How to Speed Up Data Cleaning and Exploratory Data Analysis in Python with klib

In this video, I will be showing you how you can speed up your data science projects (data cleaning and exploratory data analysis) using the klib library in Python. Particularly, the klib library enables the following task: quickly visualize missing data, perform data cleaning, visualize data distribution plot, visualize correlation plot and visualize categorical column values.

Key features:
- visualize missing data using the missingval_plot function
- perform data cleaning using the data_cleaning function
- visualize the data distribution plot using the dist_plot function
- visualize the correlation plot using the corr_plot function
- visualize categorical column values using the cat_plot function

Links for this video:

Installing klib: (Choose one)
pip install klib
conda install -c conda-forge klib

⭕ Playlist:
Check out our other videos in the following playlists.

⭕ Recommended Books:

⭕ Stock photos, graphics and videos used on this channel:

⭕ Follow us:

⭕ Disclaimer:
Recommended books and tools are affiliate links that gives me a portion of sales at no cost to you, which will contribute to the improvement of this channel's contents.

#klib #datacleaning #datapreprocessing #datawrangling #jupyter #datasciencenotebook #jupyterlab #artificialintelligence #ai #machinelearning #bigdata #datascienceproject #randomforest #decisiontree #svm #neuralnet #neuralnetwork #supportvectormachine #python #learnpython #pythonprogramming #datascience #datamining #bigdata #datascienceworkshop #dataminingworkshop #dataminingtutorial #datasciencetutorial #ai #artificialintelligence #tutorial #dataanalytics #dataanalysis #machinelearningmodel #dataprofessor

[KenJee_ds]

Definitely will be experimenting with this!

[emma_ding]

Wow! Thanks for another great video! For years, Python visualization has gone from matplotlib to pandas and seaborn. Now klib is simply amazing!

[anoopbhagat]

Thank you @DataProfessor for sharing the info. I will definitely implement it in my projects.

[thinamG]

Thanks for introducing new amazing library again, @Data Professor. It looks interesting !!

[sivaprakash]

Thank you for your valuable information

[ArunYadav-lfti]

You have really saved my time. I am also looking for time & space comparison of ML algorithms. Please make some tutorial on it.

[JosephRivera]

Thanks for this. This is something I have to implement in my project.

[CodingIsFun]

Thanks for sharing! 🙏 This looks interesting. I will definitely try it out 😀

[gohulnathv]

I just started my career over data science. I'm keen on learning some different concepts like this. ❤️ I'm waiting for your next video.😎
Thanks for much efforts you did in this Video.🎉

[aireescreates]

This is useful. Thank you. Will try to use this library.

[HVjugo]

I'll give it a try in my next project, it looks intuitive and I always struggle with data viz, so this might be a good option for me

[gohulnathv]

It's really useful sir. Keep do always sir❤️

[empowercode]

Hey! I just found your channel and subscribed, love what you're doing!

I like how clear and detailed your explanations are as well as the depth of knowledge you have surrounding the topic! Since I run a tech education channel as well, I love to see fellow Content Creators sharing, educating, and inspiring a large global audience. I wish you the best of luck on your YouTube Journey, can't wait to see you succeed! Your content really stands out and you've put so much thought into your videos!

Cheers, happy holidays, and keep up the great work :)

[ayarikhawla]

this is amazing. Thanks for sharing, Bravo

[gulabpatel]

Is there any function in klib that tells about the best distribution of your features, as distribution plot gives all the details but don't tells the closest standard distribution?
Thank you for the video. It's an amazing library.

[cesarrodriguez]

Hi, great channel really enjoy!!, could you do a video for a optimization problem when the objetive funtion is a ML model? Thanks!

[antonioverissimo]

Thank you.Very nice...Mester, can you explain, please about tidyverse 102?

[offon]

How did you make the menu section on top of your Jupyter notebook? This looks useful.

[manuelmbscorreia]

What did actually happened with the .null values? Weren't too many dropped? Shouldn't be solved knowing a bit more about the context? Sorry Professor, but I am a complete noob here.

[carrezinho]

klib doesn't running, i installed by pip, gi clone and don't recognized the lib,
I searched the stack overflow and origial page in documentation but nothing worked