nunique() for unique counts in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pandas method nunique() do?

It counts the number of unique (different) values in a Series or DataFrame column.

beginner

How do you count unique values in each column of a DataFrame using nunique()?

Use df.nunique(). It returns a Series with the count of unique values for each column.

intermediate

What parameter can you use with nunique() to include or exclude missing values?

The parameter dropna. By default, dropna=True excludes missing values from the count. Set dropna=False to include them.

beginner

How to count unique values in a single pandas Series?

Call nunique() directly on the Series, like series.nunique().

beginner

Why is nunique() useful in data analysis?

It helps quickly understand how many different values exist in data columns, which is useful for data cleaning and exploration.

What does df.nunique() return?

A Number of rows in the DataFrame

B Count of unique values per column

C Sum of all values in the DataFrame

D Mean of each column

df.nunique() returns the count of unique values for each column in the DataFrame.

How do you include missing values in the unique count using nunique()?

A Use <code>include_na=True</code>

B Set <code>dropna=True</code>

C Set <code>dropna=False</code>

D Missing values are always included

Use dropna=False to include missing values in the unique count.

Which pandas object can you call nunique() on?

A Both Series and DataFrames

B Only Series

C Only DataFrames

D Only numpy arrays

nunique() works on both pandas Series and DataFrames.

What is the default behavior of nunique() regarding missing values?

A Includes missing values in count

B Replaces missing values with zero

C Raises an error if missing values exist

D Excludes missing values from count

By default, nunique() excludes missing values (dropna=True).

If a column has values [1, 2, 2, 3, NaN], what does nunique() return by default?

A 3

B 2

C 5

D 4

By default, missing values are excluded, so unique values are 1, 2, and 3 → count is 3.

Explain how to use nunique() to find unique counts in a DataFrame and how to handle missing values.

Think about the default behavior and how to change it.

Describe a real-life example where counting unique values with nunique() helps in data analysis.

Consider something like counting unique customers or product categories.

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'