Matplotlib's Pyplot module provides a MATLAB-like interface for creating various types of plots and visualizations in Python. It is part of the Matplotlib library and is typically imported as plt. Here's an overview of how to use Pyplot to create different types of plots and customize them.
First, you need to import the pyplot module from the matplotlib library:
import matplotlib.pyplot as plt
A line plot is one of the simplest types of plots. It shows data as a series of points connected by straight lines.
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Create a line plot
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot')
plt.show()
A scatter plot shows individual data points and is useful for visualizing relationships between variables.
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Create a scatter plot
plt.scatter(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot')
plt.show()
A bar plot represents data with rectangular bars.
# Data
categories = ['A', 'B', 'C', 'D']
values = [15, 30, 45, 10]
# Create a bar plot
plt.bar(categories, values)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot')
plt.show()
A histogram shows the distribution of a dataset.
import numpy as np
# Data
data = np.random.randn(1000)
# Create a histogram
plt.hist(data, bins=30)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()
You can customize plots with titles, labels, legends, grid lines, and more.
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Create a plot with labels and title
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot with Labels')
plt.show()
# Data
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [2, 3, 5, 7, 11]
# Create a plot with multiple lines and legend
plt.plot(x, y1, label='Squared', marker='o')
plt.plot(x, y2, label='Prime', marker='s')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot with Legend')
plt.legend()
plt.show()
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Create a plot with grid lines
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot with Grid')
plt.grid(True)
plt.show()
You can create multiple plots in a single figure using subplots.
# Data
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [2, 3, 5, 7, 11]
# Create subplots
fig, axs = plt.subplots(2)
# First subplot
axs[0].plot(x, y1)
axs[0].set_title('Squared Values')
# Second subplot
axs[1].plot(x, y2)
axs[1].set_title('Prime Numbers')
# Show plot
plt.tight_layout()
plt.show()
Here's a comprehensive example that combines different types of plots and customizations:
# Data
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [2, 3, 5, 7, 11]
# Create subplots
fig, axs = plt.subplots(2)
# First subplot
axs[0].plot(x, y1)
axs[0].set_title('Squared Values')
# Second subplot
axs[1].plot(x, y2)
axs[1].set_title('Prime Numbers')
# Show plot
plt.tight_layout()
plt.show()
Here's a comprehensive example that combines different types of plots and customizations:
import matplotlib.pyplot as plt
import numpy as np
# Data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
# Create subplots
fig, axs = plt.subplots(2, 2, figsize=(10, 10))
# First subplot: Line plot
axs[0, 0].plot(x, y1, 'r-', label='sin(x)')
axs[0, 0].set_title('Line Plot')
axs[0, 0].legend()
# Second subplot: Scatter plot
axs[0, 1].scatter(x, y2, color='b', label='cos(x)')
axs[0, 1].set_title('Scatter Plot')
axs[0, 1].legend()
# Third subplot: Histogram
data = np.random.randn(1000)
axs[1, 0].hist(data, bins=30, color='g')
axs[1, 0].set_title('Histogram')
# Fourth subplot: Bar plot
categories = ['A', 'B', 'C', 'D']
values = [15, 30, 45, 10]
axs[1, 1].bar(categories, values, color='y')
axs[1, 1].set_title('Bar Plot')
# Adjust layout
plt.tight_layout()
plt.show()
You can save the plot to a file using savefig.
# Create a plot
plt.plot(x, y)
plt.title('Saved Plot')
# Save the plot to a file
plt.savefig('plot.png')
# Display the plot
plt.show()
You can specify the figure size when creating a plot.
# Create a plot with custom figure size
plt.figure(figsize=(8, 6))
plt.plot(x, y)
plt.title('Custom Figure Size')
plt.show()