value_counts() for distributions in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of value_counts() with dropna=false

What is the output of the following code snippet?

Data Analysis Python

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

A

apple     3
banana    2
dtype: int64

B

apple     2
banana    2
NaN       3
dtype: int64

C

apple     3
banana    2
NaN       1
dtype: int64

D

apple     3
banana    2
NaN       2
dtype: int64

💡 Hint

Remember that dropna=False includes missing values in the count.

The value_counts() method counts all values including NaN when dropna=False is set. Here, 'apple' appears 3 times, 'banana' 2 times, and None (NaN) 2 times.

❓ data_output

intermediate

Number of unique categories counted by value_counts()

Given the following Series, how many unique categories will value_counts() return?

Data Analysis Python

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

A 3

B 4

C 5

D 8

💡 Hint

Count how many different animals appear in the list.

The unique categories are 'cat', 'dog', 'bird', and 'fish', so value_counts() returns 4 counts.

🔧 Debug

advanced

Identify the error in value_counts() usage

What error will this code raise?

Data Analysis Python

import pandas as pd
s = pd.Series([1, 2, 2, 3, 3, 3])
result = s.value_counts(normalize=True, sort=False, ascending=True)
print(result)

A ValueError: Cannot specify both sort=False and ascending=True

B TypeError: value_counts() got an unexpected keyword argument 'ascending'

C No error; outputs normalized counts sorted ascending

D AttributeError: 'Series' object has no attribute 'value_counts'

💡 Hint

Check the allowed parameters and their combinations for value_counts().

The value_counts() method does not allow ascending=True when sort=False because sorting order only applies if sorting is enabled.

❓ visualization

advanced

Bar plot from value_counts() output

Which option shows the correct bar plot code to visualize the counts of categories in a Series s?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series(['red', 'blue', 'red', 'green', 'blue', 'blue'])

A

s.value_counts().plot(kind='bar')
plt.show()

B

s.plot(kind='bar')
plt.show()

C

s.value_counts().plot.barh()
plt.show()

D

s.value_counts().plot(kind='line')
plt.show()

💡 Hint

value_counts() returns counts; plot them as bars for category frequencies.

Option A correctly plots the counts as a vertical bar chart. Option A plots the raw Series, not counts. Option A uses line plot which is less suitable. Option A plots horizontal bars, which is valid but not the asked correct vertical bar plot.

🚀 Application

expert

Calculate relative frequency distribution with value_counts()

You have a Series s with categorical data. How do you calculate the relative frequency (percentage) of each category and round it to 2 decimals?

Data Analysis Python

import pandas as pd
s = pd.Series(['A', 'B', 'A', 'C', 'B', 'B', 'A', 'C', 'C', 'C'])

A s.value_counts(normalize=True).round(2) * 100

B s.value_counts().apply(lambda x: round(x / len(s), 2))

C (s.value_counts(normalize=True) * 100).round(2)

D s.value_counts().div(len(s)).round(2) * 100

💡 Hint

Normalize counts to get proportions, multiply by 100 for percentage, then round.

Option C correctly normalizes counts to proportions, converts to percentage, and rounds. Option C rounds proportions to 2 decimals but does not convert to percentage. Option C rounds proportions before multiplying by 100, causing incorrect percentages. Option C rounds proportions after division but before multiplying, causing incorrect percentages.

Practice

1. What does the value_counts() function do when applied to a pandas Series?

easy

A. Calculates the average of all values in the Series

B. Counts how many times each unique value appears in the Series

C. Sorts the Series in ascending order

D. Removes duplicate values from the Series

Solution

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

   This function counts each unique value's frequency in a pandas Series.

2. Step 2: Compare with other options

   Sorting, averaging, or removing duplicates are different operations not done by value_counts().

3. Final Answer:

   Counts how many times each unique value appears in the Series -> Option B

4. Quick Check:

   value_counts() = counts unique values [OK]

Hint: Remember: value_counts counts unique values frequency [OK]

Common Mistakes:

• Thinking it sorts values instead of counting
• Confusing it with mean or average calculation
• Assuming it removes duplicates instead of counting

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

easy

A. data.value_counts(percent=True)

B. data.value_counts(normalize=False)

C. data.value_counts(normalize=True)

D. data.value_counts(percentage=True)

Solution

1. Step 1: Recall the parameter for percentage in value_counts()

   The correct parameter is normalize=True to get proportions.

2. Step 2: Check other options for correctness

   Options with percent or percentage are invalid parameters and cause errors.

3. Final Answer:

   data.value_counts(normalize=True) -> Option C

4. Quick Check:

   Use normalize=True for percentages [OK]

Hint: Use normalize=True to get percentages, not percent or percentage [OK]

Common Mistakes:

• Using wrong parameter names like percent or percentage
• Forgetting to set normalize=True for percentages
• Confusing normalize=False with percentage output

3. Given the pandas Series colors = pd.Series(['red', 'blue', 'red', 'green', 'blue', 'blue']), what is the output of colors.value_counts()?

medium

A. {'blue': 3, 'red': 2, 'green': 1}

B. {'red': 3, 'blue': 2, 'green': 1}

C. {'green': 1, 'red': 2, 'blue': 3}

D. {'red': 2, 'blue': 3, 'green': 1}

Solution

1. Step 1: Count each color's occurrences

   'red' appears 2 times, 'blue' 3 times, 'green' 1 time.

2. Step 2: Understand value_counts() output order

   It sorts counts descending by default, so 'blue' (3), 'red' (2), 'green' (1).

3. Final Answer:

   {'blue': 3, 'red': 2, 'green': 1} -> Option A

4. Quick Check:

   Counts sorted descending = {'blue':3,'red':2,'green':1} [OK]

Hint: value_counts() sorts by count descending automatically [OK]

Common Mistakes:

• Ignoring the default descending sort order
• Mixing up counts of 'red' and 'blue'
• Assuming output is unordered or sorted alphabetically

4. What is wrong with this code snippet if df['Category'] is a pandas Series?

df.value_counts(normalize=True)

medium

A. It should be df['Category'].value_counts(normalize=True) to count values in the column

B. The parameter normalize is not valid for value_counts()

C. You cannot use value_counts() on a pandas Series

D. The code will count values but not normalize them

Solution

1. Step 1: Identify the object value_counts() is called on

   df is a DataFrame, but value_counts() works on Series, not DataFrame directly.

2. Step 2: Correct usage for counting a column

   Use df['Category'].value_counts(normalize=True) to count values in that column.

3. Final Answer:

   It should be df['Category'].value_counts(normalize=True) to count values in the column -> Option A

4. Quick Check:

   value_counts() needs a Series, not DataFrame [OK]

Hint: Call value_counts() on a Series, not on the whole DataFrame [OK]

Common Mistakes:

• Calling value_counts() directly on DataFrame
• Confusing DataFrame and Series methods
• Thinking normalize is invalid parameter

5. You have a pandas Series responses with values: ['yes', 'no', 'yes', 'maybe', 'no', 'yes', 'no', 'maybe', 'yes', 'no'].
How do you get the percentage distribution of each response rounded to 2 decimals?

hard

A. responses.value_counts().round(2)

B. responses.value_counts().normalize().round(2)

C. responses.value_counts(percent=True).round(2)

D. responses.value_counts(normalize=True).round(2)

Solution

1. Step 1: Use value_counts(normalize=True) to get percentages

   This returns the relative frequencies as decimals.

2. Step 2: Apply round(2) to round percentages to 2 decimals

   Chaining .round(2) rounds the decimal values properly.

3. Final Answer:

   responses.value_counts(normalize=True).round(2) -> Option D

4. Quick Check:

   Normalize then round decimals = responses.value_counts(normalize=True).round(2) [OK]

Hint: Chain normalize=True with round(2) to get rounded percentages [OK]

Common Mistakes:

• Using invalid parameter percent=True
• Calling normalize() as a method instead of parameter
• Rounding counts instead of percentages