原文：[A Technical Primer On Causality](https://medium.com/@akelleh/a-technical-primer-on-causality-181db2575e41)

原文：[AI challenge in 78 lines of Python](http://kootenpv.github.io/2016-09-07-ai-challenge-in-78-lines)

原文：[Leveraging Python to Automate GIS Processes and Provide Extended Analytical Capabilities](https://www.youtube.com/watch?v=Fmtofk8t2dg)

原文：[Python Weekly - Issue 261](http://eepurl.com/cfOTh5)

欢迎来到Python周刊第261期。让我们直奔主题。

[![](https://gallery.mailchimp.com/e2e180baf855ac797ef407fc7/images/b1a844b2-fee2-4dd3

-b56c-341147f4c21b.png)](https://software.intel.com/en-us/intel-sdp-home)

The first product release of [Intel® Distribution for Python* 2017](https://software.intel.com/en-us/intel-distribution-for-python) powered

by Anaconda* is here! Experience blazing fast Python performance with ready

access to conda and Anaconda Cloud. Get your [free download](https://registrationcenter.intel.com/en/forms/?productid=2810)

today.

[Channels adopted as an official Django project](https://www.djangoproject.com/weblog/2016/sep/09/channels-adopted-official-django-project/)

The Django team is pleased to announce that the Channels project is now

officially part of the Django project, under our new Official Projects

program. Channels is the effort to bring WebSockets, long-poll HTTP, and other

non-request-response protocol and business logic handling to Django, as part

of our ongoing effort to establish what makes a useful web framework in 2016.

[Python 3.6 dict becomes compact and gets a private version; and keywords become ordered](https://mail.python.org/pipermail/python-dev/2016-September/146327.html)

[Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c)

From being a total ml noob to start using it at work.

[The Neural Network Zoo](http://www.asimovinstitute.org/neural-network-zoo/)

并非Python专有，但是是各种类型的神经网络的一个很棒的概述。

[Working with streaming data: Using the Twitter API to capture tweets](https://www.dataquest.io/blog/streaming-data-python/)

Being able to work with streaming data is a critical skill for any aspiring

data scientist. In this post, we'll talk about strategies for working with

streaming data, and walk through an example where we stream and store data

from Twitter.

[A bite of Python](https://access.redhat.com/blogs/766093/posts/2592591)

In this article, which primarily targets people who are new to Python, a

handful of security-related quirks are looked at; experienced developers may

well be aware of the peculiarities that follow.

[Episode #75: Pythonic games at CheckIO](https://talkpython.fm/episodes/show/75/pythonic-games-at-checkio)

Do you like to play games or solve puzzles? Chances are pretty good that you

do. After all, what is programming and software development but one really

elaborate puzzle? On this episode, you meet someone who pushed this idea of

programming as a game to its logical conclusion. Alexander Liabakh is the

creator of CheckIO and Empire of Code. CheckIO is a programming adventure game

where you solve puzzles and share them with fellow players. Empire of Code is

a tower defense game where part of the strategy is to actually program AI in

Python.

[A Technical Primer On Causality](https://medium.com/@akelleh/a-technical- primer-on-causality-181db2575e41)

What does "causality" mean, and how can you represent it mathematically? How

can you encode causal assumptions, and what bearing do they have on data

analysis? These types of questions are at the core of the practice of data

science, but deep knowledge about them is surprisingly uncommon. If you

analyze data without regard to causality, you open your results up for the

possibility of enormous biases. This includes everything from recommendation

system results, to post-hoc reports on observational data, to experiments run

without proper holdout groups. This post covers causality at a more technical

level.

[Leveraging Python to Automate GIS Processes and Provide Extended Analytical Capabilities](https://www.youtube.com/watch?v=Fmtofk8t2dg)

Geographical Information Systems (GIS) is a developing spatial technology that

uses the Python programming language. Using commercial and open source

libraries, GIS analysis and development can be automated and extended beyond

the regular capabilities of desktop software. This can include spatial

analysis, data mining and automatic map generation.

[AI challenge in 78 lines of Python](http://kootenpv.github.io/2016-09-07-ai-challenge-in-78-lines)

[Real-world data cleanup with Python and Pandas](http://trendct.org/2016/08/05/real-world-data-cleanup-with-python-and-pandas)

[Getting started with Raspberry Pi - Building a Digital Photo Frame](https://paulstamatiou.com/getting-started-raspberry-pi/)

[17 places to find datasets for data science projects](https://www.dataquest.io/blog/free-datasets-for-projects/)

[The How and Why of Flask-Bitmapist](https://www.cuttlesoft.com/the-how-and-why-of-flask-bitmapist/)

[Jupyter (IPython) notebooks features](http://arogozhnikov.github.io/2016/09/10/jupyter-features.html)

[Solving Multi-Core Python](https://ericsnowcurrently.blogspot.com/2016/09/solving-mutli-core-python.html)

[Foundations for Analytics with Python: From Non-Programmer to Hacker](http://amzn.to/2cyhD4J)

If you're like many of Excel's 750 million users, you want to do more with

your data--like repeating similar analyses over hundreds of files, or

combining data in many files for analysis at one time. This practical guide

shows ambitious non-programmers how to automate and scale the processing and

analysis of data in different formats--by using Python. After author Clinton

Brownley takes you through Python basics, you'll be able to write simple

scripts for processing data in spreadsheets as well as databases. You'll also

learn how to use several Python modules for parsing files, grouping data, and

producing statistics.

[Python Developer at Headforwards](http://jobs.pythonweekly.com/jobs/python-developer-11/)

Headforwards is looking for a number of Python developers to join an existing

Agile software development team. This role will involve working on a very

exciting and cutting edge web application in the cloud space.

[py-d3](https://github.com/ResidentMario/py-d3/)

py-d3 is an IPython extension which adds D3 support to the Jupyter Notebook

environment.

[runmultithread](https://github.com/juanpabloaj/runmultithread)

Run the same process with different inputs in different threads.

[puresnmp](https://github.com/exhuma/puresnmp)

A pure Python implementation for Python 3.3+ of SNMP without any external

dependencies (neither MIBs or libsnmp).

[RevDB](https://bitbucket.org/pypy/revdb/)

RevDB is a reverse debugger for Python based on PyPy. It can be used to track

down hard-to-reproduce bugs in your Python programs (whether you usually run

them on PyPy or on CPython).

[NakedTensor](https://github.com/jostmey/NakedTensor)

Bare bottom simplest example of machine learning in TensorFlow.

[automatron](https://github.com/madflojo/automatron)

Automatron (Ah-Tom-a-tron) is an open source framework designed to detect and

remediate IT systems issues. Meaning, it can be used to monitor systems and

when it detects issues; correct them.

[wlscrape](https://github.com/bontchev/wlscrape)

A tool for scrapping the possible malware from the Wikileaks AKP leak.

[trackmac](https://github.com/MacLeek/trackmac)

Trackmac is a command line tool built for OS X users to track their time spent

on every application.It can also track the websites you visited through Chrome

or Safari every day.

[beeprint](https://github.com/panyanyany/beeprint)

Make your debug printing more friendly.

[pysession](https://github.com/FallibleInc/pysession)

Automatically saves python shell code to a local file or secret Gist.

[tensorflow-wavenet](https://github.com/ibab/tensorflow-wavenet)

A TensorFlow implementation of DeepMind's WaveNet paper.

[Python 3.6.0b1](https://mail.python.org/pipermail/python-dev/2016-September/146441.html)

[Boston Python Meetup September 2016 - Cambridge, MA](https://www.meetup.com/bostonpython/events/231638367/)

将会有以下演讲

* Hacking CPython internals

* Few lightning talks

[For Those About to Mock (We Salute You)](https://www.meetup.com/DCPython/events/232442940/)

An intro to using Python's unittest.mock library to "mock" expensive or

dangerous calls. We find it indispensable in our unittests, especially when

calling backends like databases, cloud services, or network APIs.

[PyHou 2016年九月聚会 - Houston, TX](https://www.meetup.com/python-14/events/226999489/)

[San Diego 2016年九月Python聚会 - San Diego, CA](https://www.meetup.com/pythonsd/events/233672873/)