value_counts() for frequency in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of value_counts() on a simple Series

What is the output of the following code?

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

Pandas

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

A

orange    3
banana    2
apple     1
dtype: int64

B

apple     2
banana    2
orange    2
dtype: int64

C

banana    3
apple     2
orange    1
dtype: int64

D

apple     3
banana    2
orange    1
dtype: int64

💡 Hint

Count how many times each fruit appears in the list.

The value_counts() method counts how many times each unique value appears. 'banana' appears 3 times, 'apple' 2 times, and 'orange' once.

❓ data_output

intermediate

Number of unique values counted by value_counts()

Given the Series below, how many unique values does value_counts() return?

import pandas as pd
s = pd.Series([10, 20, 10, 30, 20, 20, 40])
counts = s.value_counts()

Pandas

import pandas as pd
s = pd.Series([10, 20, 10, 30, 20, 20, 40])
counts = s.value_counts()
print(len(counts))

A 4

B 7

C 5

D 3

💡 Hint

Count how many different numbers appear in the Series.

The unique values are 10, 20, 30, and 40, so value_counts() returns 4 counts.

🔧 Debug

advanced

Identify the error in using value_counts() on a DataFrame

What error will this code produce?

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 2, 3], 'B': ['x', 'y', 'x', 'z']})
result = df.value_counts()
print(result)

Pandas

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 2, 3], 'B': ['x', 'y', 'x', 'z']})
result = df.value_counts()
print(result)

A KeyError: 'value_counts'

B AttributeError: 'DataFrame' object has no attribute 'value_counts'

C TypeError: unhashable type: 'Series'

D No error, prints counts of unique rows

💡 Hint

Check if DataFrame has value_counts method and what it returns.

Since pandas 1.1.0, DataFrame has a value_counts() method that counts unique rows. So no error occurs and it prints counts of unique rows.

🚀 Application

advanced

Using value_counts() with normalization

What does the normalize=True option do in value_counts()?
Given:

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

Pandas

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

A Shows the counts as percentages of total values

B Sorts the counts in ascending order

C Filters out values with counts less than 2

D Returns the counts as cumulative sums

💡 Hint

Think about what 'normalize' means in statistics.

normalize=True returns the relative frequencies (proportions) instead of raw counts.

🧠 Conceptual

expert

Interpreting value_counts() output with missing values

Consider this Series with missing values:

import pandas as pd
s = pd.Series(['red', 'blue', None, 'red', 'green', None, 'blue'])
result = s.value_counts(dropna=False)
print(result)

What does the output show compared to dropna=True?

Pandas

import pandas as pd
s = pd.Series(['red', 'blue', None, 'red', 'green', None, 'blue'])
result = s.value_counts(dropna=False)
print(result)

A Raises an error because None cannot be counted

B Includes counts of missing values (NaN) in the output

C Excludes missing values and only counts non-null entries

D Replaces missing values with zero before counting

💡 Hint

Check what dropna=False means for counting nulls.

dropna=False includes NaN values in the counts, so missing values are counted and shown in the output.

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