diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index adc5bc9a01bdd..7bc220acdd74c 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -95,9 +95,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.resample.Resampler.std SA01" \
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.var SA01" \
-        -i "pandas.errors.NullFrequencyError SA01" \
-        -i "pandas.errors.NumbaUtilError SA01" \
-        -i "pandas.errors.PerformanceWarning SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py
index 1de6f06ef316c..cd31ec30522c3 100644
--- a/pandas/errors/__init__.py
+++ b/pandas/errors/__init__.py
@@ -45,6 +45,11 @@ class NullFrequencyError(ValueError):
     Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
     ``PeriodIndex.shift``.
 
+    See Also
+    --------
+    Index.shift : Shift values of Index.
+    Series.shift : Shift values of Series.
+
     Examples
     --------
     >>> df = pd.DatetimeIndex(["2011-01-01 10:00", "2011-01-01"], freq=None)
@@ -58,6 +63,12 @@ class PerformanceWarning(Warning):
     """
     Warning raised when there is a possible performance impact.
 
+    See Also
+    --------
+    DataFrame.set_index : Set the DataFrame index using existing columns.
+    DataFrame.loc : Access a group of rows and columns by label(s) \
+    or a boolean array.
+
     Examples
     --------
     >>> df = pd.DataFrame(
@@ -385,6 +396,13 @@ class NumbaUtilError(Exception):
     """
     Error raised for unsupported Numba engine routines.
 
+    See Also
+    --------
+    DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.
+    Series.groupby : Group Series using a mapper or by a Series of columns.
+    DataFrame.agg : Aggregate using one or more operations over the specified axis.
+    Series.agg : Aggregate using one or more operations over the specified axis.
+
     Examples
     --------
     >>> df = pd.DataFrame(