def test_concat_categorical(self):
# GH 13524
# same categories -> category
s1 = Series([1, 2, np.nan], dtype="category")
s2 = Series([2, 1, 2], dtype="category")
exp = Series([1, 2, np.nan, 2, 1, 2], dtype="category")
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
# partially different categories => not-category
s1 = Series([3, 2], dtype="category")
s2 = Series([2, 1], dtype="category")
exp = Series([3, 2, 2, 1])
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
# completely different categories (same dtype) => not-category
s1 = Series([10, 11, np.nan], dtype="category")
s2 = Series([np.nan, 1, 3, 2], dtype="category")
exp = Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype=np.float64)
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
def test_append_tuples(self):
# GH 28410
s = Series([1, 2, 3])
list_input = [s, s]
tuple_input = (s, s)
expected = s._append(list_input)
result = s._append(tuple_input)
tm.assert_series_equal(expected, result)
def test_series_append_aware(self):
rng1 = date_range("1/1/2011 01:00",
periods=1,
freq="H",
tz="US/Eastern")
rng2 = date_range("1/1/2011 02:00",
periods=1,
freq="H",
tz="US/Eastern")
ser1 = Series([1], index=rng1)
ser2 = Series([2], index=rng2)
ts_result = ser1._append(ser2)
exp_index = DatetimeIndex(["2011-01-01 01:00", "2011-01-01 02:00"],
tz="US/Eastern",
freq="H")
exp = Series([1, 2], index=exp_index)
tm.assert_series_equal(ts_result, exp)
assert ts_result.index.tz == rng1.tz
rng1 = date_range("1/1/2011 01:00", periods=1, freq="H", tz="UTC")
rng2 = date_range("1/1/2011 02:00", periods=1, freq="H", tz="UTC")
ser1 = Series([1], index=rng1)
ser2 = Series([2], index=rng2)
ts_result = ser1._append(ser2)
exp_index = DatetimeIndex(["2011-01-01 01:00", "2011-01-01 02:00"],
tz="UTC",
freq="H")
exp = Series([1, 2], index=exp_index)
tm.assert_series_equal(ts_result, exp)
utc = rng1.tz
assert utc == ts_result.index.tz
# GH#7795
# different tz coerces to object dtype, not UTC
rng1 = date_range("1/1/2011 01:00",
periods=1,
freq="H",
tz="US/Eastern")
rng2 = date_range("1/1/2011 02:00",
periods=1,
freq="H",
tz="US/Central")
ser1 = Series([1], index=rng1)
ser2 = Series([2], index=rng2)
ts_result = ser1._append(ser2)
exp_index = Index([
Timestamp("1/1/2011 01:00", tz="US/Eastern"),
Timestamp("1/1/2011 02:00", tz="US/Central"),
])
exp = Series([1, 2], index=exp_index)
tm.assert_series_equal(ts_result, exp)
def test_concatlike_datetimetz_to_object(self, tz_aware_fixture):
tz = tz_aware_fixture
# GH 13660
# different tz coerces to object
dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"])
exp = Index(
[
pd.Timestamp("2011-01-01", tz=tz),
pd.Timestamp("2011-01-02", tz=tz),
pd.Timestamp("2012-01-01"),
pd.Timestamp("2012-01-02"),
],
dtype=object,
)
res = dti1.append(dti2)
tm.assert_index_equal(res, exp)
dts1 = Series(dti1)
dts2 = Series(dti2)
res = dts1._append(dts2)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
res = pd.concat([dts1, dts2])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
# different tz
dti3 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz="US/Pacific")
exp = Index(
[
pd.Timestamp("2011-01-01", tz=tz),
pd.Timestamp("2011-01-02", tz=tz),
pd.Timestamp("2012-01-01", tz="US/Pacific"),
pd.Timestamp("2012-01-02", tz="US/Pacific"),
],
dtype=object,
)
res = dti1.append(dti3)
tm.assert_index_equal(res, exp)
dts1 = Series(dti1)
dts3 = Series(dti3)
res = dts1._append(dts3)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
res = pd.concat([dts1, dts3])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
def test_concat_categorical_ordered(self):
# GH 13524
s1 = Series(Categorical([1, 2, np.nan], ordered=True))
s2 = Series(Categorical([2, 1, 2], ordered=True))
exp = Series(Categorical([1, 2, np.nan, 2, 1, 2], ordered=True))
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
exp = Series(Categorical([1, 2, np.nan, 2, 1, 2, 1, 2, np.nan], ordered=True))
tm.assert_series_equal(pd.concat([s1, s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s1._append([s2, s1], ignore_index=True), exp)
def test_concatlike_common_period_diff_freq_to_object(self):
# GH 13221
pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
pi2 = pd.PeriodIndex(["2012-01-01", "2012-02-01"], freq="D")
exp = Index(
[
pd.Period("2011-01", freq="M"),
pd.Period("2011-02", freq="M"),
pd.Period("2012-01-01", freq="D"),
pd.Period("2012-02-01", freq="D"),
],
dtype=object,
)
res = pi1.append(pi2)
tm.assert_index_equal(res, exp)
ps1 = Series(pi1)
ps2 = Series(pi2)
res = ps1._append(ps2)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
res = pd.concat([ps1, ps2])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
def test_concatlike_common_coerce_to_pandas_object(self):
# GH 13626
# result must be Timestamp/Timedelta, not datetime.datetime/timedelta
dti = pd.DatetimeIndex(["2011-01-01", "2011-01-02"])
tdi = pd.TimedeltaIndex(["1 days", "2 days"])
exp = Index([
pd.Timestamp("2011-01-01"),
pd.Timestamp("2011-01-02"),
pd.Timedelta("1 days"),
pd.Timedelta("2 days"),
])
res = dti.append(tdi)
tm.assert_index_equal(res, exp)
assert isinstance(res[0], pd.Timestamp)
assert isinstance(res[-1], pd.Timedelta)
dts = Series(dti)
tds = Series(tdi)
res = dts._append(tds)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
assert isinstance(res.iloc[0], pd.Timestamp)
assert isinstance(res.iloc[-1], pd.Timedelta)
res = pd.concat([dts, tds])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
assert isinstance(res.iloc[0], pd.Timestamp)
assert isinstance(res.iloc[-1], pd.Timedelta)
def test_append(self):
rng = date_range("5/8/2012 1:45", periods=10, freq="5T")
ts = Series(np.random.randn(len(rng)), rng)
df = DataFrame(np.random.randn(len(rng), 4), index=rng)
result = ts._append(ts)
result_df = df._append(df)
ex_index = DatetimeIndex(np.tile(rng.values, 2))
tm.assert_index_equal(result.index, ex_index)
tm.assert_index_equal(result_df.index, ex_index)
appended = rng.append(rng)
tm.assert_index_equal(appended, ex_index)
appended = rng.append([rng, rng])
ex_index = DatetimeIndex(np.tile(rng.values, 3))
tm.assert_index_equal(appended, ex_index)
# different index names
rng1 = rng.copy()
rng2 = rng.copy()
rng1.name = "foo"
rng2.name = "bar"
assert rng1.append(rng1).name == "foo"
assert rng1.append(rng2).name is None
def test_series_append_dst(self):
rng1 = date_range("1/1/2016 01:00",
periods=3,
freq="H",
tz="US/Eastern")
rng2 = date_range("8/1/2016 01:00",
periods=3,
freq="H",
tz="US/Eastern")
ser1 = Series([1, 2, 3], index=rng1)
ser2 = Series([10, 11, 12], index=rng2)
ts_result = ser1._append(ser2)
exp_index = DatetimeIndex(
[
"2016-01-01 01:00",
"2016-01-01 02:00",
"2016-01-01 03:00",
"2016-08-01 01:00",
"2016-08-01 02:00",
"2016-08-01 03:00",
],
tz="US/Eastern",
)
exp = Series([1, 2, 3, 10, 11, 12], index=exp_index)
tm.assert_series_equal(ts_result, exp)
assert ts_result.index.tz == rng1.tz
def test_append_tz_dateutil(self):
# see gh-2938
rng = date_range("5/8/2012 1:45",
periods=10,
freq="5T",
tz="dateutil/US/Eastern")
rng2 = date_range("5/8/2012 2:35",
periods=10,
freq="5T",
tz="dateutil/US/Eastern")
rng3 = date_range("5/8/2012 1:45",
periods=20,
freq="5T",
tz="dateutil/US/Eastern")
ts = Series(np.random.randn(len(rng)), rng)
df = DataFrame(np.random.randn(len(rng), 4), index=rng)
ts2 = Series(np.random.randn(len(rng2)), rng2)
df2 = DataFrame(np.random.randn(len(rng2), 4), index=rng2)
result = ts._append(ts2)
result_df = df._append(df2)
tm.assert_index_equal(result.index, rng3)
tm.assert_index_equal(result_df.index, rng3)
appended = rng.append(rng2)
tm.assert_index_equal(appended, rng3)
def test_append_tz_explicit_pytz(self):
# see gh-2938
from pytz import timezone as timezone
rng = date_range("5/8/2012 1:45",
periods=10,
freq="5T",
tz=timezone("US/Eastern"))
rng2 = date_range("5/8/2012 2:35",
periods=10,
freq="5T",
tz=timezone("US/Eastern"))
rng3 = date_range("5/8/2012 1:45",
periods=20,
freq="5T",
tz=timezone("US/Eastern"))
ts = Series(np.random.randn(len(rng)), rng)
df = DataFrame(np.random.randn(len(rng), 4), index=rng)
ts2 = Series(np.random.randn(len(rng2)), rng2)
df2 = DataFrame(np.random.randn(len(rng2), 4), index=rng2)
result = ts._append(ts2)
result_df = df._append(df2)
tm.assert_index_equal(result.index, rng3)
tm.assert_index_equal(result_df.index, rng3)
appended = rng.append(rng2)
tm.assert_index_equal(appended, rng3)
def test_concatlike_common_period_mixed_dt_to_object(self):
# GH 13221
# different datetimelike
pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
tdi = pd.TimedeltaIndex(["1 days", "2 days"])
exp = Index(
[
pd.Period("2011-01", freq="M"),
pd.Period("2011-02", freq="M"),
pd.Timedelta("1 days"),
pd.Timedelta("2 days"),
],
dtype=object,
)
res = pi1.append(tdi)
tm.assert_index_equal(res, exp)
ps1 = Series(pi1)
tds = Series(tdi)
res = ps1._append(tds)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
res = pd.concat([ps1, tds])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
# inverse
exp = Index(
[
pd.Timedelta("1 days"),
pd.Timedelta("2 days"),
pd.Period("2011-01", freq="M"),
pd.Period("2011-02", freq="M"),
],
dtype=object,
)
res = tdi.append(pi1)
tm.assert_index_equal(res, exp)
ps1 = Series(pi1)
tds = Series(tdi)
res = tds._append(ps1)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
res = pd.concat([tds, ps1])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
def test_concat_categorical_multi_coercion(self):
# GH 13524
s1 = Series([1, 3], dtype="category")
s2 = Series([3, 4], dtype="category")
s3 = Series([2, 3])
s4 = Series([2, 2], dtype="category")
s5 = Series([1, np.nan])
s6 = Series([1, 3, 2], dtype="category")
# mixed dtype, values are all in categories => not-category
exp = Series([1, 3, 3, 4, 2, 3, 2, 2, 1, np.nan, 1, 3, 2])
res = pd.concat([s1, s2, s3, s4, s5, s6], ignore_index=True)
tm.assert_series_equal(res, exp)
res = s1._append([s2, s3, s4, s5, s6], ignore_index=True)
tm.assert_series_equal(res, exp)
exp = Series([1, 3, 2, 1, np.nan, 2, 2, 2, 3, 3, 4, 1, 3])
res = pd.concat([s6, s5, s4, s3, s2, s1], ignore_index=True)
tm.assert_series_equal(res, exp)
res = s6._append([s5, s4, s3, s2, s1], ignore_index=True)
tm.assert_series_equal(res, exp)
def test_append_duplicates(self):
# GH 13677
s1 = Series([1, 2, 3])
s2 = Series([4, 5, 6])
exp = Series([1, 2, 3, 4, 5, 6], index=[0, 1, 2, 0, 1, 2])
tm.assert_series_equal(s1._append(s2), exp)
tm.assert_series_equal(pd.concat([s1, s2]), exp)
# the result must have RangeIndex
exp = Series([1, 2, 3, 4, 5, 6])
tm.assert_series_equal(s1._append(s2, ignore_index=True),
exp,
check_index_type=True)
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True),
exp,
check_index_type=True)
msg = "Indexes have overlapping values:"
with pytest.raises(ValueError, match=msg):
s1._append(s2, verify_integrity=True)
with pytest.raises(ValueError, match=msg):
pd.concat([s1, s2], verify_integrity=True)
def test_series_append_aware_naive(self):
rng1 = date_range("1/1/2011 01:00", periods=1, freq="H")
rng2 = date_range("1/1/2011 02:00",
periods=1,
freq="H",
tz="US/Eastern")
ser1 = Series(np.random.randn(len(rng1)), index=rng1)
ser2 = Series(np.random.randn(len(rng2)), index=rng2)
ts_result = ser1._append(ser2)
expected = ser1.index.astype(object).append(ser2.index.astype(object))
assert ts_result.index.equals(expected)
# mixed
rng1 = date_range("1/1/2011 01:00", periods=1, freq="H")
rng2 = range(100)
ser1 = Series(np.random.randn(len(rng1)), index=rng1)
ser2 = Series(np.random.randn(len(rng2)), index=rng2)
ts_result = ser1._append(ser2)
expected = ser1.index.astype(object).append(ser2.index)
assert ts_result.index.equals(expected)
def test_concat_categorical_coercion_nan(self):
# GH 13524
# some edge cases
# category + not-category => not category
s1 = Series(np.array([np.nan, np.nan], dtype=np.float64),
dtype="category")
s2 = Series([np.nan, 1])
exp = Series([np.nan, np.nan, np.nan, 1])
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
s1 = Series([1, np.nan], dtype="category")
s2 = Series([np.nan, np.nan])
exp = Series([1, np.nan, np.nan, np.nan], dtype="float")
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
# mixed dtype, all nan-likes => not-category
s1 = Series([np.nan, np.nan], dtype="category")
s2 = Series([np.nan, np.nan])
exp = Series([np.nan, np.nan, np.nan, np.nan])
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
# all category nan-likes => category
s1 = Series([np.nan, np.nan], dtype="category")
s2 = Series([np.nan, np.nan], dtype="category")
exp = Series([np.nan, np.nan, np.nan, np.nan], dtype="category")
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
def test_concatlike_common_period(self):
# GH 13660
pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
pi2 = pd.PeriodIndex(["2012-01", "2012-02"], freq="M")
exp = pd.PeriodIndex(["2011-01", "2011-02", "2012-01", "2012-02"], freq="M")
res = pi1.append(pi2)
tm.assert_index_equal(res, exp)
ps1 = Series(pi1)
ps2 = Series(pi2)
res = ps1._append(ps2)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
res = pd.concat([ps1, ps2])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
def test_concatlike_datetimetz(self, tz_aware_fixture):
tz = tz_aware_fixture
# GH 7795
dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz=tz)
exp = pd.DatetimeIndex(
["2011-01-01", "2011-01-02", "2012-01-01", "2012-01-02"], tz=tz)
res = dti1.append(dti2)
tm.assert_index_equal(res, exp)
dts1 = Series(dti1)
dts2 = Series(dti2)
res = dts1._append(dts2)
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
res = pd.concat([dts1, dts2])
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
def test_concat_categorical_3elem_coercion(self):
# GH 13524
# mixed dtypes => not-category
s1 = Series([1, 2, np.nan], dtype="category")
s2 = Series([2, 1, 2], dtype="category")
s3 = Series([1, 2, 1, 2, np.nan])
exp = Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan],
dtype="float")
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
exp = Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2],
dtype="float")
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
# values are all in either category => not-category
s1 = Series([4, 5, 6], dtype="category")
s2 = Series([1, 2, 3], dtype="category")
s3 = Series([1, 3, 4])
exp = Series([4, 5, 6, 1, 2, 3, 1, 3, 4])
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
exp = Series([1, 3, 4, 4, 5, 6, 1, 2, 3])
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
# values are all in either category => not-category
s1 = Series([4, 5, 6], dtype="category")
s2 = Series([1, 2, 3], dtype="category")
s3 = Series([10, 11, 12])
exp = Series([4, 5, 6, 1, 2, 3, 10, 11, 12])
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
exp = Series([10, 11, 12, 4, 5, 6, 1, 2, 3])
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
def test_concat_categorical_empty(self):
# GH 13524
s1 = Series([], dtype="category")
s2 = Series([1, 2], dtype="category")
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
s1 = Series([], dtype="category")
s2 = Series([], dtype="category")
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
s1 = Series([], dtype="category")
s2 = Series([], dtype="object")
# different dtype => not-category
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
s1 = Series([], dtype="category")
s2 = Series([np.nan, np.nan])
# empty Series is ignored
exp = Series([np.nan, np.nan])
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
def test_concatlike_same_dtypes(self, item):
# GH 13660
typ1, vals1 = item
vals2 = vals1
vals3 = vals1
if typ1 == "category":
exp_data = Categorical(list(vals1) + list(vals2))
exp_data3 = Categorical(list(vals1) + list(vals2) + list(vals3))
else:
exp_data = vals1 + vals2
exp_data3 = vals1 + vals2 + vals3
# ----- Index ----- #
# index.append
res = Index(vals1).append(Index(vals2))
exp = Index(exp_data)
tm.assert_index_equal(res, exp)
# 3 elements
res = Index(vals1).append([Index(vals2), Index(vals3)])
exp = Index(exp_data3)
tm.assert_index_equal(res, exp)
# index.append name mismatch
i1 = Index(vals1, name="x")
i2 = Index(vals2, name="y")
res = i1.append(i2)
exp = Index(exp_data)
tm.assert_index_equal(res, exp)
# index.append name match
i1 = Index(vals1, name="x")
i2 = Index(vals2, name="x")
res = i1.append(i2)
exp = Index(exp_data, name="x")
tm.assert_index_equal(res, exp)
# cannot append non-index
with pytest.raises(TypeError, match="all inputs must be Index"):
Index(vals1).append(vals2)
with pytest.raises(TypeError, match="all inputs must be Index"):
Index(vals1).append([Index(vals2), vals3])
# ----- Series ----- #
# series.append
res = Series(vals1)._append(Series(vals2), ignore_index=True)
exp = Series(exp_data)
tm.assert_series_equal(res, exp, check_index_type=True)
# concat
res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
tm.assert_series_equal(res, exp, check_index_type=True)
# 3 elements
res = Series(vals1)._append(
[Series(vals2), Series(vals3)], ignore_index=True)
exp = Series(exp_data3)
tm.assert_series_equal(res, exp)
res = pd.concat(
[Series(vals1), Series(vals2),
Series(vals3)],
ignore_index=True,
)
tm.assert_series_equal(res, exp)
# name mismatch
s1 = Series(vals1, name="x")
s2 = Series(vals2, name="y")
res = s1._append(s2, ignore_index=True)
exp = Series(exp_data)
tm.assert_series_equal(res, exp, check_index_type=True)
res = pd.concat([s1, s2], ignore_index=True)
tm.assert_series_equal(res, exp, check_index_type=True)
# name match
s1 = Series(vals1, name="x")
s2 = Series(vals2, name="x")
res = s1._append(s2, ignore_index=True)
exp = Series(exp_data, name="x")
tm.assert_series_equal(res, exp, check_index_type=True)
res = pd.concat([s1, s2], ignore_index=True)
tm.assert_series_equal(res, exp, check_index_type=True)
# cannot append non-index
msg = (r"cannot concatenate object of type '.+'; "
"only Series and DataFrame objs are valid")
with pytest.raises(TypeError, match=msg):
Series(vals1)._append(vals2)
with pytest.raises(TypeError, match=msg):
Series(vals1)._append([Series(vals2), vals3])
with pytest.raises(TypeError, match=msg):
pd.concat([Series(vals1), vals2])
with pytest.raises(TypeError, match=msg):
pd.concat([Series(vals1), Series(vals2), vals3])
def test_concat_categorical_coercion(self):
# GH 13524
# category + not-category => not-category
s1 = Series([1, 2, np.nan], dtype="category")
s2 = Series([2, 1, 2])
exp = Series([1, 2, np.nan, 2, 1, 2], dtype=np.float64)
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
# result shouldn't be affected by 1st elem dtype
exp = Series([2, 1, 2, 1, 2, np.nan], dtype=np.float64)
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
# all values are not in category => not-category
s1 = Series([3, 2], dtype="category")
s2 = Series([2, 1])
exp = Series([3, 2, 2, 1])
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
exp = Series([2, 1, 3, 2])
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
# completely different categories => not-category
s1 = Series([10, 11, np.nan], dtype="category")
s2 = Series([1, 3, 2])
exp = Series([10, 11, np.nan, 1, 3, 2], dtype=np.float64)
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
exp = Series([1, 3, 2, 10, 11, np.nan], dtype=np.float64)
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
# different dtype => not-category
s1 = Series([10, 11, np.nan], dtype="category")
s2 = Series(["a", "b", "c"])
exp = Series([10, 11, np.nan, "a", "b", "c"])
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
exp = Series(["a", "b", "c", 10, 11, np.nan])
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
# if normal series only contains NaN-likes => not-category
s1 = Series([10, 11], dtype="category")
s2 = Series([np.nan, np.nan, np.nan])
exp = Series([10, 11, np.nan, np.nan, np.nan])
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
exp = Series([np.nan, np.nan, np.nan, 10, 11])
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
