https://github.com/mohammadzainabbas/va-lab
👨🏻💻 Lab Work for Visual Analysis (VA) ✨
https://github.com/mohammadzainabbas/va-lab
open-data openrefine python visual-analysis web-scraping
Last synced: about 2 months ago
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👨🏻💻 Lab Work for Visual Analysis (VA) ✨
- Host: GitHub
- URL: https://github.com/mohammadzainabbas/va-lab
- Owner: mohammadzainabbas
- License: mit
- Created: 2022-10-04T08:13:43.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-15T02:37:25.000Z (almost 2 years ago)
- Last Synced: 2025-06-14T13:39:37.008Z (4 months ago)
- Topics: open-data, openrefine, python, visual-analysis, web-scraping
- Language: Jupyter Notebook
- Homepage:
- Size: 4.73 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Lab Work @ Visual Analysis 👨🏻💻
### Table of contents
- [Introduction](#introduction)
- [About the course](#about-course)
- [Labs](#labs)
* [Lab 01 - Web Scrapping via Beautiful Soup](#lab-1)
* [Lab 02 - Data Visualisation with Altair](#lab-2)
* [Lab 03 - Interaction in Visualization with Altair](#lab-3)
* [Lab 04 - Basic charts with d3.js](#lab-4)
* [Lab 05 - Basic charts with d3.js (continue)](#lab-5)
* [Lab 06 - Events, Interactivity, and Animation with d3.js](#lab-6)
- [Setup](#setup)
* [Create new enviornment](#create-new-env)
* [Setup `pre-commit` hooks](#setup-pre-commit)
#
### 1. Introduction
__`Data drives the world.`__ Nowadays, most of the data (_structured_ or _unstructured_) can be analysed via several techniques. Although, most of the data pipelines are being automated, there arises a key need to keep human in the loop.
One of the fundamental ways to keep human-machine interaction more viable is to analyse data visually (to aid the human as much as possible). `Visual Analysis` introduces some techniques and tools for _analyzing_ and _visualizing_ data.
#
### 2. About the course
During this course, we will be introduced to techniques and tools for _analyzing_ and _visualizing_ data. It emphasizes how to combine computation and visualization to perform effective analysis. The course consists of two parts: a series of lectures on analytics and a series of lectures on visualization. Both parts will include hands-on tutorials during which you will work on analysis problems and start to build your own tools.
#
### 3. Labs
The main aim of this repository is to keep track of the work we have done in __Visual Analysis (VA)__ labs.
#
#### 3.1. Lab 01 - Web Scrapping via Beautiful Soup
[Web Scrapping via Beautiful Soup](https://beautiful-soup-4.readthedocs.io/en/latest/) is a Python package for the scraping data from the internet.
Please checkout lab's details [here](https://github.com/mohammadzainabbas/VA-Lab/tree/main/src/lab1)
#### 3.2. Lab 02 - Data Visualisation with Altair
[Vega-Altair](https://altair-viz.github.io/) is a declarative statistical visualization library for Python, based on Vega and Vega-Lite.
Please checkout lab's details [here](https://github.com/mohammadzainabbas/VA-Lab/tree/main/src/lab2)
#### 3.3. Lab 03 - Interaction in Visualization with Altair
[Vega-Altair](https://altair-viz.github.io/) is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. In this lab, we will see how to use interactions with visualisation with [Vega-Altair](https://altair-viz.github.io/).
Please checkout lab's details [here](https://github.com/mohammadzainabbas/VA-Lab/tree/main/src/lab3)
#### 3.4. Lab 04 - Basic charts with d3.js
[D3.js](https://d3js.org/) is a producing dynamic, interactive data visualizations in web browsers. It makes use of Scalable Vector Graphics, HTML5, and Cascading Style Sheets standards.
Please checkout lab's details [here](https://github.com/mohammadzainabbas/VA-Lab/tree/main/src/lab4)
#### 3.5. Lab 05 - Basic charts with d3.js (continue)
[D3.js](https://d3js.org/) is a producing dynamic, interactive data visualizations in web browsers. It makes use of `Scalable Vector Graphics (SVG)`, `HTML5`, and `Cascading Style Sheets (CSS)` standards.
Please checkout lab's details [here](https://github.com/mohammadzainabbas/VA-Lab/tree/main/src/lab5)
#### 3.6. Lab 06 - Events, Interactivity, and Animation with d3.js
[D3.js](https://d3js.org/) is a producing dynamic, interactive data visualizations in web browsers. It makes use of `Scalable Vector Graphics (SVG)`, `HTML5`, and `Cascading Style Sheets (CSS)` standards.
Please checkout lab's details [here](https://github.com/mohammadzainabbas/VA-Lab/tree/main/src/lab6)
#
### 4. Setup
If you want to follow along with the lab exercises, make sure to clone and `cd` to the relevant lab's directory:
```bash
git clone https://github.com/mohammadzainabbas/VA-Lab.git
cd VA-Lab/src/
```
> For e.g: if you want to practice lab # 1, then you should do `cd VA-Lab/src/lab1`.
#### 4.1. Create new enviornment
Before starting, you may have to create new enviornment for the lab. Kindly, checkout the [documentation](https://github.com/mohammadzainabbas/VA-Lab/blob/main/docs/SETUP_ENV.md) for creating an new environment.
#
Once, you have activated your new enviornment, we may have to install all the dependencies for a given lab (kindly check if `requirements.txt` file exists for a given lab before running the below command):
```bash
pip install -r requirements.txt
```
#### 4.2. Setup `pre-commit` hooks
In order to setup `pre-commit` hooks, please refer to the [documentation](https://github.com/mohammadzainabbas/VA-Lab/blob/main/docs/SETUP_PRE-COMMIT_HOOKS.md).
#