pandas.read_hdf
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pandas.read_hdf(path_or_buf, key=None, mode='r', **kwargs)
[source]
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Read from the store, close it if we opened it.
Retrieve pandas object stored in file, optionally based on where criteria
Parameters: |
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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
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Additional keyword arguments passed to HDFStore. |
Returns: |
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item : object -
The selected object. Return type depends on the object stored. |
See also
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DataFrame.to_hdf
- Write a HDF file from a DataFrame.
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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')