pandas.HDFStore.append_to_multiple#

HDFStore.append_to_multiple(d, value, selector, data_columns=None, axes=None, dropna=<no_default>, **kwargs)[source]#

Append to multiple tables.

Splits value column-wise according to d and appends each slice to the corresponding table. The selector table is the one you query against; its columns are made data_columns so they can be used in where clauses.

Parameters:

ddict: Mapping of table_name to table_columns. None is acceptable as the values for one node (that table will get all the remaining columns).
valueDataFrame or Series: Pandas object to split across the tables.
selectorstr: Designates the indexable table; all of its columns will be made data_columns unless data_columns is passed, in which case those are used.
data_columnslist of str or True, optional: Columns to create as data columns, or True to use all columns.
axesdefault None: This parameter is currently not accepted.
dropnabool, default False: If True, drop rows from all tables if any single row in each table has all NaN.

Deprecated since version 3.1.0: The dropna keyword is deprecated and will be removed in a future version. Use DataFrame.dropna() before writing instead.
**kwargs: Additional keyword arguments forwarded to HDFStore.append().

See also

HDFStore.append: Append to a single table.
HDFStore.select_as_multiple: Read from multiple tables with a single where.

Examples

>>> df = pd.DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
>>> store = pd.HDFStore("store.h5", "w")
>>> store.append_to_multiple(
...     {"t1": ["A", "B"], "t2": None}, df, selector="t1"
... )
>>> store.close()