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Mito looks cool. I'm hopeful a tool like it can create a bridge between Excel-based analysts/researchers and more mature application flows. Another tool like Mito is Bamboo: https://bamboolib.8080labs.com/
If others are interested, Mito does not work in vscode or Google Collab. Only classic Jupyter Notebooks and Jupyter Labs are supported currently [1]. [1] https://docs.trymito.io/misc/faq
Thanks for checking that! I use vscode so this is a no go...
For the time being, because Mito generates pandas code for every edit you make, you can always use Mito in Jupyter to generate code, and then copy it over to VSCode. Admittedly, its not as nice of a workflow, but it does work!
I like this. Is a "friendlier" way to browse data. Said that, I have to add: Exploring large datasets requires a COMPLETELY different mindset. When your data starts growing, it's impossible to keep it all in a visual format (for 2 reasons[0]) and you have to start thinking analytically. You have to start looking at the statistical values of your data to understand what's its shape. That's why the `.describe()` and `.…
Or visualising it in r or pandas without meaningful subsampling.
I like this. Is a "friendlier" way to browse data. Said that, I have to add: Exploring large datasets requires a COMPLETELY different mindset. When your data starts growing, it's impossible to keep it all in a visual format (for 2 reasons[0]) and you have to start thinking analytically. You have to start looking at the statistical values of your data to understand what's its shape. That's why the `.describe()` and `.…
Some of the tools that you mentioned exist in Mito today. For example, Mito generates summary information about each column (all of the .describe() info along with a histogram of the data). And we're creating features for gaining a global understanding of the data too.
In practice, one of the main ways that we see people use Mito is for that initial exploration of the data. Often the first thing that users do when they import data into Mito is to correct the column dtype, delete columns that are irrelevant to their analysis, and filter out/replace missing values.
Looks neat - pandas is very powerful and it makes it more approachable for non-programmers. However paid product like this - I probably wouldn't make the switch to this and then have the company go belly up leaving users stranded. Too much risk. Hope for the best though - pandas is pretty fantastic.
Earlier quoted context omitted.
Thanks for checking that! I use vscode so this is a no go...
Yeah, Mito is limited to the Jupyter ecosystem (for now). We want to expand to VSCode, Google Collab, and Streamlit! For the time being, because Mito generates pandas code for every edit you make, you can always use Mito in Jupyter to generate code, and then copy it over to VSCode. Admittedly, its not as nice of a workflow, but it does work!
I like this. Is a "friendlier" way to browse data. Said that, I have to add: Exploring large datasets requires a COMPLETELY different mindset. When your data starts growing, it's impossible to keep it all in a visual format (for 2 reasons[0]) and you have to start thinking analytically. You have to start looking at the statistical values of your data to understand what's its shape. That's why the `.describe()` and `.…
> try loading a 15GB CSV in Excel. Or visualising it in r or pandas without meaningful subsampling.
It allows you to use Altair in Python for visualising data, but does the computation in the backend using Arrow DataFusion. Not for 15GB perhaps, but cool nonetheless.
Looks neat - pandas is very powerful and it makes it more approachable for non-programmers. However paid product like this - I probably wouldn't make the switch to this and then have the company go belly up leaving users stranded. Too much risk. Hope for the best though - pandas is pretty fantastic.
1. The core Mito product is open source. You can see our GitHub here [1]. We also have a pro version that has some additional, code visible, but non-open source features. The way that we think about which features belong in which version of the product is as following: Features that are needed to just get any average analysis done are open source features. On the other hand, features that are specifically useful in an organization -- connecting to company databases, formatting / styling data and graphs for a presentation, etc. -- are pro features. So if you are a team that is relying on our pro features, you're helping support the longevity & progress of Mito. If you are not one of those users and using the open source version, then you will always have access to Mito (and can even help improve it!). Of course the line between what features are specifically helpful in an organization and what feature are needed for an average analysis is a bit blurry, and is a moving target as we continue to expand Mito's offering.
2. Mito is designed specifically to not force users to make a big 'switch'. I've commented this elsewhere in this thread, but just to recap: Because Mito is an extension to Juptyer and because we generate python code for every edit you make, Mito is designed to improve your existing workflow instead of lock you into a new system. Many Mito users use Mito as a starting point! They do as much of their analysis as they can in the Mito spreadsheet and then continue writing more customized Python code to finish up their work.
Not requiring a big switch is nice for the user and its nice for Mito too! Lots of large companies have been able to get up and running with Mito in 30 minutes because it fits into their data stack.
Anyways, not that these are the only two reasons you might feel uneasy about adopting Mito, but at least wanted to share why the switch to Mito might be less scary than switching to other tools.
I have no affiliation with the project. Just found it, tried it out, and it looks very promising...