iloc for position-based selection in Pandas - Mini Project: Build & Apply

Using iloc for Position-Based Selection in pandas

📖 Scenario: You work in a small bookstore. You have a list of books with their prices and stock counts. You want to select specific rows and columns by their position to analyze the data.

🎯 Goal: Learn how to use iloc in pandas to select rows and columns by their integer position.

📋 What You'll Learn

Create a pandas DataFrame with book data

Create a variable for row and column positions

Use iloc to select data by position

Print the selected data

💡 Why This Matters

🌍 Real World

Selecting data by position is useful when you do not know the exact labels or want to quickly access data by order, such as recent sales or top products.

💼 Career

Data analysts and scientists often use iloc to slice and dice data efficiently when working with large datasets.

1

Create the book DataFrame

Import pandas as pd and create a DataFrame called books with these exact data: columns 'Title', 'Price', 'Stock' and rows: 'Python Basics' priced 25 with 10 in stock, 'Data Science' priced 40 with 5 in stock, and 'Machine Learning' priced 50 with 2 in stock.

Pandas

# Import pandas and create the DataFrame books with the given data
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of column data.

2

Set row and column positions

Create two variables: row_pos and col_pos. Set row_pos to 1 to select the second row, and col_pos to 2 to select the third column.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Python Basics', 'Data Science', 'Machine Learning'],
    'Price': [25, 40, 50],
    'Stock': [10, 5, 2]
})

# Create variables row_pos and col_pos with values 1 and 2
# Your code here

Hint

Remember, Python counting starts at 0, so 1 means the second row.

3

Select data using iloc

Use iloc on the books DataFrame with row_pos and col_pos to select the value at that row and column position. Store the result in a variable called selected_value.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Python Basics', 'Data Science', 'Machine Learning'],
    'Price': [25, 40, 50],
    'Stock': [10, 5, 2]
})

row_pos = 1
col_pos = 2

# Use iloc with row_pos and col_pos to get the value
# Your code here

Hint

Use books.iloc[row_pos, col_pos] to select by position.

4

Print the selected value

Print the variable selected_value to display the stock count of the second book.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Python Basics', 'Data Science', 'Machine Learning'],
    'Price': [25, 40, 50],
    'Stock': [10, 5, 2]
})

row_pos = 1
col_pos = 2

selected_value = books.iloc[row_pos, col_pos]

# Print the selected_value
# Your code here

Hint

Use print(selected_value) to show the result.

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