In the previous article in our series, we did an overview of Python for data analysis. Now weâll focus on one of my favorite tasks: data visualization!
Picture this: Youâre in the middle of a project and suddenly you need to make a plot to analyze the data or present the insights found. You donât have too much time, but you definitely donât want to create a plot that looks like this.
However you also donât want to get too technical and waste more time on something that isnât the main goal of your project, so what should you do?
I canât tell how many times that happened to me in the past, but by using Matplotlib & Seaborn conveniently, I came up with a simple, yet powerful way to create nice-looking and readable visualizations in Python. Forget those blue bar plots and pie charts with tiny labels, in this article, Iâll show you how to give them a better appearance without getting too technical and wasting a lot of time.
<strong>Content of this Guide
</strong>1. Graph style and Font size
2. Customization of Plots
- Color Palettes
- Figure size, figure appearance, title, and axes labels
3. The Dataset
4. Bar Plot
5. Histogram
6. Boxplot
7. Scatterplot
8. Piechart + Subplots
- Single Piechart
- Piechart side by side (subplots)
9. Line Plot