4 Ways to Load Data in Pandas You Need to Know by Nik Piepenbreier via @TDataScience

Hidden gems of the Pandas library.

A great article giving you a single place for this information.

4 Tools to Automatically Extract Data from Datetime in Python by Khuyen Tran via @TDataScience

How to Extract Datetime From Text and Data From Datetime.

This is just SO useful.

Pydantic or dataclasses? Why not both? Convert Between Them by Diego Barba via @TDataScience

Use data classes in your FastAPI projects, and speed up your pydantic model operations.

This was definitely something lacking in my knowledge and I am glad to have that gap now filled. Definitely, something to carefully think about. I'm so glad it gives some examples and sample code.

Primary Supervised Learning Algorithms Used in Machine Learning by Kevin Vu via @kdnuggets

In this tutorial, they are going to list some of the most common algorithms that are used in supervised learning along with a practical tutorial on such algorithms.

This is really useful and worth a bookmark or printout.

Explaining negative R-squared by Tan Nian Wei via @TDataScience

Why and when does R-squared, the coefficient of determination, go below zero?

Interesting and good to be able to confirm it is how I thought it was.

Python Single Responsibility Principle by/via PYTHONTUTORIAL.NET

In this tutorial, you’ll learn about the single responsibility principle and how to implement it in Python.

This was very clear and easy to use. I really think you should use this website to learn more about Python.

Parallelization with MultiProcessing in Python by Vatsal via @TDataScience

Run your data science tasks in parallel to speed up computation time.

Great examples with code to make is easier to do this with your code.

Data Cleaning Toolbox by Olivia Tanuwidjaja via @TDataScience

Compiling the aspects to look out for before analyzing your data.

This is a great checklist and I think could help you not to forget a step or even do something in the wrong order which could also produce wrong results.

How To Convert Pandas DataFrame Into NumPy Array by Giorgos Myrianthous via @TDataScience

Converting a pandas DataFrame into a NumPy array.

A great guide which shows just how easy it can be to do that conversion.

How to Use Wikipedia as a Data Source by Alan Jones via @TDataScience

How to load information from Wikipedia into Pandas by finding the best team in the English Premier League.

I like that this worked example could be used to do all sorts of things.

8 Visualizations with Python to Handle Multiple Time-Series Data by Boriharn K via @TDataScience

Visualization ideas for coping with overlapping lines in multiple time-series plots.

Some of these ideas are quite neat. Don't stick to the one you prefer the most - some sets of data may suit one of these examples more than the others.

Introduction to GraphSAGE in Python by @maximelabonne via @TDataScience

Scaling Graph Neural Networks to billions of connections.

I like this which is very clear and easy to use and understand. Great code examples.

10 SQL Queries You Should Know as a Data Scientist by Uğur Savcı via @Medium

 Learn the Most Used SQL Queries in 5 Minutes with Examples

You need to keep these somewhere so you can access them. I have in the past used text files in a directory or Evernote. It is really easy then to copy and edit the code.

How to Run 30 Machine Learning Models with a Few Lines of Code by Ismael Araujo via @TDataScience

Learn how to run multiple machine learning models using lazy predict.

This is really neat and so you need to bookmark or add it to something like Evernote so you can use this in your Python code.

6 Killer Productivity Apps for Programmers by Tapajyoti Bose via @Medium

Are you a Programmer? Want to take your productivity to a whole new level? Look no further, these apps will do the job nicely.

Some of these look good - definitely a few here worth investigation.

Python List Print — 7 Different Ways to Print a List You Must Know by Dario Radečić via @TDataScience

Explore endless possibilities of printing and formatting lists in Python.

Some of these were new for me and so I expect others will discover something new here too.

50 python one-liners everyone should know by Allwin Raju via @Medium

He was always amazed by how easily things can be done using python. Some of the tedious tasks can be done in a single line of code using python. He has gathered some of his favourite one-liners from python.

This is a great resource and something that you may learn from. 

8 Essential Python Techniques for Data Engineers and Analysts (with code samples) by @camwarrenm via @TDataScience

These are the Python code snippets he re-uses the most.

Something to bookmark for sure. I'm sure we all have a basic toolset of code scripts in various languages that we use and edit every time we need to do something.

10 Python operations for 70% of your Data Analysis by Anmol Tomar via @Medium

Day to day work of a Data Scientist involves a variety of tasks such as data pre-processing, data analysis, machine learning model creation, model deployment.

This is so good I almost feel as if we need to give a commission to Anmol every time we run this kind of code!

Take 5 Minutes to Get Started With Multi-threading in Python by Amit Chauhan via @Medium

 Run several applications at the same time.

I needed to find a way to do this so I am really grateful to Amit for showing me how to in this article. Definitely a must-read if you want to do anything interesting in Python.