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.
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.
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.
Overarching requirements for all PICSciE virtual workshops are listed on the advance setup guide for 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.
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, as described in the advance setup guide for PICSciE virtual workshops. Then, connect to “myadroit” and start a Jupyter session, as described here.
Presentation, demo, and hands-on
Presentation materials are here. Code samples for the hands-on exercises are in the following Google Colab notebooks:
A recording of the session is here (requires active Princeton NetID to view).