Read from the store, close it if we opened it.
Retrieve pandas object stored in file, optionally based on where
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.
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
Alternatively, pandas accepts an open pandas.HDFStore object.
By file-like object, we refer to objects with a read() method,
such as a file handle (e.g. via builtin open function)
The group identifier in the store. Can be omitted if the HDF file
contains a single pandas object.
Mode to use when opening the file. Ignored if path_or_buf is a
pandas.HDFStore. Default is ‘r’.
Specifies how encoding and decoding errors are to be handled.
See the errors argument for open() for a full list
A list of Term (or convertible) objects.
Row number to start selection.
Row number to stop selection.
A list of columns names to return.
Return an iterator object.
Number of rows to include in an iteration when using an iterator.
Additional keyword arguments passed to HDFStore.
The selected object. Return type depends on the object stored.
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')