Putting Text Inside a Plot in Matplotlib

In this tutorial, we will explore how to add text to a plot created using matplotlib. Specifically, we will focus on adding text inside a plot and updating its position dynamically.

Introduction

Matplotlib is a popular Python library used for creating static, animated, and interactive visualizations. One of the key features of matplotlib is its ability to customize plots with various elements such as labels, titles, legends, and more. In this tutorial, we will delve into adding text inside a plot using matplotlib’s built-in Text class.

Creating Multiple Line Plots

Let’s start by creating multiple line plots using matplotlib. We will create a simple example where we add another line to the previous plot in each iteration of a loop.

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

# Create a sample dataframe
df = pd.DataFrame({
    'Country Name': ['USA', 'Canada', 'Mexico'],
    'Year': [1960, 1970, 1980],
    'Value': [100, 200, 300]
})

# Define the years range for plotting
years = list(range(1960, 2021))

for i in years:
    # Extract relevant data from dataframe
    yearsi = list(range(1960, i+1))
    yearsi = map(str, yearsi)
    
    # Create a pivot table to plot values over country name
    ax = df.pivot_table(values=yearsi, columns='Country Name').plot()
    
    # Customize the plot as desired (e.g., set y-axis limits and x-axis tick labels)
    ax.set_ylim([0, 1500])
    ax.set_xticks(range(0,61,10))
    ax.set_xticklabels(range(1960, 2021,10))

# Show the plot
plt.show()

In this example, we create a sample dataframe with country names and values over time. We then use a loop to create multiple line plots where each iteration adds another line to the previous plot.

Adding Text Inside a Plot

To add text inside a plot using matplotlib’s Text class, you can follow these steps:

1. Create an instance of the Text class by passing in the x and y coordinates of the desired position, as well as other optional parameters (e.g., size, color).
2. Set the contents of the text using the set_text() method.
3. Optionally, update the position of the text using the set_position() method.

Here’s an example code snippet that demonstrates how to add a text label inside a plot:

import matplotlib.pyplot as plt
import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'Country Name': ['USA', 'Canada', 'Mexico'],
    'Year': [1960, 1970, 1980],
    'Value': [100, 200, 300]
})

# Define the years range for plotting
years = list(range(1960, 2021))

for i in years:
    # Extract relevant data from dataframe
    yearsi = list(range(1960, i+1))
    yearsi = map(str, yearsi)
    
    # Create a pivot table to plot values over country name
    ax = df.pivot_table(values=yearsi, columns='Country Name').plot()
    
    # Customize the plot as desired (e.g., set y-axis limits and x-axis tick labels)
    ax.set_ylim([0, 1500])
    ax.set_xticks(range(0,61,10))
    ax.set_xticklabels(range(1960, 2021,10))

    # Add a text label inside the plot
    self.look_at_me = ax.text(0, 0, 'Look at me!', size=12, color='g')
    
    # Update the position of the text based on data (optional)
    def animate(i):
        self.look_at_me.set_position((self.df['t'][i], self.df['r_mag'][i]))
        
    # Show the plot
    plt.show()

In this example, we create a Text instance and set its contents using the set_text() method. We then optionally update the position of the text using the set_position() method in an animated loop.

Conclusion

Adding text inside a plot is a powerful feature that allows you to provide additional context or information about your data visualization. By following these steps and using matplotlib’s built-in Text class, you can easily add labels to your plots and enhance their readability and interpretability.

Last modified on 2025-02-24