https://github.com/pyladiesams/vizualisation-sep2019

Use Matplotlib and Seaborn to explore data.
https://github.com/pyladiesams/vizualisation-sep2019

data-visualization matplotlib pandas python seaborn workshop

Last synced: about 2 months ago
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Use Matplotlib and Seaborn to explore data.

• Host: GitHub
• URL: https://github.com/pyladiesams/vizualisation-sep2019
• Owner: pyladiesams
• License: mit
• Created: 2019-08-17T08:47:31.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-10-03T08:05:14.000Z (over 1 year ago)
• Last Synced: 2023-10-03T19:36:49.148Z (over 1 year ago)
• Topics: data-visualization, matplotlib, pandas, python, seaborn, workshop
• Language: Jupyter Notebook
• Homepage:
• Size: 22 MB
• Stars: 3
• Watchers: 4
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# Data visualization with python

### Level: Beginner - intermediate 

### Presentation: https://pyladiesams.github.io/visualisation-sep2019/ (navigate with arrow buttons)

## Project description

Data visualization is important for both data exploration and data presentation. Hence, good visualization skills will help you to better understand your data and to find the best way to present that understanding to others. During the workshop, people will learn how to use matplotlib and seaborn to explore data and how to use these libraries to adapt the crude exploratory plots into publication ready plots.

## Preparations:

Set up a Python 3 environment that can run Jupyter notebooks (Jupyter or Jupyterlab) and install the following libraries:

* matplotlib

* pandas

* seaborn

The easiest way to do this is to install [anaconda](https://www.anaconda.com/distribution/) and install the libraries in your root environment:

```conda install matplotlib pandas seaborn```

If you already have a running (ana)conda environment, you can use `py3_pyladies_vis.yml` to set up a separate enviroment:

```conda env create -f environment.yml```

## Requirements

* Python 3

* matplotlib

* seaborn

* pandas

* jupyter or jupyterlab

## Usage

* Clone the repository

* Start jupyter(lab) and navigate to the workshop folder

## Credits

This workshop was set up by @pyladiesams and @margrietpalm