A PyQT GUI application for converting InfoLease report outputs into Excel files. Handles parsing and summarizing. Learns where files are meant to be store and compiles monthly and yearly summaries.
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InfoLeaseExtract/venv/Lib/site-packages/pandas/tests/frame/test_cumulative.py

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2.3 KiB

"""
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)