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

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

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

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

Hint

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

Practice

1. What does the value_counts() function do in pandas?

easy

A. Counts how many times each unique value appears in a column

B. Sorts the DataFrame by column values

C. Filters rows based on a condition

D. Calculates the mean of numeric columns

Solution

1. Step 1: Understand the purpose of value_counts()

   This function counts the frequency of each unique value in a pandas Series or column.

2. Step 2: Compare with other options

   Sorting, filtering, and calculating mean are different pandas operations, not value_counts().

3. Final Answer:

   Counts how many times each unique value appears in a column -> Option A

4. Quick Check:

   value_counts() = frequency count [OK]

Hint: Remember: value_counts() counts unique values fast [OK]

Common Mistakes:

• Confusing value_counts() with sorting functions
• Thinking it filters data instead of counting
• Assuming it calculates statistics like mean

2. Which of the following is the correct syntax to get the frequency of values in a pandas Series named data?

easy

A. data.count()

B. value_counts(data)

C. data.count_values()

D. data.value_counts()

Solution

1. Step 1: Recall pandas method syntax

   Methods like value_counts() are called on Series objects using dot notation.

2. Step 2: Check each option

   data.value_counts() uses correct syntax: data.value_counts(). Options B and C are invalid method calls. data.count() counts non-null entries, not frequencies.

3. Final Answer:

   data.value_counts() -> Option D

4. Quick Check:

   Method call on Series = dot notation [OK]

Hint: Use dot notation: series.value_counts() [OK]

Common Mistakes:

• Using function style like value_counts(data)
• Mixing method names like count_values()
• Confusing count() with value_counts()

3. Given the pandas Series:

import pandas as pd
s = pd.Series(['apple', 'banana', 'apple', 'orange', 'banana', 'apple'])
print(s.value_counts())

What is the output?

medium

A. apple 3
banana 2
orange 1
dtype: int64

B. apple 1
banana 1
orange 1
dtype: int64

C. 3 apple
2 banana
1 orange
dtype: object

D. apple 3
orange 2
banana 1
dtype: int64

Solution

1. Step 1: Count each unique value in the Series

   Values are 'apple' (3 times), 'banana' (2 times), and 'orange' (1 time).

2. Step 2: Understand value_counts() output format

   It returns counts sorted descending by frequency with index as values and counts as values.

3. Final Answer:

   apple 3 banana 2 orange 1 dtype: int64 -> Option A

4. Quick Check:

   Count each fruit correctly = apple 3 banana 2 orange 1 dtype: int64 [OK]

Hint: Count unique values and sort descending [OK]

Common Mistakes:

• Mixing counts with values in wrong order
• Assuming counts are sorted alphabetically
• Confusing index and values in output

4. What is wrong with this code snippet?

import pandas as pd
s = pd.Series(['cat', 'dog', 'cat', 'bird'])
counts = s.value_counts
print(counts)

medium

A. value_counts() requires a parameter

B. Series cannot contain strings for value_counts

C. Missing parentheses after value_counts when assigning to counts

D. print() cannot be used with value_counts output

Solution

1. Step 1: Identify how value_counts is assigned

   The code assigns counts = s.value_counts without parentheses, so counts is a method, not the result.

2. Step 2: Understand method call syntax

   print(counts) prints the method object itself, not the result. To fix, assign with parentheses: counts = s.value_counts() or call directly print(s.value_counts()).

3. Final Answer:

   Missing parentheses after value_counts when assigning to counts -> Option C

4. Quick Check:

   Method call needs () when assigning [OK]

Hint: Add () to call method, not just assign it [OK]

Common Mistakes:

• Forgetting parentheses when calling methods
• Thinking value_counts needs parameters
• Assuming print can't show value_counts output

5. You have a DataFrame df with a column 'color' containing some missing values (NaN). How can you use value_counts() to count frequencies including the missing values?

hard

A. df['color'].value_counts(show_na=True)

B. df['color'].value_counts(dropna=False)

C. df['color'].value_counts(include_nan=True)

D. df['color'].value_counts(keep_na=True)

Solution

1. Step 1: Recall how to include NaN in value_counts()

   The parameter dropna=False tells value_counts() to count NaN values as well.

2. Step 2: Verify parameter names

   Options A, B, and D use incorrect parameter names that do not exist in pandas.

3. Final Answer:

   df['color'].value_counts(dropna=False) -> Option B

4. Quick Check:

   Use dropna=False to count NaN [OK]

Hint: Use dropna=False to include missing values [OK]

Common Mistakes:

• Using wrong parameter names like include_nan
• Assuming NaN counted by default
• Trying non-existent options like show_na