def test_append_to_another(self):
# hits Index._concat
fst = Index(["a", "b"])
snd = CategoricalIndex(["d", "e"])
result = fst.append(snd)
expected = Index(["a", "b", "d", "e"])
tm.assert_index_equal(result, expected)
def test_append_index():
idx1 = Index([1.1, 1.2, 1.3])
idx2 = pd.date_range("2011-01-01", freq="D", periods=3, tz="Asia/Tokyo")
idx3 = Index(["A", "B", "C"])
midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
result = idx1.append(midx_lv2)
# see gh-7112
tz = pytz.timezone("Asia/Tokyo")
expected_tuples = [
(1.1, tz.localize(datetime(2011, 1, 1))),
(1.2, tz.localize(datetime(2011, 1, 2))),
(1.3, tz.localize(datetime(2011, 1, 3))),
]
expected = Index([1.1, 1.2, 1.3] + expected_tuples)
tm.assert_index_equal(result, expected)
result = midx_lv2.append(idx1)
expected = Index(expected_tuples + [1.1, 1.2, 1.3])
tm.assert_index_equal(result, expected)
result = midx_lv2.append(midx_lv2)
expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)])
tm.assert_index_equal(result, expected)
result = midx_lv2.append(midx_lv3)
tm.assert_index_equal(result, expected)
result = midx_lv3.append(midx_lv2)
expected = Index._simple_new(
np.array(
[
(1.1, tz.localize(datetime(2011, 1, 1)), "A"),
(1.2, tz.localize(datetime(2011, 1, 2)), "B"),
(1.3, tz.localize(datetime(2011, 1, 3)), "C"),
]
+ expected_tuples,
dtype=object,
),
None,
)
tm.assert_index_equal(result, expected)
def test_append_multiple(self):
index = Index(["a", "b", "c", "d", "e", "f"])
foos = [index[:2], index[2:4], index[4:]]
result = foos[0].append(foos[1:])
tm.assert_index_equal(result, index)
# empty
result = index.append([])
tm.assert_index_equal(result, index)
def _concat_index(cls, prev_index: pd.Index, cur_index: pd.Index):
if isinstance(prev_index, pd.RangeIndex) and \
isinstance(cur_index, pd.RangeIndex):
# handle RangeIndex that append may generate huge amount of data
# e.g. pd.RangeIndex(10_000) and pd.RangeIndex(10_000)
# will generate a Int64Index full of data
# for details see GH#1647
prev_stop = prev_index.start + prev_index.size * prev_index.step
cur_start = cur_index.start
if prev_stop == cur_start and prev_index.step == cur_index.step:
# continuous RangeIndex, still return RangeIndex
return prev_index.append(cur_index)
else:
# otherwise, return an empty index
return pd.Index([], dtype=prev_index.dtype)
elif isinstance(prev_index, pd.RangeIndex):
return pd.Index([], prev_index.dtype).append(cur_index)
elif isinstance(cur_index, pd.RangeIndex):
return prev_index.append(pd.Index([], cur_index.dtype))
return prev_index.append(cur_index)
def _condition_logic(base_idx: pd.Index, sub_select_idx: pd.Index, state_idx: pd.Index, condition_idx: pd.Index,
logic: str) -> pd.Index:
if str(logic).upper() == 'ALL':
return base_idx.intersection(condition_idx).sort_values()
elif str(logic).upper() == 'ANY':
return sub_select_idx.intersection(condition_idx).sort_values()
elif str(logic).upper() == 'AND':
return state_idx.intersection(condition_idx).sort_values()
elif str(logic).upper() == 'NAND':
return sub_select_idx.drop(state_idx.intersection(condition_idx)).sort_values()
elif str(logic).upper() == 'OR':
return state_idx.append(state_idx.union(condition_idx)).drop_duplicates().sort_values()
elif str(logic).upper() == 'NOR':
result = state_idx.append(state_idx.union(condition_idx)).drop_duplicates().sort_values()
return sub_select_idx.drop(result)
elif str(logic).upper() == 'NOT':
return state_idx.difference(condition_idx)
elif str(logic).upper() == 'XOR':
return state_idx.union(condition_idx).difference(state_idx.intersection(condition_idx))
raise ValueError(f"The logic '{logic}' must be AND, NAND, OR, NOR, NOT, XOR ANY or ALL")
def _reindex(self):
if self.wavelengths is not None:
wv = Index(np.round(self.wavelengths, decimals=self.tolerance))
if self._metadata_index is not None:
index = wv.append(Index(self._metadata_index))
else:
index = wv
try:
# This could be cleaner because if the df is square this will fail.
self.index = index
except:
self.columns = index
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])
