The main part of PyCon US finished just a few days ago, on Sunday evening. As usual, I had a huge amount of fun: I gave a tutorial, spoke at the education summit, and also spoke in the main conference. I told people about the newly relaunched LernerPython.com, gave away hundreds of LernerPython T-shirts, awarded people print and electronic copies of Python Workout and Pandas Workout, and had great conversations with new and old friends.
Python conferences are special, in no small part because they're run by the community; sponsors (and I'm one of them) are there because they want to support the Python ecosystem, not just get a chance to hawk their wares. I attended a commercial data-related conference a week before leaving for PyCon, and the difference was quite obvious.
If you can make it to a Python conference, I strongly encourage you to try! I'll definitely be at Euro Python in Prague this July, and I'm hoping/planning to attend a few others this year, including PyCon Taiwan in September and PyData Tel Aviv in November. Take it from me: Attending Python conferences is a great way to see the world, to make friends, and to learn a lot about Python.
Data and five questions
This week, we'll look at a small data set, one that aims to keep track of Python conferences around the world. It's far from perfect, but it's fun and still useful for practicing a number of skills. The data is in a GitHub repo:
https://github.com/python-organizers/conferences
This repo, organized by a number of volunteers in the Python community, contains a number of CSV files, each representing conferences held in a particular year.
Learning goals for this week include working with multiple CSV files, dates and times, grouping, and plotting.
Paid subscribers can download these files at the end of this message.
I'll be back tomorrow with full explanations and solutions, including a downloadable Jupyter notebook.
Meanwhile, here are my five tasks and questions:
- Download the GitHub repo for conferences. Read the CSV files for each of the
years in the repo into a single data frame. Make sure that all of the date-related
columns aredatetimedtypes. - How many Python conferences are listed for each year? Create a bar plot visualizing this information. Does anything stick out? Find, starting in 2020, the 10 countries with the greatest number of total conferences. Create a bar plot comparing their numbers.