Pandas: further reading

Author: ashwinvis

content/pandas.rst

@@ -609,9 +609,17 @@ apply your own functions to the data using :obj:`~pandas.DataFrame.apply`::
 
 
 Note that the numpy precision for integers caps at int64 while python ints are unbounded --
-limited by memory size. Thus, the result from fibonacci(99) would be erroneous when
-using numpy ints. The type of df['Number of Rabbits'][99] given by both functions above
-is in fact <class 'int'>.
+limited by memory size. Thus, the result from ``fibonacci(99)`` would be erroneous when
+using numpy ints. The type of ``df['Number of Rabbits'][99]`` given by both functions above
+is in fact ``<class 'int'>``.
+
+.. seealso::
+
+   - `Modern Pandas <https://tomaugspurger.net/posts/modern-1-intro/>`__ (2020) -- a blog series
+     on writing modern idiomatic pandas.
+   - `Python Data Science Handbook <https://jakevdp.github.io/PythonDataScienceHandbook/index.html>`__ (2016) --
+     which contains a chapter on `Data Manipulation with Pandas`.
+
 
 Alternatives to Pandas
 ----------------------