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