diff --git a/ci/code_checks.sh b/ci/code_checks.sh index 801fe7eccd1ed..65e67379a55b6 100755 --- a/ci/code_checks.sh +++ b/ci/code_checks.sh @@ -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" \ diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 0af5c753977bd..75e9b779e5094 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -1144,12 +1144,27 @@ 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 `__. 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) @@ -1157,6 +1172,11 @@ def put( 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"])