diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 7ed5103b3b796..44a6b91aeb565 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -123,13 +123,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.year GL08" \
         -i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
-        -i "pandas.api.types.is_bool PR01,SA01" \
-        -i "pandas.api.types.is_categorical_dtype SA01" \
-        -i "pandas.api.types.is_complex PR01,SA01" \
-        -i "pandas.api.types.is_complex_dtype SA01" \
-        -i "pandas.api.types.is_datetime64_dtype SA01" \
-        -i "pandas.api.types.is_datetime64_ns_dtype SA01" \
-        -i "pandas.api.types.is_datetime64tz_dtype SA01" \
         -i "pandas.api.types.is_dict_like PR07,SA01" \
         -i "pandas.api.types.is_extension_array_dtype SA01" \
         -i "pandas.api.types.is_file_like PR07,SA01" \
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index e1a2a0142c52e..47a31954b9d6c 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -1123,10 +1123,21 @@ def is_bool(obj: object) -> bool:
     """
     Return True if given object is boolean.
 
+    Parameters
+    ----------
+    obj : object
+        Object to check.
+
     Returns
     -------
     bool
 
+    See Also
+    --------
+    api.types.is_scalar : Check if the input is a scalar.
+    api.types.is_integer : Check if the input is an integer.
+    api.types.is_float : Check if the input is a float.
+
     Examples
     --------
     >>> pd.api.types.is_bool(True)
@@ -1142,10 +1153,22 @@ def is_complex(obj: object) -> bool:
     """
     Return True if given object is complex.
 
+    Parameters
+    ----------
+    obj : object
+        Object to check.
+
     Returns
     -------
     bool
 
+    See Also
+    --------
+    api.types.is_complex_dtype: Check whether the provided array or
+                                dtype is of a complex dtype.
+    api.types.is_number: Check if the object is a number.
+    api.types.is_integer: Return True if given object is integer.
+
     Examples
     --------
     >>> pd.api.types.is_complex(1 + 1j)
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index bcf1ade9b0320..16f6bd396fe93 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -279,6 +279,13 @@ def is_datetime64_dtype(arr_or_dtype) -> bool:
     boolean
         Whether or not the array-like or dtype is of the datetime64 dtype.
 
+    See Also
+    --------
+    api.types.is_datetime64_ns_dtype: Check whether the provided array or
+                                        dtype is of the datetime64[ns] dtype.
+    api.types.is_datetime64_any_dtype: Check whether the provided array or
+                                        dtype is of the datetime64 dtype.
+
     Examples
     --------
     >>> from pandas.api.types import is_datetime64_dtype
@@ -316,6 +323,13 @@ def is_datetime64tz_dtype(arr_or_dtype) -> bool:
     boolean
         Whether or not the array-like or dtype is of a DatetimeTZDtype dtype.
 
+    See Also
+    --------
+    api.types.is_datetime64_dtype: Check whether an array-like or
+                                        dtype is of the datetime64 dtype.
+    api.types.is_datetime64_any_dtype: Check whether the provided array or
+                                        dtype is of the datetime64 dtype.
+
     Examples
     --------
     >>> from pandas.api.types import is_datetime64tz_dtype
@@ -514,6 +528,12 @@ def is_categorical_dtype(arr_or_dtype) -> bool:
     boolean
         Whether or not the array-like or dtype is of the Categorical dtype.
 
+    See Also
+    --------
+    api.types.is_list_like: Check if the object is list-like.
+    api.types.is_complex_dtype: Check whether the provided array or
+                                dtype is of a complex dtype.
+
     Examples
     --------
     >>> from pandas.api.types import is_categorical_dtype
@@ -977,6 +997,13 @@ def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
     bool
         Whether or not the array or dtype is of the datetime64[ns] dtype.
 
+    See Also
+    --------
+    api.types.is_datetime64_dtype: Check whether an array-like or
+                                        dtype is of the datetime64 dtype.
+    api.types.is_datetime64_any_dtype: Check whether the provided array or
+                                        dtype is of the datetime64 dtype.
+
     Examples
     --------
     >>> from pandas.api.types import is_datetime64_ns_dtype
@@ -1436,6 +1463,14 @@ def is_complex_dtype(arr_or_dtype) -> bool:
     boolean
         Whether or not the array or dtype is of a complex dtype.
 
+    See Also
+    --------
+    api.types.is_complex: Return True if given object is complex.
+    api.types.is_numeric_dtype: Check whether the provided array or
+                                dtype is of a numeric dtype.
+    api.types.is_integer_dtype: Check whether the provided array or
+                                dtype is of an integer dtype.
+
     Examples
     --------
     >>> from pandas.api.types import is_complex_dtype