ImportPython Newsletter Issue 76.

Worthy Read

Segment is the customer data platform that developers and analysts love because of its elegant APIs and extensive partner ecosystem. Here is the Python library to integrate Segment with your Python code - https://segment.com/docs/libraries/python/ .

Interview with none other then Guido van Rossum - BDFL / Creator of Python. 

Regular expressions are basically a tiny language all their own that you can use inside of Python and many other programming languages. You will often hear regular expressions referred to as “regex”, “regexp” or just “RE”. Some languages, such as Perl and Ruby, actually support regular expression syntax directly in the language itself. Python only supports them via a library that you need to import. The primary use for regular expressions is matching strings. You create the string matching rules using a regular expression and then you apply it to a string to see if there are any matches. 

Lambda expressions provide a way to pass functionality into a function. Sadly, Python puts two annoying restrictions on lambda expressions. First, lambdas can only contain an expression, not statements. Second, lambdas can’t be serialized to disk. This blog shows how we can work around these restrictions and unleash the full power of lambdas. 

By definition, clustering is a task of grouping a set of objects in a way that objects in a particular group are more similar to each other rather than the objects in the other groups. It has multiple applications in almost every field. You can even segment your customers into different groups based on their purchase patterns. This is a Python script demonstrating the basic clustering algorithm, “k-means”. Also, it will plot the clusters using Plotly API. It uses sample data points for now, but you can easily feed in your dataset. 

If you are a flask user. You would greatly benefit by this pretty exhaustive (information wise) talk. Do you think that because Flask is a micro-framework, it must only be good for small, toy-like web applications? Well, not at all! In this tutorial I am going to show you a few patterns and best practices that can take your Flask application to the next level. 

Today we're going to make a Python library that is actually the Go webserver, for which we can write handlers in Python. It makes Python servers really fast, and—more importantly—it’s a bit fun and experimental. Andrey Petrov is the author of urllib3. If you have coded in Go you would realize this is pretty cool idea. 

You're curious how web frameworks work because you want to become a better web developer. This post aims to describe what I learned by writing a small server and framework by explaining the design and implementation process step by step, function by function. 

Lots of packages get a lot of love like django-rest-framework and wagtail, and rightfully so, they’re awesome! But I wanted to give some less well know ones some love. 

PyCon India, the premier conference in India on using and developing the Python programming language is conducted annually by the Python developer community. It attracts the best Python programmers across the country and abroad. Submit your proposal here. 

A chat tool that's actually built for teams and businesses. Try HipChat for free today. You can use Will to build a Python bot for HipChat -https://github.com/skoczen/will . Have a look.

The project I will be talking about runs along side your program, and watches not just how the code is executed but also all of the values of all variables, recording them to produce a complete trace of the project’s execution history for you to examine at your leisure. 

Caches are important in helping to solve time complexity issues, and ensure that we don’t run a time-consuming program twice. You never know when your scripts can just stop abruptly, and then you lose all the information in your cache, and you have you run everything all over again.In order to counter this, saving your cache to a disk is something that can be very helpful in that it allows state to be saved to disk, and be retrieved from it anytime as long as its there. 

In this post we will implement a simple 3-layer neural network from scratch. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details. Here I’m assuming that you are familiar with basic Calculus and Machine Learning concepts, e.g. you know what classification and regularization is. But even if you’re not familiar with any of the above this post could still turn out to be interesting ;) 

It turns out my kids have been sending each other secret messages, enciphered with a substitution cipher of their own invention! They only let me see the secret key when I agreed to help them mix up a very complicated recipe for invisible ink. 

I had a programming interview recently, a phone-screen in which we used a collaborative text editor. I was asked to implement a certain API, and chose to do so in Python. Abstracting away the problem statement, let’s say I needed a class whose instances stored some data and some other_data. As it turns out, we were both wrong. The real answer lay in understanding the distinction between class and instance attributes.