iloc for position-based selection in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does iloc do in pandas?

iloc selects rows and columns by their integer position in the DataFrame, not by label.

beginner

How do you select the first row of a DataFrame using iloc?

Use df.iloc[0] to select the first row by position.

intermediate

How to select the first 3 rows and first 2 columns using iloc?

Use df.iloc[0:3, 0:2] to select rows 0 to 2 and columns 0 to 1.

intermediate

What happens if you use a negative index with iloc?

Negative indices count from the end. For example, df.iloc[-1] selects the last row.

intermediate

Can iloc accept lists or arrays for selection?

Yes, you can pass lists or arrays of integer positions to select specific rows or columns, e.g., df.iloc[[0,2,4]].

What does df.iloc[2, 3] select?

A Rows 2 to 3

B Row with label 2 and column with label 3

C Second row and third column by position

D Third row and fourth column by position

iloc uses zero-based integer positions. So 2 means third row, 3 means fourth column.

How to select all rows but only the first column using iloc?

A df.iloc[:, 0]

B df.iloc[0, :]

C df.iloc[0]

D df.iloc[:, 1]

: means all rows, 0 means first column by position.

What will df.iloc[1:4] return?

A Rows at positions 1, 2, and 3

B Rows at positions 1 to 4 inclusive

C Rows with labels 1 to 4

D Columns 1 to 4

Slicing with iloc excludes the stop index, so rows at positions 1, 2, and 3 are selected.

Which of these is NOT a valid iloc indexer?

A An integer

B A string label

C A boolean mask

D A list of integers

iloc only accepts integer positions, lists of integers, slices, but NOT string labels.

What does df.iloc[-2] select?

A Second row

B Second column

C Second last row

D Last row

Negative indices count from the end. -2 means second last row.

Explain how to use iloc to select specific rows and columns by position in a DataFrame.

Think about selecting by counting rows and columns from zero.

Describe the difference between loc and iloc in pandas.

One uses names, the other uses counting.

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