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213 lines
7.8 KiB
Python
213 lines
7.8 KiB
Python
"""
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Tests for Series cumulative operations.
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See also
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--------
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tests.frame.test_cumulative
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"""
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import re
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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methods = {
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"cumsum": np.cumsum,
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"cumprod": np.cumprod,
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"cummin": np.minimum.accumulate,
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"cummax": np.maximum.accumulate,
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}
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class TestSeriesCumulativeOps:
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@pytest.mark.parametrize("func", [np.cumsum, np.cumprod])
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def test_datetime_series(self, datetime_series, func):
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tm.assert_numpy_array_equal(
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func(datetime_series).values,
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func(np.array(datetime_series)),
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check_dtype=True,
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)
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# with missing values
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ts = datetime_series.copy()
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ts[::2] = np.nan
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result = func(ts)[1::2]
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expected = func(np.array(ts.dropna()))
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tm.assert_numpy_array_equal(result.values, expected, check_dtype=False)
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@pytest.mark.parametrize("method", ["cummin", "cummax"])
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def test_cummin_cummax(self, datetime_series, method):
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ufunc = methods[method]
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result = getattr(datetime_series, method)().values
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expected = ufunc(np.array(datetime_series))
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tm.assert_numpy_array_equal(result, expected)
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ts = datetime_series.copy()
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ts[::2] = np.nan
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result = getattr(ts, method)()[1::2]
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expected = ufunc(ts.dropna())
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result.index = result.index._with_freq(None)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"ts",
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[
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pd.Timedelta(0),
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pd.Timestamp("1999-12-31"),
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pd.Timestamp("1999-12-31").tz_localize("US/Pacific"),
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],
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)
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@pytest.mark.parametrize(
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"method, skipna, exp_tdi",
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[
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["cummax", True, ["NaT", "2 days", "NaT", "2 days", "NaT", "3 days"]],
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["cummin", True, ["NaT", "2 days", "NaT", "1 days", "NaT", "1 days"]],
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[
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"cummax",
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False,
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["NaT", "NaT", "NaT", "NaT", "NaT", "NaT"],
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],
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[
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"cummin",
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False,
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["NaT", "NaT", "NaT", "NaT", "NaT", "NaT"],
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],
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],
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)
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def test_cummin_cummax_datetimelike(self, ts, method, skipna, exp_tdi):
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# with ts==pd.Timedelta(0), we are testing td64; with naive Timestamp
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# we are testing datetime64[ns]; with Timestamp[US/Pacific]
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# we are testing dt64tz
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tdi = pd.to_timedelta(["NaT", "2 days", "NaT", "1 days", "NaT", "3 days"])
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ser = pd.Series(tdi + ts)
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exp_tdi = pd.to_timedelta(exp_tdi)
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expected = pd.Series(exp_tdi + ts)
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result = getattr(ser, method)(skipna=skipna)
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tm.assert_series_equal(expected, result)
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@pytest.mark.parametrize(
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"func, exp",
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[
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("cummin", pd.Period("2012-1-1", freq="D")),
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("cummax", pd.Period("2012-1-2", freq="D")),
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],
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)
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def test_cummin_cummax_period(self, func, exp):
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# GH#28385
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ser = pd.Series(
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[pd.Period("2012-1-1", freq="D"), pd.NaT, pd.Period("2012-1-2", freq="D")]
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)
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result = getattr(ser, func)(skipna=False)
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expected = pd.Series([pd.Period("2012-1-1", freq="D"), pd.NaT, pd.NaT])
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tm.assert_series_equal(result, expected)
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result = getattr(ser, func)(skipna=True)
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expected = pd.Series([pd.Period("2012-1-1", freq="D"), pd.NaT, exp])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"arg",
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[
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[False, False, False, True, True, False, False],
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[False, False, False, False, False, False, False],
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],
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)
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@pytest.mark.parametrize(
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"func", [lambda x: x, lambda x: ~x], ids=["identity", "inverse"]
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)
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@pytest.mark.parametrize("method", methods.keys())
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def test_cummethods_bool(self, arg, func, method):
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# GH#6270
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# checking Series method vs the ufunc applied to the values
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ser = func(pd.Series(arg))
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ufunc = methods[method]
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exp_vals = ufunc(ser.values)
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expected = pd.Series(exp_vals)
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result = getattr(ser, method)()
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"method, expected",
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[
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["cumsum", pd.Series([0, 1, np.nan, 1], dtype=object)],
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["cumprod", pd.Series([False, 0, np.nan, 0])],
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["cummin", pd.Series([False, False, np.nan, False])],
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["cummax", pd.Series([False, True, np.nan, True])],
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],
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)
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def test_cummethods_bool_in_object_dtype(self, method, expected):
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ser = pd.Series([False, True, np.nan, False])
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result = getattr(ser, method)()
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tm.assert_series_equal(result, expected)
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def test_cumprod_timedelta(self):
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# GH#48111
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ser = pd.Series([pd.Timedelta(days=1), pd.Timedelta(days=3)])
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with pytest.raises(TypeError, match="cumprod not supported for Timedelta"):
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ser.cumprod()
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@pytest.mark.parametrize(
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"data, op, skipna, expected_data",
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[
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([], "cumsum", True, []),
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([], "cumsum", False, []),
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(["x", "z", "y"], "cumsum", True, ["x", "xz", "xzy"]),
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(["x", "z", "y"], "cumsum", False, ["x", "xz", "xzy"]),
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(["x", pd.NA, "y"], "cumsum", True, ["x", pd.NA, "xy"]),
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(["x", pd.NA, "y"], "cumsum", False, ["x", pd.NA, pd.NA]),
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([pd.NA, "x", "y"], "cumsum", True, [pd.NA, "x", "xy"]),
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([pd.NA, "x", "y"], "cumsum", False, [pd.NA, pd.NA, pd.NA]),
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([pd.NA, pd.NA, pd.NA], "cumsum", True, [pd.NA, pd.NA, pd.NA]),
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([pd.NA, pd.NA, pd.NA], "cumsum", False, [pd.NA, pd.NA, pd.NA]),
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([], "cummin", True, []),
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([], "cummin", False, []),
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(["y", "z", "x"], "cummin", True, ["y", "y", "x"]),
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(["y", "z", "x"], "cummin", False, ["y", "y", "x"]),
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(["y", pd.NA, "x"], "cummin", True, ["y", pd.NA, "x"]),
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(["y", pd.NA, "x"], "cummin", False, ["y", pd.NA, pd.NA]),
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([pd.NA, "y", "x"], "cummin", True, [pd.NA, "y", "x"]),
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([pd.NA, "y", "x"], "cummin", False, [pd.NA, pd.NA, pd.NA]),
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([pd.NA, pd.NA, pd.NA], "cummin", True, [pd.NA, pd.NA, pd.NA]),
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([pd.NA, pd.NA, pd.NA], "cummin", False, [pd.NA, pd.NA, pd.NA]),
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([], "cummax", True, []),
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([], "cummax", False, []),
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(["x", "z", "y"], "cummax", True, ["x", "z", "z"]),
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(["x", "z", "y"], "cummax", False, ["x", "z", "z"]),
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(["x", pd.NA, "y"], "cummax", True, ["x", pd.NA, "y"]),
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(["x", pd.NA, "y"], "cummax", False, ["x", pd.NA, pd.NA]),
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([pd.NA, "x", "y"], "cummax", True, [pd.NA, "x", "y"]),
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([pd.NA, "x", "y"], "cummax", False, [pd.NA, pd.NA, pd.NA]),
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([pd.NA, pd.NA, pd.NA], "cummax", True, [pd.NA, pd.NA, pd.NA]),
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([pd.NA, pd.NA, pd.NA], "cummax", False, [pd.NA, pd.NA, pd.NA]),
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],
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)
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def test_cum_methods_ea_strings(
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self, string_dtype_no_object, data, op, skipna, expected_data
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):
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# https://github.com/pandas-dev/pandas/pull/60633 - pyarrow
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# https://github.com/pandas-dev/pandas/pull/60938 - Python
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ser = pd.Series(data, dtype=string_dtype_no_object)
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method = getattr(ser, op)
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expected = pd.Series(expected_data, dtype=string_dtype_no_object)
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result = method(skipna=skipna)
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tm.assert_series_equal(result, expected)
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def test_cumprod_pyarrow_strings(self, pyarrow_string_dtype, skipna):
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# https://github.com/pandas-dev/pandas/pull/60633
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ser = pd.Series(list("xyz"), dtype=pyarrow_string_dtype)
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msg = re.escape(f"operation 'cumprod' not supported for dtype '{ser.dtype}'")
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with pytest.raises(TypeError, match=msg):
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ser.cumprod(skipna=skipna)
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