pandas.HDFStore.select#

HDFStore.select(key, where=None, start=None, stop=None, columns=None, iterator=False, chunksize=None, auto_close=False)[source]#

Retrieve pandas object stored in file, optionally based on where criteria.

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:

keystr: Object being retrieved from file.
wherelist or None: List of Term (or convertible) objects, optional.
startint or None: Row number to start selection.
stopint, default None: Row number to stop selection.
columnslist or None: A list of columns that if not None, will limit the return columns.
iteratorbool or False: Returns an iterator.
chunksizeint or None: Number or rows to include in iteration, return an iterator.
auto_closebool or False: Should automatically close the store when finished.

Returns:

object: Retrieved object from file.

See also

HDFStore.select_as_coordinates: Returns the selection as an index.
HDFStore.select_column: Returns a single column from the table.
HDFStore.select_as_multiple: Retrieves pandas objects from multiple tables.

Examples

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
>>> store = pd.HDFStore("store.h5", "w")  
>>> store.put("data", df)  
>>> store.get("data")  
>>> print(store.keys())  
['/data1', '/data2']
>>> store.select("/data1")  
   A  B
0  1  2
1  3  4
>>> store.select("/data1", where="columns == A")  
   A
0  1
1  3
>>> store.close()  