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.