I started teaching myself Python in December 2018. And observed the 💪 of the language through its libraries while pursuing the following projects:
Credits: Automate the Boring Stuff with Python 📖
This is a simple program that obtains an address either from the command line or from the clipboard and pulls up that address on Google Maps using your default browser.
This was an interesing challenge. The task here was to parse through my emails from the beginning of 2019 for all my Uber trips 🚗 and download each receipt as a separate PDF document onto my local machine.
I got to delve into message payloads within emails, HTML href tags and also learnt about writing to files!
This was a visualization task at work (with confidential data) where I was asked to create 2 simple bar graphs based on insurance claims data.
Although creating the graphs in Seaborn was easy, figuring out how to correctly add the data labels to each bar proved to be a bit challenging. Ultimately .patches from the Matplotlib library came to the rescue and by using a few numeric calculations on the width and height of each bar, I was able to position the data labels correctly!
Credits: https://github.com/WillKoehrsen/Bokeh-Python-Visualization
I wanted to take my visualization knowledge to the next level when I came across Bokeh, which would allow me to render my graphs in a web browser.
I came across Will Koehrsen's Bokeh project and recreated a part of it in my repo. It turned out to be an extremely useful and fun project where I visualized
This project involved:
- storing the Titanic 🚢 dataset as a MongoDB collection
- querying the data using the PyMongo library
- and visualizing it
Another great opportunity that presented itself through this project was being asked to collaborate on creating MongoDB coursework. Head over to this file and you'll see the progression onto a more fleshed out offering with detailed explanations for the code being used!