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<p dir="auto">At the DoJo tonight some folks were not aware of some links provided by other members of our group.</p>


<p dir="auto">Daniel Kim shared this link to his “cheat sheet” for working with Pandas.</p>


<ul>

<li>   <a href="https://nbviewer.jupyter.org/github/pybokeh/jupyter_notebooks/blob/master/pandas/PandasCheatSheet.ipynb" style="color:#3983C4">https://nbviewer.jupyter.org/github/pybokeh/jupyter_notebooks/blob/master/pandas/PandasCheatSheet.ipynb</a></li>

</ul>


<p dir="auto">He also shared his thoughts about another resource for Pandas: </p>


<ul>

<li>   “I also wholeheartedly recommend Pandas Cookbook by Ted Petrou <a href="https://www.amazon.com/Pandas-Cookbook-Scientific-Computing-Visualization/dp/1784393878" style="color:#3983C4">https://www.amazon.com/Pandas-Cookbook-Scientific-Computing-Visualization/dp/1784393878</a>.  I usually recommend it over Wes McKinney's book.  I think the official pandas docs can be used to replace Wes' book, but that is IMO.”</li>

</ul>


<p dir="auto">Here is Daniel’s suggestions for numpy resources:</p>


<ul>

<li>   <a href="https://docs.scipy.org/doc/numpy/" style="color:#3983C4">https://docs.scipy.org/doc/numpy/</a></li>

<li>   <a href="http://www.scipy-lectures.org/" style="color:#3983C4">http://www.scipy-lectures.org/</a></li>

<li>   <a href="https://datascientistnotebook.com/2017/04/01/numpy-in-50-cells-of-notebook/" style="color:#3983C4">https://datascientistnotebook.com/2017/04/01/numpy-in-50-cells-of-notebook/</a></li>

</ul>


<p dir="auto">Hopefully some of the “lurkers” on this list will find this helpful as well.</p>


<p dir="auto">Travis</p>

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