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.

Estimating Solar Panel Output with Open-Source Data by Ang Li-Lian via @TDataScience

A comprehensive guide for what it takes to estimate solar panel output using Python and QGIS.

I encourage everyone to go to her GitHub to look at this much more fully and find ways to do the same in your area or country.

Arabica: A Python Package for Exploratory Analysis of Text Data by Petr Korab via @TDataScience

Arabica provides unigrams, bigrams, and trigrams frequencies by a period in a single line of code. Learn more in this tutorial.

This looks really useful!

Don’t use Apply in Python, there are better alternatives! by Anmol Tomar via @TDataScience

Alternatives to the Apply function to improve the performance by 700x.

Some great suggestions that should definitely deliver great results.

Pivot Table Concepts by Derek Mortensen via @TDataScience

Report, Analyze, Tell Stories.

This was really interesting. Pivot tables tell us so many things and can be a great tool and so to be able to do them outside of Excel is great.

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.

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.

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.

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.

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.

101 DATA SCIENCE with Cheat Sheets (ML, DL, Scraping, Python, R, SQL, Maths & Statistics) by Anushka Bajpai via @Medium

Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That’s why we have cheat sheets and summaries. They help us access the most commonly needed reminders for making our Data Science journey fast and easy.

This is really like a one-stop-shop for cheatsheets - definitely worth a bookmark, a printout, adding to Evernote or whatever is your choice for preserving something important.

Python ETL Pipeline: The Incremental data load Techniques by Haq Nawaz via @Medium

The incremental data load approach in ETL (Extract, Transform and Load) is the ideal design pattern. In this process, we identify and process new and modified rows since the last ETL run.

Code is available on Github. I can see that it is picking up just changes but I wonder for a lot of data how efficient that actually is and whether that comparison should be done at the source or off somewhere else in the cloud where it can't affect the source's performance. Something to consider.

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.

D-Tale: One of the Best Python Libraries You Have Ever Seen by Ismael Araujo via @TDataScience

Here is his take on this must-have Python library and why you should give it a try.

I like this - it looks incredibly easy to use and very intuitive. Definitely, one to add to your list of very useful Python libraries.

Python’s F-Strings Are A Lot More Useful Than You Might Have Thought by @emmettboudgie via @TDataScience

Some cool things most people do not realize f-strings can do in Python,

Interesting to read and think about as I had no idea about some of these things.