Matplotlib is an essential tool. It’s a powerful Python library for creating static, animated, and interactive visualizations.
In this article, you’ll explore the top 10 Matplotlib functions that every data analyst should have in their toolbox, with short explanations and examples to get you going.
1. plt.plot()
Used to draw basic line plots — a go-to function for visualizing trends.
![Plot]()
2.plt.subplot() / plt.subplots()
Use this to create multiple plots in one figure — useful for dashboards.
![plt.subplot()]()
3. plt.grid()
Adds a grid to your plot — improves visual structure and accuracy.
![plt.grid()]()
4. plt.legend()
Helps distinguish multiple plotted lines or data series.
![4. plt.legend()]()
5. plt.title()
Adds a title to your plot — don’t forget this for clarity!
![plt.title()]()
6. plt.xlabel() and plt.ylabel()
Label your axes for better context and readability.
![plt.xlabel() and plt.ylabel()]()
7. plt.hist()
Creates histograms — excellent for visualizing the distribution of data.
![7. plt.hist()]()
8.plt.bar()
Use this for bar charts — great for comparing categories.
![plt.bar()]()
9. plt.scatter()
Perfect for visualizing relationships between two variables with dots.
![9. plt.scatter()]()
10. plt.show(): Display the Plot
The final call to render everything on screen.
![plt.show(): Display the Plot]()
These ten functions form the core of Matplotlib — knowing them means you can build a wide variety of plots quickly and efficiently.