groupby() basics in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of groupby sum aggregation

What is the output of this code?

import pandas as pd

data = {'Team': ['A', 'A', 'B', 'B', 'C'], 'Points': [10, 15, 10, 5, 20]}
df = pd.DataFrame(data)
result = df.groupby('Team').sum()

Pandas

import pandas as pd

data = {'Team': ['A', 'A', 'B', 'B', 'C'], 'Points': [10, 15, 10, 5, 20]}
df = pd.DataFrame(data)
result = df.groupby('Team').sum()
print(result)

A

  Team  Points
0    A      25
1    B      15
2    C      20

B

       Points
Team          
A          25
B          15
C          20

C

Team
A    25
B    15
C    20
Name: Points, dtype: int64

D

       Points
Team          
A          10
B          10
C          20

💡 Hint

Remember groupby groups rows by the 'Team' column and sums the 'Points' for each group.

The groupby('Team') groups rows by team names. Then sum() adds the Points in each group. So A has 10+15=25, B has 10+5=15, and C has 20.

❓ data_output

intermediate

Number of groups created by groupby

Given this DataFrame, how many groups will be created by grouping on 'Category' column?

import pandas as pd

data = {'Category': ['X', 'Y', 'X', 'Z', 'Y', 'Z', 'Z'], 'Value': [1, 2, 3, 4, 5, 6, 7]}
df = pd.DataFrame(data)
groups = df.groupby('Category')

Pandas

import pandas as pd

data = {'Category': ['X', 'Y', 'X', 'Z', 'Y', 'Z', 'Z'], 'Value': [1, 2, 3, 4, 5, 6, 7]}
df = pd.DataFrame(data)
groups = df.groupby('Category')
print(len(groups))

A 3

B 7

C 4

D 2

💡 Hint

Count unique values in the 'Category' column.

The unique categories are X, Y, and Z, so 3 groups are created.

🔧 Debug

advanced

Identify the error in groupby usage

What error does this code raise?

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara'], 'Score': [90, 80, 85]}
df = pd.DataFrame(data)
result = df.groupby('Age').mean()

Pandas

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara'], 'Score': [90, 80, 85]}
df = pd.DataFrame(data)
result = df.groupby('Age').mean()

A TypeError: unsupported operand type(s) for +: 'int' and 'str'

B ValueError: No numeric types to aggregate

C KeyError: 'Age'

D AttributeError: 'DataFrame' object has no attribute 'mean'

💡 Hint

Check if the column used in groupby exists in the DataFrame.

The DataFrame has no 'Age' column, so groupby('Age') raises a KeyError.

🚀 Application

advanced

Calculate average sales per region

You have sales data:

import pandas as pd

data = {'Region': ['North', 'South', 'North', 'East', 'South', 'East'], 'Sales': [100, 200, 150, 300, 250, 350]}
df = pd.DataFrame(data)

Which code correctly calculates the average sales per region?

Pandas

import pandas as pd

data = {'Region': ['North', 'South', 'North', 'East', 'South', 'East'], 'Sales': [100, 200, 150, 300, 250, 350]}
df = pd.DataFrame(data)

A df.groupby('Region').count()['Sales']

B df.groupby('Sales')['Region'].mean()

C df.groupby('Region').sum()['Sales'] / df['Sales'].count()

D df.groupby('Region')['Sales'].mean()

💡 Hint

Group by 'Region' and then calculate mean of 'Sales'.

Option D groups by 'Region' and calculates mean sales correctly. Option D groups by sales values, which is wrong. Option D divides sum by total count, not per group. Option D counts rows per region, not average sales.

🧠 Conceptual

expert

Understanding groupby object behavior

Which statement about a pandas groupby object is TRUE?

A A groupby object is an iterable that yields (group_name, DataFrame) pairs.

B A groupby object immediately computes aggregation when created.

C A groupby object can be indexed like a DataFrame to get groups directly.

D A groupby object stores the original DataFrame as a list of groups.

💡 Hint

Think about what happens when you loop over a groupby object.

A groupby object is lazy and does not compute aggregations until asked. It is iterable and yields pairs of group name and group DataFrame. It cannot be indexed like a DataFrame. It does not store groups as a list but as a special object.

Practice

1. What does the groupby() function do in pandas?

easy

A. It splits the data into groups based on column values.

B. It sorts the data in ascending order.

C. It removes duplicate rows from the data.

D. It changes the data type of columns.

Solution

1. Step 1: Understand the purpose of groupby()

   The groupby() function is used to split data into groups based on the values in one or more columns.

2. Step 2: Compare with other options

   Sorting, removing duplicates, and changing data types are different operations and not what groupby() does.

3. Final Answer:

   It splits the data into groups based on column values. -> Option A

4. Quick Check:

   groupby() = split data into groups [OK]

Hint: Remember: groupby splits data by column values [OK]

Common Mistakes:

• Confusing groupby with sorting
• Thinking groupby removes duplicates
• Assuming groupby changes data types

2. Which of the following is the correct syntax to group a DataFrame df by the column 'Category'?

easy

A. df.group_by('Category')

B. df.groupby['Category']

C. df.groupby('Category')

D. df.groupby(Category)

Solution

1. Step 1: Recall the correct method name and syntax

   The correct method is groupby() called with parentheses and the column name as a string argument.

2. Step 2: Check each option

   df.groupby('Category') uses df.groupby('Category') which is correct. df.group_by('Category') uses wrong method name group_by. df.groupby['Category'] uses square brackets instead of parentheses. df.groupby(Category) misses quotes around the column name.

3. Final Answer:

   df.groupby('Category') -> Option C

4. Quick Check:

   Correct syntax uses parentheses and string column name [OK]

Hint: Use df.groupby('column_name') with parentheses and quotes [OK]

Common Mistakes:

• Using wrong method name like group_by
• Using square brackets instead of parentheses
• Not quoting the column name string

3. Given the DataFrame:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'C'], 'Points': [10, 15, 10, 5, 20]}
df = pd.DataFrame(data)
result = df.groupby('Team').sum()

What is the output of result?

medium

A. Team Points
A 25
B 15
C 20

B. {'Team': ['A', 'B', 'C'], 'Points': [25, 15, 20]}

C. Team Points
A 10
B 10
C 20

D. Team Points
A 15
B 5
C 20

Solution

1. Step 1: Group data by 'Team'

   The groups are: A with points 10 and 15, B with points 10 and 5, C with points 20.

2. Step 2: Sum points in each group

   Sum for A = 10 + 15 = 25, B = 10 + 5 = 15, C = 20.

3. Final Answer:

   Team Points A 25 B 15 C 20 -> Option A

4. Quick Check:

   Sum points per team = 25, 15, 20 [OK]

Hint: Sum points after groupby to get totals per group [OK]

Common Mistakes:

• Not summing points, just taking first value
• Mixing up group sums
• Confusing output format

4. What is wrong with this code snippet?

df = pd.DataFrame({'City': ['X', 'Y', 'X'], 'Sales': [100, 200, 150]})
result = df.groupby.City.sum()

medium

A. DataFrame is missing import statement.

B. The column name 'City' is misspelled.

C. The sum() function cannot be used after groupby.

D. You cannot use dot notation after groupby; it should be parentheses.

Solution

1. Step 1: Check groupby syntax

   The correct way to group by a column is df.groupby('City') with parentheses and quotes.

2. Step 2: Identify the error in dot notation

   Using dot notation like df.groupby.City is invalid because groupby is a method, not an attribute.

3. Final Answer:

   You cannot use dot notation after groupby; it should be parentheses. -> Option D

4. Quick Check:

   Use parentheses with groupby, not dot notation [OK]

Hint: Always use parentheses with groupby, not dot notation [OK]

Common Mistakes:

• Using dot notation instead of parentheses
• Misspelling column names
• Thinking sum() is invalid after groupby

5. You have a sales DataFrame:

import pandas as pd
data = {'Store': ['A', 'A', 'B', 'B', 'B'], 'Product': ['X', 'Y', 'X', 'Y', 'X'], 'Sales': [100, 150, 200, 250, 300]}
df = pd.DataFrame(data)

How do you find the total sales for each store and product combination?

hard

A. df.groupby('Product').sum('Store')

B. df.groupby(['Store', 'Product']).sum()

C. df.groupby('Store').sum('Product')

D. df.groupby(['Store']).sum(['Product'])

Solution

1. Step 1: Understand grouping by multiple columns

   To group by both 'Store' and 'Product', pass a list of column names to groupby().

2. Step 2: Use sum() to aggregate sales

   After grouping, use sum() to add sales values for each group.

3. Final Answer:

   df.groupby(['Store', 'Product']).sum() -> Option B

4. Quick Check:

   Group by multiple columns using list, then sum [OK]

Hint: Group by list of columns for multi-level grouping [OK]

Common Mistakes:

• Passing column names to sum() instead of groupby()
• Grouping by single column only
• Using wrong syntax for groupby