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Understanding Matplotlib Figure and Axes
Introduction to Matplotlib
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is widely used in data analysis, scientific computing, and machine learning to visualize data patterns.
Figure and Axes
In Matplotlib, a Figure is the entire window where the plot is displayed, while Axes are the individual plots within the figure. A single figure can contain multiple axes, which allows for complex visualizations.
Creating a Simple Plot
Let's start by creating a simple line plot using Matplotlib's figure and axes.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.show()
Explanation
In this example, we create a figure and a set of subplots using plt.subplots(). The ax.plot() function is used to plot a line graph.
Adding Titles and Labels
Enhance your plot with titles and labels to make it more informative.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
ax.set_title('Simple Line Plot')
ax.set_xlabel('X-axis Label')
ax.set_ylabel('Y-axis Label')
plt.show()
Explanation
Here, the set_title(), set_xlabel(), and set_ylabel() methods are used to add a title and labels to the x and y axes respectively.
Creating Multiple Subplots
You can create multiple subplots within a single figure to compare different data sets.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot([1, 2, 3], [1, 4, 9])
axs[0, 1].plot([1, 2, 3], [1, 2, 3])
axs[1, 0].plot([1, 2, 3], [3, 2, 1])
axs[1, 1].plot([1, 2, 3], [9, 4, 1])
plt.show()
Explanation
This example demonstrates how to create a 2x2 grid of subplots using plt.subplots(2, 2). Each subplot can be accessed and customized individually.
Customizing Plot Appearance
Matplotlib allows you to customize the appearance of your plots, including colors, line styles, and markers.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [10, 20, 25, 30], color='red', linestyle='--', marker='o')
plt.show()
Explanation
In this plot, the line color is set to red, the line style is dashed, and circle markers are added at each data point using the color, linestyle, and marker parameters.
Saving Figures
Once you have created a plot, you may want to save it as an image file for later use or sharing.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.savefig('my_plot.png')
plt.show()
Explanation
The plt.savefig() function saves the current figure to a file. In this example, the plot is saved as "my_plot.png".
