Data visualization provides a pictorial representation of your data. It provides more information on what you think your data contains.
In real-life data, visualization is an important process to data analysis, which involves storytelling and recommendations.
In this practical, we will be looking at real-world data on 2020 World Population by Countries, to gather as much information and write out our findings/insights.
The visualization tools used are Matplotlib and Seaborn
- If you haven't already, clone this repo.
- We use virtual environments in ti. If you already have a virtual environment created for a previous practical, you can simply activate the environment and install pandas using
pip install matplotlib seaborn
Otherwise, create a new virtual environment then
pip install -r requirements.txt
- Go through the notebook to try your hands on the codes. 🔨🔨
- While doing that, you may realise that there are some details that weren't explored and that'll be awesome. So get at them! 🔨🔨
The dataset used in this practice was sourced here. It contains details of each country/dependent territory's world population as of 2020, their yearly growth rate, and other population-related information.
- Go through this Customers' dataset, to gain an understanding of the features it has.
- Get creative with your data exploration using the visualization tools you are familiar with.