10 Amazing Articles On Python Programming And Machine Learning
A lot is happening in the world of Python. Support for Python 2 is ending, more and more companies are referencing Python in job descriptions and it continues to gain new libraries and more support.
Since there is so much changing so fast, we got some of our favorite articles. We hope they help you on your Python programming journey.
Python 2 EOL: How to survive the end of Python 2
Python 2 support ends in 2020. Here’s what you can do if you’re stuck with Python 2 in what is fast becoming a Python 3 world
On January 1, 2020, the 2.x branch of the Python programming language will no longer be supported by its creators, the Python Software Foundation. This date will mark the culmination of a drama that has stretched on for years — the transition from an older, less capable, widely used version of Python to a newer, more powerful version that still trails its predecessor in adoption.
What do companies expect from Python devs in 2019?
What skills do I need to succeed as a Python dev in 2019?
Jointly with our team, we took 300 job specs for Python developers, scrapped from StackOverflow, AngelList, LinkedIn, and some fast-growing tech companies worldwide. From all these descriptions, we extracted the skills which were mentioned the most frequently, and here they are. (The numbers refer to the number of mentions.)
Asynchronous Programming in Python: A Walkthrough
When we talk about program execution, “asynchronous” means that the program doesn’t wait for a particular process to complete, but carries on regardless. An example of asynchronous programming is a program writing to a log file: Although it’s possible it might fail (for instance, because the log filled up the disk space), most times it doesn’t, and you can write your program to call the log routines asynchronously (or ‘fire and forget,’ as I call it).
Asynchronous execution means the main program runs a little faster. Your logging code should be written so that if it does fill the disk, it just stops logging rather than crashing.
Buggy Python Code: The 10 Most Common Mistakes That Python Developers Make
By Martin Chikilian
Python’s simple, easy-to-learn syntax can mislead Python developers — especially those who are newer to the language — into missing some of its subtleties and underestimating the power of the diverse Python language.
With that in mind, this article presents a “top 10” list of somewhat subtle, harder-to-catch mistakes that can bite even some more advanced Python developers in the rear.
(Note: This article is intended for a more advanced audience than Common Mistakes of Python Programmers, which is geared more toward those who are newer to the language.)
Creating Heatmap From Scratch in Python
Heatmap is frequently used to visualize event occurrence or density. There are some Python libraries or GIS software/tool that can be used to create a heatmap like QGIS, ArcGIS, Google Table Fusion, etc. Unfortunately, this post won’t discussed how to create a heatmap using those software/tool, but more than that, we will write our own code to create a heatmap in Python 3 from scratch using Python common library.
The algorithm which will be used to create a heatmap in Python is Kernel Density Estimation (KDE). Please refer to this post (QGIS Heatmap Using KDE Explained) to get more explanation about KDE and another post (Heatmap Calculation Tutorial) which give an example how to calculate intensity for a point from a reference point using KDE.
How to build your own Neural Network from scratch in Python
By James Loy
What’s a Neural Network?
Most introductory texts to Neural Networks brings up brain analogies when describing them. Without delving into brain analogies, I find it easier to simply describe Neural Networks as a mathematical function that maps a given input to a desired output.
Neural Networks consist of the following components
What exactly can you do with Python? Here are Python’s 3 main applications.
“What exactly can I use Python for?”
Well that’s a tricky question to answer, because there are so many applications for Python.
But over time, I have observed that there are 3 main popular applications for Python:
- Web Development
- Data Science — including machine learning, data analysis, and data visualization
Python programming language gets speed boost from latest PyPy interpreter
By Nick Heath
Good news for Python developers, thanks to a new release of the already speedy PyPy interpreter that promises to be the fastest version yet.
If you’re programming using Python then an important choice is whether to run your code using the main CPython interpreter or an alternative such as PyPy, with each option having pros and cons.
PyPy’s USP is its speed, with its integrated Just In Time (JIT) compiler allowing it to run some Python code some 7.6 times faster than CPython according to benchmarks.
How to collect, customize, and centralize Python logs
By Emily Chang and Nils Bunge
Python’s logging module basics
The logging module is included in Python’s standard library, which means that you can start using it without installing anything. The logging module’s basicConfig() method is the quickest way to configure the desired behavior of your logger. However, the Python documentation recommends creating a logger for each module in your application — and it can be difficult to configure a logger-per-module setup using basicConfig() alone. Therefore, most applications (including web frameworks like Django) automatically use file-based or dictionary-based logging configuration instead. If you’d like to get started with one of those methods, we recommend skipping directly to that section.
Three of the main parameters of basicConfig() are:
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