Updated script that can be controled by Nodejs web app
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import numpy as np
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import pandas as pd
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import pandas._testing as tm
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from pandas.tests.extension.array_with_attr import FloatAttrArray
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def test_concat_with_all_na():
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# https://github.com/pandas-dev/pandas/pull/47762
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# ensure that attribute of the column array is preserved (when it gets
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# preserved in reindexing the array) during merge/concat
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arr = FloatAttrArray(np.array([np.nan, np.nan], dtype="float64"), attr="test")
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df1 = pd.DataFrame({"col": arr, "key": [0, 1]})
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df2 = pd.DataFrame({"key": [0, 1], "col2": [1, 2]})
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result = pd.merge(df1, df2, on="key")
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expected = pd.DataFrame({"col": arr, "key": [0, 1], "col2": [1, 2]})
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tm.assert_frame_equal(result, expected)
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assert result["col"].array.attr == "test"
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df1 = pd.DataFrame({"col": arr, "key": [0, 1]})
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df2 = pd.DataFrame({"key": [0, 2], "col2": [1, 2]})
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result = pd.merge(df1, df2, on="key")
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expected = pd.DataFrame({"col": arr.take([0]), "key": [0], "col2": [1]})
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tm.assert_frame_equal(result, expected)
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assert result["col"].array.attr == "test"
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result = pd.concat([df1.set_index("key"), df2.set_index("key")], axis=1)
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expected = pd.DataFrame(
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{"col": arr.take([0, 1, -1]), "col2": [1, np.nan, 2], "key": [0, 1, 2]}
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).set_index("key")
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tm.assert_frame_equal(result, expected)
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assert result["col"].array.attr == "test"
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