Talk Python to Me: #343: Do Excel things, get notebook Python code with Mito

Here's a question: What's the most common way to explore data? Would you say pandas and matplotlib? Maybe you went more general and said Jupyter notebooks. How about Excel, or Google Sheets, or Numbers, or some other spreadsheet app? Yeah, my bet is on Excel. And while it has many drawbacks, it makes exploring tabular data very accessible to many people, most of whom aren't even developers or data scientists. <br/> <br/> On this episode, we're talking about a tool called Mito. This is an add-in for Jupyter notebooks that injects an Excel-like interface into the notebook. You pass it data via a pandas dataframe (or some other source) and then you can explore it as if you're using Excel. The cool thing is though, just below that, it's writing the pandas code you'd need to do to actually accomplish that outcome in code. <br/> <br/> I think this will make pandas and Python data exploration way more accessible to many more people. So if you've been intimidated by pandas, or know someone who has, this could be what you've been looking for.<br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Mito</b>: <a href="https://www.trymito.io/" target="_blank" rel="noopener">trymito.io</a><br/> <b>Mito summary stats</b>: <a href="https://ift.tt/3xM6Hv5" target="_blank" rel="noopener">trymito.io</a><br/> <b>pandas-profiling package</b>: <a href="https://ift.tt/2EttSgM" target="_blank" rel="noopener">github.com</a><br/> <b>Lux API</b>: <a href="https://ift.tt/3rwqFJ0" target="_blank" rel="noopener">pypi.org</a><br/> <b>Hex notebooks</b>: <a href="https://ift.tt/3o982sT" target="_blank" rel="noopener">medium.com</a><br/> <b>Deepnote</b>: <a href="https://deepnote.com/" target="_blank" rel="noopener">deepnote.com</a><br/> <b>Papermill</b>: <a href="https://ift.tt/3D6gQDT" target="_blank" rel="noopener">papermill.readthedocs.io</a><br/> <b>JupterLite</b>: <a href="https://ift.tt/2UIzTDM" target="_blank" rel="noopener">jupyter.org</a><br/> <b>Jupyter Desktop App</b>: <a href="https://ift.tt/3AN5J1J" target="_blank" rel="noopener">github.com</a><br/> <b>Jut</b>: <a href="https://ift.tt/3lwIjaP" target="_blank" rel="noopener">github.com</a><br/> <b>Jupyter project</b>: <a href="https://jupyter.org/" target="_blank" rel="noopener">jupyter.org</a><br/> <b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=XAGmSPZsYLU" target="_blank" rel="noopener">youtube.com</a><br/> <b>Episode transcripts</b>: <a href="https://ift.tt/3pdyWyP" target="_blank" rel="noopener">talkpython.fm</a><br/> <br/> <b>--- Stay in touch with us ---</b><br/> <b>Subscribe on YouTube</b>: <a href="https://ift.tt/3DznvIg" target="_blank" rel="noopener">youtube.com</a><br/> <b>Follow Talk Python on Twitter</b>: <a href="https://twitter.com/talkpython" target="_blank" rel="noopener">@talkpython</a><br/> <b>Follow Michael on Twitter</b>: <a href="https://twitter.com/mkennedy" target="_blank" rel="noopener">@mkennedy</a><br/></div><br/> <strong>Sponsors</strong><br/> <a href='https://ift.tt/3k8IUjq> <a href='https://ift.tt/3aBjB2k> <a href='https://ift.tt/3vjihuL> <a href='https://ift.tt/2PVc9qH Python Training</a>

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