The Super Bowl's biggest winner: Mexican avocados and Super Bowl guacamole

Everyone, Super Bowl What comes to mind when you think of the Super Bowl? The spectacular halftime show? The huge commercials? Today we're going to look at the Super Bowl from a different perspective. We're talking about the "biggest Super Bowl beneficiaries": Mexican avocados and guacamole!

Super Bowl's Biggest Beneficiary, Mexican Avocado Consumption Explodes

First, let's look at the surprising increase in avocado consumption: the following image shows the Python We visualized this in code. This graph clearly shows that avocado consumption has increased sixfold from 1989 to 2020. That's a whopping 6001 TP3T growth rate! One of the main reasons for this explosion is the Super Bowl.

import matplotlib.pyplot as plt

Define the # data
years = [1989, 2020]
consumption_growth = [1, 6] # consumption growth (600% increase)
super_bowl_years = [2020, 2024]
avocados_consumed = [53.5, 54] # Super Bowl season consumption (in million pounds)
import_years = [2020, 2023]
import_volume = [2, 2.48] # Mexico Imports (Billion Pounds)

Calculate the # growth rate
consumption_growth_rate = ((consumption_growth[1] - consumption_growth[0]) / consumption_growth[0]) * 100
avocados_consumed_rate = ((avocados_consumed[1] - avocados_consumed[0]) / avocados_consumed[0]) * 100
import_volume_rate = ((import_volume[1] - import_volume[0]) / import_volume[0]) * 100

Graph of # avocado consumption growth
plt.figure(figsize=(6, 4))
plt.bar(years, consumption_growth, color='green', alpha=0.7)
plt.title('Avocado Consumption Growth', fontsize=16)
plt.xlabel('Year', fontsize=14)
plt.ylabel('Consumption (Relative Scale)', fontsize=14)
plt.xticks(years, fontsize=12)

Add a numeric label to the top of the # bar
for i, value in enumerate(consumption_growth):
    plt.text(years[i], value, f'{value}', ha='center', va='bottom', fontsize=20, color='black')

# Show dotted lines and growth rates
plt.plot(years, consumption_growth, 'g--', marker='o', markersize=8)
mid_x = sum(years) / 2
mid_y = sum(consumption_growth) / 2
plt.text(mid_x, mid_y, f'{consumption_growth_rate:.1f}%', ha='center', va='bottom', fontsize=24, color='red')
plt.show()

# Graph of avocado consumption during the Super Bowl season
plt.figure(figsize=(6, 4))
plt.bar(super_bowl_years, avocados_consumed, color='green', alpha=0.7)
plt.title('Super Bowl Avocado Consumption', fontsize=16)
plt.xlabel('Year', fontsize=14)
plt.ylabel('Avocados Consumed (Million Pounds)', fontsize=14)
plt.xticks(super_bowl_years, fontsize=12)

# Add numeric labels to the top of the bar
for i, value in enumerate(avocados_consumed):
    plt.text(super_bowl_years[i], value, f'{value:.1f}M', ha='center', va='bottom', fontsize=20, color='black')

# Show the dotted line and rate of increase
plt.plot(super_bowl_years, avocados_consumed, 'g--', marker='o', markersize=8)
mid_x = sum(super_bowl_years) / 2
mid_y = sum(avocados_consumed) / 2
plt.text(mid_x, mid_y, f'{avocados_consumed_rate:.1f}%', ha='center', va='bottom', fontsize=24, color='red')
plt.show()

Graph of avocado imports from Mexico for #
plt.figure(figsize=(6, 4))
plt.bar(import_years, import_volume, color='green', alpha=0.7)
plt.title('Mexican Avocado Imports to US', fontsize=16)
plt.xlabel('Year', fontsize=14)
plt.ylabel('Import Volume (Billion Pounds)', fontsize=14)
plt.xticks(import_years, fontsize=12)

Add a numeric label to the top of the # bar
for i, value in enumerate(import_volume):
    plt.text(import_years[i], value, f'{value:.2f}B', ha='center', va='bottom', fontsize=20, color='black')

# Show dotted line and rate of increase
plt.plot(import_years, import_volume, 'g--', marker='o', markersize=8)
mid_x = sum(import_years) / 2
mid_y = sum(import_volume) / 2
plt.text(mid_x, mid_y, f'{import_volume_rate:.1f}%', ha='center', va='bottom', fontsize=24, color='red')
plt.show()

Avocado consumption during the Super Bowl

Let's look at the evolution of avocado consumption over the Super Bowl season. We can see that avocado consumption in the 2020 and 2024 Super Bowl seasons has remained at roughly the same level (around 54 million pounds). This shows that the impact of the Super Bowl on avocado consumption is consistent and stable.

Changes in avocado imports from Mexico

Finally, let's look at the change in the amount of avocados imported from Mexico to the U.S. From 2020 to 2023, we can see that Mexican avocado imports increased from 200 million pounds to 248 million pounds. That's an increase of about 241 TP3T, showing a steady increase in demand for Mexican avocados in the US.

Guacamole: the star of the Super Bowl

At the center of this growing avocado consumption is guacamole. Guacamole is a traditional Mexican sauce made from mashed avocados with onions, tomatoes, cilantro, lime juice, salt, and more. It's become a popular staple at Super Bowl parties.

Make guacamole

1. Mash the ripe avocado.
2. Add the chopped onion, tomato, and cilantro.
3. Season with lime juice and salt to taste.
4. Add jalapeños for extra spicy flavor, if desired.

During Super Bowl season, this guacamole is enjoyed with nachos, tacos, burritos, and more. It's growing in popularity because it's healthy and delicious.

Closing thoughts

The relationship between avocados and the Super Bowl is fascinating to see through the data. We've seen how a simple fruit became the centerpiece of a huge sporting event, and we're excited to see how it's done. Next time you're at the Super Bowl, why not make your own guacamole? We hope you enjoy your Super Bowl with your own special recipe!

# Glossary

• Super Bowl: The championship game of the National Football League (NFL)
• Guacamole: An avocado-based traditional Mexican sauce
• Avocado: Nutritious Tropical Fruits
• Jalapeño: A spicy pepper native to Mexico

Now you understand the special relationship between the Super Bowl, avocados, and guacamole. Use this knowledge to make your next Super Bowl party even better!

# Code Explained in detail

1. import the library

pythonimport matplotlib.pyplot as plt

• Importing modules: matplotlib.pyplotto pltas an alias. This is how Python's standard library is used.

2. define your data

pythonyears = [1989, 2020]
consumption_growth = [1, 6]

• Lists: Uses Python's list data structure to store data.

3. Calculate the percentage increase

pythonconsumption_growth_rate = ((consumption_growth[1] - consumption_growth[0]) / consumption_growth[0]) * 100

• Indexing lists: Use an index to access a specific element in the list.
• Arithmetic operationsPerforms basic math operations.

4. Create a graph

pythonplt.figure(figsize=(6, 4))
plt.bar(years, consumption_growth, color='green', alpha=0.7)

• Calling functions: Creates a graph by calling various functions in the Matplotlib library.
• Keyword arguments: Specify properties of the graph using keyword arguments when calling the function.

5. set the graph label and title

pythonplt.title('Avocado Consumption Growth', fontsize=16)
plt.xlabel('Year', fontsize=14)

• string: Specify the title and label of the graph as strings.

6. add a numeric label to the top of the bar

pythonfor i, value in enumerate(consumption_growth):
    plt.text(years[i], value, f'{value}', ha='center', va='bottom', fontsize=20, color='black')

• for Loop: Repeat for each element in the list.
• enumerate() function: Use to get the index and value of a list at the same time.
• f-string: Use F-strings for string formatting.

7. show dotted line and percentage increase

pythonmid_x = sum(years) / 2
mid_y = sum(consumption_growth) / 2
plt.text(mid_x, mid_y, f'{consumption_growth_rate:.1f}%', ha='center', va='bottom', fontsize=24, color='red')

• sum() function: Summarizes all elements in the list.
• String formatting: .1fto display the first decimal place.

8. Display the graph

pythonplt.show()

• Rendering graphs: Displays the generated graph on the screen.