Aggregation with agg() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of agg() with multiple functions

What is the output of the following code snippet using pandas agg() on a DataFrame?

Pandas

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

B

5.6  NaN  naem
0.5  NaN   nim
NaN  0.4   xam
NaN  0.01  mus
B    A

C

       A    B
sum  10.0  26.0
max   4.0   8.0
min   1.0   5.0
mean  2.5   6.5

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

💡 Hint

Remember that agg() returns a DataFrame with the aggregation functions as the index and columns as the original DataFrame columns.

The agg() method applies specified aggregation functions to each column. Here, 'A' gets 'sum' and 'max', 'B' gets 'min' and 'mean'. The result is a DataFrame with these functions as the index and columns 'A' and 'B'. Missing values appear as NaN where functions are not applied.

❓ data_output

intermediate

Number of rows after groupby and agg

Given the DataFrame and code below, how many rows does the resulting DataFrame have?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Category': ['X', 'Y', 'X', 'Z', 'Y', 'Z'],
    'Value': [10, 20, 30, 40, 50, 60]
})
result = df.groupby('Category').agg({'Value': 'sum'})
print(len(result))

A 3

B 6

C 1

D 0

💡 Hint

Groupby groups rows by unique values in 'Category'. Count unique categories.

The groupby groups rows by unique 'Category' values: 'X', 'Y', 'Z'. There are 3 unique categories, so the resulting DataFrame has 3 rows.

🔧 Debug

advanced

Identify the error in agg() usage

What error does the following code raise when run?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
result = df.agg({'A': 'sum', 'B': 'mean'})

A TypeError: unsupported operand type(s)

B KeyError: 'B'

C ValueError: Function not found

D No error, returns a DataFrame

💡 Hint

Check if all keys in the agg dictionary exist as columns in the DataFrame.

The DataFrame has only column 'A'. Specifying 'B' in agg() causes a KeyError because 'B' does not exist.

🚀 Application

advanced

Using agg() with custom functions

Which option correctly uses agg() to compute the range (max - min) of column 'Scores' in the DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({'Scores': [88, 92, 79, 93, 85]})

A df.agg({'Scores': lambda x: x.min() - x.max()})

B df.agg({'Scores': lambda x: sum(x)})

C df.agg({'Scores': lambda x: x.max() - x.min()})

D df.agg({'Scores': 'mean'})

💡 Hint

Range is max value minus min value.

Option C correctly defines a lambda function that calculates the range by subtracting min from max.

🧠 Conceptual

expert

Understanding agg() output shape with mixed aggregations

If you run agg() on a DataFrame with two columns, applying ['sum', 'mean'] to the first column and 'max' to the second column, what will be the shape of the resulting DataFrame?

A A DataFrame with 2 rows and 2 columns

B A DataFrame with 2 rows and 3 columns

C A Series with 3 elements

D A DataFrame with 3 rows and 2 columns

💡 Hint

Count total aggregation functions applied and number of columns.

The first column has 2 functions, the second has 1 function, total 3 rows. Columns remain 2. So shape is (3, 2).