the_information_nexus/tech_docs/python/Matplotlib.md

For creating and manipulating complex data visualizations, Matplotlib is an indispensable Python library. It's widely used for generating plots, histograms, bar charts, scatterplots, and more, offering extensive customization options to make the visualizations as informative and appealing as possible. Below is a concise reference guide for common use cases with Matplotlib, formatted in Markdown syntax:

Matplotlib Reference Guide

Installation

pip install matplotlib

Basic Plotting

Importing Matplotlib

import matplotlib.pyplot as plt

Creating a Simple Plot

# Prepare some data
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

# Plot data
plt.plot(x, y)

# Show plot
plt.show()

Creating a Scatter Plot

# Scatter plot
plt.scatter(x, y)

# Show plot
plt.show()

Multiple Plots on Same Axes

# Second set of data
x2 = [1, 2, 3, 4]
y2 = [30, 25, 20, 15]

# Plot data
plt.plot(x, y, label='First Line')
plt.plot(x2, y2, label='Second Line')

# Adding a legend
plt.legend()

# Show plot
plt.show()

Customizing Plots

Titles, Labels, and Legends

plt.plot(x, y)

# Title
plt.title('My First Plot')

# Axis labels
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')

# Show plot
plt.show()

Line Styles and Markers

plt.plot(x, y, color='red', linestyle='--', marker='o', label='Data Points')

# Show plot
plt.show()

Setting Axis Ranges

plt.plot(x, y)

# Setting the range for the axes
plt.xlim(0, 5)
plt.ylim(5, 35)

# Show plot
plt.show()

Adding Grid Lines

plt.plot(x, y)

# Adding grid
plt.grid(True)

# Show plot
plt.show()

Other Types of Plots

Histograms

data = [1, 2, 2, 3, 3, 3, 4, 4, 5]

# Create histogram
plt.hist(data, bins=5, alpha=0.5, color='blue')

# Show plot
plt.show()

Bar Charts

categories = ['A', 'B', 'C', 'D']
values = [10, 20, 15, 5]

# Create bar chart
plt.bar(categories, values)

# Show plot
plt.show()

Pie Charts

slices = [7, 2, 2, 13]
categories = ['A', 'B', 'C', 'D']

# Create pie chart
plt.pie(slices, labels=categories, autopct='%1.1f%%')

# Show plot
plt.show()

Saving Figures

plt.plot(x, y)

# Save the figure
plt.savefig('plot.png')

# Show plot
plt.show()

Matplotlib is a powerful library for creating static, interactive, and animated visualizations in Python. This guide covers the basics of generating and customizing plots, but Matplotlib's functionality is vast, supporting a wide range of plot types and customization options to suit various data visualization needs.