From c72868e9f19fe496486773f69e5558f90f999216 Mon Sep 17 00:00:00 2001
From: brandon-b-miller <53796099+brandon-b-miller@users.noreply.github.com>
Date: Wed, 13 Apr 2022 16:35:37 -0500
Subject: [PATCH] Remove implementation details from `apply` docstrings
 (#10651)

Removes some unnecessary implementation detail from `apply` docstrings and updates them where necessary.

Authors:
  - https://github.com/brandon-b-miller

Approvers:
  - GALI PREM SAGAR (https://github.com/galipremsagar)
  - Ashwin Srinath (https://github.com/shwina)

URL: https://github.com/rapidsai/cudf/pull/10651
---
 python/cudf/cudf/core/dataframe.py | 25 +++++++++++++------------
 python/cudf/cudf/core/series.py    | 16 +++++++++++-----
 2 files changed, 24 insertions(+), 17 deletions(-)

diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index 277fd5aae57..7c209086663 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -3564,12 +3564,13 @@ def apply(
     ):
         """
         Apply a function along an axis of the DataFrame.
-
-        Designed to mimic `pandas.DataFrame.apply`. Applies a user
-        defined function row wise over a dataframe, with true null
-        handling. Works with UDFs using `core.udf.pipeline.nulludf`
-        and returns a single series. Uses numba to jit compile the
-        function to PTX via LLVM.
+        ``apply`` relies on Numba to JIT compile ``func``.
+        Thus the allowed operations within ``func`` are limited
+        to the ones specified
+        [here](https://numba.pydata.org/numba-doc/latest/cuda/cudapysupported.html).
+        For more information, see the cuDF guide
+        to user defined functions found
+        [here](https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html).
 
         Parameters
         ----------
@@ -3590,7 +3591,7 @@ def apply(
         Examples
         --------
 
-        Simple function of a single variable which could be NA
+        Simple function of a single variable which could be NA:
 
         >>> def f(row):
         ...     if row['a'] is cudf.NA:
@@ -3606,7 +3607,7 @@ def apply(
         dtype: int64
 
         Function of multiple variables will operate in
-        a null aware manner
+        a null aware manner:
 
         >>> def f(row):
         ...     return row['a'] - row['b']
@@ -3622,7 +3623,7 @@ def apply(
         3    <NA>
         dtype: int64
 
-        Functions may conditionally return NA as in pandas
+        Functions may conditionally return NA as in pandas:
 
         >>> def f(row):
         ...     if row['a'] + row['b'] > 3:
@@ -3641,7 +3642,7 @@ def apply(
         dtype: int64
 
         Mixed types are allowed, but will return the common
-        type, rather than object as in pandas
+        type, rather than object as in pandas:
 
         >>> def f(row):
         ...     return row['a'] + row['b']
@@ -3658,7 +3659,7 @@ def apply(
 
         Functions may also return scalar values, however the
         result will be promoted to a safe type regardless of
-        the data
+        the data:
 
         >>> def f(row):
         ...     if row['a'] > 3:
@@ -3675,7 +3676,7 @@ def apply(
         2    5.0
         dtype: float64
 
-        Ops against N columns are supported generally
+        Ops against N columns are supported generally:
 
         >>> def f(row):
         ...     v, w, x, y, z = (
diff --git a/python/cudf/cudf/core/series.py b/python/cudf/cudf/core/series.py
index 5bf52ed7520..6e15c03e6b4 100644
--- a/python/cudf/cudf/core/series.py
+++ b/python/cudf/cudf/core/series.py
@@ -2021,9 +2021,15 @@ def _return_sentinel_series():
     def apply(self, func, convert_dtype=True, args=(), **kwargs):
         """
         Apply a scalar function to the values of a Series.
+        Similar to ``pandas.Series.apply``.
 
-        Similar to `pandas.Series.apply. Applies a user
-        defined function elementwise over a series.
+        ``apply`` relies on Numba to JIT compile ``func``.
+        Thus the allowed operations within ``func`` are limited
+        to the ones specified
+        [here](https://numba.pydata.org/numba-doc/latest/cuda/cudapysupported.html).
+        For more information, see the cuDF guide to
+        user defined functions found
+        [here](https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html).
 
         Parameters
         ----------
@@ -2061,7 +2067,7 @@ def apply(self, func, convert_dtype=True, args=(), **kwargs):
         2    4
         dtype: int64
 
-        Apply a basic function to a series with nulls
+        Apply a basic function to a series with nulls:
 
         >>> sr = cudf.Series([1,cudf.NA,3])
         >>> def f(x):
@@ -2073,7 +2079,7 @@ def apply(self, func, convert_dtype=True, args=(), **kwargs):
         dtype: int64
 
         Use a function that does something conditionally,
-        based on if the value is or is not null
+        based on if the value is or is not null:
 
         >>> sr = cudf.Series([1,cudf.NA,3])
         >>> def f(x):
@@ -2091,7 +2097,7 @@ def apply(self, func, convert_dtype=True, args=(), **kwargs):
         as derived from the UDFs logic. Note that this means
         the common type will be returned even if such data
         is passed that would not result in any values of that
-        dtype.
+        dtype:
 
         >>> sr = cudf.Series([1,cudf.NA,3])
         >>> def f(x):