105800 Views
82496 Views
47243 Views
47066 Views
45458 Views
38527 Views
Pi-Apps
Intermediate MicroPython
Arduino Alvik
Raspberry Pi Time machine
Now Ad-Free
Guiding Light
Intermediate level MicroPython
Introduction to FreeCAD for Beginners
Building a Robot Arm with Raspberry Pi and PCA9685
Building User Authentication for Static Sites with FastAPI
Mastering Pydantic for Robust Data Validation
Mastering Markdown for Documentation with Jekyll
KevsRobots Learning Platform
55% Percent Complete
By Kevin McAleer, 2 Minutes
Data visualization is an important part of data analysis. Python offers multiple libraries for creating static, animated, and interactive visualizations, including Matplotlib and Seaborn. In this lesson, we’ll explore the basics of these two libraries.
Data visualization is the graphical representation of data and information. It uses visual elements like charts, graphs, and maps to provide an easy way to understand trends, outliers, and patterns in data.
Matplotlib is a plotting library for Python. It provides an object-oriented API for embedding plots into applications.
import matplotlib.pyplot as plt # Create a simple line plot plt.plot([1, 2, 3, 4]) plt.ylabel('Some Numbers') plt.show()
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for creating attractive graphics.
import seaborn as sns # Load an example dataset tips = sns.load_dataset("tips") # Create a simple histogram sns.histplot(data=tips, x="total_bill")
In this lesson, you’ve learned about the basics of data visualization in Python. We’ve covered the importance of data visualization and explored two Python libraries, Matplotlib and Seaborn, for creating static visualizations. Data visualization is a powerful tool for understanding data and communicating results.
< Previous Next >