{"id":13665609,"url":"https://github.com/dlab-berkeley/Python-Fundamentals-Legacy","last_synced_at":"2025-04-26T08:32:36.167Z","repository":{"id":10360293,"uuid":"65162876","full_name":"dlab-berkeley/Python-Fundamentals-Legacy","owner":"dlab-berkeley","description":"D-Lab's 12 hour introduction to Python. 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Instructors and\nTAs are dedicated to engaging you in the classroom and answering questions in\nplain language.\n\n* **Part 1**: Introduction to Python and Jupyter Notebooks, variables, data\n  types, and functions.\n* **Part 2**: Data structures, loops, conditionals, and creating functions.\n* **Part 3**: Libraries, File I/O, and scientific computing.\n* **Part 4**: Error handling, style, and an applied, in-depth project.\n\n## Installation Instructions\n\nAnaconda is a useful package management software that allows you to run Python\nand Jupyter notebooks easily. Installing Anaconda is the easiest way to make\nsure you have all the necessary software to run the materials for this workshop.\nIf you would like to run Python on your own computer, complete the following\nsteps prior to the workshop:\n\n1. [Download and install Anaconda (Python 3.9\n   distribution)](https://www.anaconda.com/products/individual). Click the\n   \"Download\" button.\n\n2. Download the Python Fundamentals [workshop\n   materials](https://github.com/dlab-berkeley/Python-Fundamentals):\n\n   -   Click the green \"Code\" button in the top right of the repository\n        information.\n   -   Click \"Download Zip\".\n   -   Extract this file to a folder on your computer where you can easily\n        access it (we recommend Desktop).\n\n3. Optional: if you're familiar with `git`, you can instead clone this\n   repository by opening a terminal and entering the command `git clone\n   git@github.com:dlab-berkeley/Python-Fundamentals.git`.\n\n## Is Python Not Working on Your Laptop? \n\nIf you do not have Anaconda installed and the materials loaded on your workshop\nby the time it starts, we *strongly* recommend using the D-Lab Datahub to\nrun the materials for these lessons. You can access the DataHub by clicking the\nfollowing button:\n\n[![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\u0026urlpath=lab%2Ftree%2FPython-Fundamentals%2F\u0026branch=main)\n\nThe DataHub downloads this repository, along with any necessary packages, and\nallows you to run the materials in a Jupyter notebook that is stored on UC\nBerkeley's servers. No installation is necessary from your end - you only need\nan internet browser and a CalNet ID to log in. By using the DataHub, you can\nsave your work and come back to it at any time. When you want to return to your\nsaved work, just go straight to [DataHub](https://datahub.berkeley.edu), sign\nin, and you click on the `Python-Fundamentals` folder.\n\nIf you don't have a Berkeley CalNet ID, you can still run these lessons in the\ncloud, by clicking this button:\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dlab-berkeley/Python-Fundamentals/HEAD)\n\nBinder operates similarly to the D-Lab DataHub, but on a different set of\nservers. By using Binder, however, you cannot save your work.\n\n## Run the Code\n\nNow that you have all the required software and materials, you need to run the\ncode.\n\n1. Open the Anaconda Navigator application. You should see the green snake logo\n   appear on your screen. Note that this can take a few minutes to load up the\n   first time. \n\n2. Click the \"Launch\" button under \"JupyterLab\" and navigate through your file\n   system on the left hand pane to the `Python-Fundamentals` folder you\n   downloaded above. Note that, if you download the materials from GitHub, the\n   folder name may instead be `Python-Fundamentals-main`.\n\n3. Open `00_workshop_setup.ipynb` to begin.\n\n4. Press Shift + Enter (or Ctrl + Enter) to run a cell.\n\nNote that all of the above steps can be run from the terminal, if you're\nfamiliar with how to interact with Anaconda in that fashion. However, using\nAnaconda Navigator is the easiest way to get started if this is your first time\nworking with Anaconda.\n\n## Additional Resources\n\nCheck out the following online resources to learn more about Python:\n\n* [A Byte of Python](https://python.swaroopch.com)\n* [Software Carpentry](https://swcarpentry.github.io/)\n* [W3Schools](https://www.w3schools.com/python/)\n\n# About the UC Berkeley D-Lab\n\nD-Lab works with Berkeley faculty, research staff, and students to advance\ndata-intensive social science and humanities research. Our goal at D-Lab is to\nprovide practical training, staff support, resources, and space to enable you to\nuse R for your own research applications. Our services cater to all skill levels\nand no programming, statistical, or computer science backgrounds are necessary.\nWe offer these services in the form of workshops, one-to-one consulting, and\nworking groups that cover a variety of research topics, digital tools, and\nprogramming languages.  \n\nVisit the [D-Lab homepage](https://dlab.berkeley.edu/) to learn more about us.\nYou can view our [calendar](https://dlab.berkeley.edu/events/calendar) for\nupcoming events, learn about how to utilize our\n[consulting](https://dlab.berkeley.edu/consulting) and [data\nservices](https://dlab.berkeley.edu/data), and check out upcoming\n[workshops](https://dlab.berkeley.edu/events/workshops). Subscribe to our\n[newsletter](https://dlab.berkeley.edu/news/weekly-newsletter) to stay up to\ndate on D-Lab events, services, and opportunities.\n\n# Other D-Lab Python Workshops\n\nD-Lab offers a variety of Python workshops, catered toward different levels of\nexpertise.\n\n## Introductory Workshops\n\n-  [Python Data Wrangling](https://github.com/dlab-berkeley/Python-Data-Wrangling)\n-  [Python Data Visualization](https://github.com/dlab-berkeley/Python-Data-Visualization)\n\n## Intermediate and Advanced Workshops\n\n-  [Python Geospatial Fundamentals](https://github.com/dlab-berkeley/Geospatial-Data-and-Mapping-in-Python)\n-  [Python Web Scraping and APIs](https://github.com/dlab-berkeley/Python-Web-Scraping)\n-  [Python Machine Learning](https://github.com/dlab-berkeley/Python-Machine-Learning)\n-  [Python Text Analysis](https://github.com/dlab-berkeley/Python-Text-Analysis)\n-  [Python Deep Learning](https://github.com/dlab-berkeley/Python-Deep-Learning)\n\n# Contributors\n\n* Emily Grabowski\n* Pratik Sachdeva\n* Christopher Hench\n* Rochelle Terman\n","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FPython-Fundamentals-Legacy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdlab-berkeley%2FPython-Fundamentals-Legacy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FPython-Fundamentals-Legacy/lists"}