from copy import deepcopy
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import numpy as np
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import pytest
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from pandas.errors import PerformanceWarning
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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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MultiIndex,
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Series,
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concat,
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)
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import pandas._testing as tm
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class TestIndexConcat:
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def test_concat_ignore_index(self, sort):
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frame1 = DataFrame(
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{"test1": ["a", "b", "c"], "test2": [1, 2, 3], "test3": [4.5, 3.2, 1.2]}
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)
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frame2 = DataFrame({"test3": [5.2, 2.2, 4.3]})
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frame1.index = Index(["x", "y", "z"])
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frame2.index = Index(["x", "y", "q"])
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v1 = concat([frame1, frame2], axis=1, ignore_index=True, sort=sort)
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nan = np.nan
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expected = DataFrame(
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[
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[nan, nan, nan, 4.3],
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["a", 1, 4.5, 5.2],
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["b", 2, 3.2, 2.2],
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["c", 3, 1.2, nan],
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],
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index=Index(["q", "x", "y", "z"]),
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)
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if not sort:
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expected = expected.loc[["x", "y", "z", "q"]]
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tm.assert_frame_equal(v1, expected)
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@pytest.mark.parametrize(
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"name_in1,name_in2,name_in3,name_out",
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[
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("idx", "idx", "idx", "idx"),
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("idx", "idx", None, None),
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("idx", None, None, None),
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("idx1", "idx2", None, None),
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("idx1", "idx1", "idx2", None),
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("idx1", "idx2", "idx3", None),
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(None, None, None, None),
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],
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)
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def test_concat_same_index_names(self, name_in1, name_in2, name_in3, name_out):
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# GH13475
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indices = [
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Index(["a", "b", "c"], name=name_in1),
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Index(["b", "c", "d"], name=name_in2),
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Index(["c", "d", "e"], name=name_in3),
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]
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frames = [
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DataFrame({c: [0, 1, 2]}, index=i) for i, c in zip(indices, ["x", "y", "z"])
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]
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result = concat(frames, axis=1)
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exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out)
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expected = DataFrame(
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{
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"x": [0, 1, 2, np.nan, np.nan],
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"y": [np.nan, 0, 1, 2, np.nan],
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"z": [np.nan, np.nan, 0, 1, 2],
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},
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index=exp_ind,
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)
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tm.assert_frame_equal(result, expected)
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def test_concat_rename_index(self):
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a = DataFrame(
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np.random.rand(3, 3),
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columns=list("ABC"),
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index=Index(list("abc"), name="index_a"),
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)
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b = DataFrame(
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np.random.rand(3, 3),
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columns=list("ABC"),
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index=Index(list("abc"), name="index_b"),
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)
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result = concat([a, b], keys=["key0", "key1"], names=["lvl0", "lvl1"])
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exp = concat([a, b], keys=["key0", "key1"], names=["lvl0"])
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names = list(exp.index.names)
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names[1] = "lvl1"
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exp.index.set_names(names, inplace=True)
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tm.assert_frame_equal(result, exp)
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assert result.index.names == exp.index.names
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def test_concat_copy_index_series(self, axis, using_copy_on_write):
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# GH 29879
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ser = Series([1, 2])
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comb = concat([ser, ser], axis=axis, copy=True)
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if not using_copy_on_write or axis in [0, "index"]:
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assert comb.index is not ser.index
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else:
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assert comb.index is ser.index
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def test_concat_copy_index_frame(self, axis, using_copy_on_write):
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# GH 29879
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df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
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comb = concat([df, df], axis=axis, copy=True)
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if not using_copy_on_write:
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assert comb.index is not df.index
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assert comb.columns is not df.columns
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elif axis in [0, "index"]:
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assert comb.index is not df.index
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assert comb.columns is df.columns
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elif axis in [1, "columns"]:
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assert comb.index is df.index
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assert comb.columns is not df.columns
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def test_default_index(self):
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# is_series and ignore_index
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s1 = Series([1, 2, 3], name="x")
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s2 = Series([4, 5, 6], name="y")
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res = concat([s1, s2], axis=1, ignore_index=True)
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assert isinstance(res.columns, pd.RangeIndex)
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exp = DataFrame([[1, 4], [2, 5], [3, 6]])
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# use check_index_type=True to check the result have
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# RangeIndex (default index)
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tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
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# is_series and all inputs have no names
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s1 = Series([1, 2, 3])
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s2 = Series([4, 5, 6])
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res = concat([s1, s2], axis=1, ignore_index=False)
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assert isinstance(res.columns, pd.RangeIndex)
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exp = DataFrame([[1, 4], [2, 5], [3, 6]])
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exp.columns = pd.RangeIndex(2)
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tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
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# is_dataframe and ignore_index
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df1 = DataFrame({"A": [1, 2], "B": [5, 6]})
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df2 = DataFrame({"A": [3, 4], "B": [7, 8]})
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res = concat([df1, df2], axis=0, ignore_index=True)
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exp = DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], columns=["A", "B"])
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tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
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res = concat([df1, df2], axis=1, ignore_index=True)
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exp = DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]])
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tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
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def test_dups_index(self):
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# GH 4771
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# single dtypes
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df = DataFrame(
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np.random.randint(0, 10, size=40).reshape(10, 4),
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columns=["A", "A", "C", "C"],
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)
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result = concat([df, df], axis=1)
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tm.assert_frame_equal(result.iloc[:, :4], df)
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tm.assert_frame_equal(result.iloc[:, 4:], df)
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result = concat([df, df], axis=0)
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tm.assert_frame_equal(result.iloc[:10], df)
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tm.assert_frame_equal(result.iloc[10:], df)
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# multi dtypes
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df = concat(
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[
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DataFrame(np.random.randn(10, 4), columns=["A", "A", "B", "B"]),
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DataFrame(
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np.random.randint(0, 10, size=20).reshape(10, 2), columns=["A", "C"]
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),
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],
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axis=1,
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)
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result = concat([df, df], axis=1)
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tm.assert_frame_equal(result.iloc[:, :6], df)
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tm.assert_frame_equal(result.iloc[:, 6:], df)
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result = concat([df, df], axis=0)
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tm.assert_frame_equal(result.iloc[:10], df)
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tm.assert_frame_equal(result.iloc[10:], df)
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# append
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result = df.iloc[0:8, :]._append(df.iloc[8:])
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tm.assert_frame_equal(result, df)
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result = df.iloc[0:8, :]._append(df.iloc[8:9])._append(df.iloc[9:10])
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tm.assert_frame_equal(result, df)
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expected = concat([df, df], axis=0)
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result = df._append(df)
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tm.assert_frame_equal(result, expected)
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class TestMultiIndexConcat:
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def test_concat_multiindex_with_keys(self, multiindex_dataframe_random_data):
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frame = multiindex_dataframe_random_data
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index = frame.index
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result = concat([frame, frame], keys=[0, 1], names=["iteration"])
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assert result.index.names == ("iteration",) + index.names
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tm.assert_frame_equal(result.loc[0], frame)
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tm.assert_frame_equal(result.loc[1], frame)
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assert result.index.nlevels == 3
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def test_concat_multiindex_with_none_in_index_names(self):
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# GH 15787
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index = MultiIndex.from_product([[1], range(5)], names=["level1", None])
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df = DataFrame({"col": range(5)}, index=index, dtype=np.int32)
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result = concat([df, df], keys=[1, 2], names=["level2"])
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index = MultiIndex.from_product(
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[[1, 2], [1], range(5)], names=["level2", "level1", None]
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)
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expected = DataFrame({"col": list(range(5)) * 2}, index=index, dtype=np.int32)
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tm.assert_frame_equal(result, expected)
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result = concat([df, df[:2]], keys=[1, 2], names=["level2"])
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level2 = [1] * 5 + [2] * 2
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level1 = [1] * 7
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no_name = list(range(5)) + list(range(2))
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tuples = list(zip(level2, level1, no_name))
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index = MultiIndex.from_tuples(tuples, names=["level2", "level1", None])
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expected = DataFrame({"col": no_name}, index=index, dtype=np.int32)
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tm.assert_frame_equal(result, expected)
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def test_concat_multiindex_rangeindex(self):
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# GH13542
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# when multi-index levels are RangeIndex objects
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# there is a bug in concat with objects of len 1
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df = DataFrame(np.random.randn(9, 2))
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df.index = MultiIndex(
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levels=[pd.RangeIndex(3), pd.RangeIndex(3)],
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codes=[np.repeat(np.arange(3), 3), np.tile(np.arange(3), 3)],
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)
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res = concat([df.iloc[[2, 3, 4], :], df.iloc[[5], :]])
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exp = df.iloc[[2, 3, 4, 5], :]
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tm.assert_frame_equal(res, exp)
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def test_concat_multiindex_dfs_with_deepcopy(self):
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# GH 9967
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example_multiindex1 = MultiIndex.from_product([["a"], ["b"]])
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example_dataframe1 = DataFrame([0], index=example_multiindex1)
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example_multiindex2 = MultiIndex.from_product([["a"], ["c"]])
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example_dataframe2 = DataFrame([1], index=example_multiindex2)
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example_dict = {"s1": example_dataframe1, "s2": example_dataframe2}
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expected_index = MultiIndex(
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levels=[["s1", "s2"], ["a"], ["b", "c"]],
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codes=[[0, 1], [0, 0], [0, 1]],
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names=["testname", None, None],
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)
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expected = DataFrame([[0], [1]], index=expected_index)
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result_copy = concat(deepcopy(example_dict), names=["testname"])
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tm.assert_frame_equal(result_copy, expected)
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result_no_copy = concat(example_dict, names=["testname"])
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tm.assert_frame_equal(result_no_copy, expected)
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@pytest.mark.parametrize(
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"mi1_list",
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[
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[["a"], range(2)],
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[["b"], np.arange(2.0, 4.0)],
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[["c"], ["A", "B"]],
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[["d"], pd.date_range(start="2017", end="2018", periods=2)],
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],
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)
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@pytest.mark.parametrize(
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"mi2_list",
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[
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[["a"], range(2)],
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[["b"], np.arange(2.0, 4.0)],
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[["c"], ["A", "B"]],
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[["d"], pd.date_range(start="2017", end="2018", periods=2)],
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],
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)
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def test_concat_with_various_multiindex_dtypes(
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self, mi1_list: list, mi2_list: list
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):
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# GitHub #23478
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mi1 = MultiIndex.from_product(mi1_list)
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mi2 = MultiIndex.from_product(mi2_list)
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df1 = DataFrame(np.zeros((1, len(mi1))), columns=mi1)
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df2 = DataFrame(np.zeros((1, len(mi2))), columns=mi2)
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if mi1_list[0] == mi2_list[0]:
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expected_mi = MultiIndex(
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levels=[mi1_list[0], list(mi1_list[1])],
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codes=[[0, 0, 0, 0], [0, 1, 0, 1]],
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)
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else:
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expected_mi = MultiIndex(
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levels=[
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mi1_list[0] + mi2_list[0],
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list(mi1_list[1]) + list(mi2_list[1]),
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],
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codes=[[0, 0, 1, 1], [0, 1, 2, 3]],
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)
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expected_df = DataFrame(np.zeros((1, len(expected_mi))), columns=expected_mi)
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with tm.assert_produces_warning(None):
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result_df = concat((df1, df2), axis=1)
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tm.assert_frame_equal(expected_df, result_df)
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def test_concat_multiindex_(self):
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# GitHub #44786
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df = DataFrame({"col": ["a", "b", "c"]}, index=["1", "2", "2"])
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df = concat([df], keys=["X"])
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iterables = [["X"], ["1", "2", "2"]]
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result_index = df.index
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expected_index = MultiIndex.from_product(iterables)
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tm.assert_index_equal(result_index, expected_index)
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result_df = df
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expected_df = DataFrame(
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{"col": ["a", "b", "c"]}, index=MultiIndex.from_product(iterables)
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)
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tm.assert_frame_equal(result_df, expected_df)
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def test_concat_with_key_not_unique(self):
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# GitHub #46519
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df1 = DataFrame({"name": [1]})
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df2 = DataFrame({"name": [2]})
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df3 = DataFrame({"name": [3]})
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df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
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# the warning is caused by indexing unsorted multi-index
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with tm.assert_produces_warning(
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PerformanceWarning, match="indexing past lexsort depth"
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):
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out_a = df_a.loc[("x", 0), :]
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df_b = DataFrame(
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{"name": [1, 2, 3]}, index=Index([("x", 0), ("y", 0), ("x", 0)])
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)
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with tm.assert_produces_warning(
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PerformanceWarning, match="indexing past lexsort depth"
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):
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out_b = df_b.loc[("x", 0)]
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tm.assert_frame_equal(out_a, out_b)
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df1 = DataFrame({"name": ["a", "a", "b"]})
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df2 = DataFrame({"name": ["a", "b"]})
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df3 = DataFrame({"name": ["c", "d"]})
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df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
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with tm.assert_produces_warning(
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PerformanceWarning, match="indexing past lexsort depth"
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):
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out_a = df_a.loc[("x", 0), :]
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df_b = DataFrame(
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{
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"a": ["x", "x", "x", "y", "y", "x", "x"],
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"b": [0, 1, 2, 0, 1, 0, 1],
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"name": list("aababcd"),
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}
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).set_index(["a", "b"])
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df_b.index.names = [None, None]
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with tm.assert_produces_warning(
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PerformanceWarning, match="indexing past lexsort depth"
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):
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out_b = df_b.loc[("x", 0), :]
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tm.assert_frame_equal(out_a, out_b)
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def test_concat_with_duplicated_levels(self):
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# keyword levels should be unique
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df1 = DataFrame({"A": [1]}, index=["x"])
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df2 = DataFrame({"A": [1]}, index=["y"])
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msg = r"Level values not unique: \['x', 'y', 'y'\]"
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with pytest.raises(ValueError, match=msg):
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concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
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@pytest.mark.parametrize("levels", [[["x", "y"]], [["x", "y", "y"]]])
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def test_concat_with_levels_with_none_keys(self, levels):
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df1 = DataFrame({"A": [1]}, index=["x"])
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df2 = DataFrame({"A": [1]}, index=["y"])
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msg = "levels supported only when keys is not None"
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with pytest.raises(ValueError, match=msg):
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concat([df1, df2], levels=levels)
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def test_concat_range_index_result(self):
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# GH#47501
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df1 = DataFrame({"a": [1, 2]})
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df2 = DataFrame({"b": [1, 2]})
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result = concat([df1, df2], sort=True, axis=1)
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expected = DataFrame({"a": [1, 2], "b": [1, 2]})
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tm.assert_frame_equal(result, expected)
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expected_index = pd.RangeIndex(0, 2)
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tm.assert_index_equal(result.index, expected_index, exact=True)
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def test_concat_index_keep_dtype(self):
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# GH#47329
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df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype="object"))
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df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="object"))
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result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
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expected = DataFrame(
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[[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="object")
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)
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tm.assert_frame_equal(result, expected)
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def test_concat_index_keep_dtype_ea_numeric(self, any_numeric_ea_dtype):
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# GH#47329
|
df1 = DataFrame(
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[[0, 1, 1]], columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype)
|
)
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df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=any_numeric_ea_dtype))
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result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
|
expected = DataFrame(
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[[0, 1, 1.0], [0, 1, np.nan]],
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columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype),
|
)
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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))
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df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="Int32"))
|
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
|
expected = DataFrame(
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[[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):
|
# GH 46675
|
s1 = Series(["a", "b", "c"])
|
s2 = Series(["a", "b"])
|
s3 = Series(["a", "b", "c", "d"])
|
s4 = Series([], dtype=object)
|
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,
|
)
|
tm.assert_frame_equal(
|
result, expected, check_index_type=True, check_column_type=True
|
)
|
