nunique() for unique counts in Pandas - Step-by-Step Execution

Concept Flow - nunique() for unique counts

Start with DataFrame

↓

Call nunique()

↓

Count unique values per column or axis

↓

Return counts as Series or scalar

↓

End

The nunique() function counts how many unique values exist in each column or row of a DataFrame and returns those counts.

Execution Sample

Pandas

import pandas as pd

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

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
1	Create DataFrame	{'A':[1,2,2,3], 'B':['x','y','x','z']}
2	Call df.nunique()	Same as step 1	Counts unique values per column
3	Count unique in column 'A'	Values: [1,2,2,3]	3 (unique: 1,2,3)
4	Count unique in column 'B'	Values: ['x','y','x','z']	3 (unique: x,y,z)
5	Return result		A: 3, B: 3 (as Series)

💡 All columns processed, unique counts returned as Series.

Variable Tracker

Variable	Start	After nunique()
df	{'A':[1,2,2,3], 'B':['x','y','x','z']}	No change
counts	Not defined	A: 3, B: 3 (Series)

Key Moments - 2 Insights

Why does nunique() count 3 unique values in column 'A' when there are 4 rows?

Because nunique() counts distinct values, and column 'A' has values 1, 2, 2, 3. The value 2 repeats, so unique values are 1, 2, and 3, totaling 3. See execution_table rows 3 and 5.

What does nunique() return when called without arguments?

It returns a Series with the count of unique values for each column. See execution_table row 5.

Visual Quiz - 3 Questions

Look at the execution table, what is the unique count for column 'B' at step 4?

A 2

B 3

C 4

D 1

💡 Hint

Check execution_table row 4 where unique values in column 'B' are counted.

At which step does nunique() return the final counts as a Series?

A Step 5

B Step 2

C Step 3

D Step 4

💡 Hint

Look for the step where the result is returned, see execution_table row 5.

If column 'A' had all unique values, how would the count change in the variable_tracker?

A counts for 'A' would be 1

B counts for 'A' would be 3

C counts for 'A' would be 4

D counts for 'A' would be 0

💡 Hint

Unique count equals number of distinct values, see variable_tracker for current counts.

Concept Snapshot

pandas.DataFrame.nunique()
Counts unique values per column or row.
Returns a Series with counts.
Default axis=0 (columns).
Useful to find distinct values quickly.

Full Transcript

We start with a DataFrame containing columns 'A' and 'B'. Calling df.nunique() counts how many unique values each column has. For column 'A', values are 1, 2, 2, 3, so unique count is 3. For column 'B', values are 'x', 'y', 'x', 'z', so unique count is also 3. The function returns these counts as a Series. This helps us quickly see how many different values each column contains.

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'