def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None,
                       categorize=True):
    """
    Return a data hash of the Index/Series/DataFrame

    .. versionadded:: 0.19.2

    Parameters
    ----------
    index : boolean, default True
        include the index in the hash (if Series/DataFrame)
    encoding : string, default 'utf8'
        encoding for data & key when strings
    hash_key : string key to encode, default to _default_hash_key
    categorize : bool, default True
        Whether to first categorize object arrays before hashing. This is more
        efficient when the array contains duplicate values.

        .. versionadded:: 0.20.0

    Returns
    -------
    Series of uint64, same length as the object
    """
    from pandas import Series
    if hash_key is None:
        hash_key = _default_hash_key

    if isinstance(obj, ABCMultiIndex):
        return Series(hash_tuples(obj, encoding, hash_key),
                      dtype='uint64', copy=False)

    if isinstance(obj, ABCIndexClass):
        h = hash_array(obj.values, encoding, hash_key,
                       categorize).astype('uint64', copy=False)
        h = Series(h, index=obj, dtype='uint64', copy=False)
    elif isinstance(obj, ABCSeries):
        h = hash_array(obj.values, encoding, hash_key,
                       categorize).astype('uint64', copy=False)
        if index:
            index_iter = (hash_pandas_object(obj.index,
                                             index=False,
                                             encoding=encoding,
                                             hash_key=hash_key,
                                             categorize=categorize).values
                          for _ in [None])
            arrays = itertools.chain([h], index_iter)
            h = _combine_hash_arrays(arrays, 2)

        h = Series(h, index=obj.index, dtype='uint64', copy=False)

    elif isinstance(obj, ABCDataFrame):
        hashes = (hash_array(series.values) for _, series in obj.iteritems())
        num_items = len(obj.columns)
        if index:
            index_hash_generator = (hash_pandas_object(obj.index,
                                                       index=False,
                                                       encoding=encoding,
                                                       hash_key=hash_key,
                                                       categorize=categorize).values  # noqa
                                    for _ in [None])
            num_items += 1
            hashes = itertools.chain(hashes, index_hash_generator)
        h = _combine_hash_arrays(hashes, num_items)

        h = Series(h, index=obj.index, dtype='uint64', copy=False)
    else:
        raise TypeError("Unexpected type for hashing %s" % type(obj))
    return h
Return a data hash of the Index/Series/DataFrame

    .. versionadded:: 0.19.2

    Parameters
    ----------
    index : boolean, default True
        include the index in the hash (if Series/DataFrame)
    encoding : string, default 'utf8'
        encoding for data & key when strings
    hash_key : string key to encode, default to _default_hash_key
    categorize : bool, default True
        Whether to first categorize object arrays before hashing. This is more
        efficient when the array contains duplicate values.

        .. versionadded:: 0.20.0

    Returns
    -------
    Series of uint64, same length as the object