Geospatial Data and Mapping in Python: Parts 1-3

March 1, 2022, 2:00pm to March 8, 2022, 5:00pm

Trying to register, but not affiliated with the UCB campus? If you are from Berkeley Lab (LBL), UCSF, or CZ Biohub, please register via our partner portals here.

If you are from the UCB campus there's no more waitlist! But after registering above, please do fill out the affiliations form if you have not done so at least once before:

Location: Remote via Zoom. Link will be sent on the morning of the event.

Date & Time: This workshop is a 3-part series running from 2pm-5pm each day:

  • Part 1: Tuesday, March 1
  • Part 2: Thursday, March 3
  • Part 3: Tuesday, March 8

Start Time: D-Lab workshops start 10 minutes after the scheduled start time (“Berkeley Time”). We will admit all participants from the waiting room at that time.


Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research.

Geospatial Data and Mapping in Python, Part 1: Getting started with spatial dataframes

Part one of this two-part workshop series will introduce basic methods for working with geospatial data in Python using the GeoPandas library. Participants will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these. Note, this workshop focuses on vector spatial data.

Geospatial Data and Mapping in Python, Part 2: Geoprocessing and analysis

Part two of this two-part workshop series will dive deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. Note, this workshop focuses on vector spatial data.

Geospatial Data and Mapping in Python, Part 3: Description to come 

Description to come!

Knowledge Requirements

You'll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check-out prior to the workshop.

D-Lab Workshops:

Workshop Materials:

Software Requirements:Installation Instructions for Python Anaconda

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