Skip to content

TST (string): fix xfailed groupby value_counts tests #59632

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 28, 2024
Merged

TST (string): fix xfailed groupby value_counts tests #59632

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
37 changes: 26 additions & 11 deletions pandas/tests/groupby/methods/test_value_counts.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,6 @@
import numpy as np
import pytest

from pandas._config import using_string_dtype

from pandas.compat import HAS_PYARROW
import pandas.util._test_decorators as td

from pandas import (
Expand Down Expand Up @@ -276,7 +273,6 @@ def _frame_value_counts(df, keys, normalize, sort, ascending):
return df[keys].value_counts(normalize=normalize, sort=sort, ascending=ascending)


@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
@pytest.mark.parametrize("groupby", ["column", "array", "function"])
@pytest.mark.parametrize("normalize, name", [(True, "proportion"), (False, "count")])
@pytest.mark.parametrize(
Expand All @@ -289,7 +285,16 @@ def _frame_value_counts(df, keys, normalize, sort, ascending):
)
@pytest.mark.parametrize("frame", [True, False])
def test_against_frame_and_seriesgroupby(
education_df, groupby, normalize, name, sort, ascending, as_index, frame, request
education_df,
groupby,
normalize,
name,
sort,
ascending,
as_index,
frame,
request,
using_infer_string,
):
# test all parameters:
# - Use column, array or function as by= parameter
Expand Down Expand Up @@ -350,17 +355,24 @@ def test_against_frame_and_seriesgroupby(
index_frame["gender"] = index_frame["both"].str.split("-").str.get(0)
index_frame["education"] = index_frame["both"].str.split("-").str.get(1)
del index_frame["both"]
index_frame = index_frame.rename({0: None}, axis=1)
expected.index = MultiIndex.from_frame(index_frame)
index_frame2 = index_frame.rename({0: None}, axis=1)
expected.index = MultiIndex.from_frame(index_frame2)

if index_frame2.columns.isna()[0]:
# with using_infer_string, the columns in index_frame as string
# dtype, which makes the rename({0: None}) above use np.nan
# instead of None, so we need to set None more explicitly.
expected.index.names = [None] + expected.index.names[1:]
tm.assert_series_equal(result, expected)
else:
expected.insert(1, "gender", expected["both"].str.split("-").str.get(0))
expected.insert(2, "education", expected["both"].str.split("-").str.get(1))
if using_infer_string:
expected = expected.astype({"gender": "str", "education": "str"})
del expected["both"]
tm.assert_frame_equal(result, expected)


@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
@pytest.mark.parametrize(
"dtype",
[
Expand All @@ -387,6 +399,7 @@ def test_compound(
expected_count,
expected_group_size,
dtype,
using_infer_string,
):
education_df = education_df.astype(dtype)
education_df.columns = education_df.columns.astype(dtype)
Expand All @@ -409,6 +422,11 @@ def test_compound(
expected["count"] = expected_count
if dtype == "string[pyarrow]":
expected["count"] = expected["count"].convert_dtypes()
if using_infer_string and dtype == object:
expected = expected.astype(
{"country": "str", "gender": "str", "education": "str"}
)

tm.assert_frame_equal(result, expected)


Expand Down Expand Up @@ -501,9 +519,6 @@ def test_dropna_combinations(
tm.assert_series_equal(result, expected)


@pytest.mark.xfail(
using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)", strict=False
)
@pytest.mark.parametrize(
"dropna, expected_data, expected_index",
[
Expand Down