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Version:
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from itertools import product
import pytest
import sys
import warnings
from warnings import catch_warnings
from datetime import datetime, timedelta
from numpy.random import randn
import numpy as np
from distutils.version import LooseVersion
import pandas as pd
from pandas import (Series, DataFrame, bdate_range, isna,
notna, concat, Timestamp, Index)
import pandas.stats.moments as mom
import pandas.core.window as rwindow
import pandas.tseries.offsets as offsets
from pandas.core.base import SpecificationError
from pandas.errors import UnsupportedFunctionCall
import pandas.util.testing as tm
from pandas.compat import range, zip, PY3
N, K = 100, 10
def assert_equal(left, right):
if isinstance(left, Series):
tm.assert_series_equal(left, right)
else:
tm.assert_frame_equal(left, right)
class Base(object):
_nan_locs = np.arange(20, 40)
_inf_locs = np.array([])
def _create_data(self):
arr = randn(N)
arr[self._nan_locs] = np.NaN
self.arr = arr
self.rng = bdate_range(datetime(2009, 1, 1), periods=N)
self.series = Series(arr.copy(), index=self.rng)
self.frame = DataFrame(randn(N, K), index=self.rng,
columns=np.arange(K))
class TestApi(Base):
def setup_method(self, method):
self._create_data()
def test_getitem(self):
r = self.frame.rolling(window=5)
tm.assert_index_equal(r._selected_obj.columns, self.frame.columns)
r = self.frame.rolling(window=5)[1]
assert r._selected_obj.name == self.frame.columns[1]
# technically this is allowed
r = self.frame.rolling(window=5)[1, 3]
tm.assert_index_equal(r._selected_obj.columns,
self.frame.columns[[1, 3]])
r = self.frame.rolling(window=5)[[1, 3]]
tm.assert_index_equal(r._selected_obj.columns,
self.frame.columns[[1, 3]])
def test_select_bad_cols(self):
df = DataFrame([[1, 2]], columns=['A', 'B'])
g = df.rolling(window=5)
pytest.raises(KeyError, g.__getitem__, ['C']) # g[['C']]
pytest.raises(KeyError, g.__getitem__, ['A', 'C']) # g[['A', 'C']]
with tm.assert_raises_regex(KeyError, '^[^A]+$'):
# A should not be referenced as a bad column...
# will have to rethink regex if you change message!
g[['A', 'C']]
def test_attribute_access(self):
df = DataFrame([[1, 2]], columns=['A', 'B'])
r = df.rolling(window=5)
tm.assert_series_equal(r.A.sum(), r['A'].sum())
pytest.raises(AttributeError, lambda: r.F)
def tests_skip_nuisance(self):
df = DataFrame({'A': range(5), 'B': range(5, 10), 'C': 'foo'})
r = df.rolling(window=3)
result = r[['A', 'B']].sum()
expected = DataFrame({'A': [np.nan, np.nan, 3, 6, 9],
'B': [np.nan, np.nan, 18, 21, 24]},
columns=list('AB'))
tm.assert_frame_equal(result, expected)
expected = pd.concat([r[['A', 'B']].sum(), df[['C']]], axis=1)
result = r.sum()
tm.assert_frame_equal(result, expected, check_like=True)
def test_agg(self):
df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
r = df.rolling(window=3)
a_mean = r['A'].mean()
a_std = r['A'].std()
a_sum = r['A'].sum()
b_mean = r['B'].mean()
b_std = r['B'].std()
b_sum = r['B'].sum()
result = r.aggregate([np.mean, np.std])
expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
expected.columns = pd.MultiIndex.from_product([['A', 'B'], ['mean',
'std']])
tm.assert_frame_equal(result, expected)
result = r.aggregate({'A': np.mean, 'B': np.std})
expected = pd.concat([a_mean, b_std], axis=1)
tm.assert_frame_equal(result, expected, check_like=True)
result = r.aggregate({'A': ['mean', 'std']})
expected = pd.concat([a_mean, a_std], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'), ('A',
'std')])
tm.assert_frame_equal(result, expected)
result = r['A'].aggregate(['mean', 'sum'])
expected = pd.concat([a_mean, a_sum], axis=1)
expected.columns = ['mean', 'sum']
tm.assert_frame_equal(result, expected)
with catch_warnings(record=True):
result = r.aggregate({'A': {'mean': 'mean', 'sum': 'sum'}})
expected = pd.concat([a_mean, a_sum], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
('A', 'sum')])
tm.assert_frame_equal(result, expected, check_like=True)
with catch_warnings(record=True):
result = r.aggregate({'A': {'mean': 'mean',
'sum': 'sum'},
'B': {'mean2': 'mean',
'sum2': 'sum'}})
expected = pd.concat([a_mean, a_sum, b_mean, b_sum], axis=1)
exp_cols = [('A', 'mean'), ('A', 'sum'), ('B', 'mean2'), ('B', 'sum2')]
expected.columns = pd.MultiIndex.from_tuples(exp_cols)
tm.assert_frame_equal(result, expected, check_like=True)
result = r.aggregate({'A': ['mean', 'std'], 'B': ['mean', 'std']})
expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
exp_cols = [('A', 'mean'), ('A', 'std'), ('B', 'mean'), ('B', 'std')]
expected.columns = pd.MultiIndex.from_tuples(exp_cols)
tm.assert_frame_equal(result, expected, check_like=True)
# passed lambda
result = r.agg({'A': np.sum, 'B': lambda x: np.std(x, ddof=1)})
rcustom = r['B'].apply(lambda x: np.std(x, ddof=1))
expected = pd.concat([a_sum, rcustom], axis=1)
tm.assert_frame_equal(result, expected, check_like=True)
def test_agg_consistency(self):
df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
r = df.rolling(window=3)
result = r.agg([np.sum, np.mean]).columns
expected = pd.MultiIndex.from_product([list('AB'), ['sum', 'mean']])
tm.assert_index_equal(result, expected)
result = r['A'].agg([np.sum, np.mean]).columns
expected = Index(['sum', 'mean'])
tm.assert_index_equal(result, expected)
result = r.agg({'A': [np.sum, np.mean]}).columns
expected = pd.MultiIndex.from_tuples([('A', 'sum'), ('A', 'mean')])
tm.assert_index_equal(result, expected)
def test_agg_nested_dicts(self):
# API change for disallowing these types of nested dicts
df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
r = df.rolling(window=3)
def f():
r.aggregate({'r1': {'A': ['mean', 'sum']},
'r2': {'B': ['mean', 'sum']}})
pytest.raises(SpecificationError, f)
expected = pd.concat([r['A'].mean(), r['A'].std(), r['B'].mean(),
r['B'].std()], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
with catch_warnings(record=True):
result = r[['A', 'B']].agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
tm.assert_frame_equal(result, expected, check_like=True)
with catch_warnings(record=True):
result = r.agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
expected.columns = pd.MultiIndex.from_tuples([('A', 'ra', 'mean'), (
'A', 'ra', 'std'), ('B', 'rb', 'mean'), ('B', 'rb', 'std')])
tm.assert_frame_equal(result, expected, check_like=True)
def test_count_nonnumeric_types(self):
# GH12541
cols = ['int', 'float', 'string', 'datetime', 'timedelta', 'periods',
'fl_inf', 'fl_nan', 'str_nan', 'dt_nat', 'periods_nat']
df = DataFrame(
{'int': [1, 2, 3],
'float': [4., 5., 6.],
'string': list('abc'),
'datetime': pd.date_range('20170101', periods=3),
'timedelta': pd.timedelta_range('1 s', periods=3, freq='s'),
'periods': [pd.Period('2012-01'), pd.Period('2012-02'),
pd.Period('2012-03')],
'fl_inf': [1., 2., np.Inf],
'fl_nan': [1., 2., np.NaN],
'str_nan': ['aa', 'bb', np.NaN],
'dt_nat': [pd.Timestamp('20170101'), pd.Timestamp('20170203'),
pd.Timestamp(None)],
'periods_nat': [pd.Period('2012-01'), pd.Period('2012-02'),
pd.Period(None)]},
columns=cols)
expected = DataFrame(
{'int': [1., 2., 2.],
'float': [1., 2., 2.],
'string': [1., 2., 2.],
'datetime': [1., 2., 2.],
'timedelta': [1., 2., 2.],
'periods': [1., 2., 2.],
'fl_inf': [1., 2., 2.],
'fl_nan': [1., 2., 1.],
'str_nan': [1., 2., 1.],
'dt_nat': [1., 2., 1.],
'periods_nat': [1., 2., 1.]},
columns=cols)
result = df.rolling(window=2).count()
tm.assert_frame_equal(result, expected)
result = df.rolling(1).count()
expected = df.notna().astype(float)
tm.assert_frame_equal(result, expected)
def test_window_with_args(self):
tm._skip_if_no_scipy()
# make sure that we are aggregating window functions correctly with arg
r = Series(np.random.randn(100)).rolling(window=10, min_periods=1,
win_type='gaussian')
expected = pd.concat([r.mean(std=10), r.mean(std=.01)], axis=1)
expected.columns = ['<lambda>', '<lambda>']
result = r.aggregate([lambda x: x.mean(std=10),
lambda x: x.mean(std=.01)])
tm.assert_frame_equal(result, expected)
def a(x):
return x.mean(std=10)
def b(x):
return x.mean(std=0.01)
expected = pd.concat([r.mean(std=10), r.mean(std=.01)], axis=1)
expected.columns = ['a', 'b']
result = r.aggregate([a, b])
tm.assert_frame_equal(result, expected)
def test_preserve_metadata(self):
# GH 10565
s = Series(np.arange(100), name='foo')
s2 = s.rolling(30).sum()
s3 = s.rolling(20).sum()
assert s2.name == 'foo'
assert s3.name == 'foo'
def test_how_compat(self):
# in prior versions, we would allow how to be used in the resample
# now that its deprecated, we need to handle this in the actual
# aggregation functions
s = pd.Series(
np.random.randn(20),
index=pd.date_range('1/1/2000', periods=20, freq='12H'))
for how in ['min', 'max', 'median']:
for op in ['mean', 'sum', 'std', 'var', 'kurt', 'skew']:
for t in ['rolling', 'expanding']:
with catch_warnings(record=True):
dfunc = getattr(pd, "{0}_{1}".format(t, op))
if dfunc is None:
continue
if t == 'rolling':
kwargs = {'window': 5}
else:
kwargs = {}
result = dfunc(s, freq='D', how=how, **kwargs)
expected = getattr(
getattr(s, t)(freq='D', **kwargs), op)(how=how)
tm.assert_series_equal(result, expected)
class TestWindow(Base):
def setup_method(self, method):
self._create_data()
def test_constructor(self):
# GH 12669
tm._skip_if_no_scipy()
for o in [self.series, self.frame]:
c = o.rolling
# valid
c(win_type='boxcar', window=2, min_periods=1)
c(win_type='boxcar', window=2, min_periods=1, center=True)
c(win_type='boxcar', window=2, min_periods=1, center=False)
for wt in ['boxcar', 'triang', 'blackman', 'hamming', 'bartlett',
'bohman', 'blackmanharris', 'nuttall', 'barthann']:
c(win_type=wt, window=2)
# not valid
for w in [2., 'foo', np.array([2])]:
with pytest.raises(ValueError):
c(win_type='boxcar', window=2, min_periods=w)
with pytest.raises(ValueError):
c(win_type='boxcar', window=2, min_periods=1, center=w)
for wt in ['foobar', 1]:
with pytest.raises(ValueError):
c(win_type=wt, window=2)
def test_numpy_compat(self):
# see gh-12811
w = rwindow.Window(Series([2, 4, 6]), window=[0, 2])
msg = "numpy operations are not valid with window objects"
for func in ('sum', 'mean'):
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(w, func), 1, 2, 3)
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(w, func), dtype=np.float64)
class TestRolling(Base):
def setup_method(self, method):
self._create_data()
def test_doc_string(self):
df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
df
df.rolling(2).sum()
df.rolling(2, min_periods=1).sum()
def test_constructor(self):
# GH 12669
for o in [self.series, self.frame]:
c = o.rolling
# valid
c(window=2)
c(window=2, min_periods=1)
c(window=2, min_periods=1, center=True)
c(window=2, min_periods=1, center=False)
# GH 13383
c(0)
with pytest.raises(ValueError):
c(-1)
# not valid
for w in [2., 'foo', np.array([2])]:
with pytest.raises(ValueError):
c(window=w)
with pytest.raises(ValueError):
c(window=2, min_periods=w)
with pytest.raises(ValueError):
c(window=2, min_periods=1, center=w)
def test_constructor_with_win_type(self):
# GH 13383
tm._skip_if_no_scipy()
for o in [self.series, self.frame]:
c = o.rolling
c(0, win_type='boxcar')
with pytest.raises(ValueError):
c(-1, win_type='boxcar')
def test_constructor_with_timedelta_window(self):
# GH 15440
n = 10
df = pd.DataFrame({'value': np.arange(n)},
index=pd.date_range('2015-12-24',
periods=n,
freq="D"))
expected_data = np.append([0., 1.], np.arange(3., 27., 3))
for window in [timedelta(days=3), pd.Timedelta(days=3)]:
result = df.rolling(window=window).sum()
expected = pd.DataFrame({'value': expected_data},
index=pd.date_range('2015-12-24',
periods=n,
freq="D"))
tm.assert_frame_equal(result, expected)
expected = df.rolling('3D').sum()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
'window', [timedelta(days=3), pd.Timedelta(days=3), '3D'])
def test_constructor_with_timedelta_window_and_minperiods(self, window):
# GH 15305
n = 10
df = pd.DataFrame({'value': np.arange(n)},
index=pd.date_range('2017-08-08',
periods=n,
freq="D"))
expected = pd.DataFrame({'value': np.append([np.NaN, 1.],
np.arange(3., 27., 3))},
index=pd.date_range('2017-08-08',
periods=n,
freq="D"))
result_roll_sum = df.rolling(window=window, min_periods=2).sum()
result_roll_generic = df.rolling(window=window,
min_periods=2).apply(sum)
tm.assert_frame_equal(result_roll_sum, expected)
tm.assert_frame_equal(result_roll_generic, expected)
def test_numpy_compat(self):
# see gh-12811
r = rwindow.Rolling(Series([2, 4, 6]), window=2)
msg = "numpy operations are not valid with window objects"
for func in ('std', 'mean', 'sum', 'max', 'min', 'var'):
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(r, func), 1, 2, 3)
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(r, func), dtype=np.float64)
def test_closed(self):
df = DataFrame({'A': [0, 1, 2, 3, 4]})
# closed only allowed for datetimelike
with pytest.raises(ValueError):
df.rolling(window=3, closed='neither')
@pytest.mark.parametrize('roller', ['1s', 1])
def tests_empty_df_rolling(self, roller):
# GH 15819 Verifies that datetime and integer rolling windows can be
# applied to empty DataFrames
expected = DataFrame()
result = DataFrame().rolling(roller).sum()
tm.assert_frame_equal(result, expected)
# Verifies that datetime and integer rolling windows can be applied to
# empty DataFrames with datetime index
expected = DataFrame(index=pd.DatetimeIndex([]))
result = DataFrame(index=pd.DatetimeIndex([])).rolling(roller).sum()
tm.assert_frame_equal(result, expected)
def test_multi_index_names(self):
# GH 16789, 16825
cols = pd.MultiIndex.from_product([['A', 'B'], ['C', 'D', 'E']],
names=['1', '2'])
df = pd.DataFrame(np.ones((10, 6)), columns=cols)
result = df.rolling(3).cov()
tm.assert_index_equal(result.columns, df.columns)
assert result.index.names == [None, '1', '2']
class TestExpanding(Base):
def setup_method(self, method):
self._create_data()
def test_doc_string(self):
df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
df
df.expanding(2).sum()
def test_constructor(self):
# GH 12669
for o in [self.series, self.frame]:
c = o.expanding
# valid
c(min_periods=1)
c(min_periods=1, center=True)
c(min_periods=1, center=False)
# not valid
for w in [2., 'foo', np.array([2])]:
with pytest.raises(ValueError):
c(min_periods=w)
with pytest.raises(ValueError):
c(min_periods=1, center=w)
def test_numpy_compat(self):
# see gh-12811
e = rwindow.Expanding(Series([2, 4, 6]), window=2)
msg = "numpy operations are not valid with window objects"
for func in ('std', 'mean', 'sum', 'max', 'min', 'var'):
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(e, func), 1, 2, 3)
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(e, func), dtype=np.float64)
@pytest.mark.parametrize(
'expander',
[1, pytest.param('ls', marks=pytest.mark.xfail(
reason='GH 16425 expanding with '
'offset not supported'))])
def test_empty_df_expanding(self, expander):
# GH 15819 Verifies that datetime and integer expanding windows can be
# applied to empty DataFrames
expected = DataFrame()
result = DataFrame().expanding(expander).sum()
tm.assert_frame_equal(result, expected)
# Verifies that datetime and integer expanding windows can be applied
# to empty DataFrames with datetime index
expected = DataFrame(index=pd.DatetimeIndex([]))
result = DataFrame(
index=pd.DatetimeIndex([])).expanding(expander).sum()
tm.assert_frame_equal(result, expected)
class TestEWM(Base):
def setup_method(self, method):
self._create_data()
def test_doc_string(self):
df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
df
df.ewm(com=0.5).mean()
def test_constructor(self):
for o in [self.series, self.frame]:
c = o.ewm
# valid
c(com=0.5)
c(span=1.5)
c(alpha=0.5)
c(halflife=0.75)
c(com=0.5, span=None)
c(alpha=0.5, com=None)
c(halflife=0.75, alpha=None)
# not valid: mutually exclusive
with pytest.raises(ValueError):
c(com=0.5, alpha=0.5)
with pytest.raises(ValueError):
c(span=1.5, halflife=0.75)
with pytest.raises(ValueError):
c(alpha=0.5, span=1.5)
# not valid: com < 0
with pytest.raises(ValueError):
c(com=-0.5)
# not valid: span < 1
with pytest.raises(ValueError):
c(span=0.5)
# not valid: halflife <= 0
with pytest.raises(ValueError):
c(halflife=0)
# not valid: alpha <= 0 or alpha > 1
for alpha in (-0.5, 1.5):
with pytest.raises(ValueError):
c(alpha=alpha)
def test_numpy_compat(self):
# see gh-12811
e = rwindow.EWM(Series([2, 4, 6]), alpha=0.5)
msg = "numpy operations are not valid with window objects"
for func in ('std', 'mean', 'var'):
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(e, func), 1, 2, 3)
tm.assert_raises_regex(UnsupportedFunctionCall, msg,
getattr(e, func), dtype=np.float64)
class TestDeprecations(Base):
""" test that we are catching deprecation warnings """
def setup_method(self, method):
self._create_data()
def test_deprecations(self):
with catch_warnings(record=True):
mom.rolling_mean(np.ones(10), 3, center=True, axis=0)
mom.rolling_mean(Series(np.ones(10)), 3, center=True, axis=0)
# gh-12373 : rolling functions error on float32 data
# make sure rolling functions works for different dtypes
#
# NOTE that these are yielded tests and so _create_data
# is explicitly called.
#
# further note that we are only checking rolling for fully dtype
# compliance (though both expanding and ewm inherit)
class Dtype(object):
window = 2
funcs = {
'count': lambda v: v.count(),
'max': lambda v: v.max(),
'min': lambda v: v.min(),
'sum': lambda v: v.sum(),
'mean': lambda v: v.mean(),
'std': lambda v: v.std(),
'var': lambda v: v.var(),
'median': lambda v: v.median()
}
def get_expects(self):
expects = {
'sr1': {
'count': Series([1, 2, 2, 2, 2], dtype='float64'),
'max': Series([np.nan, 1, 2, 3, 4], dtype='float64'),
'min': Series([np.nan, 0, 1, 2, 3], dtype='float64'),
'sum': Series([np.nan, 1, 3, 5, 7], dtype='float64'),
'mean': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64'),
'std': Series([np.nan] + [np.sqrt(.5)] * 4, dtype='float64'),
'var': Series([np.nan, .5, .5, .5, .5], dtype='float64'),
'median': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64')
},
'sr2': {
'count': Series([1, 2, 2, 2, 2], dtype='float64'),
'max': Series([np.nan, 10, 8, 6, 4], dtype='float64'),
'min': Series([np.nan, 8, 6, 4, 2], dtype='float64'),
'sum': Series([np.nan, 18, 14, 10, 6], dtype='float64'),
'mean': Series([np.nan, 9, 7, 5, 3], dtype='float64'),
'std': Series([np.nan] + [np.sqrt(2)] * 4, dtype='float64'),
'var': Series([np.nan, 2, 2, 2, 2], dtype='float64'),
'median': Series([np.nan, 9, 7, 5, 3], dtype='float64')
},
'df': {
'count': DataFrame({0: Series([1, 2, 2, 2, 2]),
1: Series([1, 2, 2, 2, 2])},
dtype='float64'),
'max': DataFrame({0: Series([np.nan, 2, 4, 6, 8]),
1: Series([np.nan, 3, 5, 7, 9])},
dtype='float64'),
'min': DataFrame({0: Series([np.nan, 0, 2, 4, 6]),
1: Series([np.nan, 1, 3, 5, 7])},
dtype='float64'),
'sum': DataFrame({0: Series([np.nan, 2, 6, 10, 14]),
1: Series([np.nan, 4, 8, 12, 16])},
dtype='float64'),
'mean': DataFrame({0: Series([np.nan, 1, 3, 5, 7]),
1: Series([np.nan, 2, 4, 6, 8])},
dtype='float64'),
'std': DataFrame({0: Series([np.nan] + [np.sqrt(2)] * 4),
1: Series([np.nan] + [np.sqrt(2)] * 4)},
dtype='float64'),
'var': DataFrame({0: Series([np.nan, 2, 2, 2, 2]),
1: Series([np.nan, 2, 2, 2, 2])},
dtype='float64'),
'median': DataFrame({0: Series([np.nan, 1, 3, 5, 7]),
1: Series([np.nan, 2, 4, 6, 8])},
dtype='float64'),
}
}
return expects
def _create_dtype_data(self, dtype):
sr1 = Series(np.arange(5), dtype=dtype)
sr2 = Series(np.arange(10, 0, -2), dtype=dtype)
df = DataFrame(np.arange(10).reshape((5, 2)), dtype=dtype)
data = {
'sr1': sr1,
'sr2': sr2,
'df': df
}
return data
def _create_data(self):
self.data = self._create_dtype_data(self.dtype)
self.expects = self.get_expects()
def test_dtypes(self):
self._create_data()
for f_name, d_name in product(self.funcs.keys(), self.data.keys()):
f = self.funcs[f_name]
d = self.data[d_name]
exp = self.expects[d_name][f_name]
self.check_dtypes(f, f_name, d, d_name, exp)
def check_dtypes(self, f, f_name, d, d_name, exp):
roll = d.rolling(window=self.window)
result = f(roll)
tm.assert_almost_equal(result, exp)
class TestDtype_object(Dtype):
dtype = object
class Dtype_integer(Dtype):
pass
class TestDtype_int8(Dtype_integer):
dtype = np.int8
class TestDtype_int16(Dtype_integer):
dtype = np.int16
class TestDtype_int32(Dtype_integer):
dtype = np.int32
class TestDtype_int64(Dtype_integer):
dtype = np.int64
class Dtype_uinteger(Dtype):
pass
class TestDtype_uint8(Dtype_uinteger):
dtype = np.uint8
class TestDtype_uint16(Dtype_uinteger):
dtype = np.uint16
class TestDtype_uint32(Dtype_uinteger):
dtype = np.uint32
class TestDtype_uint64(Dtype_uinteger):
dtype = np.uint64
class Dtype_float(Dtype):
pass
class TestDtype_float16(Dtype_float):
dtype = np.float16
class TestDtype_float32(Dtype_float):
dtype = np.float32
class TestDtype_float64(Dtype_float):
dtype = np.float64
class TestDtype_category(Dtype):
dtype = 'category'
include_df = False
def _create_dtype_data(self, dtype):
sr1 = Series(range(5), dtype=dtype)
sr2 = Series(range(10, 0, -2), dtype=dtype)
data = {
'sr1': sr1,
'sr2': sr2
}
return data
class DatetimeLike(Dtype):
def check_dtypes(self, f, f_name, d, d_name, exp):
roll = d.rolling(window=self.window)
if f_name == 'count':
result = f(roll)
tm.assert_almost_equal(result, exp)
else:
# other methods not Implemented ATM
with pytest.raises(NotImplementedError):
f(roll)
class TestDtype_timedelta(DatetimeLike):
dtype = np.dtype('m8[ns]')
class TestDtype_datetime(DatetimeLike):
dtype = np.dtype('M8[ns]')
class TestDtype_datetime64UTC(DatetimeLike):
dtype = 'datetime64[ns, UTC]'
def _create_data(self):
pytest.skip("direct creation of extension dtype "
"datetime64[ns, UTC] is not supported ATM")
class TestMoments(Base):
def setup_method(self, method):
self._create_data()
def test_centered_axis_validation(self):
# ok
Series(np.ones(10)).rolling(window=3, center=True, axis=0).mean()
# bad axis
with pytest.raises(ValueError):
Series(np.ones(10)).rolling(window=3, center=True, axis=1).mean()
# ok ok
DataFrame(np.ones((10, 10))).rolling(window=3, center=True,
axis=0).mean()
DataFrame(np.ones((10, 10))).rolling(window=3, center=True,
axis=1).mean()
# bad axis
with pytest.raises(ValueError):
(DataFrame(np.ones((10, 10)))
.rolling(window=3, center=True, axis=2).mean())
def test_rolling_sum(self):
self._check_moment_func(mom.rolling_sum, np.sum, name='sum')
def test_rolling_count(self):
counter = lambda x: np.isfinite(x).astype(float).sum()
self._check_moment_func(mom.rolling_count, counter, name='count',
has_min_periods=False, preserve_nan=False,
fill_value=0)
def test_rolling_mean(self):
self._check_moment_func(mom.rolling_mean, np.mean, name='mean')
def test_cmov_mean(self):
# GH 8238
tm._skip_if_no_scipy()
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
10.63, 14.48])
xp = np.array([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516, 12.818,
12.952, np.nan, np.nan])
with catch_warnings(record=True):
rs = mom.rolling_mean(vals, 5, center=True)
tm.assert_almost_equal(xp, rs)
xp = Series(rs)
rs = Series(vals).rolling(5, center=True).mean()
tm.assert_series_equal(xp, rs)
def test_cmov_window(self):
# GH 8238
tm._skip_if_no_scipy()
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
10.63, 14.48])
xp = np.array([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516, 12.818,
12.952, np.nan, np.nan])
with catch_warnings(record=True):
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
tm.assert_almost_equal(xp, rs)
xp = Series(rs)
rs = Series(vals).rolling(5, win_type='boxcar', center=True).mean()
tm.assert_series_equal(xp, rs)
def test_cmov_window_corner(self):
# GH 8238
tm._skip_if_no_scipy()
# all nan
vals = np.empty(10, dtype=float)
vals.fill(np.nan)
with catch_warnings(record=True):
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
assert np.isnan(rs).all()
# empty
vals = np.array([])
with catch_warnings(record=True):
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
assert len(rs) == 0
# shorter than window
vals = np.random.randn(5)
with catch_warnings(record=True):
rs = mom.rolling_window(vals, 10, 'boxcar')
assert np.isnan(rs).all()
assert len(rs) == 5
def test_cmov_window_frame(self):
# Gh 8238
tm._skip_if_no_scipy()
vals = np.array([[12.18, 3.64], [10.18, 9.16], [13.24, 14.61],
[4.51, 8.11], [6.15, 11.44], [9.14, 6.21],
[11.31, 10.67], [2.94, 6.51], [9.42, 8.39], [12.44,
7.34]])
xp = np.array([[np.nan, np.nan], [np.nan, np.nan], [9.252, 9.392],
[8.644, 9.906], [8.87, 10.208], [6.81, 8.588],
[7.792, 8.644], [9.05, 7.824], [np.nan, np.nan
], [np.nan, np.nan]])
# DataFrame
rs = DataFrame(vals).rolling(5, win_type='boxcar', center=True).mean()
tm.assert_frame_equal(DataFrame(xp), rs)
# invalid method
with pytest.raises(AttributeError):
(DataFrame(vals).rolling(5, win_type='boxcar', center=True)
.std())
# sum
xp = np.array([[np.nan, np.nan], [np.nan, np.nan], [46.26, 46.96],
[43.22, 49.53], [44.35, 51.04], [34.05, 42.94],
[38.96, 43.22], [45.25, 39.12], [np.nan, np.nan
], [np.nan, np.nan]])
rs = DataFrame(vals).rolling(5, win_type='boxcar', center=True).sum()
tm.assert_frame_equal(DataFrame(xp), rs)
def test_cmov_window_na_min_periods(self):
tm._skip_if_no_scipy()
# min_periods
vals = Series(np.random.randn(10))
vals[4] = np.nan
vals[8] = np.nan
xp = vals.rolling(5, min_periods=4, center=True).mean()
rs = vals.rolling(5, win_type='boxcar', min_periods=4,
center=True).mean()
tm.assert_series_equal(xp, rs)
def test_cmov_window_regular(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
10.63, 14.48])
xps = {
'hamming': [np.nan, np.nan, 8.71384, 9.56348, 12.38009, 14.03687,
13.8567, 11.81473, np.nan, np.nan],
'triang': [np.nan, np.nan, 9.28667, 10.34667, 12.00556, 13.33889,
13.38, 12.33667, np.nan, np.nan],
'barthann': [np.nan, np.nan, 8.4425, 9.1925, 12.5575, 14.3675,
14.0825, 11.5675, np.nan, np.nan],
'bohman': [np.nan, np.nan, 7.61599, 9.1764, 12.83559, 14.17267,
14.65923, 11.10401, np.nan, np.nan],
'blackmanharris': [np.nan, np.nan, 6.97691, 9.16438, 13.05052,
14.02156, 15.10512, 10.74574, np.nan, np.nan],
'nuttall': [np.nan, np.nan, 7.04618, 9.16786, 13.02671, 14.03559,
15.05657, 10.78514, np.nan, np.nan],
'blackman': [np.nan, np.nan, 7.73345, 9.17869, 12.79607, 14.20036,
14.57726, 11.16988, np.nan, np.nan],
'bartlett': [np.nan, np.nan, 8.4425, 9.1925, 12.5575, 14.3675,
14.0825, 11.5675, np.nan, np.nan]
}
for wt in win_types:
xp = Series(xps[wt])
rs = Series(vals).rolling(5, win_type=wt, center=True).mean()
tm.assert_series_equal(xp, rs)
def test_cmov_window_regular_linear_range(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array(range(10), dtype=np.float)
xp = vals.copy()
xp[:2] = np.nan
xp[-2:] = np.nan
xp = Series(xp)
for wt in win_types:
rs = Series(vals).rolling(5, win_type=wt, center=True).mean()
tm.assert_series_equal(xp, rs)
def test_cmov_window_regular_missing_data(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, np.nan,
10.63, 14.48])
xps = {
'bartlett': [np.nan, np.nan, 9.70333, 10.5225, 8.4425, 9.1925,
12.5575, 14.3675, 15.61667, 13.655],
'blackman': [np.nan, np.nan, 9.04582, 11.41536, 7.73345, 9.17869,
12.79607, 14.20036, 15.8706, 13.655],
'barthann': [np.nan, np.nan, 9.70333, 10.5225, 8.4425, 9.1925,
12.5575, 14.3675, 15.61667, 13.655],
'bohman': [np.nan, np.nan, 8.9444, 11.56327, 7.61599, 9.1764,
12.83559, 14.17267, 15.90976, 13.655],
'hamming': [np.nan, np.nan, 9.59321, 10.29694, 8.71384, 9.56348,
12.38009, 14.20565, 15.24694, 13.69758],
'nuttall': [np.nan, np.nan, 8.47693, 12.2821, 7.04618, 9.16786,
13.02671, 14.03673, 16.08759, 13.65553],
'triang': [np.nan, np.nan, 9.33167, 9.76125, 9.28667, 10.34667,
12.00556, 13.82125, 14.49429, 13.765],
'blackmanharris': [np.nan, np.nan, 8.42526, 12.36824, 6.97691,
9.16438, 13.05052, 14.02175, 16.1098, 13.65509]
}
for wt in win_types:
xp = Series(xps[wt])
rs = Series(vals).rolling(5, win_type=wt, min_periods=3).mean()
tm.assert_series_equal(xp, rs)
def test_cmov_window_special(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{'beta': 1.}, {'std': 1.}, {'power': 2.,
'width': 2.}, {'width': 0.5}]
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
10.63, 14.48])
xps = {
'gaussian': [np.nan, np.nan, 8.97297, 9.76077, 12.24763, 13.89053,
13.65671, 12.01002, np.nan, np.nan],
'general_gaussian': [np.nan, np.nan, 9.85011, 10.71589, 11.73161,
13.08516, 12.95111, 12.74577, np.nan, np.nan],
'slepian': [np.nan, np.nan, 9.81073, 10.89359, 11.70284, 12.88331,
12.96079, 12.77008, np.nan, np.nan],
'kaiser': [np.nan, np.nan, 9.86851, 11.02969, 11.65161, 12.75129,
12.90702, 12.83757, np.nan, np.nan]
}
for wt, k in zip(win_types, kwds):
xp = Series(xps[wt])
rs = Series(vals).rolling(5, win_type=wt, center=True).mean(**k)
tm.assert_series_equal(xp, rs)
def test_cmov_window_special_linear_range(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{'beta': 1.}, {'std': 1.}, {'power': 2.,
'width': 2.}, {'width': 0.5}]
vals = np.array(range(10), dtype=np.float)
xp = vals.copy()
xp[:2] = np.nan
xp[-2:] = np.nan
xp = Series(xp)
for wt, k in zip(win_types, kwds):
rs = Series(vals).rolling(5, win_type=wt, center=True).mean(**k)
tm.assert_series_equal(xp, rs)
def test_rolling_median(self):
with catch_warnings(record=True):
self._check_moment_func(mom.rolling_median, np.median,
name='median')
def test_rolling_min(self):
with catch_warnings(record=True):
self._check_moment_func(mom.rolling_min, np.min, name='min')
with catch_warnings(record=True):
a = np.array([1, 2, 3, 4, 5])
b = mom.rolling_min(a, window=100, min_periods=1)
tm.assert_almost_equal(b, np.ones(len(a)))
pytest.raises(ValueError, mom.rolling_min, np.array([1, 2, 3]),
window=3, min_periods=5)
def test_rolling_max(self):
with catch_warnings(record=True):
self._check_moment_func(mom.rolling_max, np.max, name='max')
with catch_warnings(record=True):
a = np.array([1, 2, 3, 4, 5], dtype=np.float64)
b = mom.rolling_max(a, window=100, min_periods=1)
tm.assert_almost_equal(a, b)
pytest.raises(ValueError, mom.rolling_max, np.array([1, 2, 3]),
window=3, min_periods=5)
def test_rolling_quantile(self):
qs = [0.0, .1, .5, .9, 1.0]
def scoreatpercentile(a, per):
values = np.sort(a, axis=0)
idx = int(per / 1. * (values.shape[0] - 1))
if idx == values.shape[0] - 1:
retval = values[-1]
else:
qlow = float(idx) / float(values.shape[0] - 1)
qhig = float(idx + 1) / float(values.shape[0] - 1)
vlow = values[idx]
vhig = values[idx + 1]
retval = vlow + (vhig - vlow) * (per - qlow) / (qhig - qlow)
return retval
for q in qs:
def f(x, window, quantile, min_periods=None, freq=None,
center=False):
return mom.rolling_quantile(x, window, quantile,
min_periods=min_periods, freq=freq,
center=center)
def alt(x):
return scoreatpercentile(x, q)
self._check_moment_func(f, alt, name='quantile', quantile=q)
def test_rolling_quantile_np_percentile(self):
# #9413: Tests that rolling window's quantile default behavior
# is analogus to Numpy's percentile
row = 10
col = 5
idx = pd.date_range('20100101', periods=row, freq='B')
df = pd.DataFrame(np.random.rand(row * col).reshape((row, -1)),
index=idx)
df_quantile = df.quantile([0.25, 0.5, 0.75], axis=0)
np_percentile = np.percentile(df, [25, 50, 75], axis=0)
tm.assert_almost_equal(df_quantile.values, np.array(np_percentile))
def test_rolling_quantile_series(self):
# #16211: Tests that rolling window's quantile default behavior
# is analogus to pd.Series' quantile
arr = np.arange(100)
s = pd.Series(arr)
q1 = s.quantile(0.1)
q2 = s.rolling(100).quantile(0.1).iloc[-1]
tm.assert_almost_equal(q1, q2)
def test_rolling_quantile_param(self):
ser = Series([0.0, .1, .5, .9, 1.0])
with pytest.raises(ValueError):
ser.rolling(3).quantile(-0.1)
with pytest.raises(ValueError):
ser.rolling(3).quantile(10.0)
with pytest.raises(TypeError):
ser.rolling(3).quantile('foo')
def test_rolling_apply(self):
# suppress warnings about empty slices, as we are deliberately testing
# with a 0-length Series
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=".*(empty slice|0 for slice).*",
category=RuntimeWarning)
ser = Series([])
tm.assert_series_equal(ser,
ser.rolling(10).apply(lambda x: x.mean()))
f = lambda x: x[np.isfinite(x)].mean()
def roll_mean(x, window, min_periods=None, freq=None, center=False,
**kwargs):
return mom.rolling_apply(x, window, func=f,
min_periods=min_periods, freq=freq,
center=center)
self._check_moment_func(roll_mean, np.mean, name='apply', func=f)
# GH 8080
s = Series([None, None, None])
result = s.rolling(2, min_periods=0).apply(lambda x: len(x))
expected = Series([1., 2., 2.])
tm.assert_series_equal(result, expected)
result = s.rolling(2, min_periods=0).apply(len)
tm.assert_series_equal(result, expected)
def test_rolling_apply_out_of_bounds(self):
# #1850
arr = np.arange(4)
# it works!
with catch_warnings(record=True):
result = mom.rolling_apply(arr, 10, np.sum)
assert isna(result).all()
with catch_warnings(record=True):
result = mom.rolling_apply(arr, 10, np.sum, min_periods=1)
tm.assert_almost_equal(result, result)
def test_rolling_std(self):
self._check_moment_func(mom.rolling_std, lambda x: np.std(x, ddof=1),
name='std')
self._check_moment_func(mom.rolling_std, lambda x: np.std(x, ddof=0),
name='std', ddof=0)
def test_rolling_std_1obs(self):
with catch_warnings(record=True):
result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
1, min_periods=1)
expected = np.array([np.nan] * 5)
tm.assert_almost_equal(result, expected)
with catch_warnings(record=True):
result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
1, min_periods=1, ddof=0)
expected = np.zeros(5)
tm.assert_almost_equal(result, expected)
with catch_warnings(record=True):
result = mom.rolling_std(np.array([np.nan, np.nan, 3., 4., 5.]),
3, min_periods=2)
assert np.isnan(result[2])
def test_rolling_std_neg_sqrt(self):
# unit test from Bottleneck
# Test move_nanstd for neg sqrt.
a = np.array([0.0011448196318903589, 0.00028718669878572767,
0.00028718669878572767, 0.00028718669878572767,
0.00028718669878572767])
with catch_warnings(record=True):
b = mom.rolling_std(a, window=3)
assert np.isfinite(b[2:]).all()
with catch_warnings(record=True):
b = mom.ewmstd(a, span=3)
assert np.isfinite(b[2:]).all()
def test_rolling_var(self):
self._check_moment_func(mom.rolling_var, lambda x: np.var(x, ddof=1),
test_stable=True, name='var')
self._check_moment_func(mom.rolling_var, lambda x: np.var(x, ddof=0),
name='var', ddof=0)
def test_rolling_skew(self):
try:
from scipy.stats import skew
except ImportError:
pytest.skip('no scipy')
self._check_moment_func(mom.rolling_skew,
lambda x: skew(x, bias=False), name='skew')
def test_rolling_kurt(self):
try:
from scipy.stats import kurtosis
except ImportError:
pytest.skip('no scipy')
self._check_moment_func(mom.rolling_kurt,
lambda x: kurtosis(x, bias=False), name='kurt')
def test_fperr_robustness(self):
# TODO: remove this once python 2.5 out of picture
if PY3:
pytest.skip("doesn't work on python 3")
# #2114
data = '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1a@\xaa\xaa\xaa\xaa\xaa\xaa\x02@8\x8e\xe38\x8e\xe3\xe8?z\t\xed%\xb4\x97\xd0?\xa2\x0c<\xdd\x9a\x1f\xb6?\x82\xbb\xfa&y\x7f\x9d?\xac\'\xa7\xc4P\xaa\x83?\x90\xdf\xde\xb0k8j?`\xea\xe9u\xf2zQ?*\xe37\x9d\x98N7?\xe2.\xf5&v\x13\x1f?\xec\xc9\xf8\x19\xa4\xb7\x04?\x90b\xf6w\x85\x9f\xeb>\xb5A\xa4\xfaXj\xd2>F\x02\xdb\xf8\xcb\x8d\xb8>.\xac<\xfb\x87^\xa0>\xe8:\xa6\xf9_\xd3\x85>\xfb?\xe2cUU\xfd?\xfc\x7fA\xed8\x8e\xe3?\xa5\xaa\xac\x91\xf6\x12\xca?n\x1cs\xb6\xf9a\xb1?\xe8%D\xf3L-\x97?5\xddZD\x11\xe7~?#>\xe7\x82\x0b\x9ad?\xd9R4Y\x0fxK?;7x;\nP2?N\xf4JO\xb8j\x18?4\xf81\x8a%G\x00?\x9a\xf5\x97\r2\xb4\xe5>\xcd\x9c\xca\xbcB\xf0\xcc>3\x13\x87(\xd7J\xb3>\x99\x19\xb4\xe0\x1e\xb9\x99>ff\xcd\x95\x14&\x81>\x88\x88\xbc\xc7p\xddf>`\x0b\xa6_\x96|N>@\xb2n\xea\x0eS4>U\x98\x938i\x19\x1b>\x8eeb\xd0\xf0\x10\x02>\xbd\xdc-k\x96\x16\xe8=(\x93\x1e\xf2\x0e\x0f\xd0=\xe0n\xd3Bii\xb5=*\xe9\x19Y\x8c\x8c\x9c=\xc6\xf0\xbb\x90]\x08\x83=]\x96\xfa\xc0|`i=>d\xfc\xd5\xfd\xeaP=R0\xfb\xc7\xa7\x8e6=\xc2\x95\xf9_\x8a\x13\x1e=\xd6c\xa6\xea\x06\r\x04=r\xda\xdd8\t\xbc\xea<\xf6\xe6\x93\xd0\xb0\xd2\xd1<\x9d\xdeok\x96\xc3\xb7<&~\xea9s\xaf\x9f<UUUUUU\x13@q\x1c\xc7q\x1c\xc7\xf9?\xf6\x12\xdaKh/\xe1?\xf2\xc3"e\xe0\xe9\xc6?\xed\xaf\x831+\x8d\xae?\xf3\x1f\xad\xcb\x1c^\x94?\x15\x1e\xdd\xbd>\xb8\x02@\xc6\xd2&\xfd\xa8\xf5\xe8?\xd9\xe1\x19\xfe\xc5\xa3\xd0?v\x82"\xa8\xb2/\xb6?\x9dX\x835\xee\x94\x9d?h\x90W\xce\x9e\xb8\x83?\x8a\xc0th~Kj?\\\x80\xf8\x9a\xa9\x87Q?%\xab\xa0\xce\x8c_7?1\xe4\x80\x13\x11*\x1f? \x98\x00\r\xb6\xc6\x04?\x80u\xabf\x9d\xb3\xeb>UNrD\xbew\xd2>\x1c\x13C[\xa8\x9f\xb8>\x12b\xd7<pj\xa0>m-\x1fQ@\xe3\x85>\xe6\x91)l\x00/m>Da\xc6\xf2\xaatS>\x05\xd7]\xee\xe3\xf09>' # noqa
arr = np.frombuffer(data, dtype='<f8')
if sys.byteorder != "little":
arr = arr.byteswap().newbyteorder()
with catch_warnings(record=True):
result = mom.rolling_sum(arr, 2)
assert (result[1:] >= 0).all()
with catch_warnings(record=True):
result = mom.rolling_mean(arr, 2)
assert (result[1:] >= 0).all()
with catch_warnings(record=True):
result = mom.rolling_var(arr, 2)
assert (result[1:] >= 0).all()
# #2527, ugh
arr = np.array([0.00012456, 0.0003, 0])
with catch_warnings(record=True):
result = mom.rolling_mean(arr, 1)
assert result[-1] >= 0
with catch_warnings(record=True):
result = mom.rolling_mean(-arr, 1)
assert result[-1] <= 0
def _check_moment_func(self, f, static_comp, name=None, window=50,
has_min_periods=True, has_center=True,
has_time_rule=True, preserve_nan=True,
fill_value=None, test_stable=False, **kwargs):
with warnings.catch_warnings(record=True):
self._check_ndarray(f, static_comp, window=window,
has_min_periods=has_min_periods,
preserve_nan=preserve_nan,
has_center=has_center, fill_value=fill_value,
test_stable=test_stable, **kwargs)
with warnings.catch_warnings(record=True):
self._check_structures(f, static_comp,
has_min_periods=has_min_periods,
has_time_rule=has_time_rule,
fill_value=fill_value,
has_center=has_center, **kwargs)
# new API
if name is not None:
self._check_structures(f, static_comp, name=name,
has_min_periods=has_min_periods,
has_time_rule=has_time_rule,
fill_value=fill_value,
has_center=has_center, **kwargs)
def _check_ndarray(self, f, static_comp, window=50, has_min_periods=True,
preserve_nan=True, has_center=True, fill_value=None,
test_stable=False, test_window=True, **kwargs):
def get_result(arr, window, min_periods=None, center=False):
return f(arr, window, min_periods=min_periods, center=center, **
kwargs)
result = get_result(self.arr, window)
tm.assert_almost_equal(result[-1], static_comp(self.arr[-50:]))
if preserve_nan:
assert (np.isnan(result[self._nan_locs]).all())
# excluding NaNs correctly
arr = randn(50)
arr[:10] = np.NaN
arr[-10:] = np.NaN
if has_min_periods:
result = get_result(arr, 50, min_periods=30)
tm.assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# min_periods is working correctly
result = get_result(arr, 20, min_periods=15)
assert np.isnan(result[23])
assert not np.isnan(result[24])
assert not np.isnan(result[-6])
assert np.isnan(result[-5])
arr2 = randn(20)
result = get_result(arr2, 10, min_periods=5)
assert isna(result[3])
assert notna(result[4])
# min_periods=0
result0 = get_result(arr, 20, min_periods=0)
result1 = get_result(arr, 20, min_periods=1)
tm.assert_almost_equal(result0, result1)
else:
result = get_result(arr, 50)
tm.assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# GH 7925
if has_center:
if has_min_periods:
result = get_result(arr, 20, min_periods=15, center=True)
expected = get_result(
np.concatenate((arr, np.array([np.NaN] * 9))), 20,
min_periods=15)[9:]
else:
result = get_result(arr, 20, center=True)
expected = get_result(
np.concatenate((arr, np.array([np.NaN] * 9))), 20)[9:]
tm.assert_numpy_array_equal(result, expected)
if test_stable:
result = get_result(self.arr + 1e9, window)
tm.assert_almost_equal(result[-1],
static_comp(self.arr[-50:] + 1e9))
# Test window larger than array, #7297
if test_window:
if has_min_periods:
for minp in (0, len(self.arr) - 1, len(self.arr)):
result = get_result(self.arr, len(self.arr) + 1,
min_periods=minp)
expected = get_result(self.arr, len(self.arr),
min_periods=minp)
nan_mask = np.isnan(result)
tm.assert_numpy_array_equal(nan_mask, np.isnan(expected))
nan_mask = ~nan_mask
tm.assert_almost_equal(result[nan_mask],
expected[nan_mask])
else:
result = get_result(self.arr, len(self.arr) + 1)
expected = get_result(self.arr, len(self.arr))
nan_mask = np.isnan(result)
tm.assert_numpy_array_equal(nan_mask, np.isnan(expected))
nan_mask = ~nan_mask
tm.assert_almost_equal(result[nan_mask], expected[nan_mask])
def _check_structures(self, f, static_comp, name=None,
has_min_periods=True, has_time_rule=True,
has_center=True, fill_value=None, **kwargs):
def get_result(obj, window, min_periods=None, freq=None, center=False):
# check via the API calls if name is provided
if name is not None:
# catch a freq deprecation warning if freq is provided and not
# None
with catch_warnings(record=True):
r = obj.rolling(window=window, min_periods=min_periods,
freq=freq, center=center)
return getattr(r, name)(**kwargs)
# check via the moments API
with catch_warnings(record=True):
return f(obj, window=window, min_periods=min_periods,
freq=freq, center=center, **kwargs)
series_result = get_result(self.series, window=50)
frame_result = get_result(self.frame, window=50)
assert isinstance(series_result, Series)
assert type(frame_result) == DataFrame
# check time_rule works
if has_time_rule:
win = 25
minp = 10
if has_min_periods:
series_result = get_result(self.series[::2], window=win,
min_periods=minp, freq='B')
frame_result = get_result(self.frame[::2], window=win,
min_periods=minp, freq='B')
else:
series_result = get_result(self.series[::2], window=win,
freq='B')
frame_result = get_result(self.frame[::2], window=win,
freq='B')
last_date = series_result.index[-1]
prev_date = last_date - 24 * offsets.BDay()
trunc_series = self.series[::2].truncate(prev_date, last_date)
trunc_frame = self.frame[::2].truncate(prev_date, last_date)
tm.assert_almost_equal(series_result[-1],
static_comp(trunc_series))
tm.assert_series_equal(frame_result.xs(last_date),
trunc_frame.apply(static_comp),
check_names=False)
# GH 7925
if has_center:
# shifter index
s = ['x%d' % x for x in range(12)]
if has_min_periods:
minp = 10
series_xp = get_result(
self.series.reindex(list(self.series.index) + s),
window=25,
min_periods=minp).shift(-12).reindex(self.series.index)
frame_xp = get_result(
self.frame.reindex(list(self.frame.index) + s),
window=25,
min_periods=minp).shift(-12).reindex(self.frame.index)
series_rs = get_result(self.series, window=25,
min_periods=minp, center=True)
frame_rs = get_result(self.frame, window=25, min_periods=minp,
center=True)
else:
series_xp = get_result(
self.series.reindex(list(self.series.index) + s),
window=25).shift(-12).reindex(self.series.index)
frame_xp = get_result(
self.frame.reindex(list(self.frame.index) + s),
window=25).shift(-12).reindex(self.frame.index)
series_rs = get_result(self.series, window=25, center=True)
frame_rs = get_result(self.frame, window=25, center=True)
if fill_value is not None:
series_xp = series_xp.fillna(fill_value)
frame_xp = frame_xp.fillna(fill_value)
tm.assert_series_equal(series_xp, series_rs)
tm.assert_frame_equal(frame_xp, frame_rs)
def test_ewma(self):
self._check_ew(mom.ewma, name='mean')
arr = np.zeros(1000)
arr[5] = 1
with catch_warnings(record=True):
result = mom.ewma(arr, span=100, adjust=False).sum()
assert np.abs(result - 1) < 1e-2
s = Series([1.0, 2.0, 4.0, 8.0])
expected = Series([1.0, 1.6, 2.736842, 4.923077])
for f in [lambda s: s.ewm(com=2.0, adjust=True).mean(),
lambda s: s.ewm(com=2.0, adjust=True,
ignore_na=False).mean(),
lambda s: s.ewm(com=2.0, adjust=True, ignore_na=True).mean(),
]:
result = f(s)
tm.assert_series_equal(result, expected)
expected = Series([1.0, 1.333333, 2.222222, 4.148148])
for f in [lambda s: s.ewm(com=2.0, adjust=False).mean(),
lambda s: s.ewm(com=2.0, adjust=False,
ignore_na=False).mean(),
lambda s: s.ewm(com=2.0, adjust=False,
ignore_na=True).mean(),
]:
result = f(s)
tm.assert_series_equal(result, expected)
def test_ewma_nan_handling(self):
s = Series([1.] + [np.nan] * 5 + [1.])
result = s.ewm(com=5).mean()
tm.assert_series_equal(result, Series([1.] * len(s)))
s = Series([np.nan] * 2 + [1.] + [np.nan] * 2 + [1.])
result = s.ewm(com=5).mean()
tm.assert_series_equal(result, Series([np.nan] * 2 + [1.] * 4))
# GH 7603
s0 = Series([np.nan, 1., 101.])
s1 = Series([1., np.nan, 101.])
s2 = Series([np.nan, 1., np.nan, np.nan, 101., np.nan])
s3 = Series([1., np.nan, 101., 50.])
com = 2.
alpha = 1. / (1. + com)
def simple_wma(s, w):
return (s.multiply(w).cumsum() / w.cumsum()).fillna(method='ffill')
for (s, adjust, ignore_na, w) in [
(s0, True, False, [np.nan, (1. - alpha), 1.]),
(s0, True, True, [np.nan, (1. - alpha), 1.]),
(s0, False, False, [np.nan, (1. - alpha), alpha]),
(s0, False, True, [np.nan, (1. - alpha), alpha]),
(s1, True, False, [(1. - alpha) ** 2, np.nan, 1.]),
(s1, True, True, [(1. - alpha), np.nan, 1.]),
(s1, False, False, [(1. - alpha) ** 2, np.nan, alpha]),
(s1, False, True, [(1. - alpha), np.nan, alpha]),
(s2, True, False, [np.nan, (1. - alpha) **
3, np.nan, np.nan, 1., np.nan]),
(s2, True, True, [np.nan, (1. - alpha),
np.nan, np.nan, 1., np.nan]),
(s2, False, False, [np.nan, (1. - alpha) **
3, np.nan, np.nan, alpha, np.nan]),
(s2, False, True, [np.nan, (1. - alpha),
np.nan, np.nan, alpha, np.nan]),
(s3, True, False, [(1. - alpha) **
3, np.nan, (1. - alpha), 1.]),
(s3, True, True, [(1. - alpha) **
2, np.nan, (1. - alpha), 1.]),
(s3, False, False, [(1. - alpha) ** 3, np.nan,
(1. - alpha) * alpha,
alpha * ((1. - alpha) ** 2 + alpha)]),
(s3, False, True, [(1. - alpha) ** 2,
np.nan, (1. - alpha) * alpha, alpha])]:
expected = simple_wma(s, Series(w))
result = s.ewm(com=com, adjust=adjust, ignore_na=ignore_na).mean()
tm.assert_series_equal(result, expected)
if ignore_na is False:
# check that ignore_na defaults to False
result = s.ewm(com=com, adjust=adjust).mean()
tm.assert_series_equal(result, expected)
def test_ewmvar(self):
self._check_ew(mom.ewmvar, name='var')
def test_ewmvol(self):
self._check_ew(mom.ewmvol, name='vol')
def test_ewma_span_com_args(self):
with catch_warnings(record=True):
A = mom.ewma(self.arr, com=9.5)
B = mom.ewma(self.arr, span=20)
tm.assert_almost_equal(A, B)
pytest.raises(ValueError, mom.ewma, self.arr, com=9.5, span=20)
pytest.raises(ValueError, mom.ewma, self.arr)
def test_ewma_halflife_arg(self):
with catch_warnings(record=True):
A = mom.ewma(self.arr, com=13.932726172912965)
B = mom.ewma(self.arr, halflife=10.0)
tm.assert_almost_equal(A, B)
pytest.raises(ValueError, mom.ewma, self.arr, span=20,
halflife=50)
pytest.raises(ValueError, mom.ewma, self.arr, com=9.5,
halflife=50)
pytest.raises(ValueError, mom.ewma, self.arr, com=9.5, span=20,
halflife=50)
pytest.raises(ValueError, mom.ewma, self.arr)
def test_ewma_alpha_old_api(self):
# GH 10789
with catch_warnings(record=True):
a = mom.ewma(self.arr, alpha=0.61722699889169674)
b = mom.ewma(self.arr, com=0.62014947789973052)
c = mom.ewma(self.arr, span=2.240298955799461)
d = mom.ewma(self.arr, halflife=0.721792864318)
tm.assert_numpy_array_equal(a, b)
tm.assert_numpy_array_equal(a, c)
tm.assert_numpy_array_equal(a, d)
def test_ewma_alpha_arg_old_api(self):
# GH 10789
with catch_warnings(record=True):
pytest.raises(ValueError, mom.ewma, self.arr)
pytest.raises(ValueError, mom.ewma, self.arr,
com=10.0, alpha=0.5)
pytest.raises(ValueError, mom.ewma, self.arr,
span=10.0, alpha=0.5)
pytest.raises(ValueError, mom.ewma, self.arr,
halflife=10.0, alpha=0.5)
def test_ewm_alpha(self):
# GH 10789
s = Series(self.arr)
a = s.ewm(alpha=0.61722699889169674).mean()
b = s.ewm(com=0.62014947789973052).mean()
c = s.ewm(span=2.240298955799461).mean()
d = s.ewm(halflife=0.721792864318).mean()
tm.assert_series_equal(a, b)
tm.assert_series_equal(a, c)
tm.assert_series_equal(a, d)
def test_ewm_alpha_arg(self):
# GH 10789
s = Series(self.arr)
pytest.raises(ValueError, s.ewm)
pytest.raises(ValueError, s.ewm, com=10.0, alpha=0.5)
pytest.raises(ValueError, s.ewm, span=10.0, alpha=0.5)
pytest.raises(ValueError, s.ewm, halflife=10.0, alpha=0.5)
def test_ewm_domain_checks(self):
# GH 12492
s = Series(self.arr)
# com must satisfy: com >= 0
pytest.raises(ValueError, s.ewm, com=-0.1)
s.ewm(com=0.0)
s.ewm(com=0.1)
# span must satisfy: span >= 1
pytest.raises(ValueError, s.ewm, span=-0.1)
pytest.raises(ValueError, s.ewm, span=0.0)
pytest.raises(ValueError, s.ewm, span=0.9)
s.ewm(span=1.0)
s.ewm(span=1.1)
# halflife must satisfy: halflife > 0
pytest.raises(ValueError, s.ewm, halflife=-0.1)
pytest.raises(ValueError, s.ewm, halflife=0.0)
s.ewm(halflife=0.1)
# alpha must satisfy: 0 < alpha <= 1
pytest.raises(ValueError, s.ewm, alpha=-0.1)
pytest.raises(ValueError, s.ewm, alpha=0.0)
s.ewm(alpha=0.1)
s.ewm(alpha=1.0)
pytest.raises(ValueError, s.ewm, alpha=1.1)
def test_ew_empty_arrays(self):
arr = np.array([], dtype=np.float64)
funcs = [mom.ewma, mom.ewmvol, mom.ewmvar]
for f in funcs:
with catch_warnings(record=True):
result = f(arr, 3)
tm.assert_almost_equal(result, arr)
def _check_ew(self, func, name=None):
with catch_warnings(record=True):
self._check_ew_ndarray(func, name=name)
self._check_ew_structures(func, name=name)
def _check_ew_ndarray(self, func, preserve_nan=False, name=None):
result = func(self.arr, com=10)
if preserve_nan:
assert (np.isnan(result[self._nan_locs]).all())
# excluding NaNs correctly
arr = randn(50)
arr[:10] = np.NaN
arr[-10:] = np.NaN
s = Series(arr)
# check min_periods
# GH 7898
result = func(s, 50, min_periods=2)
assert np.isnan(result.values[:11]).all()
assert not np.isnan(result.values[11:]).any()
for min_periods in (0, 1):
result = func(s, 50, min_periods=min_periods)
if func == mom.ewma:
assert np.isnan(result.values[:10]).all()
assert not np.isnan(result.values[10:]).any()
else:
# ewmstd, ewmvol, ewmvar (with bias=False) require at least two
# values
assert np.isnan(result.values[:11]).all()
assert not np.isnan(result.values[11:]).any()
# check series of length 0
result = func(Series([]), 50, min_periods=min_periods)
tm.assert_series_equal(result, Series([]))
# check series of length 1
result = func(Series([1.]), 50, min_periods=min_periods)
if func == mom.ewma:
tm.assert_series_equal(result, Series([1.]))
else:
# ewmstd, ewmvol, ewmvar with bias=False require at least two
# values
tm.assert_series_equal(result, Series([np.NaN]))
# pass in ints
result2 = func(np.arange(50), span=10)
assert result2.dtype == np.float_
def _check_ew_structures(self, func, name):
series_result = getattr(self.series.ewm(com=10), name)()
assert isinstance(series_result, Series)
frame_result = getattr(self.frame.ewm(com=10), name)()
assert type(frame_result) == DataFrame
class TestPairwise(object):
# GH 7738
df1s = [DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0, 1]),
DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 0]),
DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 1]),
DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]],
columns=['C', 'C']),
DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1., 0]),
DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0., 1]),
DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=['C', 1]),
DataFrame([[2., 4.], [1., 2.], [5., 2.], [8., 1.]],
columns=[1, 0.]),
DataFrame([[2, 4.], [1, 2.], [5, 2.], [8, 1.]],
columns=[0, 1.]),
DataFrame([[2, 4], [1, 2], [5, 2], [8, 1.]],
columns=[1., 'X']), ]
df2 = DataFrame([[None, 1, 1], [None, 1, 2],
[None, 3, 2], [None, 8, 1]], columns=['Y', 'Z', 'X'])
s = Series([1, 1, 3, 8])
def compare(self, result, expected):
# since we have sorted the results
# we can only compare non-nans
result = result.dropna().values
expected = expected.dropna().values
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize('f', [lambda x: x.cov(), lambda x: x.corr()])
def test_no_flex(self, f):
# DataFrame methods (which do not call _flex_binary_moment())
results = [f(df) for df in self.df1s]
for (df, result) in zip(self.df1s, results):
tm.assert_index_equal(result.index, df.columns)
tm.assert_index_equal(result.columns, df.columns)
for i, result in enumerate(results):
if i > 0:
self.compare(result, results[0])
@pytest.mark.parametrize(
'f', [lambda x: x.expanding().cov(pairwise=True),
lambda x: x.expanding().corr(pairwise=True),
lambda x: x.rolling(window=3).cov(pairwise=True),
lambda x: x.rolling(window=3).corr(pairwise=True),
lambda x: x.ewm(com=3).cov(pairwise=True),
lambda x: x.ewm(com=3).corr(pairwise=True)])
def test_pairwise_with_self(self, f):
# DataFrame with itself, pairwise=True
results = [f(df) for df in self.df1s]
for (df, result) in zip(self.df1s, results):
tm.assert_index_equal(result.index.levels[0],
df.index,
check_names=False)
tm.assert_index_equal(result.index.levels[1],
df.columns,
check_names=False)
tm.assert_index_equal(result.columns, df.columns)
for i, result in enumerate(results):
if i > 0:
self.compare(result, results[0])
@pytest.mark.parametrize(
'f', [lambda x: x.expanding().cov(pairwise=False),
lambda x: x.expanding().corr(pairwise=False),
lambda x: x.rolling(window=3).cov(pairwise=False),
lambda x: x.rolling(window=3).corr(pairwise=False),
lambda x: x.ewm(com=3).cov(pairwise=False),
lambda x: x.ewm(com=3).corr(pairwise=False), ])
def test_no_pairwise_with_self(self, f):
# DataFrame with itself, pairwise=False
results = [f(df) for df in self.df1s]
for (df, result) in zip(self.df1s, results):
tm.assert_index_equal(result.index, df.index)
tm.assert_index_equal(result.columns, df.columns)
for i, result in enumerate(results):
if i > 0:
self.compare(result, results[0])
@pytest.mark.parametrize(
'f', [lambda x, y: x.expanding().cov(y, pairwise=True),
lambda x, y: x.expanding().corr(y, pairwise=True),
lambda x, y: x.rolling(window=3).cov(y, pairwise=True),
lambda x, y: x.rolling(window=3).corr(y, pairwise=True),
lambda x, y: x.ewm(com=3).cov(y, pairwise=True),
lambda x, y: x.ewm(com=3).corr(y, pairwise=True), ])
def test_pairwise_with_other(self, f):
# DataFrame with another DataFrame, pairwise=True
results = [f(df, self.df2) for df in self.df1s]
for (df, result) in zip(self.df1s, results):
tm.assert_index_equal(result.index.levels[0],
df.index,
check_names=False)
tm.assert_index_equal(result.index.levels[1],
self.df2.columns,
check_names=False)
for i, result in enumerate(results):
if i > 0:
self.compare(result, results[0])
@pytest.mark.parametrize(
'f', [lambda x, y: x.expanding().cov(y, pairwise=False),
lambda x, y: x.expanding().corr(y, pairwise=False),
lambda x, y: x.rolling(window=3).cov(y, pairwise=False),
lambda x, y: x.rolling(window=3).corr(y, pairwise=False),
lambda x, y: x.ewm(com=3).cov(y, pairwise=False),
lambda x, y: x.ewm(com=3).corr(y, pairwise=False), ])
def test_no_pairwise_with_other(self, f):
# DataFrame with another DataFrame, pairwise=False
results = [f(df, self.df2) if df.columns.is_unique else None
for df in self.df1s]
for (df, result) in zip(self.df1s, results):
if result is not None:
with catch_warnings(record=True):
# we can have int and str columns
expected_index = df.index.union(self.df2.index)
expected_columns = df.columns.union(self.df2.columns)
tm.assert_index_equal(result.index, expected_index)
tm.assert_index_equal(result.columns, expected_columns)
else:
tm.assert_raises_regex(
ValueError, "'arg1' columns are not unique", f, df,
self.df2)
tm.assert_raises_regex(
ValueError, "'arg2' columns are not unique", f,
self.df2, df)
@pytest.mark.parametrize(
'f', [lambda x, y: x.expanding().cov(y),
lambda x, y: x.expanding().corr(y),
lambda x, y: x.rolling(window=3).cov(y),
lambda x, y: x.rolling(window=3).corr(y),
lambda x, y: x.ewm(com=3).cov(y),
lambda x, y: x.ewm(com=3).corr(y), ])
def test_pairwise_with_series(self, f):
# DataFrame with a Series
results = ([f(df, self.s) for df in self.df1s] +
[f(self.s, df) for df in self.df1s])
for (df, result) in zip(self.df1s, results):
tm.assert_index_equal(result.index, df.index)
tm.assert_index_equal(result.columns, df.columns)
for i, result in enumerate(results):
if i > 0:
self.compare(result, results[0])
# create the data only once as we are not setting it
def _create_consistency_data():
def create_series():
return [Series(),
Series([np.nan]),
Series([np.nan, np.nan]),
Series([3.]),
Series([np.nan, 3.]),
Series([3., np.nan]),
Series([1., 3.]),
Series([2., 2.]),
Series([3., 1.]),
Series([5., 5., 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan,
np.nan]),
Series([np.nan, 5., 5., 5., np.nan, np.nan, np.nan, 5., 5.,
np.nan, np.nan]),
Series([np.nan, np.nan, 5., 5., np.nan, np.nan, np.nan, 5., 5.,
np.nan, np.nan]),
Series([np.nan, 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7.,
12., 13., 14., 15.]),
Series([np.nan, 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3.,
12., 13., 14., 15.]),
Series([2., 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7.,
12., 13., 14., 15.]),
Series([2., 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3.,
12., 13., 14., 15.]),
Series(range(10)),
Series(range(20, 0, -2)), ]
def create_dataframes():
return ([DataFrame(),
DataFrame(columns=['a']),
DataFrame(columns=['a', 'a']),
DataFrame(columns=['a', 'b']),
DataFrame(np.arange(10).reshape((5, 2))),
DataFrame(np.arange(25).reshape((5, 5))),
DataFrame(np.arange(25).reshape((5, 5)),
columns=['a', 'b', 99, 'd', 'd'])] +
[DataFrame(s) for s in create_series()])
def is_constant(x):
values = x.values.ravel()
return len(set(values[notna(values)])) == 1
def no_nans(x):
return x.notna().all().all()
# data is a tuple(object, is_contant, no_nans)
data = create_series() + create_dataframes()
return [(x, is_constant(x), no_nans(x)) for x in data]
_consistency_data = _create_consistency_data()
def _rolling_consistency_cases():
for window in [1, 2, 3, 10, 20]:
for min_periods in set([0, 1, 2, 3, 4, window]):
if min_periods and (min_periods > window):
continue
for center in [False, True]:
yield window, min_periods, center
class TestMomentsConsistency(Base):
base_functions = [
(lambda v: Series(v).count(), None, 'count'),
(lambda v: Series(v).max(), None, 'max'),
(lambda v: Series(v).min(), None, 'min'),
(lambda v: Series(v).sum(), None, 'sum'),
(lambda v: Series(v).mean(), None, 'mean'),
(lambda v: Series(v).std(), 1, 'std'),
(lambda v: Series(v).cov(Series(v)), None, 'cov'),
(lambda v: Series(v).corr(Series(v)), None, 'corr'),
(lambda v: Series(v).var(), 1, 'var'),
# restore once GH 8086 is fixed
# lambda v: Series(v).skew(), 3, 'skew'),
# (lambda v: Series(v).kurt(), 4, 'kurt'),
# (lambda x, min_periods: mom.expanding_quantile(x, 0.3,
# min_periods=min_periods, 'quantile'),
# restore once GH 8084 is fixed
# lambda v: Series(v).quantile(0.3), None, 'quantile'),
(lambda v: Series(v).median(), None, 'median'),
(np.nanmax, 1, 'max'),
(np.nanmin, 1, 'min'),
(np.nansum, 1, 'sum'),
]
if np.__version__ >= LooseVersion('1.8.0'):
base_functions += [
(np.nanmean, 1, 'mean'),
(lambda v: np.nanstd(v, ddof=1), 1, 'std'),
(lambda v: np.nanvar(v, ddof=1), 1, 'var'),
]
if np.__version__ >= LooseVersion('1.9.0'):
base_functions += [(np.nanmedian, 1, 'median'), ]
no_nan_functions = [
(np.max, None, 'max'),
(np.min, None, 'min'),
(np.sum, None, 'sum'),
(np.mean, None, 'mean'),
(lambda v: np.std(v, ddof=1), 1, 'std'),
(lambda v: np.var(v, ddof=1), 1, 'var'),
(np.median, None, 'median'),
]
def _create_data(self):
super(TestMomentsConsistency, self)._create_data()
self.data = _consistency_data
def setup_method(self, method):
self._create_data()
def _test_moments_consistency(self, min_periods, count, mean, mock_mean,
corr, var_unbiased=None, std_unbiased=None,
cov_unbiased=None, var_biased=None,
std_biased=None, cov_biased=None,
var_debiasing_factors=None):
def _non_null_values(x):
values = x.values.ravel()
return set(values[notna(values)].tolist())
for (x, is_constant, no_nans) in self.data:
count_x = count(x)
mean_x = mean(x)
if mock_mean:
# check that mean equals mock_mean
expected = mock_mean(x)
assert_equal(mean_x, expected.astype('float64'))
# check that correlation of a series with itself is either 1 or NaN
corr_x_x = corr(x, x)
# assert _non_null_values(corr_x_x).issubset(set([1.]))
# restore once rolling_cov(x, x) is identically equal to var(x)
if is_constant:
exp = x.max() if isinstance(x, Series) else x.max().max()
# check mean of constant series
expected = x * np.nan
expected[count_x >= max(min_periods, 1)] = exp
assert_equal(mean_x, expected)
# check correlation of constant series with itself is NaN
expected[:] = np.nan
assert_equal(corr_x_x, expected)
if var_unbiased and var_biased and var_debiasing_factors:
# check variance debiasing factors
var_unbiased_x = var_unbiased(x)
var_biased_x = var_biased(x)
var_debiasing_factors_x = var_debiasing_factors(x)
assert_equal(var_unbiased_x, var_biased_x *
var_debiasing_factors_x)
for (std, var, cov) in [(std_biased, var_biased, cov_biased),
(std_unbiased, var_unbiased, cov_unbiased)
]:
# check that var(x), std(x), and cov(x) are all >= 0
var_x = var(x)
std_x = std(x)
assert not (var_x < 0).any().any()
assert not (std_x < 0).any().any()
if cov:
cov_x_x = cov(x, x)
assert not (cov_x_x < 0).any().any()
# check that var(x) == cov(x, x)
assert_equal(var_x, cov_x_x)
# check that var(x) == std(x)^2
assert_equal(var_x, std_x * std_x)
if var is var_biased:
# check that biased var(x) == mean(x^2) - mean(x)^2
mean_x2 = mean(x * x)
assert_equal(var_x, mean_x2 - (mean_x * mean_x))
if is_constant:
# check that variance of constant series is identically 0
assert not (var_x > 0).any().any()
expected = x * np.nan
expected[count_x >= max(min_periods, 1)] = 0.
if var is var_unbiased:
expected[count_x < 2] = np.nan
assert_equal(var_x, expected)
if isinstance(x, Series):
for (y, is_constant, no_nans) in self.data:
if not x.isna().equals(y.isna()):
# can only easily test two Series with similar
# structure
continue
# check that cor(x, y) is symmetric
corr_x_y = corr(x, y)
corr_y_x = corr(y, x)
assert_equal(corr_x_y, corr_y_x)
if cov:
# check that cov(x, y) is symmetric
cov_x_y = cov(x, y)
cov_y_x = cov(y, x)
assert_equal(cov_x_y, cov_y_x)
# check that cov(x, y) == (var(x+y) - var(x) -
# var(y)) / 2
var_x_plus_y = var(x + y)
var_y = var(y)
assert_equal(cov_x_y, 0.5 *
(var_x_plus_y - var_x - var_y))
# check that corr(x, y) == cov(x, y) / (std(x) *
# std(y))
std_y = std(y)
assert_equal(corr_x_y, cov_x_y / (std_x * std_y))
if cov is cov_biased:
# check that biased cov(x, y) == mean(x*y) -
# mean(x)*mean(y)
mean_y = mean(y)
mean_x_times_y = mean(x * y)
assert_equal(cov_x_y, mean_x_times_y -
(mean_x * mean_y))
@pytest.mark.slow
@pytest.mark.parametrize(
'min_periods, adjust, ignore_na', product([0, 1, 2, 3, 4],
[True, False],
[False, True]))
def test_ewm_consistency(self, min_periods, adjust, ignore_na):
def _weights(s, com, adjust, ignore_na):
if isinstance(s, DataFrame):
if not len(s.columns):
return DataFrame(index=s.index, columns=s.columns)
w = concat([
_weights(s.iloc[:, i], com=com, adjust=adjust,
ignore_na=ignore_na)
for i, _ in enumerate(s.columns)], axis=1)
w.index = s.index
w.columns = s.columns
return w
w = Series(np.nan, index=s.index)
alpha = 1. / (1. + com)
if ignore_na:
w[s.notna()] = _weights(s[s.notna()], com=com,
adjust=adjust, ignore_na=False)
elif adjust:
for i in range(len(s)):
if s.iat[i] == s.iat[i]:
w.iat[i] = pow(1. / (1. - alpha), i)
else:
sum_wts = 0.
prev_i = -1
for i in range(len(s)):
if s.iat[i] == s.iat[i]:
if prev_i == -1:
w.iat[i] = 1.
else:
w.iat[i] = alpha * sum_wts / pow(1. - alpha,
i - prev_i)
sum_wts += w.iat[i]
prev_i = i
return w
def _variance_debiasing_factors(s, com, adjust, ignore_na):
weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
cum_sum = weights.cumsum().fillna(method='ffill')
cum_sum_sq = (weights * weights).cumsum().fillna(method='ffill')
numerator = cum_sum * cum_sum
denominator = numerator - cum_sum_sq
denominator[denominator <= 0.] = np.nan
return numerator / denominator
def _ewma(s, com, min_periods, adjust, ignore_na):
weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
result = s.multiply(weights).cumsum().divide(weights.cumsum(
)).fillna(method='ffill')
result[s.expanding().count() < (max(min_periods, 1) if min_periods
else 1)] = np.nan
return result
com = 3.
# test consistency between different ewm* moments
self._test_moments_consistency(
min_periods=min_periods,
count=lambda x: x.expanding().count(),
mean=lambda x: x.ewm(com=com, min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).mean(),
mock_mean=lambda x: _ewma(x, com=com,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na),
corr=lambda x, y: x.ewm(com=com, min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).corr(y),
var_unbiased=lambda x: (
x.ewm(com=com, min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).var(bias=False)),
std_unbiased=lambda x: (
x.ewm(com=com, min_periods=min_periods,
adjust=adjust, ignore_na=ignore_na)
.std(bias=False)),
cov_unbiased=lambda x, y: (
x.ewm(com=com, min_periods=min_periods,
adjust=adjust, ignore_na=ignore_na)
.cov(y, bias=False)),
var_biased=lambda x: (
x.ewm(com=com, min_periods=min_periods,
adjust=adjust, ignore_na=ignore_na)
.var(bias=True)),
std_biased=lambda x: x.ewm(com=com, min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).std(bias=True),
cov_biased=lambda x, y: (
x.ewm(com=com, min_periods=min_periods,
adjust=adjust, ignore_na=ignore_na)
.cov(y, bias=True)),
var_debiasing_factors=lambda x: (
_variance_debiasing_factors(x, com=com, adjust=adjust,
ignore_na=ignore_na)))
@pytest.mark.slow
@pytest.mark.parametrize(
'min_periods', [0, 1, 2, 3, 4])
def test_expanding_consistency(self, min_periods):
# suppress warnings about empty slices, as we are deliberately testing
# with empty/0-length Series/DataFrames
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=".*(empty slice|0 for slice).*",
category=RuntimeWarning)
# test consistency between different expanding_* moments
self._test_moments_consistency(
min_periods=min_periods,
count=lambda x: x.expanding().count(),
mean=lambda x: x.expanding(
min_periods=min_periods).mean(),
mock_mean=lambda x: x.expanding(
min_periods=min_periods).sum() / x.expanding().count(),
corr=lambda x, y: x.expanding(
min_periods=min_periods).corr(y),
var_unbiased=lambda x: x.expanding(
min_periods=min_periods).var(),
std_unbiased=lambda x: x.expanding(
min_periods=min_periods).std(),
cov_unbiased=lambda x, y: x.expanding(
min_periods=min_periods).cov(y),
var_biased=lambda x: x.expanding(
min_periods=min_periods).var(ddof=0),
std_biased=lambda x: x.expanding(
min_periods=min_periods).std(ddof=0),
cov_biased=lambda x, y: x.expanding(
min_periods=min_periods).cov(y, ddof=0),
var_debiasing_factors=lambda x: (
x.expanding().count() /
(x.expanding().count() - 1.)
.replace(0., np.nan)))
# test consistency between expanding_xyz() and either (a)
# expanding_apply of Series.xyz(), or (b) expanding_apply of
# np.nanxyz()
for (x, is_constant, no_nans) in self.data:
functions = self.base_functions
# GH 8269
if no_nans:
functions = self.base_functions + self.no_nan_functions
for (f, require_min_periods, name) in functions:
expanding_f = getattr(
x.expanding(min_periods=min_periods), name)
if (require_min_periods and
(min_periods is not None) and
(min_periods < require_min_periods)):
continue
if name == 'count':
expanding_f_result = expanding_f()
expanding_apply_f_result = x.expanding(
min_periods=0).apply(func=f)
else:
if name in ['cov', 'corr']:
expanding_f_result = expanding_f(
pairwise=False)
else:
expanding_f_result = expanding_f()
expanding_apply_f_result = x.expanding(
min_periods=min_periods).apply(func=f)
# GH 9422
if name in ['sum', 'prod']:
assert_equal(expanding_f_result,
expanding_apply_f_result)
@pytest.mark.slow
@pytest.mark.parametrize(
'window,min_periods,center', list(_rolling_consistency_cases()))
def test_rolling_consistency(self, window, min_periods, center):
# suppress warnings about empty slices, as we are deliberately testing
# with empty/0-length Series/DataFrames
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=".*(empty slice|0 for slice).*",
category=RuntimeWarning)
# test consistency between different rolling_* moments
self._test_moments_consistency(
min_periods=min_periods,
count=lambda x: (
x.rolling(window=window, center=center)
.count()),
mean=lambda x: (
x.rolling(window=window, min_periods=min_periods,
center=center).mean()),
mock_mean=lambda x: (
x.rolling(window=window,
min_periods=min_periods,
center=center).sum()
.divide(x.rolling(window=window,
min_periods=min_periods,
center=center).count())),
corr=lambda x, y: (
x.rolling(window=window, min_periods=min_periods,
center=center).corr(y)),
var_unbiased=lambda x: (
x.rolling(window=window, min_periods=min_periods,
center=center).var()),
std_unbiased=lambda x: (
x.rolling(window=window, min_periods=min_periods,
center=center).std()),
cov_unbiased=lambda x, y: (
x.rolling(window=window, min_periods=min_periods,
center=center).cov(y)),
var_biased=lambda x: (
x.rolling(window=window, min_periods=min_periods,
center=center).var(ddof=0)),
std_biased=lambda x: (
x.rolling(window=window, min_periods=min_periods,
center=center).std(ddof=0)),
cov_biased=lambda x, y: (
x.rolling(window=window, min_periods=min_periods,
center=center).cov(y, ddof=0)),
var_debiasing_factors=lambda x: (
x.rolling(window=window, center=center).count()
.divide((x.rolling(window=window, center=center)
.count() - 1.)
.replace(0., np.nan))))
# test consistency between rolling_xyz() and either (a)
# rolling_apply of Series.xyz(), or (b) rolling_apply of
# np.nanxyz()
for (x, is_constant, no_nans) in self.data:
functions = self.base_functions
# GH 8269
if no_nans:
functions = self.base_functions + self.no_nan_functions
for (f, require_min_periods, name) in functions:
rolling_f = getattr(
x.rolling(window=window, center=center,
min_periods=min_periods), name)
if require_min_periods and (
min_periods is not None) and (
min_periods < require_min_periods):
continue
if name == 'count':
rolling_f_result = rolling_f()
rolling_apply_f_result = x.rolling(
window=window, min_periods=0,
center=center).apply(func=f)
else:
if name in ['cov', 'corr']:
rolling_f_result = rolling_f(
pairwise=False)
else:
rolling_f_result = rolling_f()
rolling_apply_f_result = x.rolling(
window=window, min_periods=min_periods,
center=center).apply(func=f)
# GH 9422
if name in ['sum', 'prod']:
assert_equal(rolling_f_result,
rolling_apply_f_result)
# binary moments
def test_rolling_cov(self):
A = self.series
B = A + randn(len(A))
result = A.rolling(window=50, min_periods=25).cov(B)
tm.assert_almost_equal(result[-1], np.cov(A[-50:], B[-50:])[0, 1])
def test_rolling_cov_pairwise(self):
self._check_pairwise_moment('rolling', 'cov', window=10, min_periods=5)
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = A.rolling(window=50, min_periods=25).corr(B)
tm.assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
# test for correct bias correction
a = tm.makeTimeSeries()
b = tm.makeTimeSeries()
a[:5] = np.nan
b[:10] = np.nan
result = a.rolling(window=len(a), min_periods=1).corr(b)
tm.assert_almost_equal(result[-1], a.corr(b))
def test_rolling_corr_pairwise(self):
self._check_pairwise_moment('rolling', 'corr', window=10,
min_periods=5)
@pytest.mark.parametrize('window', range(7))
def test_rolling_corr_with_zero_variance(self, window):
# GH 18430
s = pd.Series(np.zeros(20))
other = pd.Series(np.arange(20))
assert s.rolling(window=window).corr(other=other).isna().all()
def _check_pairwise_moment(self, dispatch, name, **kwargs):
def get_result(obj, obj2=None):
return getattr(getattr(obj, dispatch)(**kwargs), name)(obj2)
result = get_result(self.frame)
result = result.loc[(slice(None), 1), 5]
result.index = result.index.droplevel(1)
expected = get_result(self.frame[1], self.frame[5])
tm.assert_series_equal(result, expected, check_names=False)
def test_flex_binary_moment(self):
# GH3155
# don't blow the stack
pytest.raises(TypeError, rwindow._flex_binary_moment, 5, 6, None)
def test_corr_sanity(self):
# GH 3155
df = DataFrame(np.array(
[[0.87024726, 0.18505595], [0.64355431, 0.3091617],
[0.92372966, 0.50552513], [0.00203756, 0.04520709],
[0.84780328, 0.33394331], [0.78369152, 0.63919667]]))
res = df[0].rolling(5, center=True).corr(df[1])
assert all([np.abs(np.nan_to_num(x)) <= 1 for x in res])
# and some fuzzing
for _ in range(10):
df = DataFrame(np.random.rand(30, 2))
res = df[0].rolling(5, center=True).corr(df[1])
try:
assert all([np.abs(np.nan_to_num(x)) <= 1 for x in res])
except AssertionError:
print(res)
def test_flex_binary_frame(self):
def _check(method):
series = self.frame[1]
res = getattr(series.rolling(window=10), method)(self.frame)
res2 = getattr(self.frame.rolling(window=10), method)(series)
exp = self.frame.apply(lambda x: getattr(
series.rolling(window=10), method)(x))
tm.assert_frame_equal(res, exp)
tm.assert_frame_equal(res2, exp)
frame2 = self.frame.copy()
frame2.values[:] = np.random.randn(*frame2.shape)
res3 = getattr(self.frame.rolling(window=10), method)(frame2)
exp = DataFrame(dict((k, getattr(self.frame[k].rolling(
window=10), method)(frame2[k])) for k in self.frame))
tm.assert_frame_equal(res3, exp)
methods = ['corr', 'cov']
for meth in methods:
_check(meth)
def test_ewmcov(self):
self._check_binary_ew('cov')
def test_ewmcov_pairwise(self):
self._check_pairwise_moment('ewm', 'cov', span=10, min_periods=5)
def test_ewmcorr(self):
self._check_binary_ew('corr')
def test_ewmcorr_pairwise(self):
self._check_pairwise_moment('ewm', 'corr', span=10, min_periods=5)
def _check_binary_ew(self, name):
def func(A, B, com, **kwargs):
return getattr(A.ewm(com, **kwargs), name)(B)
A = Series(randn(50), index=np.arange(50))
B = A[2:] + randn(48)
A[:10] = np.NaN
B[-10:] = np.NaN
result = func(A, B, 20, min_periods=5)
assert np.isnan(result.values[:14]).all()
assert not np.isnan(result.values[14:]).any()
# GH 7898
for min_periods in (0, 1, 2):
result = func(A, B, 20, min_periods=min_periods)
# binary functions (ewmcov, ewmcorr) with bias=False require at
# least two values
assert np.isnan(result.values[:11]).all()
assert not np.isnan(result.values[11:]).any()
# check series of length 0
result = func(Series([]), Series([]), 50, min_periods=min_periods)
tm.assert_series_equal(result, Series([]))
# check series of length 1
result = func(
Series([1.]), Series([1.]), 50, min_periods=min_periods)
tm.assert_series_equal(result, Series([np.NaN]))
pytest.raises(Exception, func, A, randn(50), 20, min_periods=5)
def test_expanding_apply(self):
ser = Series([])
tm.assert_series_equal(ser, ser.expanding().apply(lambda x: x.mean()))
def expanding_mean(x, min_periods=1, freq=None):
return mom.expanding_apply(x, lambda x: x.mean(),
min_periods=min_periods, freq=freq)
self._check_expanding(expanding_mean, np.mean)
# GH 8080
s = Series([None, None, None])
result = s.expanding(min_periods=0).apply(lambda x: len(x))
expected = Series([1., 2., 3.])
tm.assert_series_equal(result, expected)
def test_expanding_apply_args_kwargs(self):
def mean_w_arg(x, const):
return np.mean(x) + const
df = DataFrame(np.random.rand(20, 3))
expected = df.expanding().apply(np.mean) + 20.
tm.assert_frame_equal(df.expanding().apply(mean_w_arg, args=(20, )),
expected)
tm.assert_frame_equal(df.expanding().apply(mean_w_arg,
kwargs={'const': 20}),
expected)
def test_expanding_corr(self):
A = self.series.dropna()
B = (A + randn(len(A)))[:-5]
result = A.expanding().corr(B)
rolling_result = A.rolling(window=len(A), min_periods=1).corr(B)
tm.assert_almost_equal(rolling_result, result)
def test_expanding_count(self):
result = self.series.expanding().count()
tm.assert_almost_equal(result, self.series.rolling(
window=len(self.series)).count())
def test_expanding_quantile(self):
result = self.series.expanding().quantile(0.5)
rolling_result = self.series.rolling(window=len(self.series),
min_periods=1).quantile(0.5)
tm.assert_almost_equal(result, rolling_result)
def test_expanding_cov(self):
A = self.series
B = (A + randn(len(A)))[:-5]
result = A.expanding().cov(B)
rolling_result = A.rolling(window=len(A), min_periods=1).cov(B)
tm.assert_almost_equal(rolling_result, result)
def test_expanding_max(self):
self._check_expanding(mom.expanding_max, np.max, preserve_nan=False)
def test_expanding_cov_pairwise(self):
result = self.frame.expanding().corr()
rolling_result = self.frame.rolling(window=len(self.frame),
min_periods=1).corr()
tm.assert_frame_equal(result, rolling_result)
def test_expanding_corr_pairwise(self):
result = self.frame.expanding().corr()
rolling_result = self.frame.rolling(window=len(self.frame),
min_periods=1).corr()
tm.assert_frame_equal(result, rolling_result)
def test_expanding_cov_diff_index(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = s1.expanding().cov(s2)
expected = Series([None, None, 2.0])
tm.assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = s1.expanding().cov(s2a)
tm.assert_series_equal(result, expected)
s1 = Series([7, 8, 10], index=[0, 1, 3])
s2 = Series([7, 9, 10], index=[0, 2, 3])
result = s1.expanding().cov(s2)
expected = Series([None, None, None, 4.5])
tm.assert_series_equal(result, expected)
def test_expanding_corr_diff_index(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = s1.expanding().corr(s2)
expected = Series([None, None, 1.0])
tm.assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = s1.expanding().corr(s2a)
tm.assert_series_equal(result, expected)
s1 = Series([7, 8, 10], index=[0, 1, 3])
s2 = Series([7, 9, 10], index=[0, 2, 3])
result = s1.expanding().corr(s2)
expected = Series([None, None, None, 1.])
tm.assert_series_equal(result, expected)
def test_rolling_cov_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = s1.rolling(window=3, min_periods=2).cov(s2)
expected = Series([None, None, 2.0])
tm.assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = s1.rolling(window=3, min_periods=2).cov(s2a)
tm.assert_series_equal(result, expected)
def test_rolling_corr_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = s1.rolling(window=3, min_periods=2).corr(s2)
expected = Series([None, None, 1.0])
tm.assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = s1.rolling(window=3, min_periods=2).corr(s2a)
tm.assert_series_equal(result, expected)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
functions = [lambda x: (x.rolling(window=10, min_periods=5)
.cov(x, pairwise=False)),
lambda x: (x.rolling(window=10, min_periods=5)
.corr(x, pairwise=False)),
lambda x: x.rolling(window=10, min_periods=5).max(),
lambda x: x.rolling(window=10, min_periods=5).min(),
lambda x: x.rolling(window=10, min_periods=5).sum(),
lambda x: x.rolling(window=10, min_periods=5).mean(),
lambda x: x.rolling(window=10, min_periods=5).std(),
lambda x: x.rolling(window=10, min_periods=5).var(),
lambda x: x.rolling(window=10, min_periods=5).skew(),
lambda x: x.rolling(window=10, min_periods=5).kurt(),
lambda x: x.rolling(
window=10, min_periods=5).quantile(quantile=0.5),
lambda x: x.rolling(window=10, min_periods=5).median(),
lambda x: x.rolling(window=10, min_periods=5).apply(sum),
lambda x: x.rolling(win_type='boxcar',
window=10, min_periods=5).mean()]
for f in functions:
try:
s_result = f(s)
tm.assert_series_equal(s_result, s_expected)
df_result = f(df)
tm.assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
def test_rolling_functions_window_non_shrinkage_binary(self):
# corr/cov return a MI DataFrame
df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]],
columns=Index(['A', 'B'], name='foo'),
index=Index(range(4), name='bar'))
df_expected = DataFrame(
columns=Index(['A', 'B'], name='foo'),
index=pd.MultiIndex.from_product([df.index, df.columns],
names=['bar', 'foo']),
dtype='float64')
functions = [lambda x: (x.rolling(window=10, min_periods=5)
.cov(x, pairwise=True)),
lambda x: (x.rolling(window=10, min_periods=5)
.corr(x, pairwise=True))]
for f in functions:
df_result = f(df)
tm.assert_frame_equal(df_result, df_expected)
def test_moment_functions_zero_length(self):
# GH 8056
s = Series()
s_expected = s
df1 = DataFrame()
df1_expected = df1
df2 = DataFrame(columns=['a'])
df2['a'] = df2['a'].astype('float64')
df2_expected = df2
functions = [lambda x: x.expanding().count(),
lambda x: x.expanding(min_periods=5).cov(
x, pairwise=False),
lambda x: x.expanding(min_periods=5).corr(
x, pairwise=False),
lambda x: x.expanding(min_periods=5).max(),
lambda x: x.expanding(min_periods=5).min(),
lambda x: x.expanding(min_periods=5).sum(),
lambda x: x.expanding(min_periods=5).mean(),
lambda x: x.expanding(min_periods=5).std(),
lambda x: x.expanding(min_periods=5).var(),
lambda x: x.expanding(min_periods=5).skew(),
lambda x: x.expanding(min_periods=5).kurt(),
lambda x: x.expanding(min_periods=5).quantile(0.5),
lambda x: x.expanding(min_periods=5).median(),
lambda x: x.expanding(min_periods=5).apply(sum),
lambda x: x.rolling(window=10).count(),
lambda x: x.rolling(window=10, min_periods=5).cov(
x, pairwise=False),
lambda x: x.rolling(window=10, min_periods=5).corr(
x, pairwise=False),
lambda x: x.rolling(window=10, min_periods=5).max(),
lambda x: x.rolling(window=10, min_periods=5).min(),
lambda x: x.rolling(window=10, min_periods=5).sum(),
lambda x: x.rolling(window=10, min_periods=5).mean(),
lambda x: x.rolling(window=10, min_periods=5).std(),
lambda x: x.rolling(window=10, min_periods=5).var(),
lambda x: x.rolling(window=10, min_periods=5).skew(),
lambda x: x.rolling(window=10, min_periods=5).kurt(),
lambda x: x.rolling(
window=10, min_periods=5).quantile(0.5),
lambda x: x.rolling(window=10, min_periods=5).median(),
lambda x: x.rolling(window=10, min_periods=5).apply(sum),
lambda x: x.rolling(win_type='boxcar',
window=10, min_periods=5).mean(),
]
for f in functions:
try:
s_result = f(s)
tm.assert_series_equal(s_result, s_expected)
df1_result = f(df1)
tm.assert_frame_equal(df1_result, df1_expected)
df2_result = f(df2)
tm.assert_frame_equal(df2_result, df2_expected)
except (ImportError):
# scipy needed for rolling_window
continue
def test_moment_functions_zero_length_pairwise(self):
df1 = DataFrame()
df1_expected = df1
df2 = DataFrame(columns=Index(['a'], name='foo'),
index=Index([], name='bar'))
df2['a'] = df2['a'].astype('float64')
df1_expected = DataFrame(
index=pd.MultiIndex.from_product([df1.index, df1.columns]),
columns=Index([]))
df2_expected = DataFrame(
index=pd.MultiIndex.from_product([df2.index, df2.columns],
names=['bar', 'foo']),
columns=Index(['a'], name='foo'),
dtype='float64')
functions = [lambda x: (x.expanding(min_periods=5)
.cov(x, pairwise=True)),
lambda x: (x.expanding(min_periods=5)
.corr(x, pairwise=True)),
lambda x: (x.rolling(window=10, min_periods=5)
.cov(x, pairwise=True)),
lambda x: (x.rolling(window=10, min_periods=5)
.corr(x, pairwise=True)),
]
for f in functions:
df1_result = f(df1)
tm.assert_frame_equal(df1_result, df1_expected)
df2_result = f(df2)
tm.assert_frame_equal(df2_result, df2_expected)
def test_expanding_cov_pairwise_diff_length(self):
# GH 7512
df1 = DataFrame([[1, 5], [3, 2], [3, 9]],
columns=Index(['A', 'B'], name='foo'))
df1a = DataFrame([[1, 5], [3, 9]],
index=[0, 2],
columns=Index(['A', 'B'], name='foo'))
df2 = DataFrame([[5, 6], [None, None], [2, 1]],
columns=Index(['X', 'Y'], name='foo'))
df2a = DataFrame([[5, 6], [2, 1]],
index=[0, 2],
columns=Index(['X', 'Y'], name='foo'))
# TODO: xref gh-15826
# .loc is not preserving the names
result1 = df1.expanding().cov(df2a, pairwise=True).loc[2]
result2 = df1.expanding().cov(df2a, pairwise=True).loc[2]
result3 = df1a.expanding().cov(df2, pairwise=True).loc[2]
result4 = df1a.expanding().cov(df2a, pairwise=True).loc[2]
expected = DataFrame([[-3.0, -6.0], [-5.0, -10.0]],
columns=Index(['A', 'B'], name='foo'),
index=Index(['X', 'Y'], name='foo'))
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
tm.assert_frame_equal(result3, expected)
tm.assert_frame_equal(result4, expected)
def test_expanding_corr_pairwise_diff_length(self):
# GH 7512
df1 = DataFrame([[1, 2], [3, 2], [3, 4]],
columns=['A', 'B'],
index=Index(range(3), name='bar'))
df1a = DataFrame([[1, 2], [3, 4]],
index=Index([0, 2], name='bar'),
columns=['A', 'B'])
df2 = DataFrame([[5, 6], [None, None], [2, 1]],
columns=['X', 'Y'],
index=Index(range(3), name='bar'))
df2a = DataFrame([[5, 6], [2, 1]],
index=Index([0, 2], name='bar'),
columns=['X', 'Y'])
result1 = df1.expanding().corr(df2, pairwise=True).loc[2]
result2 = df1.expanding().corr(df2a, pairwise=True).loc[2]
result3 = df1a.expanding().corr(df2, pairwise=True).loc[2]
result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2]
expected = DataFrame([[-1.0, -1.0], [-1.0, -1.0]],
columns=['A', 'B'],
index=Index(['X', 'Y']))
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
tm.assert_frame_equal(result3, expected)
tm.assert_frame_equal(result4, expected)
def test_rolling_skew_edge_cases(self):
all_nan = Series([np.NaN] * 5)
# yields all NaN (0 variance)
d = Series([1] * 5)
x = d.rolling(window=5).skew()
tm.assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
x = d.rolling(window=2).skew()
tm.assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 0.177994, 1.548824]
d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401
])
expected = Series([np.NaN, np.NaN, np.NaN, 0.177994, 1.548824])
x = d.rolling(window=4).skew()
tm.assert_series_equal(expected, x)
def test_rolling_kurt_edge_cases(self):
all_nan = Series([np.NaN] * 5)
# yields all NaN (0 variance)
d = Series([1] * 5)
x = d.rolling(window=5).kurt()
tm.assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
x = d.rolling(window=3).kurt()
tm.assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 1.224307, 2.671499]
d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401
])
expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])
x = d.rolling(window=4).kurt()
tm.assert_series_equal(expected, x)
def test_rolling_skew_eq_value_fperr(self):
# #18804 all rolling skew for all equal values should return Nan
a = pd.Series([1.1] * 15).rolling(window=10).skew()
assert np.isnan(a).all()
def test_rolling_kurt_eq_value_fperr(self):
# #18804 all rolling kurt for all equal values should return Nan
a = pd.Series([1.1] * 15).rolling(window=10).kurt()
assert np.isnan(a).all()
def _check_expanding_ndarray(self, func, static_comp, has_min_periods=True,
has_time_rule=True, preserve_nan=True):
result = func(self.arr)
tm.assert_almost_equal(result[10], static_comp(self.arr[:11]))
if preserve_nan:
assert (np.isnan(result[self._nan_locs]).all())
arr = randn(50)
if has_min_periods:
result = func(arr, min_periods=30)
assert (np.isnan(result[:29]).all())
tm.assert_almost_equal(result[-1], static_comp(arr[:50]))
# min_periods is working correctly
result = func(arr, min_periods=15)
assert np.isnan(result[13])
assert not np.isnan(result[14])
arr2 = randn(20)
result = func(arr2, min_periods=5)
assert isna(result[3])
assert notna(result[4])
# min_periods=0
result0 = func(arr, min_periods=0)
result1 = func(arr, min_periods=1)
tm.assert_almost_equal(result0, result1)
else:
result = func(arr)
tm.assert_almost_equal(result[-1], static_comp(arr[:50]))
def _check_expanding_structures(self, func):
series_result = func(self.series)
assert isinstance(series_result, Series)
frame_result = func(self.frame)
assert type(frame_result) == DataFrame
def _check_expanding(self, func, static_comp, has_min_periods=True,
has_time_rule=True, preserve_nan=True):
with warnings.catch_warnings(record=True):
self._check_expanding_ndarray(func, static_comp,
has_min_periods=has_min_periods,
has_time_rule=has_time_rule,
preserve_nan=preserve_nan)
with warnings.catch_warnings(record=True):
self._check_expanding_structures(func)
def test_rolling_max_gh6297(self):
"""Replicate result expected in GH #6297"""
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 2 datapoints on one of the days
indices.append(datetime(1975, 1, 3, 6, 0))
series = Series(range(1, 7), index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
expected = Series([1.0, 2.0, 6.0, 4.0, 5.0],
index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
with catch_warnings(record=True):
x = series.rolling(window=1, freq='D').max()
tm.assert_series_equal(expected, x)
def test_rolling_max_how_resample(self):
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 3 datapoints on last day (4, 10, and 20)
indices.append(datetime(1975, 1, 5, 1))
indices.append(datetime(1975, 1, 5, 2))
series = Series(list(range(0, 5)) + [10, 20], index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
# Default how should be max
expected = Series([0.0, 1.0, 2.0, 3.0, 20.0],
index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
with catch_warnings(record=True):
x = series.rolling(window=1, freq='D').max()
tm.assert_series_equal(expected, x)
# Now specify median (10.0)
expected = Series([0.0, 1.0, 2.0, 3.0, 10.0],
index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
with catch_warnings(record=True):
x = series.rolling(window=1, freq='D').max(how='median')
tm.assert_series_equal(expected, x)
# Now specify mean (4+10+20)/3
v = (4.0 + 10.0 + 20.0) / 3.0
expected = Series([0.0, 1.0, 2.0, 3.0, v],
index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
with catch_warnings(record=True):
x = series.rolling(window=1, freq='D').max(how='mean')
tm.assert_series_equal(expected, x)
def test_rolling_min_how_resample(self):
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 3 datapoints on last day (4, 10, and 20)
indices.append(datetime(1975, 1, 5, 1))
indices.append(datetime(1975, 1, 5, 2))
series = Series(list(range(0, 5)) + [10, 20], index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
# Default how should be min
expected = Series([0.0, 1.0, 2.0, 3.0, 4.0],
index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
with catch_warnings(record=True):
r = series.rolling(window=1, freq='D')
tm.assert_series_equal(expected, r.min())
def test_rolling_median_how_resample(self):
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 3 datapoints on last day (4, 10, and 20)
indices.append(datetime(1975, 1, 5, 1))
indices.append(datetime(1975, 1, 5, 2))
series = Series(list(range(0, 5)) + [10, 20], index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
# Default how should be median
expected = Series([0.0, 1.0, 2.0, 3.0, 10],
index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
with catch_warnings(record=True):
x = series.rolling(window=1, freq='D').median()
tm.assert_series_equal(expected, x)
def test_rolling_median_memory_error(self):
# GH11722
n = 20000
Series(np.random.randn(n)).rolling(window=2, center=False).median()
Series(np.random.randn(n)).rolling(window=2, center=False).median()
def test_rolling_min_max_numeric_types(self):
# GH12373
types_test = [np.dtype("f{}".format(width)) for width in [4, 8]]
types_test.extend([np.dtype("{}{}".format(sign, width))
for width in [1, 2, 4, 8] for sign in "ui"])
for data_type in types_test:
# Just testing that these don't throw exceptions and that
# the return type is float64. Other tests will cover quantitative
# correctness
result = (DataFrame(np.arange(20, dtype=data_type))
.rolling(window=5).max())
assert result.dtypes[0] == np.dtype("f8")
result = (DataFrame(np.arange(20, dtype=data_type))
.rolling(window=5).min())
assert result.dtypes[0] == np.dtype("f8")
class TestGrouperGrouping(object):
def setup_method(self, method):
self.series = Series(np.arange(10))
self.frame = DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.arange(40)})
def test_mutated(self):
def f():
self.frame.groupby('A', foo=1)
pytest.raises(TypeError, f)
g = self.frame.groupby('A')
assert not g.mutated
g = self.frame.groupby('A', mutated=True)
assert g.mutated
def test_getitem(self):
g = self.frame.groupby('A')
g_mutated = self.frame.groupby('A', mutated=True)
expected = g_mutated.B.apply(lambda x: x.rolling(2).mean())
result = g.rolling(2).mean().B
tm.assert_series_equal(result, expected)
result = g.rolling(2).B.mean()
tm.assert_series_equal(result, expected)
result = g.B.rolling(2).mean()
tm.assert_series_equal(result, expected)
result = self.frame.B.groupby(self.frame.A).rolling(2).mean()
tm.assert_series_equal(result, expected)
def test_getitem_multiple(self):
# GH 13174
g = self.frame.groupby('A')
r = g.rolling(2)
g_mutated = self.frame.groupby('A', mutated=True)
expected = g_mutated.B.apply(lambda x: x.rolling(2).count())
result = r.B.count()
tm.assert_series_equal(result, expected)
result = r.B.count()
tm.assert_series_equal(result, expected)
def test_rolling(self):
g = self.frame.groupby('A')
r = g.rolling(window=4)
for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']:
result = getattr(r, f)()
expected = g.apply(lambda x: getattr(x.rolling(4), f)())
tm.assert_frame_equal(result, expected)
for f in ['std', 'var']:
result = getattr(r, f)(ddof=1)
expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1))
tm.assert_frame_equal(result, expected)
result = r.quantile(0.5)
expected = g.apply(lambda x: x.rolling(4).quantile(0.5))
tm.assert_frame_equal(result, expected)
def test_rolling_corr_cov(self):
g = self.frame.groupby('A')
r = g.rolling(window=4)
for f in ['corr', 'cov']:
result = getattr(r, f)(self.frame)
def func(x):
return getattr(x.rolling(4), f)(self.frame)
expected = g.apply(func)
tm.assert_frame_equal(result, expected)
result = getattr(r.B, f)(pairwise=True)
def func(x):
return getattr(x.B.rolling(4), f)(pairwise=True)
expected = g.apply(func)
tm.assert_series_equal(result, expected)
def test_rolling_apply(self):
g = self.frame.groupby('A')
r = g.rolling(window=4)
# reduction
result = r.apply(lambda x: x.sum())
expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum()))
tm.assert_frame_equal(result, expected)
def test_expanding(self):
g = self.frame.groupby('A')
r = g.expanding()
for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']:
result = getattr(r, f)()
expected = g.apply(lambda x: getattr(x.expanding(), f)())
tm.assert_frame_equal(result, expected)
for f in ['std', 'var']:
result = getattr(r, f)(ddof=0)
expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
tm.assert_frame_equal(result, expected)
result = r.quantile(0.5)
expected = g.apply(lambda x: x.expanding().quantile(0.5))
tm.assert_frame_equal(result, expected)
def test_expanding_corr_cov(self):
g = self.frame.groupby('A')
r = g.expanding()
for f in ['corr', 'cov']:
result = getattr(r, f)(self.frame)
def func(x):
return getattr(x.expanding(), f)(self.frame)
expected = g.apply(func)
tm.assert_frame_equal(result, expected)
result = getattr(r.B, f)(pairwise=True)
def func(x):
return getattr(x.B.expanding(), f)(pairwise=True)
expected = g.apply(func)
tm.assert_series_equal(result, expected)
def test_expanding_apply(self):
g = self.frame.groupby('A')
r = g.expanding()
# reduction
result = r.apply(lambda x: x.sum())
expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum()))
tm.assert_frame_equal(result, expected)
class TestRollingTS(object):
# rolling time-series friendly
# xref GH13327
def setup_method(self, method):
self.regular = DataFrame({'A': pd.date_range('20130101',
periods=5,
freq='s'),
'B': range(5)}).set_index('A')
self.ragged = DataFrame({'B': range(5)})
self.ragged.index = [Timestamp('20130101 09:00:00'),
Timestamp('20130101 09:00:02'),
Timestamp('20130101 09:00:03'),
Timestamp('20130101 09:00:05'),
Timestamp('20130101 09:00:06')]
def test_doc_string(self):
df = DataFrame({'B': [0, 1, 2, np.nan, 4]},
index=[Timestamp('20130101 09:00:00'),
Timestamp('20130101 09:00:02'),
Timestamp('20130101 09:00:03'),
Timestamp('20130101 09:00:05'),
Timestamp('20130101 09:00:06')])
df
df.rolling('2s').sum()
def test_valid(self):
df = self.regular
# not a valid freq
with pytest.raises(ValueError):
df.rolling(window='foobar')
# not a datetimelike index
with pytest.raises(ValueError):
df.reset_index().rolling(window='foobar')
# non-fixed freqs
for freq in ['2MS', pd.offsets.MonthBegin(2)]:
with pytest.raises(ValueError):
df.rolling(window=freq)
for freq in ['1D', pd.offsets.Day(2), '2ms']:
df.rolling(window=freq)
# non-integer min_periods
for minp in [1.0, 'foo', np.array([1, 2, 3])]:
with pytest.raises(ValueError):
df.rolling(window='1D', min_periods=minp)
# center is not implemented
with pytest.raises(NotImplementedError):
df.rolling(window='1D', center=True)
def test_on(self):
df = self.regular
# not a valid column
with pytest.raises(ValueError):
df.rolling(window='2s', on='foobar')
# column is valid
df = df.copy()
df['C'] = pd.date_range('20130101', periods=len(df))
df.rolling(window='2d', on='C').sum()
# invalid columns
with pytest.raises(ValueError):
df.rolling(window='2d', on='B')
# ok even though on non-selected
df.rolling(window='2d', on='C').B.sum()
def test_monotonic_on(self):
# on/index must be monotonic
df = DataFrame({'A': pd.date_range('20130101',
periods=5,
freq='s'),
'B': range(5)})
assert df.A.is_monotonic
df.rolling('2s', on='A').sum()
df = df.set_index('A')
assert df.index.is_monotonic
df.rolling('2s').sum()
# non-monotonic
df.index = reversed(df.index.tolist())
assert not df.index.is_monotonic
with pytest.raises(ValueError):
df.rolling('2s').sum()
df = df.reset_index()
with pytest.raises(ValueError):
df.rolling('2s', on='A').sum()
def test_frame_on(self):
df = DataFrame({'B': range(5),
'C': pd.date_range('20130101 09:00:00',
periods=5,
freq='3s')})
df['A'] = [Timestamp('20130101 09:00:00'),
Timestamp('20130101 09:00:02'),
Timestamp('20130101 09:00:03'),
Timestamp('20130101 09:00:05'),
Timestamp('20130101 09:00:06')]
# we are doing simulating using 'on'
expected = (df.set_index('A')
.rolling('2s')
.B
.sum()
.reset_index(drop=True)
)
result = (df.rolling('2s', on='A')
.B
.sum()
)
tm.assert_series_equal(result, expected)
# test as a frame
# we should be ignoring the 'on' as an aggregation column
# note that the expected is setting, computing, and reseting
# so the columns need to be switched compared
# to the actual result where they are ordered as in the
# original
expected = (df.set_index('A')
.rolling('2s')[['B']]
.sum()
.reset_index()[['B', 'A']]
)
result = (df.rolling('2s', on='A')[['B']]
.sum()
)
tm.assert_frame_equal(result, expected)
def test_frame_on2(self):
# using multiple aggregation columns
df = DataFrame({'A': [0, 1, 2, 3, 4],
'B': [0, 1, 2, np.nan, 4],
'C': pd.Index([pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:05'),
pd.Timestamp('20130101 09:00:06')])},
columns=['A', 'C', 'B'])
expected1 = DataFrame({'A': [0., 1, 3, 3, 7],
'B': [0, 1, 3, np.nan, 4],
'C': df['C']},
columns=['A', 'C', 'B'])
result = df.rolling('2s', on='C').sum()
expected = expected1
tm.assert_frame_equal(result, expected)
expected = Series([0, 1, 3, np.nan, 4], name='B')
result = df.rolling('2s', on='C').B.sum()
tm.assert_series_equal(result, expected)
expected = expected1[['A', 'B', 'C']]
result = df.rolling('2s', on='C')[['A', 'B', 'C']].sum()
tm.assert_frame_equal(result, expected)
def test_basic_regular(self):
df = self.regular.copy()
df.index = pd.date_range('20130101', periods=5, freq='D')
expected = df.rolling(window=1, min_periods=1).sum()
result = df.rolling(window='1D').sum()
tm.assert_frame_equal(result, expected)
df.index = pd.date_range('20130101', periods=5, freq='2D')
expected = df.rolling(window=1, min_periods=1).sum()
result = df.rolling(window='2D', min_periods=1).sum()
tm.assert_frame_equal(result, expected)
expected = df.rolling(window=1, min_periods=1).sum()
result = df.rolling(window='2D', min_periods=1).sum()
tm.assert_frame_equal(result, expected)
expected = df.rolling(window=1).sum()
result = df.rolling(window='2D').sum()
tm.assert_frame_equal(result, expected)
def test_min_periods(self):
# compare for min_periods
df = self.regular
# these slightly different
expected = df.rolling(2, min_periods=1).sum()
result = df.rolling('2s').sum()
tm.assert_frame_equal(result, expected)
expected = df.rolling(2, min_periods=1).sum()
result = df.rolling('2s', min_periods=1).sum()
tm.assert_frame_equal(result, expected)
def test_closed(self):
# xref GH13965
df = DataFrame({'A': [1] * 5},
index=[pd.Timestamp('20130101 09:00:01'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:04'),
pd.Timestamp('20130101 09:00:06')])
# closed must be 'right', 'left', 'both', 'neither'
with pytest.raises(ValueError):
self.regular.rolling(window='2s', closed="blabla")
expected = df.copy()
expected["A"] = [1.0, 2, 2, 2, 1]
result = df.rolling('2s', closed='right').sum()
tm.assert_frame_equal(result, expected)
# default should be 'right'
result = df.rolling('2s').sum()
tm.assert_frame_equal(result, expected)
expected = df.copy()
expected["A"] = [1.0, 2, 3, 3, 2]
result = df.rolling('2s', closed='both').sum()
tm.assert_frame_equal(result, expected)
expected = df.copy()
expected["A"] = [np.nan, 1.0, 2, 2, 1]
result = df.rolling('2s', closed='left').sum()
tm.assert_frame_equal(result, expected)
expected = df.copy()
expected["A"] = [np.nan, 1.0, 1, 1, np.nan]
result = df.rolling('2s', closed='neither').sum()
tm.assert_frame_equal(result, expected)
def test_ragged_sum(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).sum()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).sum()
expected = df.copy()
expected['B'] = [0.0, 1, 3, 3, 7]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=2).sum()
expected = df.copy()
expected['B'] = [np.nan, np.nan, 3, np.nan, 7]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='3s', min_periods=1).sum()
expected = df.copy()
expected['B'] = [0.0, 1, 3, 5, 7]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='3s').sum()
expected = df.copy()
expected['B'] = [0.0, 1, 3, 5, 7]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='4s', min_periods=1).sum()
expected = df.copy()
expected['B'] = [0.0, 1, 3, 6, 9]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='4s', min_periods=3).sum()
expected = df.copy()
expected['B'] = [np.nan, np.nan, 3, 6, 9]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).sum()
expected = df.copy()
expected['B'] = [0.0, 1, 3, 6, 10]
tm.assert_frame_equal(result, expected)
def test_ragged_mean(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).mean()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).mean()
expected = df.copy()
expected['B'] = [0.0, 1, 1.5, 3.0, 3.5]
tm.assert_frame_equal(result, expected)
def test_ragged_median(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).median()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).median()
expected = df.copy()
expected['B'] = [0.0, 1, 1.5, 3.0, 3.5]
tm.assert_frame_equal(result, expected)
def test_ragged_quantile(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).quantile(0.5)
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).quantile(0.5)
expected = df.copy()
expected['B'] = [0.0, 1, 1.5, 3.0, 3.5]
tm.assert_frame_equal(result, expected)
def test_ragged_std(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).std(ddof=0)
expected = df.copy()
expected['B'] = [0.0] * 5
tm.assert_frame_equal(result, expected)
result = df.rolling(window='1s', min_periods=1).std(ddof=1)
expected = df.copy()
expected['B'] = [np.nan] * 5
tm.assert_frame_equal(result, expected)
result = df.rolling(window='3s', min_periods=1).std(ddof=0)
expected = df.copy()
expected['B'] = [0.0] + [0.5] * 4
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).std(ddof=1)
expected = df.copy()
expected['B'] = [np.nan, 0.707107, 1.0, 1.0, 1.290994]
tm.assert_frame_equal(result, expected)
def test_ragged_var(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).var(ddof=0)
expected = df.copy()
expected['B'] = [0.0] * 5
tm.assert_frame_equal(result, expected)
result = df.rolling(window='1s', min_periods=1).var(ddof=1)
expected = df.copy()
expected['B'] = [np.nan] * 5
tm.assert_frame_equal(result, expected)
result = df.rolling(window='3s', min_periods=1).var(ddof=0)
expected = df.copy()
expected['B'] = [0.0] + [0.25] * 4
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).var(ddof=1)
expected = df.copy()
expected['B'] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.]
tm.assert_frame_equal(result, expected)
def test_ragged_skew(self):
df = self.ragged
result = df.rolling(window='3s', min_periods=1).skew()
expected = df.copy()
expected['B'] = [np.nan] * 5
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).skew()
expected = df.copy()
expected['B'] = [np.nan] * 2 + [0.0, 0.0, 0.0]
tm.assert_frame_equal(result, expected)
def test_ragged_kurt(self):
df = self.ragged
result = df.rolling(window='3s', min_periods=1).kurt()
expected = df.copy()
expected['B'] = [np.nan] * 5
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).kurt()
expected = df.copy()
expected['B'] = [np.nan] * 4 + [-1.2]
tm.assert_frame_equal(result, expected)
def test_ragged_count(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).count()
expected = df.copy()
expected['B'] = [1.0, 1, 1, 1, 1]
tm.assert_frame_equal(result, expected)
df = self.ragged
result = df.rolling(window='1s').count()
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).count()
expected = df.copy()
expected['B'] = [1.0, 1, 2, 1, 2]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=2).count()
expected = df.copy()
expected['B'] = [np.nan, np.nan, 2, np.nan, 2]
tm.assert_frame_equal(result, expected)
def test_regular_min(self):
df = DataFrame({'A': pd.date_range('20130101',
periods=5,
freq='s'),
'B': [0.0, 1, 2, 3, 4]}).set_index('A')
result = df.rolling('1s').min()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
df = DataFrame({'A': pd.date_range('20130101',
periods=5,
freq='s'),
'B': [5, 4, 3, 4, 5]}).set_index('A')
tm.assert_frame_equal(result, expected)
result = df.rolling('2s').min()
expected = df.copy()
expected['B'] = [5.0, 4, 3, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling('5s').min()
expected = df.copy()
expected['B'] = [5.0, 4, 3, 3, 3]
tm.assert_frame_equal(result, expected)
def test_ragged_min(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).min()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).min()
expected = df.copy()
expected['B'] = [0.0, 1, 1, 3, 3]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).min()
expected = df.copy()
expected['B'] = [0.0, 0, 0, 1, 1]
tm.assert_frame_equal(result, expected)
def test_perf_min(self):
N = 10000
dfp = DataFrame({'B': np.random.randn(N)},
index=pd.date_range('20130101',
periods=N,
freq='s'))
expected = dfp.rolling(2, min_periods=1).min()
result = dfp.rolling('2s').min()
assert ((result - expected) < 0.01).all().bool()
expected = dfp.rolling(200, min_periods=1).min()
result = dfp.rolling('200s').min()
assert ((result - expected) < 0.01).all().bool()
def test_ragged_max(self):
df = self.ragged
result = df.rolling(window='1s', min_periods=1).max()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).max()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).max()
expected = df.copy()
expected['B'] = [0.0, 1, 2, 3, 4]
tm.assert_frame_equal(result, expected)
def test_ragged_apply(self):
df = self.ragged
f = lambda x: 1
result = df.rolling(window='1s', min_periods=1).apply(f)
expected = df.copy()
expected['B'] = 1.
tm.assert_frame_equal(result, expected)
result = df.rolling(window='2s', min_periods=1).apply(f)
expected = df.copy()
expected['B'] = 1.
tm.assert_frame_equal(result, expected)
result = df.rolling(window='5s', min_periods=1).apply(f)
expected = df.copy()
expected['B'] = 1.
tm.assert_frame_equal(result, expected)
def test_all(self):
# simple comparision of integer vs time-based windowing
df = self.regular * 2
er = df.rolling(window=1)
r = df.rolling(window='1s')
for f in ['sum', 'mean', 'count', 'median', 'std',
'var', 'kurt', 'skew', 'min', 'max']:
result = getattr(r, f)()
expected = getattr(er, f)()
tm.assert_frame_equal(result, expected)
result = r.quantile(0.5)
expected = er.quantile(0.5)
tm.assert_frame_equal(result, expected)
result = r.apply(lambda x: 1)
expected = er.apply(lambda x: 1)
tm.assert_frame_equal(result, expected)
def test_all2(self):
# more sophisticated comparision of integer vs.
# time-based windowing
df = DataFrame({'B': np.arange(50)},
index=pd.date_range('20130101',
periods=50, freq='H')
)
# in-range data
dft = df.between_time("09:00", "16:00")
r = dft.rolling(window='5H')
for f in ['sum', 'mean', 'count', 'median', 'std',
'var', 'kurt', 'skew', 'min', 'max']:
result = getattr(r, f)()
# we need to roll the days separately
# to compare with a time-based roll
# finally groupby-apply will return a multi-index
# so we need to drop the day
def agg_by_day(x):
x = x.between_time("09:00", "16:00")
return getattr(x.rolling(5, min_periods=1), f)()
expected = df.groupby(df.index.day).apply(
agg_by_day).reset_index(level=0, drop=True)
tm.assert_frame_equal(result, expected)
def test_groupby_monotonic(self):
# GH 15130
# we don't need to validate monotonicity when grouping
data = [
['David', '1/1/2015', 100], ['David', '1/5/2015', 500],
['David', '5/30/2015', 50], ['David', '7/25/2015', 50],
['Ryan', '1/4/2014', 100], ['Ryan', '1/19/2015', 500],
['Ryan', '3/31/2016', 50], ['Joe', '7/1/2015', 100],
['Joe', '9/9/2015', 500], ['Joe', '10/15/2015', 50]]
df = pd.DataFrame(data=data, columns=['name', 'date', 'amount'])
df['date'] = pd.to_datetime(df['date'])
expected = df.set_index('date').groupby('name').apply(
lambda x: x.rolling('180D')['amount'].sum())
result = df.groupby('name').rolling('180D', on='date')['amount'].sum()
tm.assert_series_equal(result, expected)
def test_non_monotonic(self):
# GH 13966 (similar to #15130, closed by #15175)
dates = pd.date_range(start='2016-01-01 09:30:00',
periods=20, freq='s')
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.concatenate((dates, dates)),
'C': np.arange(40)})
result = df.groupby('A').rolling('4s', on='B').C.mean()
expected = df.set_index('B').groupby('A').apply(
lambda x: x.rolling('4s')['C'].mean())
tm.assert_series_equal(result, expected)
df2 = df.sort_values('B')
result = df2.groupby('A').rolling('4s', on='B').C.mean()
tm.assert_series_equal(result, expected)
def test_rolling_cov_offset(self):
# GH16058
idx = pd.date_range('2017-01-01', periods=24, freq='1h')
ss = pd.Series(np.arange(len(idx)), index=idx)
result = ss.rolling('2h').cov()
expected = pd.Series([np.nan] + [0.5 for _ in range(len(idx) - 1)],
index=idx)
tm.assert_series_equal(result, expected)
expected2 = ss.rolling(2, min_periods=1).cov()
tm.assert_series_equal(result, expected2)
result = ss.rolling('3h').cov()
expected = pd.Series([np.nan, 0.5] +
[1.0 for _ in range(len(idx) - 2)],
index=idx)
tm.assert_series_equal(result, expected)
expected2 = ss.rolling(3, min_periods=1).cov()
tm.assert_series_equal(result, expected2)