value_counts() for frequency in Pandas - Mini Project: Build & Apply

Counting Fruit Frequencies with value_counts()

📖 Scenario: Imagine you have a basket with different types of fruits. You want to know how many of each fruit you have.

🎯 Goal: You will create a list of fruits, then use value_counts() to find out the frequency of each fruit type.

📋 What You'll Learn

Create a pandas Series from a list of fruits

Use the value_counts() method to count the frequency of each fruit

Print the frequency counts

💡 Why This Matters

🌍 Real World

Counting how often items appear is useful in sales data, survey answers, or any list of repeated entries.

💼 Career

Data analysts and scientists often use frequency counts to summarize and understand data quickly.

1

Create a pandas Series of fruits

Import pandas as pd and create a pandas Series called fruits with these exact values: 'apple', 'banana', 'apple', 'orange', 'banana', 'banana'.

Pandas

# Import pandas and create the Series called fruits
# Your code here

Need a hint?

Use pd.Series() to create a Series from a list.

2

Use value_counts() to count fruit frequencies

Create a variable called fruit_counts that stores the result of calling value_counts() on the fruits Series.

Pandas

import pandas as pd
fruits = pd.Series(['apple', 'banana', 'apple', 'orange', 'banana', 'banana'])
# Use value_counts() on fruits and save to fruit_counts
# Your code here

Need a hint?

Call value_counts() on the Series to get counts.

3

Print the fruit frequency counts

Print the variable fruit_counts to display the frequency of each fruit.

Pandas

import pandas as pd
fruits = pd.Series(['apple', 'banana', 'apple', 'orange', 'banana', 'banana'])
fruit_counts = fruits.value_counts()
# Print fruit_counts
# Your code here

Need a hint?

Use print(fruit_counts) to show the counts.

4

Filter fruits with frequency greater than 1

Create a variable called common_fruits that contains only the fruits from fruit_counts with counts greater than 1. Then print common_fruits.

Pandas

import pandas as pd
fruits = pd.Series(['apple', 'banana', 'apple', 'orange', 'banana', 'banana'])
fruit_counts = fruits.value_counts()
print(fruit_counts)
# Filter fruit_counts for counts > 1 and save to common_fruits
# Print common_fruits
# Your code here

Need a hint?

Use boolean indexing like fruit_counts[fruit_counts > 1] to filter.