pandas.read_hdf#
- pandas.read_hdf(path_or_buf, key=None, mode='r', errors='strict', where=None, start=None, stop=None, columns=None, iterator=False, chunksize=None, **kwargs)[source]#
- Read from the store, close it if we opened it. - 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
- path_or_bufstr, path object, pandas.HDFStore
- Any valid string path is acceptable. Only supports the local file system, remote URLs and file-like objects are not supported. - If you want to pass in a path object, pandas accepts any - os.PathLike.- Alternatively, pandas accepts an open - pandas.HDFStoreobject.
- 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’.
- 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.
- 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. 
- **kwargs
- Additional keyword arguments passed to HDFStore. 
 
- Returns
- 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')