Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
2883 lines
102 KiB
2883 lines
102 KiB
from collections import OrderedDict, abc, deque
|
|
import datetime as dt
|
|
from datetime import datetime
|
|
from decimal import Decimal
|
|
from io import StringIO
|
|
from itertools import combinations
|
|
from warnings import catch_warnings
|
|
|
|
import dateutil
|
|
import numpy as np
|
|
from numpy.random import randn
|
|
import pytest
|
|
|
|
from pandas.core.dtypes.dtypes import CategoricalDtype
|
|
|
|
import pandas as pd
|
|
from pandas import (
|
|
Categorical,
|
|
DataFrame,
|
|
DatetimeIndex,
|
|
Index,
|
|
MultiIndex,
|
|
Series,
|
|
Timestamp,
|
|
concat,
|
|
date_range,
|
|
isna,
|
|
read_csv,
|
|
)
|
|
import pandas._testing as tm
|
|
from pandas.core.arrays import SparseArray
|
|
from pandas.core.construction import create_series_with_explicit_dtype
|
|
from pandas.tests.extension.decimal import to_decimal
|
|
|
|
|
|
@pytest.fixture(params=[True, False])
|
|
def sort(request):
|
|
"""Boolean sort keyword for concat and DataFrame.append."""
|
|
return request.param
|
|
|
|
|
|
class TestConcatAppendCommon:
|
|
"""
|
|
Test common dtype coercion rules between concat and append.
|
|
"""
|
|
|
|
def setup_method(self, method):
|
|
|
|
dt_data = [
|
|
pd.Timestamp("2011-01-01"),
|
|
pd.Timestamp("2011-01-02"),
|
|
pd.Timestamp("2011-01-03"),
|
|
]
|
|
tz_data = [
|
|
pd.Timestamp("2011-01-01", tz="US/Eastern"),
|
|
pd.Timestamp("2011-01-02", tz="US/Eastern"),
|
|
pd.Timestamp("2011-01-03", tz="US/Eastern"),
|
|
]
|
|
|
|
td_data = [
|
|
pd.Timedelta("1 days"),
|
|
pd.Timedelta("2 days"),
|
|
pd.Timedelta("3 days"),
|
|
]
|
|
|
|
period_data = [
|
|
pd.Period("2011-01", freq="M"),
|
|
pd.Period("2011-02", freq="M"),
|
|
pd.Period("2011-03", freq="M"),
|
|
]
|
|
|
|
self.data = {
|
|
"bool": [True, False, True],
|
|
"int64": [1, 2, 3],
|
|
"float64": [1.1, np.nan, 3.3],
|
|
"category": pd.Categorical(["X", "Y", "Z"]),
|
|
"object": ["a", "b", "c"],
|
|
"datetime64[ns]": dt_data,
|
|
"datetime64[ns, US/Eastern]": tz_data,
|
|
"timedelta64[ns]": td_data,
|
|
"period[M]": period_data,
|
|
}
|
|
|
|
def _check_expected_dtype(self, obj, label):
|
|
"""
|
|
Check whether obj has expected dtype depending on label
|
|
considering not-supported dtypes
|
|
"""
|
|
if isinstance(obj, pd.Index):
|
|
if label == "bool":
|
|
assert obj.dtype == "object"
|
|
else:
|
|
assert obj.dtype == label
|
|
elif isinstance(obj, pd.Series):
|
|
if label.startswith("period"):
|
|
assert obj.dtype == "Period[M]"
|
|
else:
|
|
assert obj.dtype == label
|
|
else:
|
|
raise ValueError
|
|
|
|
def test_dtypes(self):
|
|
# to confirm test case covers intended dtypes
|
|
for typ, vals in self.data.items():
|
|
self._check_expected_dtype(pd.Index(vals), typ)
|
|
self._check_expected_dtype(pd.Series(vals), typ)
|
|
|
|
def test_concatlike_same_dtypes(self):
|
|
# GH 13660
|
|
for typ1, vals1 in self.data.items():
|
|
|
|
vals2 = vals1
|
|
vals3 = vals1
|
|
|
|
if typ1 == "category":
|
|
exp_data = pd.Categorical(list(vals1) + list(vals2))
|
|
exp_data3 = pd.Categorical(list(vals1) + list(vals2) + list(vals3))
|
|
else:
|
|
exp_data = vals1 + vals2
|
|
exp_data3 = vals1 + vals2 + vals3
|
|
|
|
# ----- Index ----- #
|
|
|
|
# index.append
|
|
res = pd.Index(vals1).append(pd.Index(vals2))
|
|
exp = pd.Index(exp_data)
|
|
tm.assert_index_equal(res, exp)
|
|
|
|
# 3 elements
|
|
res = pd.Index(vals1).append([pd.Index(vals2), pd.Index(vals3)])
|
|
exp = pd.Index(exp_data3)
|
|
tm.assert_index_equal(res, exp)
|
|
|
|
# index.append name mismatch
|
|
i1 = pd.Index(vals1, name="x")
|
|
i2 = pd.Index(vals2, name="y")
|
|
res = i1.append(i2)
|
|
exp = pd.Index(exp_data)
|
|
tm.assert_index_equal(res, exp)
|
|
|
|
# index.append name match
|
|
i1 = pd.Index(vals1, name="x")
|
|
i2 = pd.Index(vals2, name="x")
|
|
res = i1.append(i2)
|
|
exp = pd.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"):
|
|
pd.Index(vals1).append(vals2)
|
|
|
|
with pytest.raises(TypeError, match="all inputs must be Index"):
|
|
pd.Index(vals1).append([pd.Index(vals2), vals3])
|
|
|
|
# ----- Series ----- #
|
|
|
|
# series.append
|
|
res = pd.Series(vals1).append(pd.Series(vals2), ignore_index=True)
|
|
exp = pd.Series(exp_data)
|
|
tm.assert_series_equal(res, exp, check_index_type=True)
|
|
|
|
# concat
|
|
res = pd.concat([pd.Series(vals1), pd.Series(vals2)], ignore_index=True)
|
|
tm.assert_series_equal(res, exp, check_index_type=True)
|
|
|
|
# 3 elements
|
|
res = pd.Series(vals1).append(
|
|
[pd.Series(vals2), pd.Series(vals3)], ignore_index=True
|
|
)
|
|
exp = pd.Series(exp_data3)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
res = pd.concat(
|
|
[pd.Series(vals1), pd.Series(vals2), pd.Series(vals3)],
|
|
ignore_index=True,
|
|
)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
# name mismatch
|
|
s1 = pd.Series(vals1, name="x")
|
|
s2 = pd.Series(vals2, name="y")
|
|
res = s1.append(s2, ignore_index=True)
|
|
exp = pd.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 = pd.Series(vals1, name="x")
|
|
s2 = pd.Series(vals2, name="x")
|
|
res = s1.append(s2, ignore_index=True)
|
|
exp = pd.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):
|
|
pd.Series(vals1).append(vals2)
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
pd.Series(vals1).append([pd.Series(vals2), vals3])
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
pd.concat([pd.Series(vals1), vals2])
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
pd.concat([pd.Series(vals1), pd.Series(vals2), vals3])
|
|
|
|
def test_concatlike_dtypes_coercion(self):
|
|
# GH 13660
|
|
for typ1, vals1 in self.data.items():
|
|
for typ2, vals2 in self.data.items():
|
|
|
|
vals3 = vals2
|
|
|
|
# basically infer
|
|
exp_index_dtype = None
|
|
exp_series_dtype = None
|
|
|
|
if typ1 == typ2:
|
|
# same dtype is tested in test_concatlike_same_dtypes
|
|
continue
|
|
elif typ1 == "category" or typ2 == "category":
|
|
# TODO: suspicious
|
|
continue
|
|
|
|
# specify expected dtype
|
|
if typ1 == "bool" and typ2 in ("int64", "float64"):
|
|
# series coerces to numeric based on numpy rule
|
|
# index doesn't because bool is object dtype
|
|
exp_series_dtype = typ2
|
|
elif typ2 == "bool" and typ1 in ("int64", "float64"):
|
|
exp_series_dtype = typ1
|
|
elif (
|
|
typ1 == "datetime64[ns, US/Eastern]"
|
|
or typ2 == "datetime64[ns, US/Eastern]"
|
|
or typ1 == "timedelta64[ns]"
|
|
or typ2 == "timedelta64[ns]"
|
|
):
|
|
exp_index_dtype = object
|
|
exp_series_dtype = object
|
|
|
|
exp_data = vals1 + vals2
|
|
exp_data3 = vals1 + vals2 + vals3
|
|
|
|
# ----- Index ----- #
|
|
|
|
# index.append
|
|
res = pd.Index(vals1).append(pd.Index(vals2))
|
|
exp = pd.Index(exp_data, dtype=exp_index_dtype)
|
|
tm.assert_index_equal(res, exp)
|
|
|
|
# 3 elements
|
|
res = pd.Index(vals1).append([pd.Index(vals2), pd.Index(vals3)])
|
|
exp = pd.Index(exp_data3, dtype=exp_index_dtype)
|
|
tm.assert_index_equal(res, exp)
|
|
|
|
# ----- Series ----- #
|
|
|
|
# series.append
|
|
res = pd.Series(vals1).append(pd.Series(vals2), ignore_index=True)
|
|
exp = pd.Series(exp_data, dtype=exp_series_dtype)
|
|
tm.assert_series_equal(res, exp, check_index_type=True)
|
|
|
|
# concat
|
|
res = pd.concat([pd.Series(vals1), pd.Series(vals2)], ignore_index=True)
|
|
tm.assert_series_equal(res, exp, check_index_type=True)
|
|
|
|
# 3 elements
|
|
res = pd.Series(vals1).append(
|
|
[pd.Series(vals2), pd.Series(vals3)], ignore_index=True
|
|
)
|
|
exp = pd.Series(exp_data3, dtype=exp_series_dtype)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
res = pd.concat(
|
|
[pd.Series(vals1), pd.Series(vals2), pd.Series(vals3)],
|
|
ignore_index=True,
|
|
)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
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 = pd.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 = pd.Series(dti)
|
|
tds = pd.Series(tdi)
|
|
res = dts.append(tds)
|
|
tm.assert_series_equal(res, pd.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, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
assert isinstance(res.iloc[0], pd.Timestamp)
|
|
assert isinstance(res.iloc[-1], pd.Timedelta)
|
|
|
|
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 = pd.Series(dti1)
|
|
dts2 = pd.Series(dti2)
|
|
res = dts1.append(dts2)
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
res = pd.concat([dts1, dts2])
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
@pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo", "EST5EDT"])
|
|
def test_concatlike_datetimetz_short(self, tz):
|
|
# GH#7795
|
|
ix1 = pd.date_range(start="2014-07-15", end="2014-07-17", freq="D", tz=tz)
|
|
ix2 = pd.DatetimeIndex(["2014-07-11", "2014-07-21"], tz=tz)
|
|
df1 = pd.DataFrame(0, index=ix1, columns=["A", "B"])
|
|
df2 = pd.DataFrame(0, index=ix2, columns=["A", "B"])
|
|
|
|
exp_idx = pd.DatetimeIndex(
|
|
["2014-07-15", "2014-07-16", "2014-07-17", "2014-07-11", "2014-07-21"],
|
|
tz=tz,
|
|
)
|
|
exp = pd.DataFrame(0, index=exp_idx, columns=["A", "B"])
|
|
|
|
tm.assert_frame_equal(df1.append(df2), exp)
|
|
tm.assert_frame_equal(pd.concat([df1, df2]), 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 = pd.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 = pd.Series(dti1)
|
|
dts2 = pd.Series(dti2)
|
|
res = dts1.append(dts2)
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
res = pd.concat([dts1, dts2])
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
# different tz
|
|
dti3 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz="US/Pacific")
|
|
|
|
exp = pd.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 = pd.Series(dti1)
|
|
dts3 = pd.Series(dti3)
|
|
res = dts1.append(dts3)
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
res = pd.concat([dts1, dts3])
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
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 = pd.Series(pi1)
|
|
ps2 = pd.Series(pi2)
|
|
res = ps1.append(ps2)
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
res = pd.concat([ps1, ps2])
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
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 = pd.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 = pd.Series(pi1)
|
|
ps2 = pd.Series(pi2)
|
|
res = ps1.append(ps2)
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
res = pd.concat([ps1, ps2])
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
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 = pd.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 = pd.Series(pi1)
|
|
tds = pd.Series(tdi)
|
|
res = ps1.append(tds)
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
res = pd.concat([ps1, tds])
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
# inverse
|
|
exp = pd.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 = pd.Series(pi1)
|
|
tds = pd.Series(tdi)
|
|
res = tds.append(ps1)
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
res = pd.concat([tds, ps1])
|
|
tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
|
|
|
|
def test_concat_categorical(self):
|
|
# GH 13524
|
|
|
|
# same categories -> category
|
|
s1 = pd.Series([1, 2, np.nan], dtype="category")
|
|
s2 = pd.Series([2, 1, 2], dtype="category")
|
|
|
|
exp = pd.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 = pd.Series([3, 2], dtype="category")
|
|
s2 = pd.Series([2, 1], dtype="category")
|
|
|
|
exp = pd.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 = pd.Series([10, 11, np.nan], dtype="category")
|
|
s2 = pd.Series([np.nan, 1, 3, 2], dtype="category")
|
|
|
|
exp = pd.Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype="object")
|
|
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_union_categorical_same_categories_different_order(self):
|
|
# https://github.com/pandas-dev/pandas/issues/19096
|
|
a = pd.Series(Categorical(["a", "b", "c"], categories=["a", "b", "c"]))
|
|
b = pd.Series(Categorical(["a", "b", "c"], categories=["b", "a", "c"]))
|
|
result = pd.concat([a, b], ignore_index=True)
|
|
expected = pd.Series(
|
|
Categorical(["a", "b", "c", "a", "b", "c"], categories=["a", "b", "c"])
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_concat_categorical_coercion(self):
|
|
# GH 13524
|
|
|
|
# category + not-category => not-category
|
|
s1 = pd.Series([1, 2, np.nan], dtype="category")
|
|
s2 = pd.Series([2, 1, 2])
|
|
|
|
exp = pd.Series([1, 2, np.nan, 2, 1, 2], dtype="object")
|
|
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 = pd.Series([2, 1, 2, 1, 2, np.nan], dtype="object")
|
|
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 = pd.Series([3, 2], dtype="category")
|
|
s2 = pd.Series([2, 1])
|
|
|
|
exp = pd.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 = pd.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 = pd.Series([10, 11, np.nan], dtype="category")
|
|
s2 = pd.Series([1, 3, 2])
|
|
|
|
exp = pd.Series([10, 11, np.nan, 1, 3, 2], dtype="object")
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1.append(s2, ignore_index=True), exp)
|
|
|
|
exp = pd.Series([1, 3, 2, 10, 11, np.nan], dtype="object")
|
|
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 = pd.Series([10, 11, np.nan], dtype="category")
|
|
s2 = pd.Series(["a", "b", "c"])
|
|
|
|
exp = pd.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 = pd.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 = pd.Series([10, 11], dtype="category")
|
|
s2 = pd.Series([np.nan, np.nan, np.nan])
|
|
|
|
exp = pd.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 = pd.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)
|
|
|
|
def test_concat_categorical_3elem_coercion(self):
|
|
# GH 13524
|
|
|
|
# mixed dtypes => not-category
|
|
s1 = pd.Series([1, 2, np.nan], dtype="category")
|
|
s2 = pd.Series([2, 1, 2], dtype="category")
|
|
s3 = pd.Series([1, 2, 1, 2, np.nan])
|
|
|
|
exp = pd.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 = pd.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 = pd.Series([4, 5, 6], dtype="category")
|
|
s2 = pd.Series([1, 2, 3], dtype="category")
|
|
s3 = pd.Series([1, 3, 4])
|
|
|
|
exp = pd.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 = pd.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 = pd.Series([4, 5, 6], dtype="category")
|
|
s2 = pd.Series([1, 2, 3], dtype="category")
|
|
s3 = pd.Series([10, 11, 12])
|
|
|
|
exp = pd.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 = pd.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_multi_coercion(self):
|
|
# GH 13524
|
|
|
|
s1 = pd.Series([1, 3], dtype="category")
|
|
s2 = pd.Series([3, 4], dtype="category")
|
|
s3 = pd.Series([2, 3])
|
|
s4 = pd.Series([2, 2], dtype="category")
|
|
s5 = pd.Series([1, np.nan])
|
|
s6 = pd.Series([1, 3, 2], dtype="category")
|
|
|
|
# mixed dtype, values are all in categories => not-category
|
|
exp = pd.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 = pd.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_concat_categorical_ordered(self):
|
|
# GH 13524
|
|
|
|
s1 = pd.Series(pd.Categorical([1, 2, np.nan], ordered=True))
|
|
s2 = pd.Series(pd.Categorical([2, 1, 2], ordered=True))
|
|
|
|
exp = pd.Series(pd.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 = pd.Series(
|
|
pd.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_concat_categorical_coercion_nan(self):
|
|
# GH 13524
|
|
|
|
# some edge cases
|
|
# category + not-category => not category
|
|
s1 = pd.Series(np.array([np.nan, np.nan], dtype=np.float64), dtype="category")
|
|
s2 = pd.Series([np.nan, 1])
|
|
|
|
exp = pd.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 = pd.Series([1, np.nan], dtype="category")
|
|
s2 = pd.Series([np.nan, np.nan])
|
|
|
|
exp = pd.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 = pd.Series([np.nan, np.nan], dtype="category")
|
|
s2 = pd.Series([np.nan, np.nan])
|
|
|
|
exp = pd.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 = pd.Series([np.nan, np.nan], dtype="category")
|
|
s2 = pd.Series([np.nan, np.nan], dtype="category")
|
|
|
|
exp = pd.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_concat_categorical_empty(self):
|
|
# GH 13524
|
|
|
|
s1 = pd.Series([], dtype="category")
|
|
s2 = pd.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 = pd.Series([], dtype="category")
|
|
s2 = pd.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 = pd.Series([], dtype="category")
|
|
s2 = pd.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 = pd.Series([], dtype="category")
|
|
s2 = pd.Series([np.nan, np.nan])
|
|
|
|
# empty Series is ignored
|
|
exp = pd.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)
|
|
|
|
|
|
class TestAppend:
|
|
def test_append(self, sort, float_frame):
|
|
mixed_frame = float_frame.copy()
|
|
mixed_frame["foo"] = "bar"
|
|
|
|
begin_index = float_frame.index[:5]
|
|
end_index = float_frame.index[5:]
|
|
|
|
begin_frame = float_frame.reindex(begin_index)
|
|
end_frame = float_frame.reindex(end_index)
|
|
|
|
appended = begin_frame.append(end_frame)
|
|
tm.assert_almost_equal(appended["A"], float_frame["A"])
|
|
|
|
del end_frame["A"]
|
|
partial_appended = begin_frame.append(end_frame, sort=sort)
|
|
assert "A" in partial_appended
|
|
|
|
partial_appended = end_frame.append(begin_frame, sort=sort)
|
|
assert "A" in partial_appended
|
|
|
|
# mixed type handling
|
|
appended = mixed_frame[:5].append(mixed_frame[5:])
|
|
tm.assert_frame_equal(appended, mixed_frame)
|
|
|
|
# what to test here
|
|
mixed_appended = mixed_frame[:5].append(float_frame[5:], sort=sort)
|
|
mixed_appended2 = float_frame[:5].append(mixed_frame[5:], sort=sort)
|
|
|
|
# all equal except 'foo' column
|
|
tm.assert_frame_equal(
|
|
mixed_appended.reindex(columns=["A", "B", "C", "D"]),
|
|
mixed_appended2.reindex(columns=["A", "B", "C", "D"]),
|
|
)
|
|
|
|
def test_append_empty(self, float_frame):
|
|
empty = DataFrame()
|
|
|
|
appended = float_frame.append(empty)
|
|
tm.assert_frame_equal(float_frame, appended)
|
|
assert appended is not float_frame
|
|
|
|
appended = empty.append(float_frame)
|
|
tm.assert_frame_equal(float_frame, appended)
|
|
assert appended is not float_frame
|
|
|
|
def test_append_overlap_raises(self, float_frame):
|
|
msg = "Indexes have overlapping values"
|
|
with pytest.raises(ValueError, match=msg):
|
|
float_frame.append(float_frame, verify_integrity=True)
|
|
|
|
def test_append_new_columns(self):
|
|
# see gh-6129: new columns
|
|
df = DataFrame({"a": {"x": 1, "y": 2}, "b": {"x": 3, "y": 4}})
|
|
row = Series([5, 6, 7], index=["a", "b", "c"], name="z")
|
|
expected = DataFrame(
|
|
{
|
|
"a": {"x": 1, "y": 2, "z": 5},
|
|
"b": {"x": 3, "y": 4, "z": 6},
|
|
"c": {"z": 7},
|
|
}
|
|
)
|
|
result = df.append(row)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_append_length0_frame(self, sort):
|
|
df = DataFrame(columns=["A", "B", "C"])
|
|
df3 = DataFrame(index=[0, 1], columns=["A", "B"])
|
|
df5 = df.append(df3, sort=sort)
|
|
|
|
expected = DataFrame(index=[0, 1], columns=["A", "B", "C"])
|
|
tm.assert_frame_equal(df5, expected)
|
|
|
|
def test_append_records(self):
|
|
arr1 = np.zeros((2,), dtype=("i4,f4,a10"))
|
|
arr1[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
|
|
|
|
arr2 = np.zeros((3,), dtype=("i4,f4,a10"))
|
|
arr2[:] = [(3, 4.0, "foo"), (5, 6.0, "bar"), (7.0, 8.0, "baz")]
|
|
|
|
df1 = DataFrame(arr1)
|
|
df2 = DataFrame(arr2)
|
|
|
|
result = df1.append(df2, ignore_index=True)
|
|
expected = DataFrame(np.concatenate((arr1, arr2)))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# rewrite sort fixture, since we also want to test default of None
|
|
def test_append_sorts(self, sort):
|
|
df1 = pd.DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
|
|
df2 = pd.DataFrame({"a": [1, 2], "c": [3, 4]}, index=[2, 3])
|
|
|
|
with tm.assert_produces_warning(None):
|
|
result = df1.append(df2, sort=sort)
|
|
|
|
# for None / True
|
|
expected = pd.DataFrame(
|
|
{"b": [1, 2, None, None], "a": [1, 2, 1, 2], "c": [None, None, 3, 4]},
|
|
columns=["a", "b", "c"],
|
|
)
|
|
if sort is False:
|
|
expected = expected[["b", "a", "c"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_append_different_columns(self, sort):
|
|
df = DataFrame(
|
|
{
|
|
"bools": np.random.randn(10) > 0,
|
|
"ints": np.random.randint(0, 10, 10),
|
|
"floats": np.random.randn(10),
|
|
"strings": ["foo", "bar"] * 5,
|
|
}
|
|
)
|
|
|
|
a = df[:5].loc[:, ["bools", "ints", "floats"]]
|
|
b = df[5:].loc[:, ["strings", "ints", "floats"]]
|
|
|
|
appended = a.append(b, sort=sort)
|
|
assert isna(appended["strings"][0:4]).all()
|
|
assert isna(appended["bools"][5:]).all()
|
|
|
|
def test_append_many(self, sort, float_frame):
|
|
chunks = [
|
|
float_frame[:5],
|
|
float_frame[5:10],
|
|
float_frame[10:15],
|
|
float_frame[15:],
|
|
]
|
|
|
|
result = chunks[0].append(chunks[1:])
|
|
tm.assert_frame_equal(result, float_frame)
|
|
|
|
chunks[-1] = chunks[-1].copy()
|
|
chunks[-1]["foo"] = "bar"
|
|
result = chunks[0].append(chunks[1:], sort=sort)
|
|
tm.assert_frame_equal(result.loc[:, float_frame.columns], float_frame)
|
|
assert (result["foo"][15:] == "bar").all()
|
|
assert result["foo"][:15].isna().all()
|
|
|
|
def test_append_preserve_index_name(self):
|
|
# #980
|
|
df1 = DataFrame(columns=["A", "B", "C"])
|
|
df1 = df1.set_index(["A"])
|
|
df2 = DataFrame(data=[[1, 4, 7], [2, 5, 8], [3, 6, 9]], columns=["A", "B", "C"])
|
|
df2 = df2.set_index(["A"])
|
|
|
|
result = df1.append(df2)
|
|
assert result.index.name == "A"
|
|
|
|
indexes_can_append = [
|
|
pd.RangeIndex(3),
|
|
pd.Index([4, 5, 6]),
|
|
pd.Index([4.5, 5.5, 6.5]),
|
|
pd.Index(list("abc")),
|
|
pd.CategoricalIndex("A B C".split()),
|
|
pd.CategoricalIndex("D E F".split(), ordered=True),
|
|
pd.IntervalIndex.from_breaks([7, 8, 9, 10]),
|
|
pd.DatetimeIndex(
|
|
[
|
|
dt.datetime(2013, 1, 3, 0, 0),
|
|
dt.datetime(2013, 1, 3, 6, 10),
|
|
dt.datetime(2013, 1, 3, 7, 12),
|
|
]
|
|
),
|
|
]
|
|
|
|
indexes_cannot_append_with_other = [
|
|
pd.MultiIndex.from_arrays(["A B C".split(), "D E F".split()])
|
|
]
|
|
|
|
all_indexes = indexes_can_append + indexes_cannot_append_with_other
|
|
|
|
@pytest.mark.parametrize("index", all_indexes, ids=lambda x: type(x).__name__)
|
|
def test_append_same_columns_type(self, index):
|
|
# GH18359
|
|
|
|
# df wider than ser
|
|
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=index)
|
|
ser_index = index[:2]
|
|
ser = pd.Series([7, 8], index=ser_index, name=2)
|
|
result = df.append(ser)
|
|
expected = pd.DataFrame(
|
|
[[1.0, 2.0, 3.0], [4, 5, 6], [7, 8, np.nan]], index=[0, 1, 2], columns=index
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# ser wider than df
|
|
ser_index = index
|
|
index = index[:2]
|
|
df = pd.DataFrame([[1, 2], [4, 5]], columns=index)
|
|
ser = pd.Series([7, 8, 9], index=ser_index, name=2)
|
|
result = df.append(ser)
|
|
expected = pd.DataFrame(
|
|
[[1, 2, np.nan], [4, 5, np.nan], [7, 8, 9]],
|
|
index=[0, 1, 2],
|
|
columns=ser_index,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"df_columns, series_index",
|
|
combinations(indexes_can_append, r=2),
|
|
ids=lambda x: type(x).__name__,
|
|
)
|
|
def test_append_different_columns_types(self, df_columns, series_index):
|
|
# GH18359
|
|
# See also test 'test_append_different_columns_types_raises' below
|
|
# for errors raised when appending
|
|
|
|
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=df_columns)
|
|
ser = pd.Series([7, 8, 9], index=series_index, name=2)
|
|
|
|
result = df.append(ser)
|
|
idx_diff = ser.index.difference(df_columns)
|
|
combined_columns = Index(df_columns.tolist()).append(idx_diff)
|
|
expected = pd.DataFrame(
|
|
[
|
|
[1.0, 2.0, 3.0, np.nan, np.nan, np.nan],
|
|
[4, 5, 6, np.nan, np.nan, np.nan],
|
|
[np.nan, np.nan, np.nan, 7, 8, 9],
|
|
],
|
|
index=[0, 1, 2],
|
|
columns=combined_columns,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"index_can_append", indexes_can_append, ids=lambda x: type(x).__name__
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"index_cannot_append_with_other",
|
|
indexes_cannot_append_with_other,
|
|
ids=lambda x: type(x).__name__,
|
|
)
|
|
def test_append_different_columns_types_raises(
|
|
self, index_can_append, index_cannot_append_with_other
|
|
):
|
|
# GH18359
|
|
# Dataframe.append will raise if MultiIndex appends
|
|
# or is appended to a different index type
|
|
#
|
|
# See also test 'test_append_different_columns_types' above for
|
|
# appending without raising.
|
|
|
|
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=index_can_append)
|
|
ser = pd.Series([7, 8, 9], index=index_cannot_append_with_other, name=2)
|
|
msg = (
|
|
r"Expected tuple, got (int|long|float|str|"
|
|
r"pandas._libs.interval.Interval)|"
|
|
r"object of type '(int|float|Timestamp|"
|
|
r"pandas._libs.interval.Interval)' has no len\(\)|"
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.append(ser)
|
|
|
|
df = pd.DataFrame(
|
|
[[1, 2, 3], [4, 5, 6]], columns=index_cannot_append_with_other
|
|
)
|
|
ser = pd.Series([7, 8, 9], index=index_can_append, name=2)
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.append(ser)
|
|
|
|
def test_append_dtype_coerce(self, sort):
|
|
|
|
# GH 4993
|
|
# appending with datetime will incorrectly convert datetime64
|
|
|
|
df1 = DataFrame(
|
|
index=[1, 2],
|
|
data=[dt.datetime(2013, 1, 1, 0, 0), dt.datetime(2013, 1, 2, 0, 0)],
|
|
columns=["start_time"],
|
|
)
|
|
df2 = DataFrame(
|
|
index=[4, 5],
|
|
data=[
|
|
[dt.datetime(2013, 1, 3, 0, 0), dt.datetime(2013, 1, 3, 6, 10)],
|
|
[dt.datetime(2013, 1, 4, 0, 0), dt.datetime(2013, 1, 4, 7, 10)],
|
|
],
|
|
columns=["start_time", "end_time"],
|
|
)
|
|
|
|
expected = concat(
|
|
[
|
|
Series(
|
|
[
|
|
pd.NaT,
|
|
pd.NaT,
|
|
dt.datetime(2013, 1, 3, 6, 10),
|
|
dt.datetime(2013, 1, 4, 7, 10),
|
|
],
|
|
name="end_time",
|
|
),
|
|
Series(
|
|
[
|
|
dt.datetime(2013, 1, 1, 0, 0),
|
|
dt.datetime(2013, 1, 2, 0, 0),
|
|
dt.datetime(2013, 1, 3, 0, 0),
|
|
dt.datetime(2013, 1, 4, 0, 0),
|
|
],
|
|
name="start_time",
|
|
),
|
|
],
|
|
axis=1,
|
|
sort=sort,
|
|
)
|
|
result = df1.append(df2, ignore_index=True, sort=sort)
|
|
if sort:
|
|
expected = expected[["end_time", "start_time"]]
|
|
else:
|
|
expected = expected[["start_time", "end_time"]]
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_append_missing_column_proper_upcast(self, sort):
|
|
df1 = DataFrame({"A": np.array([1, 2, 3, 4], dtype="i8")})
|
|
df2 = DataFrame({"B": np.array([True, False, True, False], dtype=bool)})
|
|
|
|
appended = df1.append(df2, ignore_index=True, sort=sort)
|
|
assert appended["A"].dtype == "f8"
|
|
assert appended["B"].dtype == "O"
|
|
|
|
def test_append_empty_frame_to_series_with_dateutil_tz(self):
|
|
# GH 23682
|
|
date = Timestamp("2018-10-24 07:30:00", tz=dateutil.tz.tzutc())
|
|
s = Series({"date": date, "a": 1.0, "b": 2.0})
|
|
df = DataFrame(columns=["c", "d"])
|
|
result_a = df.append(s, ignore_index=True)
|
|
expected = DataFrame(
|
|
[[np.nan, np.nan, 1.0, 2.0, date]], columns=["c", "d", "a", "b", "date"]
|
|
)
|
|
# These columns get cast to object after append
|
|
expected["c"] = expected["c"].astype(object)
|
|
expected["d"] = expected["d"].astype(object)
|
|
tm.assert_frame_equal(result_a, expected)
|
|
|
|
expected = DataFrame(
|
|
[[np.nan, np.nan, 1.0, 2.0, date]] * 2, columns=["c", "d", "a", "b", "date"]
|
|
)
|
|
expected["c"] = expected["c"].astype(object)
|
|
expected["d"] = expected["d"].astype(object)
|
|
|
|
result_b = result_a.append(s, ignore_index=True)
|
|
tm.assert_frame_equal(result_b, expected)
|
|
|
|
# column order is different
|
|
expected = expected[["c", "d", "date", "a", "b"]]
|
|
result = df.append([s, s], ignore_index=True)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_append_empty_tz_frame_with_datetime64ns(self):
|
|
# https://github.com/pandas-dev/pandas/issues/35460
|
|
df = pd.DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
|
|
|
|
# pd.NaT gets inferred as tz-naive, so append result is tz-naive
|
|
result = df.append({"a": pd.NaT}, ignore_index=True)
|
|
expected = pd.DataFrame({"a": [pd.NaT]}).astype("datetime64[ns]")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# also test with typed value to append
|
|
df = pd.DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
|
|
result = df.append(
|
|
pd.Series({"a": pd.NaT}, dtype="datetime64[ns]"), ignore_index=True
|
|
)
|
|
expected = pd.DataFrame({"a": [pd.NaT]}).astype("datetime64[ns]")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
class TestConcatenate:
|
|
def test_concat_copy(self):
|
|
df = DataFrame(np.random.randn(4, 3))
|
|
df2 = DataFrame(np.random.randint(0, 10, size=4).reshape(4, 1))
|
|
df3 = DataFrame({5: "foo"}, index=range(4))
|
|
|
|
# These are actual copies.
|
|
result = concat([df, df2, df3], axis=1, copy=True)
|
|
|
|
for b in result._mgr.blocks:
|
|
assert b.values.base is None
|
|
|
|
# These are the same.
|
|
result = concat([df, df2, df3], axis=1, copy=False)
|
|
|
|
for b in result._mgr.blocks:
|
|
if b.is_float:
|
|
assert b.values.base is df._mgr.blocks[0].values.base
|
|
elif b.is_integer:
|
|
assert b.values.base is df2._mgr.blocks[0].values.base
|
|
elif b.is_object:
|
|
assert b.values.base is not None
|
|
|
|
# Float block was consolidated.
|
|
df4 = DataFrame(np.random.randn(4, 1))
|
|
result = concat([df, df2, df3, df4], axis=1, copy=False)
|
|
for b in result._mgr.blocks:
|
|
if b.is_float:
|
|
assert b.values.base is None
|
|
elif b.is_integer:
|
|
assert b.values.base is df2._mgr.blocks[0].values.base
|
|
elif b.is_object:
|
|
assert b.values.base is not None
|
|
|
|
def test_concat_with_group_keys(self):
|
|
df = DataFrame(np.random.randn(4, 3))
|
|
df2 = DataFrame(np.random.randn(4, 4))
|
|
|
|
# axis=0
|
|
df = DataFrame(np.random.randn(3, 4))
|
|
df2 = DataFrame(np.random.randn(4, 4))
|
|
|
|
result = concat([df, df2], keys=[0, 1])
|
|
exp_index = MultiIndex.from_arrays(
|
|
[[0, 0, 0, 1, 1, 1, 1], [0, 1, 2, 0, 1, 2, 3]]
|
|
)
|
|
expected = DataFrame(np.r_[df.values, df2.values], index=exp_index)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat([df, df], keys=[0, 1])
|
|
exp_index2 = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
|
|
expected = DataFrame(np.r_[df.values, df.values], index=exp_index2)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# axis=1
|
|
df = DataFrame(np.random.randn(4, 3))
|
|
df2 = DataFrame(np.random.randn(4, 4))
|
|
|
|
result = concat([df, df2], keys=[0, 1], axis=1)
|
|
expected = DataFrame(np.c_[df.values, df2.values], columns=exp_index)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat([df, df], keys=[0, 1], axis=1)
|
|
expected = DataFrame(np.c_[df.values, df.values], columns=exp_index2)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_keys_specific_levels(self):
|
|
df = DataFrame(np.random.randn(10, 4))
|
|
pieces = [df.iloc[:, [0, 1]], df.iloc[:, [2]], df.iloc[:, [3]]]
|
|
level = ["three", "two", "one", "zero"]
|
|
result = concat(
|
|
pieces,
|
|
axis=1,
|
|
keys=["one", "two", "three"],
|
|
levels=[level],
|
|
names=["group_key"],
|
|
)
|
|
|
|
tm.assert_index_equal(result.columns.levels[0], Index(level, name="group_key"))
|
|
tm.assert_index_equal(result.columns.levels[1], Index([0, 1, 2, 3]))
|
|
|
|
assert result.columns.names == ["group_key", None]
|
|
|
|
def test_concat_dataframe_keys_bug(self, sort):
|
|
t1 = DataFrame(
|
|
{"value": Series([1, 2, 3], index=Index(["a", "b", "c"], name="id"))}
|
|
)
|
|
t2 = DataFrame({"value": Series([7, 8], index=Index(["a", "b"], name="id"))})
|
|
|
|
# it works
|
|
result = concat([t1, t2], axis=1, keys=["t1", "t2"], sort=sort)
|
|
assert list(result.columns) == [("t1", "value"), ("t2", "value")]
|
|
|
|
def test_concat_series_partial_columns_names(self):
|
|
# GH10698
|
|
foo = Series([1, 2], name="foo")
|
|
bar = Series([1, 2])
|
|
baz = Series([4, 5])
|
|
|
|
result = concat([foo, bar, baz], axis=1)
|
|
expected = DataFrame(
|
|
{"foo": [1, 2], 0: [1, 2], 1: [4, 5]}, columns=["foo", 0, 1]
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat([foo, bar, baz], axis=1, keys=["red", "blue", "yellow"])
|
|
expected = DataFrame(
|
|
{"red": [1, 2], "blue": [1, 2], "yellow": [4, 5]},
|
|
columns=["red", "blue", "yellow"],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat([foo, bar, baz], axis=1, ignore_index=True)
|
|
expected = DataFrame({0: [1, 2], 1: [1, 2], 2: [4, 5]})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("mapping", ["mapping", "dict"])
|
|
def test_concat_mapping(self, mapping, non_dict_mapping_subclass):
|
|
constructor = dict if mapping == "dict" else non_dict_mapping_subclass
|
|
frames = constructor(
|
|
{
|
|
"foo": DataFrame(np.random.randn(4, 3)),
|
|
"bar": DataFrame(np.random.randn(4, 3)),
|
|
"baz": DataFrame(np.random.randn(4, 3)),
|
|
"qux": DataFrame(np.random.randn(4, 3)),
|
|
}
|
|
)
|
|
|
|
sorted_keys = list(frames.keys())
|
|
|
|
result = concat(frames)
|
|
expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat(frames, axis=1)
|
|
expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys, axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
keys = ["baz", "foo", "bar"]
|
|
result = concat(frames, keys=keys)
|
|
expected = concat([frames[k] for k in keys], keys=keys)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_ignore_index(self, sort):
|
|
frame1 = DataFrame(
|
|
{"test1": ["a", "b", "c"], "test2": [1, 2, 3], "test3": [4.5, 3.2, 1.2]}
|
|
)
|
|
frame2 = DataFrame({"test3": [5.2, 2.2, 4.3]})
|
|
frame1.index = Index(["x", "y", "z"])
|
|
frame2.index = Index(["x", "y", "q"])
|
|
|
|
v1 = concat([frame1, frame2], axis=1, ignore_index=True, sort=sort)
|
|
|
|
nan = np.nan
|
|
expected = DataFrame(
|
|
[
|
|
[nan, nan, nan, 4.3],
|
|
["a", 1, 4.5, 5.2],
|
|
["b", 2, 3.2, 2.2],
|
|
["c", 3, 1.2, nan],
|
|
],
|
|
index=Index(["q", "x", "y", "z"]),
|
|
)
|
|
if not sort:
|
|
expected = expected.loc[["x", "y", "z", "q"]]
|
|
|
|
tm.assert_frame_equal(v1, expected)
|
|
|
|
def test_concat_multiindex_with_keys(self):
|
|
index = MultiIndex(
|
|
levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
|
|
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
|
|
names=["first", "second"],
|
|
)
|
|
frame = DataFrame(
|
|
np.random.randn(10, 3),
|
|
index=index,
|
|
columns=Index(["A", "B", "C"], name="exp"),
|
|
)
|
|
result = concat([frame, frame], keys=[0, 1], names=["iteration"])
|
|
|
|
assert result.index.names == ("iteration",) + index.names
|
|
tm.assert_frame_equal(result.loc[0], frame)
|
|
tm.assert_frame_equal(result.loc[1], frame)
|
|
assert result.index.nlevels == 3
|
|
|
|
def test_concat_multiindex_with_tz(self):
|
|
# GH 6606
|
|
df = DataFrame(
|
|
{
|
|
"dt": [
|
|
datetime(2014, 1, 1),
|
|
datetime(2014, 1, 2),
|
|
datetime(2014, 1, 3),
|
|
],
|
|
"b": ["A", "B", "C"],
|
|
"c": [1, 2, 3],
|
|
"d": [4, 5, 6],
|
|
}
|
|
)
|
|
df["dt"] = df["dt"].apply(lambda d: Timestamp(d, tz="US/Pacific"))
|
|
df = df.set_index(["dt", "b"])
|
|
|
|
exp_idx1 = DatetimeIndex(
|
|
["2014-01-01", "2014-01-02", "2014-01-03"] * 2, tz="US/Pacific", name="dt"
|
|
)
|
|
exp_idx2 = Index(["A", "B", "C"] * 2, name="b")
|
|
exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2])
|
|
expected = DataFrame(
|
|
{"c": [1, 2, 3] * 2, "d": [4, 5, 6] * 2}, index=exp_idx, columns=["c", "d"]
|
|
)
|
|
|
|
result = concat([df, df])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_multiindex_with_none_in_index_names(self):
|
|
# GH 15787
|
|
index = pd.MultiIndex.from_product([[1], range(5)], names=["level1", None])
|
|
df = pd.DataFrame({"col": range(5)}, index=index, dtype=np.int32)
|
|
|
|
result = concat([df, df], keys=[1, 2], names=["level2"])
|
|
index = pd.MultiIndex.from_product(
|
|
[[1, 2], [1], range(5)], names=["level2", "level1", None]
|
|
)
|
|
expected = pd.DataFrame(
|
|
{"col": list(range(5)) * 2}, index=index, dtype=np.int32
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat([df, df[:2]], keys=[1, 2], names=["level2"])
|
|
level2 = [1] * 5 + [2] * 2
|
|
level1 = [1] * 7
|
|
no_name = list(range(5)) + list(range(2))
|
|
tuples = list(zip(level2, level1, no_name))
|
|
index = pd.MultiIndex.from_tuples(tuples, names=["level2", "level1", None])
|
|
expected = pd.DataFrame({"col": no_name}, index=index, dtype=np.int32)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_keys_and_levels(self):
|
|
df = DataFrame(np.random.randn(1, 3))
|
|
df2 = DataFrame(np.random.randn(1, 4))
|
|
|
|
levels = [["foo", "baz"], ["one", "two"]]
|
|
names = ["first", "second"]
|
|
result = concat(
|
|
[df, df2, df, df2],
|
|
keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
|
|
levels=levels,
|
|
names=names,
|
|
)
|
|
expected = concat([df, df2, df, df2])
|
|
exp_index = MultiIndex(
|
|
levels=levels + [[0]],
|
|
codes=[[0, 0, 1, 1], [0, 1, 0, 1], [0, 0, 0, 0]],
|
|
names=names + [None],
|
|
)
|
|
expected.index = exp_index
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# no names
|
|
result = concat(
|
|
[df, df2, df, df2],
|
|
keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
|
|
levels=levels,
|
|
)
|
|
assert result.index.names == (None,) * 3
|
|
|
|
# no levels
|
|
result = concat(
|
|
[df, df2, df, df2],
|
|
keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
|
|
names=["first", "second"],
|
|
)
|
|
assert result.index.names == ("first", "second", None)
|
|
tm.assert_index_equal(
|
|
result.index.levels[0], Index(["baz", "foo"], name="first")
|
|
)
|
|
|
|
def test_concat_keys_levels_no_overlap(self):
|
|
# GH #1406
|
|
df = DataFrame(np.random.randn(1, 3), index=["a"])
|
|
df2 = DataFrame(np.random.randn(1, 4), index=["b"])
|
|
|
|
msg = "Values not found in passed level"
|
|
with pytest.raises(ValueError, match=msg):
|
|
concat([df, df], keys=["one", "two"], levels=[["foo", "bar", "baz"]])
|
|
|
|
msg = "Key one not in level"
|
|
with pytest.raises(ValueError, match=msg):
|
|
concat([df, df2], keys=["one", "two"], levels=[["foo", "bar", "baz"]])
|
|
|
|
def test_concat_rename_index(self):
|
|
a = DataFrame(
|
|
np.random.rand(3, 3),
|
|
columns=list("ABC"),
|
|
index=Index(list("abc"), name="index_a"),
|
|
)
|
|
b = DataFrame(
|
|
np.random.rand(3, 3),
|
|
columns=list("ABC"),
|
|
index=Index(list("abc"), name="index_b"),
|
|
)
|
|
|
|
result = concat([a, b], keys=["key0", "key1"], names=["lvl0", "lvl1"])
|
|
|
|
exp = concat([a, b], keys=["key0", "key1"], names=["lvl0"])
|
|
names = list(exp.index.names)
|
|
names[1] = "lvl1"
|
|
exp.index.set_names(names, inplace=True)
|
|
|
|
tm.assert_frame_equal(result, exp)
|
|
assert result.index.names == exp.index.names
|
|
|
|
def test_crossed_dtypes_weird_corner(self):
|
|
columns = ["A", "B", "C", "D"]
|
|
df1 = DataFrame(
|
|
{
|
|
"A": np.array([1, 2, 3, 4], dtype="f8"),
|
|
"B": np.array([1, 2, 3, 4], dtype="i8"),
|
|
"C": np.array([1, 2, 3, 4], dtype="f8"),
|
|
"D": np.array([1, 2, 3, 4], dtype="i8"),
|
|
},
|
|
columns=columns,
|
|
)
|
|
|
|
df2 = DataFrame(
|
|
{
|
|
"A": np.array([1, 2, 3, 4], dtype="i8"),
|
|
"B": np.array([1, 2, 3, 4], dtype="f8"),
|
|
"C": np.array([1, 2, 3, 4], dtype="i8"),
|
|
"D": np.array([1, 2, 3, 4], dtype="f8"),
|
|
},
|
|
columns=columns,
|
|
)
|
|
|
|
appended = df1.append(df2, ignore_index=True)
|
|
expected = DataFrame(
|
|
np.concatenate([df1.values, df2.values], axis=0), columns=columns
|
|
)
|
|
tm.assert_frame_equal(appended, expected)
|
|
|
|
df = DataFrame(np.random.randn(1, 3), index=["a"])
|
|
df2 = DataFrame(np.random.randn(1, 4), index=["b"])
|
|
result = concat([df, df2], keys=["one", "two"], names=["first", "second"])
|
|
assert result.index.names == ("first", "second")
|
|
|
|
def test_dups_index(self):
|
|
# GH 4771
|
|
|
|
# single dtypes
|
|
df = DataFrame(
|
|
np.random.randint(0, 10, size=40).reshape(10, 4),
|
|
columns=["A", "A", "C", "C"],
|
|
)
|
|
|
|
result = concat([df, df], axis=1)
|
|
tm.assert_frame_equal(result.iloc[:, :4], df)
|
|
tm.assert_frame_equal(result.iloc[:, 4:], df)
|
|
|
|
result = concat([df, df], axis=0)
|
|
tm.assert_frame_equal(result.iloc[:10], df)
|
|
tm.assert_frame_equal(result.iloc[10:], df)
|
|
|
|
# multi dtypes
|
|
df = concat(
|
|
[
|
|
DataFrame(np.random.randn(10, 4), columns=["A", "A", "B", "B"]),
|
|
DataFrame(
|
|
np.random.randint(0, 10, size=20).reshape(10, 2), columns=["A", "C"]
|
|
),
|
|
],
|
|
axis=1,
|
|
)
|
|
|
|
result = concat([df, df], axis=1)
|
|
tm.assert_frame_equal(result.iloc[:, :6], df)
|
|
tm.assert_frame_equal(result.iloc[:, 6:], df)
|
|
|
|
result = concat([df, df], axis=0)
|
|
tm.assert_frame_equal(result.iloc[:10], df)
|
|
tm.assert_frame_equal(result.iloc[10:], df)
|
|
|
|
# append
|
|
result = df.iloc[0:8, :].append(df.iloc[8:])
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.iloc[0:8, :].append(df.iloc[8:9]).append(df.iloc[9:10])
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
expected = concat([df, df], axis=0)
|
|
result = df.append(df)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_with_mixed_tuples(self, sort):
|
|
# 10697
|
|
# columns have mixed tuples, so handle properly
|
|
df1 = DataFrame({"A": "foo", ("B", 1): "bar"}, index=range(2))
|
|
df2 = DataFrame({"B": "foo", ("B", 1): "bar"}, index=range(2))
|
|
|
|
# it works
|
|
concat([df1, df2], sort=sort)
|
|
|
|
def test_handle_empty_objects(self, sort):
|
|
df = DataFrame(np.random.randn(10, 4), columns=list("abcd"))
|
|
|
|
baz = df[:5].copy()
|
|
baz["foo"] = "bar"
|
|
empty = df[5:5]
|
|
|
|
frames = [baz, empty, empty, df[5:]]
|
|
concatted = concat(frames, axis=0, sort=sort)
|
|
|
|
expected = df.reindex(columns=["a", "b", "c", "d", "foo"])
|
|
expected["foo"] = expected["foo"].astype("O")
|
|
expected.loc[0:4, "foo"] = "bar"
|
|
|
|
tm.assert_frame_equal(concatted, expected)
|
|
|
|
# empty as first element with time series
|
|
# GH3259
|
|
df = DataFrame(
|
|
dict(A=range(10000)), index=date_range("20130101", periods=10000, freq="s")
|
|
)
|
|
empty = DataFrame()
|
|
result = concat([df, empty], axis=1)
|
|
tm.assert_frame_equal(result, df)
|
|
result = concat([empty, df], axis=1)
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = concat([df, empty])
|
|
tm.assert_frame_equal(result, df)
|
|
result = concat([empty, df])
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
def test_concat_mixed_objs(self):
|
|
|
|
# concat mixed series/frames
|
|
# G2385
|
|
|
|
# axis 1
|
|
index = date_range("01-Jan-2013", periods=10, freq="H")
|
|
arr = np.arange(10, dtype="int64")
|
|
s1 = Series(arr, index=index)
|
|
s2 = Series(arr, index=index)
|
|
df = DataFrame(arr.reshape(-1, 1), index=index)
|
|
|
|
expected = DataFrame(
|
|
np.repeat(arr, 2).reshape(-1, 2), index=index, columns=[0, 0]
|
|
)
|
|
result = concat([df, df], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(
|
|
np.repeat(arr, 2).reshape(-1, 2), index=index, columns=[0, 1]
|
|
)
|
|
result = concat([s1, s2], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(
|
|
np.repeat(arr, 3).reshape(-1, 3), index=index, columns=[0, 1, 2]
|
|
)
|
|
result = concat([s1, s2, s1], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(
|
|
np.repeat(arr, 5).reshape(-1, 5), index=index, columns=[0, 0, 1, 2, 3]
|
|
)
|
|
result = concat([s1, df, s2, s2, s1], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# with names
|
|
s1.name = "foo"
|
|
expected = DataFrame(
|
|
np.repeat(arr, 3).reshape(-1, 3), index=index, columns=["foo", 0, 0]
|
|
)
|
|
result = concat([s1, df, s2], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
s2.name = "bar"
|
|
expected = DataFrame(
|
|
np.repeat(arr, 3).reshape(-1, 3), index=index, columns=["foo", 0, "bar"]
|
|
)
|
|
result = concat([s1, df, s2], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# ignore index
|
|
expected = DataFrame(
|
|
np.repeat(arr, 3).reshape(-1, 3), index=index, columns=[0, 1, 2]
|
|
)
|
|
result = concat([s1, df, s2], axis=1, ignore_index=True)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# axis 0
|
|
expected = DataFrame(
|
|
np.tile(arr, 3).reshape(-1, 1), index=index.tolist() * 3, columns=[0]
|
|
)
|
|
result = concat([s1, df, s2])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(np.tile(arr, 3).reshape(-1, 1), columns=[0])
|
|
result = concat([s1, df, s2], ignore_index=True)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_empty_dtype_coerce(self):
|
|
|
|
# xref to #12411
|
|
# xref to #12045
|
|
# xref to #11594
|
|
# see below
|
|
|
|
# 10571
|
|
df1 = DataFrame(data=[[1, None], [2, None]], columns=["a", "b"])
|
|
df2 = DataFrame(data=[[3, None], [4, None]], columns=["a", "b"])
|
|
result = concat([df1, df2])
|
|
expected = df1.dtypes
|
|
tm.assert_series_equal(result.dtypes, expected)
|
|
|
|
def test_dtype_coerceion(self):
|
|
|
|
# 12411
|
|
df = DataFrame({"date": [pd.Timestamp("20130101").tz_localize("UTC"), pd.NaT]})
|
|
|
|
result = concat([df.iloc[[0]], df.iloc[[1]]])
|
|
tm.assert_series_equal(result.dtypes, df.dtypes)
|
|
|
|
# 12045
|
|
import datetime
|
|
|
|
df = DataFrame(
|
|
{"date": [datetime.datetime(2012, 1, 1), datetime.datetime(1012, 1, 2)]}
|
|
)
|
|
result = concat([df.iloc[[0]], df.iloc[[1]]])
|
|
tm.assert_series_equal(result.dtypes, df.dtypes)
|
|
|
|
# 11594
|
|
df = DataFrame({"text": ["some words"] + [None] * 9})
|
|
result = concat([df.iloc[[0]], df.iloc[[1]]])
|
|
tm.assert_series_equal(result.dtypes, df.dtypes)
|
|
|
|
def test_concat_series(self):
|
|
|
|
ts = tm.makeTimeSeries()
|
|
ts.name = "foo"
|
|
|
|
pieces = [ts[:5], ts[5:15], ts[15:]]
|
|
|
|
result = concat(pieces)
|
|
tm.assert_series_equal(result, ts)
|
|
assert result.name == ts.name
|
|
|
|
result = concat(pieces, keys=[0, 1, 2])
|
|
expected = ts.copy()
|
|
|
|
ts.index = DatetimeIndex(np.array(ts.index.values, dtype="M8[ns]"))
|
|
|
|
exp_codes = [np.repeat([0, 1, 2], [len(x) for x in pieces]), np.arange(len(ts))]
|
|
exp_index = MultiIndex(levels=[[0, 1, 2], ts.index], codes=exp_codes)
|
|
expected.index = exp_index
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_concat_series_axis1(self, sort=sort):
|
|
ts = tm.makeTimeSeries()
|
|
|
|
pieces = [ts[:-2], ts[2:], ts[2:-2]]
|
|
|
|
result = concat(pieces, axis=1)
|
|
expected = DataFrame(pieces).T
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat(pieces, keys=["A", "B", "C"], axis=1)
|
|
expected = DataFrame(pieces, index=["A", "B", "C"]).T
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# preserve series names, #2489
|
|
s = Series(randn(5), name="A")
|
|
s2 = Series(randn(5), name="B")
|
|
|
|
result = concat([s, s2], axis=1)
|
|
expected = DataFrame({"A": s, "B": s2})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
s2.name = None
|
|
result = concat([s, s2], axis=1)
|
|
tm.assert_index_equal(result.columns, Index(["A", 0], dtype="object"))
|
|
|
|
# must reindex, #2603
|
|
s = Series(randn(3), index=["c", "a", "b"], name="A")
|
|
s2 = Series(randn(4), index=["d", "a", "b", "c"], name="B")
|
|
result = concat([s, s2], axis=1, sort=sort)
|
|
expected = DataFrame({"A": s, "B": s2})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_series_axis1_names_applied(self):
|
|
# ensure names argument is not ignored on axis=1, #23490
|
|
s = Series([1, 2, 3])
|
|
s2 = Series([4, 5, 6])
|
|
result = concat([s, s2], axis=1, keys=["a", "b"], names=["A"])
|
|
expected = DataFrame(
|
|
[[1, 4], [2, 5], [3, 6]], columns=pd.Index(["a", "b"], name="A")
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat([s, s2], axis=1, keys=[("a", 1), ("b", 2)], names=["A", "B"])
|
|
expected = DataFrame(
|
|
[[1, 4], [2, 5], [3, 6]],
|
|
columns=MultiIndex.from_tuples([("a", 1), ("b", 2)], names=["A", "B"]),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_single_with_key(self):
|
|
df = DataFrame(np.random.randn(10, 4))
|
|
|
|
result = concat([df], keys=["foo"])
|
|
expected = concat([df, df], keys=["foo", "bar"])
|
|
tm.assert_frame_equal(result, expected[:10])
|
|
|
|
def test_concat_exclude_none(self):
|
|
df = DataFrame(np.random.randn(10, 4))
|
|
|
|
pieces = [df[:5], None, None, df[5:]]
|
|
result = concat(pieces)
|
|
tm.assert_frame_equal(result, df)
|
|
with pytest.raises(ValueError, match="All objects passed were None"):
|
|
concat([None, None])
|
|
|
|
def test_concat_datetime64_block(self):
|
|
from pandas.core.indexes.datetimes import date_range
|
|
|
|
rng = date_range("1/1/2000", periods=10)
|
|
|
|
df = DataFrame({"time": rng})
|
|
|
|
result = concat([df, df])
|
|
assert (result.iloc[:10]["time"] == rng).all()
|
|
assert (result.iloc[10:]["time"] == rng).all()
|
|
|
|
def test_concat_timedelta64_block(self):
|
|
from pandas import to_timedelta
|
|
|
|
rng = to_timedelta(np.arange(10), unit="s")
|
|
|
|
df = DataFrame({"time": rng})
|
|
|
|
result = concat([df, df])
|
|
assert (result.iloc[:10]["time"] == rng).all()
|
|
assert (result.iloc[10:]["time"] == rng).all()
|
|
|
|
def test_concat_keys_with_none(self):
|
|
# #1649
|
|
df0 = DataFrame([[10, 20, 30], [10, 20, 30], [10, 20, 30]])
|
|
|
|
result = concat(dict(a=None, b=df0, c=df0[:2], d=df0[:1], e=df0))
|
|
expected = concat(dict(b=df0, c=df0[:2], d=df0[:1], e=df0))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = concat(
|
|
[None, df0, df0[:2], df0[:1], df0], keys=["a", "b", "c", "d", "e"]
|
|
)
|
|
expected = concat([df0, df0[:2], df0[:1], df0], keys=["b", "c", "d", "e"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_bug_1719(self):
|
|
ts1 = tm.makeTimeSeries()
|
|
ts2 = tm.makeTimeSeries()[::2]
|
|
|
|
# to join with union
|
|
# these two are of different length!
|
|
left = concat([ts1, ts2], join="outer", axis=1)
|
|
right = concat([ts2, ts1], join="outer", axis=1)
|
|
|
|
assert len(left) == len(right)
|
|
|
|
def test_concat_bug_2972(self):
|
|
ts0 = Series(np.zeros(5))
|
|
ts1 = Series(np.ones(5))
|
|
ts0.name = ts1.name = "same name"
|
|
result = concat([ts0, ts1], axis=1)
|
|
|
|
expected = DataFrame({0: ts0, 1: ts1})
|
|
expected.columns = ["same name", "same name"]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_bug_3602(self):
|
|
|
|
# GH 3602, duplicate columns
|
|
df1 = DataFrame(
|
|
{
|
|
"firmNo": [0, 0, 0, 0],
|
|
"prc": [6, 6, 6, 6],
|
|
"stringvar": ["rrr", "rrr", "rrr", "rrr"],
|
|
}
|
|
)
|
|
df2 = DataFrame(
|
|
{"C": [9, 10, 11, 12], "misc": [1, 2, 3, 4], "prc": [6, 6, 6, 6]}
|
|
)
|
|
expected = DataFrame(
|
|
[
|
|
[0, 6, "rrr", 9, 1, 6],
|
|
[0, 6, "rrr", 10, 2, 6],
|
|
[0, 6, "rrr", 11, 3, 6],
|
|
[0, 6, "rrr", 12, 4, 6],
|
|
]
|
|
)
|
|
expected.columns = ["firmNo", "prc", "stringvar", "C", "misc", "prc"]
|
|
|
|
result = concat([df1, df2], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_inner_join_empty(self):
|
|
# GH 15328
|
|
df_empty = pd.DataFrame()
|
|
df_a = pd.DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
|
|
df_expected = pd.DataFrame({"a": []}, index=[], dtype="int64")
|
|
|
|
for how, expected in [("inner", df_expected), ("outer", df_a)]:
|
|
result = pd.concat([df_a, df_empty], axis=1, join=how)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_series_axis1_same_names_ignore_index(self):
|
|
dates = date_range("01-Jan-2013", "01-Jan-2014", freq="MS")[0:-1]
|
|
s1 = Series(randn(len(dates)), index=dates, name="value")
|
|
s2 = Series(randn(len(dates)), index=dates, name="value")
|
|
|
|
result = concat([s1, s2], axis=1, ignore_index=True)
|
|
expected = Index([0, 1])
|
|
|
|
tm.assert_index_equal(result.columns, expected)
|
|
|
|
def test_concat_iterables(self):
|
|
# GH8645 check concat works with tuples, list, generators, and weird
|
|
# stuff like deque and custom iterables
|
|
df1 = DataFrame([1, 2, 3])
|
|
df2 = DataFrame([4, 5, 6])
|
|
expected = DataFrame([1, 2, 3, 4, 5, 6])
|
|
tm.assert_frame_equal(concat((df1, df2), ignore_index=True), expected)
|
|
tm.assert_frame_equal(concat([df1, df2], ignore_index=True), expected)
|
|
tm.assert_frame_equal(
|
|
concat((df for df in (df1, df2)), ignore_index=True), expected
|
|
)
|
|
tm.assert_frame_equal(concat(deque((df1, df2)), ignore_index=True), expected)
|
|
|
|
class CustomIterator1:
|
|
def __len__(self) -> int:
|
|
return 2
|
|
|
|
def __getitem__(self, index):
|
|
try:
|
|
return {0: df1, 1: df2}[index]
|
|
except KeyError as err:
|
|
raise IndexError from err
|
|
|
|
tm.assert_frame_equal(pd.concat(CustomIterator1(), ignore_index=True), expected)
|
|
|
|
class CustomIterator2(abc.Iterable):
|
|
def __iter__(self):
|
|
yield df1
|
|
yield df2
|
|
|
|
tm.assert_frame_equal(pd.concat(CustomIterator2(), ignore_index=True), expected)
|
|
|
|
def test_concat_invalid(self):
|
|
|
|
# trying to concat a ndframe with a non-ndframe
|
|
df1 = tm.makeCustomDataframe(10, 2)
|
|
for obj in [1, dict(), [1, 2], (1, 2)]:
|
|
|
|
msg = (
|
|
f"cannot concatenate object of type '{type(obj)}'; "
|
|
"only Series and DataFrame objs are valid"
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
concat([df1, obj])
|
|
|
|
def test_concat_invalid_first_argument(self):
|
|
df1 = tm.makeCustomDataframe(10, 2)
|
|
df2 = tm.makeCustomDataframe(10, 2)
|
|
msg = (
|
|
"first argument must be an iterable of pandas "
|
|
'objects, you passed an object of type "DataFrame"'
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
concat(df1, df2)
|
|
|
|
# generator ok though
|
|
concat(DataFrame(np.random.rand(5, 5)) for _ in range(3))
|
|
|
|
# text reader ok
|
|
# GH6583
|
|
data = """index,A,B,C,D
|
|
foo,2,3,4,5
|
|
bar,7,8,9,10
|
|
baz,12,13,14,15
|
|
qux,12,13,14,15
|
|
foo2,12,13,14,15
|
|
bar2,12,13,14,15
|
|
"""
|
|
|
|
reader = read_csv(StringIO(data), chunksize=1)
|
|
result = concat(reader, ignore_index=True)
|
|
expected = read_csv(StringIO(data))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_NaT_series(self):
|
|
# GH 11693
|
|
# test for merging NaT series with datetime series.
|
|
x = Series(
|
|
date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="US/Eastern")
|
|
)
|
|
y = Series(pd.NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]")
|
|
expected = Series([x[0], x[1], pd.NaT, pd.NaT])
|
|
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# all NaT with tz
|
|
expected = Series(pd.NaT, index=range(4), dtype="datetime64[ns, US/Eastern]")
|
|
result = pd.concat([y, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# without tz
|
|
x = pd.Series(pd.date_range("20151124 08:00", "20151124 09:00", freq="1h"))
|
|
y = pd.Series(pd.date_range("20151124 10:00", "20151124 11:00", freq="1h"))
|
|
y[:] = pd.NaT
|
|
expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT])
|
|
result = pd.concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# all NaT without tz
|
|
x[:] = pd.NaT
|
|
expected = pd.Series(pd.NaT, index=range(4), dtype="datetime64[ns]")
|
|
result = pd.concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_concat_tz_frame(self):
|
|
df2 = DataFrame(
|
|
dict(
|
|
A=pd.Timestamp("20130102", tz="US/Eastern"),
|
|
B=pd.Timestamp("20130603", tz="CET"),
|
|
),
|
|
index=range(5),
|
|
)
|
|
|
|
# concat
|
|
df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1)
|
|
tm.assert_frame_equal(df2, df3)
|
|
|
|
def test_concat_tz_series(self):
|
|
# gh-11755: tz and no tz
|
|
x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="UTC"))
|
|
y = Series(date_range("2012-01-01", "2012-01-02"))
|
|
expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# gh-11887: concat tz and object
|
|
x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="UTC"))
|
|
y = Series(["a", "b"])
|
|
expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# see gh-12217 and gh-12306
|
|
# Concatenating two UTC times
|
|
first = pd.DataFrame([[datetime(2016, 1, 1)]])
|
|
first[0] = first[0].dt.tz_localize("UTC")
|
|
|
|
second = pd.DataFrame([[datetime(2016, 1, 2)]])
|
|
second[0] = second[0].dt.tz_localize("UTC")
|
|
|
|
result = pd.concat([first, second])
|
|
assert result[0].dtype == "datetime64[ns, UTC]"
|
|
|
|
# Concatenating two London times
|
|
first = pd.DataFrame([[datetime(2016, 1, 1)]])
|
|
first[0] = first[0].dt.tz_localize("Europe/London")
|
|
|
|
second = pd.DataFrame([[datetime(2016, 1, 2)]])
|
|
second[0] = second[0].dt.tz_localize("Europe/London")
|
|
|
|
result = pd.concat([first, second])
|
|
assert result[0].dtype == "datetime64[ns, Europe/London]"
|
|
|
|
# Concatenating 2+1 London times
|
|
first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]])
|
|
first[0] = first[0].dt.tz_localize("Europe/London")
|
|
|
|
second = pd.DataFrame([[datetime(2016, 1, 3)]])
|
|
second[0] = second[0].dt.tz_localize("Europe/London")
|
|
|
|
result = pd.concat([first, second])
|
|
assert result[0].dtype == "datetime64[ns, Europe/London]"
|
|
|
|
# Concat'ing 1+2 London times
|
|
first = pd.DataFrame([[datetime(2016, 1, 1)]])
|
|
first[0] = first[0].dt.tz_localize("Europe/London")
|
|
|
|
second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]])
|
|
second[0] = second[0].dt.tz_localize("Europe/London")
|
|
|
|
result = pd.concat([first, second])
|
|
assert result[0].dtype == "datetime64[ns, Europe/London]"
|
|
|
|
def test_concat_tz_series_with_datetimelike(self):
|
|
# see gh-12620: tz and timedelta
|
|
x = [
|
|
pd.Timestamp("2011-01-01", tz="US/Eastern"),
|
|
pd.Timestamp("2011-02-01", tz="US/Eastern"),
|
|
]
|
|
y = [pd.Timedelta("1 day"), pd.Timedelta("2 day")]
|
|
result = concat([pd.Series(x), pd.Series(y)], ignore_index=True)
|
|
tm.assert_series_equal(result, pd.Series(x + y, dtype="object"))
|
|
|
|
# tz and period
|
|
y = [pd.Period("2011-03", freq="M"), pd.Period("2011-04", freq="M")]
|
|
result = concat([pd.Series(x), pd.Series(y)], ignore_index=True)
|
|
tm.assert_series_equal(result, pd.Series(x + y, dtype="object"))
|
|
|
|
def test_concat_tz_series_tzlocal(self):
|
|
# see gh-13583
|
|
x = [
|
|
pd.Timestamp("2011-01-01", tz=dateutil.tz.tzlocal()),
|
|
pd.Timestamp("2011-02-01", tz=dateutil.tz.tzlocal()),
|
|
]
|
|
y = [
|
|
pd.Timestamp("2012-01-01", tz=dateutil.tz.tzlocal()),
|
|
pd.Timestamp("2012-02-01", tz=dateutil.tz.tzlocal()),
|
|
]
|
|
|
|
result = concat([pd.Series(x), pd.Series(y)], ignore_index=True)
|
|
tm.assert_series_equal(result, pd.Series(x + y))
|
|
assert result.dtype == "datetime64[ns, tzlocal()]"
|
|
|
|
@pytest.mark.parametrize("tz1", [None, "UTC"])
|
|
@pytest.mark.parametrize("tz2", [None, "UTC"])
|
|
@pytest.mark.parametrize("s", [pd.NaT, pd.Timestamp("20150101")])
|
|
def test_concat_NaT_dataframes_all_NaT_axis_0(self, tz1, tz2, s):
|
|
# GH 12396
|
|
|
|
# tz-naive
|
|
first = pd.DataFrame([[pd.NaT], [pd.NaT]]).apply(
|
|
lambda x: x.dt.tz_localize(tz1)
|
|
)
|
|
second = pd.DataFrame([s]).apply(lambda x: x.dt.tz_localize(tz2))
|
|
|
|
result = pd.concat([first, second], axis=0)
|
|
expected = pd.DataFrame(pd.Series([pd.NaT, pd.NaT, s], index=[0, 1, 0]))
|
|
expected = expected.apply(lambda x: x.dt.tz_localize(tz2))
|
|
if tz1 != tz2:
|
|
expected = expected.astype(object)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("tz1", [None, "UTC"])
|
|
@pytest.mark.parametrize("tz2", [None, "UTC"])
|
|
def test_concat_NaT_dataframes_all_NaT_axis_1(self, tz1, tz2):
|
|
# GH 12396
|
|
|
|
first = pd.DataFrame(pd.Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1))
|
|
second = pd.DataFrame(pd.Series([pd.NaT]).dt.tz_localize(tz2), columns=[1])
|
|
expected = pd.DataFrame(
|
|
{
|
|
0: pd.Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1),
|
|
1: pd.Series([pd.NaT, pd.NaT]).dt.tz_localize(tz2),
|
|
}
|
|
)
|
|
result = pd.concat([first, second], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("tz1", [None, "UTC"])
|
|
@pytest.mark.parametrize("tz2", [None, "UTC"])
|
|
def test_concat_NaT_series_dataframe_all_NaT(self, tz1, tz2):
|
|
# GH 12396
|
|
|
|
# tz-naive
|
|
first = pd.Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1)
|
|
second = pd.DataFrame(
|
|
[
|
|
[pd.Timestamp("2015/01/01", tz=tz2)],
|
|
[pd.Timestamp("2016/01/01", tz=tz2)],
|
|
],
|
|
index=[2, 3],
|
|
)
|
|
|
|
expected = pd.DataFrame(
|
|
[
|
|
pd.NaT,
|
|
pd.NaT,
|
|
pd.Timestamp("2015/01/01", tz=tz2),
|
|
pd.Timestamp("2016/01/01", tz=tz2),
|
|
]
|
|
)
|
|
if tz1 != tz2:
|
|
expected = expected.astype(object)
|
|
|
|
result = pd.concat([first, second])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("tz", [None, "UTC"])
|
|
def test_concat_NaT_dataframes(self, tz):
|
|
# GH 12396
|
|
|
|
first = pd.DataFrame([[pd.NaT], [pd.NaT]])
|
|
first = first.apply(lambda x: x.dt.tz_localize(tz))
|
|
second = pd.DataFrame(
|
|
[[pd.Timestamp("2015/01/01", tz=tz)], [pd.Timestamp("2016/01/01", tz=tz)]],
|
|
index=[2, 3],
|
|
)
|
|
expected = pd.DataFrame(
|
|
[
|
|
pd.NaT,
|
|
pd.NaT,
|
|
pd.Timestamp("2015/01/01", tz=tz),
|
|
pd.Timestamp("2016/01/01", tz=tz),
|
|
]
|
|
)
|
|
|
|
result = pd.concat([first, second], axis=0)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_period_series(self):
|
|
x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
|
|
y = Series(pd.PeriodIndex(["2015-10-01", "2016-01-01"], freq="D"))
|
|
expected = Series([x[0], x[1], y[0], y[1]], dtype="Period[D]")
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_concat_period_multiple_freq_series(self):
|
|
x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
|
|
y = Series(pd.PeriodIndex(["2015-10-01", "2016-01-01"], freq="M"))
|
|
expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
assert result.dtype == "object"
|
|
|
|
def test_concat_period_other_series(self):
|
|
x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
|
|
y = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="M"))
|
|
expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
assert result.dtype == "object"
|
|
|
|
# non-period
|
|
x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
|
|
y = Series(pd.DatetimeIndex(["2015-11-01", "2015-12-01"]))
|
|
expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
assert result.dtype == "object"
|
|
|
|
x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
|
|
y = Series(["A", "B"])
|
|
expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
|
|
result = concat([x, y], ignore_index=True)
|
|
tm.assert_series_equal(result, expected)
|
|
assert result.dtype == "object"
|
|
|
|
def test_concat_empty_series(self):
|
|
# GH 11082
|
|
s1 = pd.Series([1, 2, 3], name="x")
|
|
s2 = pd.Series(name="y", dtype="float64")
|
|
res = pd.concat([s1, s2], axis=1)
|
|
exp = pd.DataFrame(
|
|
{"x": [1, 2, 3], "y": [np.nan, np.nan, np.nan]},
|
|
index=pd.Index([0, 1, 2], dtype="O"),
|
|
)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
s1 = pd.Series([1, 2, 3], name="x")
|
|
s2 = pd.Series(name="y", dtype="float64")
|
|
res = pd.concat([s1, s2], axis=0)
|
|
# name will be reset
|
|
exp = pd.Series([1, 2, 3])
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
# empty Series with no name
|
|
s1 = pd.Series([1, 2, 3], name="x")
|
|
s2 = pd.Series(name=None, dtype="float64")
|
|
res = pd.concat([s1, s2], axis=1)
|
|
exp = pd.DataFrame(
|
|
{"x": [1, 2, 3], 0: [np.nan, np.nan, np.nan]},
|
|
columns=["x", 0],
|
|
index=pd.Index([0, 1, 2], dtype="O"),
|
|
)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
@pytest.mark.parametrize("tz", [None, "UTC"])
|
|
@pytest.mark.parametrize("values", [[], [1, 2, 3]])
|
|
def test_concat_empty_series_timelike(self, tz, values):
|
|
# GH 18447
|
|
|
|
first = Series([], dtype="M8[ns]").dt.tz_localize(tz)
|
|
dtype = None if values else np.float64
|
|
second = Series(values, dtype=dtype)
|
|
|
|
expected = DataFrame(
|
|
{
|
|
0: pd.Series([pd.NaT] * len(values), dtype="M8[ns]").dt.tz_localize(tz),
|
|
1: values,
|
|
}
|
|
)
|
|
result = concat([first, second], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_default_index(self):
|
|
# is_series and ignore_index
|
|
s1 = pd.Series([1, 2, 3], name="x")
|
|
s2 = pd.Series([4, 5, 6], name="y")
|
|
res = pd.concat([s1, s2], axis=1, ignore_index=True)
|
|
assert isinstance(res.columns, pd.RangeIndex)
|
|
exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]])
|
|
# use check_index_type=True to check the result have
|
|
# RangeIndex (default index)
|
|
tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
|
|
|
|
# is_series and all inputs have no names
|
|
s1 = pd.Series([1, 2, 3])
|
|
s2 = pd.Series([4, 5, 6])
|
|
res = pd.concat([s1, s2], axis=1, ignore_index=False)
|
|
assert isinstance(res.columns, pd.RangeIndex)
|
|
exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]])
|
|
exp.columns = pd.RangeIndex(2)
|
|
tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
|
|
|
|
# is_dataframe and ignore_index
|
|
df1 = pd.DataFrame({"A": [1, 2], "B": [5, 6]})
|
|
df2 = pd.DataFrame({"A": [3, 4], "B": [7, 8]})
|
|
|
|
res = pd.concat([df1, df2], axis=0, ignore_index=True)
|
|
exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], columns=["A", "B"])
|
|
tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
|
|
|
|
res = pd.concat([df1, df2], axis=1, ignore_index=True)
|
|
exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]])
|
|
tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
|
|
|
|
def test_concat_multiindex_rangeindex(self):
|
|
# GH13542
|
|
# when multi-index levels are RangeIndex objects
|
|
# there is a bug in concat with objects of len 1
|
|
|
|
df = DataFrame(np.random.randn(9, 2))
|
|
df.index = MultiIndex(
|
|
levels=[pd.RangeIndex(3), pd.RangeIndex(3)],
|
|
codes=[np.repeat(np.arange(3), 3), np.tile(np.arange(3), 3)],
|
|
)
|
|
|
|
res = concat([df.iloc[[2, 3, 4], :], df.iloc[[5], :]])
|
|
exp = df.iloc[[2, 3, 4, 5], :]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_concat_multiindex_dfs_with_deepcopy(self):
|
|
# GH 9967
|
|
from copy import deepcopy
|
|
|
|
example_multiindex1 = pd.MultiIndex.from_product([["a"], ["b"]])
|
|
example_dataframe1 = pd.DataFrame([0], index=example_multiindex1)
|
|
|
|
example_multiindex2 = pd.MultiIndex.from_product([["a"], ["c"]])
|
|
example_dataframe2 = pd.DataFrame([1], index=example_multiindex2)
|
|
|
|
example_dict = {"s1": example_dataframe1, "s2": example_dataframe2}
|
|
expected_index = pd.MultiIndex(
|
|
levels=[["s1", "s2"], ["a"], ["b", "c"]],
|
|
codes=[[0, 1], [0, 0], [0, 1]],
|
|
names=["testname", None, None],
|
|
)
|
|
expected = pd.DataFrame([[0], [1]], index=expected_index)
|
|
result_copy = pd.concat(deepcopy(example_dict), names=["testname"])
|
|
tm.assert_frame_equal(result_copy, expected)
|
|
result_no_copy = pd.concat(example_dict, names=["testname"])
|
|
tm.assert_frame_equal(result_no_copy, expected)
|
|
|
|
def test_categorical_concat_append(self):
|
|
cat = Categorical(["a", "b"], categories=["a", "b"])
|
|
vals = [1, 2]
|
|
df = DataFrame({"cats": cat, "vals": vals})
|
|
cat2 = Categorical(["a", "b", "a", "b"], categories=["a", "b"])
|
|
vals2 = [1, 2, 1, 2]
|
|
exp = DataFrame({"cats": cat2, "vals": vals2}, index=Index([0, 1, 0, 1]))
|
|
|
|
tm.assert_frame_equal(pd.concat([df, df]), exp)
|
|
tm.assert_frame_equal(df.append(df), exp)
|
|
|
|
# GH 13524 can concat different categories
|
|
cat3 = Categorical(["a", "b"], categories=["a", "b", "c"])
|
|
vals3 = [1, 2]
|
|
df_different_categories = DataFrame({"cats": cat3, "vals": vals3})
|
|
|
|
res = pd.concat([df, df_different_categories], ignore_index=True)
|
|
exp = DataFrame({"cats": list("abab"), "vals": [1, 2, 1, 2]})
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.append(df_different_categories, ignore_index=True)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_categorical_concat_dtypes(self):
|
|
|
|
# GH8143
|
|
index = ["cat", "obj", "num"]
|
|
cat = Categorical(["a", "b", "c"])
|
|
obj = Series(["a", "b", "c"])
|
|
num = Series([1, 2, 3])
|
|
df = pd.concat([Series(cat), obj, num], axis=1, keys=index)
|
|
|
|
result = df.dtypes == "object"
|
|
expected = Series([False, True, False], index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.dtypes == "int64"
|
|
expected = Series([False, False, True], index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.dtypes == "category"
|
|
expected = Series([True, False, False], index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_categorical_concat(self, sort):
|
|
# See GH 10177
|
|
df1 = DataFrame(
|
|
np.arange(18, dtype="int64").reshape(6, 3), columns=["a", "b", "c"]
|
|
)
|
|
|
|
df2 = DataFrame(np.arange(14, dtype="int64").reshape(7, 2), columns=["a", "c"])
|
|
|
|
cat_values = ["one", "one", "two", "one", "two", "two", "one"]
|
|
df2["h"] = Series(Categorical(cat_values))
|
|
|
|
res = pd.concat((df1, df2), axis=0, ignore_index=True, sort=sort)
|
|
exp = DataFrame(
|
|
{
|
|
"a": [0, 3, 6, 9, 12, 15, 0, 2, 4, 6, 8, 10, 12],
|
|
"b": [
|
|
1,
|
|
4,
|
|
7,
|
|
10,
|
|
13,
|
|
16,
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
],
|
|
"c": [2, 5, 8, 11, 14, 17, 1, 3, 5, 7, 9, 11, 13],
|
|
"h": [None] * 6 + cat_values,
|
|
}
|
|
)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_categorical_concat_gh7864(self):
|
|
# GH 7864
|
|
# make sure ordering is preserved
|
|
df = DataFrame({"id": [1, 2, 3, 4, 5, 6], "raw_grade": list("abbaae")})
|
|
df["grade"] = Categorical(df["raw_grade"])
|
|
df["grade"].cat.set_categories(["e", "a", "b"])
|
|
|
|
df1 = df[0:3]
|
|
df2 = df[3:]
|
|
|
|
tm.assert_index_equal(df["grade"].cat.categories, df1["grade"].cat.categories)
|
|
tm.assert_index_equal(df["grade"].cat.categories, df2["grade"].cat.categories)
|
|
|
|
dfx = pd.concat([df1, df2])
|
|
tm.assert_index_equal(df["grade"].cat.categories, dfx["grade"].cat.categories)
|
|
|
|
dfa = df1.append(df2)
|
|
tm.assert_index_equal(df["grade"].cat.categories, dfa["grade"].cat.categories)
|
|
|
|
def test_categorical_concat_preserve(self):
|
|
|
|
# GH 8641 series concat not preserving category dtype
|
|
# GH 13524 can concat different categories
|
|
s = Series(list("abc"), dtype="category")
|
|
s2 = Series(list("abd"), dtype="category")
|
|
|
|
exp = Series(list("abcabd"))
|
|
res = pd.concat([s, s2], ignore_index=True)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
exp = Series(list("abcabc"), dtype="category")
|
|
res = pd.concat([s, s], ignore_index=True)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
exp = Series(list("abcabc"), index=[0, 1, 2, 0, 1, 2], dtype="category")
|
|
res = pd.concat([s, s])
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
a = Series(np.arange(6, dtype="int64"))
|
|
b = Series(list("aabbca"))
|
|
|
|
df2 = DataFrame({"A": a, "B": b.astype(CategoricalDtype(list("cab")))})
|
|
res = pd.concat([df2, df2])
|
|
exp = DataFrame(
|
|
{
|
|
"A": pd.concat([a, a]),
|
|
"B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
|
|
}
|
|
)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_categorical_index_preserver(self):
|
|
|
|
a = Series(np.arange(6, dtype="int64"))
|
|
b = Series(list("aabbca"))
|
|
|
|
df2 = DataFrame(
|
|
{"A": a, "B": b.astype(CategoricalDtype(list("cab")))}
|
|
).set_index("B")
|
|
result = pd.concat([df2, df2])
|
|
expected = DataFrame(
|
|
{
|
|
"A": pd.concat([a, a]),
|
|
"B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
|
|
}
|
|
).set_index("B")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# wrong categories
|
|
df3 = DataFrame(
|
|
{"A": a, "B": Categorical(b, categories=list("abe"))}
|
|
).set_index("B")
|
|
msg = "categories must match existing categories when appending"
|
|
with pytest.raises(TypeError, match=msg):
|
|
pd.concat([df2, df3])
|
|
|
|
def test_concat_categoricalindex(self):
|
|
# GH 16111, categories that aren't lexsorted
|
|
categories = [9, 0, 1, 2, 3]
|
|
|
|
a = pd.Series(1, index=pd.CategoricalIndex([9, 0], categories=categories))
|
|
b = pd.Series(2, index=pd.CategoricalIndex([0, 1], categories=categories))
|
|
c = pd.Series(3, index=pd.CategoricalIndex([1, 2], categories=categories))
|
|
|
|
result = pd.concat([a, b, c], axis=1)
|
|
|
|
exp_idx = pd.CategoricalIndex([9, 0, 1, 2], categories=categories)
|
|
exp = pd.DataFrame(
|
|
{
|
|
0: [1, 1, np.nan, np.nan],
|
|
1: [np.nan, 2, 2, np.nan],
|
|
2: [np.nan, np.nan, 3, 3],
|
|
},
|
|
columns=[0, 1, 2],
|
|
index=exp_idx,
|
|
)
|
|
tm.assert_frame_equal(result, exp)
|
|
|
|
def test_concat_order(self):
|
|
# GH 17344
|
|
dfs = [pd.DataFrame(index=range(3), columns=["a", 1, None])]
|
|
dfs += [
|
|
pd.DataFrame(index=range(3), columns=[None, 1, "a"]) for i in range(100)
|
|
]
|
|
|
|
result = pd.concat(dfs, sort=True).columns
|
|
expected = dfs[0].columns
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
def test_concat_datetime_timezone(self):
|
|
# GH 18523
|
|
idx1 = pd.date_range("2011-01-01", periods=3, freq="H", tz="Europe/Paris")
|
|
idx2 = pd.date_range(start=idx1[0], end=idx1[-1], freq="H")
|
|
df1 = pd.DataFrame({"a": [1, 2, 3]}, index=idx1)
|
|
df2 = pd.DataFrame({"b": [1, 2, 3]}, index=idx2)
|
|
result = pd.concat([df1, df2], axis=1)
|
|
|
|
exp_idx = (
|
|
DatetimeIndex(
|
|
[
|
|
"2011-01-01 00:00:00+01:00",
|
|
"2011-01-01 01:00:00+01:00",
|
|
"2011-01-01 02:00:00+01:00",
|
|
],
|
|
freq="H",
|
|
)
|
|
.tz_convert("UTC")
|
|
.tz_convert("Europe/Paris")
|
|
)
|
|
|
|
expected = pd.DataFrame(
|
|
[[1, 1], [2, 2], [3, 3]], index=exp_idx, columns=["a", "b"]
|
|
)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
idx3 = pd.date_range("2011-01-01", periods=3, freq="H", tz="Asia/Tokyo")
|
|
df3 = pd.DataFrame({"b": [1, 2, 3]}, index=idx3)
|
|
result = pd.concat([df1, df3], axis=1)
|
|
|
|
exp_idx = DatetimeIndex(
|
|
[
|
|
"2010-12-31 15:00:00+00:00",
|
|
"2010-12-31 16:00:00+00:00",
|
|
"2010-12-31 17:00:00+00:00",
|
|
"2010-12-31 23:00:00+00:00",
|
|
"2011-01-01 00:00:00+00:00",
|
|
"2011-01-01 01:00:00+00:00",
|
|
]
|
|
)
|
|
|
|
expected = pd.DataFrame(
|
|
[
|
|
[np.nan, 1],
|
|
[np.nan, 2],
|
|
[np.nan, 3],
|
|
[1, np.nan],
|
|
[2, np.nan],
|
|
[3, np.nan],
|
|
],
|
|
index=exp_idx,
|
|
columns=["a", "b"],
|
|
)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# GH 13783: Concat after resample
|
|
result = pd.concat(
|
|
[df1.resample("H").mean(), df2.resample("H").mean()], sort=True
|
|
)
|
|
expected = pd.DataFrame(
|
|
{"a": [1, 2, 3] + [np.nan] * 3, "b": [np.nan] * 3 + [1, 2, 3]},
|
|
index=idx1.append(idx1),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_different_extension_dtypes_upcasts(self):
|
|
a = pd.Series(pd.core.arrays.integer_array([1, 2]))
|
|
b = pd.Series(to_decimal([1, 2]))
|
|
|
|
result = pd.concat([a, b], ignore_index=True)
|
|
expected = pd.Series([1, 2, Decimal(1), Decimal(2)], dtype=object)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_concat_odered_dict(self):
|
|
# GH 21510
|
|
expected = pd.concat(
|
|
[pd.Series(range(3)), pd.Series(range(4))], keys=["First", "Another"]
|
|
)
|
|
result = pd.concat(
|
|
OrderedDict(
|
|
[("First", pd.Series(range(3))), ("Another", pd.Series(range(4)))]
|
|
)
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("pdt", [pd.Series, pd.DataFrame])
|
|
@pytest.mark.parametrize("dt", np.sctypes["float"])
|
|
def test_concat_no_unnecessary_upcast(dt, pdt):
|
|
# GH 13247
|
|
dims = pdt(dtype=object).ndim
|
|
|
|
dfs = [
|
|
pdt(np.array([1], dtype=dt, ndmin=dims)),
|
|
pdt(np.array([np.nan], dtype=dt, ndmin=dims)),
|
|
pdt(np.array([5], dtype=dt, ndmin=dims)),
|
|
]
|
|
x = pd.concat(dfs)
|
|
assert x.values.dtype == dt
|
|
|
|
|
|
@pytest.mark.parametrize("pdt", [create_series_with_explicit_dtype, pd.DataFrame])
|
|
@pytest.mark.parametrize("dt", np.sctypes["int"])
|
|
def test_concat_will_upcast(dt, pdt):
|
|
with catch_warnings(record=True):
|
|
dims = pdt().ndim
|
|
dfs = [
|
|
pdt(np.array([1], dtype=dt, ndmin=dims)),
|
|
pdt(np.array([np.nan], ndmin=dims)),
|
|
pdt(np.array([5], dtype=dt, ndmin=dims)),
|
|
]
|
|
x = pd.concat(dfs)
|
|
assert x.values.dtype == "float64"
|
|
|
|
|
|
def test_concat_empty_and_non_empty_frame_regression():
|
|
# GH 18178 regression test
|
|
df1 = pd.DataFrame({"foo": [1]})
|
|
df2 = pd.DataFrame({"foo": []})
|
|
expected = pd.DataFrame({"foo": [1.0]})
|
|
result = pd.concat([df1, df2])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_empty_and_non_empty_series_regression():
|
|
# GH 18187 regression test
|
|
s1 = pd.Series([1])
|
|
s2 = pd.Series([], dtype=object)
|
|
|
|
expected = s1
|
|
result = pd.concat([s1, s2])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_concat_sorts_columns(sort):
|
|
# GH-4588
|
|
df1 = pd.DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
|
|
df2 = pd.DataFrame({"a": [3, 4], "c": [5, 6]})
|
|
|
|
# for sort=True/None
|
|
expected = pd.DataFrame(
|
|
{"a": [1, 2, 3, 4], "b": [1, 2, None, None], "c": [None, None, 5, 6]},
|
|
columns=["a", "b", "c"],
|
|
)
|
|
|
|
if sort is False:
|
|
expected = expected[["b", "a", "c"]]
|
|
|
|
# default
|
|
with tm.assert_produces_warning(None):
|
|
result = pd.concat([df1, df2], ignore_index=True, sort=sort)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_sorts_index(sort):
|
|
df1 = pd.DataFrame({"a": [1, 2, 3]}, index=["c", "a", "b"])
|
|
df2 = pd.DataFrame({"b": [1, 2]}, index=["a", "b"])
|
|
|
|
# For True/None
|
|
expected = pd.DataFrame(
|
|
{"a": [2, 3, 1], "b": [1, 2, None]}, index=["a", "b", "c"], columns=["a", "b"]
|
|
)
|
|
if sort is False:
|
|
expected = expected.loc[["c", "a", "b"]]
|
|
|
|
# Warn and sort by default
|
|
with tm.assert_produces_warning(None):
|
|
result = pd.concat([df1, df2], axis=1, sort=sort)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_inner_sort(sort):
|
|
# https://github.com/pandas-dev/pandas/pull/20613
|
|
df1 = pd.DataFrame({"a": [1, 2], "b": [1, 2], "c": [1, 2]}, columns=["b", "a", "c"])
|
|
df2 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[3, 4])
|
|
|
|
with tm.assert_produces_warning(None):
|
|
# unset sort should *not* warn for inner join
|
|
# since that never sorted
|
|
result = pd.concat([df1, df2], sort=sort, join="inner", ignore_index=True)
|
|
|
|
expected = pd.DataFrame({"b": [1, 2, 3, 4], "a": [1, 2, 1, 2]}, columns=["b", "a"])
|
|
if sort is True:
|
|
expected = expected[["a", "b"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_aligned_sort():
|
|
# GH-4588
|
|
df = pd.DataFrame({"c": [1, 2], "b": [3, 4], "a": [5, 6]}, columns=["c", "b", "a"])
|
|
result = pd.concat([df, df], sort=True, ignore_index=True)
|
|
expected = pd.DataFrame(
|
|
{"a": [5, 6, 5, 6], "b": [3, 4, 3, 4], "c": [1, 2, 1, 2]},
|
|
columns=["a", "b", "c"],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = pd.concat([df, df[["c", "b"]]], join="inner", sort=True, ignore_index=True)
|
|
expected = expected[["b", "c"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_aligned_sort_does_not_raise():
|
|
# GH-4588
|
|
# We catch TypeErrors from sorting internally and do not re-raise.
|
|
df = pd.DataFrame({1: [1, 2], "a": [3, 4]}, columns=[1, "a"])
|
|
expected = pd.DataFrame({1: [1, 2, 1, 2], "a": [3, 4, 3, 4]}, columns=[1, "a"])
|
|
result = pd.concat([df, df], ignore_index=True, sort=True)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("s1name,s2name", [(np.int64(190), (43, 0)), (190, (43, 0))])
|
|
def test_concat_series_name_npscalar_tuple(s1name, s2name):
|
|
# GH21015
|
|
s1 = pd.Series({"a": 1, "b": 2}, name=s1name)
|
|
s2 = pd.Series({"c": 5, "d": 6}, name=s2name)
|
|
result = pd.concat([s1, s2])
|
|
expected = pd.Series({"a": 1, "b": 2, "c": 5, "d": 6})
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_concat_categorical_tz():
|
|
# GH-23816
|
|
a = pd.Series(pd.date_range("2017-01-01", periods=2, tz="US/Pacific"))
|
|
b = pd.Series(["a", "b"], dtype="category")
|
|
result = pd.concat([a, b], ignore_index=True)
|
|
expected = pd.Series(
|
|
[
|
|
pd.Timestamp("2017-01-01", tz="US/Pacific"),
|
|
pd.Timestamp("2017-01-02", tz="US/Pacific"),
|
|
"a",
|
|
"b",
|
|
]
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_concat_categorical_unchanged():
|
|
# GH-12007
|
|
# test fix for when concat on categorical and float
|
|
# coerces dtype categorical -> float
|
|
df = pd.DataFrame(pd.Series(["a", "b", "c"], dtype="category", name="A"))
|
|
ser = pd.Series([0, 1, 2], index=[0, 1, 3], name="B")
|
|
result = pd.concat([df, ser], axis=1)
|
|
expected = pd.DataFrame(
|
|
{
|
|
"A": pd.Series(["a", "b", "c", np.nan], dtype="category"),
|
|
"B": pd.Series([0, 1, np.nan, 2], dtype="float"),
|
|
}
|
|
)
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
def test_concat_datetimeindex_freq():
|
|
# GH 3232
|
|
# Monotonic index result
|
|
dr = pd.date_range("01-Jan-2013", periods=100, freq="50L", tz="UTC")
|
|
data = list(range(100))
|
|
expected = pd.DataFrame(data, index=dr)
|
|
result = pd.concat([expected[:50], expected[50:]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# Non-monotonic index result
|
|
result = pd.concat([expected[50:], expected[:50]])
|
|
expected = pd.DataFrame(data[50:] + data[:50], index=dr[50:].append(dr[:50]))
|
|
expected.index._data.freq = None
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_empty_df_object_dtype():
|
|
# GH 9149
|
|
df_1 = pd.DataFrame({"Row": [0, 1, 1], "EmptyCol": np.nan, "NumberCol": [1, 2, 3]})
|
|
df_2 = pd.DataFrame(columns=df_1.columns)
|
|
result = pd.concat([df_1, df_2], axis=0)
|
|
expected = df_1.astype(object)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_sparse():
|
|
# GH 23557
|
|
a = pd.Series(SparseArray([0, 1, 2]))
|
|
expected = pd.DataFrame(data=[[0, 0], [1, 1], [2, 2]]).astype(
|
|
pd.SparseDtype(np.int64, 0)
|
|
)
|
|
result = pd.concat([a, a], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_concat_dense_sparse():
|
|
# GH 30668
|
|
a = pd.Series(pd.arrays.SparseArray([1, None]), dtype=float)
|
|
b = pd.Series([1], dtype=float)
|
|
expected = pd.Series(data=[1, None, 1], index=[0, 1, 0]).astype(
|
|
pd.SparseDtype(np.float64, None)
|
|
)
|
|
result = pd.concat([a, b], axis=0)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("test_series", [True, False])
|
|
def test_concat_copy_index(test_series, axis):
|
|
# GH 29879
|
|
if test_series:
|
|
ser = Series([1, 2])
|
|
comb = concat([ser, ser], axis=axis, copy=True)
|
|
assert comb.index is not ser.index
|
|
else:
|
|
df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
|
|
comb = concat([df, df], axis=axis, copy=True)
|
|
assert comb.index is not df.index
|
|
assert comb.columns is not df.columns
|
|
|
|
|
|
def test_concat_multiindex_datetime_object_index():
|
|
# https://github.com/pandas-dev/pandas/issues/11058
|
|
s = Series(
|
|
["a", "b"],
|
|
index=MultiIndex.from_arrays(
|
|
[[1, 2], Index([dt.date(2013, 1, 1), dt.date(2014, 1, 1)], dtype="object")],
|
|
names=["first", "second"],
|
|
),
|
|
)
|
|
s2 = Series(
|
|
["a", "b"],
|
|
index=MultiIndex.from_arrays(
|
|
[[1, 2], Index([dt.date(2013, 1, 1), dt.date(2015, 1, 1)], dtype="object")],
|
|
names=["first", "second"],
|
|
),
|
|
)
|
|
expected = DataFrame(
|
|
[["a", "a"], ["b", np.nan], [np.nan, "b"]],
|
|
index=MultiIndex.from_arrays(
|
|
[
|
|
[1, 2, 2],
|
|
DatetimeIndex(
|
|
["2013-01-01", "2014-01-01", "2015-01-01"],
|
|
dtype="datetime64[ns]",
|
|
freq=None,
|
|
),
|
|
],
|
|
names=["first", "second"],
|
|
),
|
|
)
|
|
result = concat([s, s2], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("keys", [["e", "f", "f"], ["f", "e", "f"]])
|
|
def test_duplicate_keys(keys):
|
|
# GH 33654
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
s1 = Series([7, 8, 9], name="c")
|
|
s2 = Series([10, 11, 12], name="d")
|
|
result = concat([df, s1, s2], axis=1, keys=keys)
|
|
expected_values = [[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]
|
|
expected_columns = pd.MultiIndex.from_tuples(
|
|
[(keys[0], "a"), (keys[0], "b"), (keys[1], "c"), (keys[2], "d")]
|
|
)
|
|
expected = DataFrame(expected_values, columns=expected_columns)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
tm.SubclassedDataFrame({"A": np.arange(0, 10)}),
|
|
tm.SubclassedSeries(np.arange(0, 10), name="A"),
|
|
],
|
|
)
|
|
def test_concat_preserves_subclass(obj):
|
|
# GH28330 -- preserve subclass
|
|
|
|
result = concat([obj, obj])
|
|
assert isinstance(result, type(obj))
|
|
|
|
|
|
def test_concat_frame_axis0_extension_dtypes():
|
|
# preserve extension dtype (through common_dtype mechanism)
|
|
df1 = pd.DataFrame({"a": pd.array([1, 2, 3], dtype="Int64")})
|
|
df2 = pd.DataFrame({"a": np.array([4, 5, 6])})
|
|
|
|
result = pd.concat([df1, df2], ignore_index=True)
|
|
expected = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6]}, dtype="Int64")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = pd.concat([df2, df1], ignore_index=True)
|
|
expected = pd.DataFrame({"a": [4, 5, 6, 1, 2, 3]}, dtype="Int64")
|
|
tm.assert_frame_equal(result, expected)
|
|
|