This repository contains the materials for D-Lab’s Python Fundamentals workshop series.

No prior experience with Python is required.

Check out D-Lab’s Workshop Catalog to browse all workshops, see what’s running now, and review prerequisites.

This six-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience, with a focus on data science application. In Parts 1-3, we cover the basics of Python and Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas. In Parts 4-6, we cover loops and conditionals, creating your own functions, analysis and visualization in Pandas, and the workflow of a data science project.

After completing Python Fundamentals, you will be able to:

• Navigate Jupyter Notebooks.
• Assign variables.
• Distinguish common data types and structures.
• Apply methods to data types.
• Inspect documentation to deal with error messages.
• Implement loops to do repeated computations.
• Understand how to handle conditions.
• Write your own functions.
• Perform basic operations in Pandas, including visualization.
• Understand the basic workflow for a data science project.

This workshop does not cover the following:

• Advanced DataFrame manipulation. This is covered in Python Data Wrangling.
• Advanced Data visualization. This is covered Python Data Visualization.

Python Fundamentals has 6 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walkthrough interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language.

1. Part 1: Introduction to Jupyter and Python
2. Part 2: Data Types and Structures
3. Part 3: Introduction to Pandas
4. Part 4: Functions and Conditionals
5. Part 5: Data Analysis and Visualization
6. Part 6: Project

Before attending the workshop, you should install Python and Jupyter to your computer. If you need help, please submit a consulting request with D-Lab prior to the start of the workshop.

Anaconda is software that allows you to run Python and Jupyter notebooks on your computer. Installing Anaconda is the easiest way to make sure you have all the necessary software to run the materials for this workshop. We recommend Python 3.12 or later (the latest Anaconda distribution includes this by default). Complete the following steps:

1. Download and install Anaconda. Click "Download" and then click 64-bit "Graphical Installer" for your current operating system.
2. Download the materials in this repository:

• 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 https://github.com/dlab-berkeley/Python-Fundamentals.git.

You can also follow a step-by-step Anaconda Installation tutorial with visuals here.

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 Lab" and navigate through your file system to the Python-Fundamentals folder you downloaded above.

3. Navigate to the lessons folder

4. Open the 01_Jupyter_and_Python.ipynb to begin.

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

Note: if you are having trouble accessing the Downloads/Desktop folder from Anaconda Navigator, check out this post or submit a consulting request to get help!

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 Binder, which is another cloud-based option. Click this button:

Note: Using Binder, you unfortunately 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:

• Python Data Wrangling
• Python Data Visualization
• Geospatial Fundamentals in Python

• Python Text Analysis
• Python Machine Learning
• Python Deep Learning

• Tom van Nuenen
• Emily Grabowski
• Pratik Sachdeva