Data Visualization in Python

This session provides an introduction to effective data visualization in Python. Several plotting packages will be discussed, including 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 research-quality plots using Python.

Knowledge prerequisites:

Participants should have reasonable facility with the Python programming language, including a basic familiarity with NumPy arrays and Pandas data frames. This session is not appropriate for those with no prior Python experience. However, no previous experience with Python plotting tools is required.

Hardware/software prerequisites:

Overarching requirements for all PICSciE virtual workshops are listed on the advance setup guide for PICSciE virtual workshops (https://researchcomputing.princeton.edu/learn/workshops-live-training/requirements-picscie-virtual-workshops). In addition, for the hands-on portions of this session, participants should install the Anaconda Python 3 distribution –" which includes Jupyter notebooks, NumPy, Pandas, and Matplotlib –" on their laptops in advance. Instructions can be found on the PICSciE virtual workshops requirements page (https://researchcomputing.princeton.edu/learn/workshops-live-training/requirements-picscie-virtual-workshops).

Alternately, participants without Python 3 installed on their laptops who prefer to run Jupyter Notebooks remotely on one of Princeton’s systems can do so the “myadroit” web interface to the Adroit cluster. To access myadroit, you should first register for an account on Adroit (https://researchcomputing.princeton.edu/systems/adroit), as described in the advance setup guide for PICSciE virtual workshops (https://researchcomputing.princeton.edu/learn/workshops-live-training/requirements-picscie-virtual-workshops). Then, connect to “myadroit” and start a Jupyter session, as described at https://github.com/PrincetonUniversity/hpc_beginning_workshop/tree/master/03_web_interface.

Session format:

Presentation, demo, and hands-on

What to expect:
Single workshop (one-off workshop –" 2 hours total)

Meet the facilitator:
Jose Garrido Torres is a data scientist at the Center for Statistics and Machine Learning at Princeton University. He is part of the Schmidt Data X Fund and Princeton Catalysis Initiative in collaboration with Professor Abigal G. Doyle and Professor Ryan P. Adams. He focuses on developing computational methods for the exploration of chemical space using artificial intelligence.

**Please note that this session is virtual; you will receive a Zoom link a few days prior to the session**

To request accommodations for this event, please contact the workshop or event facilitator at least 3 working days prior to the event.

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Co-hosted with: PICSciE/Research Computing

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