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https://github.com/dlab-berkeley/Python-Data-Wrangling-Legacy

D-Lab's 3 hour introduction to data wrangling in Python. Learn how to import and manipulate dataframes using pandas in Python.
https://github.com/dlab-berkeley/Python-Data-Wrangling-Legacy

data-science pandas python

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D-Lab's 3 hour introduction to data wrangling in Python. Learn how to import and manipulate dataframes using pandas in Python.

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# D-Lab Python Data Wrangling Workshop



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

[![Binder](http://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/dlab-berkeley/Python-Data-Wrangling/HEAD)



This repository contains the materials for D-Lab's Python Data Wrangling workshop. Prior experience with [Python Fundamentals](https://github.com/dlab-berkeley/python-fundamentals) is assumed.



## Workshop Goals



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](https://github.com/dlab-berkeley/python-fundamentals), we recommend attending that workshop first.



## 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 3.8 distribution)](https://www.anaconda.com/products/individual). Click "Download" and then click 64-bit "Graphical Installer" for your current operating system.



2. Download the [Python-Data-Wrangling workshop materials](https://github.com/dlab-berkeley/Python-Data-Wrangling):



* 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-Data-Wrangling.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-Data-Visualization` folder you downloaded above.



3. Open the `lessons` folder, and click `01_pandas.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.



## 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: 



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



[![Binder](http://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/dlab-berkeley/Python-Data-Wrangling/HEAD)



By using this button, however, you cannot save your work.



# Additional Resources



* [The official pandas documentation](http://pandas.pydata.org/pandas-docs/stable/)

* [Visualization with pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html)



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



# Other D-Lab Python Workshops



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



## Introductory Workshops



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

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

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

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



## Advanced Workshops



* [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)