pandas.HDFStore.select_as_multiple#

HDFStore.select_as_multiple(keys, where=None, selector=None, columns=None, start=None, stop=None, iterator=False, chunksize=None, auto_close=False)[source]#

Retrieve pandas objects from multiple tables.

Warning

Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the “fixed” format. Loading pickled data received from untrusted sources can be unsafe.

See: https://docs.python.org/3/library/pickle.html for more.

Parameters:

keyslist of str: Names of the tables to read.
wherelist, optional: List of Term (or convertible) objects.
selectorstr, optional: The table to apply the where criteria to. Defaults to keys[0].
columnslist, optional: Columns to return.
startint, optional: Row number to start selection. Applied to each table before where is evaluated.
stopint, optional: Row number to stop selection. Applied to each table before where is evaluated.
iteratorbool, default False: Return an iterator.
chunksizeint, optional: Number of rows to include in each iteration; implies iterator=True.
auto_closebool, default False: Should automatically close the store when finished.

Returns:

DataFrame or TableIterator: Concatenated result from the selected tables. A TableIterator is returned instead when iterator=True or chunksize is given.

Raises:

KeyError: If keys or selector is not found, or keys is empty.
TypeError: If keys is not a list or tuple.
ValueError: If the tables do not all have the same number of rows.

See also

HDFStore.append_to_multiple: Append to multiple tables, splitting a single object into a dict of column groups.
HDFStore.select: Retrieve a single stored object.

Examples

>>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["C", "D"])
>>> store = pd.HDFStore("store.h5", "w")
>>> store.append("t1", df1, format="table")
>>> store.append("t2", df2, format="table")
>>> store.select_as_multiple(["t1", "t2"])
>>> store.close()