Skip to content

DOC: fix PR07,SA01 for pandas.util.hash_array #58877

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
Jun 1, 2024
Merged

DOC: fix PR07,SA01 for pandas.util.hash_array #58877

Show file tree
Hide file tree
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
1 change: 0 additions & 1 deletion ci/code_checks.sh
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -788,7 +788,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
-i "pandas.tseries.offsets.YearEnd.normalize GL08" \
-i "pandas.tseries.offsets.YearEnd.rule_code GL08" \
-i "pandas.unique PR07" \
-i "pandas.util.hash_array PR07,SA01" \
-i "pandas.util.hash_pandas_object PR07,SA01" # There should be no backslash in the final line, please keep this comment in the last ignored function

RET=$(($RET + $?)) ; echo $MSG "DONE"
Expand Down
6 changes: 6 additions & 0 deletions pandas/core/util/hashing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -244,6 +244,7 @@ def hash_array(
Parameters
----------
vals : ndarray or ExtensionArray
The input array to hash.
encoding : str, default 'utf8'
Encoding for data & key when strings.
hash_key : str, default _default_hash_key
Expand All @@ -257,6 +258,11 @@ def hash_array(
ndarray[np.uint64, ndim=1]
Hashed values, same length as the vals.

See Also
--------
util.hash_pandas_object : Return a data hash of the Index/Series/DataFrame.
util.hash_tuples : Hash an MultiIndex / listlike-of-tuples efficiently.

Examples
--------
>>> pd.util.hash_array(np.array([1, 2, 3]))
Expand Down