Past Meetings • Recent Topics

Why Learn PySpark?
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

Tour of job scheduling in Python
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.

Defining services with grpc and protocol buffers
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
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
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
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.

Mocking with MITM
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.

Pandas MultiIndex Tutorial and Best Practices
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.

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.

Interactive Introspection with `ls`
By: Aly Sivji
Experience Level: Novice

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

Data Classes in Python 3.7: Why and How do They Compare to Existing Solutions?
By: Brian Stempin, Yiu Ming Huynh
Experience Level: Intermediate

Python prides itself on being a language where “There should be one – and preferably only one – obvious way to do it” (PEP 20). One place where this isn’t really true is when it comes to the question of how to store data. There are several options: dictionaries, tuples, named tuples, vanilla Python classes, and Python classes decorated with the attrs library. PEP 557 adds a new way: Data classes. In this talk we will compare and contrast each approach, give listeners a way to figure out which one is best for their particular project, and share some performance metrics for those who are concerned with speed and memory footprints.

Python 3.7 Below the Fold: `mock.seal`
By: Aly Sivji
Experience Level: Intermediate

`unittest.mock` provides a flexible implementation of mock objects we can use to write isolated unit tests. In this lightning talk, we will explore the new `mock.seal()` function that was added in Python 3.7.

Intro to SaltStack
By: Erik Johnson
Experience Level: Novice

SaltStack is open-source software for modern IT automation. The project was created in 2012 and today is used by tens of thousands of DevOps and enterprise IT organizations to automate the management of data center infrastructure and application environments. With its core remote-execution functionality, it is flexible enough to run shell commands, perform configuration management tasks, orchestration, and more. Erik Johnson, a SaltStack core developer and Chicago-area native, will demo the basics of how to get started using Salt, as well as how to use its powerful event bus for automation tasks.