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This document is a self-learning document for a course in Python programming. This course contains (1) a part for beginners, (2) a discussion of several advanced topics that are of interest to Python programmers, and (3) a Python workbook with lots of exercises.

My project lets you try on virtual pairs of sunglasses or masks. To achieve this I utilized python, Dlib, OpenCV, Scipy, and Numpy. The pipeline involves opening a live webcam feed, detecting keypoints on faces in the feed, warping a png image of sunglasses to match the face, rotating the png with face movements, and blending the two images together so they look like one real image.

Some Python programmers like to think of their language as flawless. I personally know some Python programmers claiming that Python is superior to other languages in its clean design and unmatched elegance. This however isn’t true. Python has at least as many deep and fundamental flaws as most other languages, despite parts of its community claiming something different.

How Do You Compare? Compare yourself to over 1,000 DevOps peers to see how they manage their processes.

Hi, Have you ever try to package your code/project to became .deb or in official Debian or Ubuntu Repository to share to the rest of the world?

What changed in my reasoning? First of all, I’m working on other problems. Whereas I used to do a lot of work that was very easy to map to numpy operations (which are fast as they use compiled code), now I write a lot of code which is not straight numerics. And, then, if I have to write it in standard Python, it is slow as molasses. I don’t mean slower in the sense of “wait a couple of seconds”, I mean “wait several hours instead of 2 minutes.”

This post explores a real-world use case calculating complex credit models in Python using Dask. It is an example of a complex parallel system that is well outside of the traditional “big data” workloads.

Numpy is the most basic and a powerful package for scientific computing in python. This is part 1 of a mega-tutorial covering all the core aspects of performing data manipulation and analysis with numpy’s ndarrays.

This is a dashboard to track progress of porting Fedora packages to Python 3.

In layman terms, a future is an object which can hold the value or result of some computation done asynchronously. What does that mean ?

google sheets

Exploring the overlapping functionality of SQL and Python can help those of us familiar with one language become more adept with the other. And with a deep understanding of both, we can all make smarter decisions about how to combine and leverage each, making it easy to always choose the right tool for every task.


algnuth - 240 Stars, 6 Fork
Algebraic Number Theory package.

grafana-scripts - 54 Stars, 5 Fork
API scripts written (both pushing and pulling) to aggregate data into influxdb for grafana.

cryptory - 35 Stars, 3 Fork
Retrieve historical crpytocurrency data.

automation - 33 Stars, 8 Fork
code generator

electrum-personal-server - 31 Stars, 3 Fork
Maximally lightweight electrum server for a single user.

r2kit - 28 Stars, 5 Fork
A set of scripts for a radare-based malware code analysis workflow.

git-ctm - 24 Stars, 0 Fork
git Commit Time Machine.

augmented-reality - 18 Stars, 4 Fork
Snapchat-like augmented reality filter.

brutepanel - 10 Stars, 0 Fork
Brute Panel is a modern admin login path finder written in Python.

feed - 7 Stars, 1 Fork
Some feeds output from feedly.

Simple-OpenCV-Calculator - 5 Stars, 4 Fork
A gesture controlled calculator.