Updated script that can be controled by Nodejs web app
This commit is contained in:
mac OS
@ -0,0 +1,7 @@
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import pytest
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@pytest.fixture(params=[True, False])
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def sort(request):
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"""Boolean sort keyword for concat and DataFrame.append."""
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return request.param
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@ -0,0 +1,389 @@
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import datetime as dt
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from itertools import combinations
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import dateutil
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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Series,
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Timestamp,
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concat,
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isna,
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)
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import pandas._testing as tm
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class TestAppend:
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def test_append(self, sort, float_frame):
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mixed_frame = float_frame.copy()
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mixed_frame["foo"] = "bar"
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begin_index = float_frame.index[:5]
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end_index = float_frame.index[5:]
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begin_frame = float_frame.reindex(begin_index)
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end_frame = float_frame.reindex(end_index)
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appended = begin_frame._append(end_frame)
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tm.assert_almost_equal(appended["A"], float_frame["A"])
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del end_frame["A"]
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partial_appended = begin_frame._append(end_frame, sort=sort)
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assert "A" in partial_appended
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partial_appended = end_frame._append(begin_frame, sort=sort)
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assert "A" in partial_appended
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# mixed type handling
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appended = mixed_frame[:5]._append(mixed_frame[5:])
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tm.assert_frame_equal(appended, mixed_frame)
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# what to test here
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mixed_appended = mixed_frame[:5]._append(float_frame[5:], sort=sort)
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mixed_appended2 = float_frame[:5]._append(mixed_frame[5:], sort=sort)
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# all equal except 'foo' column
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tm.assert_frame_equal(
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mixed_appended.reindex(columns=["A", "B", "C", "D"]),
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mixed_appended2.reindex(columns=["A", "B", "C", "D"]),
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)
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def test_append_empty(self, float_frame):
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empty = DataFrame()
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appended = float_frame._append(empty)
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tm.assert_frame_equal(float_frame, appended)
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assert appended is not float_frame
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appended = empty._append(float_frame)
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tm.assert_frame_equal(float_frame, appended)
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assert appended is not float_frame
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def test_append_overlap_raises(self, float_frame):
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msg = "Indexes have overlapping values"
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with pytest.raises(ValueError, match=msg):
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float_frame._append(float_frame, verify_integrity=True)
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def test_append_new_columns(self):
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# see gh-6129: new columns
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df = DataFrame({"a": {"x": 1, "y": 2}, "b": {"x": 3, "y": 4}})
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row = Series([5, 6, 7], index=["a", "b", "c"], name="z")
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expected = DataFrame(
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{
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"a": {"x": 1, "y": 2, "z": 5},
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"b": {"x": 3, "y": 4, "z": 6},
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"c": {"z": 7},
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}
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)
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result = df._append(row)
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tm.assert_frame_equal(result, expected)
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def test_append_length0_frame(self, sort):
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df = DataFrame(columns=["A", "B", "C"])
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df3 = DataFrame(index=[0, 1], columns=["A", "B"])
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df5 = df._append(df3, sort=sort)
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expected = DataFrame(index=[0, 1], columns=["A", "B", "C"])
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tm.assert_frame_equal(df5, expected)
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def test_append_records(self):
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arr1 = np.zeros((2,), dtype=("i4,f4,S10"))
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arr1[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
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arr2 = np.zeros((3,), dtype=("i4,f4,S10"))
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arr2[:] = [(3, 4.0, "foo"), (5, 6.0, "bar"), (7.0, 8.0, "baz")]
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df1 = DataFrame(arr1)
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df2 = DataFrame(arr2)
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result = df1._append(df2, ignore_index=True)
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expected = DataFrame(np.concatenate((arr1, arr2)))
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tm.assert_frame_equal(result, expected)
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# rewrite sort fixture, since we also want to test default of None
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def test_append_sorts(self, sort):
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df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
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df2 = DataFrame({"a": [1, 2], "c": [3, 4]}, index=[2, 3])
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result = df1._append(df2, sort=sort)
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# for None / True
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expected = DataFrame(
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{"b": [1, 2, None, None], "a": [1, 2, 1, 2], "c": [None, None, 3, 4]},
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columns=["a", "b", "c"],
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)
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if sort is False:
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expected = expected[["b", "a", "c"]]
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tm.assert_frame_equal(result, expected)
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def test_append_different_columns(self, sort):
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df = DataFrame(
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{
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"bools": np.random.default_rng(2).standard_normal(10) > 0,
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"ints": np.random.default_rng(2).integers(0, 10, 10),
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"floats": np.random.default_rng(2).standard_normal(10),
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"strings": ["foo", "bar"] * 5,
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}
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)
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a = df[:5].loc[:, ["bools", "ints", "floats"]]
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b = df[5:].loc[:, ["strings", "ints", "floats"]]
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appended = a._append(b, sort=sort)
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assert isna(appended["strings"][0:4]).all()
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assert isna(appended["bools"][5:]).all()
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def test_append_many(self, sort, float_frame):
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chunks = [
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float_frame[:5],
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float_frame[5:10],
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float_frame[10:15],
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float_frame[15:],
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]
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result = chunks[0]._append(chunks[1:])
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tm.assert_frame_equal(result, float_frame)
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chunks[-1] = chunks[-1].copy()
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chunks[-1]["foo"] = "bar"
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result = chunks[0]._append(chunks[1:], sort=sort)
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tm.assert_frame_equal(result.loc[:, float_frame.columns], float_frame)
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assert (result["foo"][15:] == "bar").all()
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assert result["foo"][:15].isna().all()
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def test_append_preserve_index_name(self):
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# #980
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df1 = DataFrame(columns=["A", "B", "C"])
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df1 = df1.set_index(["A"])
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df2 = DataFrame(data=[[1, 4, 7], [2, 5, 8], [3, 6, 9]], columns=["A", "B", "C"])
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df2 = df2.set_index(["A"])
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msg = "The behavior of array concatenation with empty entries is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = df1._append(df2)
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assert result.index.name == "A"
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indexes_can_append = [
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pd.RangeIndex(3),
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Index([4, 5, 6]),
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Index([4.5, 5.5, 6.5]),
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Index(list("abc")),
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pd.CategoricalIndex("A B C".split()),
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pd.CategoricalIndex("D E F".split(), ordered=True),
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pd.IntervalIndex.from_breaks([7, 8, 9, 10]),
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pd.DatetimeIndex(
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[
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dt.datetime(2013, 1, 3, 0, 0),
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dt.datetime(2013, 1, 3, 6, 10),
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dt.datetime(2013, 1, 3, 7, 12),
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]
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),
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pd.MultiIndex.from_arrays(["A B C".split(), "D E F".split()]),
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]
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@pytest.mark.parametrize(
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"index", indexes_can_append, ids=lambda x: type(x).__name__
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)
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def test_append_same_columns_type(self, index):
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# GH18359
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# df wider than ser
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df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=index)
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ser_index = index[:2]
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ser = Series([7, 8], index=ser_index, name=2)
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result = df._append(ser)
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expected = DataFrame(
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[[1, 2, 3.0], [4, 5, 6], [7, 8, np.nan]], index=[0, 1, 2], columns=index
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)
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# integer dtype is preserved for columns present in ser.index
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assert expected.dtypes.iloc[0].kind == "i"
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assert expected.dtypes.iloc[1].kind == "i"
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tm.assert_frame_equal(result, expected)
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# ser wider than df
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ser_index = index
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index = index[:2]
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df = DataFrame([[1, 2], [4, 5]], columns=index)
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ser = Series([7, 8, 9], index=ser_index, name=2)
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result = df._append(ser)
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expected = DataFrame(
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[[1, 2, np.nan], [4, 5, np.nan], [7, 8, 9]],
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index=[0, 1, 2],
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columns=ser_index,
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"df_columns, series_index",
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combinations(indexes_can_append, r=2),
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ids=lambda x: type(x).__name__,
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)
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def test_append_different_columns_types(self, df_columns, series_index):
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# GH18359
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# See also test 'test_append_different_columns_types_raises' below
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# for errors raised when appending
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df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=df_columns)
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ser = Series([7, 8, 9], index=series_index, name=2)
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result = df._append(ser)
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idx_diff = ser.index.difference(df_columns)
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combined_columns = Index(df_columns.tolist()).append(idx_diff)
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expected = DataFrame(
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[
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[1.0, 2.0, 3.0, np.nan, np.nan, np.nan],
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[4, 5, 6, np.nan, np.nan, np.nan],
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[np.nan, np.nan, np.nan, 7, 8, 9],
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],
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index=[0, 1, 2],
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columns=combined_columns,
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)
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tm.assert_frame_equal(result, expected)
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def test_append_dtype_coerce(self, sort):
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# GH 4993
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# appending with datetime will incorrectly convert datetime64
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df1 = DataFrame(
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index=[1, 2],
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data=[dt.datetime(2013, 1, 1, 0, 0), dt.datetime(2013, 1, 2, 0, 0)],
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columns=["start_time"],
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)
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df2 = DataFrame(
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index=[4, 5],
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data=[
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[dt.datetime(2013, 1, 3, 0, 0), dt.datetime(2013, 1, 3, 6, 10)],
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[dt.datetime(2013, 1, 4, 0, 0), dt.datetime(2013, 1, 4, 7, 10)],
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],
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columns=["start_time", "end_time"],
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)
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expected = concat(
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[
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Series(
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[
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pd.NaT,
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pd.NaT,
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dt.datetime(2013, 1, 3, 6, 10),
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dt.datetime(2013, 1, 4, 7, 10),
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],
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name="end_time",
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),
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Series(
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[
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dt.datetime(2013, 1, 1, 0, 0),
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dt.datetime(2013, 1, 2, 0, 0),
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dt.datetime(2013, 1, 3, 0, 0),
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dt.datetime(2013, 1, 4, 0, 0),
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],
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name="start_time",
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),
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],
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axis=1,
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sort=sort,
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)
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result = df1._append(df2, ignore_index=True, sort=sort)
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if sort:
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expected = expected[["end_time", "start_time"]]
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else:
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expected = expected[["start_time", "end_time"]]
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tm.assert_frame_equal(result, expected)
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def test_append_missing_column_proper_upcast(self, sort):
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df1 = DataFrame({"A": np.array([1, 2, 3, 4], dtype="i8")})
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df2 = DataFrame({"B": np.array([True, False, True, False], dtype=bool)})
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appended = df1._append(df2, ignore_index=True, sort=sort)
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assert appended["A"].dtype == "f8"
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assert appended["B"].dtype == "O"
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def test_append_empty_frame_to_series_with_dateutil_tz(self):
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# GH 23682
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date = Timestamp("2018-10-24 07:30:00", tz=dateutil.tz.tzutc())
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ser = Series({"a": 1.0, "b": 2.0, "date": date})
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df = DataFrame(columns=["c", "d"])
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result_a = df._append(ser, ignore_index=True)
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expected = DataFrame(
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[[np.nan, np.nan, 1.0, 2.0, date]], columns=["c", "d", "a", "b", "date"]
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)
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# These columns get cast to object after append
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expected["c"] = expected["c"].astype(object)
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expected["d"] = expected["d"].astype(object)
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tm.assert_frame_equal(result_a, expected)
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expected = DataFrame(
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[[np.nan, np.nan, 1.0, 2.0, date]] * 2, columns=["c", "d", "a", "b", "date"]
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)
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expected["c"] = expected["c"].astype(object)
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expected["d"] = expected["d"].astype(object)
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result_b = result_a._append(ser, ignore_index=True)
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tm.assert_frame_equal(result_b, expected)
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result = df._append([ser, ser], ignore_index=True)
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tm.assert_frame_equal(result, expected)
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def test_append_empty_tz_frame_with_datetime64ns(self, using_array_manager):
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# https://github.com/pandas-dev/pandas/issues/35460
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df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
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# pd.NaT gets inferred as tz-naive, so append result is tz-naive
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result = df._append({"a": pd.NaT}, ignore_index=True)
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if using_array_manager:
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expected = DataFrame({"a": [pd.NaT]}, dtype=object)
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else:
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expected = DataFrame({"a": [np.nan]}, dtype=object)
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tm.assert_frame_equal(result, expected)
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# also test with typed value to append
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df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
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other = Series({"a": pd.NaT}, dtype="datetime64[ns]")
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result = df._append(other, ignore_index=True)
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tm.assert_frame_equal(result, expected)
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# mismatched tz
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other = Series({"a": pd.NaT}, dtype="datetime64[ns, US/Pacific]")
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result = df._append(other, ignore_index=True)
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expected = DataFrame({"a": [pd.NaT]}).astype(object)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
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)
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@pytest.mark.parametrize("val", [1, "NaT"])
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def test_append_empty_frame_with_timedelta64ns_nat(
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self, dtype_str, val, using_array_manager
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):
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# https://github.com/pandas-dev/pandas/issues/35460
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df = DataFrame(columns=["a"]).astype(dtype_str)
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other = DataFrame({"a": [np.timedelta64(val, "ns")]})
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result = df._append(other, ignore_index=True)
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expected = other.astype(object)
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if isinstance(val, str) and dtype_str != "int64" and not using_array_manager:
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# TODO: expected used to be `other.astype(object)` which is a more
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# reasonable result. This was changed when tightening
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# assert_frame_equal's treatment of mismatched NAs to match the
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# existing behavior.
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expected = DataFrame({"a": [np.nan]}, dtype=object)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
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)
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@pytest.mark.parametrize("val", [1, "NaT"])
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def test_append_frame_with_timedelta64ns_nat(self, dtype_str, val):
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# https://github.com/pandas-dev/pandas/issues/35460
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df = DataFrame({"a": pd.array([1], dtype=dtype_str)})
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other = DataFrame({"a": [np.timedelta64(val, "ns")]})
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result = df._append(other, ignore_index=True)
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expected = DataFrame({"a": [df.iloc[0, 0], other.iloc[0, 0]]}, dtype=object)
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tm.assert_frame_equal(result, expected)
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@ -0,0 +1,753 @@
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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Index,
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Series,
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)
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import pandas._testing as tm
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@pytest.fixture(
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params=list(
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{
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"bool": [True, False, True],
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"int64": [1, 2, 3],
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"float64": [1.1, np.nan, 3.3],
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"category": Categorical(["X", "Y", "Z"]),
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"object": ["a", "b", "c"],
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"datetime64[ns]": [
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pd.Timestamp("2011-01-01"),
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pd.Timestamp("2011-01-02"),
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pd.Timestamp("2011-01-03"),
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],
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"datetime64[ns, US/Eastern]": [
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pd.Timestamp("2011-01-01", tz="US/Eastern"),
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pd.Timestamp("2011-01-02", tz="US/Eastern"),
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pd.Timestamp("2011-01-03", tz="US/Eastern"),
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],
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"timedelta64[ns]": [
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pd.Timedelta("1 days"),
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pd.Timedelta("2 days"),
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pd.Timedelta("3 days"),
|
||||
],
|
||||
"period[M]": [
|
||||
pd.Period("2011-01", freq="M"),
|
||||
pd.Period("2011-02", freq="M"),
|
||||
pd.Period("2011-03", freq="M"),
|
||||
],
|
||||
}.items()
|
||||
)
|
||||
)
|
||||
def item(request):
|
||||
key, data = request.param
|
||||
return key, data
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def item2(item):
|
||||
return item
|
||||
|
||||
|
||||
class TestConcatAppendCommon:
|
||||
"""
|
||||
Test common dtype coercion rules between concat and append.
|
||||
"""
|
||||
|
||||
def test_dtypes(self, item, index_or_series, using_infer_string):
|
||||
# to confirm test case covers intended dtypes
|
||||
typ, vals = item
|
||||
obj = index_or_series(vals)
|
||||
if typ == "object" and using_infer_string:
|
||||
typ = "string"
|
||||
if isinstance(obj, Index):
|
||||
assert obj.dtype == typ
|
||||
elif isinstance(obj, Series):
|
||||
if typ.startswith("period"):
|
||||
assert obj.dtype == "Period[M]"
|
||||
else:
|
||||
assert obj.dtype == typ
|
||||
|
||||
def test_concatlike_same_dtypes(self, item):
|
||||
# GH 13660
|
||||
typ1, vals1 = item
|
||||
|
||||
vals2 = vals1
|
||||
vals3 = vals1
|
||||
|
||||
if typ1 == "category":
|
||||
exp_data = Categorical(list(vals1) + list(vals2))
|
||||
exp_data3 = Categorical(list(vals1) + list(vals2) + list(vals3))
|
||||
else:
|
||||
exp_data = vals1 + vals2
|
||||
exp_data3 = vals1 + vals2 + vals3
|
||||
|
||||
# ----- Index ----- #
|
||||
|
||||
# index.append
|
||||
res = Index(vals1).append(Index(vals2))
|
||||
exp = Index(exp_data)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
# 3 elements
|
||||
res = Index(vals1).append([Index(vals2), Index(vals3)])
|
||||
exp = Index(exp_data3)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
# index.append name mismatch
|
||||
i1 = Index(vals1, name="x")
|
||||
i2 = Index(vals2, name="y")
|
||||
res = i1.append(i2)
|
||||
exp = Index(exp_data)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
# index.append name match
|
||||
i1 = Index(vals1, name="x")
|
||||
i2 = Index(vals2, name="x")
|
||||
res = i1.append(i2)
|
||||
exp = Index(exp_data, name="x")
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
# cannot append non-index
|
||||
with pytest.raises(TypeError, match="all inputs must be Index"):
|
||||
Index(vals1).append(vals2)
|
||||
|
||||
with pytest.raises(TypeError, match="all inputs must be Index"):
|
||||
Index(vals1).append([Index(vals2), vals3])
|
||||
|
||||
# ----- Series ----- #
|
||||
|
||||
# series.append
|
||||
res = Series(vals1)._append(Series(vals2), ignore_index=True)
|
||||
exp = Series(exp_data)
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
# concat
|
||||
res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
# 3 elements
|
||||
res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
|
||||
exp = Series(exp_data3)
|
||||
tm.assert_series_equal(res, exp)
|
||||
|
||||
res = pd.concat(
|
||||
[Series(vals1), Series(vals2), Series(vals3)],
|
||||
ignore_index=True,
|
||||
)
|
||||
tm.assert_series_equal(res, exp)
|
||||
|
||||
# name mismatch
|
||||
s1 = Series(vals1, name="x")
|
||||
s2 = Series(vals2, name="y")
|
||||
res = s1._append(s2, ignore_index=True)
|
||||
exp = Series(exp_data)
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
res = pd.concat([s1, s2], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
# name match
|
||||
s1 = Series(vals1, name="x")
|
||||
s2 = Series(vals2, name="x")
|
||||
res = s1._append(s2, ignore_index=True)
|
||||
exp = Series(exp_data, name="x")
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
res = pd.concat([s1, s2], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
# cannot append non-index
|
||||
msg = (
|
||||
r"cannot concatenate object of type '.+'; "
|
||||
"only Series and DataFrame objs are valid"
|
||||
)
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
Series(vals1)._append(vals2)
|
||||
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
Series(vals1)._append([Series(vals2), vals3])
|
||||
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
pd.concat([Series(vals1), vals2])
|
||||
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
pd.concat([Series(vals1), Series(vals2), vals3])
|
||||
|
||||
def test_concatlike_dtypes_coercion(self, item, item2, request):
|
||||
# GH 13660
|
||||
typ1, vals1 = item
|
||||
typ2, vals2 = item2
|
||||
|
||||
vals3 = vals2
|
||||
|
||||
# basically infer
|
||||
exp_index_dtype = None
|
||||
exp_series_dtype = None
|
||||
|
||||
if typ1 == typ2:
|
||||
pytest.skip("same dtype is tested in test_concatlike_same_dtypes")
|
||||
elif typ1 == "category" or typ2 == "category":
|
||||
pytest.skip("categorical type tested elsewhere")
|
||||
|
||||
# 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
|
||||
mark = pytest.mark.xfail(reason="GH#39187 casting to object")
|
||||
request.applymarker(mark)
|
||||
elif typ2 == "bool" and typ1 in ("int64", "float64"):
|
||||
exp_series_dtype = typ1
|
||||
mark = pytest.mark.xfail(reason="GH#39187 casting to object")
|
||||
request.applymarker(mark)
|
||||
elif typ1 in {"datetime64[ns, US/Eastern]", "timedelta64[ns]"} or typ2 in {
|
||||
"datetime64[ns, US/Eastern]",
|
||||
"timedelta64[ns]",
|
||||
}:
|
||||
exp_index_dtype = object
|
||||
exp_series_dtype = object
|
||||
|
||||
exp_data = vals1 + vals2
|
||||
exp_data3 = vals1 + vals2 + vals3
|
||||
|
||||
# ----- Index ----- #
|
||||
|
||||
# index.append
|
||||
# GH#39817
|
||||
res = Index(vals1).append(Index(vals2))
|
||||
exp = Index(exp_data, dtype=exp_index_dtype)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
# 3 elements
|
||||
res = Index(vals1).append([Index(vals2), Index(vals3)])
|
||||
exp = Index(exp_data3, dtype=exp_index_dtype)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
# ----- Series ----- #
|
||||
|
||||
# series._append
|
||||
# GH#39817
|
||||
res = Series(vals1)._append(Series(vals2), ignore_index=True)
|
||||
exp = Series(exp_data, dtype=exp_series_dtype)
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
# concat
|
||||
# GH#39817
|
||||
res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp, check_index_type=True)
|
||||
|
||||
# 3 elements
|
||||
# GH#39817
|
||||
res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
|
||||
exp = Series(exp_data3, dtype=exp_series_dtype)
|
||||
tm.assert_series_equal(res, exp)
|
||||
|
||||
# GH#39817
|
||||
res = pd.concat(
|
||||
[Series(vals1), Series(vals2), 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 = Index(
|
||||
[
|
||||
pd.Timestamp("2011-01-01"),
|
||||
pd.Timestamp("2011-01-02"),
|
||||
pd.Timedelta("1 days"),
|
||||
pd.Timedelta("2 days"),
|
||||
]
|
||||
)
|
||||
|
||||
res = dti.append(tdi)
|
||||
tm.assert_index_equal(res, exp)
|
||||
assert isinstance(res[0], pd.Timestamp)
|
||||
assert isinstance(res[-1], pd.Timedelta)
|
||||
|
||||
dts = Series(dti)
|
||||
tds = Series(tdi)
|
||||
res = dts._append(tds)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
assert isinstance(res.iloc[0], pd.Timestamp)
|
||||
assert isinstance(res.iloc[-1], pd.Timedelta)
|
||||
|
||||
res = pd.concat([dts, tds])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
assert isinstance(res.iloc[0], pd.Timestamp)
|
||||
assert isinstance(res.iloc[-1], pd.Timedelta)
|
||||
|
||||
def test_concatlike_datetimetz(self, tz_aware_fixture):
|
||||
tz = tz_aware_fixture
|
||||
# GH 7795
|
||||
dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
|
||||
dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz=tz)
|
||||
|
||||
exp = pd.DatetimeIndex(
|
||||
["2011-01-01", "2011-01-02", "2012-01-01", "2012-01-02"], tz=tz
|
||||
)
|
||||
|
||||
res = dti1.append(dti2)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
dts1 = Series(dti1)
|
||||
dts2 = Series(dti2)
|
||||
res = dts1._append(dts2)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
res = pd.concat([dts1, dts2])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
@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 = DataFrame(0, index=ix1, columns=["A", "B"])
|
||||
df2 = 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,
|
||||
).as_unit("ns")
|
||||
exp = 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 = Index(
|
||||
[
|
||||
pd.Timestamp("2011-01-01", tz=tz),
|
||||
pd.Timestamp("2011-01-02", tz=tz),
|
||||
pd.Timestamp("2012-01-01"),
|
||||
pd.Timestamp("2012-01-02"),
|
||||
],
|
||||
dtype=object,
|
||||
)
|
||||
|
||||
res = dti1.append(dti2)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
dts1 = Series(dti1)
|
||||
dts2 = Series(dti2)
|
||||
res = dts1._append(dts2)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
res = pd.concat([dts1, dts2])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
# different tz
|
||||
dti3 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz="US/Pacific")
|
||||
|
||||
exp = Index(
|
||||
[
|
||||
pd.Timestamp("2011-01-01", tz=tz),
|
||||
pd.Timestamp("2011-01-02", tz=tz),
|
||||
pd.Timestamp("2012-01-01", tz="US/Pacific"),
|
||||
pd.Timestamp("2012-01-02", tz="US/Pacific"),
|
||||
],
|
||||
dtype=object,
|
||||
)
|
||||
|
||||
res = dti1.append(dti3)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
dts1 = Series(dti1)
|
||||
dts3 = Series(dti3)
|
||||
res = dts1._append(dts3)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
res = pd.concat([dts1, dts3])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
def test_concatlike_common_period(self):
|
||||
# GH 13660
|
||||
pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
|
||||
pi2 = pd.PeriodIndex(["2012-01", "2012-02"], freq="M")
|
||||
|
||||
exp = pd.PeriodIndex(["2011-01", "2011-02", "2012-01", "2012-02"], freq="M")
|
||||
|
||||
res = pi1.append(pi2)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
ps1 = Series(pi1)
|
||||
ps2 = Series(pi2)
|
||||
res = ps1._append(ps2)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
res = pd.concat([ps1, ps2])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
def test_concatlike_common_period_diff_freq_to_object(self):
|
||||
# GH 13221
|
||||
pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
|
||||
pi2 = pd.PeriodIndex(["2012-01-01", "2012-02-01"], freq="D")
|
||||
|
||||
exp = Index(
|
||||
[
|
||||
pd.Period("2011-01", freq="M"),
|
||||
pd.Period("2011-02", freq="M"),
|
||||
pd.Period("2012-01-01", freq="D"),
|
||||
pd.Period("2012-02-01", freq="D"),
|
||||
],
|
||||
dtype=object,
|
||||
)
|
||||
|
||||
res = pi1.append(pi2)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
ps1 = Series(pi1)
|
||||
ps2 = Series(pi2)
|
||||
res = ps1._append(ps2)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
res = pd.concat([ps1, ps2])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
def test_concatlike_common_period_mixed_dt_to_object(self):
|
||||
# GH 13221
|
||||
# different datetimelike
|
||||
pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
|
||||
tdi = pd.TimedeltaIndex(["1 days", "2 days"])
|
||||
exp = Index(
|
||||
[
|
||||
pd.Period("2011-01", freq="M"),
|
||||
pd.Period("2011-02", freq="M"),
|
||||
pd.Timedelta("1 days"),
|
||||
pd.Timedelta("2 days"),
|
||||
],
|
||||
dtype=object,
|
||||
)
|
||||
|
||||
res = pi1.append(tdi)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
ps1 = Series(pi1)
|
||||
tds = Series(tdi)
|
||||
res = ps1._append(tds)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
res = pd.concat([ps1, tds])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
# inverse
|
||||
exp = Index(
|
||||
[
|
||||
pd.Timedelta("1 days"),
|
||||
pd.Timedelta("2 days"),
|
||||
pd.Period("2011-01", freq="M"),
|
||||
pd.Period("2011-02", freq="M"),
|
||||
],
|
||||
dtype=object,
|
||||
)
|
||||
|
||||
res = tdi.append(pi1)
|
||||
tm.assert_index_equal(res, exp)
|
||||
|
||||
ps1 = Series(pi1)
|
||||
tds = Series(tdi)
|
||||
res = tds._append(ps1)
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
res = pd.concat([tds, ps1])
|
||||
tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
|
||||
|
||||
def test_concat_categorical(self):
|
||||
# GH 13524
|
||||
|
||||
# same categories -> category
|
||||
s1 = Series([1, 2, np.nan], dtype="category")
|
||||
s2 = Series([2, 1, 2], dtype="category")
|
||||
|
||||
exp = Series([1, 2, np.nan, 2, 1, 2], dtype="category")
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
# partially different categories => not-category
|
||||
s1 = Series([3, 2], dtype="category")
|
||||
s2 = Series([2, 1], dtype="category")
|
||||
|
||||
exp = Series([3, 2, 2, 1])
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
# completely different categories (same dtype) => not-category
|
||||
s1 = Series([10, 11, np.nan], dtype="category")
|
||||
s2 = Series([np.nan, 1, 3, 2], dtype="category")
|
||||
|
||||
exp = Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype=np.float64)
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
def test_union_categorical_same_categories_different_order(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/19096
|
||||
a = Series(Categorical(["a", "b", "c"], categories=["a", "b", "c"]))
|
||||
b = Series(Categorical(["a", "b", "c"], categories=["b", "a", "c"]))
|
||||
result = pd.concat([a, b], ignore_index=True)
|
||||
expected = 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 = Series([1, 2, np.nan], dtype="category")
|
||||
s2 = Series([2, 1, 2])
|
||||
|
||||
exp = Series([1, 2, np.nan, 2, 1, 2], dtype=np.float64)
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
# result shouldn't be affected by 1st elem dtype
|
||||
exp = Series([2, 1, 2, 1, 2, np.nan], dtype=np.float64)
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
||||
|
||||
# all values are not in category => not-category
|
||||
s1 = Series([3, 2], dtype="category")
|
||||
s2 = Series([2, 1])
|
||||
|
||||
exp = Series([3, 2, 2, 1])
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
exp = Series([2, 1, 3, 2])
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
||||
|
||||
# completely different categories => not-category
|
||||
s1 = Series([10, 11, np.nan], dtype="category")
|
||||
s2 = Series([1, 3, 2])
|
||||
|
||||
exp = Series([10, 11, np.nan, 1, 3, 2], dtype=np.float64)
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
exp = Series([1, 3, 2, 10, 11, np.nan], dtype=np.float64)
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
||||
|
||||
# different dtype => not-category
|
||||
s1 = Series([10, 11, np.nan], dtype="category")
|
||||
s2 = Series(["a", "b", "c"])
|
||||
|
||||
exp = Series([10, 11, np.nan, "a", "b", "c"])
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
exp = Series(["a", "b", "c", 10, 11, np.nan])
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
||||
|
||||
# if normal series only contains NaN-likes => not-category
|
||||
s1 = Series([10, 11], dtype="category")
|
||||
s2 = Series([np.nan, np.nan, np.nan])
|
||||
|
||||
exp = Series([10, 11, np.nan, np.nan, np.nan])
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
exp = Series([np.nan, np.nan, np.nan, 10, 11])
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
||||
|
||||
def test_concat_categorical_3elem_coercion(self):
|
||||
# GH 13524
|
||||
|
||||
# mixed dtypes => not-category
|
||||
s1 = Series([1, 2, np.nan], dtype="category")
|
||||
s2 = Series([2, 1, 2], dtype="category")
|
||||
s3 = Series([1, 2, 1, 2, np.nan])
|
||||
|
||||
exp = Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan], dtype="float")
|
||||
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
|
||||
|
||||
exp = Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2], dtype="float")
|
||||
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
|
||||
|
||||
# values are all in either category => not-category
|
||||
s1 = Series([4, 5, 6], dtype="category")
|
||||
s2 = Series([1, 2, 3], dtype="category")
|
||||
s3 = Series([1, 3, 4])
|
||||
|
||||
exp = Series([4, 5, 6, 1, 2, 3, 1, 3, 4])
|
||||
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
|
||||
|
||||
exp = Series([1, 3, 4, 4, 5, 6, 1, 2, 3])
|
||||
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
|
||||
|
||||
# values are all in either category => not-category
|
||||
s1 = Series([4, 5, 6], dtype="category")
|
||||
s2 = Series([1, 2, 3], dtype="category")
|
||||
s3 = Series([10, 11, 12])
|
||||
|
||||
exp = Series([4, 5, 6, 1, 2, 3, 10, 11, 12])
|
||||
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
|
||||
|
||||
exp = Series([10, 11, 12, 4, 5, 6, 1, 2, 3])
|
||||
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
|
||||
|
||||
def test_concat_categorical_multi_coercion(self):
|
||||
# GH 13524
|
||||
|
||||
s1 = Series([1, 3], dtype="category")
|
||||
s2 = Series([3, 4], dtype="category")
|
||||
s3 = Series([2, 3])
|
||||
s4 = Series([2, 2], dtype="category")
|
||||
s5 = Series([1, np.nan])
|
||||
s6 = Series([1, 3, 2], dtype="category")
|
||||
|
||||
# mixed dtype, values are all in categories => not-category
|
||||
exp = Series([1, 3, 3, 4, 2, 3, 2, 2, 1, np.nan, 1, 3, 2])
|
||||
res = pd.concat([s1, s2, s3, s4, s5, s6], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp)
|
||||
res = s1._append([s2, s3, s4, s5, s6], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp)
|
||||
|
||||
exp = Series([1, 3, 2, 1, np.nan, 2, 2, 2, 3, 3, 4, 1, 3])
|
||||
res = pd.concat([s6, s5, s4, s3, s2, s1], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp)
|
||||
res = s6._append([s5, s4, s3, s2, s1], ignore_index=True)
|
||||
tm.assert_series_equal(res, exp)
|
||||
|
||||
def test_concat_categorical_ordered(self):
|
||||
# GH 13524
|
||||
|
||||
s1 = Series(Categorical([1, 2, np.nan], ordered=True))
|
||||
s2 = Series(Categorical([2, 1, 2], ordered=True))
|
||||
|
||||
exp = Series(Categorical([1, 2, np.nan, 2, 1, 2], ordered=True))
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
exp = Series(Categorical([1, 2, np.nan, 2, 1, 2, 1, 2, np.nan], ordered=True))
|
||||
tm.assert_series_equal(pd.concat([s1, s2, s1], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append([s2, s1], ignore_index=True), exp)
|
||||
|
||||
def test_concat_categorical_coercion_nan(self):
|
||||
# GH 13524
|
||||
|
||||
# some edge cases
|
||||
# category + not-category => not category
|
||||
s1 = Series(np.array([np.nan, np.nan], dtype=np.float64), dtype="category")
|
||||
s2 = Series([np.nan, 1])
|
||||
|
||||
exp = Series([np.nan, np.nan, np.nan, 1])
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
s1 = Series([1, np.nan], dtype="category")
|
||||
s2 = Series([np.nan, np.nan])
|
||||
|
||||
exp = Series([1, np.nan, np.nan, np.nan], dtype="float")
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
# mixed dtype, all nan-likes => not-category
|
||||
s1 = Series([np.nan, np.nan], dtype="category")
|
||||
s2 = Series([np.nan, np.nan])
|
||||
|
||||
exp = Series([np.nan, np.nan, np.nan, np.nan])
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
||||
|
||||
# all category nan-likes => category
|
||||
s1 = Series([np.nan, np.nan], dtype="category")
|
||||
s2 = Series([np.nan, np.nan], dtype="category")
|
||||
|
||||
exp = Series([np.nan, np.nan, np.nan, np.nan], dtype="category")
|
||||
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
def test_concat_categorical_empty(self):
|
||||
# GH 13524
|
||||
|
||||
s1 = Series([], dtype="category")
|
||||
s2 = Series([1, 2], dtype="category")
|
||||
|
||||
msg = "The behavior of array concatenation with empty entries is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
|
||||
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
|
||||
|
||||
s1 = Series([], dtype="category")
|
||||
s2 = Series([], dtype="category")
|
||||
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
|
||||
|
||||
s1 = Series([], dtype="category")
|
||||
s2 = Series([], dtype="object")
|
||||
|
||||
# different dtype => not-category
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
|
||||
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
|
||||
tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
|
||||
|
||||
s1 = Series([], dtype="category")
|
||||
s2 = Series([np.nan, np.nan])
|
||||
|
||||
# empty Series is ignored
|
||||
exp = Series([np.nan, np.nan])
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
||||
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
||||
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
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_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)
|
||||
@ -0,0 +1,273 @@
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
|
||||
from pandas.core.dtypes.dtypes import CategoricalDtype
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
Categorical,
|
||||
DataFrame,
|
||||
Series,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestCategoricalConcat:
|
||||
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,
|
||||
}
|
||||
)
|
||||
exp["h"] = exp["h"].astype(df2["h"].dtype)
|
||||
tm.assert_frame_equal(res, exp)
|
||||
|
||||
def test_categorical_concat_dtypes(self, using_infer_string):
|
||||
# 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 if not using_infer_string else "string[pyarrow_numpy]"
|
||||
)
|
||||
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_concat_categoricalindex(self):
|
||||
# GH 16111, categories that aren't lexsorted
|
||||
categories = [9, 0, 1, 2, 3]
|
||||
|
||||
a = Series(1, index=pd.CategoricalIndex([9, 0], categories=categories))
|
||||
b = Series(2, index=pd.CategoricalIndex([0, 1], categories=categories))
|
||||
c = 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 = 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_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 -> uses concat_compat, which casts to object
|
||||
df3 = DataFrame(
|
||||
{"A": a, "B": Categorical(b, categories=list("abe"))}
|
||||
).set_index("B")
|
||||
result = pd.concat([df2, df3])
|
||||
expected = pd.concat(
|
||||
[
|
||||
df2.set_axis(df2.index.astype(object), axis=0),
|
||||
df3.set_axis(df3.index.astype(object), axis=0),
|
||||
]
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_categorical_tz(self):
|
||||
# GH-23816
|
||||
a = Series(pd.date_range("2017-01-01", periods=2, tz="US/Pacific"))
|
||||
b = Series(["a", "b"], dtype="category")
|
||||
result = pd.concat([a, b], ignore_index=True)
|
||||
expected = 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_datetime(self):
|
||||
# GH-39443
|
||||
df1 = DataFrame(
|
||||
{"x": Series(datetime(2021, 1, 1), index=[0], dtype="category")}
|
||||
)
|
||||
df2 = DataFrame(
|
||||
{"x": Series(datetime(2021, 1, 2), index=[1], dtype="category")}
|
||||
)
|
||||
|
||||
result = pd.concat([df1, df2])
|
||||
expected = DataFrame(
|
||||
{"x": Series([datetime(2021, 1, 1), datetime(2021, 1, 2)])}
|
||||
)
|
||||
|
||||
tm.assert_equal(result, expected)
|
||||
|
||||
def test_concat_categorical_unchanged(self):
|
||||
# GH-12007
|
||||
# test fix for when concat on categorical and float
|
||||
# coerces dtype categorical -> float
|
||||
df = DataFrame(Series(["a", "b", "c"], dtype="category", name="A"))
|
||||
ser = Series([0, 1, 2], index=[0, 1, 3], name="B")
|
||||
result = pd.concat([df, ser], axis=1)
|
||||
expected = DataFrame(
|
||||
{
|
||||
"A": Series(["a", "b", "c", np.nan], dtype="category"),
|
||||
"B": Series([0, 1, np.nan, 2], dtype="float"),
|
||||
}
|
||||
)
|
||||
tm.assert_equal(result, expected)
|
||||
|
||||
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_index_upcast(self):
|
||||
# GH 17629
|
||||
# test upcasting to object when concatenating on categorical indexes
|
||||
# with non-identical categories
|
||||
|
||||
a = DataFrame({"foo": [1, 2]}, index=Categorical(["foo", "bar"]))
|
||||
b = DataFrame({"foo": [4, 3]}, index=Categorical(["baz", "bar"]))
|
||||
|
||||
res = pd.concat([a, b])
|
||||
exp = DataFrame({"foo": [1, 2, 4, 3]}, index=["foo", "bar", "baz", "bar"])
|
||||
|
||||
tm.assert_equal(res, exp)
|
||||
|
||||
a = Series([1, 2], index=Categorical(["foo", "bar"]))
|
||||
b = Series([4, 3], index=Categorical(["baz", "bar"]))
|
||||
|
||||
res = pd.concat([a, b])
|
||||
exp = Series([1, 2, 4, 3], index=["foo", "bar", "baz", "bar"])
|
||||
|
||||
tm.assert_equal(res, exp)
|
||||
|
||||
def test_categorical_missing_from_one_frame(self):
|
||||
# GH 25412
|
||||
df1 = DataFrame({"f1": [1, 2, 3]})
|
||||
df2 = DataFrame({"f1": [2, 3, 1], "f2": Series([4, 4, 4]).astype("category")})
|
||||
result = pd.concat([df1, df2], sort=True)
|
||||
dtype = CategoricalDtype([4])
|
||||
expected = DataFrame(
|
||||
{
|
||||
"f1": [1, 2, 3, 2, 3, 1],
|
||||
"f2": Categorical.from_codes([-1, -1, -1, 0, 0, 0], dtype=dtype),
|
||||
},
|
||||
index=[0, 1, 2, 0, 1, 2],
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_categorical_same_categories_different_order(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/24845
|
||||
|
||||
c1 = pd.CategoricalIndex(["a", "a"], categories=["a", "b"], ordered=False)
|
||||
c2 = pd.CategoricalIndex(["b", "b"], categories=["b", "a"], ordered=False)
|
||||
c3 = pd.CategoricalIndex(
|
||||
["a", "a", "b", "b"], categories=["a", "b"], ordered=False
|
||||
)
|
||||
|
||||
df1 = DataFrame({"A": [1, 2]}, index=c1)
|
||||
df2 = DataFrame({"A": [3, 4]}, index=c2)
|
||||
|
||||
result = pd.concat((df1, df2))
|
||||
expected = DataFrame({"A": [1, 2, 3, 4]}, index=c3)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
@ -0,0 +1,912 @@
|
||||
from collections import (
|
||||
abc,
|
||||
deque,
|
||||
)
|
||||
from collections.abc import Iterator
|
||||
from datetime import datetime
|
||||
from decimal import Decimal
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas.errors import InvalidIndexError
|
||||
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="YE-DEC")
|
||||
d2 = date_range("12/31/2000", "12/31/2009", freq="YE-DEC")
|
||||
|
||||
s1 = Series(np.random.default_rng(2).standard_normal(10), d1)
|
||||
s2 = Series(np.random.default_rng(2).standard_normal(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.default_rng(2).standard_normal((4, 3)))
|
||||
df2 = DataFrame(np.random.default_rng(2).integers(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 not any(
|
||||
np.shares_memory(arr, y)
|
||||
for x in [df, df2, df3]
|
||||
for y in x._mgr.arrays
|
||||
)
|
||||
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.default_rng(2).standard_normal((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.default_rng(2).standard_normal((3, 4)))
|
||||
df2 = DataFrame(np.random.default_rng(2).standard_normal((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.default_rng(2).standard_normal((4, 3)))
|
||||
df2 = DataFrame(np.random.default_rng(2).standard_normal((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.default_rng(2).standard_normal((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]
|
||||
|
||||
@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.default_rng(2).standard_normal((4, 3))),
|
||||
"bar": DataFrame(np.random.default_rng(2).standard_normal((4, 3))),
|
||||
"baz": DataFrame(np.random.default_rng(2).standard_normal((4, 3))),
|
||||
"qux": DataFrame(np.random.default_rng(2).standard_normal((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_keys_and_levels(self):
|
||||
df = DataFrame(np.random.default_rng(2).standard_normal((1, 3)))
|
||||
df2 = DataFrame(np.random.default_rng(2).standard_normal((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.default_rng(2).standard_normal((1, 3)), index=["a"])
|
||||
df2 = DataFrame(np.random.default_rng(2).standard_normal((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.default_rng(2).standard_normal((1, 3)), index=["a"])
|
||||
df2 = DataFrame(np.random.default_rng(2).standard_normal((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_columns(self):
|
||||
# Test column-wise concat for mixed series/frames (axis=1)
|
||||
# G2385
|
||||
|
||||
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)
|
||||
|
||||
def test_concat_mixed_objs_index(self):
|
||||
# Test row-wise concat for mixed series/frames with a common name
|
||||
# GH2385, GH15047
|
||||
|
||||
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.tile(arr, 3).reshape(-1, 1), index=index.tolist() * 3, columns=[0]
|
||||
)
|
||||
result = concat([s1, df, s2])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_mixed_objs_index_names(self):
|
||||
# Test row-wise concat for mixed series/frames with distinct names
|
||||
# GH2385, GH15047
|
||||
|
||||
index = date_range("01-Jan-2013", periods=10, freq="h")
|
||||
arr = np.arange(10, dtype="int64")
|
||||
s1 = Series(arr, index=index, name="foo")
|
||||
s2 = Series(arr, index=index, name="bar")
|
||||
df = DataFrame(arr.reshape(-1, 1), index=index)
|
||||
|
||||
expected = DataFrame(
|
||||
np.kron(np.where(np.identity(3) == 1, 1, np.nan), arr).T,
|
||||
index=index.tolist() * 3,
|
||||
columns=["foo", 0, "bar"],
|
||||
)
|
||||
result = concat([s1, df, s2])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# Rename all series to 0 when ignore_index=True
|
||||
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_coercion(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.default_rng(2).standard_normal((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.default_rng(2).standard_normal((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 = Series(
|
||||
np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
|
||||
)
|
||||
ts2 = ts1.copy()[::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.default_rng(2).standard_normal(5),
|
||||
index=[0, 1, 2, 3, 3],
|
||||
columns=["a"],
|
||||
)
|
||||
df2 = DataFrame(
|
||||
np.random.default_rng(2).standard_normal(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)
|
||||
|
||||
|
||||
def test_concat_no_unnecessary_upcast(float_numpy_dtype, frame_or_series):
|
||||
# GH 13247
|
||||
dims = frame_or_series(dtype=object).ndim
|
||||
dt = float_numpy_dtype
|
||||
|
||||
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])
|
||||
def test_concat_will_upcast(pdt, any_signed_int_numpy_dtype):
|
||||
dt = any_signed_int_numpy_dtype
|
||||
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)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings(
|
||||
"ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
|
||||
)
|
||||
@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": np.array([None, np.nan], dtype=object),
|
||||
"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)
|
||||
|
||||
msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
|
||||
warn = None
|
||||
if df_dtype == "datetime64[ns]" or (
|
||||
df_dtype == "float64" and empty_dtype != "float64"
|
||||
):
|
||||
warn = FutureWarning
|
||||
with tm.assert_produces_warning(warn, match=msg):
|
||||
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)
|
||||
|
||||
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"
|
||||
|
||||
msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
|
||||
warn = None
|
||||
if empty_dtype != df_dtype and empty_dtype is not None:
|
||||
warn = FutureWarning
|
||||
elif df_dtype == "datetime64[ns]":
|
||||
warn = FutureWarning
|
||||
|
||||
with tm.assert_produces_warning(warn, match=msg):
|
||||
result = concat([empty, df], ignore_index=True)
|
||||
|
||||
expected = DataFrame({"foo": [np.nan, 1, 2], "bar": [np.nan, 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]})
|
||||
|
||||
aligned = df2.reindex(columns=df1.columns)
|
||||
|
||||
msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = concat([df1, aligned], ignore_index=True)
|
||||
expected = df1 = DataFrame({"a": [1, 2], "b": [pd.Timestamp("2012-01-01"), pd.NaT]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
def test_concat_mismatched_keys_length():
|
||||
# GH#43485
|
||||
ser = Series(range(5))
|
||||
sers = [ser + n for n in range(4)]
|
||||
keys = ["A", "B", "C"]
|
||||
|
||||
msg = r"The behavior of pd.concat with len\(keys\) != len\(objs\) is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
concat(sers, keys=keys, axis=1)
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
concat(sers, keys=keys, axis=0)
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
concat((x for x in sers), keys=(y for y in keys), axis=1)
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
concat((x for x in sers), keys=(y for y in keys), axis=0)
|
||||
|
||||
|
||||
def test_concat_multiindex_with_category():
|
||||
df1 = DataFrame(
|
||||
{
|
||||
"c1": Series(list("abc"), dtype="category"),
|
||||
"c2": Series(list("eee"), dtype="category"),
|
||||
"i2": Series([1, 2, 3]),
|
||||
}
|
||||
)
|
||||
df1 = df1.set_index(["c1", "c2"])
|
||||
df2 = DataFrame(
|
||||
{
|
||||
"c1": Series(list("abc"), dtype="category"),
|
||||
"c2": Series(list("eee"), dtype="category"),
|
||||
"i2": Series([4, 5, 6]),
|
||||
}
|
||||
)
|
||||
df2 = df2.set_index(["c1", "c2"])
|
||||
result = concat([df1, df2])
|
||||
expected = DataFrame(
|
||||
{
|
||||
"c1": Series(list("abcabc"), dtype="category"),
|
||||
"c2": Series(list("eeeeee"), dtype="category"),
|
||||
"i2": Series([1, 2, 3, 4, 5, 6]),
|
||||
}
|
||||
)
|
||||
expected = expected.set_index(["c1", "c2"])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
def test_concat_ea_upcast():
|
||||
# GH#54848
|
||||
df1 = DataFrame(["a"], dtype="string")
|
||||
df2 = DataFrame([1], dtype="Int64")
|
||||
result = concat([df1, df2])
|
||||
expected = DataFrame(["a", 1], index=[0, 0])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
def test_concat_none_with_timezone_timestamp():
|
||||
# GH#52093
|
||||
df1 = DataFrame([{"A": None}])
|
||||
df2 = DataFrame([{"A": pd.Timestamp("1990-12-20 00:00:00+00:00")}])
|
||||
msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = concat([df1, df2], ignore_index=True)
|
||||
expected = DataFrame({"A": [None, pd.Timestamp("1990-12-20 00:00:00+00:00")]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
@ -0,0 +1,230 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
DataFrame,
|
||||
Index,
|
||||
Series,
|
||||
concat,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestDataFrameConcat:
|
||||
def test_concat_multiple_frames_dtypes(self):
|
||||
# GH#2759
|
||||
df1 = DataFrame(data=np.ones((10, 2)), columns=["foo", "bar"], dtype=np.float64)
|
||||
df2 = DataFrame(data=np.ones((10, 2)), dtype=np.float32)
|
||||
results = concat((df1, df2), axis=1).dtypes
|
||||
expected = Series(
|
||||
[np.dtype("float64")] * 2 + [np.dtype("float32")] * 2,
|
||||
index=["foo", "bar", 0, 1],
|
||||
)
|
||||
tm.assert_series_equal(results, expected)
|
||||
|
||||
def test_concat_tuple_keys(self):
|
||||
# GH#14438
|
||||
df1 = DataFrame(np.ones((2, 2)), columns=list("AB"))
|
||||
df2 = DataFrame(np.ones((3, 2)) * 2, columns=list("AB"))
|
||||
results = concat((df1, df2), keys=[("bee", "bah"), ("bee", "boo")])
|
||||
expected = DataFrame(
|
||||
{
|
||||
"A": {
|
||||
("bee", "bah", 0): 1.0,
|
||||
("bee", "bah", 1): 1.0,
|
||||
("bee", "boo", 0): 2.0,
|
||||
("bee", "boo", 1): 2.0,
|
||||
("bee", "boo", 2): 2.0,
|
||||
},
|
||||
"B": {
|
||||
("bee", "bah", 0): 1.0,
|
||||
("bee", "bah", 1): 1.0,
|
||||
("bee", "boo", 0): 2.0,
|
||||
("bee", "boo", 1): 2.0,
|
||||
("bee", "boo", 2): 2.0,
|
||||
},
|
||||
}
|
||||
)
|
||||
tm.assert_frame_equal(results, expected)
|
||||
|
||||
def test_concat_named_keys(self):
|
||||
# GH#14252
|
||||
df = DataFrame({"foo": [1, 2], "bar": [0.1, 0.2]})
|
||||
index = Index(["a", "b"], name="baz")
|
||||
concatted_named_from_keys = concat([df, df], keys=index)
|
||||
expected_named = DataFrame(
|
||||
{"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
|
||||
index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=["baz", None]),
|
||||
)
|
||||
tm.assert_frame_equal(concatted_named_from_keys, expected_named)
|
||||
|
||||
index_no_name = Index(["a", "b"], name=None)
|
||||
concatted_named_from_names = concat([df, df], keys=index_no_name, names=["baz"])
|
||||
tm.assert_frame_equal(concatted_named_from_names, expected_named)
|
||||
|
||||
concatted_unnamed = concat([df, df], keys=index_no_name)
|
||||
expected_unnamed = DataFrame(
|
||||
{"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
|
||||
index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=[None, None]),
|
||||
)
|
||||
tm.assert_frame_equal(concatted_unnamed, expected_unnamed)
|
||||
|
||||
def test_concat_axis_parameter(self):
|
||||
# GH#14369
|
||||
df1 = DataFrame({"A": [0.1, 0.2]}, index=range(2))
|
||||
df2 = DataFrame({"A": [0.3, 0.4]}, index=range(2))
|
||||
|
||||
# Index/row/0 DataFrame
|
||||
expected_index = DataFrame({"A": [0.1, 0.2, 0.3, 0.4]}, index=[0, 1, 0, 1])
|
||||
|
||||
concatted_index = concat([df1, df2], axis="index")
|
||||
tm.assert_frame_equal(concatted_index, expected_index)
|
||||
|
||||
concatted_row = concat([df1, df2], axis="rows")
|
||||
tm.assert_frame_equal(concatted_row, expected_index)
|
||||
|
||||
concatted_0 = concat([df1, df2], axis=0)
|
||||
tm.assert_frame_equal(concatted_0, expected_index)
|
||||
|
||||
# Columns/1 DataFrame
|
||||
expected_columns = DataFrame(
|
||||
[[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=["A", "A"]
|
||||
)
|
||||
|
||||
concatted_columns = concat([df1, df2], axis="columns")
|
||||
tm.assert_frame_equal(concatted_columns, expected_columns)
|
||||
|
||||
concatted_1 = concat([df1, df2], axis=1)
|
||||
tm.assert_frame_equal(concatted_1, expected_columns)
|
||||
|
||||
series1 = Series([0.1, 0.2])
|
||||
series2 = Series([0.3, 0.4])
|
||||
|
||||
# Index/row/0 Series
|
||||
expected_index_series = Series([0.1, 0.2, 0.3, 0.4], index=[0, 1, 0, 1])
|
||||
|
||||
concatted_index_series = concat([series1, series2], axis="index")
|
||||
tm.assert_series_equal(concatted_index_series, expected_index_series)
|
||||
|
||||
concatted_row_series = concat([series1, series2], axis="rows")
|
||||
tm.assert_series_equal(concatted_row_series, expected_index_series)
|
||||
|
||||
concatted_0_series = concat([series1, series2], axis=0)
|
||||
tm.assert_series_equal(concatted_0_series, expected_index_series)
|
||||
|
||||
# Columns/1 Series
|
||||
expected_columns_series = DataFrame(
|
||||
[[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=[0, 1]
|
||||
)
|
||||
|
||||
concatted_columns_series = concat([series1, series2], axis="columns")
|
||||
tm.assert_frame_equal(concatted_columns_series, expected_columns_series)
|
||||
|
||||
concatted_1_series = concat([series1, series2], axis=1)
|
||||
tm.assert_frame_equal(concatted_1_series, expected_columns_series)
|
||||
|
||||
# Testing ValueError
|
||||
with pytest.raises(ValueError, match="No axis named"):
|
||||
concat([series1, series2], axis="something")
|
||||
|
||||
def test_concat_numerical_names(self):
|
||||
# GH#15262, GH#12223
|
||||
df = DataFrame(
|
||||
{"col": range(9)},
|
||||
dtype="int32",
|
||||
index=(
|
||||
pd.MultiIndex.from_product(
|
||||
[["A0", "A1", "A2"], ["B0", "B1", "B2"]], names=[1, 2]
|
||||
)
|
||||
),
|
||||
)
|
||||
result = concat((df.iloc[:2, :], df.iloc[-2:, :]))
|
||||
expected = DataFrame(
|
||||
{"col": [0, 1, 7, 8]},
|
||||
dtype="int32",
|
||||
index=pd.MultiIndex.from_tuples(
|
||||
[("A0", "B0"), ("A0", "B1"), ("A2", "B1"), ("A2", "B2")], names=[1, 2]
|
||||
),
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_astype_dup_col(self):
|
||||
# GH#23049
|
||||
df = DataFrame([{"a": "b"}])
|
||||
df = concat([df, df], axis=1)
|
||||
|
||||
result = df.astype("category")
|
||||
expected = DataFrame(
|
||||
np.array(["b", "b"]).reshape(1, 2), columns=["a", "a"]
|
||||
).astype("category")
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
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_bool_with_int(self):
|
||||
# GH#42092 we may want to change this to return object, but that
|
||||
# would need a deprecation
|
||||
df1 = DataFrame(Series([True, False, True, True], dtype="bool"))
|
||||
df2 = DataFrame(Series([1, 0, 1], dtype="int64"))
|
||||
|
||||
result = concat([df1, df2])
|
||||
expected = concat([df1.astype("int64"), df2])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_duplicates_in_index_with_keys(self):
|
||||
# GH#42651
|
||||
index = [1, 1, 3]
|
||||
data = [1, 2, 3]
|
||||
|
||||
df = DataFrame(data=data, index=index)
|
||||
result = concat([df], keys=["A"], names=["ID", "date"])
|
||||
mi = pd.MultiIndex.from_product([["A"], index], names=["ID", "date"])
|
||||
expected = DataFrame(data=data, index=mi)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
tm.assert_index_equal(result.index.levels[1], Index([1, 3], name="date"))
|
||||
|
||||
@pytest.mark.parametrize("ignore_index", [True, False])
|
||||
@pytest.mark.parametrize("order", ["C", "F"])
|
||||
@pytest.mark.parametrize("axis", [0, 1])
|
||||
def test_concat_copies(self, axis, order, ignore_index, using_copy_on_write):
|
||||
# based on asv ConcatDataFrames
|
||||
df = DataFrame(np.zeros((10, 5), dtype=np.float32, order=order))
|
||||
|
||||
res = concat([df] * 5, axis=axis, ignore_index=ignore_index, copy=True)
|
||||
|
||||
if not using_copy_on_write:
|
||||
for arr in res._iter_column_arrays():
|
||||
for arr2 in df._iter_column_arrays():
|
||||
assert not np.shares_memory(arr, arr2)
|
||||
|
||||
def test_outer_sort_columns(self):
|
||||
# GH#47127
|
||||
df1 = DataFrame({"A": [0], "B": [1], 0: 1})
|
||||
df2 = DataFrame({"A": [100]})
|
||||
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
|
||||
expected = DataFrame({0: [1.0, np.nan], "A": [0, 100], "B": [1.0, np.nan]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_inner_sort_columns(self):
|
||||
# GH#47127
|
||||
df1 = DataFrame({"A": [0], "B": [1], 0: 1})
|
||||
df2 = DataFrame({"A": [100], 0: 2})
|
||||
result = concat([df1, df2], ignore_index=True, join="inner", sort=True)
|
||||
expected = DataFrame({0: [1, 2], "A": [0, 100]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_sort_columns_one_df(self):
|
||||
# GH#47127
|
||||
df1 = DataFrame({"A": [100], 0: 2})
|
||||
result = concat([df1], ignore_index=True, join="inner", sort=True)
|
||||
expected = DataFrame({0: [2], "A": [100]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
@ -0,0 +1,606 @@
|
||||
import datetime as dt
|
||||
from datetime import datetime
|
||||
|
||||
import dateutil
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
DataFrame,
|
||||
DatetimeIndex,
|
||||
Index,
|
||||
MultiIndex,
|
||||
Series,
|
||||
Timestamp,
|
||||
concat,
|
||||
date_range,
|
||||
to_timedelta,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestDatetimeConcat:
|
||||
def test_concat_datetime64_block(self):
|
||||
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_datetime_datetime64_frame(self):
|
||||
# GH#2624
|
||||
rows = []
|
||||
rows.append([datetime(2010, 1, 1), 1])
|
||||
rows.append([datetime(2010, 1, 2), "hi"])
|
||||
|
||||
df2_obj = DataFrame.from_records(rows, columns=["date", "test"])
|
||||
|
||||
ind = date_range(start="2000/1/1", freq="D", periods=10)
|
||||
df1 = DataFrame({"date": ind, "test": range(10)})
|
||||
|
||||
# it works!
|
||||
concat([df1, df2_obj])
|
||||
|
||||
def test_concat_datetime_timezone(self):
|
||||
# GH 18523
|
||||
idx1 = date_range("2011-01-01", periods=3, freq="h", tz="Europe/Paris")
|
||||
idx2 = date_range(start=idx1[0], end=idx1[-1], freq="h")
|
||||
df1 = DataFrame({"a": [1, 2, 3]}, index=idx1)
|
||||
df2 = DataFrame({"b": [1, 2, 3]}, index=idx2)
|
||||
result = 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",
|
||||
],
|
||||
dtype="M8[ns, Europe/Paris]",
|
||||
freq="h",
|
||||
)
|
||||
expected = DataFrame(
|
||||
[[1, 1], [2, 2], [3, 3]], index=exp_idx, columns=["a", "b"]
|
||||
)
|
||||
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
idx3 = date_range("2011-01-01", periods=3, freq="h", tz="Asia/Tokyo")
|
||||
df3 = DataFrame({"b": [1, 2, 3]}, index=idx3)
|
||||
result = 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",
|
||||
]
|
||||
).as_unit("ns")
|
||||
|
||||
expected = 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 = concat([df1.resample("h").mean(), df2.resample("h").mean()], sort=True)
|
||||
expected = 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_datetimeindex_freq(self):
|
||||
# GH 3232
|
||||
# Monotonic index result
|
||||
dr = date_range("01-Jan-2013", periods=100, freq="50ms", tz="UTC")
|
||||
data = list(range(100))
|
||||
expected = DataFrame(data, index=dr)
|
||||
result = concat([expected[:50], expected[50:]])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# Non-monotonic index result
|
||||
result = concat([expected[50:], expected[:50]])
|
||||
expected = 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_multiindex_datetime_object_index(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/11058
|
||||
idx = Index(
|
||||
[dt.date(2013, 1, 1), dt.date(2014, 1, 1), dt.date(2015, 1, 1)],
|
||||
dtype="object",
|
||||
)
|
||||
|
||||
s = Series(
|
||||
["a", "b"],
|
||||
index=MultiIndex.from_arrays(
|
||||
[
|
||||
[1, 2],
|
||||
idx[:-1],
|
||||
],
|
||||
names=["first", "second"],
|
||||
),
|
||||
)
|
||||
s2 = Series(
|
||||
["a", "b"],
|
||||
index=MultiIndex.from_arrays(
|
||||
[[1, 2], idx[::2]],
|
||||
names=["first", "second"],
|
||||
),
|
||||
)
|
||||
mi = MultiIndex.from_arrays(
|
||||
[[1, 2, 2], idx],
|
||||
names=["first", "second"],
|
||||
)
|
||||
assert mi.levels[1].dtype == object
|
||||
|
||||
expected = DataFrame(
|
||||
[["a", "a"], ["b", np.nan], [np.nan, "b"]],
|
||||
index=mi,
|
||||
)
|
||||
result = concat([s, s2], axis=1)
|
||||
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 = concat([y, y], ignore_index=True)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_concat_NaT_series2(self):
|
||||
# without tz
|
||||
x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h"))
|
||||
y = Series(date_range("20151124 10:00", "20151124 11:00", freq="1h"))
|
||||
y[:] = pd.NaT
|
||||
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 without tz
|
||||
x[:] = pd.NaT
|
||||
expected = Series(pd.NaT, index=range(4), dtype="datetime64[ns]")
|
||||
result = concat([x, y], ignore_index=True)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("tz", [None, "UTC"])
|
||||
def test_concat_NaT_dataframes(self, tz):
|
||||
# GH 12396
|
||||
|
||||
dti = DatetimeIndex([pd.NaT, pd.NaT], tz=tz)
|
||||
first = DataFrame({0: dti})
|
||||
second = DataFrame(
|
||||
[[Timestamp("2015/01/01", tz=tz)], [Timestamp("2016/01/01", tz=tz)]],
|
||||
index=[2, 3],
|
||||
)
|
||||
expected = DataFrame(
|
||||
[
|
||||
pd.NaT,
|
||||
pd.NaT,
|
||||
Timestamp("2015/01/01", tz=tz),
|
||||
Timestamp("2016/01/01", tz=tz),
|
||||
]
|
||||
)
|
||||
|
||||
result = concat([first, second], axis=0)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("tz1", [None, "UTC"])
|
||||
@pytest.mark.parametrize("tz2", [None, "UTC"])
|
||||
@pytest.mark.parametrize("item", [pd.NaT, Timestamp("20150101")])
|
||||
def test_concat_NaT_dataframes_all_NaT_axis_0(
|
||||
self, tz1, tz2, item, using_array_manager
|
||||
):
|
||||
# GH 12396
|
||||
|
||||
# tz-naive
|
||||
first = DataFrame([[pd.NaT], [pd.NaT]]).apply(lambda x: x.dt.tz_localize(tz1))
|
||||
second = DataFrame([item]).apply(lambda x: x.dt.tz_localize(tz2))
|
||||
|
||||
result = concat([first, second], axis=0)
|
||||
expected = DataFrame(Series([pd.NaT, pd.NaT, item], index=[0, 1, 0]))
|
||||
expected = expected.apply(lambda x: x.dt.tz_localize(tz2))
|
||||
if tz1 != tz2:
|
||||
expected = expected.astype(object)
|
||||
if item is pd.NaT and not using_array_manager:
|
||||
# GH#18463
|
||||
# TODO: setting nan here is to keep the test passing as we
|
||||
# make assert_frame_equal stricter, but is nan really the
|
||||
# ideal behavior here?
|
||||
if tz1 is not None:
|
||||
expected.iloc[-1, 0] = np.nan
|
||||
else:
|
||||
expected.iloc[:-1, 0] = np.nan
|
||||
|
||||
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 = DataFrame(Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1))
|
||||
second = DataFrame(Series([pd.NaT]).dt.tz_localize(tz2), columns=[1])
|
||||
expected = DataFrame(
|
||||
{
|
||||
0: Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1),
|
||||
1: Series([pd.NaT, pd.NaT]).dt.tz_localize(tz2),
|
||||
}
|
||||
)
|
||||
result = 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 = Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1)
|
||||
second = DataFrame(
|
||||
[
|
||||
[Timestamp("2015/01/01", tz=tz2)],
|
||||
[Timestamp("2016/01/01", tz=tz2)],
|
||||
],
|
||||
index=[2, 3],
|
||||
)
|
||||
|
||||
expected = DataFrame(
|
||||
[
|
||||
pd.NaT,
|
||||
pd.NaT,
|
||||
Timestamp("2015/01/01", tz=tz2),
|
||||
Timestamp("2016/01/01", tz=tz2),
|
||||
]
|
||||
)
|
||||
if tz1 != tz2:
|
||||
expected = expected.astype(object)
|
||||
|
||||
result = concat([first, second])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
class TestTimezoneConcat:
|
||||
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)
|
||||
|
||||
def test_concat_tz_series2(self):
|
||||
# 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)
|
||||
|
||||
def test_concat_tz_series3(self, unit, unit2):
|
||||
# see gh-12217 and gh-12306
|
||||
# Concatenating two UTC times
|
||||
first = DataFrame([[datetime(2016, 1, 1)]], dtype=f"M8[{unit}]")
|
||||
first[0] = first[0].dt.tz_localize("UTC")
|
||||
|
||||
second = DataFrame([[datetime(2016, 1, 2)]], dtype=f"M8[{unit2}]")
|
||||
second[0] = second[0].dt.tz_localize("UTC")
|
||||
|
||||
result = concat([first, second])
|
||||
exp_unit = tm.get_finest_unit(unit, unit2)
|
||||
assert result[0].dtype == f"datetime64[{exp_unit}, UTC]"
|
||||
|
||||
def test_concat_tz_series4(self, unit, unit2):
|
||||
# Concatenating two London times
|
||||
first = DataFrame([[datetime(2016, 1, 1)]], dtype=f"M8[{unit}]")
|
||||
first[0] = first[0].dt.tz_localize("Europe/London")
|
||||
|
||||
second = DataFrame([[datetime(2016, 1, 2)]], dtype=f"M8[{unit2}]")
|
||||
second[0] = second[0].dt.tz_localize("Europe/London")
|
||||
|
||||
result = concat([first, second])
|
||||
exp_unit = tm.get_finest_unit(unit, unit2)
|
||||
assert result[0].dtype == f"datetime64[{exp_unit}, Europe/London]"
|
||||
|
||||
def test_concat_tz_series5(self, unit, unit2):
|
||||
# Concatenating 2+1 London times
|
||||
first = DataFrame(
|
||||
[[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]], dtype=f"M8[{unit}]"
|
||||
)
|
||||
first[0] = first[0].dt.tz_localize("Europe/London")
|
||||
|
||||
second = DataFrame([[datetime(2016, 1, 3)]], dtype=f"M8[{unit2}]")
|
||||
second[0] = second[0].dt.tz_localize("Europe/London")
|
||||
|
||||
result = concat([first, second])
|
||||
exp_unit = tm.get_finest_unit(unit, unit2)
|
||||
assert result[0].dtype == f"datetime64[{exp_unit}, Europe/London]"
|
||||
|
||||
def test_concat_tz_series6(self, unit, unit2):
|
||||
# Concatenating 1+2 London times
|
||||
first = DataFrame([[datetime(2016, 1, 1)]], dtype=f"M8[{unit}]")
|
||||
first[0] = first[0].dt.tz_localize("Europe/London")
|
||||
|
||||
second = DataFrame(
|
||||
[[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]], dtype=f"M8[{unit2}]"
|
||||
)
|
||||
second[0] = second[0].dt.tz_localize("Europe/London")
|
||||
|
||||
result = concat([first, second])
|
||||
exp_unit = tm.get_finest_unit(unit, unit2)
|
||||
assert result[0].dtype == f"datetime64[{exp_unit}, Europe/London]"
|
||||
|
||||
def test_concat_tz_series_tzlocal(self):
|
||||
# see gh-13583
|
||||
x = [
|
||||
Timestamp("2011-01-01", tz=dateutil.tz.tzlocal()),
|
||||
Timestamp("2011-02-01", tz=dateutil.tz.tzlocal()),
|
||||
]
|
||||
y = [
|
||||
Timestamp("2012-01-01", tz=dateutil.tz.tzlocal()),
|
||||
Timestamp("2012-02-01", tz=dateutil.tz.tzlocal()),
|
||||
]
|
||||
|
||||
result = concat([Series(x), Series(y)], ignore_index=True)
|
||||
tm.assert_series_equal(result, Series(x + y))
|
||||
assert result.dtype == "datetime64[ns, tzlocal()]"
|
||||
|
||||
def test_concat_tz_series_with_datetimelike(self):
|
||||
# see gh-12620: tz and timedelta
|
||||
x = [
|
||||
Timestamp("2011-01-01", tz="US/Eastern"),
|
||||
Timestamp("2011-02-01", tz="US/Eastern"),
|
||||
]
|
||||
y = [pd.Timedelta("1 day"), pd.Timedelta("2 day")]
|
||||
result = concat([Series(x), Series(y)], ignore_index=True)
|
||||
tm.assert_series_equal(result, Series(x + y, dtype="object"))
|
||||
|
||||
# tz and period
|
||||
y = [pd.Period("2011-03", freq="M"), pd.Period("2011-04", freq="M")]
|
||||
result = concat([Series(x), Series(y)], ignore_index=True)
|
||||
tm.assert_series_equal(result, Series(x + y, dtype="object"))
|
||||
|
||||
def test_concat_tz_frame(self):
|
||||
df2 = DataFrame(
|
||||
{
|
||||
"A": Timestamp("20130102", tz="US/Eastern"),
|
||||
"B": Timestamp("20130603", tz="CET"),
|
||||
},
|
||||
index=range(5),
|
||||
)
|
||||
|
||||
# concat
|
||||
df3 = concat([df2.A.to_frame(), df2.B.to_frame()], axis=1)
|
||||
tm.assert_frame_equal(df2, df3)
|
||||
|
||||
def test_concat_multiple_tzs(self):
|
||||
# GH#12467
|
||||
# combining datetime tz-aware and naive DataFrames
|
||||
ts1 = Timestamp("2015-01-01", tz=None)
|
||||
ts2 = Timestamp("2015-01-01", tz="UTC")
|
||||
ts3 = Timestamp("2015-01-01", tz="EST")
|
||||
|
||||
df1 = DataFrame({"time": [ts1]})
|
||||
df2 = DataFrame({"time": [ts2]})
|
||||
df3 = DataFrame({"time": [ts3]})
|
||||
|
||||
results = concat([df1, df2]).reset_index(drop=True)
|
||||
expected = DataFrame({"time": [ts1, ts2]}, dtype=object)
|
||||
tm.assert_frame_equal(results, expected)
|
||||
|
||||
results = concat([df1, df3]).reset_index(drop=True)
|
||||
expected = DataFrame({"time": [ts1, ts3]}, dtype=object)
|
||||
tm.assert_frame_equal(results, expected)
|
||||
|
||||
results = concat([df2, df3]).reset_index(drop=True)
|
||||
expected = DataFrame({"time": [ts2, ts3]})
|
||||
tm.assert_frame_equal(results, expected)
|
||||
|
||||
def test_concat_multiindex_with_tz(self):
|
||||
# GH 6606
|
||||
df = DataFrame(
|
||||
{
|
||||
"dt": DatetimeIndex(
|
||||
[
|
||||
datetime(2014, 1, 1),
|
||||
datetime(2014, 1, 2),
|
||||
datetime(2014, 1, 3),
|
||||
],
|
||||
dtype="M8[ns, US/Pacific]",
|
||||
),
|
||||
"b": ["A", "B", "C"],
|
||||
"c": [1, 2, 3],
|
||||
"d": [4, 5, 6],
|
||||
}
|
||||
)
|
||||
df = df.set_index(["dt", "b"])
|
||||
|
||||
exp_idx1 = DatetimeIndex(
|
||||
["2014-01-01", "2014-01-02", "2014-01-03"] * 2,
|
||||
dtype="M8[ns, 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_tz_not_aligned(self):
|
||||
# GH#22796
|
||||
ts = pd.to_datetime([1, 2]).tz_localize("UTC")
|
||||
a = DataFrame({"A": ts})
|
||||
b = DataFrame({"A": ts, "B": ts})
|
||||
result = concat([a, b], sort=True, ignore_index=True)
|
||||
expected = DataFrame(
|
||||
{"A": list(ts) + list(ts), "B": [pd.NaT, pd.NaT] + list(ts)}
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"t1",
|
||||
[
|
||||
"2015-01-01",
|
||||
pytest.param(
|
||||
pd.NaT,
|
||||
marks=pytest.mark.xfail(
|
||||
reason="GH23037 incorrect dtype when concatenating"
|
||||
),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_concat_tz_NaT(self, t1):
|
||||
# GH#22796
|
||||
# Concatenating tz-aware multicolumn DataFrames
|
||||
ts1 = Timestamp(t1, tz="UTC")
|
||||
ts2 = Timestamp("2015-01-01", tz="UTC")
|
||||
ts3 = Timestamp("2015-01-01", tz="UTC")
|
||||
|
||||
df1 = DataFrame([[ts1, ts2]])
|
||||
df2 = DataFrame([[ts3]])
|
||||
|
||||
result = concat([df1, df2])
|
||||
expected = DataFrame([[ts1, ts2], [ts3, pd.NaT]], index=[0, 0])
|
||||
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_tz_with_empty(self):
|
||||
# GH 9188
|
||||
result = concat(
|
||||
[DataFrame(date_range("2000", periods=1, tz="UTC")), DataFrame()]
|
||||
)
|
||||
expected = DataFrame(date_range("2000", periods=1, tz="UTC"))
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
class TestPeriodConcat:
|
||||
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"
|
||||
|
||||
def test_concat_period_other_series2(self):
|
||||
# non-period
|
||||
x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
|
||||
y = Series(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"
|
||||
|
||||
def test_concat_period_other_series3(self):
|
||||
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_timedelta64_block():
|
||||
rng = to_timedelta(np.arange(10), unit="s")
|
||||
|
||||
df = DataFrame({"time": rng})
|
||||
|
||||
result = concat([df, df])
|
||||
tm.assert_frame_equal(result.iloc[:10], df)
|
||||
tm.assert_frame_equal(result.iloc[10:], df)
|
||||
|
||||
|
||||
def test_concat_multiindex_datetime_nat():
|
||||
# GH#44900
|
||||
left = DataFrame({"a": 1}, index=MultiIndex.from_tuples([(1, pd.NaT)]))
|
||||
right = DataFrame(
|
||||
{"b": 2}, index=MultiIndex.from_tuples([(1, pd.NaT), (2, pd.NaT)])
|
||||
)
|
||||
result = concat([left, right], axis="columns")
|
||||
expected = DataFrame(
|
||||
{"a": [1.0, np.nan], "b": 2}, MultiIndex.from_tuples([(1, pd.NaT), (2, pd.NaT)])
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
def test_concat_float_datetime64(using_array_manager):
|
||||
# GH#32934
|
||||
df_time = DataFrame({"A": pd.array(["2000"], dtype="datetime64[ns]")})
|
||||
df_float = DataFrame({"A": pd.array([1.0], dtype="float64")})
|
||||
|
||||
expected = DataFrame(
|
||||
{
|
||||
"A": [
|
||||
pd.array(["2000"], dtype="datetime64[ns]")[0],
|
||||
pd.array([1.0], dtype="float64")[0],
|
||||
]
|
||||
},
|
||||
index=[0, 0],
|
||||
)
|
||||
result = concat([df_time, df_float])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
expected = DataFrame({"A": pd.array([], dtype="object")})
|
||||
result = concat([df_time.iloc[:0], df_float.iloc[:0]])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
expected = DataFrame({"A": pd.array([1.0], dtype="object")})
|
||||
result = concat([df_time.iloc[:0], df_float])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
if not using_array_manager:
|
||||
expected = DataFrame({"A": pd.array(["2000"], dtype="datetime64[ns]")})
|
||||
msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = concat([df_time, df_float.iloc[:0]])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
else:
|
||||
expected = DataFrame({"A": pd.array(["2000"], dtype="datetime64[ns]")}).astype(
|
||||
{"A": "object"}
|
||||
)
|
||||
result = concat([df_time, df_float.iloc[:0]])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
@ -0,0 +1,295 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
DataFrame,
|
||||
RangeIndex,
|
||||
Series,
|
||||
concat,
|
||||
date_range,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestEmptyConcat:
|
||||
def test_handle_empty_objects(self, sort, using_infer_string):
|
||||
df = DataFrame(
|
||||
np.random.default_rng(2).standard_normal((10, 4)), columns=list("abcd")
|
||||
)
|
||||
|
||||
dfcopy = df[:5].copy()
|
||||
dfcopy["foo"] = "bar"
|
||||
empty = df[5:5]
|
||||
|
||||
frames = [dfcopy, 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(
|
||||
object if not using_infer_string else "string[pyarrow_numpy]"
|
||||
)
|
||||
expected.loc[0:4, "foo"] = "bar"
|
||||
|
||||
tm.assert_frame_equal(concatted, expected)
|
||||
|
||||
# empty as first element with time series
|
||||
# GH3259
|
||||
df = DataFrame(
|
||||
{"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_empty_series(self):
|
||||
# GH 11082
|
||||
s1 = Series([1, 2, 3], name="x")
|
||||
s2 = Series(name="y", dtype="float64")
|
||||
res = concat([s1, s2], axis=1)
|
||||
exp = DataFrame(
|
||||
{"x": [1, 2, 3], "y": [np.nan, np.nan, np.nan]},
|
||||
index=RangeIndex(3),
|
||||
)
|
||||
tm.assert_frame_equal(res, exp)
|
||||
|
||||
s1 = Series([1, 2, 3], name="x")
|
||||
s2 = Series(name="y", dtype="float64")
|
||||
msg = "The behavior of array concatenation with empty entries is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
res = concat([s1, s2], axis=0)
|
||||
# name will be reset
|
||||
exp = Series([1, 2, 3])
|
||||
tm.assert_series_equal(res, exp)
|
||||
|
||||
# empty Series with no name
|
||||
s1 = Series([1, 2, 3], name="x")
|
||||
s2 = Series(name=None, dtype="float64")
|
||||
res = concat([s1, s2], axis=1)
|
||||
exp = DataFrame(
|
||||
{"x": [1, 2, 3], 0: [np.nan, np.nan, np.nan]},
|
||||
columns=["x", 0],
|
||||
index=RangeIndex(3),
|
||||
)
|
||||
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: 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)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"left,right,expected",
|
||||
[
|
||||
# booleans
|
||||
(np.bool_, np.int32, np.object_), # changed from int32 in 2.0 GH#39817
|
||||
(np.bool_, np.float32, np.object_),
|
||||
# datetime-like
|
||||
("m8[ns]", np.bool_, np.object_),
|
||||
("m8[ns]", np.int64, np.object_),
|
||||
("M8[ns]", np.bool_, np.object_),
|
||||
("M8[ns]", np.int64, np.object_),
|
||||
# categorical
|
||||
("category", "category", "category"),
|
||||
("category", "object", "object"),
|
||||
],
|
||||
)
|
||||
def test_concat_empty_series_dtypes(self, left, right, expected):
|
||||
# GH#39817, GH#45101
|
||||
result = concat([Series(dtype=left), Series(dtype=right)])
|
||||
assert result.dtype == expected
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"dtype", ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"]
|
||||
)
|
||||
def test_concat_empty_series_dtypes_match_roundtrips(self, dtype):
|
||||
dtype = np.dtype(dtype)
|
||||
|
||||
result = concat([Series(dtype=dtype)])
|
||||
assert result.dtype == dtype
|
||||
|
||||
result = concat([Series(dtype=dtype), Series(dtype=dtype)])
|
||||
assert result.dtype == dtype
|
||||
|
||||
@pytest.mark.parametrize("dtype", ["float64", "int8", "uint8", "m8[ns]", "M8[ns]"])
|
||||
@pytest.mark.parametrize(
|
||||
"dtype2",
|
||||
["float64", "int8", "uint8", "m8[ns]", "M8[ns]"],
|
||||
)
|
||||
def test_concat_empty_series_dtypes_roundtrips(self, dtype, dtype2):
|
||||
# round-tripping with self & like self
|
||||
if dtype == dtype2:
|
||||
pytest.skip("same dtype is not applicable for test")
|
||||
|
||||
def int_result_type(dtype, dtype2):
|
||||
typs = {dtype.kind, dtype2.kind}
|
||||
if not len(typs - {"i", "u", "b"}) and (
|
||||
dtype.kind == "i" or dtype2.kind == "i"
|
||||
):
|
||||
return "i"
|
||||
elif not len(typs - {"u", "b"}) and (
|
||||
dtype.kind == "u" or dtype2.kind == "u"
|
||||
):
|
||||
return "u"
|
||||
return None
|
||||
|
||||
def float_result_type(dtype, dtype2):
|
||||
typs = {dtype.kind, dtype2.kind}
|
||||
if not len(typs - {"f", "i", "u"}) and (
|
||||
dtype.kind == "f" or dtype2.kind == "f"
|
||||
):
|
||||
return "f"
|
||||
return None
|
||||
|
||||
def get_result_type(dtype, dtype2):
|
||||
result = float_result_type(dtype, dtype2)
|
||||
if result is not None:
|
||||
return result
|
||||
result = int_result_type(dtype, dtype2)
|
||||
if result is not None:
|
||||
return result
|
||||
return "O"
|
||||
|
||||
dtype = np.dtype(dtype)
|
||||
dtype2 = np.dtype(dtype2)
|
||||
expected = get_result_type(dtype, dtype2)
|
||||
result = concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype
|
||||
assert result.kind == expected
|
||||
|
||||
def test_concat_empty_series_dtypes_triple(self):
|
||||
assert (
|
||||
concat(
|
||||
[Series(dtype="M8[ns]"), Series(dtype=np.bool_), Series(dtype=np.int64)]
|
||||
).dtype
|
||||
== np.object_
|
||||
)
|
||||
|
||||
def test_concat_empty_series_dtype_category_with_array(self):
|
||||
# GH#18515
|
||||
assert (
|
||||
concat(
|
||||
[Series(np.array([]), dtype="category"), Series(dtype="float64")]
|
||||
).dtype
|
||||
== "float64"
|
||||
)
|
||||
|
||||
def test_concat_empty_series_dtypes_sparse(self):
|
||||
result = concat(
|
||||
[
|
||||
Series(dtype="float64").astype("Sparse"),
|
||||
Series(dtype="float64").astype("Sparse"),
|
||||
]
|
||||
)
|
||||
assert result.dtype == "Sparse[float64]"
|
||||
|
||||
result = concat(
|
||||
[Series(dtype="float64").astype("Sparse"), Series(dtype="float64")]
|
||||
)
|
||||
expected = pd.SparseDtype(np.float64)
|
||||
assert result.dtype == expected
|
||||
|
||||
result = concat(
|
||||
[Series(dtype="float64").astype("Sparse"), Series(dtype="object")]
|
||||
)
|
||||
expected = pd.SparseDtype("object")
|
||||
assert result.dtype == expected
|
||||
|
||||
def test_concat_empty_df_object_dtype(self):
|
||||
# GH 9149
|
||||
df_1 = DataFrame({"Row": [0, 1, 1], "EmptyCol": np.nan, "NumberCol": [1, 2, 3]})
|
||||
df_2 = DataFrame(columns=df_1.columns)
|
||||
result = concat([df_1, df_2], axis=0)
|
||||
expected = df_1.astype(object)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_empty_dataframe_dtypes(self):
|
||||
df = DataFrame(columns=list("abc"))
|
||||
df["a"] = df["a"].astype(np.bool_)
|
||||
df["b"] = df["b"].astype(np.int32)
|
||||
df["c"] = df["c"].astype(np.float64)
|
||||
|
||||
result = concat([df, df])
|
||||
assert result["a"].dtype == np.bool_
|
||||
assert result["b"].dtype == np.int32
|
||||
assert result["c"].dtype == np.float64
|
||||
|
||||
result = concat([df, df.astype(np.float64)])
|
||||
assert result["a"].dtype == np.object_
|
||||
assert result["b"].dtype == np.float64
|
||||
assert result["c"].dtype == np.float64
|
||||
|
||||
def test_concat_inner_join_empty(self):
|
||||
# GH 15328
|
||||
df_empty = DataFrame()
|
||||
df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
|
||||
df_expected = DataFrame({"a": []}, index=RangeIndex(0), dtype="int64")
|
||||
|
||||
result = concat([df_a, df_empty], axis=1, join="inner")
|
||||
tm.assert_frame_equal(result, df_expected)
|
||||
|
||||
result = concat([df_a, df_empty], axis=1, join="outer")
|
||||
tm.assert_frame_equal(result, df_a)
|
||||
|
||||
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_concat_empty_dataframe(self):
|
||||
# 39037
|
||||
df1 = DataFrame(columns=["a", "b"])
|
||||
df2 = DataFrame(columns=["b", "c"])
|
||||
result = concat([df1, df2, df1])
|
||||
expected = DataFrame(columns=["a", "b", "c"])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
df3 = DataFrame(columns=["a", "b"])
|
||||
df4 = DataFrame(columns=["b"])
|
||||
result = concat([df3, df4])
|
||||
expected = DataFrame(columns=["a", "b"])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_empty_dataframe_different_dtypes(self, using_infer_string):
|
||||
# 39037
|
||||
df1 = DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
|
||||
df2 = DataFrame({"a": [1, 2, 3]})
|
||||
|
||||
result = concat([df1[:0], df2[:0]])
|
||||
assert result["a"].dtype == np.int64
|
||||
assert result["b"].dtype == np.object_ if not using_infer_string else "string"
|
||||
|
||||
def test_concat_to_empty_ea(self):
|
||||
"""48510 `concat` to an empty EA should maintain type EA dtype."""
|
||||
df_empty = DataFrame({"a": pd.array([], dtype=pd.Int64Dtype())})
|
||||
df_new = DataFrame({"a": pd.array([1, 2, 3], dtype=pd.Int64Dtype())})
|
||||
expected = df_new.copy()
|
||||
result = concat([df_empty, df_new])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
@ -0,0 +1,472 @@
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas.errors import PerformanceWarning
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
DataFrame,
|
||||
Index,
|
||||
MultiIndex,
|
||||
Series,
|
||||
concat,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestIndexConcat:
|
||||
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)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"name_in1,name_in2,name_in3,name_out",
|
||||
[
|
||||
("idx", "idx", "idx", "idx"),
|
||||
("idx", "idx", None, None),
|
||||
("idx", None, None, None),
|
||||
("idx1", "idx2", None, None),
|
||||
("idx1", "idx1", "idx2", None),
|
||||
("idx1", "idx2", "idx3", None),
|
||||
(None, None, None, None),
|
||||
],
|
||||
)
|
||||
def test_concat_same_index_names(self, name_in1, name_in2, name_in3, name_out):
|
||||
# GH13475
|
||||
indices = [
|
||||
Index(["a", "b", "c"], name=name_in1),
|
||||
Index(["b", "c", "d"], name=name_in2),
|
||||
Index(["c", "d", "e"], name=name_in3),
|
||||
]
|
||||
frames = [
|
||||
DataFrame({c: [0, 1, 2]}, index=i) for i, c in zip(indices, ["x", "y", "z"])
|
||||
]
|
||||
result = concat(frames, axis=1)
|
||||
|
||||
exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out)
|
||||
expected = DataFrame(
|
||||
{
|
||||
"x": [0, 1, 2, np.nan, np.nan],
|
||||
"y": [np.nan, 0, 1, 2, np.nan],
|
||||
"z": [np.nan, np.nan, 0, 1, 2],
|
||||
},
|
||||
index=exp_ind,
|
||||
)
|
||||
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_rename_index(self):
|
||||
a = DataFrame(
|
||||
np.random.default_rng(2).random((3, 3)),
|
||||
columns=list("ABC"),
|
||||
index=Index(list("abc"), name="index_a"),
|
||||
)
|
||||
b = DataFrame(
|
||||
np.random.default_rng(2).random((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_concat_copy_index_series(self, axis, using_copy_on_write):
|
||||
# GH 29879
|
||||
ser = Series([1, 2])
|
||||
comb = concat([ser, ser], axis=axis, copy=True)
|
||||
if not using_copy_on_write or axis in [0, "index"]:
|
||||
assert comb.index is not ser.index
|
||||
else:
|
||||
assert comb.index is ser.index
|
||||
|
||||
def test_concat_copy_index_frame(self, axis, using_copy_on_write):
|
||||
# GH 29879
|
||||
df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
|
||||
comb = concat([df, df], axis=axis, copy=True)
|
||||
if not using_copy_on_write:
|
||||
assert not comb.index.is_(df.index)
|
||||
assert not comb.columns.is_(df.columns)
|
||||
elif axis in [0, "index"]:
|
||||
assert not comb.index.is_(df.index)
|
||||
assert comb.columns.is_(df.columns)
|
||||
elif axis in [1, "columns"]:
|
||||
assert comb.index.is_(df.index)
|
||||
assert not comb.columns.is_(df.columns)
|
||||
|
||||
def test_default_index(self):
|
||||
# is_series and ignore_index
|
||||
s1 = Series([1, 2, 3], name="x")
|
||||
s2 = Series([4, 5, 6], name="y")
|
||||
res = concat([s1, s2], axis=1, ignore_index=True)
|
||||
assert isinstance(res.columns, pd.RangeIndex)
|
||||
exp = 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 = Series([1, 2, 3])
|
||||
s2 = Series([4, 5, 6])
|
||||
res = concat([s1, s2], axis=1, ignore_index=False)
|
||||
assert isinstance(res.columns, pd.RangeIndex)
|
||||
exp = 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 = DataFrame({"A": [1, 2], "B": [5, 6]})
|
||||
df2 = DataFrame({"A": [3, 4], "B": [7, 8]})
|
||||
|
||||
res = concat([df1, df2], axis=0, ignore_index=True)
|
||||
exp = 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 = concat([df1, df2], axis=1, ignore_index=True)
|
||||
exp = 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_dups_index(self):
|
||||
# GH 4771
|
||||
|
||||
# single dtypes
|
||||
df = DataFrame(
|
||||
np.random.default_rng(2).integers(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.default_rng(2).standard_normal((10, 4)),
|
||||
columns=["A", "A", "B", "B"],
|
||||
),
|
||||
DataFrame(
|
||||
np.random.default_rng(2).integers(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)
|
||||
|
||||
|
||||
class TestMultiIndexConcat:
|
||||
def test_concat_multiindex_with_keys(self, multiindex_dataframe_random_data):
|
||||
frame = multiindex_dataframe_random_data
|
||||
index = frame.index
|
||||
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_none_in_index_names(self):
|
||||
# GH 15787
|
||||
index = MultiIndex.from_product([[1], range(5)], names=["level1", None])
|
||||
df = DataFrame({"col": range(5)}, index=index, dtype=np.int32)
|
||||
|
||||
result = concat([df, df], keys=[1, 2], names=["level2"])
|
||||
index = MultiIndex.from_product(
|
||||
[[1, 2], [1], range(5)], names=["level2", "level1", None]
|
||||
)
|
||||
expected = 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 = MultiIndex.from_tuples(tuples, names=["level2", "level1", None])
|
||||
expected = DataFrame({"col": no_name}, index=index, dtype=np.int32)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
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.default_rng(2).standard_normal((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
|
||||
example_multiindex1 = MultiIndex.from_product([["a"], ["b"]])
|
||||
example_dataframe1 = DataFrame([0], index=example_multiindex1)
|
||||
|
||||
example_multiindex2 = MultiIndex.from_product([["a"], ["c"]])
|
||||
example_dataframe2 = DataFrame([1], index=example_multiindex2)
|
||||
|
||||
example_dict = {"s1": example_dataframe1, "s2": example_dataframe2}
|
||||
expected_index = MultiIndex(
|
||||
levels=[["s1", "s2"], ["a"], ["b", "c"]],
|
||||
codes=[[0, 1], [0, 0], [0, 1]],
|
||||
names=["testname", None, None],
|
||||
)
|
||||
expected = DataFrame([[0], [1]], index=expected_index)
|
||||
result_copy = concat(deepcopy(example_dict), names=["testname"])
|
||||
tm.assert_frame_equal(result_copy, expected)
|
||||
result_no_copy = concat(example_dict, names=["testname"])
|
||||
tm.assert_frame_equal(result_no_copy, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"mi1_list",
|
||||
[
|
||||
[["a"], range(2)],
|
||||
[["b"], np.arange(2.0, 4.0)],
|
||||
[["c"], ["A", "B"]],
|
||||
[["d"], pd.date_range(start="2017", end="2018", periods=2)],
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"mi2_list",
|
||||
[
|
||||
[["a"], range(2)],
|
||||
[["b"], np.arange(2.0, 4.0)],
|
||||
[["c"], ["A", "B"]],
|
||||
[["d"], pd.date_range(start="2017", end="2018", periods=2)],
|
||||
],
|
||||
)
|
||||
def test_concat_with_various_multiindex_dtypes(
|
||||
self, mi1_list: list, mi2_list: list
|
||||
):
|
||||
# GitHub #23478
|
||||
mi1 = MultiIndex.from_product(mi1_list)
|
||||
mi2 = MultiIndex.from_product(mi2_list)
|
||||
|
||||
df1 = DataFrame(np.zeros((1, len(mi1))), columns=mi1)
|
||||
df2 = DataFrame(np.zeros((1, len(mi2))), columns=mi2)
|
||||
|
||||
if mi1_list[0] == mi2_list[0]:
|
||||
expected_mi = MultiIndex(
|
||||
levels=[mi1_list[0], list(mi1_list[1])],
|
||||
codes=[[0, 0, 0, 0], [0, 1, 0, 1]],
|
||||
)
|
||||
else:
|
||||
expected_mi = MultiIndex(
|
||||
levels=[
|
||||
mi1_list[0] + mi2_list[0],
|
||||
list(mi1_list[1]) + list(mi2_list[1]),
|
||||
],
|
||||
codes=[[0, 0, 1, 1], [0, 1, 2, 3]],
|
||||
)
|
||||
|
||||
expected_df = DataFrame(np.zeros((1, len(expected_mi))), columns=expected_mi)
|
||||
|
||||
with tm.assert_produces_warning(None):
|
||||
result_df = concat((df1, df2), axis=1)
|
||||
|
||||
tm.assert_frame_equal(expected_df, result_df)
|
||||
|
||||
def test_concat_multiindex_(self):
|
||||
# GitHub #44786
|
||||
df = DataFrame({"col": ["a", "b", "c"]}, index=["1", "2", "2"])
|
||||
df = concat([df], keys=["X"])
|
||||
|
||||
iterables = [["X"], ["1", "2", "2"]]
|
||||
result_index = df.index
|
||||
expected_index = MultiIndex.from_product(iterables)
|
||||
|
||||
tm.assert_index_equal(result_index, expected_index)
|
||||
|
||||
result_df = df
|
||||
expected_df = DataFrame(
|
||||
{"col": ["a", "b", "c"]}, index=MultiIndex.from_product(iterables)
|
||||
)
|
||||
tm.assert_frame_equal(result_df, expected_df)
|
||||
|
||||
def test_concat_with_key_not_unique(self):
|
||||
# GitHub #46519
|
||||
df1 = DataFrame({"name": [1]})
|
||||
df2 = DataFrame({"name": [2]})
|
||||
df3 = DataFrame({"name": [3]})
|
||||
df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
|
||||
# the warning is caused by indexing unsorted multi-index
|
||||
with tm.assert_produces_warning(
|
||||
PerformanceWarning, match="indexing past lexsort depth"
|
||||
):
|
||||
out_a = df_a.loc[("x", 0), :]
|
||||
|
||||
df_b = DataFrame(
|
||||
{"name": [1, 2, 3]}, index=Index([("x", 0), ("y", 0), ("x", 0)])
|
||||
)
|
||||
with tm.assert_produces_warning(
|
||||
PerformanceWarning, match="indexing past lexsort depth"
|
||||
):
|
||||
out_b = df_b.loc[("x", 0)]
|
||||
|
||||
tm.assert_frame_equal(out_a, out_b)
|
||||
|
||||
df1 = DataFrame({"name": ["a", "a", "b"]})
|
||||
df2 = DataFrame({"name": ["a", "b"]})
|
||||
df3 = DataFrame({"name": ["c", "d"]})
|
||||
df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
|
||||
with tm.assert_produces_warning(
|
||||
PerformanceWarning, match="indexing past lexsort depth"
|
||||
):
|
||||
out_a = df_a.loc[("x", 0), :]
|
||||
|
||||
df_b = DataFrame(
|
||||
{
|
||||
"a": ["x", "x", "x", "y", "y", "x", "x"],
|
||||
"b": [0, 1, 2, 0, 1, 0, 1],
|
||||
"name": list("aababcd"),
|
||||
}
|
||||
).set_index(["a", "b"])
|
||||
df_b.index.names = [None, None]
|
||||
with tm.assert_produces_warning(
|
||||
PerformanceWarning, match="indexing past lexsort depth"
|
||||
):
|
||||
out_b = df_b.loc[("x", 0), :]
|
||||
|
||||
tm.assert_frame_equal(out_a, out_b)
|
||||
|
||||
def test_concat_with_duplicated_levels(self):
|
||||
# keyword levels should be unique
|
||||
df1 = DataFrame({"A": [1]}, index=["x"])
|
||||
df2 = DataFrame({"A": [1]}, index=["y"])
|
||||
msg = r"Level values not unique: \['x', 'y', 'y'\]"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
|
||||
|
||||
@pytest.mark.parametrize("levels", [[["x", "y"]], [["x", "y", "y"]]])
|
||||
def test_concat_with_levels_with_none_keys(self, levels):
|
||||
df1 = DataFrame({"A": [1]}, index=["x"])
|
||||
df2 = DataFrame({"A": [1]}, index=["y"])
|
||||
msg = "levels supported only when keys is not None"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
concat([df1, df2], levels=levels)
|
||||
|
||||
def test_concat_range_index_result(self):
|
||||
# GH#47501
|
||||
df1 = DataFrame({"a": [1, 2]})
|
||||
df2 = DataFrame({"b": [1, 2]})
|
||||
|
||||
result = concat([df1, df2], sort=True, axis=1)
|
||||
expected = DataFrame({"a": [1, 2], "b": [1, 2]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
expected_index = pd.RangeIndex(0, 2)
|
||||
tm.assert_index_equal(result.index, expected_index, exact=True)
|
||||
|
||||
def test_concat_index_keep_dtype(self):
|
||||
# GH#47329
|
||||
df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype="object"))
|
||||
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="object"))
|
||||
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
|
||||
expected = DataFrame(
|
||||
[[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="object")
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_index_keep_dtype_ea_numeric(self, any_numeric_ea_dtype):
|
||||
# GH#47329
|
||||
df1 = DataFrame(
|
||||
[[0, 1, 1]], columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype)
|
||||
)
|
||||
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=any_numeric_ea_dtype))
|
||||
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
|
||||
expected = DataFrame(
|
||||
[[0, 1, 1.0], [0, 1, np.nan]],
|
||||
columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype),
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("dtype", ["Int8", "Int16", "Int32"])
|
||||
def test_concat_index_find_common(self, dtype):
|
||||
# GH#47329
|
||||
df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype))
|
||||
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="Int32"))
|
||||
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
|
||||
expected = DataFrame(
|
||||
[[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="Int32")
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_axis_1_sort_false_rangeindex(self, using_infer_string):
|
||||
# GH 46675
|
||||
s1 = Series(["a", "b", "c"])
|
||||
s2 = Series(["a", "b"])
|
||||
s3 = Series(["a", "b", "c", "d"])
|
||||
s4 = Series(
|
||||
[], dtype=object if not using_infer_string else "string[pyarrow_numpy]"
|
||||
)
|
||||
result = concat(
|
||||
[s1, s2, s3, s4], sort=False, join="outer", ignore_index=False, axis=1
|
||||
)
|
||||
expected = DataFrame(
|
||||
[
|
||||
["a"] * 3 + [np.nan],
|
||||
["b"] * 3 + [np.nan],
|
||||
["c", np.nan] * 2,
|
||||
[np.nan] * 2 + ["d"] + [np.nan],
|
||||
],
|
||||
dtype=object if not using_infer_string else "string[pyarrow_numpy]",
|
||||
)
|
||||
tm.assert_frame_equal(
|
||||
result, expected, check_index_type=True, check_column_type=True
|
||||
)
|
||||
@ -0,0 +1,54 @@
|
||||
from io import StringIO
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas import (
|
||||
DataFrame,
|
||||
concat,
|
||||
read_csv,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestInvalidConcat:
|
||||
@pytest.mark.parametrize("obj", [1, {}, [1, 2], (1, 2)])
|
||||
def test_concat_invalid(self, obj):
|
||||
# trying to concat a ndframe with a non-ndframe
|
||||
df1 = DataFrame(range(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 = DataFrame(range(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)
|
||||
|
||||
def test_concat_generator_obj(self):
|
||||
# generator ok though
|
||||
concat(DataFrame(np.random.default_rng(2).random((5, 5))) for _ in range(3))
|
||||
|
||||
def test_concat_textreader_obj(self):
|
||||
# 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
|
||||
"""
|
||||
|
||||
with read_csv(StringIO(data), chunksize=1) as reader:
|
||||
result = concat(reader, ignore_index=True)
|
||||
expected = read_csv(StringIO(data))
|
||||
tm.assert_frame_equal(result, expected)
|
||||
@ -0,0 +1,175 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas import (
|
||||
DataFrame,
|
||||
DatetimeIndex,
|
||||
Index,
|
||||
MultiIndex,
|
||||
Series,
|
||||
concat,
|
||||
date_range,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestSeriesConcat:
|
||||
def test_concat_series(self):
|
||||
ts = Series(
|
||||
np.arange(20, dtype=np.float64),
|
||||
index=date_range("2020-01-01", periods=20),
|
||||
name="foo",
|
||||
)
|
||||
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_empty_and_non_empty_series_regression(self):
|
||||
# GH 18187 regression test
|
||||
s1 = Series([1])
|
||||
s2 = Series([], dtype=object)
|
||||
|
||||
expected = s1
|
||||
msg = "The behavior of array concatenation with empty entries is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = concat([s1, s2])
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_concat_series_axis1(self):
|
||||
ts = Series(
|
||||
np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
|
||||
)
|
||||
|
||||
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)
|
||||
|
||||
def test_concat_series_axis1_preserves_series_names(self):
|
||||
# preserve series names, #2489
|
||||
s = Series(np.random.default_rng(2).standard_normal(5), name="A")
|
||||
s2 = Series(np.random.default_rng(2).standard_normal(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"))
|
||||
|
||||
def test_concat_series_axis1_with_reindex(self, sort):
|
||||
# must reindex, #2603
|
||||
s = Series(
|
||||
np.random.default_rng(2).standard_normal(3), index=["c", "a", "b"], name="A"
|
||||
)
|
||||
s2 = Series(
|
||||
np.random.default_rng(2).standard_normal(4),
|
||||
index=["d", "a", "b", "c"],
|
||||
name="B",
|
||||
)
|
||||
result = concat([s, s2], axis=1, sort=sort)
|
||||
expected = DataFrame({"A": s, "B": s2}, index=["c", "a", "b", "d"])
|
||||
if sort:
|
||||
expected = expected.sort_index()
|
||||
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=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_series_axis1_same_names_ignore_index(self):
|
||||
dates = date_range("01-Jan-2013", "01-Jan-2014", freq="MS")[0:-1]
|
||||
s1 = Series(
|
||||
np.random.default_rng(2).standard_normal(len(dates)),
|
||||
index=dates,
|
||||
name="value",
|
||||
)
|
||||
s2 = Series(
|
||||
np.random.default_rng(2).standard_normal(len(dates)),
|
||||
index=dates,
|
||||
name="value",
|
||||
)
|
||||
|
||||
result = concat([s1, s2], axis=1, ignore_index=True)
|
||||
expected = Index(range(2))
|
||||
|
||||
tm.assert_index_equal(result.columns, expected, exact=True)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"s1name,s2name", [(np.int64(190), (43, 0)), (190, (43, 0))]
|
||||
)
|
||||
def test_concat_series_name_npscalar_tuple(self, s1name, s2name):
|
||||
# GH21015
|
||||
s1 = Series({"a": 1, "b": 2}, name=s1name)
|
||||
s2 = Series({"c": 5, "d": 6}, name=s2name)
|
||||
result = concat([s1, s2])
|
||||
expected = Series({"a": 1, "b": 2, "c": 5, "d": 6})
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_concat_series_partial_columns_names(self):
|
||||
# GH10698
|
||||
named_series = Series([1, 2], name="foo")
|
||||
unnamed_series1 = Series([1, 2])
|
||||
unnamed_series2 = Series([4, 5])
|
||||
|
||||
result = concat([named_series, unnamed_series1, unnamed_series2], 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(
|
||||
[named_series, unnamed_series1, unnamed_series2],
|
||||
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(
|
||||
[named_series, unnamed_series1, unnamed_series2], axis=1, ignore_index=True
|
||||
)
|
||||
expected = DataFrame({0: [1, 2], 1: [1, 2], 2: [4, 5]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_series_length_one_reversed(self, frame_or_series):
|
||||
# GH39401
|
||||
obj = frame_or_series([100])
|
||||
result = concat([obj.iloc[::-1]])
|
||||
tm.assert_equal(result, obj)
|
||||
@ -0,0 +1,118 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import DataFrame
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestConcatSort:
|
||||
def test_concat_sorts_columns(self, sort):
|
||||
# GH-4588
|
||||
df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
|
||||
df2 = DataFrame({"a": [3, 4], "c": [5, 6]})
|
||||
|
||||
# for sort=True/None
|
||||
expected = 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(self, sort):
|
||||
df1 = DataFrame({"a": [1, 2, 3]}, index=["c", "a", "b"])
|
||||
df2 = DataFrame({"b": [1, 2]}, index=["a", "b"])
|
||||
|
||||
# For True/None
|
||||
expected = 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(self, sort):
|
||||
# https://github.com/pandas-dev/pandas/pull/20613
|
||||
df1 = DataFrame(
|
||||
{"a": [1, 2], "b": [1, 2], "c": [1, 2]}, columns=["b", "a", "c"]
|
||||
)
|
||||
df2 = 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 = 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(self):
|
||||
# GH-4588
|
||||
df = 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 = 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(self):
|
||||
# GH-4588
|
||||
# We catch TypeErrors from sorting internally and do not re-raise.
|
||||
df = DataFrame({1: [1, 2], "a": [3, 4]}, columns=[1, "a"])
|
||||
expected = 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)
|
||||
|
||||
def test_concat_frame_with_sort_false(self):
|
||||
# GH 43375
|
||||
result = pd.concat(
|
||||
[DataFrame({i: i}, index=[i]) for i in range(2, 0, -1)], sort=False
|
||||
)
|
||||
expected = DataFrame([[2, np.nan], [np.nan, 1]], index=[2, 1], columns=[2, 1])
|
||||
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# GH 37937
|
||||
df1 = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=[1, 2, 3])
|
||||
df2 = DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]}, index=[3, 1, 6])
|
||||
result = pd.concat([df2, df1], axis=1, sort=False)
|
||||
expected = DataFrame(
|
||||
[
|
||||
[7.0, 10.0, 3.0, 6.0],
|
||||
[8.0, 11.0, 1.0, 4.0],
|
||||
[9.0, 12.0, np.nan, np.nan],
|
||||
[np.nan, np.nan, 2.0, 5.0],
|
||||
],
|
||||
index=[3, 1, 6, 2],
|
||||
columns=["c", "d", "a", "b"],
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_concat_sort_none_raises(self):
|
||||
# GH#41518
|
||||
df = DataFrame({1: [1, 2], "a": [3, 4]})
|
||||
msg = "The 'sort' keyword only accepts boolean values; None was passed."
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
pd.concat([df, df], sort=None)
|
||||
Reference in New Issue
Block a user