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

Concept Flow - nunique() for cardinality

Start with DataFrame

↓

Call nunique()

↓

Count unique values per column

↓

Return counts as Series

↓

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.