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REF: refactor cumulative op tests from test_analytics #30358

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Dec 20, 2019
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REF: refactor cumulative op tests from test_analytics #30358

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106 changes: 0 additions & 106 deletions pandas/tests/frame/test_analytics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1495,112 +1495,6 @@ def test_sum_bools(self):
bools = isna(df)
assert bools.sum(axis=1)[0] == 10

# ---------------------------------------------------------------------
# Cumulative Reductions - cumsum, cummax, ...

def test_cumsum_corner(self):
dm = DataFrame(np.arange(20).reshape(4, 5), index=range(4), columns=range(5))
# ?(wesm)
result = dm.cumsum() # noqa

def test_cumsum(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cumsum = datetime_frame.cumsum()
expected = datetime_frame.apply(Series.cumsum)
tm.assert_frame_equal(cumsum, expected)

# axis = 1
cumsum = datetime_frame.cumsum(axis=1)
expected = datetime_frame.apply(Series.cumsum, axis=1)
tm.assert_frame_equal(cumsum, expected)

# works
df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
result = df.cumsum() # noqa

# fix issue
cumsum_xs = datetime_frame.cumsum(axis=1)
assert np.shape(cumsum_xs) == np.shape(datetime_frame)

def test_cumprod(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cumprod = datetime_frame.cumprod()
expected = datetime_frame.apply(Series.cumprod)
tm.assert_frame_equal(cumprod, expected)

# axis = 1
cumprod = datetime_frame.cumprod(axis=1)
expected = datetime_frame.apply(Series.cumprod, axis=1)
tm.assert_frame_equal(cumprod, expected)

# fix issue
cumprod_xs = datetime_frame.cumprod(axis=1)
assert np.shape(cumprod_xs) == np.shape(datetime_frame)

# ints
df = datetime_frame.fillna(0).astype(int)
df.cumprod(0)
df.cumprod(1)

# ints32
df = datetime_frame.fillna(0).astype(np.int32)
df.cumprod(0)
df.cumprod(1)

def test_cummin(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cummin = datetime_frame.cummin()
expected = datetime_frame.apply(Series.cummin)
tm.assert_frame_equal(cummin, expected)

# axis = 1
cummin = datetime_frame.cummin(axis=1)
expected = datetime_frame.apply(Series.cummin, axis=1)
tm.assert_frame_equal(cummin, expected)

# it works
df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
result = df.cummin() # noqa

# fix issue
cummin_xs = datetime_frame.cummin(axis=1)
assert np.shape(cummin_xs) == np.shape(datetime_frame)

def test_cummax(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cummax = datetime_frame.cummax()
expected = datetime_frame.apply(Series.cummax)
tm.assert_frame_equal(cummax, expected)

# axis = 1
cummax = datetime_frame.cummax(axis=1)
expected = datetime_frame.apply(Series.cummax, axis=1)
tm.assert_frame_equal(cummax, expected)

# it works
df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
result = df.cummax() # noqa

# fix issue
cummax_xs = datetime_frame.cummax(axis=1)
assert np.shape(cummax_xs) == np.shape(datetime_frame)

# ---------------------------------------------------------------------
# Miscellanea

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120 changes: 120 additions & 0 deletions pandas/tests/frame/test_cumulative.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,120 @@
"""
Tests for DataFrame cumulative operations

See also
--------
tests.series.test_cumulative
"""

import numpy as np

from pandas import DataFrame, Series
import pandas.util.testing as tm


class TestDataFrameCumulativeOps:
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Not a huge deal but probably don't even need the classes now

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for whatever reason i still find this style more comfortable, but agreed on the "not a huge deal"

# ---------------------------------------------------------------------
# Cumulative Operations - cumsum, cummax, ...

def test_cumsum_corner(self):
dm = DataFrame(np.arange(20).reshape(4, 5), index=range(4), columns=range(5))
# TODO(wesm): do something with this?
result = dm.cumsum() # noqa

def test_cumsum(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cumsum = datetime_frame.cumsum()
expected = datetime_frame.apply(Series.cumsum)
tm.assert_frame_equal(cumsum, expected)

# axis = 1
cumsum = datetime_frame.cumsum(axis=1)
expected = datetime_frame.apply(Series.cumsum, axis=1)
tm.assert_frame_equal(cumsum, expected)

# works
df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
df.cumsum()

# fix issue
cumsum_xs = datetime_frame.cumsum(axis=1)
assert np.shape(cumsum_xs) == np.shape(datetime_frame)

def test_cumprod(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cumprod = datetime_frame.cumprod()
expected = datetime_frame.apply(Series.cumprod)
tm.assert_frame_equal(cumprod, expected)

# axis = 1
cumprod = datetime_frame.cumprod(axis=1)
expected = datetime_frame.apply(Series.cumprod, axis=1)
tm.assert_frame_equal(cumprod, expected)

# fix issue
cumprod_xs = datetime_frame.cumprod(axis=1)
assert np.shape(cumprod_xs) == np.shape(datetime_frame)

# ints
df = datetime_frame.fillna(0).astype(int)
df.cumprod(0)
df.cumprod(1)

# ints32
df = datetime_frame.fillna(0).astype(np.int32)
df.cumprod(0)
df.cumprod(1)

def test_cummin(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cummin = datetime_frame.cummin()
expected = datetime_frame.apply(Series.cummin)
tm.assert_frame_equal(cummin, expected)

# axis = 1
cummin = datetime_frame.cummin(axis=1)
expected = datetime_frame.apply(Series.cummin, axis=1)
tm.assert_frame_equal(cummin, expected)

# it works
df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
df.cummin()

# fix issue
cummin_xs = datetime_frame.cummin(axis=1)
assert np.shape(cummin_xs) == np.shape(datetime_frame)

def test_cummax(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
datetime_frame.loc[15:, 2] = np.nan

# axis = 0
cummax = datetime_frame.cummax()
expected = datetime_frame.apply(Series.cummax)
tm.assert_frame_equal(cummax, expected)

# axis = 1
cummax = datetime_frame.cummax(axis=1)
expected = datetime_frame.apply(Series.cummax, axis=1)
tm.assert_frame_equal(cummax, expected)

# it works
df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
df.cummax()

# fix issue
cummax_xs = datetime_frame.cummax(axis=1)
assert np.shape(cummax_xs) == np.shape(datetime_frame)
111 changes: 0 additions & 111 deletions pandas/tests/series/test_analytics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
import numpy as np
import pytest

from pandas.compat.numpy import _np_version_under1p18
import pandas.util._test_decorators as td

import pandas as pd
Expand Down Expand Up @@ -125,116 +124,6 @@ def test_argsort_stable(self):
with pytest.raises(AssertionError, match=msg):
tm.assert_numpy_array_equal(qindexer, mindexer)

def test_cumsum(self, datetime_series):
self._check_accum_op("cumsum", datetime_series)

def test_cumprod(self, datetime_series):
self._check_accum_op("cumprod", datetime_series)

def test_cummin(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummin().values,
np.minimum.accumulate(np.array(datetime_series)),
)
ts = datetime_series.copy()
ts[::2] = np.NaN
result = ts.cummin()[1::2]
expected = np.minimum.accumulate(ts.dropna())

tm.assert_series_equal(result, expected)

def test_cummax(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummax().values,
np.maximum.accumulate(np.array(datetime_series)),
)
ts = datetime_series.copy()
ts[::2] = np.NaN
result = ts.cummax()[1::2]
expected = np.maximum.accumulate(ts.dropna())

tm.assert_series_equal(result, expected)

@pytest.mark.xfail(
not _np_version_under1p18, reason="numpy 1.18 changed min/max behavior for NaT"
)
def test_cummin_datetime64(self):
s = pd.Series(
pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-3"])
)

expected = pd.Series(
pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-1"])
)
result = s.cummin(skipna=True)
tm.assert_series_equal(expected, result)

expected = pd.Series(
pd.to_datetime(
["NaT", "2000-1-2", "2000-1-2", "2000-1-1", "2000-1-1", "2000-1-1"]
)
)
result = s.cummin(skipna=False)
tm.assert_series_equal(expected, result)

@pytest.mark.xfail(
not _np_version_under1p18, reason="numpy 1.18 changed min/max behavior for NaT"
)
def test_cummax_datetime64(self):
s = pd.Series(
pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-3"])
)

expected = pd.Series(
pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-2", "NaT", "2000-1-3"])
)
result = s.cummax(skipna=True)
tm.assert_series_equal(expected, result)

expected = pd.Series(
pd.to_datetime(
["NaT", "2000-1-2", "2000-1-2", "2000-1-2", "2000-1-2", "2000-1-3"]
)
)
result = s.cummax(skipna=False)
tm.assert_series_equal(expected, result)

@pytest.mark.xfail(
not _np_version_under1p18, reason="numpy 1.18 changed min/max behavior for NaT"
)
def test_cummin_timedelta64(self):
s = pd.Series(pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "3 min"]))

expected = pd.Series(
pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "1 min"])
)
result = s.cummin(skipna=True)
tm.assert_series_equal(expected, result)

expected = pd.Series(
pd.to_timedelta(["NaT", "2 min", "2 min", "1 min", "1 min", "1 min"])
)
result = s.cummin(skipna=False)
tm.assert_series_equal(expected, result)

@pytest.mark.xfail(
not _np_version_under1p18, reason="numpy 1.18 changed min/max behavior for NaT"
)
def test_cummax_timedelta64(self):
s = pd.Series(pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "3 min"]))

expected = pd.Series(
pd.to_timedelta(["NaT", "2 min", "NaT", "2 min", "NaT", "3 min"])
)
result = s.cummax(skipna=True)
tm.assert_series_equal(expected, result)

expected = pd.Series(
pd.to_timedelta(["NaT", "2 min", "2 min", "2 min", "2 min", "3 min"])
)
result = s.cummax(skipna=False)
tm.assert_series_equal(expected, result)

def test_np_diff(self):
pytest.skip("skipping due to Series no longer being an ndarray")

Expand Down
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