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Revert addition of 'None' to default na_values of the parser #53912

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Revert addition of 'None' to default na_values of the parser #53912

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2 changes: 1 addition & 1 deletion doc/source/user_guide/io.rst
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Expand Up @@ -1149,7 +1149,7 @@ To completely override the default values that are recognized as missing, specif
.. _io.navaluesconst:

The default ``NaN`` recognized values are ``['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', '#N/A', 'N/A',
'n/a', 'NA', '<NA>', '#NA', 'NULL', 'null', 'NaN', '-NaN', 'nan', '-nan', 'None', '']``.
'n/a', 'NA', '<NA>', '#NA', 'NULL', 'null', 'NaN', '-NaN', 'nan', '-nan', '']``.

Let us consider some examples:

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1 change: 0 additions & 1 deletion pandas/_libs/parsers.pyx
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Expand Up @@ -1410,7 +1410,6 @@ STR_NA_VALUES = {
"nan",
"-nan",
"",
"None",
}
_NA_VALUES = _ensure_encoded(list(STR_NA_VALUES))

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1 change: 0 additions & 1 deletion pandas/tests/io/parser/test_na_values.py
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Expand Up @@ -113,7 +113,6 @@ def test_default_na_values(all_parsers):
"-nan",
"#N/A N/A",
"",
"None",
}
assert _NA_VALUES == STR_NA_VALUES

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1 change: 1 addition & 0 deletions pandas/tests/io/test_html.py
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Expand Up @@ -178,6 +178,7 @@ def test_dtype_backend(self, string_storage, dtype_backend):
"b": Series([1, 2, 3], dtype="Int64"),
"c": Series([1.5, np.nan, 2.5], dtype="Float64"),
"d": Series([1.5, 2.0, 2.5], dtype="Float64"),
# "e": Series([True, False, None], dtype="boolean"),
"e": [True, False, None],
"f": [True, False, True],
"g": ["a", "b", "c"],
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