Aggregation with agg() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the 'score' column using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'score': [85, 90, 78]}
df = pd.DataFrame(data)

result = df['score'].agg([1])
print(result)

Drag options to blanks, or click blank then click option'

A'mean'

B'sum'

C'max'

D'min'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' instead of 'mean' will add all scores instead of averaging.

Using 'max' or 'min' will return the highest or lowest score, not the average.

The agg() function applies the aggregation function specified. Here, 'mean' calculates the average score.

2fill in blank

medium

Complete the code to calculate both the minimum and maximum of the 'score' column using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'score': [85, 90, 78]}
df = pd.DataFrame(data)

result = df['score'].agg([1])
print(result)

Drag options to blanks, or click blank then click option'

A['min', 'max']

B'min', 'max'

C'min'

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string with commas instead of a list causes errors.

Passing only one function name returns a single aggregation, not both.

The agg() function accepts a list of function names to apply multiple aggregations. The list ['min', 'max'] calculates both minimum and maximum values.

3fill in blank

hard

Fix the error in the code to calculate the sum of 'score' and the mean of 'age' columns using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'score': [85, 90], 'age': [25, 30]}
df = pd.DataFrame(data)

result = df.agg({'score': [1], 'age': 'mean'})
print(result)

Drag options to blanks, or click blank then click option'

A'mean'

Bsum

C['sum']

D'sum'

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum without quotes causes a NameError.

Using a list like ['sum'] is not accepted in this context.

When specifying aggregation functions in a dictionary for agg(), function names must be strings like 'sum' or 'mean'.

4fill in blank

hard

Fill both blanks to create a dictionary that calculates the mean of 'score' and the max of 'age' using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'score': [85, 90], 'age': [25, 30]}
df = pd.DataFrame(data)

result = df.agg({'score': [1], 'age': [2])
print(result)

Drag options to blanks, or click blank then click option'

A'mean'

B'max'

C'sum'

D'min'

Attempts:

3 left

💡 Hint

Common Mistakes

Using function names without quotes.

Mixing up which function applies to which column.

The dictionary keys are column names and values are aggregation functions as strings. Here, 'mean' for 'score' and 'max' for 'age' calculate the average score and maximum age.

5fill in blank

hard

Fill all three blanks to create a dictionary that calculates the sum of 'score', the mean of 'age', and the minimum of 'score' using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'score': [85, 90], 'age': [25, 30]}
df = pd.DataFrame(data)

result = df.agg({'score': [[1], [2]], 'age': [3])
print(result)

Drag options to blanks, or click blank then click option'

A'sum'

B'mean'

C'min'

D'max'

Attempts:

3 left

💡 Hint

Common Mistakes

Using function names without quotes.

Passing a string instead of a list for multiple functions.

Mixing up the order of functions in the list.

For the 'score' column, we pass a list of functions ['sum', 'min'] to calculate total and minimum scores. For 'age', 'mean' calculates the average age.

Practice

1.

What does the agg() function do in pandas?

easy

A. It sorts the DataFrame rows by index.

B. It applies one or more aggregation functions to DataFrame columns.

C. It filters rows based on a condition.

D. It merges two DataFrames together.

Solution

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

   The agg() function is used to apply aggregation functions like sum, mean, count, etc., to DataFrame columns.

2. Step 2: Compare with other options

   Sorting, filtering, and merging are done by other pandas functions, not agg().

3. Final Answer:

   It applies one or more aggregation functions to DataFrame columns. -> Option B

4. Quick Check:

   agg() = aggregation [OK]

Hint: Remember agg() means aggregate functions on columns [OK]

Common Mistakes:

• Confusing agg() with sorting or filtering functions
• Thinking agg() modifies row order
• Assuming agg() merges DataFrames

2.

Which of the following is the correct syntax to apply agg() with multiple functions on a DataFrame df?

df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']})

easy

A. df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']})

B. df.aggregate(['sum', 'mean'], ['min', 'max'])

C. df.agg(['col1': 'sum', 'col2': 'min'])

D. df.agg({'sum', 'mean', 'min', 'max'})

Solution

1. Step 1: Review correct agg() syntax

   The correct syntax uses a dictionary mapping column names to lists of functions: {'col1': ['sum', 'mean'], 'col2': ['min', 'max']}.

2. Step 2: Check each option

   df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']}) matches the correct syntax. Options A, B, and D have syntax errors or wrong argument formats.

3. Final Answer:

   df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']}) -> Option A

4. Quick Check:

   Dictionary with column keys and function lists = df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']}) [OK]

Hint: Use dict with columns as keys and list of funcs as values [OK]

Common Mistakes:

• Passing functions as separate arguments instead of dict
• Using sets {} instead of dict for agg()
• Incorrect list or dict syntax inside agg()

3.

Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
result = df.agg({'A': ['sum', 'max'], 'B': ['sum', 'max']})
print(result)

What is the output?

medium

A. A B
sum 6 15
max 3 6

B. A B
sum 6 4
max 3 4

C. A B
sum 6 5
max 3 6

D. A B
sum 6 5
max 3 4

Solution

1. Step 1: Calculate aggregation for column 'A'

   Sum of [1, 2, 3] is 6; max is 3.

2. Step 2: Calculate aggregation for column 'B'

   Sum of [4, 5, 6] is 15; max is 6.

3. Final Answer:

   A B sum 6 15 max 3 6 -> Option A

4. Quick Check:

   Sum A=6, Max A=3, Sum B=15, Max B=6 [OK]

Hint: Apply each function only to specified columns [OK]

Common Mistakes:

• Assuming max applies to column B
• Mixing up min and max values
• Confusing row and column labels in output

4.

What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
result = df.agg({'X': 'sum', 'Y': ['mean', 'max']})
print(result)

medium

A. Error because 'agg()' requires all functions as strings.

B. Error because 'sum' should be in a list for column 'X'.

C. Error because 'agg()' cannot mix single and multiple functions.

D. No error; code runs and prints correct aggregation.

Solution

1. Step 1: Check if mixing single and multiple functions is allowed

   In pandas, you can pass a single function as a string or multiple as a list per column in agg().

2. Step 2: Verify syntax correctness

   Passing 'sum' as a string for 'X' and ['mean', 'max'] for 'Y' is valid syntax.

3. Final Answer:

   No error; code runs and prints correct aggregation. -> Option D

4. Quick Check:

   Single or list functions per column both allowed [OK]

Hint: Single function can be string; multiple must be list [OK]

Common Mistakes:

• Thinking all functions must be lists
• Assuming mixing single and list causes error
• Confusing function names with strings vs objects

5.

You have a DataFrame df with columns 'Product', 'Sales', and 'Quantity'. You want to group by 'Product' and get the total sales, average quantity, and maximum sales per product. Which agg() call achieves this?

hard

A. df.groupby('Product').agg({'Sales': ['mean'], 'Quantity': ['sum', 'max']})

B. df.groupby('Product').agg({'Sales': 'mean', 'Quantity': ['sum', 'max']})

C. df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'})

D. df.groupby('Product').agg({'Product': ['sum'], 'Sales': 'max', 'Quantity': 'mean'})

Solution

1. Step 1: Identify required aggregations per column

   We want total sales (sum), average quantity (mean), and maximum sales (max) grouped by product.

2. Step 2: Match functions to columns

   For 'Sales', apply 'sum' and 'max'; for 'Quantity', apply 'mean'. Grouping is by 'Product'.

3. Step 3: Check options

   df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'}) matches the requirements exactly. Others have wrong functions or columns.

4. Final Answer:

   df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'}) -> Option C

5. Quick Check:

   Group by Product, Sales sum & max, Quantity mean = df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'}) [OK]

Hint: Map each column to needed functions in a dict inside agg() [OK]

Common Mistakes:

• Applying wrong functions to columns
• Including grouping column in agg()
• Mixing aggregation functions incorrectly