from collections import (
|
abc,
|
deque,
|
)
|
from datetime import datetime
|
from decimal import Decimal
|
from typing import Iterator
|
from warnings import (
|
catch_warnings,
|
simplefilter,
|
)
|
|
import numpy as np
|
import pytest
|
|
from pandas.errors import (
|
InvalidIndexError,
|
PerformanceWarning,
|
)
|
import pandas.util._test_decorators as td
|
|
import pandas as pd
|
from pandas import (
|
DataFrame,
|
Index,
|
MultiIndex,
|
PeriodIndex,
|
Series,
|
concat,
|
date_range,
|
)
|
import pandas._testing as tm
|
from pandas.core.arrays import SparseArray
|
from pandas.tests.extension.decimal import to_decimal
|
|
|
class TestConcatenate:
|
def test_append_concat(self):
|
# GH#1815
|
d1 = date_range("12/31/1990", "12/31/1999", freq="A-DEC")
|
d2 = date_range("12/31/2000", "12/31/2009", freq="A-DEC")
|
|
s1 = Series(np.random.randn(10), d1)
|
s2 = Series(np.random.randn(10), d2)
|
|
s1 = s1.to_period()
|
s2 = s2.to_period()
|
|
# drops index
|
result = concat([s1, s2])
|
assert isinstance(result.index, PeriodIndex)
|
assert result.index[0] == s1.index[0]
|
|
def test_concat_copy(self, using_array_manager, using_copy_on_write):
|
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)
|
|
if not using_copy_on_write:
|
for arr in result._mgr.arrays:
|
assert arr.base is None
|
else:
|
for arr in result._mgr.arrays:
|
assert arr.base is not None
|
|
# These are the same.
|
result = concat([df, df2, df3], axis=1, copy=False)
|
|
for arr in result._mgr.arrays:
|
if arr.dtype.kind == "f":
|
assert arr.base is df._mgr.arrays[0].base
|
elif arr.dtype.kind in ["i", "u"]:
|
assert arr.base is df2._mgr.arrays[0].base
|
elif arr.dtype == object:
|
if using_array_manager:
|
# we get the same array object, which has no base
|
assert arr is df3._mgr.arrays[0]
|
else:
|
assert arr.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 arr in result._mgr.arrays:
|
if arr.dtype.kind == "f":
|
if using_array_manager or using_copy_on_write:
|
# this is a view on some array in either df or df4
|
assert any(
|
np.shares_memory(arr, other)
|
for other in df._mgr.arrays + df4._mgr.arrays
|
)
|
else:
|
# the block was consolidated, so we got a copy anyway
|
assert arr.base is None
|
elif arr.dtype.kind in ["i", "u"]:
|
assert arr.base is df2._mgr.arrays[0].base
|
elif arr.dtype == object:
|
# this is a view on df3
|
assert any(np.shares_memory(arr, other) for other in df3._mgr.arrays)
|
|
def test_concat_with_group_keys(self):
|
# 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)
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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)
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tm.assert_frame_equal(result, expected)
|
|
# axis=1
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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)
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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"]
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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]))
|
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assert result.columns.names == ["group_key", None]
|
|
@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)
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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_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_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 = concat([df1, 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_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_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_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
|
df = DataFrame({"date": [datetime(2012, 1, 1), 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_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_no_items_raises(self):
|
with pytest.raises(ValueError, match="No objects to concatenate"):
|
concat([])
|
|
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_keys_with_none(self):
|
# #1649
|
df0 = DataFrame([[10, 20, 30], [10, 20, 30], [10, 20, 30]])
|
|
result = concat({"a": None, "b": df0, "c": df0[:2], "d": df0[:1], "e": df0})
|
expected = concat({"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_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(concat(CustomIterator1(), ignore_index=True), expected)
|
|
class CustomIterator2(abc.Iterable):
|
def __iter__(self) -> Iterator:
|
yield df1
|
yield df2
|
|
tm.assert_frame_equal(concat(CustomIterator2(), ignore_index=True), expected)
|
|
def test_concat_order(self):
|
# GH 17344, GH#47331
|
dfs = [DataFrame(index=range(3), columns=["a", 1, None])]
|
dfs += [DataFrame(index=range(3), columns=[None, 1, "a"]) for _ in range(100)]
|
|
result = concat(dfs, sort=True).columns
|
expected = Index([1, "a", None])
|
tm.assert_index_equal(result, expected)
|
|
def test_concat_different_extension_dtypes_upcasts(self):
|
a = Series(pd.array([1, 2], dtype="Int64"))
|
b = Series(to_decimal([1, 2]))
|
|
result = concat([a, b], ignore_index=True)
|
expected = Series([1, 2, Decimal(1), Decimal(2)], dtype=object)
|
tm.assert_series_equal(result, expected)
|
|
def test_concat_ordered_dict(self):
|
# GH 21510
|
expected = concat(
|
[Series(range(3)), Series(range(4))], keys=["First", "Another"]
|
)
|
result = concat({"First": Series(range(3)), "Another": Series(range(4))})
|
tm.assert_series_equal(result, expected)
|
|
def test_concat_duplicate_indices_raise(self):
|
# GH 45888: test raise for concat DataFrames with duplicate indices
|
# https://github.com/pandas-dev/pandas/issues/36263
|
df1 = DataFrame(np.random.randn(5), index=[0, 1, 2, 3, 3], columns=["a"])
|
df2 = DataFrame(np.random.randn(5), index=[0, 1, 2, 2, 4], columns=["b"])
|
msg = "Reindexing only valid with uniquely valued Index objects"
|
with pytest.raises(InvalidIndexError, match=msg):
|
concat([df1, df2], axis=1)
|
|
|
@pytest.mark.parametrize("dt", np.sctypes["float"])
|
def test_concat_no_unnecessary_upcast(dt, frame_or_series):
|
# GH 13247
|
dims = frame_or_series(dtype=object).ndim
|
|
dfs = [
|
frame_or_series(np.array([1], dtype=dt, ndmin=dims)),
|
frame_or_series(np.array([np.nan], dtype=dt, ndmin=dims)),
|
frame_or_series(np.array([5], dtype=dt, ndmin=dims)),
|
]
|
x = concat(dfs)
|
assert x.values.dtype == dt
|
|
|
@pytest.mark.parametrize("pdt", [Series, 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 = concat(dfs)
|
assert x.values.dtype == "float64"
|
|
|
def test_concat_empty_and_non_empty_frame_regression():
|
# GH 18178 regression test
|
df1 = DataFrame({"foo": [1]})
|
df2 = DataFrame({"foo": []})
|
expected = DataFrame({"foo": [1.0]})
|
result = concat([df1, df2])
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_concat_sparse():
|
# GH 23557
|
a = Series(SparseArray([0, 1, 2]))
|
expected = DataFrame(data=[[0, 0], [1, 1], [2, 2]]).astype(
|
pd.SparseDtype(np.int64, 0)
|
)
|
result = concat([a, a], axis=1)
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_concat_dense_sparse():
|
# GH 30668
|
dtype = pd.SparseDtype(np.float64, None)
|
a = Series(pd.arrays.SparseArray([1, None]), dtype=dtype)
|
b = Series([1], dtype=float)
|
expected = Series(data=[1, None, 1], index=[0, 1, 0]).astype(dtype)
|
result = concat([a, b], axis=0)
|
tm.assert_series_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 = 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)
|
|
|
def test_duplicate_keys_same_frame():
|
# GH 43595
|
keys = ["e", "e"]
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
result = concat([df, df], axis=1, keys=keys)
|
expected_values = [[1, 4, 1, 4], [2, 5, 2, 5], [3, 6, 3, 6]]
|
expected_columns = MultiIndex.from_tuples(
|
[(keys[0], "a"), (keys[0], "b"), (keys[1], "a"), (keys[1], "b")]
|
)
|
expected = DataFrame(expected_values, columns=expected_columns)
|
with catch_warnings():
|
# result.columns not sorted, resulting in performance warning
|
simplefilter("ignore", PerformanceWarning)
|
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 = DataFrame({"a": pd.array([1, 2, 3], dtype="Int64")})
|
df2 = DataFrame({"a": np.array([4, 5, 6])})
|
|
result = concat([df1, df2], ignore_index=True)
|
expected = DataFrame({"a": [1, 2, 3, 4, 5, 6]}, dtype="Int64")
|
tm.assert_frame_equal(result, expected)
|
|
result = concat([df2, df1], ignore_index=True)
|
expected = DataFrame({"a": [4, 5, 6, 1, 2, 3]}, dtype="Int64")
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_concat_preserves_extension_int64_dtype():
|
# GH 24768
|
df_a = DataFrame({"a": [-1]}, dtype="Int64")
|
df_b = DataFrame({"b": [1]}, dtype="Int64")
|
result = concat([df_a, df_b], ignore_index=True)
|
expected = DataFrame({"a": [-1, None], "b": [None, 1]}, dtype="Int64")
|
tm.assert_frame_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"dtype1,dtype2,expected_dtype",
|
[
|
("bool", "bool", "bool"),
|
("boolean", "bool", "boolean"),
|
("bool", "boolean", "boolean"),
|
("boolean", "boolean", "boolean"),
|
],
|
)
|
def test_concat_bool_types(dtype1, dtype2, expected_dtype):
|
# GH 42800
|
ser1 = Series([True, False], dtype=dtype1)
|
ser2 = Series([False, True], dtype=dtype2)
|
result = concat([ser1, ser2], ignore_index=True)
|
expected = Series([True, False, False, True], dtype=expected_dtype)
|
tm.assert_series_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
("keys", "integrity"),
|
[
|
(["red"] * 3, True),
|
(["red"] * 3, False),
|
(["red", "blue", "red"], False),
|
(["red", "blue", "red"], True),
|
],
|
)
|
def test_concat_repeated_keys(keys, integrity):
|
# GH: 20816
|
series_list = [Series({"a": 1}), Series({"b": 2}), Series({"c": 3})]
|
result = concat(series_list, keys=keys, verify_integrity=integrity)
|
tuples = list(zip(keys, ["a", "b", "c"]))
|
expected = Series([1, 2, 3], index=MultiIndex.from_tuples(tuples))
|
tm.assert_series_equal(result, expected)
|
|
|
def test_concat_null_object_with_dti():
|
# GH#40841
|
dti = pd.DatetimeIndex(
|
["2021-04-08 21:21:14+00:00"], dtype="datetime64[ns, UTC]", name="Time (UTC)"
|
)
|
right = DataFrame(data={"C": [0.5274]}, index=dti)
|
|
idx = Index([None], dtype="object", name="Maybe Time (UTC)")
|
left = DataFrame(data={"A": [None], "B": [np.nan]}, index=idx)
|
|
result = concat([left, right], axis="columns")
|
|
exp_index = Index([None, dti[0]], dtype=object)
|
expected = DataFrame(
|
{"A": [None, None], "B": [np.nan, np.nan], "C": [np.nan, 0.5274]},
|
index=exp_index,
|
)
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_concat_multiindex_with_empty_rangeindex():
|
# GH#41234
|
mi = MultiIndex.from_tuples([("B", 1), ("C", 1)])
|
df1 = DataFrame([[1, 2]], columns=mi)
|
df2 = DataFrame(index=[1], columns=pd.RangeIndex(0))
|
|
result = concat([df1, df2])
|
expected = DataFrame([[1, 2], [np.nan, np.nan]], columns=mi)
|
tm.assert_frame_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"data",
|
[
|
Series(data=[1, 2]),
|
DataFrame(
|
data={
|
"col1": [1, 2],
|
}
|
),
|
DataFrame(dtype=float),
|
Series(dtype=float),
|
],
|
)
|
def test_concat_drop_attrs(data):
|
# GH#41828
|
df1 = data.copy()
|
df1.attrs = {1: 1}
|
df2 = data.copy()
|
df2.attrs = {1: 2}
|
df = concat([df1, df2])
|
assert len(df.attrs) == 0
|
|
|
@pytest.mark.parametrize(
|
"data",
|
[
|
Series(data=[1, 2]),
|
DataFrame(
|
data={
|
"col1": [1, 2],
|
}
|
),
|
DataFrame(dtype=float),
|
Series(dtype=float),
|
],
|
)
|
def test_concat_retain_attrs(data):
|
# GH#41828
|
df1 = data.copy()
|
df1.attrs = {1: 1}
|
df2 = data.copy()
|
df2.attrs = {1: 1}
|
df = concat([df1, df2])
|
assert df.attrs[1] == 1
|
|
|
@td.skip_array_manager_invalid_test
|
@pytest.mark.parametrize("df_dtype", ["float64", "int64", "datetime64[ns]"])
|
@pytest.mark.parametrize("empty_dtype", [None, "float64", "object"])
|
def test_concat_ignore_empty_object_float(empty_dtype, df_dtype):
|
# https://github.com/pandas-dev/pandas/issues/45637
|
df = DataFrame({"foo": [1, 2], "bar": [1, 2]}, dtype=df_dtype)
|
empty = DataFrame(columns=["foo", "bar"], dtype=empty_dtype)
|
result = concat([empty, df])
|
expected = df
|
if df_dtype == "int64":
|
# TODO what exact behaviour do we want for integer eventually?
|
if empty_dtype == "float64":
|
expected = df.astype("float64")
|
else:
|
expected = df.astype("object")
|
tm.assert_frame_equal(result, expected)
|
|
|
@td.skip_array_manager_invalid_test
|
@pytest.mark.parametrize("df_dtype", ["float64", "int64", "datetime64[ns]"])
|
@pytest.mark.parametrize("empty_dtype", [None, "float64", "object"])
|
def test_concat_ignore_all_na_object_float(empty_dtype, df_dtype):
|
df = DataFrame({"foo": [1, 2], "bar": [1, 2]}, dtype=df_dtype)
|
empty = DataFrame({"foo": [np.nan], "bar": [np.nan]}, dtype=empty_dtype)
|
result = concat([empty, df], ignore_index=True)
|
|
if df_dtype == "int64":
|
# TODO what exact behaviour do we want for integer eventually?
|
if empty_dtype == "object":
|
df_dtype = "object"
|
else:
|
df_dtype = "float64"
|
expected = DataFrame({"foo": [None, 1, 2], "bar": [None, 1, 2]}, dtype=df_dtype)
|
tm.assert_frame_equal(result, expected)
|
|
|
@td.skip_array_manager_invalid_test
|
def test_concat_ignore_empty_from_reindex():
|
# https://github.com/pandas-dev/pandas/pull/43507#issuecomment-920375856
|
df1 = DataFrame({"a": [1], "b": [pd.Timestamp("2012-01-01")]})
|
df2 = DataFrame({"a": [2]})
|
|
result = concat([df1, df2.reindex(columns=df1.columns)], ignore_index=True)
|
expected = df1 = DataFrame({"a": [1, 2], "b": [pd.Timestamp("2012-01-01"), pd.NaT]})
|
tm.assert_frame_equal(result, expected)
|
