put(self, key: 'str', value: 'DataFrame | Series', format=None, index=True, append=False, complib=None, complevel: 'int | None' = None, min_itemsize: 'int | dict[str, int] | None' = None, nan_rep=None, data_columns: 'Literal[True] | list[str] | None' = None, encoding=None, errors: 'str' = 'strict', track_times: 'bool' = True, dropna: 'bool' = False)
Format to use when storing object in HDFStore. Value can be one of:
'fixed'
Fixed format. Fast writing/reading. Not-appendable, nor searchable.
'table'
Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
This will force Table format, append the input data to the existing.
List of columns to create as data columns, or True to use all columns. See :None:None:`here
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`.
Provide an encoding for strings.
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.
Store object in HDFStore.
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