nunique() for cardinality in Data Analysis Python - Step-by-Step Execution

Concept Flow - nunique() for cardinality

Start with DataFrame

↓

Call nunique()

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Count unique values per column

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Return counts as Series

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Use counts for cardinality analysis

The nunique() function counts unique values in each column of a DataFrame, helping us understand the cardinality of data.

Execution Sample

Data Analysis Python

import pandas as pd

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

This code creates a DataFrame and uses nunique() to count unique values in each column.

Execution Table

Step	Action	DataFrame State	nunique() Result	Explanation
1	Create DataFrame	{'A':[1,2,2,3], 'B':['x','y','x','z']}	N/A	DataFrame with 4 rows and 2 columns created
2	Call df.nunique()	Same DataFrame	A: 3, B: 3	Counts unique values: A has 3 unique (1,2,3), B has 3 unique (x,y,z)
3	Store result in unique_counts	Same DataFrame	unique_counts = A:3, B:3	Result stored as Series for further use

💡 All columns processed, unique counts returned as Series

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
df	None	{'A':[1,2,2,3], 'B':['x','y','x','z']}	Same	Same
unique_counts	None	None	A: 3, B: 3	A: 3, B: 3

Key Moments - 2 Insights

Why does column 'A' show 3 unique values even though it has 4 rows?

Because nunique() counts distinct values, and '2' appears twice, so unique values are 1, 2, and 3 (3 total). See execution_table row 2.

Does nunique() count missing values (NaN) as unique?

By default, nunique() ignores NaN values when counting unique values. This is why only actual distinct values are counted.

Visual Quiz - 3 Questions

Look at the execution_table at Step 2, what is the unique count for column 'B'?

A 3

B 2

C 4

D 1

💡 Hint

Check the 'nunique() Result' column in execution_table row 2.

At which step is the unique count stored in a variable?

A Step 1

B Step 2

C Step 3

D After all steps

💡 Hint

Look at the 'Action' column in execution_table to see when unique_counts is assigned.

If column 'A' had a missing value (NaN), how would nunique() count change?

A It would count NaN as a unique value

B It would ignore NaN and count only actual unique values

C It would count NaN twice

D It would cause an error

💡 Hint

Recall the key moment about how nunique() treats NaN values.

Concept Snapshot

nunique() counts unique values per DataFrame column
Returns a Series with counts
Ignores NaN by default
Useful for cardinality analysis
Syntax: df.nunique()

Full Transcript

We start with a DataFrame containing columns A and B. Calling df.nunique() counts how many unique values each column has. For example, column A has values 1, 2, 2, 3, so unique count is 3 because 2 repeats. Column B has 'x', 'y', 'x', 'z', so unique count is also 3. The result is stored in unique_counts as a Series. Note that nunique() ignores missing values by default. This helps us understand the variety of data in each column, called cardinality.

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