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DOC: add SA01 for pandas.api.types.is_int64_dtype #59216

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DOC: add SA01 for pandas.api.types.is_int64_dtype #59216

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1 change: 0 additions & 1 deletion ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -302,7 +302,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
-i "pandas.api.types.is_float PR01,SA01" \
-i "pandas.api.types.is_float_dtype SA01" \
-i "pandas.api.types.is_hashable PR01,RT03,SA01" \
-i "pandas.api.types.is_int64_dtype SA01" \
-i "pandas.api.types.is_integer PR01,SA01" \
-i "pandas.api.types.is_integer_dtype SA01" \
-i "pandas.api.types.is_interval_dtype SA01" \
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7 changes: 7 additions & 0 deletions pandas/core/dtypes/common.py
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Expand Up @@ -829,6 +829,13 @@ def is_int64_dtype(arr_or_dtype) -> bool:
int)` will be True if the OS uses 64-bit integers and False if the OS
uses 32-bit integers.

See Also
--------
pandas.api.types.is_string_dtype : Check whether the provided array or dtype is of the string dtype.
pandas.api.types.is_float_dtype : Check whether the provided array or dtype is of a float dtype.
pandas.api.types.is_integer_dtype : Check whether the provided array or dtype is of an integer dtype.
pandas.api.types.is_numeric_dtype : Check whether the provided array or dtype is of a numeric dtype.

Examples
--------
>>> from pandas.api.types import is_int64_dtype
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