Python pandas.core.index.MultiIndex.from_arrays() Examples
The following are 30
code examples of pandas.core.index.MultiIndex.from_arrays().
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Example #1
| Source File: reshape.py From Splunking-Crime with GNU Affero General Public License v3.0 | 6 votes |
|
def pivot(self, index=None, columns=None, values=None):
"""
See DataFrame.pivot
"""
if values is None:
cols = [columns] if index is None else [index, columns]
append = index is None
indexed = self.set_index(cols, append=append)
return indexed.unstack(columns)
else:
if index is None:
index = self.index
else:
index = self[index]
indexed = Series(self[values].values,
index=MultiIndex.from_arrays([index, self[columns]]))
return indexed.unstack(columns)
Example #2
| Source File: test_multilevel.py From vnpy_crypto with MIT License | 6 votes |
|
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)
Example #3
| 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 #4
| Source File: test_multilevel.py From vnpy_crypto with MIT License | 6 votes |
|
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
Example #5
| 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 #6
| Source File: test_multilevel.py From recruit with Apache License 2.0 | 6 votes |
|
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
Example #7
| Source File: test_analytics.py From recruit with Apache License 2.0 | 6 votes |
|
def test_count(self, datetime_series):
assert datetime_series.count() == len(datetime_series)
datetime_series[::2] = np.NaN
assert datetime_series.count() == np.isfinite(datetime_series).sum()
mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
ts = Series(np.arange(len(mi)), index=mi)
left = ts.count(level=1)
right = Series([2, 3, 1], index=[1, 2, nan])
assert_series_equal(left, right)
ts.iloc[[0, 3, 5]] = nan
assert_series_equal(ts.count(level=1), right - 1)
Example #8
| Source File: test_multilevel.py From vnpy_crypto with MIT License | 6 votes |
|
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)
Example #9
| Source File: reshape.py From vnpy_crypto with MIT License | 6 votes |
|
def pivot(self, index=None, columns=None, values=None):
"""
See DataFrame.pivot
"""
if values is None:
cols = [columns] if index is None else [index, columns]
append = index is None
indexed = self.set_index(cols, append=append)
else:
if index is None:
index = self.index
else:
index = self[index]
index = MultiIndex.from_arrays([index, self[columns]])
if is_list_like(values) and not isinstance(values, tuple):
# Exclude tuple because it is seen as a single column name
indexed = self._constructor(self[values].values, index=index,
columns=values)
else:
indexed = self._constructor_sliced(self[values].values,
index=index)
return indexed.unstack(columns)
Example #10
| Source File: groupby.py From Computable with MIT License | 6 votes |
|
def _index_with_as_index(self, b):
"""
Take boolean mask of index to be returned from apply, if as_index=True
"""
# TODO perf, it feels like this should already be somewhere...
from itertools import chain
original = self.obj.index
gp = self.grouper
levels = chain((gp.levels[i][gp.labels[i][b]]
for i in range(len(gp.groupings))),
(original.get_level_values(i)[b]
for i in range(original.nlevels)))
new = MultiIndex.from_arrays(list(levels))
new.names = gp.names + original.names
return new
Example #11
| Source File: parsers.py From Computable with MIT License | 6 votes |
|
def _agg_index(self, index, try_parse_dates=True):
arrays = []
for i, arr in enumerate(index):
if (try_parse_dates and self._should_parse_dates(i)):
arr = self._date_conv(arr)
col_na_values = self.na_values
col_na_fvalues = self.na_fvalues
if isinstance(self.na_values, dict):
col_name = self.index_names[i]
if col_name is not None:
col_na_values, col_na_fvalues = _get_na_values(
col_name, self.na_values, self.na_fvalues)
arr, _ = self._convert_types(arr, col_na_values | col_na_fvalues)
arrays.append(arr)
index = MultiIndex.from_arrays(arrays, names=self.index_names)
return index
Example #12
| Source File: test_multilevel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
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)
Example #13
| 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 #14
| Source File: test_multilevel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
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
Example #15
| Source File: pivot.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def pivot(data, index=None, columns=None, values=None):
if values is None:
cols = [columns] if index is None else [index, columns]
append = index is None
indexed = data.set_index(cols, append=append)
else:
if index is None:
index = data.index
else:
index = data[index]
index = MultiIndex.from_arrays([index, data[columns]])
if is_list_like(values) and not isinstance(values, tuple):
# Exclude tuple because it is seen as a single column name
indexed = data._constructor(data[values].values, index=index,
columns=values)
else:
indexed = data._constructor_sliced(data[values].values,
index=index)
return indexed.unstack(columns)
Example #16
| Source File: test_analytics.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_count(self, datetime_series):
assert datetime_series.count() == len(datetime_series)
datetime_series[::2] = np.NaN
assert datetime_series.count() == np.isfinite(datetime_series).sum()
mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
ts = Series(np.arange(len(mi)), index=mi)
left = ts.count(level=1)
right = Series([2, 3, 1], index=[1, 2, nan])
assert_series_equal(left, right)
ts.iloc[[0, 3, 5]] = nan
assert_series_equal(ts.count(level=1), right - 1)
Example #17
| Source File: test_multilevel.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
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)
Example #18
| Source File: test_multilevel.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
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)
Example #19
| 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 #20
| Source File: test_multilevel.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
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
Example #21
| Source File: test_analytics.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_count(self):
assert self.ts.count() == len(self.ts)
self.ts[::2] = np.NaN
assert self.ts.count() == np.isfinite(self.ts).sum()
mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
ts = Series(np.arange(len(mi)), index=mi)
left = ts.count(level=1)
right = Series([2, 3, 1], index=[1, 2, nan])
assert_series_equal(left, right)
ts.iloc[[0, 3, 5]] = nan
assert_series_equal(ts.count(level=1), right - 1)
Example #22
| Source File: reshape.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def pivot(self, index=None, columns=None, values=None):
"""
See DataFrame.pivot
"""
if values is None:
cols = [columns] if index is None else [index, columns]
append = index is None
indexed = self.set_index(cols, append=append)
return indexed.unstack(columns)
else:
if index is None:
index = self.index
else:
index = self[index]
indexed = Series(self[values].values,
index=MultiIndex.from_arrays([index, self[columns]]))
return indexed.unstack(columns)
Example #23
| Source File: test_multilevel.py From coffeegrindsize with MIT License | 6 votes |
|
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)
Example #24
| 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 #25
| Source File: test_multilevel.py From coffeegrindsize with MIT License | 6 votes |
|
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
Example #26
| Source File: test_multilevel.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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)
Example #27
| Source File: test_multilevel.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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)
Example #28
| 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 #29
| Source File: test_multilevel.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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
Example #30
| Source File: test_analytics.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_count(self):
assert self.ts.count() == len(self.ts)
self.ts[::2] = np.NaN
assert self.ts.count() == np.isfinite(self.ts).sum()
mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
ts = Series(np.arange(len(mi)), index=mi)
left = ts.count(level=1)
right = Series([2, 3, 1], index=[1, 2, nan])
assert_series_equal(left, right)
ts.iloc[[0, 3, 5]] = nan
assert_series_equal(ts.count(level=1), right - 1)
