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TST/CLN: parametrize tests\resample\test_time_grouper.py #24013

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Merged
merged 5 commits into from
Dec 3, 2018
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TST/CLN: parametrize tests\resample\test_time_grouper.py #24013

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05fa12b
parametrize tests\resample\test_time_grouper.py
simonjayhawkins Nov 28, 2018
2740e0e
Merge remote-tracking branch 'upstream/master' into time-grouper
simonjayhawkins Nov 30, 2018
e92cd2f
resample method fixture
simonjayhawkins Nov 30, 2018
8e65852
use import in conftest.py to avoid duplication
simonjayhawkins Dec 3, 2018
ed6a781
Merge remote-tracking branch 'upstream/master' into time-grouper
simonjayhawkins Dec 3, 2018
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142 changes: 53 additions & 89 deletions pandas/tests/resample/test_time_grouper.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,9 @@
# pylint: disable=E1101

from datetime import datetime
from operator import methodcaller

import numpy as np
import pytest

from pandas.compat import zip

import pandas as pd
from pandas import DataFrame, Panel, Series
from pandas.core.indexes.datetimes import date_range
Expand Down Expand Up @@ -104,20 +100,21 @@ def f(x):
tm.assert_panel_equal(result, binagg)


def test_fails_on_no_datetime_index():
index_names = ('Int64Index', 'Index', 'Float64Index', 'MultiIndex')
index_funcs = (tm.makeIntIndex,
tm.makeUnicodeIndex, tm.makeFloatIndex,
lambda m: tm.makeCustomIndex(m, 2))
@pytest.mark.parametrize('name, func', [
('Int64Index', tm.makeIntIndex),
('Index', tm.makeUnicodeIndex),
('Float64Index', tm.makeFloatIndex),
('MultiIndex', lambda m: tm.makeCustomIndex(m, 2))
])
def test_fails_on_no_datetime_index(name, func):
n = 2
for name, func in zip(index_names, index_funcs):
index = func(n)
df = DataFrame({'a': np.random.randn(n)}, index=index)
index = func(n)
df = DataFrame({'a': np.random.randn(n)}, index=index)

msg = ("Only valid with DatetimeIndex, TimedeltaIndex "
"or PeriodIndex, but got an instance of %r" % name)
with pytest.raises(TypeError, match=msg):
df.groupby(TimeGrouper('D'))
msg = ("Only valid with DatetimeIndex, TimedeltaIndex "
"or PeriodIndex, but got an instance of %r" % name)
with pytest.raises(TypeError, match=msg):
df.groupby(TimeGrouper('D'))


def test_aaa_group_order():
Expand All @@ -143,7 +140,20 @@ def test_aaa_group_order():
df[4::5])


def test_aggregate_normal():
@pytest.mark.parametrize('func, assert_func', [
('min', assert_frame_equal),
('max', assert_frame_equal),
('prod', assert_frame_equal),
('var', assert_frame_equal),
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i think you could move all of the func to a fixture in pandas/tests/resample/conftest (might be usable elsewhere)

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so that the fixture is reusable, std has been added to the downsample methods in test_base.py and sem, median, ohlc, quantile and nunique added to the test_aggregate_normal test, although ohlc fails.

('std', assert_frame_equal),
('mean', assert_frame_equal),
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you could avoid the assert_func and just change it to do tm.assert_equal

('count', assert_frame_equal),
('sum', assert_frame_equal),
('size', assert_series_equal), # GH 7453
('first', assert_frame_equal), # GH 7453
('last', assert_frame_equal), # GH 7453
])
def test_aggregate_normal(func, assert_func):
# check TimeGrouper's aggregation is identical as normal groupby

n = 20
Expand All @@ -159,35 +169,11 @@ def test_aggregate_normal():
normal_grouped = normal_df.groupby('key')
dt_grouped = dt_df.groupby(TimeGrouper(key='key', freq='D'))

for func in ['min', 'max', 'prod', 'var', 'std', 'mean']:
expected = getattr(normal_grouped, func)()
dt_result = getattr(dt_grouped, func)()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
assert_frame_equal(expected, dt_result)

for func in ['count', 'sum']:
expected = getattr(normal_grouped, func)()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
dt_result = getattr(dt_grouped, func)()
assert_frame_equal(expected, dt_result)

# GH 7453
for func in ['size']:
expected = getattr(normal_grouped, func)()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
dt_result = getattr(dt_grouped, func)()
assert_series_equal(expected, dt_result)

# GH 7453
for func in ['first', 'last']:
expected = getattr(normal_grouped, func)()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
dt_result = getattr(dt_grouped, func)()
assert_frame_equal(expected, dt_result)
expected = getattr(normal_grouped, func)()
dt_result = getattr(dt_grouped, func)()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
assert_func(expected, dt_result)

# if TimeGrouper is used included, 'nth' doesn't work yet

Expand All @@ -201,34 +187,23 @@ def test_aggregate_normal():
"""


@pytest.mark.parametrize('method, unit', [
('sum', 0),
('prod', 1),
@pytest.mark.parametrize('method, method_args, unit', [
('sum', dict(), 0),
('sum', dict(min_count=0), 0),
('sum', dict(min_count=1), np.nan),
('prod', dict(), 1),
('prod', dict(min_count=0), 1),
('prod', dict(min_count=1), np.nan)
])
def test_resample_entirly_nat_window(method, unit):
def test_resample_entirly_nat_window(method, method_args, unit):
s = pd.Series([0] * 2 + [np.nan] * 2,
index=pd.date_range('2017', periods=4))
# 0 / 1 by default
result = methodcaller(method)(s.resample("2d"))
expected = pd.Series([0.0, unit],
index=pd.to_datetime(['2017-01-01',
'2017-01-03']))
tm.assert_series_equal(result, expected)

# min_count=0
result = methodcaller(method, min_count=0)(s.resample("2d"))
result = methodcaller(method, **method_args)(s.resample("2d"))
expected = pd.Series([0.0, unit],
index=pd.to_datetime(['2017-01-01',
'2017-01-03']))
tm.assert_series_equal(result, expected)

# min_count=1
result = methodcaller(method, min_count=1)(s.resample("2d"))
expected = pd.Series([0.0, np.nan],
index=pd.to_datetime(['2017-01-01',
'2017-01-03']))
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize('func, fill_value', [
('min', np.nan),
Expand Down Expand Up @@ -302,33 +277,22 @@ def test_repr():
assert result == expected


@pytest.mark.parametrize('method, unit', [
('sum', 0),
('prod', 1),
@pytest.mark.parametrize('method, method_args, expected_values', [
('sum', dict(), [1, 0, 1]),
('sum', dict(min_count=0), [1, 0, 1]),
('sum', dict(min_count=1), [1, np.nan, 1]),
('sum', dict(min_count=2), [np.nan, np.nan, np.nan]),
('prod', dict(), [1, 1, 1]),
('prod', dict(min_count=0), [1, 1, 1]),
('prod', dict(min_count=1), [1, np.nan, 1]),
('prod', dict(min_count=2), [np.nan, np.nan, np.nan]),
])
def test_upsample_sum(method, unit):
def test_upsample_sum(method, method_args, expected_values):
s = pd.Series(1, index=pd.date_range("2017", periods=2, freq="H"))
resampled = s.resample("30T")
index = pd.to_datetime(['2017-01-01T00:00:00',
'2017-01-01T00:30:00',
'2017-01-01T01:00:00'])

# 0 / 1 by default
result = methodcaller(method)(resampled)
expected = pd.Series([1, unit, 1], index=index)
tm.assert_series_equal(result, expected)

# min_count=0
result = methodcaller(method, min_count=0)(resampled)
expected = pd.Series([1, unit, 1], index=index)
tm.assert_series_equal(result, expected)

# min_count=1
result = methodcaller(method, min_count=1)(resampled)
expected = pd.Series([1, np.nan, 1], index=index)
tm.assert_series_equal(result, expected)

# min_count>1
result = methodcaller(method, min_count=2)(resampled)
expected = pd.Series([np.nan, np.nan, np.nan], index=index)
result = methodcaller(method, **method_args)(resampled)
expected = pd.Series(expected_values, index=index)
tm.assert_series_equal(result, expected)