def test_datetime_series_binops_pandas(lhs_dtype, rhs_dtype):
pd_data_1 = pd.Series(
pd.date_range("20010101", "20020215", freq="400h", name="times"))
pd_data_2 = pd.Series(
pd.date_range("20010101", "20020215", freq="401h", name="times"))
gdf_data_1 = Series(pd_data_1).astype(lhs_dtype)
gdf_data_2 = Series(pd_data_2).astype(rhs_dtype)
assert_eq(pd_data_1, gdf_data_1.astype("datetime64[ns]"))
assert_eq(pd_data_2, gdf_data_2.astype("datetime64[ns]"))
assert_eq(pd_data_1 < pd_data_2, gdf_data_1 < gdf_data_2)
assert_eq(pd_data_1 > pd_data_2, gdf_data_1 > gdf_data_2)
assert_eq(pd_data_1 == pd_data_2, gdf_data_1 == gdf_data_2)
assert_eq(pd_data_1 <= pd_data_2, gdf_data_1 <= gdf_data_2)
assert_eq(pd_data_1 >= pd_data_2, gdf_data_1 >= gdf_data_2)
def test_string_numeric_astype(dtype):
if dtype.startswith("bool"):
data = [1, 0, 1, 0, 1]
elif dtype.startswith("int"):
data = [1, 2, 3, 4, 5]
elif dtype.startswith("float"):
data = [1.0, 2.0, 3.0, 4.0, 5.0]
elif dtype.startswith("datetime64"):
data = [1000000000, 2000000000, 3000000000, 4000000000, 5000000000]
if dtype.startswith("datetime64"):
ps = pd.Series(data, dtype="datetime64[ns]")
gs = Series.from_pandas(ps)
else:
ps = pd.Series(data, dtype=dtype)
gs = Series(data, dtype=dtype)
# Pandas datetime64 --> str typecasting returns arbitrary format depending
# on the data, so making it consistent unless we choose to match the
# behavior
if dtype.startswith("datetime64"):
expect = ps.dt.strftime("%Y-%m-%dT%H:%M:%SZ")
else:
expect = ps.astype("str")
got = gs.astype("str")
assert_eq(expect, got)
def test_string_astype(dtype):
if dtype.startswith("int"):
data = ["1", "2", "3", "4", "5"]
elif dtype.startswith("float"):
data = ["1.0", "2.0", "3.0", "4.0", "5.0"]
elif dtype.startswith("bool"):
data = ["True", "False", "True", "False", "False"]
elif dtype.startswith("datetime64"):
data = [
"2019-06-04T00:00:00Z",
"2019-06-04T12:12:12Z",
"2019-06-03T00:00:00Z",
"2019-05-04T00:00:00Z",
"2018-06-04T00:00:00Z",
]
elif dtype == "str" or dtype == "object":
data = ["ab", "cd", "ef", "gh", "ij"]
ps = pd.Series(data)
gs = Series(data)
# Pandas str --> bool typecasting always returns True if there's a string
if dtype.startswith("bool"):
expect = ps == "True"
else:
expect = ps.astype(dtype)
got = gs.astype(dtype)
assert_eq(expect, got)
def test_typecast_to_from_datetime(data, from_dtype, to_dtype):
np_data = data.astype(from_dtype)
gdf_data = Series(np_data)
np_casted = np_data.astype(to_dtype).astype(from_dtype)
gdf_casted = gdf_data.astype(to_dtype).astype(from_dtype)
np.testing.assert_equal(np_casted, np.array(gdf_casted))
def test_typecast_from_datetime_to_datetime(data, from_dtype, to_dtype):
np_data = data.astype(from_dtype)
gdf_col = Series(np_data)._column
np_casted = np_data.astype(to_dtype)
gdf_casted = gdf_col.astype(to_dtype)
np.testing.assert_equal(np_casted, gdf_casted.to_array())
def test_typecast_to_datetime(data, dtype):
np_data = data.astype(dtype)
gdf_data = Series(np_data)
np_casted = np_data.astype('datetime64[ms]')
gdf_casted = gdf_data.astype('datetime64[ms]')
np.testing.assert_equal(np_casted, np.array(gdf_casted))
def func(index):
arr = np.random.random(100) * 10
sr = Series(arr)
result = binop(sr.astype('int32'), sr)
expect = binop(arr.astype('int32'), arr)
np.testing.assert_almost_equal(result.to_array(), expect, decimal=5)
def test_string_empty_astype(dtype):
data = []
ps = pd.Series(data, dtype="str")
gs = Series(data, dtype="str")
expect = ps.astype(dtype)
got = gs.astype(dtype)
assert_eq(expect, got)
def test_typecast_from_datetime_to_int64_to_datetime(data, dtype):
pd_data = pd.Series(data.copy())
np_data = np.array(pd_data)
gdf_data = Series(pd_data)
np_casted = np_data.astype(np.int64).astype(dtype)
gdf_casted = gdf_data.astype(np.int64).astype(dtype)
np.testing.assert_equal(np_casted, np.array(gdf_casted))
def test_string_astype(dtype):
if dtype.startswith('int'):
data = ["1", "2", "3", "4", "5"]
elif dtype.startswith('float'):
data = ["1.0", "2.0", "3.0", "4.0", "5.0"]
ps = pd.Series(data)
gs = Series(data)
expect = ps.astype(dtype)
got = gs.astype(dtype)
assert_eq(expect, got)
def test_string_empty_numeric_astype(dtype):
data = []
if dtype.startswith("datetime64"):
ps = pd.Series(data, dtype="datetime64[ns]")
else:
ps = pd.Series(data, dtype=dtype)
gs = Series(data, dtype=dtype)
expect = ps.astype("str")
got = gs.astype("str")
assert_eq(expect, got)
def test_string_empty_numeric_astype(dtype):
data = []
if dtype.startswith('datetime64'):
ps = pd.Series(data, dtype='datetime64[ns]')
else:
ps = pd.Series(data, dtype=dtype)
gs = Series(data, dtype=dtype)
expect = ps.astype('str')
got = gs.astype('str')
assert_eq(expect, got)
def test_date_minmax():
np_data = np.random.normal(size=10 ** 3)
gdf_data = Series(np_data)
np_casted = np_data.astype("datetime64[ms]")
gdf_casted = gdf_data.astype("datetime64[ms]")
np_min = np_casted.min()
gdf_min = gdf_casted.min()
assert np_min == gdf_min
np_max = np_casted.max()
gdf_max = gdf_casted.max()
assert np_max == gdf_max
def test_string_empty_numeric_astype(dtype):
if dtype.startswith('bool'):
pytest.xfail("booleans not yet supported")
elif dtype.startswith('float'):
pytest.xfail("floats not yet supported")
data = []
ps = pd.Series(data, dtype=dtype)
gs = Series(data, dtype=dtype)
expect = ps.astype('str')
got = gs.astype('str')
assert_eq(expect, got)
def test_string_numeric_astype(dtype):
if dtype.startswith('bool'):
pytest.xfail("booleans not yet supported")
data = [1, 0, 1, 0, 1]
elif dtype.startswith('int'):
data = [1, 2, 3, 4, 5]
elif dtype.startswith('float'):
pytest.xfail("floats not yet supported")
data = [1.0, 2.0, 3.0, 4.0, 5.0]
ps = pd.Series(data, dtype=dtype)
gs = Series(data, dtype=dtype)
expect = ps.astype('str')
got = gs.astype('str')
assert_eq(expect, got)
def test_string_astype(dtype):
if dtype.startswith('int'):
data = ["1", "2", "3", "4", "5"]
elif dtype.startswith('float'):
data = ["1.0", "2.0", "3.0", "4.0", "5.0"]
elif dtype.startswith('bool'):
data = ["True", "False", "True", "False", "False"]
elif dtype.startswith('datetime64'):
data = [
"2019-06-04T00:00:00Z", "2019-06-04T12:12:12Z",
"2019-06-03T00:00:00Z", "2019-05-04T00:00:00Z",
"2018-06-04T00:00:00Z"
]
ps = pd.Series(data)
gs = Series(data)
# Pandas str --> bool typecasting always returns True if there's a string
if dtype.startswith('bool'):
expect = (ps == 'True')
else:
expect = ps.astype(dtype)
got = gs.astype(dtype)
assert_eq(expect, got)