This intermediate level workshop will introduce you to data visualization in Python using the Matplotlib and Seaborn libraries. Participants in this interactive class will write Python code in Jupyter Notebooks to visualize a prepared dataset using both the Matplotlib and Seaborn libraries. By the end of this workshop, participants will be able to:
- Understand different plotting libraries in Python, and which is appropriate when.
- Understand difference between pyplot imperative syntax (state-based interface) and Object-Oriented syntax.
- Be able to perform a simple visualization using provided data in both Matplotlib and Seaborn.
Prerequisites: Participants should be familiar with basic programming concepts, including variable assignment, data types, function calls, and installing packages or libraries. Introductory experience in Python or R will be especially helpful for this workshop.
Website: dataservices.library.jhu.edu/
Contact us: [email protected]
JHU Data Services, part of the Johns Hopkins University Sheridan Libraries, helps the JHU community find, use, visualize, manage, and share data. We offer live webinars and self-paced online trainings on computational research and coding, GIS, data management, data visualization, and more. See all of our training topics on our website.
This repository contains materials for one of our live webinars open to JHU students, faculty, and staff. Please contact us with any questions.
If you do not have Python already installed, please visit https://www.python.org/ and choose the installation type appropriate to your operating system.
We will be using the following packages:
matplotlib: Primary visualization library in Python. Install using pip install matplotlib.
seaborn: Statistical data visualization in Python. Install using pip install seaborn.
Jupyter Notebook: We will use Jupyter Notebook for running Python code and viewing our resulting plots. Install using pip install notebook.
Use our Jupyter Notebook Tutorial to learn the basics of opening and running Jupyter Notebooks. You can view the Jupyter Notebook Tutorial online here.
- Data: This folder contains raw data files to be used during hands-on activities in the workshop
- In-ClassScripts: This folder contains Jupyter Notebook file you will need for the workshop:
data-visualization-in-python-student-notebook.ipynb: Jupyter Notebook containing interactive exercises for the workshopsaving-matplotlib-seaborn-figures.ipynb: Instructions on how to save figures inmatplotlibandseaborn
If you have taken the live webinar for this class, please take this survey: https://www.surveymonkey.com/r/python-data-vis
The presentation materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), attributable to Data Services, Johns Hopkins University.
See LICENSE file for code licensing and re-use information.
The images, external resources, and cheatsheets linked in this repository may have other licenses and terms of use.
Please cite this material as:
Johns Hopkins University Data Services. September 19th, 2024. Introduction to Data Visualization in Python. https://github.com/jhu-data-services/intro-to-data-visualization-in-python