Matplotlib: Legends

Legends

• A legend in Python (using matplotlib) is a box that provides a description of different elements in a plot, helping users understand what each color, line, or marker represents.

Uses of legends

1. Legends are used to identify multiple data series in the same plot.

2.It helps to identify different categories, trends or variables.

3. Legends make the plot more informative and readable.

4.Legends offer context and explanations for the data being presented, helping viewers make sense of the information.

5. By labeling different data series or categories, legends make it easier to compare and contrast various aspects of the plot.

How to add legends ?

• In matplotlib, we use plt.legend() to add a legend to a plot. It can be represented in a box as shown by following screenshot.

Demo example

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]

y1 = [1, 4, 9, 16]

y2 = [1, 2, 3, 4]

plt.plot(x,y1,label='Quadratic')

plt.plot(x,y2, label='Linear")

plt.legend()

plt.title('Demo for Legend')

plt.xlabel('X‒axis')

plt.ylabel('Y‒axis')

plt.show()

Output

Code explanation: In above program,

1.We have created three different data sets namely x,y1 and y2.

2. Using plt.plot we draw two lines ‒ Using x,y1 the quadratic line is drawn and using x,y2 the linear line is drawn. When we plot data, you can add a label parameter to each plot element. These labels will be used in the legend.

3.To add a legend to the plot, we use the plt.legend() function.

4. Finally display the plot with title, xlabel and ylabel.

Customization of legends

• We can customize the position, frame, title and more. Here are some common customization options:

1) Positioning: We can position the legend in various locations using the loc parameter.

Some common positions include:

•  upper right

•  upper left

•  lower right

•  lower left

For example ‒ plt.legend(loc='upper right')

2) Adding title: We can add title to legend using the parameter title. For example plt.legend(title="Types of Items')

3) Changing font size: We can define the font sizes as small, medium or large using legend. The parameter fontsize is used for this purpose. You can also use specific numeric values to set the font size in points. For example ‒

plt.legend(fontsize='medium')

or

plt.legend(fontsize=12) # Font size in points

4) Shadow: With shadow=True, we can add a shadow effect.

Example:1

In the competitive business world, tracking sales trends over time is crucial for making informed decisions. Companies analyze sales data to identify which products perform well and to strategize future marketing efforts.

Write a Python code to plot the sales of three products in the four months. Make use of following data. And to distinguish each product sale make use of legend in your program.

months = ['Jan', 'Feb', 'Mar', 'Apr']

sales_a = [10, 15, 20, 25]

sales_b = [5, 10, 15, 20]

sales c= [7, 14, 21, 28]

Solution:

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr']

sales a = [10, 15, 20, 25]

sales_b = [5, 10, 15, 20]

sales_c = [7, 14, 21, 28]

plt.plot(months, sales_a, label='Product A')

plt.plot(months, sales_b, label='Product B')

plt.plot(months, sales_c, label='Product C')

plt.legend(title='Products', loc='upper left')

plt.title('Monthly Sales Data')

plt.xlabel('Month')

plt.ylabel('Sales')

plt.show()

Output

Example:2

Educational institutions often analyze student performance to identify trends and improve teaching strategies. One important aspect of this analysis is understanding how students of different genders perform in academics across different age groups. Take hypothetical sample data of students' ages and marks for both males and females. Write a Python program Represent the data using a scatter plot where:

(a) Male students are marked with blue circles (0).

(b) Female students are marked with red triangles (^).

(c) Add a legend to indicate gender categories.

Solution :

import matplotlib.pyplot as plt

# Sample data (Age vs. Marks)

male_ages = [18, 19, 20, 21, 22]

male_marks = [85, 78, 90, 88, 76]

female_ages = [18, 19, 20, 21, 22]

female_marks = [92, 80, 89, 95, 84]

# Scatter plot

plt.scatter(male_ages, male_marks, color='blue', marker='o', label="Male") plt.scatter(female_ages, female_marks, color='red', marker='^', label="Female")

plt.xlabel("Age")

plt.ylabel("Marks")

plt.title("Student Sample Data")

plt.legend()

plt.show()

Output