November Meetup

Summary

• Introduction (15 min)

• Talk: Introduction to Selenium (20 min)

• Talk: A Gentle Introduction to Generators and Coroutines in Python (15 min)

• Networking over Tea, sponsored by Zilogic Systems (http://www.zilogic.com) (15 min)

• Talk: Getting started with SQLAlchemy (30 min)

• Talk: Fun with Pandas (30 min)

Details

Introduction to Selenium

Speaker: Mayur Shah (https://www.linkedin.com/in/thetestingguy)

Selenium is a portable software testing framework for web applications. Selenium provides a record/playback tool for authoring tests without learning a test scripting language. This talk will be an introduction to Selenium and its usage in Test Automation.

Slides: http://www.slideshare.net/kagrana_software/selenium-introductionchennaipy

A Gentle Introduction to Generators and Coroutines in Python

Speaker: Kiran Gangadharan (http://kirang.in/)

Though a lot of people know Python, very few people actually understand generators and coroutines enough to understand how awesome they can be. This talk aims to provide a basic understanding of how and when they can be used, and why they are an important utility in your Python toolkit.

Slides: https://speakerdeck.com/kirang89/a-gentle-introduction-to-generators-and-coroutines

Getting started with SQLAlchemy

Speaker: Shrayas Rajagopal (https://github.com/shrayasr)

SQLAlchemy is a really quirky and awesome way to do ORMs in python. And it has quite a learning curve at least at the initial stages. Once you get to know the general idea of how things work, it becomes pretty awesome. I've just started working with it and just want to talk about some things that'll help with getting started with SQLAlchemy.

Slides: https://speakerdeck.com/shrayasr/introduction-to-sqlalchemy-orms

Fun with Pandas

Speaker: Sharmila Gopirajan (http://www.minvolai.com/blog/)

Pandas is Python's answer to R's Dataframes. If that does not make sense, not to worry. Just think of Excel's functions in Python, only more versatile and able to handle much larger amounts of data. This will be a gentle introduction to Pandas, following which, we will try to apply the concepts to a Crunchbase's public dataset.

IPython Notebook: http://nbviewer.ipython.org/github/sharmi/crunchbase_analysis