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Matplotlib Tutorial for Beginners

Matplotlib Tutorial for Beginners


Matplotlib is a popular data visualization library for Python that provides a wide range of tools for creating various types of plots and charts. In this tutorial, we'll cover 15 key concepts of Matplotlib to help you get started with creating compelling visualizations.



1. Installation and Import

To begin, install Matplotlib using pip:

bash
pip install matplotlib

Import the library in your Python script:


import matplotlib.pyplot as plt

2. Basic Line Plot

Create a simple line plot to visualize data:



x = [1, 2, 3, 4, 5]
y = [10, 25, 18, 30, 15]
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Basic Line Plot')
plt.show()


3. Customizing Plots

Customize plot appearance using various options:


plt.plot(x, y, color='red', linestyle='dashed', marker='o', markersize=8, label='Data Points')
plt.legend()
plt.grid(True)

4. Multiple Subplots

Create multiple subplots in a single figure:


plt.subplot(2, 1, 1)
plt.plot(x, y)
plt.subplot(2, 1, 2)
plt.scatter(x, y)


5. Scatter Plot

Generate a scatter plot to display individual data points:


plt.scatter(x, y, color='blue', marker='x', label='Scatter Points')
plt.legend()

6. Bar Plot

Visualize categorical data using bar plots:


categories = ['A', 'B', 'C', 'D']
values = [15, 30, 10, 25]
plt.bar(categories, values, color='green')

7. Histogram

Create a histogram to visualize data distribution:


data = [12, 15, 20, 22, 25, 30, 32, 35, 40, 45]
plt.hist(data, bins=5, color='orange', edgecolor='black')


8. Pie Chart

Display proportions with a pie chart:


labels = ['Apples', 'Bananas', 'Grapes', 'Oranges']
sizes = [35, 20, 25, 20]
plt.pie(sizes, labels=labels, autopct='%1.1f%%', colors=['red', 'yellow', 'purple', 'orange'])

9. Box Plot

Create a box plot to visualize data spread and outliers:


data = [10, 15, 18, 22, 25, 30, 32, 35, 40, 45]
plt.boxplot(data)


10. Heatmap

Generate a heatmap to visualize a matrix of values:


import numpy as np
data = np.random.rand(5, 5)
plt.imshow(data, cmap='viridis')
plt.colorbar()

11. 3D Plotting

Create 3D plots for visualizing 3D data:


from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z)

12. Error Bars

Add error bars to visualize uncertainty in data points:



x = [1, 2, 3, 4, 5]
y = [10, 25, 18, 30, 15]
error = [2, 3, 1, 4, 2]
plt.errorbar(x, y, yerr=error, fmt='o', capsize=5)

13. Annotations and Text

Add annotations and text to enhance plot readability:


plt.plot(x, y)
plt.annotate('Max Value', xy=(4, 30), xytext=(3, 27),
arrowprops=dict(facecolor='black', shrink=0.05))
plt.text(1, 12, 'Start', fontsize=12)

14. Saving and Exporting

Save plots as image files:


plt.plot(x, y)
plt.savefig('plot.png', dpi=300, bbox_inches='tight')

15. Advanced Customization

Explore advanced customization options for fonts, colors, and styles:



plt.plot(x, y, color='red', linestyle='dashed', linewidth=2, label='Data')
plt.xlabel('X-axis', fontsize=14, color='blue')
plt.ylabel('Y-axis', fontsize=14, color='green')
plt.title('Advanced Customization', fontsize=16, fontweight='bold')
plt.legend(loc='upper right', fontsize=12)
plt.xticks(fontsize=10, rotation=45)
plt.yticks(fontsize=10)
plt.tight_layout()
plt.show()



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