This free reference guide will take you back to the basics. You’ll find visuals and definitions on key concepts and questions you need to answer about your teams to determine your readiness for continuous delivery. Download and share with your team.
This article explains the new features in Python 3.7, compared to 3.6.
If you read data science articles, you may have already stumbled upon FiveThirtyEight’s content. Naturally, you were impressed by their awesome visualizations. You wanted to make your own awesome visualizations and so asked Quora and Reddit how to do it. You received some answers, but they were rather vague. You still can’t get the graphs done yourself. In this post, we’ll help you. Using Python’s matplotlib and pandas, we’ll see that it’s rather easy to replicate the core parts of any FiveThirtyEight (FTE) visualization.
This post is about how to set up multiple Python versions and environments on a development machine (and why I don’t use conda).
It is much much easier to run PySpark with docker now, especially using an image from the repository of Jupyter. When you just want to try or learn Python. it is very convenient to use Jupyter Notebook for an interactive developing environment. The same reason makes me want to run Spark through PySpark in Jupyter Notenook.
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How to profile your python code to improve performance?
Content based image retrieval (CBIR) systems enable to find similar images to a query image among an image dataset. The most famous CBIR system is the search per image feature of Google search. This article is a keras tutorial that demonstrates how to create a CBIR system on MNIST dataset. Our CBIR system will be based on a convolutional denoising autoencoder. It is a class of unsupervised deep learning algorithms.
You need to iterate over an infinite series of numbers, breaking when a condition is met.
Test the API for free.
I thought it would be nice to show how one can leverage Python’s Pandas library to get stock ticker symbols from Wikipedia.
In this article we will. Extract twitter data using tweepy and learn how to handle it using pandas. Do some basic statistics and visualizations with numpy, matplotlib and seaborn. Do sentiment analysis of extracted (Trump's) tweets using textblob.
Django Girls Foundation is an initiative that aims to introduce women and girls who never coded before to the world of technology and increase the diversity of the tech industry. We achieve this by organising one-day workshops and inviting women to come and learn how to build the internet using HTML, CSS, Python and Django. Django Girls is a volunteer run organisation with volunteers all over the world. Django Girls has two part-time paid staff members and the support team (six awesome ladies who are also volunteers) to help provide support to all other volunteers.
Logistic regression can be used to solve problems like classifying images.
A recent article by Jason Goldstein expressed the author’s difficulty understanding and using Asyncio, especially in a Flask context. Asyncio in a Flask context is the exact experience I have with Quart, so I hope I can add something to the conversation this author started.
future-fstrings - 80 Stars, 2 Fork
A backport of fstrings to python<3.6
python-switch - 57 Stars, 4 Fork
Adds switch blocks to Python.
socksmon - 31 Stars, 3 Fork
Monitor arbitrary TCP traffic using your HTTP interception proxy of choice.
s3tk - 30 Stars, 0 Fork
A security toolkit for Amazon S3.
Octomender - 22 Stars, 0 Fork
Get repo recommendation based on your GitHub star history.
web-traffic-forecasting - 13 Stars, 3 Fork
Kaggle | Web Traffic Forecasting.
list_dict_DB - 13 Stars, 0 Fork
In-Memory noSQL-like data structure.
pyprof-timer - 0 Stars, 0 Fork
A timer for profiling a Python function or snippet.