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.

Practice

1. What does df.iloc[2] select from a pandas DataFrame df?

easy

A. The third row of the DataFrame by position

B. The row with index label 2

C. The third column of the DataFrame by position

D. The column with index label 2

Solution

1. Step 1: Understand iloc usage

   iloc selects rows or columns by integer position, starting at 0.

2. Step 2: Interpret df.iloc[2]

   This selects the row at position 2, which is the third row counting from zero.

3. Final Answer:

   The third row of the DataFrame by position -> Option A

4. Quick Check:

   iloc uses position, so 2 means third row [OK]

Hint: Remember: iloc counts rows/columns from zero position [OK]

Common Mistakes:

• Confusing iloc with loc (label-based selection)
• Thinking iloc uses index labels instead of positions
• Mixing row and column selection

2. Which of the following is the correct syntax to select the first 3 rows and first 2 columns using iloc?

easy

A. df.iloc[3, 2]

B. df.iloc[1:3, 1:2]

C. df.iloc[0:2, 0:3]

D. df.iloc[:3, :2]

Solution

1. Step 1: Understand slicing with iloc

   Slices in iloc are start:stop and select positions from start up to but not including stop.

2. Step 2: Apply slice for first 3 rows and 2 columns

   First 3 rows are positions 0,1,2 so slice is :3. First 2 columns are positions 0,1 so slice is :2.

3. Final Answer:

   df.iloc[:3, :2] -> Option D

4. Quick Check:

   Slices exclude stop, so :3 means 0,1,2 [OK]

Hint: Use slice :n to get first n positions [OK]

Common Mistakes:

• Using wrong slice ranges that exclude needed rows/columns
• Confusing row and column order in iloc
• Using single integers instead of slices for multiple rows/columns

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({
  'A': [10, 20, 30],
  'B': [40, 50, 60],
  'C': [70, 80, 90]
})
print(df.iloc[1, 2])

What is the output?

medium

A. 50

B. 30

C. 80

D. 60

Solution

1. Step 1: Identify row and column positions

   iloc[1, 2] means row at position 1 (second row), column at position 2 (third column).

2. Step 2: Find value at row 1, column 2

   Row 1 values: A=20, B=50, C=80. Column 2 is 'C', so value is 80.

3. Final Answer:

   80 -> Option C

4. Quick Check:

   Row 1, Col 2 = 80 [OK]

Hint: Remember iloc[row, column] uses zero-based positions [OK]

Common Mistakes:

• Mixing up row and column positions
• Using index labels instead of positions
• Selecting wrong column number

4. What is wrong with this code snippet?

df.iloc[1:3, 2:1]

It aims to select rows 1 and 2 and columns 2 and 1.

medium

A. iloc cannot slice columns, only rows

B. Column slice 2:1 is empty because stop is less than start

C. Row slice 1:3 is invalid syntax

D. The code will select columns 1 and 2 correctly

Solution

1. Step 1: Analyze column slice 2:1

   Slices select positions from start up to but not including stop. Here start=2, stop=1, which is reversed and results in an empty selection.

2. Step 2: Check row slice 1:3

   Row slice 1:3 selects rows at positions 1 and 2 correctly.

3. Final Answer:

   Column slice 2:1 is empty because stop is less than start -> Option B

4. Quick Check:

   Slices with stop less than start return empty [OK]

Hint: Slice stop must be greater than start to select data [OK]

Common Mistakes:

• Assuming reversed slices select columns in reverse
• Thinking iloc syntax is invalid for slices
• Confusing iloc with loc label slicing

5. You have a DataFrame df with 5 rows and 4 columns. You want to select the last 2 rows and the last 3 columns using iloc. Which code correctly does this?

hard

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

B. df.iloc[2:4, 1:3]

C. df.iloc[-2:, -2:]

D. df.iloc[3:5, 1:4]

Solution

1. Step 1: Understand row positions for last 2 rows

   With 5 rows (positions 0 to 4), last 2 rows are positions 3 and 4, so slice is 3:5 or equivalently -2:.

2. Step 2: Understand column positions for last 3 columns

   With 4 columns (positions 0 to 3), last 3 columns are positions 1, 2, 3, so slice is 1:4 or equivalently -3:.

3. Step 3: Match correct iloc syntax

   Using negative indices, df.iloc[-2:, -3:] would be correct. Among options, df.iloc[2:, 1:] selects rows from position 2 to end (rows 2,3,4) and columns from position 1 to end (columns 1,2,3), which includes the last 2 rows and last 3 columns.

4. Final Answer:

   df.iloc[2:, 1:] -> Option A

5. Quick Check:

   Rows 2-4 and columns 1-3 selected correctly [OK]

Hint: Count positions from zero; slice stop is one past last index [OK]

Common Mistakes:

• Using negative slices incorrectly
• Off-by-one errors in slice ranges
• Confusing row and column slice order