@@ -45,6 +45,11 @@ class NullFrequencyError(ValueError):
4545 Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
4646 ``PeriodIndex.shift``.
4747
48+ See Also
49+ --------
50+ Index.shift : Shift values of Index.
51+ Series.shift : Shift values of Series.
52+
4853 Examples
4954 --------
5055 >>> df = pd.DatetimeIndex(["2011-01-01 10:00", "2011-01-01"], freq=None)
@@ -58,6 +63,12 @@ class PerformanceWarning(Warning):
5863 """
5964 Warning raised when there is a possible performance impact.
6065
66+ See Also
67+ --------
68+ DataFrame.set_index : Set the DataFrame index using existing columns.
69+ DataFrame.loc : Access a group of rows and columns by label(s) \
70+ or a boolean array.
71+
6172 Examples
6273 --------
6374 >>> df = pd.DataFrame(
@@ -385,17 +396,24 @@ class NumbaUtilError(Exception):
385396 """
386397 Error raised for unsupported Numba engine routines.
387398
399+ See Also
400+ --------
401+ DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.
402+ Series.groupby : Group Series using a mapper or by a Series of columns.
403+ DataFrame.agg : Aggregate using one or more operations over the specified axis.
404+ Series.agg : Aggregate using one or more operations over the specified axis.
405+
388406 Examples
389407 --------
390408 >>> df = pd.DataFrame(
391409 ... {"key": ["a", "a", "b", "b"], "data": [1, 2, 3, 4]}, columns=["key", "data"]
392410 ... )
393411 >>> def incorrect_function(x):
394412 ... return sum(x) * 2.7
395- >>> df.groupby ("key").agg(incorrect_function, engine="numba")
413+ >>> df.("key").agg(incorrect_function, engine="numba")
396414 Traceback (most recent call last):
397415 NumbaUtilError: The first 2 arguments to incorrect_function
398- must be ['values', 'index']
416+ must be ['values', 'index']groupby
399417 """
400418
401419
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