20
It’s Dangerous To Go Alone!
Heed this advice! One of the best ways to learn a language is to work on a problem with other people. For example, in pair-programming, two people program together. Alternatively, one person can do the typing while the other person talks through the code. This allows two sets of eyes to look at the code, improves communication between the two colleagues, and gives a sense of ownership. These shared-programming techniques both contribute to higher-quality code and make programming fun, which means you’re more likely to improve by doing it more often.
20.1 Local Meetups
Many cities have a Meetup culture in which people can find a common hobby or topic and have a place to “meet up”.1 Python-specific meetups exist, but it’s worth going to others that focus on data cleaning, visualization, or machine learning. Even meetups in other languages can be helpful. The more you expose yourself to the community and the field, the more connections you can make with your own work.
1. Meetup: https://www.meetup.com/
If there isn’t a meetup in your city, create one! You can start with friends and people who are interested, and begin to host regular times to meet and talk. Keep it fun. Talk about topics of interest at a bar. Again, the more enjoyable something is, the more likely you are to do it.
Since the COVID-19 pandemic, many meetups have moved to virtual + online, and hybrid options for meetups are becoming the norm.
20.2 Conferences
Conferences are a great way to learn about the latest libraries and techniques. You also get to meet new people as well as library maintainers. Many conferences sponsor a “sprint day”, during which people are encouraged to work on code and contribute to a library. This is a great way to learn about the library itself, to improve your programming skills, and to contribute to the community.
PyCon is the main Python conference.2 It includes topics across the entire Python ecosystem, such as Django3 and Flask4 for web development. The talks for these conferences are usually recorded and freely available.5 The SciPy6 and EuroSciPy7 conferences focus more on the scientific and analytics stack aspects of Python. I have attended SciPy over the past few years, and I can assure you that the tutorials cover a vast set of topics. The best way to view the conference tutorials and talks is to find the respective YouTube page for the conference.
2. PyCon conference: https://us.pycon.org
3. Django: www.djangoproject.com
4. Flask: https://flask.palletsprojects.com
5. Python 2017 talks: www.youtube.com/channel/UCrJhliKNQ8g0qoE_zvL8eVg
6. SciPy Conference: https://conference.scipy.org
7. EuroSciPy Conference: https://www.euroscipy.org/
AnacondaCon is a newer conference that likewise has videos posted online.8 Jupyter also hosts its own conferences. Jupyter Days and JupyterCon have videos, and you can hear when the next conference is on the main Jupyter blog.9 Finally, PyData, the nonprofit that supports many open-source projects, sponsors conferences and provides videos.10
8. AnacondaCon Conference: https://anacondacon.io/
9. JupyterCon Conference https://jupytercon.com/
10. PyData: https://pydata.org/
20.3 The Carpentries
The Carpentries is a nonprofit organization that aims to teach all the programming and data skills to researchers. It’s where I got my start into data science education. Software-Carpentry, Data Carpentry, and Library Carpentry are sister organizations under The Carpentries.
The Carpentries does a great job sharing their lesson materials. If you ever need a resource to learn or teach out of, I cannot recommend the materials from The Carpentries enough: https://carpentries.org/workshops-curricula/.
20.4 Podcasts
Data science related podcasts are plentiful. Here are some that I listen to (in no particular order):
Vanishing Gradients:
https://vanishinggradients.fireside.fm/Data Skeptic:
https://dataskeptic.com/Talk Python to Me:
https://talkpython.fm/Python Bytes:
https://pythonbytes.fm/Super Data Science:
https://www.superdatascience.com/podcastShiny Developer Series:
https://shinydevseries.com/R Weekly Highlights:
https://rweekly.fireside.fm/Not So Standard Deviations:
https://nssdeviations.com/Partially Derivative (discontinued):
http://partiallyderivative.com/Linear Digressions (discontinued):
http://lineardigressions.com/Becoming a Data Scientist (discontinued):
www.becomingadatascientist.com
While this isn’t an exhaustive list, these podcasts will give you a good sense of the Python and data science community and the tools, news, and thinking behind many data science methods.
20.5 Other Resources
Instead of trying to create a list of Python resources in a book, I’ve started a project called “The Big Book of Python” that aims to parallel “The Big Book of R”. These resources aim to curate a bunch of free resources into a single page. I hope these resources help you with your future data science journey.
Conclusion
This book was intended to provide you with a solid foundation from which to learn more about Pandas and its related libraries. Be sure to check out the accompanying github repository for the book for updates and additional resources: https://github.com/chendaniely/pandas_for_everyone.