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TST/CLN: Prepare moving tests out of groupby/test_allowlist (pandas-d…
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rhshadrach authored and Yi Wei committed May 19, 2023
1 parent 45a4c5b commit d133890
Showing 1 changed file with 19 additions and 52 deletions.
71 changes: 19 additions & 52 deletions pandas/tests/groupby/test_allowlist.py
Original file line number Diff line number Diff line change
Expand Up @@ -3,72 +3,38 @@
Do not add tests here!
"""

from string import ascii_lowercase

import numpy as np
import pytest

from pandas import (
DataFrame,
Series,
date_range,
)
import pandas._testing as tm

AGG_FUNCTIONS = [
"sum",
"prod",
"min",
"max",
"median",
"mean",
"skew",
"std",
"var",
"sem",
]
AGG_FUNCTIONS_WITH_SKIPNA = ["skew"]


@pytest.fixture
def df():
return DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.randn(8),
"D": np.random.randn(8),
}
)


@pytest.fixture
def df_letters():
letters = np.array(list(ascii_lowercase))
N = 10
random_letters = letters.take(np.random.randint(0, 26, N))
df = DataFrame(
{
"floats": N / 10 * Series(np.random.random(N)),
"letters": Series(random_letters),
}
)
return df


@pytest.fixture
def raw_frame():
return DataFrame([0])


@pytest.mark.parametrize("op", AGG_FUNCTIONS)
@pytest.mark.parametrize(
"op",
[
"sum",
"prod",
"min",
"max",
"median",
"mean",
"skew",
"std",
"var",
"sem",
],
)
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize("skipna", [True, False])
@pytest.mark.parametrize("sort", [True, False])
def test_regression_allowlist_methods(raw_frame, op, axis, skipna, sort):
def test_regression_allowlist_methods(op, axis, skipna, sort):
# GH6944
# GH 17537
# explicitly test the allowlist methods
raw_frame = DataFrame([0])
if axis == 0:
frame = raw_frame
msg = "The 'axis' keyword in DataFrame.groupby is deprecated and will be"
Expand All @@ -79,7 +45,8 @@ def test_regression_allowlist_methods(raw_frame, op, axis, skipna, sort):
with tm.assert_produces_warning(FutureWarning, match=msg):
grouped = frame.groupby(level=0, axis=axis, sort=sort)

if op in AGG_FUNCTIONS_WITH_SKIPNA:
if op == "skew":
# skew has skipna
result = getattr(grouped, op)(skipna=skipna)
expected = frame.groupby(level=0).apply(
lambda h: getattr(h, op)(axis=axis, skipna=skipna)
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