82 lines
2.3 KiB
Python
82 lines
2.3 KiB
Python
"""
|
|
Tests for DataFrame cumulative operations
|
|
|
|
See also
|
|
--------
|
|
tests.series.test_cumulative
|
|
"""
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas import (
|
|
DataFrame,
|
|
Series,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestDataFrameCumulativeOps:
|
|
# ---------------------------------------------------------------------
|
|
# Cumulative Operations - cumsum, cummax, ...
|
|
|
|
def test_cumulative_ops_smoke(self):
|
|
# it works
|
|
df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
|
|
df.cummax()
|
|
df.cummin()
|
|
df.cumsum()
|
|
|
|
dm = DataFrame(np.arange(20).reshape(4, 5), index=range(4), columns=range(5))
|
|
# TODO(wesm): do something with this?
|
|
dm.cumsum()
|
|
|
|
def test_cumprod_smoke(self, datetime_frame):
|
|
datetime_frame.iloc[5:10, 0] = np.nan
|
|
datetime_frame.iloc[10:15, 1] = np.nan
|
|
datetime_frame.iloc[15:, 2] = np.nan
|
|
|
|
# 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)
|
|
|
|
@pytest.mark.parametrize("method", ["cumsum", "cumprod", "cummin", "cummax"])
|
|
def test_cumulative_ops_match_series_apply(self, datetime_frame, method):
|
|
datetime_frame.iloc[5:10, 0] = np.nan
|
|
datetime_frame.iloc[10:15, 1] = np.nan
|
|
datetime_frame.iloc[15:, 2] = np.nan
|
|
|
|
# axis = 0
|
|
result = getattr(datetime_frame, method)()
|
|
expected = datetime_frame.apply(getattr(Series, method))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# axis = 1
|
|
result = getattr(datetime_frame, method)(axis=1)
|
|
expected = datetime_frame.apply(getattr(Series, method), axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# fix issue TODO: GH ref?
|
|
assert np.shape(result) == np.shape(datetime_frame)
|
|
|
|
def test_cumsum_preserve_dtypes(self):
|
|
# GH#19296 dont incorrectly upcast to object
|
|
df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3.0], "C": [True, False, False]})
|
|
|
|
result = df.cumsum()
|
|
|
|
expected = DataFrame(
|
|
{
|
|
"A": Series([1, 3, 6], dtype=np.int64),
|
|
"B": Series([1, 3, 6], dtype=np.float64),
|
|
"C": df["C"].cumsum(),
|
|
}
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|