iloc for position-based selection in Pandas

Introduction

We use iloc to pick rows and columns from a table by their number positions, not by their names. This helps when we want to work with data based on where it is in the table.

You want to get the first 5 rows of a data table.

You want to select the 2nd and 3rd columns from a table without knowing their names.

You want to pick a single cell by its row and column number.

You want to slice a part of the table using row and column positions.

Syntax

Pandas

df.iloc[row_selection, column_selection]

row_selection and column_selection can be a single number, a list of numbers, or a slice like start:stop.

Positions start at 0, so the first row or column is at position 0.

Examples

This picks the first row of the table.

Pandas

df.iloc[0]
# Selects the first row

The colon : means all rows, and 1 means the second column.

Pandas

df.iloc[:, 1]
# Selects all rows in the second column

This picks a small block from the table using row and column positions.

Pandas

df.iloc[1:4, 0:2]
# Selects rows 2 to 4 and columns 1 to 2

This picks specific rows and columns by their positions.

Pandas

df.iloc[[0, 2], [1, 3]]
# Selects rows 1 and 3, columns 2 and 4

Sample Program

This program creates a small table with names, ages, and cities. Then it shows how to use iloc to pick the first row, the entire second column, and a small block of rows and columns.

Pandas

import pandas as pd

# Create a simple data table
data = {'Name': ['Anna', 'Bob', 'Cara', 'Dan'],
        'Age': [23, 35, 45, 28],
        'City': ['NY', 'LA', 'Chicago', 'Houston']}
df = pd.DataFrame(data)

# Select the first row
first_row = df.iloc[0]

# Select the second column for all rows
second_column = df.iloc[:, 1]

# Select rows 1 to 3 and columns 0 to 1
subset = df.iloc[1:4, 0:2]

print("First row:")
print(first_row)
print("\nSecond column:")
print(second_column)
print("\nSubset of rows and columns:")
print(subset)

Important Notes

Remember that iloc uses zero-based counting, so the first row or column is at position 0.

If you use a number outside the range of rows or columns, you will get an error.

You can mix single numbers, lists, and slices to select exactly what you want.

Summary

iloc lets you pick data by position, not by name.

Positions start at 0, like counting from the beginning.

You can select rows, columns, or both using numbers, lists, or slices.

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