This repository contains the materials for D-Lab's Python Data Wrangling workshop. Prior experience with Python Fundamentals is assumed.
In this workshop, we provide an introduction to data wrangling with Python. We will do so largely with the pandas package, which provides a rich set of tools to manipulate and interact with data frames, the most common data structure used when analyzing tabular data. We'll learn how to manipulate, index, merge, group, and plot data frames using pandas functions.
Basic familiarity with Python is assumed. If you are not familiar with the material in Python Fundamentals, we recommend attending that workshop first.
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-Data-Wrangling 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 git@github.com:dlab-berkeley/Python-Data-Wrangling.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-Data-Visualizationfolder you downloaded above. -
Open the
lessonsfolder, and click01_pandas.ipynbto begin. -
Press Shift + Enter (or Ctrl + Enter) to run a cell.
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 this 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-Data-Wrangling 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: