21 November | 10:00 AM Central Time
Data management is a crucial component of any scientific work. Being able to establish relationships between environmental datasets or query a large database with ease in order to isolate data of interest are vital skills that are required for effective scientific work. However, depending on your level of programming experience, it can be tough to figure out what’s the best route to take when starting to explore data management codes. Python’s Pandas and GeoPandas packages are versatile and easy to use, but how do you get started with them? How do they pair up against SQL and R? How do you create, query, and manipulate a dataframe or geodataframe in Python? These questions (and more) will be answered in this third installment of the Open Source Software Series’ Python workshops.
THIS WORKSHOP WILL REQUIRE YOU TO ALREADY HAVE PYTHON, PANDAS, GEOPANDAS, AND ANY PREREQUISITE PACKAGES ALREADY INSTALLED.
Watch recordings from the first two Python workshops in the SEG Open Source Software YouTube playlist here to learn how to install Python and to learn about data types in Python:
Chris Terra is a 4th-year PhD student at Rutgers University Newark working with Dr. Lee Slater. Born and raised south of Boston, MA – Chris got his B.S. in Geoscience from the University of Massachusetts Amherst and spent the next few years working as a field geologist for a private environmental consulting company in southern MA before deciding to pursue his PhD at Rutgers. Chris’s PhD research focuses on using time-lapse electrical methods (primarily ERT and EMI) to investigate vertical saltwater intrusion occurring in coastal environments during large storm events. As a near-surface geophysicist needing to deal with large datasets, data visualization, and inversions, he has worked a considerable amount with Python’s various packages and third-party software. This past summer, Chris worked as an intern at Pacific Northwest National Laboratory creating a Jupyter Notebook based tutorial for the open-source 3D geophysical modeling and inversion code – E4D – which has helped to expand his proficiency in Python overall but has also helped him become more comfortable with integrating Python in both Windows and Linux environments.
Joshua Pwavodi is presently serving as a postdoctoral researcher at the ITES, University of Strasbourg. He successfully earned his PhD in Geophysics from Université Grenoble Alpes, France in 2023. In 2017, he achieved an MSc in Petroleum Geosciences from the Centre of Excellence in Geosciences and Petroleum Engineering at the University of Benin, along with a BSc in Geology from Ahmadu Bello University, Nigeria. His research primarily revolves around leveraging machine learning and artificial intelligence to enhance the petrophysical and geophysical processes related to geothermal exploration, seismicity, and borehole studies. In 2023, Joshua was honored with the European Consortium for Ocean Research Drilling (ECORD) early career research grant. In 2021, his work was selected as a finalist for the innovation award at the ICEG-SEG conference. Furthermore, he clinched the title of the Outstanding Student Paper Presentation at the 5th SEG EURASIA conference in 2021 and has been the recipient of more than 8 scholarships during his academic journey. Currently, Joshua actively volunteers with the Near-Surface SEG Geophysics Student Subcommittee and the Digital User Group of the American Geophysical Union (AGU).
Noah Dewar grew up surrounded by the Atlantic Ocean in Nova Scotia and received his B.Sc. from Mcgill University in 2012 where he completed a Joint Major in Physics and Geophysics. He then worked at Sander Geophysics Limited in Ottawa, Canada, for three and a half years where he quickly moved from geophysical analyst to field crew chief and ran multiple surveys around the world. He then attended Stanford University and joined the environmental geophysics research group run by Professor Rosemary Knight and received his Ph.D. in 2020. During his time at Stanford he published research on data-driven methodologies for the use of near surface geophysical methods to inform coupled groundwater-surface flow models. After completing his degree at Stanford he worked for just under a year as a data scientist at Omniscience Corporation before taking on the role of Chief Technology Officer at HighTide Intelligence, a flood risk and analytics startup that leverages big data and scalable code to provide consulting services and an innovative platform for flood mitigation and adaption.