Function test_chunked_array_asarray

python/pyarrow/tests/test_table.py:426–463

()

@pytest.mark.pandas
@pytest.mark.nopandas
def test_chunked_array_asarray():
    # ensure this is tested both when pandas is present or not (ARROW-6564)

    data = [
        pa.array([0]),
        pa.array([1, 2, 3])
    ]
    chunked_arr = pa.chunked_array(data)

    np_arr = np.asarray(chunked_arr)
    assert np_arr.tolist() == [0, 1, 2, 3]
    assert np_arr.dtype == np.dtype('int64')

    # An optional type can be specified when calling np.asarray
    np_arr = np.asarray(chunked_arr, dtype='str')
    assert np_arr.tolist() == ['0', '1', '2', '3']

    # Types are modified when there are nulls
    data = [
        pa.array([1, None]),
        pa.array([1, 2, 3])
    ]
    chunked_arr = pa.chunked_array(data)

    np_arr = np.asarray(chunked_arr)
    elements = np_arr.tolist()
    assert elements[0] == 1.
    assert np.isnan(elements[1])
    assert elements[2:] == [1., 2., 3.]
    assert np_arr.dtype == np.dtype('float64')

    # DictionaryType data will be converted to dense numpy array
    arr = pa.DictionaryArray.from_arrays(
        pa.array([0, 1, 2, 0, 1]), pa.array(['a', 'b', 'c']))
    chunked_arr = pa.chunked_array([arr, arr])
    np_arr = np.asarray(chunked_arr)
    assert np_arr.dtype == np.dtype('object')
    assert np_arr.tolist() == ['a', 'b', 'c', 'a', 'b'] * 2


def test_chunked_array_flatten():