Plotting Multiple Graphs on the Same Axes in Matplotlib

Matplotlib is a powerful plotting library for Python that provides an easy-to-use interface for creating high-quality plots. However, it can be challenging to plot multiple graphs on the same axes when they have different types or styles.

In this article, we will explore how to show both bar and line graphs on the same plot in Matplotlib.

Introduction

Matplotlib is a popular plotting library that provides an easy-to-use interface for creating high-quality plots. One of its most powerful features is the ability to customize the appearance of individual plots, including changing the type of graph, adding titles and labels, and customizing colors and styles.

When working with multiple graphs on the same plot, it can be challenging to choose a library that meets all of your needs. However, Matplotlib provides several options for plotting multiple graphs on the same axes, including using subplots, creating separate plots with shared axes, and using twinx() function to create secondary axes.

Understanding Subplots

Subplots are a great way to plot multiple graphs on the same axes without having to worry about coordinate limits or overlapping data. In Matplotlib, you can create subplots by calling the add_subplot() function and specifying the number of rows and columns you want to use.

Here is an example of how to create a subplot with two bars:

import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

ax1.bar([1, 2, 3], [10, 15, 20])
ax2.bar([4, 5, 6], [25, 30, 35])

plt.show()

This code will create two subplots, one for each bar graph.

Plotting Multiple Graphs on the Same Axes

However, sometimes you want to plot both a bar and a line graph on the same axes. This is where things get tricky.

The main issue with plotting multiple graphs on the same axes is that Matplotlib uses its own coordinate system, which means that each plot has its own set of x and y limits. When you try to add two plots to the same axes, it can lead to overlapping data or inconsistent scales.

To overcome this challenge, we need to use a different approach.

Using Twin Axes

One solution is to create separate axes for each graph using the twinx() function. This function creates a secondary set of axes that shares the x-axis with the original plot but has its own y-axis.

Here is an example:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

Df = pd.DataFrame(data=np.random.randn(10,4), index=pd.DatetimeIndex(start='2005', freq='M', periods=10), columns=['A','B','C','D'])

fig = plt.figure()
ax1 = Df['A','B'].plot(kind="bar")
plt.xticks(rotation=0)

ax2 = ax1.twinx()
ax2.plot(ax1.get_xticks(),Df['C','D'],marker='o')

plt.show()

In this code, we first create the bar graph using Df['A','B'].plot(). Then, we use twinx() to create a secondary set of axes that shares the x-axis with the original plot. Finally, we add the line graph to the secondary axes.

This approach allows us to plot multiple graphs on the same axes without having to worry about coordinate limits or overlapping data.

Best Practices

When plotting multiple graphs on the same axes, here are some best practices to keep in mind:

• Use subplots when you need to compare two or more plots.
• Use twinx() function when you need to plot a bar graph alongside a line graph.
• Make sure each graph has its own set of x and y limits.
• Customize the appearance of each graph to make it stand out from the others.

Advanced Techniques

If you want to get really advanced with your plots, there are several techniques you can use:

• Using multiple ax.plot() calls: Instead of using a single ax.plot() call for both graphs, you can create separate lines by calling the function multiple times.
• Customizing colors and styles: You can customize the appearance of each graph by changing the line color, marker style, or even adding more complex patterns to your data points.
• Adding labels and titles: Make sure to label your axes and add a title to help viewers understand what they’re looking at.

Conclusion

Plotting multiple graphs on the same axes is an essential skill in data analysis. By using subplots, twinx() function, or even customizing individual lines, you can create beautiful and informative plots that show multiple relationships between different variables.

Remember to follow best practices for creating effective plots, and don’t be afraid to experiment with different techniques to achieve your desired results. Happy plotting!

Last modified on 2024-12-08