Python pandas.core.index.MultiIndex.from_product() Examples
The following are 30
code examples of pandas.core.index.MultiIndex.from_product().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
pandas.core.index.MultiIndex
, or try the search function
.
.
Example #1
| Source File: test_multilevel.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
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 = pd.MultiIndex.from_product([[d1], [d2]])
assert isinstance(index.levels[0], pd.DatetimeIndex)
assert isinstance(index.levels[1], pd.DatetimeIndex)
Example #2
| Source File: test_constructors.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
Example #3
| Source File: test_multilevel.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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)
Example #4
| Source File: test_multilevel.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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)
Example #5
| Source File: test_multilevel.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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)
Example #6
| Source File: test_multilevel.py From coffeegrindsize with MIT License | 6 votes |
|
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)
Example #7
| Source File: test_multilevel.py From coffeegrindsize with MIT License | 6 votes |
|
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)
Example #8
| Source File: test_multilevel.py From coffeegrindsize with MIT License | 6 votes |
|
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)
Example #9
| Source File: test_constructors.py From coffeegrindsize with MIT License | 6 votes |
|
def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
Example #10
| Source File: test_categorical.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = pd.Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = pd.Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
Example #11
| Source File: test_multilevel.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_sort_index_reorder_on_ops(self):
# 15687
df = pd.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)
Example #12
| Source File: test_multilevel.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_reset_index_period(self):
# GH 7746
idx = pd.MultiIndex.from_product([pd.period_range('20130101',
periods=3, freq='M'),
['a', 'b', 'c']],
names=['month', 'feature'])
df = pd.DataFrame(np.arange(9, dtype='int64')
.reshape(-1, 1),
index=idx, columns=['a'])
expected = pd.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)
Example #13
| Source File: test_constructors.py From recruit with Apache License 2.0 | 6 votes |
|
def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
Example #14
| Source File: test_multilevel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
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)
Example #15
| Source File: test_multilevel.py From recruit with Apache License 2.0 | 6 votes |
|
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)
Example #16
| Source File: test_multilevel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
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)
Example #17
| Source File: test_multilevel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
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)
Example #18
| Source File: test_multilevel.py From recruit with Apache License 2.0 | 6 votes |
|
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)
Example #19
| Source File: test_constructors.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
Example #20
| Source File: test_constructors.py From vnpy_crypto with MIT License | 6 votes |
|
def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
Example #21
| Source File: test_multilevel.py From recruit with Apache License 2.0 | 6 votes |
|
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)
Example #22
| Source File: test_multilevel.py From vnpy_crypto with MIT License | 6 votes |
|
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)
Example #23
| Source File: test_multilevel.py From vnpy_crypto with MIT License | 6 votes |
|
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)
Example #24
| Source File: test_multilevel.py From vnpy_crypto with MIT License | 6 votes |
|
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)
Example #25
| Source File: scipy_sparse.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _coo_to_sparse_series(A, dense_index=False):
""" Convert a scipy.sparse.coo_matrix to a SparseSeries.
Use the defaults given in the SparseSeries constructor.
"""
s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
s = s.sort_index()
s = s.to_sparse() # TODO: specify kind?
if dense_index:
# is there a better constructor method to use here?
i = range(A.shape[0])
j = range(A.shape[1])
ind = MultiIndex.from_product([i, j])
s = s.reindex(ind)
return s
Example #26
| Source File: scipy_sparse.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def _coo_to_sparse_series(A, dense_index=False):
""" Convert a scipy.sparse.coo_matrix to a SparseSeries.
Use the defaults given in the SparseSeries constructor.
"""
s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
s = s.sort_index()
s = s.to_sparse() # TODO: specify kind?
if dense_index:
# is there a better constructor method to use here?
i = range(A.shape[0])
j = range(A.shape[1])
ind = MultiIndex.from_product([i, j])
s = s.reindex(ind)
return s
Example #27
| Source File: test_multilevel.py From coffeegrindsize with MIT License | 4 votes |
|
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
Example #28
| Source File: test_multilevel.py From vnpy_crypto with MIT License | 4 votes |
|
def test_sort_index_and_reconstruction(self):
# 15622
# lexsortedness should be identical
# across MultiIndex consruction methods
df = DataFrame([[1, 1], [2, 2]], index=list('ab'))
expected = DataFrame([[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_tuples([(0.5, 'a'),
(0.5, 'b'),
(0.8, 'a'),
(0.8, 'b')]))
assert expected.index.is_lexsorted()
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_product([[0.5, 0.8], list('ab')]))
result = result.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex(levels=[[0.5, 0.8], ['a', 'b']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]]))
result = result.sort_index()
assert result.index.is_lexsorted()
tm.assert_frame_equal(result, expected)
concatted = pd.concat([df, df], keys=[0.8, 0.5])
result = concatted.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
# 14015
df = DataFrame([[1, 2], [6, 7]],
columns=MultiIndex.from_tuples(
[(0, '20160811 12:00:00'),
(0, '20160809 12:00:00')],
names=['l1', 'Date']))
df.columns.set_levels(pd.to_datetime(df.columns.levels[1]),
level=1,
inplace=True)
assert not df.columns.is_lexsorted()
assert not df.columns.is_monotonic
result = df.sort_index(axis=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
result = df.sort_index(axis=1, level=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
Example #29
| Source File: test_multilevel.py From recruit with Apache License 2.0 | 4 votes |
|
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
Example #30
| Source File: test_multilevel.py From twitter-stock-recommendation with MIT License | 4 votes |
|
def test_sort_index_and_reconstruction(self):
# 15622
# lexsortedness should be identical
# across MultiIndex consruction methods
df = DataFrame([[1, 1], [2, 2]], index=list('ab'))
expected = DataFrame([[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_tuples([(0.5, 'a'),
(0.5, 'b'),
(0.8, 'a'),
(0.8, 'b')]))
assert expected.index.is_lexsorted()
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_product([[0.5, 0.8], list('ab')]))
result = result.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex(levels=[[0.5, 0.8], ['a', 'b']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]]))
result = result.sort_index()
assert result.index.is_lexsorted()
tm.assert_frame_equal(result, expected)
concatted = pd.concat([df, df], keys=[0.8, 0.5])
result = concatted.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
# 14015
df = DataFrame([[1, 2], [6, 7]],
columns=MultiIndex.from_tuples(
[(0, '20160811 12:00:00'),
(0, '20160809 12:00:00')],
names=['l1', 'Date']))
df.columns.set_levels(pd.to_datetime(df.columns.levels[1]),
level=1,
inplace=True)
assert not df.columns.is_lexsorted()
assert not df.columns.is_monotonic
result = df.sort_index(axis=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
result = df.sort_index(axis=1, level=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
