# pandas.read_hdf

`pandas.``read_hdf`(path_or_buf, key=None, mode='r', **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_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.

  **kwargs

  > Additional keyword arguments passed to HDFStore.

  Returns:

  item : object

  > 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')

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