docs: Add a video tutorial by Naren Castellon

Closes #19

README.md

@@ -106,7 +106,8 @@ To see the latest entries in the list : <a href="https://www.trackawesomelist.co
 - [Polars Introduction for Python with a 128GB Ryzen 24-core Benchmark vs Pandas](https://www.youtube.com/watch?v=J0wpRP-ExVg&ab_channel=JeffHeaton) &#9203; `12 min` - A video that provides an introduction to Polars for Python by [\@jeffheaton](https://github.com/jeffheaton). Notebook used for the video [in this github repo](https://github.com/jeffheaton/present/blob/master/youtube/polars/polars.ipynb).
 - [Polars: The main alternative to pandas in Python!?](https://www.youtube.com/playlist?list=PLo9Vi5B84_dfAuwJqNYG4XhZMrGTF3sBx) &#9203; `57 min` - A Polars tutorial series on Youtube by [\@martinbel](https://github.com/martinbel). Notebooks and datasets used for the videos available [in this github repo](https://github.com/martinbel/polars-tutorial).
 - [Polars vs Pandas](https://www.youtube.com/watch?v=jU8Ghp7tRCU&ab_channel=hu-po) &#9203; `37 min` - A detailed video on Youtube that compares Polars and Pandas by [\@hu-po](https://github.com/hu-po).
-- [Polars: The Super Fast Dataframe Library for Python — Goodbye Pandas?](https://artificialcorner.com/polars-the-super-fast-dataframe-library-for-python-goodbye-pandas-85156e84337f) - An article and a [video](https://www.youtube.com/watch?v=CByx7XjYMhw) &#9203; `19 min` that explores some basic features of Polars by [@ifrankandrade](https://github.com/ifrankandrade).
+- [Polars: The Super Fast Dataframe Library for Python — Goodbye Pandas?](https://artificialcorner.com/polars-the-super-fast-dataframe-library-for-python-goodbye-pandas-85156e84337f) - An article and a [video](https://www.youtube.com/watch?v=CByx7XjYMhw) &#9203; `19 min` that explores some basic features of Polars by [@ifrankandrade](https://github.com/ifrankand
+- [Manipulación de Datos con Polars en python](https://www.youtube.com/watch?v=3RM3pWw2iRQ) - A detailed tutorial video in Spanish :es: that shows 20 Polars functions to perform 80% of the tasks of a data scientist by [Naren Castellon](https://www.linkedin.com/in/naren-castellon-1541b8101/?originalSubdomain=pa).
 
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