def unconvert(values, dtype, compress=None):

    as_is_ext = isinstance(values, ExtType) and values.code == 0

    if as_is_ext:
        values = values.data

    if is_categorical_dtype(dtype):
        return values

    elif is_object_dtype(dtype):
        return np.array(values, dtype=object)

    dtype = pandas_dtype(dtype).base

    if not as_is_ext:
        values = values.encode('latin1')

    if compress:
        if compress == u'zlib':
            _check_zlib()
            decompress = zlib.decompress
        elif compress == u'blosc':
            _check_blosc()
            decompress = blosc.decompress
        else:
            raise ValueError("compress must be one of 'zlib' or 'blosc'")

        try:
            return np.frombuffer(
                _move_into_mutable_buffer(decompress(values)),
                dtype=dtype,
            )
        except _BadMove as e:
            # Pull the decompressed data off of the `_BadMove` exception.
            # We don't just store this in the locals because we want to
            # minimize the risk of giving users access to a `bytes` object
            # whose data is also given to a mutable buffer.
            values = e.args[0]
            if len(values) <= 1:
                # The empty string and single characters are memoized in many
                # string creating functions in the capi. This case should not
                # warn even though we need to make a copy because we are only
                # copying at most 1 byte.
                warnings.warn(
                    'copying data after decompressing; this may mean that'
                    ' decompress is caching its result',
                    PerformanceWarning,
                )
                # fall through to copying `np.fromstring`

    # Copy the string into a numpy array.
    return np.fromstring(values, dtype=dtype)
dataset/ETHPy150Open pydata/pandas/pandas/io/packers.py/unconvert/operator `>` -> `<=`