70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
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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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import pandas._testing as tm
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@pytest.mark.parametrize(
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"to_concat_dtypes, result_dtype",
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[
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(["Int64", "Int64"], "Int64"),
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(["UInt64", "UInt64"], "UInt64"),
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(["Int8", "Int8"], "Int8"),
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(["Int8", "Int16"], "Int16"),
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(["UInt8", "Int8"], "Int16"),
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(["Int32", "UInt32"], "Int64"),
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(["Int64", "UInt64"], "Float64"),
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(["Int64", "boolean"], "object"),
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(["UInt8", "boolean"], "object"),
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],
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)
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def test_concat_series(to_concat_dtypes, result_dtype):
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# we expect the same dtypes as we would get with non-masked inputs,
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# just masked where available.
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result = pd.concat([pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes])
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expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype(
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result_dtype
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)
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tm.assert_series_equal(result, expected)
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# order doesn't matter for result
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result = pd.concat(
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[pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes[::-1]]
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)
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expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype(
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result_dtype
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)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"to_concat_dtypes, result_dtype",
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[
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(["Int64", "int64"], "Int64"),
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(["UInt64", "uint64"], "UInt64"),
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(["Int8", "int8"], "Int8"),
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(["Int8", "int16"], "Int16"),
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(["UInt8", "int8"], "Int16"),
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(["Int32", "uint32"], "Int64"),
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(["Int64", "uint64"], "Float64"),
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(["Int64", "bool"], "object"),
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(["UInt8", "bool"], "object"),
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],
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)
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def test_concat_series_with_numpy(to_concat_dtypes, result_dtype):
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# we expect the same dtypes as we would get with non-masked inputs,
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# just masked where available.
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s1 = pd.Series([0, 1, pd.NA], dtype=to_concat_dtypes[0])
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s2 = pd.Series(np.array([0, 1], dtype=to_concat_dtypes[1]))
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result = pd.concat([s1, s2], ignore_index=True)
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expected = pd.Series([0, 1, pd.NA, 0, 1], dtype=object).astype(result_dtype)
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tm.assert_series_equal(result, expected)
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# order doesn't matter for result
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result = pd.concat([s2, s1], ignore_index=True)
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expected = pd.Series([0, 1, 0, 1, pd.NA], dtype=object).astype(result_dtype)
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tm.assert_series_equal(result, expected)
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