Datasist

Open source Python library for easy data analysis, visualization and modeling. Follow our page to get daily tips, tricks, and major announcements.

[Datasist @datasistlibrary]

Looking to get started with Datasist, check out this post by @risin_developer
on medium. It's a two part series that covers some of the most important features of available.

Rt, someone should be using this... ☺️

Part 1 https://t.co/QYMRiA1tR6

[Datasist @datasistlibrary]

👐👐 Go tryout Autoviz 📊

[W. @wale_int]

I got on a mini-project today and decided to use @risingodegua's @datasistlibrary. I must say its a pretty neat, helpful tool! Especially love the autoviz visualization method. It literally assists you by churning out all the relevant plots. Love it!

[Datasist @datasistlibrary]

This tutorial by @risingodegua teaches you how to to create reproducible data science project by Leveraging datasist's project management features.

https://t.co/iwcBJaXLrY

[Datasist @datasistlibrary]

Interested in working on datasist? Indicate below 👇👇

[RO2⚡️ @risingodegua]

Pythonistas 🐍, whether beginners or experts looking to contribute to open source, I'm having a video call session to explain @datasistlibrary code base, and show you how to make your first or 100th contribution.

If interested drop your email here:
https://t.co/MwO8aXffUU

[RO2⚡️ @risingodegua]

Considering converting @datasistlibrary visualization module to using @plotlygraphs by default. This will make the graphs interactive by default. 🤔🤔

[Datasist @datasistlibrary]

You can manage your data science work flow seamlessly by leveraging the project module of datasist. It contains functions to create directories, save and retrieve models, data and temporary outputs.

More details here: 👇👇👇 https://t.co/XiA3NGIvx6

[Datasist @datasistlibrary]

Have an idea for improvement or a new feature for datasist? Head over to our contribution guide page. 👇🏼👇🏼👇🏼

https://t.co/h13MjzL0k4

[Datasist @datasistlibrary]

Have some spare time this weekend?
Then, help us complete our new documentation. It uses markdown and is built with Gitbook.

Check guide here: https://t.co/9575pUK0s9

[Datasist @datasistlibrary]

👏👏

[Pizzle The One @pizzledee1]

Got plugged to an amazing library @datasistlibrary created by @risin_developer. Can't deny it's some badass stuff. Nice one!

[Yuwa @sir_yuwa]

I recently got acquainted with the @datasistlibrary created by @risin_developer for exploratory data analysis, and I was mind blown... it’s a real time saver and you’d be surprised how one line of code could do what you’d have originally done with 10-20 lines... Kudos !!!👏🏽

[RO2⚡️ @risingodegua]

If you're learning to code, don't just copy and paste code you see in a tutorial. Type it out yourself, change the variables, remove some parts, play with it, break it, and fix it. Try to understand each component and what they do.

Remembrance and writing goes hand in hand!

[Datasist @datasistlibrary]

Have some spare time this weekend?
Then, help us complete our new documentation. It uses markdown and is built with Gitbook.

Check guide here: https://t.co/9575pUK0s9

[Datasist @datasistlibrary]

Datasist Tip of the Day (9)

Ever sent a data science project to another data scientist and have them give you that look?

Well, you can now create and manage a well structured data science project with Datasist.

Check out this doc below:
https://t.co/eoXKhKBsYv

[Data knight @emeka_boris]

200 Subscribers 🎉

Thank you so much fam!

Let's make it 400

https://t.co/wD5pGTyZVJ

#BlackTechTwitter

[Jeff Dean @JeffDean]

Google Dataset Search is now officially out of beta.

"Dataset Search has indexed almost 25 million of these datasets, giving you a single place to search for datasets & find links to where the data is."

Nice work, Natasha Noy and everyone else involved!

https://t.co/7eMIfTOd36

[Datasist @datasistlibrary]

@JFPuget @Nasereliver @SadeeqAkintola @_Kennyrich @AbozeBrain @GiftOscarT1 @emeka_boris @bimbolawale @CalebEmelike @IrekponorVictor 👍👍

[Datasist @datasistlibrary]

Datasist Tip of the Day (8)

You can automatically drop features with a specified percentage of missing values by using the drop_missing function.

import datasist.feature_engineering as fe
fe.drop_missing(df, percent=7.0)

[DSN - Data Science Nigeria @dsn_ai_network]

See how Artificial Intelligence explains the success of Liverpool @LFC in the @premierleague with its amazing use in goal probability, pitch control & pass accuracy.

A great work led by Ian Graham, Director of Liverpool Data unit- a PhD in Physics

Read https://t.co/kLosBSql5v

[Datasist @datasistlibrary]

@JFPuget @Nasereliver @SadeeqAkintola @_Kennyrich @AbozeBrain @GiftOscarT1 @emeka_boris @bimbolawale @CalebEmelike @IrekponorVictor True, features with missing values are rarely dropped, but features with say 90, 95, 98 percent missing values surely are arguably less useful.

[Data knight @emeka_boris]

😍😍😍😍😍 My lover

[Datasist @datasistlibrary]

A sneak peek at our new documention. We are building this using Gitbook. Final version will be out in the coming weeks.
To contribute to this documentation, clone the markdown files with the link below, and make your contributions.

#datasist

https://t.co/5KZXCo7LkS