Data visualization is fundamental to the data science process. Using plots and graphs to convey a complex idea makes your data more accessible to everyone. In this session, you will learn the fundamentals of plotting with Pandas in Jupyter by building an interactive visualization prototype that can also run as a standalone web application/dashboard. This session is for anyone who wants to be more familiar with data visualization, hands-on, with Python, Pandas, Matplotlib, interactive widgets, and Flask.
The majority of our session will be in Jupyter notebooks and writing code hands-on. Please go through the environment setup ahead of time.
- Be familiar with data visualization conventions within Jupyter Lab
- Implement standard plots using Pandas
- Be able to describe the integration between matplotlib and Pandas
- Understand the concept of "figure" and "axes"
- Be familiar with other visual frameworks and assess their strengths
- Panel fundamentals
- Implement a basic prototype visualization for exploring data with interactive elements
- Tradeoffs between sharing notebooks vs. custom dashboards
- Flask Fundamentals
- Migrate a Panel visualization in Jupyter to Flask
This session is hands-on and interactive. To get the most out of this session, attendees should dedicate some time before the event to ensure their Python environments' have the proper configuration. We will mainly be using Jupyter Lab and Python libraries. Check out the environment setup guide for full-dteails.
If you show up without first reviewing the environment setup guide, it's very likely your machine won't be ready to go once we start coding so please review the guide and perform any configuration prior to our session.
- Python >= 3.8.x
- A dedicated code editor such as Visual Studio, PyCharm, or Sublime
Gitbash - https://gitforwindows.org/
- Panel
- Flask
- Jupyter Lab
- Pandas
- Matplotlib
Slides will be updated prior to event: https://www.icloud.com/keynote/0n4K_EMonbhwp2s064MrWJHDw#From_Jupyter_to_Dashboards