Comprehensive Guide on Data Visualization with Pandas

I really am not sure how "comprehensive" I would call this, after glancing over it it looks like one of the million currently existing basic pandas plotting guides.

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.<p>Pandas feels like the wrong tool for this job. I don&#x27;t use multi-indexes or any statistical methods. I don&#x27;t chart anything.<p>But it&#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.<p>Are there better options in this space?

Other graphing libs for jupyter notebooks include:<p><pre><code> - Bokeh - Plotly - Seaborn </code></pre> These libraries were built to improve upon matplotlib or each other, weren&#x27;t they? Yet, people continue to reach for &quot;the original&quot; ¯\(ツ)&#x2F;¯

The matlab plotting syntax got transferred to Python through matplotlib and got very deeply ingrained - was first, got popular, built into pandas and statsmodels, foundation of seaborn etc. Recently I saw a snippet of Julia code that uses &quot;pyplot&quot;, I assume because people find it familiar and convenient. That API just refuses to die.

The pandas wrappers around matplotlib are convenient but for anything that needs customising, you&#x27;ll need to reach for the full matplotlib API anyway.

Does anybody here think that Pandas API design is ugly and inconsistent? It feels like hack after hack.

I strongly recommend Altair (<a href="https:&#x2F;&#x2F;altair-viz.github.io&#x2F;" rel="nofollow">https:&#x2F;&#x2F;altair-viz.github.io&#x2F;</a>) as an extremely Pandas-friendly alternative approach to data visualization. It&#x27;s the first library that has successfully &quot;hidden&quot; the ugly, gnarled matplotlib layer underneath for me. It also looks killer.

I recommend reading the official docs. I think they improved the plotting interface recently, and I learned a lot from reading through the guide:<p><a href="https:&#x2F;&#x2F;pandas.pydata.org&#x2F;pandas-docs&#x2F;stable&#x2F;user_guide&#x2F;visualization.html" rel="nofollow">https:&#x2F;&#x2F;pandas.pydata.org&#x2F;pandas-docs&#x2F;stable&#x2F;user_guide&#x2F;visu...</a>

Here&#x27;s a dataset link that doesn&#x27;t require registering with Kaggle:<p><pre><code> https:&#x2F;&#x2F;www.teodorheggelund.com&#x2F;static&#x2F;world-happiness-report-2019.zip</code></pre>

I had no idea what Pandas was (other than the plural of the cute fluffy creature). I was really hoping this was going to talk about how to use panda images to visualize data!

The name “Pandas” is really misleading, especially in the title of this article. It&#x27;s missing the phrase, “No animals were harmed in the making of these graphs”.

It shows how to generate the different charts but it doesn&#x27;t show you how to save them as a image.

Pandas should listen to the unix philosophy a bit and remove its plotting API.

I would have thought if you focused on bamboo leaves as the main visual motif then that would keep their attention.

Leave those peaceful furry creatures alone, please.