Past Meetings • Recent Topics

From Python to Rust
(0:15:00 Minutes)
By: Kevin Nasto
Experience Level: Intermediate

Ever been curious about the Rust programming language? Although Rust is a low level language, some similarities exist with Python. This talk describes it from the point of view of a Python user. Discover the alternatives to pip, functions and passing values, lists, classes, import statements, exception handling, and more.

Python in a Pod in a Kube in a Pi
(0:30:00 Minutes)
By: Joe Jasinski
Experience Level: Intermediate

Have some extra Raspberry Pi's laying around? Ever want to learn what this Kubernetes thing is about? Do you love running Python inside of Docker? Then this talk is for you! This talk will dive into some core Kubernetes concepts, using a Raspberry Pi cluster as a learning tool.

Beating Mastermind: Winning Games, Translating Math to Code, and Learning from Donald Knuth
(0:35:00 Minutes)
By: Adam Forsyth
Experience Level: Novice

Mastermind is a logic-based guessing game. Many years ago, Donald Knuth described a way to win the game in 5 moves or less. We’ll implement the game and the algorithm from the article. Come learn how to beat Mastermind and turn a paper by a famous scientist into code!

Tour of job scheduling in Python
(0:30:00 Minutes)
By: Raymond Buhr
Experience Level: Novice

Once you've started to learn python, you're going to want to use it to automate tasks. Their are lots of ways to do this, each with it's own set of pros and cons. This talk will go over a few options for scheduling the execution of python scripts and the tradeoffs that come with each. Tools that will be covered include crontab, schedule, celery, airflow, and cloud options AWS Lambda and GCP functions.

Why Learn PySpark?
(0:30:00 Minutes)
By: David Liao
Experience Level: Novice

Grubhub has chosen to adopt the Spark Big Data computing framework to underpin it’s internal Grubhub Data Platform Spark was adopted very early by Silicon Valley FANG companies.. What features make Spark a great computing platform for both Analytical reporting and Machine Learning? Tips on how to install PySpark on a Mac OSX system so one can play wit PySpark without paying for a cloud cluster

Defining services with grpc and protocol buffers
(0:25:00 Minutes)
By: Patrick Boland
Experience Level: Intermediate

gRPC and protocol buffers offer a high performance, open source way to define services and messages for the future. Think of it like REST, but for the http2 protocol.

How to install Anaconda
(0:05:00 Minutes)
By: Kevin Nasto
Experience Level: Novice

Ever want to avoid installing Python packages with complex dependencies such as sklearn? Ever have permissions issues installing a package? Anaconda is the answer. This talk describes why use it and how to get it set up.

Machine Learning and Deduplication
(0:30:00 Minutes)
By: Forest Gregg
Experience Level: Intermediate

Machine learning and record linkage: Finding duplicates or matching data when you don't have primary keys is one of the biggest challenges in preparing data for data science. At DataMade we have built a python, open source machine learning library to help developers, and a product Dedupe.io to help everyone else. We describe the problem and how we use machine learning to scale to tens of millions of records.

Why python is the best first programming language and here is how to make it even better
(0:15:00 Minutes)
By: Jhankar Mahbub
Experience Level: Novice

Meet Simon! He doesn't have a technology background, but he wants to be a programmer. Should he go to a boot camp with 17k or read 29,900,000 options provided by Google when he searched "Learn Python". Or he can join ChiPy mentorship program. While all of these will work, I would like to make his journey bit more enjoyable by presenting a more natural, friendlier, and a more interactive way to learn programming concepts. In this talk, we will look at functions, for loops, list comprehensions, and generators in a way that is easy for people like Simon to understand and use.

Pandas MultiIndex Tutorial and Best Practices
(0:25:00 Minutes)
By: Zax
Experience Level: Novice

While Pandas is one of the most well known Python libraries for working with array-like data, many users limit themselves to just two dimensions of data. This talk will walk through Pandas' MultiIndex DataFrames, which extend traditional DataFrames by enabling effective storage and manipulation of arbitrarily high dimension data in a 2-dimensional tabular structure. ((If that sentence doesn't make sense yet, don't worry - it should by the end of the tutorial.)) While the displayed version of a multiindexed DataFrame doesn't appear to be much more than a prettily-organized regular DataFrame, it's actually a pretty powerful structure if the data warrants its use. This talk is beginner friendly, and will start from the assumption of having never used Pandas, though some Pandas experience will aid understanding.

Interactive Introspection with `ls`
(0:05:00 Minutes)
By: Aly Sivji
Experience Level: Novice

Walkthrough of `python-ls`, a new utility that allows users to interactively introspect Python objects.

Mocking with MITM
(0:30:00 Minutes)
By: Quentin Bayart
Experience Level: Intermediate

Every developer (eventually) writes tests. Unit tests, Integration tests, End-to-end tests, Regression tests.. All of those tests are necessary but can become a nightmare when you need to refactor some code. I personally don't like the amount of time I spend to manually mock my dependencies / functions / objects. This talk is about a simple docker-compose / pytest / mitm setup which aims at speeding up the mocking process and the maintenance of those mocks when refactoring or when updating the interface of your services. Q&A: Many of you are dealing with this mocking process regularly so you can expect many comments / questions if you come to this talk :) Contact: Quentin Bayart, Software Engineer @ Nielsen qbayart@hawk.iit.edu https://github.com/QuentinBay A couple of days before the presentation, I will push my demo to my github so you should be able to find it there after the presentation.

Python Magic Methods
By: Nick Timkovich
Experience Level: Intermediate

Everything in Python is an object and nothing is special. Python's built-in objects can be added, called, indexed, or with'd, and with a little magic, so can yours! Use of magic methods, those prefixed/suffixed with double underscores, can increase the flexibility of your code while also making it shorter and simpler.