pandas.read_hdf

pandas.read_hdf(path_or_buf, key=None, mode: str = 'r', errors: str = 'strict', where=None, start: Union[int, NoneType] = None, stop: Union[int, NoneType] = None, columns=None, iterator=False, chunksize: Union[int, NoneType] = None, **kwargs)

Read from the store, close it if we opened it.

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

Parameters

path_or_bufstr, path object, pandas.HDFStore or file-like object

Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.h5.

If you want to pass in a path object, pandas accepts any os.PathLike.

Alternatively, pandas accepts an open pandas.HDFStore object.

By file-like object, we refer to objects with a read() method, such as a file handler (e.g. via builtin open function) or StringIO.

New in version 0.21.0: support for __fspath__ protocol.

keyobject, optional

The group identifier in the store. Can be omitted if the HDF file contains a single pandas object.

mode{‘r’, ‘r+’, ‘a’}, default ‘r’

Mode to use when opening the file. Ignored if path_or_buf is a pandas.HDFStore. Default is ‘r’.

wherelist, optional

A list of Term (or convertible) objects.

startint, optional

Row number to start selection.

stopint, optional

Row number to stop selection.

columnslist, optional

A list of columns names to return.

iteratorbool, optional

Return an iterator object.

chunksizeint, optional

Number of rows to include in an iteration when using an iterator.

errorsstr, default ‘strict’

Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.

**kwargs

Additional keyword arguments passed to HDFStore.

Returns

itemobject

The selected object. Return type depends on the object stored.

See also

DataFrame.to_hdf

Write a HDF file from a DataFrame.

HDFStore

Low-level access to HDF files.

Examples

>>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])
>>> df.to_hdf('./store.h5', 'data')
>>> reread = pd.read_hdf('./store.h5')

pandas.read_html pandas.HDFStore.put