Data Visualization in Python

by PICSciE/Research Computing

Training/Workshop Programming Languages Research & Data Analysis Undergraduate Research

Thu, Apr 6, 2023

4:30 PM – 6 PM EDT (GMT-4)

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This workshop provides an introduction to effective data visualization in Python. The training focuses on three plotting packages: Matplotlib, Seaborn and Plotly. Examples may include simple static 1D plots, 2D contour maps, heat maps, violin plots, and box plots. The session may also touch on more advanced interactive plots.

Learning objectives: Attendees will be exposed to different plotting packages in Python, along with how to integrate them with NumPy and Pandas, at least at a basic level. After the session, participants will know the basic mechanics of how to generate publication-quality plots using Python.

Session format: Presentation, demo and hands-on

Knowledge prerequisites: Participants should have reasonable facility with the Python programming language, including a basic familiarity with NumPy arrays and Pandas data frames. No previous experience with Python plotting tools is required.

Hardware/software prerequisites: Participants have two options: (1) Come with your own installation of Anaconda Python 3 distribution on your laptop. This will provide Jupyter notebooks, NumPy, Pandas and Matplotlib. (2) Create an account on Adroit at least a few hours before the workshop (https://forms.rc.princeton.edu/registration/?q=adroit) and use the MyAdroit web interface for the workshop. Directions for using the MyAdroit interface are here:
https://researchcomputing.princeton.edu/jupyter

Speakers

Michal Grzadkowski's profile photo

Michal Grzadkowski

Michal joined Princeton Research Computing in 2021 after five years working as a Research Software Engineer at Oregon Health & Science University, where his primary project involved studying the application of machine learning models to better understand the impacts of mutations commonly implicated in tumorigenesis. This involved implementing novel methods for representing the taxonomies of mutations present in cancer cohorts, as well as developing software for deploying and consolidating thousands of classification models on a high-performance compute cluster. His present work focuses on optimizing pipelines for generating quantitative assessments of the contributions various types of assets can make to a power grid’s ability to satisfy the demand for electricity over a given time frame.

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Co-hosted with: GradFUTURES

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