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https://github.com/dlab-berkeley/Python-Fundamentals-Legacy

D-Lab's 12 hour introduction to Python. Learn how to create variables and functions, use control flow structures, use libraries, import data, and more, using Python and Jupyter Notebooks.
https://github.com/dlab-berkeley/Python-Fundamentals-Legacy

data-science introduction-to-python jupyter python

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D-Lab's 12 hour introduction to Python. Learn how to create variables and functions, use control flow structures, use libraries, import data, and more, using Python and Jupyter Notebooks.

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# D-Lab Python Fundamentals Workshop



[![Datahub](https://img.shields.io/badge/launch-datahub-blue)](https://dlab.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FPython-Fundamentals&urlpath=lab%2Ftree%2FPython-Fundamentals%2F&branch=main)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dlab-berkeley/Python-Fundamentals/HEAD)

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)



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

No prior experience with Python is required to attend this workshop.



## Workshop Goals



This four-part, interactive workshop series is your complete introduction to

programming Python for people with little or no previous programming experience.

By the end of the series, you will be able to apply your knowledge of basic

principles of programming and data manipulation to a real-world social science

application.



Each of the parts is divided into a lecture-style coding walkthrough interrupted

by challenge problems, discussions of the solutions, and breaks. Instructors and

TAs are dedicated to engaging you in the classroom and answering questions in

plain language.



* **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 easily. Installing Anaconda is the easiest way to make

sure you have all the necessary software to run the materials for this workshop.

If you would like to run Python on your own computer, complete the following

steps prior to the workshop:



1. [Download and install Anaconda (Python 3.9

   distribution)](https://www.anaconda.com/products/individual). Click the

   "Download" button.



2. Download the Python Fundamentals [workshop

   materials](https://github.com/dlab-berkeley/Python-Fundamentals):



   -   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 the command `git clone

   [email protected]:dlab-berkeley/Python-Fundamentals.git`.



## 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 D-Lab Datahub to

run the materials for these lessons. You can access the DataHub by clicking the

following button:



[![Datahub](https://img.shields.io/badge/launch-datahub-blue)](https://dlab.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FPython-Fundamentals&urlpath=lab%2Ftree%2FPython-Fundamentals%2F&branch=main)



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](https://datahub.berkeley.edu), 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:



[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dlab-berkeley/Python-Fundamentals/HEAD)



Binder operates similarly to the D-Lab DataHub, but on a different set of

servers. By using Binder, however, you cannot save your work.



## 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 "JupyterLab" and navigate through your file

   system on the left hand pane to the `Python-Fundamentals` folder you

   downloaded above. Note that, if you download the materials from GitHub, the

   folder name may instead be `Python-Fundamentals-main`.



3. Open `00_workshop_setup.ipynb` to begin.



4. 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.



## Additional Resources



Check out the following online resources to learn more about Python:



* [A Byte of Python](https://python.swaroopch.com)

* [Software Carpentry](https://swcarpentry.github.io/)

* [W3Schools](https://www.w3schools.com/python/)



# 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](https://dlab.berkeley.edu/) to learn more about us.

You can view our [calendar](https://dlab.berkeley.edu/events/calendar) for

upcoming events, learn about how to utilize our

[consulting](https://dlab.berkeley.edu/consulting) and [data

services](https://dlab.berkeley.edu/data), and check out upcoming

[workshops](https://dlab.berkeley.edu/events/workshops). Subscribe to our

[newsletter](https://dlab.berkeley.edu/news/weekly-newsletter) to stay up to

date on D-Lab events, services, and opportunities.



# Other D-Lab Python Workshops



D-Lab offers a variety of Python workshops, catered toward different levels of

expertise.



## Introductory Workshops



-  [Python Data Wrangling](https://github.com/dlab-berkeley/Python-Data-Wrangling)

-  [Python Data Visualization](https://github.com/dlab-berkeley/Python-Data-Visualization)



## Intermediate and Advanced Workshops



-  [Python Geospatial Fundamentals](https://github.com/dlab-berkeley/Geospatial-Data-and-Mapping-in-Python)

-  [Python Web Scraping and APIs](https://github.com/dlab-berkeley/Python-Web-Scraping)

-  [Python Machine Learning](https://github.com/dlab-berkeley/Python-Machine-Learning)

-  [Python Text Analysis](https://github.com/dlab-berkeley/Python-Text-Analysis)

-  [Python Deep Learning](https://github.com/dlab-berkeley/Python-Deep-Learning)



# Contributors



* Emily Grabowski

* Pratik Sachdeva

* Christopher Hench

* Rochelle Terman