136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
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"""
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Tests for DataFrame cumulative operations
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See also
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--------
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tests.series.test_cumulative
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"""
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import numpy as np
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from pandas import DataFrame, Series
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import pandas._testing as tm
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class TestDataFrameCumulativeOps:
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# ---------------------------------------------------------------------
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# Cumulative Operations - cumsum, cummax, ...
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def test_cumsum_corner(self):
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dm = DataFrame(np.arange(20).reshape(4, 5), index=range(4), columns=range(5))
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# TODO(wesm): do something with this?
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result = dm.cumsum() # noqa
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def test_cumsum(self, datetime_frame):
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datetime_frame.iloc[5:10, 0] = np.nan
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datetime_frame.iloc[10:15, 1] = np.nan
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datetime_frame.iloc[15:, 2] = np.nan
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# axis = 0
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cumsum = datetime_frame.cumsum()
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expected = datetime_frame.apply(Series.cumsum)
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tm.assert_frame_equal(cumsum, expected)
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# axis = 1
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cumsum = datetime_frame.cumsum(axis=1)
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expected = datetime_frame.apply(Series.cumsum, axis=1)
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tm.assert_frame_equal(cumsum, expected)
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# works
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df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
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df.cumsum()
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# fix issue
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cumsum_xs = datetime_frame.cumsum(axis=1)
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assert np.shape(cumsum_xs) == np.shape(datetime_frame)
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def test_cumprod(self, datetime_frame):
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datetime_frame.iloc[5:10, 0] = np.nan
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datetime_frame.iloc[10:15, 1] = np.nan
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datetime_frame.iloc[15:, 2] = np.nan
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# axis = 0
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cumprod = datetime_frame.cumprod()
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expected = datetime_frame.apply(Series.cumprod)
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tm.assert_frame_equal(cumprod, expected)
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# axis = 1
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cumprod = datetime_frame.cumprod(axis=1)
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expected = datetime_frame.apply(Series.cumprod, axis=1)
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tm.assert_frame_equal(cumprod, expected)
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# fix issue
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cumprod_xs = datetime_frame.cumprod(axis=1)
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assert np.shape(cumprod_xs) == np.shape(datetime_frame)
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# ints
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df = datetime_frame.fillna(0).astype(int)
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df.cumprod(0)
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df.cumprod(1)
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# ints32
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df = datetime_frame.fillna(0).astype(np.int32)
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df.cumprod(0)
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df.cumprod(1)
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def test_cummin(self, datetime_frame):
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datetime_frame.iloc[5:10, 0] = np.nan
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datetime_frame.iloc[10:15, 1] = np.nan
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datetime_frame.iloc[15:, 2] = np.nan
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# axis = 0
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cummin = datetime_frame.cummin()
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expected = datetime_frame.apply(Series.cummin)
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tm.assert_frame_equal(cummin, expected)
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# axis = 1
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cummin = datetime_frame.cummin(axis=1)
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expected = datetime_frame.apply(Series.cummin, axis=1)
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tm.assert_frame_equal(cummin, expected)
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# it works
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df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
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df.cummin()
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# fix issue
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cummin_xs = datetime_frame.cummin(axis=1)
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assert np.shape(cummin_xs) == np.shape(datetime_frame)
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def test_cummax(self, datetime_frame):
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datetime_frame.iloc[5:10, 0] = np.nan
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datetime_frame.iloc[10:15, 1] = np.nan
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datetime_frame.iloc[15:, 2] = np.nan
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# axis = 0
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cummax = datetime_frame.cummax()
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expected = datetime_frame.apply(Series.cummax)
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tm.assert_frame_equal(cummax, expected)
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# axis = 1
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cummax = datetime_frame.cummax(axis=1)
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expected = datetime_frame.apply(Series.cummax, axis=1)
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tm.assert_frame_equal(cummax, expected)
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# it works
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df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
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df.cummax()
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# fix issue
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cummax_xs = datetime_frame.cummax(axis=1)
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assert np.shape(cummax_xs) == np.shape(datetime_frame)
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def test_cumulative_ops_preserve_dtypes(self):
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# GH#19296 dont incorrectly upcast to object
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df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3.0], "C": [True, False, False]})
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result = df.cumsum()
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expected = DataFrame(
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{
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"A": Series([1, 3, 6], dtype=np.int64),
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"B": Series([1, 3, 6], dtype=np.float64),
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"C": df["C"].cumsum(),
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}
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)
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tm.assert_frame_equal(result, expected)
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