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| 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((10000, 200), 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)
|
|
