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DOC: Enforce Numpy Docstring Validation for pandas.HDFStore.put #58384

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1 change: 0 additions & 1 deletion ci/code_checks.sh
Original file line number Diff line number Diff line change
Expand Up @@ -119,7 +119,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
-i "pandas.DatetimeTZDtype.tz SA01" \
-i "pandas.DatetimeTZDtype.unit SA01" \
-i "pandas.Grouper PR02" \
-i "pandas.HDFStore.put PR01,SA01" \
-i "pandas.HDFStore.walk SA01" \
-i "pandas.Index PR07" \
-i "pandas.Index.T SA01" \
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20 changes: 20 additions & 0 deletions pandas/io/pytables.py
Original file line number Diff line number Diff line change
Expand Up @@ -1144,19 +1144,39 @@ def put(
Write DataFrame index as a column.
append : bool, default False
This will force Table format, append the input data to the existing.
complib : default None
This parameter is currently not accepted.
complevel : int, 0-9, default None
Specifies a compression level for data.
A value of 0 or None disables compression.
min_itemsize : int, dict, or None
Dict of columns that specify minimum str sizes.
nan_rep : str
Str to use as str nan representation.
data_columns : list of columns or True, default None
List of columns to create as data columns, or True to use all columns.
See `here
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
encoding : str, default None
Provide an encoding for strings.
errors : str, default 'strict'
The error handling scheme to use for encoding errors.
The default is 'strict' meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
track_times : bool, default True
Parameter is propagated to 'create_table' method of 'PyTables'.
If set to False it enables to have the same h5 files (same hashes)
independent on creation time.
dropna : bool, default False, optional
Remove missing values.

See Also
--------
HDFStore.info : Prints detailed information on the store.
HDFStore.get_storer : Returns the storer object for a key.

Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
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