agg() for multiple aggregations in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of multiple aggregations with agg()

What is the output of this code snippet using pandas agg() for multiple aggregations?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8]
})

result = df.agg({'A': ['sum', 'max'], 'B': ['min', 'mean']})
print(result)

A {'A': {'sum': 10, 'max': 4}, 'B': {'min': 5, 'mean': 6.5}}

B

       A    B
sum  10.0  26.0
max   4.0   8.0

C

       A    B
sum  10.0  NaN
max   4.0  NaN
min   NaN  5.0
mean  NaN  6.5

D

A    10.0
B     5.0
dtype: float64

💡 Hint

agg() with dict applies each function per column and returns a DataFrame with function names as index.

The agg() method applies multiple aggregation functions per column. The result is a DataFrame where rows are the aggregation functions and columns are the original columns. Functions not applied to a column show NaN.

❓ data_output

intermediate

Number of rows in agg() result with multiple aggregations

Given this DataFrame and agg() call, how many rows does the result have?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'X': [10, 20, 30],
    'Y': [5, 15, 25]
})

result = df.agg({'X': ['min', 'max', 'mean'], 'Y': ['sum', 'mean']})

A 4

B 5

C 3

D 2

💡 Hint

Count unique aggregation functions used across all columns.

The agg() result has one row per unique aggregation function name used. Here, functions are 'min', 'max', 'mean', 'sum'. That's 4 rows.

🔧 Debug

advanced

Identify the error in agg() usage

What error does this code raise when using agg() with multiple aggregations?

Data Analysis Python

import pandas as pd

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

result = df.agg({'A': 'sum', 'B': ['min', 'max']})

A TypeError: unhashable type: 'list'

B AttributeError: 'list' object has no attribute 'agg'

C ValueError: No axis named B for object type DataFrame

D KeyError: 'B'

💡 Hint

Check if all columns in agg dict exist in DataFrame.

The DataFrame has no column 'B', so trying to aggregate on 'B' raises a KeyError.

🚀 Application

advanced

Using agg() to summarize grouped data

You have this DataFrame with groups. Which option shows the correct agg() output for multiple aggregations per group?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Group': ['X', 'X', 'Y', 'Y'],
    'Value': [10, 20, 30, 40]
})

grouped = df.groupby('Group').agg({'Value': ['min', 'max', 'mean']})
print(grouped)

A

       Value
min  max  mean
Group               
X    10   20  15.0
Y    30   40  35.0

B

       Value
min max mean
Group               
X   10  20  15.0
Y   30  40  35.0

C

       Value
        min max mean
Group               
X         10  20  15.0
Y         30  40  35.0

D

       Value
min max mean
Group               
X  10  20  15.0
Y  30  40  35.0

💡 Hint

Check the multi-level column index format pandas produces for multiple aggregations.

When grouping and aggregating multiple functions, pandas creates a multi-level column index with the original column name and aggregation function names.

🧠 Conceptual

expert

Understanding agg() output shape with overlapping functions

If you run agg() on a DataFrame with columns 'A' and 'B' using {'A': ['sum', 'mean'], 'B': ['mean', 'max']}, how many rows and columns will the output DataFrame have?

A 4 rows and 4 columns

B 3 rows and 2 columns

C 2 rows and 4 columns

D 2 rows and 2 columns

💡 Hint

Rows correspond to unique aggregation functions, columns to original columns.

The output DataFrame has one row per unique aggregation function: 'sum', 'mean', 'max' → 3 unique functions. But since 'sum' applies only to 'A', 'max' only to 'B', the result shows NaN where function not applied. The index will have 3 rows, columns 2.