iloc for position-based selection in Pandas - Step-by-Step Execution

Concept Flow - iloc for position-based selection

Start with DataFrame

↓

Use iloc with positions

↓

Select rows/columns by integer index

↓

Return subset DataFrame or Series

↓

Use or display selected data

iloc lets you pick rows and columns by their integer position in the DataFrame, not by labels.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
  'A': [10, 20, 30],
  'B': [40, 50, 60]
})

subset = df.iloc[1:3, 0:2]

This code selects rows 1 and 2, and columns 0 and 1 by position from the DataFrame.

Execution Table

Step	Action	Row positions selected	Column positions selected	Resulting DataFrame
1	Create DataFrame	-	-	[[10, 40], [20, 50], [30, 60]]
2	Apply iloc[1:3, 0:2]	Rows 1 and 2	Columns 0 and 1	[[20, 50], [30, 60]]
3	Display subset	-	-	DataFrame with rows index 1 and 2, columns 'A' and 'B'

💡 Selection complete, subset DataFrame returned with specified rows and columns by position.

Variable Tracker

Variable	Start	After iloc selection
df	[[10, 40], [20, 50], [30, 60]]	[[10, 40], [20, 50], [30, 60]]
subset	Not defined	[[20, 50], [30, 60]]

Key Moments - 3 Insights

Why does iloc use numbers instead of column names?

iloc always uses integer positions to select rows and columns, not labels. This is shown in the execution_table step 2 where positions 1:3 and 0:2 are used.

What happens if I use iloc with a position outside the DataFrame size?

It will raise an IndexError because iloc expects valid integer positions within the DataFrame size. This is not shown here but is important to remember.

Does iloc include the stop index in slicing?

No, iloc slicing excludes the stop index, like normal Python slicing. Step 2 shows rows 1 and 2 selected, but not row 3.

Visual Quiz - 3 Questions

Look at the execution_table, what rows does iloc select at step 2?

A Rows 0 and 1

B Rows 2 and 3

C Rows 1 and 2

D Rows 0, 1, and 2

💡 Hint

Check the 'Row positions selected' column in execution_table step 2.

According to variable_tracker, what is the value of 'subset' after iloc selection?

A [[10, 40], [20, 50], [30, 60]]

B [[20, 50], [30, 60]]

C [[40, 50], [60, 70]]

D Not defined

💡 Hint

Look at the 'After iloc selection' value for 'subset' in variable_tracker.

If we change iloc to df.iloc[0:2, 1:2], what columns will be selected?

A Column 1 only

B Column 0 only

C Columns 0 and 1

D Columns 1 and 2

💡 Hint

Recall that iloc slicing excludes the stop index; see concept_flow and execution_table for slicing behavior.

Concept Snapshot

iloc selects rows and columns by integer position.
Syntax: df.iloc[row_start:row_stop, col_start:col_stop]
Slices exclude the stop index.
Returns a DataFrame or Series subset.
Positions start at 0, not labels.

Full Transcript

We start with a DataFrame of 3 rows and 2 columns labeled 'A' and 'B'. Using iloc, we select rows by their integer positions 1 and 2, and columns 0 and 1. iloc uses integer positions, not labels, so the first row is position 0. The slicing excludes the stop index, so 1:3 means rows 1 and 2 only. The resulting subset DataFrame contains the selected rows and columns. Variables 'df' and 'subset' track the original and selected data. Common confusions include iloc using positions not labels, slicing excluding the stop index, and errors if positions are out of range. The visual quiz checks understanding of row selection, variable values, and column slicing behavior.

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