def test_cummax_masked():
data = [1, 2, None, 4, 5]
float_types = ["float32", "float64"]
for type_ in float_types:
gs = Series(data).astype(type_)
ps = pd.Series(data).astype(type_)
assert_eq(gs.cummax(), ps.cummax())
for type_ in INTEGER_TYPES:
gs = Series(data).astype(type_)
expected = pd.Series([1, 2, np.nan, 4, 5]).astype("float64")
assert_eq(gs.cummax(), expected)
def test_cummax_masked():
data = [1, 2, None, 4, 5]
float_types = ["float32", "float64"]
int_types = ["int8", "int16", "int32", "int64"]
for type_ in float_types:
gs = Series(data).astype(type_)
ps = pd.Series(data).astype(type_)
assert_eq(gs.cummax(), ps.cummax())
for type_ in int_types:
expected = pd.Series([1, 2, -1, 4, 5]).astype(type_)
gs = Series(data).astype(type_)
assert_eq(gs.cummax(), expected)
def test_cummax(dtype, nelem):
if dtype == np.int8:
# to keep data in range
data = gen_rand(dtype, nelem, low=-2, high=2)
else:
data = gen_rand(dtype, nelem)
decimal = 4 if dtype == np.float32 else 6
# series
gs = Series(data)
ps = pd.Series(data)
np.testing.assert_array_almost_equal(gs.cummax().to_array(),
ps.cummax(),
decimal=decimal)
# dataframe series (named series)
gdf = DataFrame()
gdf["a"] = Series(data)
pdf = pd.DataFrame()
pdf["a"] = pd.Series(data)
np.testing.assert_array_almost_equal(gdf.a.cummax().to_array(),
pdf.a.cummax(),
decimal=decimal)
