nunique() for unique counts in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of nunique() on a DataFrame column

What is the output of the following code snippet?

Pandas

import pandas as pd

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

A 2

B 3

C 6

D 1

💡 Hint

Count how many different numbers appear in column 'A'.

The column 'A' has values [1, 2, 2, 3, 3, 3]. The unique values are 1, 2, and 3, so the count is 3.

❓ data_output

intermediate

Unique counts per column in a DataFrame

What is the output of the following code?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 1, 2, 2], 'Y': ['a', 'b', 'a', 'b'], 'Z': [5, 5, 5, 5]})
result = df.nunique()
print(result)

A

X    1
Y    1
Z    1
dtype: int64

B

X    4
Y    4
Z    4
dtype: int64

C

X    2
Y    2
Z    1
dtype: int64

D

X    2
Y    1
Z    1
dtype: int64

💡 Hint

Count unique values in each column separately.

Column 'X' has unique values 1 and 2 (2 unique), 'Y' has 'a' and 'b' (2 unique), 'Z' has only 5 (1 unique).

🔧 Debug

advanced

Error raised by incorrect use of nunique()

What error does the following code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
result = df.nunique(axis=2)
print(result)

A ValueError: No axis named 2 for object type DataFrame

B TypeError: nunique() got an unexpected keyword argument 'axis'

C AttributeError: 'DataFrame' object has no attribute 'nunique'

D No error, prints the unique counts

💡 Hint

Check valid axis values for DataFrame methods.

DataFrames only support axis=0 (rows) or axis=1 (columns). axis=2 is invalid and raises ValueError.

🚀 Application

advanced

Counting unique values after filtering

Given the DataFrame below, what is the output of the code?

Pandas

import pandas as pd

df = pd.DataFrame({'Category': ['A', 'B', 'A', 'C', 'B', 'C'], 'Value': [10, 20, 10, 30, 20, 40]})
filtered = df[df['Value'] > 15]
result = filtered['Category'].nunique()
print(result)

A 1

B 3

C 4

D 2

💡 Hint

Filter rows where Value > 15, then count unique categories.

Filtered rows have Values 20, 30, 20, 40 with Categories B, C, B, C. Unique categories are B and C, count is 2.

🧠 Conceptual

expert

Understanding nunique() with dropna parameter

What is the output of this code snippet?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2, 2, np.nan, np.nan]})
result = df['A'].nunique(dropna=False)
print(result)

A 3

B 2

C 4

D 5

💡 Hint

dropna=False counts NaN as a unique value.

The column 'A' has values [1, 2, 2, NaN, NaN]. The unique values are 1, 2, and NaN. Since dropna=False, NaN is counted as a unique value, so the count is 3.

Practice

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

easy

A. Sorts the values in ascending order

B. Calculates the sum of all values in a DataFrame

C. Replaces missing values with zero

D. Counts the number of unique values in a Series or DataFrame column

Solution

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

   The nunique() function counts how many different (unique) values exist in a pandas Series or DataFrame column.

2. Step 2: Compare with other functions

   Other options describe sum, sorting, or filling missing values, which are not what nunique() does.

3. Final Answer:

   Counts the number of unique values in a Series or DataFrame column -> Option D

4. Quick Check:

   nunique() = unique count [OK]

Hint: Remember: 'nunique' means 'number of unique' [OK]

Common Mistakes:

• Confusing with sum() function
• Thinking it sorts data
• Assuming it fills missing values

2. Which of the following is the correct syntax to count unique values in the 'Age' column of a DataFrame named df?

easy

A. df['Age'].nunique()

B. df.nunique('Age')

C. df.nunique['Age']

D. df.nunique.Age()

Solution

1. Step 1: Identify correct method call on a column

   To count unique values in a specific column, select the column first with df['Age'], then call nunique().

2. Step 2: Check syntax of options

   df['Age'].nunique() uses correct syntax: df['Age'].nunique(). Other options misuse parentheses or indexing.

3. Final Answer:

   df['Age'].nunique() -> Option A

4. Quick Check:

   Use df['column'].nunique() for unique count [OK]

Hint: Select column first, then call nunique() [OK]

Common Mistakes:

• Passing column name as argument to nunique()
• Using dot notation with parentheses incorrectly
• Trying to index nunique as if a dictionary

3. Given the DataFrame:

import pandas as pd
data = {'City': ['NY', 'LA', 'NY', 'Chicago', 'LA', 'NY'], 'Score': [10, 20, 10, 30, 20, 40]}
df = pd.DataFrame(data)
print(df['City'].nunique())

What is the output?

medium

A. 4

B. 6

C. 3

D. 2

Solution

1. Step 1: Identify unique cities in the 'City' column

   The cities are ['NY', 'LA', 'NY', 'Chicago', 'LA', 'NY']. Unique values are 'NY', 'LA', and 'Chicago'.

2. Step 2: Count unique values

   There are 3 unique cities.

3. Final Answer:

   3 -> Option C

4. Quick Check:

   Unique cities count = 3 [OK]

Hint: Count distinct city names ignoring repeats [OK]

Common Mistakes:

• Counting total rows instead of unique values
• Including duplicates in count
• Confusing with number of columns

4. You run this code:

import pandas as pd
data = {'A': [1, 2, 2, 3], 'B': [4, 4, 5, 6]}
df = pd.DataFrame(data)
print(df['A'].nunique(axis=1))

But it gives an error. What is the problem?

medium

A. The axis parameter is invalid for nunique()

B. nunique() cannot be used on DataFrames

C. The DataFrame has duplicate values causing error

D. Missing parentheses after nunique

Solution

1. Step 1: Check nunique() parameters

   The nunique() function in pandas does not support the axis parameter to count unique values row-wise.

2. Step 2: Understand correct usage

   It works column-wise by default. To count unique values per row, a different approach is needed.

3. Final Answer:

   The axis parameter is invalid for nunique() -> Option A

4. Quick Check:

   nunique() has no axis argument [OK]

Hint: Remember: nunique() counts column-wise only [OK]

Common Mistakes:

• Trying to use axis=1 with nunique()
• Assuming nunique() works row-wise by default
• Confusing with other functions that accept axis

5. You have a DataFrame df with columns 'Name', 'City', and 'Age'. You want to find how many unique cities each person has appeared in, but the data has multiple rows per person. Which code correctly counts unique cities per 'Name'?

hard

A. df['City'].nunique(groupby='Name')

B. df.groupby('Name')['City'].nunique()

C. df.nunique('City', by='Name')

D. df.groupby('City')['Name'].nunique()

Solution

1. Step 1: Understand the goal

   We want to count unique cities for each person (grouped by 'Name').

2. Step 2: Use groupby and nunique correctly

   The correct syntax is to group by 'Name' and then apply nunique() on the 'City' column.

3. Step 3: Check options

   df.groupby('Name')['City'].nunique() matches this syntax. Others misuse parameters or group by wrong columns.

4. Final Answer:

   df.groupby('Name')['City'].nunique() -> Option B

5. Quick Check:

   Group by 'Name', then count unique 'City' [OK]

Hint: Group by person, then count unique cities with nunique() [OK]

Common Mistakes:

• Using nunique() without groupby
• Passing wrong parameters to nunique()
• Grouping by 'City' instead of 'Name'