def test_series_fillna_invalid_dtype(data_dtype):
gdf = Series([1, 2, None, 3], dtype=data_dtype)
fill_value = 2.5
with pytest.raises(TypeError) as raises:
gdf.fillna(fill_value)
raises.match("Cannot safely cast non-equivalent {} to {}".format(
np.dtype(type(fill_value)).type.__name__, gdf.dtype.type.__name__))
def test_series_replace():
a1 = np.array([0, 1, 2, 3, 4])
# Numerical
a2 = np.array([5, 1, 2, 3, 4])
sr1 = Series(a1)
sr2 = sr1.replace(0, 5)
np.testing.assert_equal(sr2.to_array(), a2)
# Categorical
psr3 = pd.Series(["one", "two", "three"], dtype="category")
psr4 = psr3.replace("one", "two")
sr3 = Series.from_pandas(psr3)
sr4 = sr3.replace("one", "two")
pd.testing.assert_series_equal(sr4.to_pandas(), psr4)
# List input
a6 = np.array([5, 6, 2, 3, 4])
sr6 = sr1.replace([0, 1], [5, 6])
np.testing.assert_equal(sr6.to_array(), a6)
a7 = np.array([5.5, 6.5, 2, 3, 4])
sr7 = sr1.replace([0, 1], [5.5, 6.5])
np.testing.assert_equal(sr7.to_array(), a7)
# Series input
a8 = np.array([5, 5, 5, 3, 4])
sr8 = sr1.replace(sr1[:3], 5)
np.testing.assert_equal(sr8.to_array(), a8)
# large input containing null
sr9 = Series(list(range(400)) + [None])
sr10 = sr9.replace([22, 323, 27, 0], None)
assert sr10.null_count == 5
assert len(sr10.to_array()) == (401 - 5)
sr11 = sr9.replace([22, 323, 27, 0], -1)
assert sr11.null_count == 1
assert len(sr11.to_array()) == (401 - 1)
# large input not containing nulls
sr9 = sr9.fillna(-11)
sr12 = sr9.replace([22, 323, 27, 0], None)
assert sr12.null_count == 4
assert len(sr12.to_array()) == (401 - 4)
sr13 = sr9.replace([22, 323, 27, 0], -1)
assert sr13.null_count == 0
assert len(sr13.to_array()) == 401
def test_series_fillna_numerical(dtype, fill_type, null_value, inplace):
data = np.array([0, 1, null_value, 2, null_value], dtype='float64')
sr = Series(data).astype(dtype)
if fill_type == 'scalar':
fill_value = np.random.randint(0, 5)
expect = np.array([0, 1, fill_value, 2, fill_value], dtype=dtype)
elif fill_type == 'series':
fill_value = Series(np.random.randint(0, 5, (5, )))
expect = np.array([0, 1, fill_value[2], 2, fill_value[4]], dtype=dtype)
got = sr.fillna(fill_value, inplace=inplace)
if inplace:
got = sr
else:
got = got.to_array()
np.testing.assert_equal(expect, got)
def test_series_fillna_numerical(data_dtype, fill_dtype, fill_type, null_value,
inplace):
# TODO: These tests should use Pandas' nullable int type
# when we support a recent enough version of Pandas
# https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
if fill_type == 'scalar':
fill_value = np.random.randint(0, 5)
expect = np.array([0, 1, fill_value, 2, fill_value], dtype=data_dtype)
elif fill_type == 'series':
data = np.random.randint(0, 5, (5, ))
fill_value = pd.Series(data, dtype=data_dtype)
expect = np.array([0, 1, fill_value[2], 2, fill_value[4]],
dtype=data_dtype)
sr = Series([0, 1, null_value, 2, null_value], dtype=data_dtype)
result = sr.fillna(fill_value, inplace=inplace)
if inplace:
result = sr
got = result.to_array()
np.testing.assert_equal(expect, got)
