Data Visualization

Matplotlib, a comprehensive plotting library, serves as the foundation of data visualization in Python. However, it is not alone in this realm. Python offers a diverse range of data visualization libraries like Seaborn, Plotly, and Pandas, each excelling in specific visualization styles.

# Example: Data visualization with Matplotlib
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5] y = [10, 12, 8, 15, 11]

plt.plot(x, y, marker=’o’, linestyle=’-‘, color=’b’, label=’Data Points’)
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Simple Line Plot’)
plt.legend()
plt.grid(True)
plt.show()

Python offers a myriad of options for data visualization, including line plots, bar charts, scatter plots, histograms, and more. These visualizations can help reveal trends, patterns, and relationships within the data.

# Example: Bar chart with Seaborn
import seaborn as sns

data = {‘Category’: [‘A’, ‘B’, ‘C’, ‘D’, ‘E’],
‘Values’: [25, 30, 15, 20, 10]}

sns.barplot(x=’Category’, y=’Values’, data=data)
plt.xlabel(‘Categories’)
plt.ylabel(‘Values’)
plt.title(‘Bar Chart Example’)
plt.show()