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