
index=[['i', 'i', 'j'], ['X', 'X', 'Y']])
mi_int = DataFrame(np.random.randn(3, 3),
columns=[[2, 2, 4], [6, 8, 10]],
index=[[4, 4, 8], [8, 10, 12]])
# the first row
rs = mi_labels.loc['i']
with catch_warnings(record=True):
xp = mi_labels.ix['i']
tm.assert_frame_equal(rs, xp)
# 2nd (last) columns
rs = mi_labels.loc[:, 'j']
with catch_warnings(record=True):
xp = mi_labels.ix[:, 'j']
tm.assert_frame_equal(rs, xp)
# corner column
rs = mi_labels.loc['j'].loc[:, 'j']
with catch_warnings(record=True):
xp = mi_labels.ix['j'].ix[:, 'j']
tm.assert_frame_equal(rs, xp)
# with a tuple
rs = mi_labels.loc[('i', 'X')]
with catch_warnings(record=True):
xp = mi_labels.ix[('i', 'X')]
tm.assert_frame_equal(rs, xp)
rs = mi_int.loc[4]
with catch_warnings(record=True):
xp = mi_int.ix[4]
tm.assert_frame_equal(rs, xp)
def test_getitem_partial_int(self):
# GH 12416
# with single item
l1 = [10, 20]
l2 = ['a', 'b']
df = DataFrame(index=range(2),
columns=pd.MultiIndex.from_product([l1, l2]))
expected = DataFrame(index=range(2),
columns=l2)
result = df[20]
tm.assert_frame_equal(result, expected)
# with list
expected = DataFrame(index=range(2),
columns=pd.MultiIndex.from_product([l1[1:], l2]))
result = df[[20]]
tm.assert_frame_equal(result, expected)
# missing item:
with tm.assert_raises_regex(KeyError, '1'):
df[1]
with tm.assert_raises_regex(KeyError, "'\[1\] not in index'"):
df[[1]]
def test_loc_multiindex_indexer_none(self):
# GH6788
# multi-index indexer is None (meaning take all)
attributes = ['Attribute' + str(i) for i in range(1)]
attribute_values = ['Value' + str(i) for i in range(5)]
index = MultiIndex.from_product([attributes, attribute_values])
df = 0.1 * np.random.randn(10, 1 * 5) + 0.5
df = DataFrame(df, columns=index)
result = df[attributes]
tm.assert_frame_equal(result, df)
# GH 7349
# loc with a multi-index seems to be doing fallback
df = DataFrame(np.arange(12).reshape(-1, 1),
index=pd.MultiIndex.from_product([[1, 2, 3, 4],
[1, 2, 3]]))
expected = df.loc[([1, 2], ), :]
result = df.loc[[1, 2]]
tm.assert_frame_equal(result, expected)
def test_loc_multiindex_incomplete(self):
# GH 7399
# incomplete indexers
s = pd.Series(np.arange(15, dtype='int64'),
MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
expected = s.loc[:, 'a':'c']
result = s.loc[0:4, 'a':'c']
tm.assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)
result = s.loc[:4, 'a':'c']
tm.assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)
result = s.loc[0:, 'a':'c']
tm.assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)
# GH 7400
# multiindexer gettitem with list of indexers skips wrong element
s = pd.Series(np.arange(15, dtype='int64'),
MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
expected = s.iloc[[6, 7, 8, 12, 13, 14]]
result = s.loc[2:4:2, 'a':'c']
tm.assert_series_equal(result, expected)
def test_multiindex_perf_warn(self):
df = DataFrame({'jim': [0, 0, 1, 1],
'joe': ['x', 'x', 'z', 'y'],
'jolie': np.random.rand(4)}).set_index(['jim', 'joe'])
with tm.assert_produces_warning(PerformanceWarning,
clear=[pd.core.index]):
df.loc[(1, 'z')]
df = df.iloc[[2, 1, 3, 0]]
with tm.assert_produces_warning(PerformanceWarning):
df.loc[(0, )]
def test_series_getitem_multiindex(self):
# GH 6018
# series regression getitem with a multi-index
s = Series([1, 2, 3])
s.index = MultiIndex.from_tuples([(0, 0), (1, 1), (2, 1)])
result = s[:, 0]
expected = Series([1], index=[0])
tm.assert_series_equal(result, expected)
result = s.loc[:, 1]
expected = Series([2, 3], index=[1, 2])
tm.assert_series_equal(result, expected)
# xs
result = s.xs(0, level=0)
expected = Series([1], index=[0])
tm.assert_series_equal(result, expected)
result = s.xs(1, level=1)
expected = Series([2, 3], index=[1, 2])
tm.assert_series_equal(result, expected)
# GH6258
dt = list(date_range('20130903', periods=3))
idx = MultiIndex.from_product([list('AB'), dt])
s = Series([1, 3, 4, 1, 3, 4], index=idx)
result = s.xs('20130903', level=1)
expected = Series([1, 1], index=list('AB'))
tm.assert_series_equal(result, expected)
# GH5684
idx = MultiIndex.from_tuples([('a', 'one'), ('a', 'two'), ('b', 'one'),
('b', 'two')])
s = Series([1, 2, 3, 4], index=idx)
s.index.set_names(['L1', 'L2'], inplace=True)
result = s.xs('one', level='L2')
expected = Series([1, 3], index=['a', 'b'])
expected.index.set_names(['L1'], inplace=True)
tm.assert_series_equal(result, expected)
def test_xs_multiindex(self):
# GH2903
columns = MultiIndex.from_tuples(
[('a', 'foo'), ('a', 'bar'), ('b', 'hello'),
('b', 'world')], names=['lvl0', 'lvl1'])
df = DataFrame(np.random.randn(4, 4), columns=columns)
df.sort_index(axis=1, inplace=True)
result = df.xs('a', level='lvl0', axis=1)
expected = df.iloc[:, 0:2].loc[:, 'a']
tm.assert_frame_equal(result, expected)
result = df.xs('foo', level='lvl1', axis=1)
expected = df.iloc[:, 1:2].copy()
expected.columns = expected.columns.droplevel('lvl1')
tm.assert_frame_equal(result, expected)
def test_multiindex_setitem(self):
# GH 3738
# setting with a multi-index right hand side
arrays = [np.array(['bar', 'bar', 'baz', 'qux', 'qux', 'bar']),
np.array(['one', 'two', 'one', 'one', 'two', 'one']),
np.arange(0, 6, 1)]
df_orig = pd.DataFrame(np.random.randn(6, 3),
index=arrays,
columns=['A', 'B', 'C']).sort_index()
expected = df_orig.loc[['bar']] * 2
df = df_orig.copy()
df.loc[['bar']] *= 2
tm.assert_frame_equal(df.loc[['bar']], expected)
# raise because these have differing levels
def f():
df.loc['bar'] *= 2
pytest.raises(TypeError, f)
# from SO
# http://stackoverflow.com/questions/24572040/pandas-access-the-level-of-multiindex-for-inplace-operation
df_orig = DataFrame.from_dict({'price': {
('DE', 'Coal', 'Stock'): 2,
('DE', 'Gas', 'Stock'): 4,
('DE', 'Elec', 'Demand'): 1,
('FR', 'Gas', 'Stock'): 5,
('FR', 'Solar', 'SupIm'): 0,
('FR', 'Wind', 'SupIm'): 0
}})
df_orig.index = MultiIndex.from_tuples(df_orig.index,
names=['Sit', 'Com', 'Type'])
expected = df_orig.copy()
expected.iloc[[0, 2, 3]] *= 2
idx = pd.IndexSlice
df = df_orig.copy()
df.loc[idx[:, :, 'Stock'], :] *= 2
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[idx[:, :, 'Stock'], 'price'] *= 2
tm.assert_frame_equal(df, expected)
def test_getitem_duplicates_multiindex(self):
# GH 5725 the 'A' happens to be a valid Timestamp so the doesn't raise
# the appropriate error, only in PY3 of course!
index = MultiIndex(levels=[['D', 'B', 'C'],
[0, 26, 27, 37, 57, 67, 75, 82]],
labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2],
[1, 3, 4, 6, 0, 2, 2, 3, 5, 7]],
names=['tag', 'day'])
arr = np.random.randn(len(index), 1)
df = DataFrame(arr, index=index, columns=['val'])
result = df.val['D']
expected = Series(arr.ravel()[0:3], name='val', index=Index(
[26, 37, 57], name='day'))
tm.assert_series_equal(result, expected)
def f():
df.val['A']
pytest.raises(KeyError, f)
def f():
df.val['X']
pytest.raises(KeyError, f)
# A is treated as a special Timestamp
index = MultiIndex(levels=[['A', 'B', 'C'],
[0, 26, 27, 37, 57, 67, 75, 82]],
labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2],
[1, 3, 4, 6, 0, 2, 2, 3, 5, 7]],
names=['tag', 'day'])
df = DataFrame(arr, index=index, columns=['val'])
result = df.val['A']
expected = Series(arr.ravel()[0:3], name='val', index=Index(
[26, 37, 57], name='day'))
tm.assert_series_equal(result, expected)
def f():
df.val['X']
pytest.raises(KeyError, f)
# GH 7866
# multi-index slicing with missing indexers
idx = pd.MultiIndex.from_product([['A', 'B', 'C'],
['foo', 'bar', 'baz']],
names=['one', 'two'])
s = pd.Series(np.arange(9, dtype='int64'), index=idx).sort_index()
exp_idx = pd.MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']],
names=['one', 'two'])
expected = pd.Series(np.arange(3, dtype='int64'),
index=exp_idx).sort_index()
result = s.loc[['A']]
tm.assert_series_equal(result, expected)
result = s.loc[['A', 'D']]
tm.assert_series_equal(result, expected)
# not any values found
pytest.raises(KeyError, lambda: s.loc[['D']])
# empty ok
result = s.loc[[]]
expected = s.iloc[[]]
tm.assert_series_equal(result, expected)
idx = pd.IndexSlice
expected = pd.Series([0, 3, 6], index=pd.MultiIndex.from_product(
[['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index()
result = s.loc[idx[:, ['foo']]]
tm.assert_series_equal(result, expected)
result = s.loc[idx[:, ['foo', 'bah']]]
tm.assert_series_equal(result, expected)
# GH 8737
# empty indexer
multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'],
['alpha', 'beta']))
df = DataFrame(
np.random.randn(5, 6), index=range(5), columns=multi_index)
df = df.sort_index(level=0, axis=1)
expected = DataFrame(index=range(5),
columns=multi_index.reindex([])[0])
result1 = df.loc[:, ([], slice(None))]
result2 = df.loc[:, (['foo'], [])]
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
# regression from < 0.14.0
# GH 7914
df = DataFrame([[np.mean, np.median], ['mean', 'median']],
columns=MultiIndex.from_tuples([('functs', 'mean'),
('functs', 'median')]),
index=['function', 'name'])
result = df.loc['function', ('functs', 'mean')]
assert result == np.mean
def test_multiindex_assignment(self):
# GH3777 part 2
# mixed dtype
df = DataFrame(np.random.randint(5, 10, size=9).reshape(3, 3),
columns=list('abc'),
index=[[4, 4, 8], [8, 10, 12]])
df['d'] = np.nan
arr = np.array([0., 1.])
with catch_warnings(record=True):
df.ix[4, 'd'] = arr
tm.assert_series_equal(df.ix[4, 'd'],
Series(arr, index=[8, 10], name='d'))
# single dtype
df = DataFrame(np.random.randint(5, 10, size=9).reshape(3, 3),
columns=list('abc'),
index=[[4, 4, 8], [8, 10, 12]])
with catch_warnings(record=True):
df.ix[4, 'c'] = arr
exp = Series(arr, index=[8, 10], name='c', dtype='float64')
tm.assert_series_equal(df.ix[4, 'c'], exp)
# scalar ok
with catch_warnings(record=True):
df.ix[4, 'c'] = 10
exp = Series(10, index=[8, 10], name='c', dtype='float64')
tm.assert_series_equal(df.ix[4, 'c'], exp)
# invalid assignments
def f():
with catch_warnings(record=True):
df.ix[4, 'c'] = [0, 1, 2, 3]
pytest.raises(ValueError, f)
def f():
with catch_warnings(record=True):
df.ix[4, 'c'] = [0]
pytest.raises(ValueError, f)
# groupby example
NUM_ROWS = 100
NUM_COLS = 10
col_names = ['A' + num for num in
map(str, np.arange(NUM_COLS).tolist())]
index_cols = col_names[:5]
df = DataFrame(np.random.randint(5, size=(NUM_ROWS, NUM_COLS)),
dtype=np.int64, columns=col_names)
df = df.set_index(index_cols).sort_index()
grp = df.groupby(level=index_cols[:4])
df['new_col'] = np.nan
f_index = np.arange(5)
def f(name, df2):
return Series(np.arange(df2.shape[0]),
name=df2.index.values[0]).reindex(f_index)
# TODO(wesm): unused?
# new_df = pd.concat([f(name, df2) for name, df2 in grp], axis=1).T
# we are actually operating on a copy here
# but in this case, that's ok
for name, df2 in grp:
new_vals = np.arange(df2.shape[0])
with catch_warnings(record=True):
df.ix[name, 'new_col'] = new_vals
def test_multiindex_label_slicing_with_negative_step(self):
s = Series(np.arange(20),
MultiIndex.from_product([list('abcde'), np.arange(4)]))
SLC = pd.IndexSlice
def assert_slices_equivalent(l_slc, i_slc):
tm.assert_series_equal(s.loc[l_slc], s.iloc[i_slc])
tm.assert_series_equal(s[l_slc], s.iloc[i_slc])
with catch_warnings(record=True):
tm.assert_series_equal(s.ix[l_slc], s.iloc[i_slc])
assert_slices_equivalent(SLC[::-1], SLC[::-1])
assert_slices_equivalent(SLC['d'::-1], SLC[15::-1])
assert_slices_equivalent(SLC[('d', )::-1], SLC[15::-1])
assert_slices_equivalent(SLC[:'d':-1], SLC[:11:-1])
assert_slices_equivalent(SLC[:('d', ):-1], SLC[:11:-1])
assert_slices_equivalent(SLC['d':'b':-1], SLC[15:3:-1])
assert_slices_equivalent(SLC[('d', ):'b':-1], SLC[15:3:-1])
assert_slices_equivalent(SLC['d':('b', ):-1], SLC[15:3:-1])
assert_slices_equivalent(SLC[('d', ):('b', ):-1], SLC[15:3:-1])
assert_slices_equivalent(SLC['b':'d':-1], SLC[:0])
assert_slices_equivalent(SLC[('c', 2)::-1], SLC[10::-1])
assert_slices_equivalent(SLC[:('c', 2):-1], SLC[:9:-1])
assert_slices_equivalent(SLC[('e', 0):('c', 2):-1], SLC[16:9:-1])
def test_multiindex_slice_first_level(self):
# GH 12697
freq = ['a', 'b', 'c', 'd']
idx = pd.MultiIndex.from_product([freq, np.arange(500)])
df = pd.DataFrame(list(range(2000)), index=idx, columns=['Test'])
df_slice = df.loc[pd.IndexSlice[:, 30:70], :]
result = df_slice.loc['a']
expected = pd.DataFrame(list(range(30, 71)),
columns=['Test'],
index=range(30, 71))
tm.assert_frame_equal(result, expected)
result = df_slice.loc['d']
expected = pd.DataFrame(list(range(1530, 1571)),
columns=['Test'],
index=range(30, 71))
tm.assert_frame_equal(result, expected)
def test_multiindex_symmetric_difference(self):
# GH 13490
idx = MultiIndex.from_product([['a', 'b'], ['A', 'B']],
names=['a', 'b'])
result = idx ^ idx
assert result.names == idx.names
idx2 = idx.copy().rename(['A', 'B'])
result = idx ^ idx2
assert result.names == [None, None]
class TestMultiIndexSlicers(object):
def test_per_axis_per_level_getitem(self):
# GH6134
# example test case
ix = MultiIndex.from_product([_mklbl('A', 5), _mklbl('B', 7), _mklbl(
'C', 4), _mklbl('D', 2)])
df = DataFrame(np.arange(len(ix.get_values())), index=ix)
result = df.loc[(slice('A1', 'A3'), slice(None), ['C1', 'C3']), :]
expected = df.loc[[tuple([a, b, c, d])
for a, b, c, d in df.index.values
if (a == 'A1' or a == 'A2' or a == 'A3') and (
c == 'C1' or c == 'C3')]]
tm.assert_frame_equal(result, expected)
expected = df.loc[[tuple([a, b, c, d])
for a, b, c, d in df.index.values
if (a == 'A1' or a == 'A2' or a == 'A3') and (
c == 'C1' or c == 'C2' or c == 'C3')]]
result = df.loc[(slice('A1', 'A3'), slice(None), slice('C1', 'C3')), :]
tm.assert_frame_equal(result, expected)
# test multi-index slicing with per axis and per index controls
index = MultiIndex.from_tuples([('A', 1), ('A', 2),
('A', 3), ('B', 1)],
names=['one', 'two'])
columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'),
('b', 'foo'), ('b', 'bah')],
names=['lvl0', 'lvl1'])
df = DataFrame(
np.arange(16, dtype='int64').reshape(
4, 4), index=index, columns=columns)
df = df.sort_index(axis=0).sort_index(axis=1)
# identity
result = df.loc[(slice(None), slice(None)), :]
tm.assert_frame_equal(result, df)
result = df.loc[(slice(None), slice(None)), (slice(None), slice(None))]
tm.assert_frame_equal(result, df)
result = df.loc[:, (slice(None), slice(None))]
tm.assert_frame_equal(result, df)
# index
result = df.loc[(slice(None), [1]), :]
expected = df.iloc[[0, 3]]
tm.assert_frame_equal(result, expected)
result = df.loc[(slice(None), 1), :]
expected = df.iloc[[0, 3]]
tm.assert_frame_equal(result, expected)
# columns
result = df.loc[:, (slice(None), ['foo'])]
expected = df.iloc[:, [1, 3]]
tm.assert_frame_equal(result, expected)
# both
result = df.loc[(slice(None), 1), (slice(None), ['foo'])]
expected = df.iloc[[0, 3], [1, 3]]
tm.assert_frame_equal(result, expected)
result = df.loc['A', 'a']
expected = DataFrame(dict(bar=[1, 5, 9], foo=[0, 4, 8]),
index=Index([1, 2, 3], name='two'),
columns=Index(['bar', 'foo'], name='lvl1'))
tm.assert_frame_equal(result, expected)
result = df.loc[(slice(None), [1, 2]), :]
expected = df.iloc[[0, 1, 3]]
tm.assert_frame_equal(result, expected)
# multi-level series
s = Series(np.arange(len(ix.get_values())), index=ix)
result = s.loc['A1':'A3', :, ['C1', 'C3']]
expected = s.loc[[tuple([a, b, c, d])
for a, b, c, d in s.index.values
if (a == 'A1' or a == 'A2' or a == 'A3') and (
c == 'C1' or c == 'C3')]]
tm.assert_series_equal(result, expected)
# boolean indexers
result = df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :]
expected = df.iloc[[2, 3]]
tm.assert_frame_equal(result, expected)
def f():
df.loc[(slice(None), np.array([True, False])), :]
pytest.raises(ValueError, f)
# ambiguous cases
# these can be multiply interpreted (e.g. in this case
# as df.loc[slice(None),[1]] as well
pytest.raises(KeyError, lambda: df.loc[slice(None), [1]])
result = df.loc[(slice(None), [1]), :]
expected = df.iloc[[0, 3]]
tm.assert_frame_equal(result, expected)
# not lexsorted
assert df.index.lexsort_depth == 2
df = df.sort_index(level=1, axis=0)
assert df.index.lexsort_depth == 0
with tm.assert_raises_regex(
UnsortedIndexError,
'MultiIndex slicing requires the index to be '
r'lexsorted: slicing on levels \[1\], lexsort depth 0'):
df.loc[(slice(None), slice('bar')), :]
# GH 16734: not sorted, but no real slicing
result = df.loc[(slice(None), df.loc[:, ('a', 'bar')] > 5), :]
tm.assert_frame_equal(result, df.iloc[[1, 3], :])
def test_multiindex_slicers_non_unique(self):
# GH 7106
# non-unique mi index support
df = (DataFrame(dict(A=['foo', 'foo', 'foo', 'foo'],
B=['a', 'a', 'a', 'a'],
C=[1, 2, 1, 3],
D=[1, 2, 3, 4]))
.set_index(['A', 'B', 'C']).sort_index())
assert not df.index.is_unique
expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'],
C=[1, 1], D=[1, 3]))
.set_index(['A', 'B', 'C']).sort_index())
result = df.loc[(slice(None), slice(None), 1), :]
tm.assert_frame_equal(result, expected)
# this is equivalent of an xs expression
result = df.xs(1, level=2, drop_level=False)
tm.assert_frame_equal(result, expected)
df = (DataFrame(dict(A=['foo', 'foo', 'foo', 'foo'],
B=['a', 'a', 'a', 'a'],
C=[1, 2, 1, 2],
D=[1, 2, 3, 4]))
.set_index(['A', 'B', 'C']).sort_index())
assert not df.index.is_unique
expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'],
C=[1, 1], D=[1, 3]))
.set_index(['A', 'B', 'C']).sort_index())
result = df.loc[(slice(None), slice(None), 1), :]
assert not result.index.is_unique
tm.assert_frame_equal(result, expected)
# GH12896
# numpy-implementation dependent bug
ints = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 13, 14, 14, 16,
17, 18, 19, 200000, 200000]
n = len(ints)
idx = MultiIndex.from_arrays([['a'] * n, ints])
result = Series([1] * n, index=idx)
result = result.sort_index()
result = result.loc[(slice(None), slice(100000))]
expected = Series([1] * (n - 2), index=idx[:-2]).sort_index()
tm.assert_series_equal(result, expected)
def test_multiindex_slicers_datetimelike(self):
# GH 7429
# buggy/inconsistent behavior when slicing with datetime-like
import datetime
dates = [datetime.datetime(2012, 1, 1, 12, 12, 12) +
datetime.timedelta(days=i) for i in range(6)]
freq = [1, 2]
index = MultiIndex.from_product(
[dates, freq], names=['date', 'frequency'])
df = DataFrame(
np.arange(6 * 2 * 4, dtype='int64').reshape(
-1, 4), index=index, columns=list('ABCD'))
# multi-axis slicing
idx = pd.IndexSlice
expected = df.iloc[[0, 2, 4], [0, 1]]
result = df.loc[(slice(Timestamp('2012-01-01 12:12:12'),
Timestamp('2012-01-03 12:12:12')),
slice(1, 1)), slice('A', 'B')]
tm.assert_frame_equal(result, expected)
result = df.loc[(idx[Timestamp('2012-01-01 12:12:12'):Timestamp(
'2012-01-03 12:12:12')], idx[1:1]), slice('A', 'B')]
tm.assert_frame_equal(result, expected)
result = df.loc[(slice(Timestamp('2012-01-01 12:12:12'),
Timestamp('2012-01-03 12:12:12')), 1),
slice('A', 'B')]
tm.assert_frame_equal(result, expected)
# with strings
result = df.loc[(slice('2012-01-01 12:12:12', '2012-01-03 12:12:12'),
slice(1, 1)), slice('A', 'B')]
tm.assert_frame_equal(result, expected)
result = df.loc[(idx['2012-01-01 12:12:12':'2012-01-03 12:12:12'], 1),
idx['A', 'B']]
tm.assert_frame_equal(result, expected)
def test_multiindex_slicers_edges(self):
# GH 8132
# various edge cases
df = DataFrame(
{'A': ['A0'] * 5 + ['A1'] * 5 + ['A2'] * 5,
'B': ['B0', 'B0', 'B1', 'B1', 'B2'] * 3,
'DATE': ["2013-06-11", "2013-07-02", "2013-07-09", "2013-07-30",
"2013-08-06", "2013-06-11", "2013-07-02", "2013-07-09",
"2013-07-30", "2013-08-06", "2013-09-03", "2013-10-01",
"2013-07-09", "2013-08-06", "2013-09-03"],
'VALUES': [22, 35, 14, 9, 4, 40, 18, 4, 2, 5, 1, 2, 3, 4, 2]})
df['DATE'] = pd.to_datetime(df['DATE'])
df1 = df.set_index(['A', 'B', 'DATE'])
df1 = df1.sort_index()
# A1 - Get all values under "A0" and "A1"
result = df1.loc[(slice('A1')), :]
expected = df1.iloc[0:10]
tm.assert_frame_equal(result, expected)
# A2 - Get all values from the start to "A2"
result = df1.loc[(slice('A2')), :]
expected = df1
tm.assert_frame_equal(result, expected)
# A3 - Get all values under "B1" or "B2"
result = df1.loc[(slice(None), slice('B1', 'B2')), :]
expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13, 14]]
tm.assert_frame_equal(result, expected)
# A4 - Get all values between 2013-07-02 and 2013-07-09
result = df1.loc[(slice(None), slice(None),
slice('20130702', '20130709')), :]
expected = df1.iloc[[1, 2, 6, 7, 12]]
tm.assert_frame_equal(result, expected)
# B1 - Get all values in B0 that are also under A0, A1 and A2
result = df1.loc[(slice('A2'), slice('B0')), :]
expected = df1.iloc[[0, 1, 5, 6, 10, 11]]
tm.assert_frame_equal(result, expected)
# B2 - Get all values in B0, B1 and B2 (similar to what #2 is doing for
# the As)
result = df1.loc[(slice(None), slice('B2')), :]
expected = df1
tm.assert_frame_equal(result, expected)
# B3 - Get all values from B1 to B2 and up to 2013-08-06
result = df1.loc[(slice(None), slice('B1', 'B2'),
slice('2013-08-06')), :]
expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13]]
tm.assert_frame_equal(result, expected)
# B4 - Same as A4 but the start of the date slice is not a key.
# shows indexing on a partial selection slice
result = df1.loc[(slice(None), slice(None),
slice('20130701', '20130709')), :]
expected = df1.iloc[[1, 2, 6, 7, 12]]
tm.assert_frame_equal(result, expected)
def test_per_axis_per_level_doc_examples(self):
# test index maker
idx = pd.IndexSlice
# from indexing.rst / advanced
index = MultiIndex.from_product([_mklbl('A', 4), _mklbl('B', 2),
_mklbl('C', 4), _mklbl('D', 2)])
columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'),
('b', 'foo'), ('b', 'bah')],
names=['lvl0', 'lvl1'])
df = DataFrame(np.arange(len(index) * len(columns), dtype='int64')
.reshape((len(index), len(columns))),
index=index, columns=columns)
result = df.loc[(slice('A1', 'A3'), slice(None), ['C1', 'C3']), :]
expected = df.loc[[tuple([a, b, c, d])
for a, b, c, d in df.index.values
if (a == 'A1' or a == 'A2' or a == 'A3') and (
c == 'C1' or c == 'C3')]]
tm.assert_frame_equal(result, expected)
result = df.loc[idx['A1':'A3', :, ['C1', 'C3']], :]
tm.assert_frame_equal(result, expected)
result = df.loc[(slice(None), slice(None), ['C1', 'C3']), :]
expected = df.loc[[tuple([a, b, c, d])
for a, b, c, d in df.index.values
if (c == 'C1' or c == 'C3')]]
tm.assert_frame_equal(result, expected)
result = df.loc[idx[:, :, ['C1', 'C3']], :]
tm.assert_frame_equal(result, expected)
# not sorted
def f():
df.loc['A1', ('a', slice('foo'))]
pytest.raises(UnsortedIndexError, f)
# GH 16734: not sorted, but no real slicing
tm.assert_frame_equal(df.loc['A1', (slice(None), 'foo')],
df.loc['A1'].iloc[:, [0, 2]])
df = df.sort_index(axis=1)
# slicing
df.loc['A1', (slice(None), 'foo')]
df.loc[(slice(None), slice(None), ['C1', 'C3']), (slice(None), 'foo')]
# setitem
df.loc(axis=0)[:, :, ['C1', 'C3']] = -10
def test_loc_axis_arguments(self):
index = MultiIndex.from_product([_mklbl('A', 4), _mklbl('B', 2),
_mklbl('C', 4), _mklbl('D', 2)])
columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'),
('b', 'foo'), ('b', 'bah')],
names=['lvl0', 'lvl1'])
df = DataFrame(np.arange(len(index) * len(columns), dtype='int64')
.reshape((len(index), len(columns))),
index=index,
columns=columns).sort_index().sort_index(axis=1)
# axis 0
result = df.loc(axis=0)['A1':'A3', :, ['C1', 'C3']]
expected = df.loc[[tuple([a, b, c, d])
for a, b, c, d in df.index.values
if (a == 'A1' or a == 'A2' or a == 'A3') and (
c == 'C1' or c == 'C3')]]
tm.assert_frame_equal(result, expected)
result = df.loc(axis='index')[:, :, ['C1', 'C3']]
expected = df.loc[[tuple([a, b, c, d])
for a, b, c, d in df.index.values
if (c == 'C1' or c == 'C3')]]
tm.assert_frame_equal(result, expected)
# axis 1
result = df.loc(axis=1)[:, 'foo']
expected = df.loc[:, (slice(None), 'foo')]
tm.assert_frame_equal(result, expected)
result = df.loc(axis='columns')[:, 'foo']
expected = df.loc[:, (slice(None), 'foo')]
tm.assert_frame_equal(result, expected)
# invalid axis
def f():
df.loc(axis=-1)[:, :, ['C1', 'C3']]
pytest.raises(ValueError, f)
def f():
df.loc(axis=2)[:, :, ['C1', 'C3']]
pytest.raises(ValueError, f)
def f():
df.loc(axis='foo')[:, :, ['C1', 'C3']]
pytest.raises(ValueError, f)
def test_per_axis_per_level_setitem(self):
# test index maker
idx = pd.IndexSlice
# test multi-index slicing with per axis and per index controls
index = MultiIndex.from_tuples([('A', 1), ('A', 2),
('A', 3), ('B', 1)],
names=['one', 'two'])
columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'),
('b', 'foo'), ('b', 'bah')],
names=['lvl0', 'lvl1'])
df_orig = DataFrame(
np.arange(16, dtype='int64').reshape(
4, 4), index=index, columns=columns)
df_orig = df_orig.sort_index(axis=0).sort_index(axis=1)
# identity
df = df_orig.copy()
df.loc[(slice(None), slice(None)), :] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc(axis=0)[:, :] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[(slice(None), slice(None)), (slice(None), slice(None))] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[:, (slice(None), slice(None))] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
# index
df = df_orig.copy()
df.loc[(slice(None), [1]), :] = 100
expected = df_orig.copy()
expected.iloc[[0, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[(slice(None), 1), :] = 100
expected = df_orig.copy()
expected.iloc[[0, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc(axis=0)[:, 1] = 100
expected = df_orig.copy()
expected.iloc[[0, 3]] = 100
tm.assert_frame_equal(df, expected)
# columns
df = df_orig.copy()
df.loc[:, (slice(None), ['foo'])] = 100
expected = df_orig.copy()
expected.iloc[:, [1, 3]] = 100
tm.assert_frame_equal(df, expected)
# both
df = df_orig.copy()
df.loc[(slice(None), 1), (slice(None), ['foo'])] = 100
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[idx[:, 1], idx[:, ['foo']]] = 100
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc['A', 'a'] = 100
expected = df_orig.copy()
expected.iloc[0:3, 0:2] = 100
tm.assert_frame_equal(df, expected)
# setting with a list-like
df = df_orig.copy()
df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array(
[[100, 100], [100, 100]], dtype='int64')
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = 100
tm.assert_frame_equal(df, expected)
# not enough values
df = df_orig.copy()
def f():
df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array(
[[100], [100, 100]], dtype='int64')
pytest.raises(ValueError, f)
def f():
df.loc[(slice(None), 1), (slice(None), ['foo'])] = np.array(
[100, 100, 100, 100], dtype='int64')
pytest.raises(ValueError, f)
# with an alignable rhs
df = df_orig.copy()
df.loc[(slice(None), 1), (slice(None), ['foo'])] = df.loc[(slice(
None), 1), (slice(None), ['foo'])] * 5
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = expected.iloc[[0, 3], [1, 3]] * 5
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[(slice(None), 1), (slice(None), ['foo'])] *= df.loc[(slice(
None), 1), (slice(None), ['foo'])]
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]]
tm.assert_frame_equal(df, expected)
rhs = df_orig.loc[(slice(None), 1), (slice(None), ['foo'])].copy()
rhs.loc[:, ('c', 'bah')] = 10
df = df_orig.copy()
df.loc[(slice(None), 1), (slice(None), ['foo'])] *= rhs
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]]
tm.assert_frame_equal(df, expected)
class TestMultiIndexPanel(object):
def test_iloc_getitem_panel_multiindex(self):
with catch_warnings(record=True):
# GH 7199
# Panel with multi-index
multi_index = pd.MultiIndex.from_tuples([('ONE', 'one'),
('TWO', 'two'),
('THREE', 'three')],
names=['UPPER', 'lower'])
simple_index = [x[0] for x in multi_index]
wd1 = Panel(items=['First', 'Second'],
major_axis=['a', 'b', 'c', 'd'],
minor_axis=multi_index)
wd2 = Panel(items=['First', 'Second'],
major_axis=['a', 'b', 'c', 'd'],
minor_axis=simple_index)
expected1 = wd1['First'].iloc[[True, True, True, False], [0, 2]]
result1 = wd1.iloc[0, [True, True, True, False], [0, 2]] # WRONG
tm.assert_frame_equal(result1, expected1)
expected2 = wd2['First'].iloc[[True, True, True, False], [0, 2]]
result2 = wd2.iloc[0, [True, True, True, False], [0, 2]]
tm.assert_frame_equal(result2, expected2)
expected1 = DataFrame(index=['a'], columns=multi_index,
dtype='float64')
result1 = wd1.iloc[0, [0], [0, 1, 2]]
tm.assert_frame_equal(result1, expected1)
expected2 = DataFrame(index=['a'], columns=simple_index,
dtype='float64')
result2 = wd2.iloc[0, [0], [0, 1, 2]]
tm.assert_frame_equal(result2, expected2)
# GH 7516
mi = MultiIndex.from_tuples([(0, 'x'), (1, 'y'), (2, 'z')])
p = Panel(np.arange(3 * 3 * 3, dtype='int64').reshape(3, 3, 3),
items=['a', 'b', 'c'], major_axis=mi,
minor_axis=['u', 'v', 'w'])
result = p.iloc[:, 1, 0]
expected = Series([3, 12, 21], index=['a', 'b', 'c'], name='u')
tm.assert_series_equal(result, expected)
result = p.loc[:, (1, 'y'), 'u']
tm.assert_series_equal(result, expected)
def test_panel_setitem_with_multiindex(self):
with catch_warnings(record=True):
# 10360
# failing with a multi-index
arr = np.array([[[1, 2, 3], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0]]],
dtype=np.float64)
# reg index
axes = dict(items=['A', 'B'], major_axis=[0, 1],
minor_axis=['X', 'Y', 'Z'])
p1 = Panel(0., **axes)
p1.iloc[0, 0, :] = [1, 2, 3]
expected = Panel(arr, **axes)
tm.assert_panel_equal(p1, expected)
# multi-indexes
axes['items'] = pd.MultiIndex.from_tuples(
[('A', 'a'), ('B', 'b')])
p2 = Panel(0., **axes)
p2.iloc[0, 0, :] = [1, 2, 3]
expected = Panel(arr, **axes)
tm.assert_panel_equal(p2, expected)
axes['major_axis'] = pd.MultiIndex.from_tuples(
[('A', 1), ('A', 2)])
p3 = Panel(0., **axes)
p3.iloc[0, 0, :] = [1, 2, 3]
expected = Panel(arr, **axes)
tm.assert_panel_equal(p3, expected)
axes['minor_axis'] = pd.MultiIndex.from_product(
[['X'], range(3)])
p4 = Panel(0., **axes)
p4.iloc[0, 0, :] = [1, 2, 3]
expected = Panel(arr, **axes)
tm.assert_panel_equal(p4, expected)
arr = np.array(
[[[1, 0, 0], [2, 0, 0]], [[0, 0, 0], [0, 0, 0]]],
dtype=np.float64)
p5 = Panel(0., **axes)
p5.iloc[0, :, 0] = [1, 2]
expected = Panel(arr, **axes)
tm.assert_panel_equal(p5, expected)