<story><title>Comprehensive Guide on Data Visualization with Pandas</title><url>https://kanoki.org/2019/09/16/dataframe-visualization-with-pandas-plot/</url></story><parent_chain></parent_chain><comment><author>lawlorino</author><text>I really am not sure how &amp;quot;comprehensive&amp;quot; I would call this, after glancing over it it looks like one of the million currently existing basic pandas plotting guides.</text></comment>
<story><title>Comprehensive Guide on Data Visualization with Pandas</title><url>https://kanoki.org/2019/09/16/dataframe-visualization-with-pandas-plot/</url></story><parent_chain></parent_chain><comment><author>whitehouse3</author><text>Lately, `pip install pandas` is my first step after making a new virtual environment. Its read_sql and read_csv methods are magic. The resulting DataFrames are just like DataTables in C#. And for complex joins and aggregations, I can DataFrame.to_sql into an in-memory SQLite database.&lt;p&gt;Pandas feels like the wrong tool for this job. I don&amp;#x27;t use multi-indexes or any statistical methods. I don&amp;#x27;t chart anything.&lt;p&gt;But it&amp;#x27;s so darn convenient. If the time comes to optimize I can `import csv` directly and improve performance. But nothing beats it for prototyping.&lt;p&gt;Are there better options in this space?</text></comment>