# Handling Matplotlib’s ‘No Handles With Labels Found’ Warning When Creating Legends

## Handling the “No Handles with Labels Found to Put in Legend” Warning in Matplotlib

Matplotlib is a powerful data visualization tool widely used by data scientists, analysts, and researchers. However, when working with matplotlib, one may encounter the warning “No handles with labels found to put in legend.” This warning typically appears when attempting to create a legend for a plot, but no labels have been specified.

In this article, we will explore two methods for handling this warning, which will enable you to create effective legends for your matplotlib plots.

## 1. Labeling the Data in the Plot

The first method for handling the warning “No handles with labels found to put in legend” involves labeling the data in your plot. Labeling the data in a plot is an essential step as it provides context for the information being presented.

In this case, it also allows us to create a legend for the plot. To label the data in your plot, you can use the “label” parameter when plotting the data.

For example, consider the following code snippet:

``````import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
plt.plot(x, y1, label="sin(x)")
plt.plot(x, y2, label="cos(x)")
plt.legend()
plt.show()
``````

In this code, we plot two mathematical functions: sine and cosine. We use the “label” parameter to provide a label for each line in the plot.

Finally, we call the `legend` function to create the legend. Running this code produces the following plot:

As you can see, the legend has been created successfully because we labeled the data during the plotting process.

## 2. Creating the Legend after Adding Lines to the Plot

The second method for handling the matplotlib warning “No handles with labels found to put in legend” involves adding lines to the plot and then creating the legend. This method is useful if you have already plotted the data without labels and now want to add a legend.

To use this method, you need to create and store each line in the plot as a variable. You can then pass these variables to the “legend” function, which will create the legend.

### Consider the following code snippet:

``````import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
line1, = plt.plot(x, y1)
line2, = plt.plot(x, y2)
plt.legend([line1, line2], ["sin(x)", "cos(x)"])
plt.show()
``````

In this code, we first create the plot without any labels. We then store each line in the plot as a variable (i.e., line1 and line2).

Finally, we call the `legend` function and pass in the line variables and label strings as separate arguments. Running this code produces the following plot:

As you can see, we have successfully created the legend by adding lines to the plot and then calling the `legend` function.

• Matplotlib documentation: The official documentation for matplotlib is an excellent resource for learning more about the library and its various functions.
• The Python Graph Gallery: This website offers many examples of matplotlib plots, along with the associated code and explanations.
• DataCamp’s Matplotlib tutorial: This tutorial covers the basics of working with matplotlib, including creating plots, formatting axes, and creating legends.

## Conclusion

In this article, we explored two methods for handling the matplotlib warning “No handles with labels found to put in legend” when creating legends for your plots. By labeling the data in your plot or by adding lines to the plot and then creating the legend, you can effectively add context to your visualizations and make them more informative.

With these techniques and the additional resources provided, you should be well on your way to creating beautiful and informative plots with matplotlib. The use of legends in graphs and charts is essential as it allows the reader to understand the data represented in the graph. By using the techniques outlined in this article, you can create professional and informative visualizations that effectively communicate your data to your audience.