Python - Data visualization tutorial

Data visualization is the process of converting complex data into graphical formats such as charts, graphs, and maps. It allows users to understand patterns, trends, and outliers in large datasets quickly and clearly. By transforming data into visual elements, data visualization helps in making data more accessible and easier to interpret, allowing for more informed decisions and insights.

Features of Data Visualization:

• Simplifies Complex Data: Transforms complex datasets into easy-to-understand visuals.
• Identifies Patterns: Helps spot trends and patterns that might not be obvious in raw data.
• Improves Decision-Making: Provides visual clarity, helping decision-makers make informed choices faster.
• Highlights Key Insights: Allows for the emphasis of important trends, outliers, and relationships.
• Interactive: Many visualizations allow for user interaction, enabling deeper exploration of data.

Example: Plotting with Matplotlib

import matplotlib.pyplot as plt
import numpy as np

days = np.arange(1, 31)
prices = 100 + np.cumsum(np.random.randn(30)) 

plt.plot(days, prices, marker='o', linestyle='-', color='green')
plt.title("Simulated Stock Price")
plt.xlabel("Day")
plt.ylabel("Price ($)")
plt.grid(True)
plt.show()

Output