pandas.HDFStore.put

HDFStore.put(self, key: str, value: ~FrameOrSeries, format=None, index=True, append=False, complib=None, complevel: Union[int, NoneType] = None, min_itemsize: Union[int, Dict[str, int], NoneType] = None, nan_rep=None, data_columns: Union[List[str], NoneType] = None, encoding=None, errors: str = 'strict', track_times: bool = True)

Store object in HDFStore.

Parameters

keystr

value{Series, DataFrame}

format‘fixed(f)|table(t)’, default is ‘fixed’

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.

appendbool, default False

This will force Table format, append the input data to the existing.

data_columnslist, default None

List of columns to create as data columns, or True to use all columns. See here.

encodingstr, default None

Provide an encoding for strings.

dropnabool, default False, do not write an ALL nan row to

The store settable by the option ‘io.hdf.dropna_table’.

track_timesbool, 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.

New in version 1.1.0.

pandas.read_hdf pandas.HDFStore.append