iloc for position-based selection in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this iloc selection?

Given the DataFrame df below, what is the output of df.iloc[1:3, 0:2]?

Pandas

import pandas as pd

data = {'A': [10, 20, 30, 40], 'B': [5, 15, 25, 35], 'C': [100, 200, 300, 400]}
df = pd.DataFrame(data)

result = df.iloc[1:3, 0:2]
print(result)

A

    A   C
1  20  200
2  30  300

B

    A   B
0  10   5
1  20  15

C

    A   B
1  20  15
2  30  25

D

    B   C
1  15  200
2  25  300

💡 Hint

Remember iloc uses integer positions for rows and columns, starting at 0, and the end index is exclusive.

iloc[1:3, 0:2] selects rows at positions 1 and 2, and columns at positions 0 and 1. So rows with index 1 and 2, columns 'A' and 'B'.

❓ data_output

intermediate

How many rows and columns does this iloc slice return?

Given a DataFrame df with 5 rows and 4 columns, what is the shape of df.iloc[2:5, 1:3]?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame(np.arange(20).reshape(5,4), columns=list('ABCD'))

result = df.iloc[2:5, 1:3].shape
print(result)

A (3, 2)

B (2, 3)

C (3, 3)

D (2, 2)

💡 Hint

Count how many rows and columns are included between the start and end indices.

Rows 2, 3, 4 are included (3 rows). Columns 1 and 2 are included (2 columns). So shape is (3, 2).

🔧 Debug

advanced

What error does this iloc code raise?

Consider the code below. What error will it raise when executed?

Pandas

import pandas as pd

data = {'X': [1, 2, 3], 'Y': [4, 5, 6]}
df = pd.DataFrame(data)

result = df.iloc[3, 1]
print(result)

A KeyError: 3

B IndexError: single positional indexer is out-of-bounds

C ValueError: cannot index with vector containing NA / NaN values

D TypeError: unhashable type: 'slice'

💡 Hint

Check the number of rows and the index used in iloc.

The DataFrame has rows at positions 0,1,2. Accessing position 3 is out of bounds, causing IndexError.

🚀 Application

advanced

Select the correct iloc code to get the last two rows and first column

You have a DataFrame df with 10 rows and 4 columns. Which iloc code correctly selects the last two rows and only the first column?

A df.iloc[-2:, 0:1]

B df.iloc[8:10, 0]

C df.iloc[8:10, 0:1]

D df.iloc[-2:, 0]

💡 Hint

Remember that iloc slicing with a single column index returns a Series, but slicing with a range returns a DataFrame.

df.iloc[-2:, 0:1] selects last two rows and first column as a DataFrame. Other options either return Series or are valid but return Series.

🧠 Conceptual

expert

What is the output of this complex iloc selection?

Given the DataFrame df below, what is the output of df.iloc[[0, 2, 4], [1, 3]]?

Pandas

import pandas as pd
import numpy as np

np.random.seed(0)
df = pd.DataFrame(np.arange(20).reshape(5,4), columns=['W', 'X', 'Y', 'Z'])

result = df.iloc[[0, 2, 4], [1, 3]]
print(result)

A

    X   Z
0   1   3
1   5   7
2   9  11

B

    W   Y
0   0   2
2   8  10
4  16  18

C

    X   Z
1   5   7
3  13  15
4  17  19

D

    X   Z
0   1   3
2   9  11
4  17  19

💡 Hint

iloc with lists selects specific rows and columns by position, not slices.

Rows at positions 0, 2, 4 and columns at positions 1 and 3 correspond to columns 'X' and 'Z'. Values are from the DataFrame accordingly.