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10 great Python packages for Data Science not known to many:

1️⃣ CleanLab
You're missing out on a lot if you haven't started using Cleanlab yet!
Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.
It's like a magic wand! 🪄

Check this out👇
https://lnkd.in/dY2fp5YW

2️⃣ LazyPredict
A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.
Supports both regression & classification!

Check this out👇
https://lnkd.in/ggZ-HByv

3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

Check this out👇
https://lnkd.in/genaV395

4️⃣ PyForest
A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

Check this out👇
https://lnkd.in/gv2tsjMe

5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

Check this out👇
https://lnkd.in/dapGg-AS

6️⃣ Drawdata
Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.
Very handy for learning & understanding the behaviour of ML algorithms!

Check this out👇
https://lnkd.in/gBSrQ-e4

7️⃣ black
The Uncompromising Code Formatter
Arguably the best, I use it everyday!!

Check this out 👇
https://lnkd.in/gsz2Mxqn

8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.

Check this out👇
https://lnkd.in/gUzh-YZM

9️⃣ PyTorch-Lightning by @LightningAI⚡️
If you like PyTorch, you'll love PyTorch Lightning!
Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

Check this out👇
https://lnkd.in/dir2Xej7

🔟 Reflex
Build Performant, customizable web apps in pure Python that you can deploy in seconds
Reflex is a library to build full-stack web apps in pure Python.

Check this out👇
https://lnkd.in/gz_vsNba

https://www.tgoop.com/CodeProgrammer 🆘
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10 great Python packages for Data Science not known to many:

1️⃣ CleanLab
You're missing out on a lot if you haven't started using Cleanlab yet!
Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.
It's like a magic wand! 🪄

Check this out👇
https://lnkd.in/dY2fp5YW

2️⃣ LazyPredict
A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.
Supports both regression & classification!

Check this out👇
https://lnkd.in/ggZ-HByv

3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

Check this out👇
https://lnkd.in/genaV395

4️⃣ PyForest
A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

Check this out👇
https://lnkd.in/gv2tsjMe

5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

Check this out👇
https://lnkd.in/dapGg-AS

6️⃣ Drawdata
Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.
Very handy for learning & understanding the behaviour of ML algorithms!

Check this out👇
https://lnkd.in/gBSrQ-e4

7️⃣ black
The Uncompromising Code Formatter
Arguably the best, I use it everyday!!

Check this out 👇
https://lnkd.in/gsz2Mxqn

8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.

Check this out👇
https://lnkd.in/gUzh-YZM

9️⃣ PyTorch-Lightning by @LightningAI⚡️
If you like PyTorch, you'll love PyTorch Lightning!
Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

Check this out👇
https://lnkd.in/dir2Xej7

🔟 Reflex
Build Performant, customizable web apps in pure Python that you can deploy in seconds
Reflex is a library to build full-stack web apps in pure Python.

Check this out👇
https://lnkd.in/gz_vsNba

https://www.tgoop.com/CodeProgrammer 🆘

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