iloc for position-based selection in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the first row of the DataFrame using iloc.

Pandas

first_row = df.iloc[[1]]

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

A0

B-1

C2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 instead of 0 to select the first row.

Using negative indices incorrectly.

The first row in pandas DataFrame is at position 0, so df.iloc[0] selects it.

2fill in blank

medium

Complete the code to select the first three rows of the DataFrame using iloc.

Pandas

subset = df.iloc[[1]]

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

A0,3

B1:4

C0:3

D[0,1,2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1:4 which skips the first row.

Using a tuple or list instead of a slice.

Using slice 0:3 with iloc selects rows at positions 0, 1, and 2 (first three rows).

3fill in blank

hard

Fix the error in the code to select the last two rows using iloc.

Pandas

last_two = df.iloc[[1]]

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

A-2:

B[-2:]

Cdf[-2:]

Dlen(df)-2:len(df)

Attempts:

3 left

💡 Hint

Common Mistakes

Using brackets around the slice which causes a TypeError.

Using df[-2:] which is label-based slicing, not iloc.

Using iloc with slice -2: selects the last two rows by position. Brackets or df slicing won't work with iloc.

4fill in blank

hard

Fill both blanks to select rows 1 to 3 and columns 0 to 2 using iloc.

Pandas

subset = df.iloc[[1], [2]]

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

A1:4

B0:3

C[1,2,3]

D[0,1,2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using lists instead of slices for iloc.

Off-by-one errors in slice ranges.

Rows 1 to 3 are selected with slice 1:4 (stop exclusive). Columns 0 to 2 are selected with 0:3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its length only if length is greater than 3.

Pandas

lengths = { [1] : [2] for [3] in words if len([3]) > 3 }

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

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using the length as the key instead of the value.

The comprehension maps each word to its length. 'word' is the variable, 'len(word)' is the value, and 'word' is used in the loop.

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