def test_series_get_item(psr, arg):
gsr = Series.from_pandas(psr)
expect = psr[arg]
got = gsr[arg]
assert_eq(expect, got)
def test_series_loc_datetime():
if PANDAS_GE_110:
kwargs = {"check_freq": False}
else:
kwargs = {}
ps = pd.Series([1, 2, 3, 4, 5],
index=pd.date_range("20010101", "20010105"))
gs = Series.from_pandas(ps)
# a few different ways of specifying a datetime label:
assert_eq(ps.loc["20010101"], gs.loc["20010101"])
assert_eq(ps.loc["2001-01-01"], gs.loc["2001-01-01"])
assert_eq(
ps.loc[pd.to_datetime("2001-01-01")],
gs.loc[pd.to_datetime("2001-01-01")],
)
assert_eq(
ps.loc[np.datetime64("2001-01-01")],
gs.loc[np.datetime64("2001-01-01")],
)
assert_eq(
ps.loc["2001-01-02":"2001-01-05"],
gs.loc["2001-01-02":"2001-01-05"],
**kwargs,
)
assert_eq(ps.loc["2001-01-02":], gs.loc["2001-01-02":], **kwargs)
assert_eq(ps.loc[:"2001-01-04"], gs.loc[:"2001-01-04"], **kwargs)
assert_eq(ps.loc[::2], gs.loc[::2], **kwargs)
assert_eq(
ps.loc[["2001-01-01", "2001-01-04", "2001-01-05"]],
gs.loc[["2001-01-01", "2001-01-04", "2001-01-05"]],
)
assert_eq(
ps.loc[[
pd.to_datetime("2001-01-01"),
pd.to_datetime("2001-01-04"),
pd.to_datetime("2001-01-05"),
]],
gs.loc[[
pd.to_datetime("2001-01-01"),
pd.to_datetime("2001-01-04"),
pd.to_datetime("2001-01-05"),
]],
)
assert_eq(
ps.loc[[True, False, True, False, True]],
gs.loc[[True, False, True, False, True]],
**kwargs,
)
just_less_than_max = ps.index.max() - pd.Timedelta("5m")
assert_eq(ps.loc[:just_less_than_max], gs.loc[:just_less_than_max],
**kwargs)
def test_series_take_positional():
psr = pd.Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"])
gsr = Series.from_pandas(psr)
take_indices = [1, 2, 0, 3]
expect = psr.take(take_indices)
got = gsr.take(take_indices, keep_index=True)
assert_eq(expect, got)
def test_series_loc_float_index():
ps = pd.Series([1, 2, 3, 4, 5], index=[5.43, 6.34, 7.34, 8.0, 9.1])
gs = Series.from_pandas(ps)
assert_eq(ps.loc[5.43], gs.loc[5.43])
assert_eq(ps.loc[8], gs.loc[8])
assert_eq(ps.loc[6.1:8], gs.loc[6.1:8])
assert_eq(ps.loc[:7.1], gs.loc[:7.1])
assert_eq(ps.loc[6.345:], gs.loc[6.345:])
assert_eq(ps.loc[::2], gs.loc[::2])
assert_eq(
ps.loc[[True, False, True, False, True]],
gs.loc[[True, False, True, False, True]],
)
def test_to_from_pandas_nulls(data, nulls):
pd_data = pd.Series(data.copy().astype("datetime64[ns]"))
if nulls == "some":
# Fill half the values with NaT
pd_data[list(range(0, len(pd_data), 2))] = np.datetime64("nat", "ns")
elif nulls == "all":
# Fill all the values with NaT
pd_data[:] = np.datetime64("nat", "ns")
gdf_data = Series.from_pandas(pd_data)
expect = pd_data
got = gdf_data.to_pandas()
assert_eq(expect, got)
def test_series_loc_datetime():
ps = pd.Series(
[1, 2, 3, 4, 5], index=pd.date_range("20010101", "20010105")
)
gs = Series.from_pandas(ps)
# a few different ways of specifying a datetime label:
assert_eq(ps.loc["20010101"], gs.loc["20010101"])
assert_eq(ps.loc["2001-01-01"], gs.loc["2001-01-01"])
assert_eq(
ps.loc[pd.to_datetime("2001-01-01")],
gs.loc[pd.to_datetime("2001-01-01")],
)
assert_eq(
ps.loc[np.datetime64("2001-01-01")],
gs.loc[np.datetime64("2001-01-01")],
)
assert_eq(
ps.loc["2001-01-02":"2001-01-05"], gs.loc["2001-01-02":"2001-01-05"]
)
assert_eq(ps.loc["2001-01-02":], gs.loc["2001-01-02":])
assert_eq(ps.loc[:"2001-01-04"], gs.loc[:"2001-01-04"])
assert_eq(ps.loc[::2], gs.loc[::2])
#
# assert_eq(ps.loc[['2001-01-01', '2001-01-04', '2001-01-05']],
# gs.loc[['2001-01-01', '2001-01-04', '2001-01-05']])
# looks like a bug in Pandas doesn't let us check for the above,
# so instead:
assert_eq(
ps.loc[
[
pd.to_datetime("2001-01-01"),
pd.to_datetime("2001-01-04"),
pd.to_datetime("2001-01-05"),
]
],
gs.loc[
[
pd.to_datetime("2001-01-01"),
pd.to_datetime("2001-01-04"),
pd.to_datetime("2001-01-05"),
]
],
)
assert_eq(
ps.loc[[True, False, True, False, True]],
gs.loc[[True, False, True, False, True]],
)
def test_series_loc_numerical():
ps = pd.Series([1, 2, 3, 4, 5], index=[5, 6, 7, 8, 9])
gs = Series.from_pandas(ps)
assert_eq(ps.loc[5], gs.loc[5])
assert_eq(ps.loc[6], gs.loc[6])
assert_eq(ps.loc[6:8], gs.loc[6:8])
assert_eq(ps.loc[:8], gs.loc[:8])
assert_eq(ps.loc[6:], gs.loc[6:])
assert_eq(ps.loc[::2], gs.loc[::2])
assert_eq(ps.loc[[5, 8, 9]], gs.loc[[5, 8, 9]])
assert_eq(
ps.loc[[True, False, True, False, True]],
gs.loc[[True, False, True, False, True]],
)
def test_series_loc_string():
ps = pd.Series([1, 2, 3, 4, 5],
index=["one", "two", "three", "four", "five"])
gs = Series.from_pandas(ps)
assert_eq(ps.loc["one"], gs.loc["one"])
assert_eq(ps.loc["five"], gs.loc["five"])
assert_eq(ps.loc["two":"four"], gs.loc["two":"four"])
assert_eq(ps.loc[:"four"], gs.loc[:"four"])
assert_eq(ps.loc["two":], gs.loc["two":])
assert_eq(ps.loc[::2], gs.loc[::2])
assert_eq(ps.loc[["one", "four", "five"]], gs.loc[["one", "four", "five"]])
assert_eq(
ps.loc[[True, False, True, False, True]],
gs.loc[[True, False, True, False, True]],
)
def test_series_loc_categorical():
ps = pd.Series([1, 2, 3, 4, 5],
index=pd.Categorical(["a", "b", "c", "d", "e"]))
gs = Series.from_pandas(ps)
assert_eq(ps.loc["a"], gs.loc["a"])
assert_eq(ps.loc["e"], gs.loc["e"])
assert_eq(ps.loc["b":"d"], gs.loc["b":"d"])
assert_eq(ps.loc[:"d"], gs.loc[:"d"])
assert_eq(ps.loc["b":], gs.loc["b":])
assert_eq(ps.loc[::2], gs.loc[::2])
# order of categories changes, so we can only
# compare values:
assert_eq(ps.loc[["a", "d", "e"]].values, gs.loc[["a", "d",
"e"]].to_array())
assert_eq(
ps.loc[[True, False, True, False, True]],
gs.loc[[True, False, True, False, True]],
)
