nunique() for unique counts in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

Acount

Bunique

Cnunique

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts all non-null values, not unique ones.

Using unique() which returns unique values but does not count them.

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

2fill in blank

medium

Complete the code to count unique values in each column of the DataFrame.

Pandas

unique_counts = df.[1]()

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

Anunique

Bunique

Ccount

Dvalue_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using unique() which returns arrays of unique values, not counts.

Using value_counts() which works on Series, not DataFrame.

The nunique() method applied to the whole DataFrame returns unique counts per column.

3fill in blank

hard

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

Pandas

unique_ages = df['age'].[1]

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

Anunique()

Bnunique

Cunique()

Dcount()

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after nunique.

Using unique() which returns unique values, not 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.

Pandas

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

Bword_list

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable name for the list.

Using the wrong comparison operator in the condition.

We loop over word_list and select words longer than 3 letters to count unique 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 for words longer than 4 letters.

Pandas

result = [1]: [2] for [3] in words if len([3]) > 4

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

Aword.upper()

Blen(set(word))

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using the wrong variable name in the loop.

The dictionary comprehension uses word.upper() as keys, counts unique letters with len(set(word)), and loops over word in words.

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'