D-Lab's Python Fundamentals Workshop

This repository contains the materials for D-Lab’s Python Fundamentals workshop. No prior experience with Python is required.

Workshop Goals

In this workshop, we provide an introduction to Python. We do so in four main parts:

• Part 1: Introduction to Python and Jupyter Notebooks, variables, data types, and functions.
• Part 2: Data structures, loops, conditionals, and creating functions.
• Part 3: Libraries, File I/O, and scientific computing.
• Part 4: Error handling, style, and an applied, in-depth project.

Installation Instructions

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:

1. Download and install Anaconda (Python 3.9 distribution). Click "Download" and then click 64-bit "Graphical Installer" for your current operating system.

2. Download the Python-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).

3. Optional: if you're familiar with git, you can instead clone this repository by opening a terminal and entering git clone [email protected]:dlab-berkeley/Python-Fundamentals.git.

Run the code

Now that you have all the required software and materials, you need to run the code:

1. 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.

2. Click the "Launch" button under "Jupyter Notebooks" and navigate through your file system to the Python-Fundamentals folder you downloaded above.

3. Open the lessons folder, then Part1, and open 00_workshop_setup.ipynb to begin.

4. Press Shift + Enter (or Ctrl + Enter) to run a cell.

Is Python not working on your laptop?

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-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, you cannot save your work unfortunately.

About the UC Berkeley D-Lab

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.

Other D-Lab Python Workshops

Here are other Python workshops offered by the D-Lab:

Basic competency

• Python Fundamentals
• Introduction to Pandas
• Geospatial Fundamentals in Python

Intermediate/advanced competency

• Computational Text Analysis in Python
• Introduction to Machine Learning in Python
• Introduction to Artificial Neural Networks in Python
• Fairness and Bias in Machine Learning

Contributors

• Emily Grabowski
• Pratik Sachdeva
• Christopher Hench
• Rochelle Terman