loc vs iloc mental model in Pandas - Visual Side-by-Side Comparison

Concept Flow - loc vs iloc mental model

Start with DataFrame

↓

Use loc: label-based

↓

Select rows/cols by labels

↓

Use iloc: position-based

↓

Select rows/cols by integer index

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Return selected data

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End

The flow shows starting with a DataFrame, then choosing loc for label-based selection or iloc for position-based selection, and finally returning the selected data.

Execution Sample

Pandas

import pandas as pd

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

print(df.loc['y'])
print(df.iloc[1])

This code creates a DataFrame with labels x,y,z and selects the row labeled 'y' using loc and the row at position 1 using iloc.

Execution Table

Step	Operation	Input	Selection Method	Result
1	Create DataFrame	{'A':[10,20,30],'B':[40,50,60]}, index=['x','y','z']	N/A	DataFrame with rows x,y,z and columns A,B
2	Select row with loc	label='y'	loc (label-based)	Row with index 'y': A=20, B=50
3	Select row with iloc	position=1	iloc (position-based)	Row at position 1: A=20, B=50
4	Select row with loc	label='z'	loc (label-based)	Row with index 'z': A=30, B=60
5	Select row with iloc	position=2	iloc (position-based)	Row at position 2: A=30, B=60
6	Select row with loc	label='x'	loc (label-based)	Row with index 'x': A=10, B=40
7	Select row with iloc	position=0	iloc (position-based)	Row at position 0: A=10, B=40
8	Try loc with non-existing label	label='a'	loc (label-based)	KeyError (label 'a' not found)
9	Try iloc with out-of-range position	position=5	iloc (position-based)	IndexError (position 5 out of range)
10	End	N/A	N/A	Selection complete

💡 Execution stops after all selections and errors are demonstrated.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7
df	undefined	DataFrame with index x,y,z and columns A,B	Same	Same	Same	Same	Same	Same
selected_row	undefined	undefined	{A:20, B:50} (loc 'y')	{A:20, B:50} (iloc 1)	{A:30, B:60} (loc 'z')	{A:30, B:60} (iloc 2)	{A:10, B:40} (loc 'x')	{A:10, B:40} (iloc 0)

Key Moments - 3 Insights

Why does df.loc['y'] and df.iloc[1] return the same row?

Because the label 'y' is at position 1 in the DataFrame index, so loc with label 'y' and iloc with position 1 select the same row (see execution_table rows 2 and 3).

What happens if you use loc with a label that does not exist?

A KeyError occurs because loc expects exact labels present in the index (see execution_table row 8).

What happens if you use iloc with a position outside the DataFrame range?

An IndexError occurs because iloc expects integer positions within the DataFrame size (see execution_table row 9).

Visual Quiz - 3 Questions

Look at the execution table at step 3. What row does df.iloc[1] select?

A Row with index 'z' (A=30, B=60)

B Row with index 'x' (A=10, B=40)

C Row with index 'y' (A=20, B=50)

D KeyError

💡 Hint

Check execution_table row 3 under Result column.

At which step does using loc cause an error due to a missing label?

A Step 8

B Step 9

C Step 7

D Step 10

💡 Hint

Look for KeyError in execution_table rows.

If you want to select the first row by position, which method and index do you use?

A loc with label 'x'

B iloc with position 0

C loc with label 'y'

D iloc with position 1

💡 Hint

Check variable_tracker for iloc position 0 and corresponding row.

Concept Snapshot

pandas loc vs iloc quick guide:
- loc selects by label (index names)
- iloc selects by integer position (0-based)
- loc raises KeyError if label missing
- iloc raises IndexError if position out of range
- Use loc for named indexes, iloc for position-based access

Full Transcript

This visual execution shows how pandas loc and iloc select rows from a DataFrame. We start with a DataFrame with index labels x,y,z. Using loc with label 'y' selects the row labeled 'y'. Using iloc with position 1 selects the second row by position, which is also 'y'. We see that loc uses labels and iloc uses integer positions. Errors occur if loc uses a missing label or iloc uses an invalid position. Variable tracking shows the selected rows at each step. This helps understand the difference between label-based and position-based selection in pandas.