Thu, Sep 13 2018 at 06:00 PM at Metis
(30 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.
(5 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.
(15 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.
Thu, Aug 09 2018 at 06:00 PM at Sully's House
(30 Minutes)
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.
(5 Minutes)
By: Aly Sivji
Experience Level: Novice
Walkthrough of `python-ls`, a new utility that allows users to interactively introspect Python objects.
(30 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.
(25 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.
Thu, Jul 12 2018 at 06:00 PM at Telnyx
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.
(5 Minutes)
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.
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.
Thu, Apr 12 2018 at 06:00 PM at Blue Lacuna
By: Matt Inwood
Experience Level: Intermediate
Airflow is a great open source resource for managing ETL, or any other scheduled jobs. We'll go over the DAG-Task-Script Hierarchy; job triggers, logging, and the web interface. I'll also talk about some best practices, and different caveats and gotchas that you can come across from my personal experience implementing it.
(10 Minutes)
By: Joshua Herman
Experience Level: Intermediate
Slides Link
After giving a whirlwind tour of what Ethereum and Solidity are I will show how to use Django and Web3py to deploy a smart contract that performs division.
(20 Minutes)
By: Leon Shernoff
Experience Level: Novice
Working on a substantial Python project at home can be confusing and frustrating. A work environment can suddenly impact the direction of a project in unexpected ways, because of the many stakeholders; but they usually have a robust process in place for actually doing the coding (otherwise nothing gets done). Implementing a solid and productive workflow routine at home can be a challenge, but it is of great benefit for complex projects. This talk uses a sample text-processing project to demonstrate a home workflow design featuring sandboxing in Jupyter notebooks, migration of working routines to project-specific modules and straight-ahead Python files, and writing unit tests for these in PyTest.