This repository contains the materials for D-Lab's Python Text Analysis. Prior experience with Python Fundamentals and Python Data Wrangling is assumed.
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.
This workshop is divided in two parts:
- 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 that this workshop focuses on vector spatial data.
- Part 2: Geoprocessing and analysis. In Part 2, we 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.
Anaconda is a useful package management software that allows you to run Python and Jupyter notebooks very easily. Installing Anaconda is the easiest way to make sure you have all the necessary software to run the materials for this workshop. Complete the following steps:
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Download and install Anaconda (Python 3.8 distribution). Click "Download" and then click 64-bit "Graphical Installer" for your current operating system.
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Download the Python-Geospatial-Fundamentals workshop materials:
- Click the green "Code" button in the top right of the repository information.
- Click "Download Zip".
- Extract this file to a folder on your computer where you can easily access it (we recommend Desktop).
- Optional: if you're familiar with
git, you can instead clone this repository by opening a terminal and enteringgit clone [email protected]:dlab-berkeley/Python-Geospatial-Fundamentals.git.
Now that you have all the required software and materials, you need to run the code:
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Open the Anaconda Navigator application. You should see the green snake logo appear on your screen. Note that this can take a few minutes to load up the first time.
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Click the "Launch" button under "Jupyter Notebooks" and navigate through your file system to the
Python-Geospatial-Fundamentalsfolder you downloaded above. -
Go to the
lessonsfolder and find the notebook corresponding to the workshop you are attending. -
Press Shift + Enter (or Ctrl + Enter) to run a cell.
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You will need to install additional packages depending on which workshop you are attending.
Note that all of the above steps can be run from the terminal, if you're familiar with how to interact with Anaconda in that fashion. However, using Anaconda Navigator is the easiest way to get started if this is your first time working with Anaconda.
If you do not have Anaconda installed and the materials loaded on your workshop by the time it starts, we strongly recommend using the UC Berkeley Datahub to run the materials for these lessons. You can access the DataHub by clicking the following button:
The DataHub downloads this repository, along with any necessary packages, and
allows you to run the materials in a Jupyter notebook that is stored on UC
Berkeley's servers. No installation is necessary from your end - you only need
an internet browser and a CalNet ID to log in. By using the DataHub, you can
save your work and come back to it at any time. When you want to return to your
saved work, just go straight to DataHub, sign
in, and you click on the Python-Geospatial-Fundamentals folder.
If you don't have a Berkeley CalNet ID, you can still run these lessons in the cloud, by clicking this button:
By using this button, however, you cannot save your work.
D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.
Visit the D-Lab homepage to learn more about us. You can view our calendar for upcoming events, learn about how to utilize our consulting and data services, and check out upcoming workshops.
Here are other Python workshops offered by the D-Lab: