Data Visualization with Matplotlib
Matplotlib is a powerful Python library used for creating static, interactive, and animated visualizations . It provides a wide variety of plotting functions and customization options, making it suitable for many types of data visualization tasks.
Here's a basic example of how to create a simple plot using Matplotlib:
Python Code
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Create a plot
plt.plot(x, y)
# Add labels and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Sample Plot')
# Show the plot
plt.show()
This will generate a simple line plot with the given data points.
Matplotlib can create a wide variety of plots, including line plots, scatter plots, bar plots, histogram, pie charts, etc. Here are some examples:
Scatter Plot:
Python Code
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Create a scatter plot
plt.scatter(x, y)
# Add labels and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot')
# Show the plot
plt.show()
Bar Plot:
Python Code
import matplotlib.pyplot as plt
# Sample data
x = ['A', 'B', 'C', 'D', 'E']
y = [10, 15, 7, 10, 5]
# Create a bar plot
plt.bar(x, y)
# Add labels and title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot')
# Show the plot
plt.show()
Histogram:
Python Code
import matplotlib.pyplot as plt
import numpy as np
# Generate some random data
data = np.random.randn(1000)
# Create a histogram
plt.hist(data, bins=30)
# Add labels and title
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
# Show the plot
plt.show()
These are just a few examples of what you can do with Matplotlib. can customize your plots further by changing colors, adding legends, annotations, using different styles, and much more. Matplotlib's documentation is comprehensive and provides many examples to help you get started and explore its capabilities further.