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230 lines
8.6 KiB
230 lines
8.6 KiB
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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concat,
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)
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import pandas._testing as tm
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class TestDataFrameConcat:
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def test_concat_multiple_frames_dtypes(self):
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# GH#2759
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df1 = DataFrame(data=np.ones((10, 2)), columns=["foo", "bar"], dtype=np.float64)
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df2 = DataFrame(data=np.ones((10, 2)), dtype=np.float32)
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results = concat((df1, df2), axis=1).dtypes
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expected = Series(
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[np.dtype("float64")] * 2 + [np.dtype("float32")] * 2,
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index=["foo", "bar", 0, 1],
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)
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tm.assert_series_equal(results, expected)
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def test_concat_tuple_keys(self):
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# GH#14438
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df1 = DataFrame(np.ones((2, 2)), columns=list("AB"))
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df2 = DataFrame(np.ones((3, 2)) * 2, columns=list("AB"))
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results = concat((df1, df2), keys=[("bee", "bah"), ("bee", "boo")])
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expected = DataFrame(
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{
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"A": {
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("bee", "bah", 0): 1.0,
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("bee", "bah", 1): 1.0,
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("bee", "boo", 0): 2.0,
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("bee", "boo", 1): 2.0,
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("bee", "boo", 2): 2.0,
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},
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"B": {
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("bee", "bah", 0): 1.0,
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("bee", "bah", 1): 1.0,
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("bee", "boo", 0): 2.0,
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("bee", "boo", 1): 2.0,
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("bee", "boo", 2): 2.0,
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},
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}
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)
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tm.assert_frame_equal(results, expected)
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def test_concat_named_keys(self):
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# GH#14252
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df = DataFrame({"foo": [1, 2], "bar": [0.1, 0.2]})
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index = Index(["a", "b"], name="baz")
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concatted_named_from_keys = concat([df, df], keys=index)
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expected_named = DataFrame(
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{"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
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index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=["baz", None]),
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)
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tm.assert_frame_equal(concatted_named_from_keys, expected_named)
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index_no_name = Index(["a", "b"], name=None)
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concatted_named_from_names = concat([df, df], keys=index_no_name, names=["baz"])
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tm.assert_frame_equal(concatted_named_from_names, expected_named)
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concatted_unnamed = concat([df, df], keys=index_no_name)
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expected_unnamed = DataFrame(
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{"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
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index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=[None, None]),
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)
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tm.assert_frame_equal(concatted_unnamed, expected_unnamed)
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def test_concat_axis_parameter(self):
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# GH#14369
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df1 = DataFrame({"A": [0.1, 0.2]}, index=range(2))
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df2 = DataFrame({"A": [0.3, 0.4]}, index=range(2))
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# Index/row/0 DataFrame
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expected_index = DataFrame({"A": [0.1, 0.2, 0.3, 0.4]}, index=[0, 1, 0, 1])
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concatted_index = concat([df1, df2], axis="index")
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tm.assert_frame_equal(concatted_index, expected_index)
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concatted_row = concat([df1, df2], axis="rows")
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tm.assert_frame_equal(concatted_row, expected_index)
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concatted_0 = concat([df1, df2], axis=0)
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tm.assert_frame_equal(concatted_0, expected_index)
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# Columns/1 DataFrame
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expected_columns = DataFrame(
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[[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=["A", "A"]
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)
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concatted_columns = concat([df1, df2], axis="columns")
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tm.assert_frame_equal(concatted_columns, expected_columns)
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concatted_1 = concat([df1, df2], axis=1)
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tm.assert_frame_equal(concatted_1, expected_columns)
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series1 = Series([0.1, 0.2])
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series2 = Series([0.3, 0.4])
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# Index/row/0 Series
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expected_index_series = Series([0.1, 0.2, 0.3, 0.4], index=[0, 1, 0, 1])
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concatted_index_series = concat([series1, series2], axis="index")
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tm.assert_series_equal(concatted_index_series, expected_index_series)
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concatted_row_series = concat([series1, series2], axis="rows")
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tm.assert_series_equal(concatted_row_series, expected_index_series)
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concatted_0_series = concat([series1, series2], axis=0)
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tm.assert_series_equal(concatted_0_series, expected_index_series)
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# Columns/1 Series
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expected_columns_series = DataFrame(
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[[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=[0, 1]
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)
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concatted_columns_series = concat([series1, series2], axis="columns")
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tm.assert_frame_equal(concatted_columns_series, expected_columns_series)
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concatted_1_series = concat([series1, series2], axis=1)
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tm.assert_frame_equal(concatted_1_series, expected_columns_series)
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# Testing ValueError
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with pytest.raises(ValueError, match="No axis named"):
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concat([series1, series2], axis="something")
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def test_concat_numerical_names(self):
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# GH#15262, GH#12223
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df = DataFrame(
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{"col": range(9)},
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dtype="int32",
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index=(
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pd.MultiIndex.from_product(
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[["A0", "A1", "A2"], ["B0", "B1", "B2"]], names=[1, 2]
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)
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),
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)
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result = concat((df.iloc[:2, :], df.iloc[-2:, :]))
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expected = DataFrame(
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{"col": [0, 1, 7, 8]},
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dtype="int32",
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index=pd.MultiIndex.from_tuples(
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[("A0", "B0"), ("A0", "B1"), ("A2", "B1"), ("A2", "B2")], names=[1, 2]
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),
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)
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tm.assert_frame_equal(result, expected)
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def test_concat_astype_dup_col(self):
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# GH#23049
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df = DataFrame([{"a": "b"}])
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df = concat([df, df], axis=1)
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result = df.astype("category")
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expected = DataFrame(
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np.array(["b", "b"]).reshape(1, 2), columns=["a", "a"]
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).astype("category")
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tm.assert_frame_equal(result, expected)
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def test_concat_dataframe_keys_bug(self, sort):
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t1 = DataFrame(
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{"value": Series([1, 2, 3], index=Index(["a", "b", "c"], name="id"))}
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)
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t2 = DataFrame({"value": Series([7, 8], index=Index(["a", "b"], name="id"))})
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# it works
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result = concat([t1, t2], axis=1, keys=["t1", "t2"], sort=sort)
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assert list(result.columns) == [("t1", "value"), ("t2", "value")]
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def test_concat_bool_with_int(self):
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# GH#42092 we may want to change this to return object, but that
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# would need a deprecation
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df1 = DataFrame(Series([True, False, True, True], dtype="bool"))
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df2 = DataFrame(Series([1, 0, 1], dtype="int64"))
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result = concat([df1, df2])
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expected = concat([df1.astype("int64"), df2])
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tm.assert_frame_equal(result, expected)
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def test_concat_duplicates_in_index_with_keys(self):
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# GH#42651
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index = [1, 1, 3]
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data = [1, 2, 3]
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df = DataFrame(data=data, index=index)
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result = concat([df], keys=["A"], names=["ID", "date"])
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mi = pd.MultiIndex.from_product([["A"], index], names=["ID", "date"])
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expected = DataFrame(data=data, index=mi)
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tm.assert_frame_equal(result, expected)
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tm.assert_index_equal(result.index.levels[1], Index([1, 3], name="date"))
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@pytest.mark.parametrize("ignore_index", [True, False])
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@pytest.mark.parametrize("order", ["C", "F"])
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@pytest.mark.parametrize("axis", [0, 1])
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def test_concat_copies(self, axis, order, ignore_index):
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# based on asv ConcatDataFrames
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df = DataFrame(np.zeros((10000, 200), dtype=np.float32, order=order))
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res = concat([df] * 5, axis=axis, ignore_index=ignore_index, copy=True)
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for arr in res._iter_column_arrays():
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for arr2 in df._iter_column_arrays():
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assert not np.shares_memory(arr, arr2)
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def test_outer_sort_columns(self):
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# GH#47127
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df1 = DataFrame({"A": [0], "B": [1], 0: 1})
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df2 = DataFrame({"A": [100]})
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result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
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expected = DataFrame({0: [1.0, np.nan], "A": [0, 100], "B": [1.0, np.nan]})
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tm.assert_frame_equal(result, expected)
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def test_inner_sort_columns(self):
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# GH#47127
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df1 = DataFrame({"A": [0], "B": [1], 0: 1})
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df2 = DataFrame({"A": [100], 0: 2})
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result = concat([df1, df2], ignore_index=True, join="inner", sort=True)
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expected = DataFrame({0: [1, 2], "A": [0, 100]})
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tm.assert_frame_equal(result, expected)
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def test_sort_columns_one_df(self):
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# GH#47127
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df1 = DataFrame({"A": [100], 0: 2})
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result = concat([df1], ignore_index=True, join="inner", sort=True)
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expected = DataFrame({0: [2], "A": [100]})
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tm.assert_frame_equal(result, expected)
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