nunique() for cardinality in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count unique values in the 'color' column.

Data Analysis Python

unique_colors = df['color'].[1]()

Drag options to blanks, or click blank then click option'

Anunique

Bcount

Csum

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() instead of nunique()

Using sum() which adds values instead of counting unique ones

The nunique() function counts the number of unique values in a column.

2fill in blank

medium

Complete the code to find the number of unique values in the 'city' column of the DataFrame.

Data Analysis Python

unique_cities = df['city'].[1]()

Drag options to blanks, or click blank then click option'

Acount

Bvalue_counts

Cunique

Dnunique

Attempts:

3 left

💡 Hint

Common Mistakes

Using unique() which returns an array instead of a count

Using value_counts() which returns counts of all values

The nunique() method counts unique values directly when called on a DataFrame column.

3fill in blank

hard

Fix the error in the code to correctly count unique values in the 'product' column.

Data Analysis Python

unique_products = df['product'].[1]

Drag options to blanks, or click blank then click option'

Anunique

Bnunique()

Ccount()

Dunique()

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after nunique

Using unique() which returns unique values, not the count

The nunique() method must be called with parentheses to execute and return the count.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their unique letter counts as values.

Data Analysis Python

unique_letters = {word: len(set(word)) for word in [1] if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Awords

B>

C<

Dletters

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'letters' instead of 'words' as iterable

Using '<' instead of '>' in the condition

The dictionary comprehension loops over words and includes only words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their unique letter counts as values, only for words longer than 4 letters.

Data Analysis Python

result = [1]: len(set(word)) for word in words if len(word) [2] 4 and word.isalpha() and word.islower() == [3]

Drag options to blanks, or click blank then click option'

Aword.upper()

B>

CFalse

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of word.upper() for keys

Using '<' instead of '>' for length condition

Using True instead of False for word.islower() check

The dictionary keys are uppercase words, filtered for length > 4, and only words that are not lowercase (hence word.islower() == False).

Practice

1. What does the nunique() function do when applied to a pandas DataFrame column?

easy

A. Finds the maximum value in that column

B. Calculates the sum of all values in that column

C. Returns the total number of rows in the DataFrame

D. Counts the number of unique values in that column

Solution

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

   The nunique() function counts how many distinct or unique values exist in a column.

2. Step 2: Compare with other functions

   Unlike sum or max, nunique() focuses on counting unique entries, not numerical calculations.

3. Final Answer:

   Counts the number of unique values in that column -> Option D

4. Quick Check:

   nunique() = unique count [OK]

Hint: Remember: nunique() counts unique items only [OK]

Common Mistakes:

• Confusing nunique() with count()
• Thinking it sums values instead of counting unique ones
• Assuming it returns total rows instead of unique values

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

easy

A. df.nunique('Age')

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

C. df.nunique['Age']

D. df.nunique.Age()

Solution

1. Step 1: Access the column correctly

   To apply nunique() on a column, first select the column using df['Age'].

2. Step 2: Apply the nunique() method

   Then call nunique() as a method: df['Age'].nunique().

3. Final Answer:

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

4. Quick Check:

   Correct syntax = df['column'].nunique() [OK]

Hint: Use bracket notation for columns before nunique() [OK]

Common Mistakes:

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

3. Given the DataFrame below, what is the output of df['City'].nunique()?

import pandas as pd
data = {'City': ['Paris', 'London', 'Paris', 'Berlin', 'London', 'Rome']}
df = pd.DataFrame(data)

medium

A. 4

B. 3

C. 5

D. 6

Solution

1. Step 1: Identify unique cities in the list

   The cities are: 'Paris', 'London', 'Paris', 'Berlin', 'London', 'Rome'. Unique cities are 'Paris', 'London', 'Berlin', and 'Rome'.

2. Step 2: Count unique cities

   There are 4 unique cities in total.

3. Final Answer:

   4 -> Option A

4. Quick Check:

   Unique cities count = 4 [OK]

Hint: Count distinct values ignoring duplicates [OK]

Common Mistakes:

• Counting all entries instead of unique ones
• Miscounting duplicates as unique
• Confusing number of rows with unique count

4. What is wrong with this code snippet if we want to count unique values in the 'Product' column?

import pandas as pd
data = {'Product': ['A', 'B', 'A', 'C']}
df = pd.DataFrame(data)
unique_count = df.nunique('Product')

medium

A. The nunique() method does not take column names as arguments

B. The DataFrame is missing the 'Product' column

C. The nunique() method should be called without parentheses

D. The DataFrame constructor syntax is incorrect

Solution

1. Step 1: Check how nunique() works on DataFrames

   When called on a DataFrame, nunique() counts unique values for each column and does not accept column names as arguments.

2. Step 2: Correct way to count unique values in one column

   To count unique values in 'Product', select the column first: df['Product'].nunique().

3. Final Answer:

   The nunique() method does not take column names as arguments -> Option A

4. Quick Check:

   nunique() on DataFrame has no column argument [OK]

Hint: Call nunique() on column, not DataFrame with argument [OK]

Common Mistakes:

• Passing column name as argument to nunique() on DataFrame
• Calling nunique without parentheses
• Misunderstanding DataFrame constructor syntax

5. You have a DataFrame df with a column 'Score' containing some missing values (NaN). How do you count unique scores including the missing values as a unique category?

hard

A. df['Score'].count()

B. df['Score'].nunique(dropna=True)

C. df['Score'].nunique(dropna=False)

D. df['Score'].unique()

Solution

1. Step 1: Understand dropna parameter in nunique()

   By default, nunique() ignores missing values (NaN). Setting dropna=False counts NaN as a unique value.

2. Step 2: Apply nunique() with dropna=False

   Use df['Score'].nunique(dropna=False) to include NaN in the unique count.

3. Final Answer:

   df['Score'].nunique(dropna=False) -> Option C

4. Quick Check:

   Include NaN by setting dropna=False [OK]

Hint: Use dropna=False to count NaN as unique [OK]

Common Mistakes:

• Ignoring dropna and missing NaN count
• Using count() which excludes NaN
• Using unique() without counting length