Welcome to issue 271 of Python Weekly. Enjoy this week's issue and if you celebrate it, Happy Thanksgiving!
Articles, Tutorials and Talks
Episode #85: Parsing horrible things with Python
Do you have horribly convoluted things that need parsing? Obviously you'll learn a bunch of tips and tricks from this episode. But you'll see that advanced parsing is a gateway to many interesting computer science techniques. Listen in as I speak with Erik Rose about his journey to parse weird things at Mozilla.
A Deep Dive into Geospatial Analysis
Many of the datasets that data scientists handle have some kind of geospatial component to them, and that information is oftentimes useful-to-critical for understanding the problem at hand. As such, an understanding of spatial data and how to work with it is a valuable skill for any data scientist to have. Even better, Python provides a rich toolset for working in this domain, and recent advances have greatly simplified and consolidated these. In this tutorial we will take a deep dive into geospatial analysis in Python, using tools like geopandas, shapely, and pysal to analyze a dataset, provided by Kaggle (and originally from Inside AirBnB), of sample AirBnB locations in Boston, Massachusetts.
Building a Financial Model with Pandas
In my previous articles, I have discussed how to use pandas as a replacement for Excel as a data wrangling tool. In many cases, a python + pandas solution is superior to the highly manual processes many people use for manipulating data in Excel. However, Excel is used for many scenarios in a business environment - not just data wrangling. This specific post will discuss how to do financial modeling in pandas instead of Excel. For this example, I will build a simple amortization table in pandas and show how to model various outcomes.
Watermarking images on Django sites
Techniques to generate visible and invisible watermarks using Pillow and django-imagekit.
Bayesian Linear Regression
Quick demonstration of Bayesian linear regression -- particularly, I want to show how you can find the parameters of a Gaussian distribution from which you can sample weights to fit your dataset! Then you can use this distribution as a prior to find the predictive distribution and make use of confidence levels. I will try to explain everything I'm doing, but you should know linear regression, some linear algebra and some Bayesian stats.
AsyncIO for the Working Python Developer
iSee: Using deep learning to remove eyeglasses from faces
Books
Algorithms of the Intelligent Web
Algorithms of the Intelligent Web teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you'll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python's scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You'll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning.
Interesting Projects, Tools and Libraries
Speech-Hacker
Makes famous people speak whatever you wish by linking their words.
pipfile
A Pipfile, and its related Pipfile.lock, are a new (and much better!) replacement for pip's requirements.txt files.
Kalliope
Kalliope is a modular always-on voice controlled personal assistant designed for home automation.
Pydoc
Pydoc is a project to build auto-generated API documentation for every package that is uploaded to PyPI. This is similar to projects like http://rubydoc.info/ and https://godoc.org/ -- but for the Python community. Once we have gotten all package in PyPI, we hope to expand it to GitHub packages as well.
gzint
A python3 library for efficiently storing massive integers (stands for gzipped-integer).
NBA-prediction
Predicts scores of NBA games using matrix completion.
Quiver
Interactive convnet features visualization for Keras.
Streamlink
Streamlink is a CLI utility that pipes flash videos from online streaming services to a variety of video players such as VLC, or alternatively, a browser. The main purpose of streamlink is to convert CPU-heavy flash plugins to a less CPU-intensive format.
colorcet
A set of useful perceptually uniform colormaps for plotting scientific data.
New Releases
Python 3.6.0 beta 4
PyCharm 2016.3
Upcoming Events and Webinars
AnacondaCON 2017
A conference dedicated to bringing together the brightest minds in Open Data Science and the Anaconda community. At AnacondaCON, you will engage with enterprise customers that use Anaconda, as well as foundational contributors and thought leaders in the Open Data Science movement who are harnessing the power and innovation of our community. The agenda will be filled with educational, informative and thought-provoking sessions to ensure you walk away with the knowledge and connections you need to move your Open Data Science initiatives forward.
San Francisco Django Meetup December 2016 - San Francisco, CA
There will be following talks - Django + Ionic: From POC to Production
- REST Websockets API with Django Channels
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