diff --git a/ci/code_checks.sh b/ci/code_checks.sh index c53265e126bf0..73f532ac1d081 100755 --- a/ci/code_checks.sh +++ b/ci/code_checks.sh @@ -284,12 +284,9 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then -i "pandas.api.types.is_iterator PR07,SA01" \ -i "pandas.api.types.is_list_like SA01" \ -i "pandas.api.types.is_named_tuple PR07,SA01" \ - -i "pandas.api.types.is_numeric_dtype SA01" \ -i "pandas.api.types.is_object_dtype SA01" \ - -i "pandas.api.types.is_period_dtype SA01" \ -i "pandas.api.types.is_re PR07,SA01" \ -i "pandas.api.types.is_re_compilable PR07,SA01" \ - -i "pandas.api.types.is_timedelta64_ns_dtype SA01" \ -i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \ -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \ -i "pandas.arrays.BooleanArray SA01" \ diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py index 3c11b9d723c14..64b5278424192 100644 --- a/pandas/core/dtypes/common.py +++ b/pandas/core/dtypes/common.py @@ -412,6 +412,13 @@ def is_period_dtype(arr_or_dtype) -> bool: boolean Whether or not the array-like or dtype is of the Period dtype. + See Also + -------- + api.types.is_timedelta64_ns_dtype : Check whether the provided array or dtype is + of the timedelta64[ns] dtype. + api.types.is_timedelta64_dtype: Check whether an array-like or dtype + is of the timedelta64 dtype. + Examples -------- >>> from pandas.core.dtypes.common import is_period_dtype @@ -1021,6 +1028,11 @@ def is_timedelta64_ns_dtype(arr_or_dtype) -> bool: boolean Whether or not the array or dtype is of the timedelta64[ns] dtype. + See Also + -------- + api.types.is_timedelta64_dtype: Check whether an array-like or dtype + is of the timedelta64 dtype. + Examples -------- >>> from pandas.core.dtypes.common import is_timedelta64_ns_dtype @@ -1140,6 +1152,15 @@ def is_numeric_dtype(arr_or_dtype) -> bool: boolean Whether or not the array or dtype is of a numeric dtype. + See Also + -------- + api.types.is_integer_dtype: Check whether the provided array or dtype + is of an integer dtype. + api.types.is_unsigned_integer_dtype: Check whether the provided array + or dtype is of an unsigned integer dtype. + api.types.is_signed_integer_dtype: Check whether the provided array + or dtype is of an signed integer dtype. + Examples -------- >>> from pandas.api.types import is_numeric_dtype