pandas.read_hdf(path_or_buf, key=None, mode='r', **kwargs)[source]

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

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

path_or_buf : str, 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 0.19.0: support for pathlib, py.path.

New in version 0.21.0: support for __fspath__ protocol.

key : object, optional

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

mode : {‘r’, ‘r+’, ‘a’}, optional

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

where : list, optional

A list of Term (or convertible) objects.

start : int, optional

Row number to start selection.

stop : int, optional

Row number to stop selection.

columns : list, optional

A list of columns names to return.

iterator : bool, optional

Return an iterator object.

chunksize : int, optional

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

errors : str, default ‘strict’

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


Additional keyword arguments passed to HDFStore.

item : object

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

See also

Write a HDF file from a DataFrame.
Low-level access to HDF files.


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