loc vs iloc mental model in Pandas - Hands-On Comparison

Understanding loc vs iloc in pandas

📖 Scenario: You work in a small shop and keep track of daily sales in a table. You want to learn how to pick data from this table using two different methods: loc and iloc.

🎯 Goal: Learn how to select rows and columns from a pandas DataFrame using loc (label-based) and iloc (position-based) to understand their differences.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Create a variable to select a specific row label

Use loc to select data by label

Use iloc to select data by position

Print the selected data to compare results

💡 Why This Matters

🌍 Real World

In real life, data tables often have labels like dates or names. Knowing how to pick data by label or by position helps you get exactly what you want.

💼 Career

Data scientists and analysts frequently use <code>loc</code> and <code>iloc</code> to filter and select data efficiently for analysis and reporting.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with these exact data: rows labeled "Mon", "Tue", "Wed", columns "apples" and "oranges", and values: Mon: 3, 2; Tue: 5, 1; Wed: 2, 4.

Pandas

import pandas as pd
# Create the DataFrame 'sales' with the given data and labels
# Your code here

Hint

Use pd.DataFrame with a dictionary for columns and index for row labels.

2

Set the row label to select

Create a variable called day_label and set it to the string "Tue" to select Tuesday's sales by label.

Pandas

import pandas as pd
sales = pd.DataFrame({"apples": [3, 5, 2], "oranges": [2, 1, 4]}, index=["Mon", "Tue", "Wed"])
# Create variable 'day_label' with value 'Tue'
# Your code here

Hint

Just assign the string "Tue" to day_label.

3

Select Tuesday's sales using loc and iloc

Use loc with day_label to select Tuesday's sales by label and save it in sales_loc. Use iloc with the integer 1 to select Tuesday's sales by position and save it in sales_iloc.

Pandas

import pandas as pd
sales = pd.DataFrame({"apples": [3, 5, 2], "oranges": [2, 1, 4]}, index=["Mon", "Tue", "Wed"])
day_label = "Tue"
# Use loc with day_label to get sales_loc
# Use iloc with 1 to get sales_iloc
# Your code here

Hint

Use sales.loc[day_label] and sales.iloc[1] to select the row.

4

Print the selected sales data

Print sales_loc and sales_iloc to see the sales for Tuesday selected by label and by position.

Pandas

import pandas as pd
sales = pd.DataFrame({"apples": [3, 5, 2], "oranges": [2, 1, 4]}, index=["Mon", "Tue", "Wed"])
day_label = "Tue"
sales_loc = sales.loc[day_label]
sales_iloc = sales.iloc[1]
# Print sales_loc and sales_iloc
# Your code here

Hint

Use two print() statements, one for each variable.

Practice

1. What is the main difference between loc and iloc in pandas?

easy

A. loc uses labels, iloc uses integer positions

B. loc uses integer positions, iloc uses labels

C. loc selects columns only, iloc selects rows only

D. loc is faster than iloc

Solution

1. Step 1: Understand loc selection method

   loc selects data by row and column labels, meaning it uses the actual names of rows and columns.

2. Step 2: Understand iloc selection method

   iloc selects data by integer positions, starting from 0, ignoring labels.

3. Final Answer:

   loc uses labels, iloc uses integer positions -> Option A

4. Quick Check:

   loc = labels, iloc = positions [OK]

Hint: Remember: loc = labels, iloc = integer positions [OK]

Common Mistakes:

• Confusing labels with positions
• Thinking loc uses positions
• Assuming iloc uses labels

2. Which of the following is the correct syntax to select rows with labels 'a' to 'c' and columns 'x' to 'z' using loc?

easy

A. df.loc['a':'c', 'x':'z']

B. df.iloc['a':'c', 'x':'z']

C. df.loc[0:2, 0:2]

D. df.iloc[0:2, 'x':'z']

Solution

1. Step 1: Identify loc syntax

   loc uses labels, so slicing with labels like 'a':'c' and 'x':'z' is valid.

2. Step 2: Check other options

   Selections using iloc with label slices are invalid because iloc requires integer positions. Using integer slices with loc is incorrect when the labels are strings like 'a', 'c', etc.

3. Final Answer:

   df.loc['a':'c', 'x':'z'] -> Option A

4. Quick Check:

   loc uses labels in slices [OK]

Hint: Use labels inside loc brackets, positions inside iloc [OK]

Common Mistakes:

• Using integer positions with loc
• Using labels with iloc
• Mixing loc and iloc syntax

3. Given the DataFrame:

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

What is the output of df.iloc[1, 0]?

medium

A. 30

B. 40

C. 50

D. 20

Solution

1. Step 1: Understand DataFrame structure

   Rows are labeled 'x','y','z' with values in column 'A' as [10,20,30]. Positions start at 0, so row 1 is 'y', column 0 is 'A'.

2. Step 2: Apply iloc selection

   df.iloc[1, 0] selects row at position 1 ('y') and column at position 0 ('A'), value is 20.

3. Final Answer:

   20 -> Option D

4. Quick Check:

   iloc uses positions: row 1, col 0 = 20 [OK]

Hint: iloc uses zero-based positions, count rows and columns from 0 [OK]

Common Mistakes:

• Using labels instead of positions
• Confusing row and column indices
• Selecting wrong cell by mixing loc and iloc

4. Consider this code:

import pandas as pd
df = pd.DataFrame({'A':[1,2,3]}, index=[10,20,30])
print(df.loc[1])

What error will occur and why?

medium

A. No error, prints 2

B. KeyError because label 1 does not exist in index

C. IndexError because position 1 is out of range

D. TypeError because loc expects string labels

Solution

1. Step 1: Check DataFrame index labels

   Index labels are [10, 20, 30], so label 1 is not present.

2. Step 2: Understand loc behavior

   loc looks for label 1, which does not exist, causing a KeyError.

3. Final Answer:

   KeyError because label 1 does not exist in index -> Option B

4. Quick Check:

   loc needs existing labels, else KeyError [OK]

Hint: loc needs exact labels; missing label causes KeyError [OK]

Common Mistakes:

• Thinking loc uses positions
• Expecting no error when label missing
• Confusing KeyError with IndexError

5. You have a DataFrame with non-numeric row labels:

import pandas as pd
df = pd.DataFrame({'Score':[88, 92, 95]}, index=['John', 'Jane', 'Doe'])

You want to select the second row using loc and iloc. Which code will correctly select the second row and what will be the difference in output?

hard

A. df.loc['Jane'] and df.iloc['Jane'] both select by label 'Jane'; output differs

B. df.loc[1] selects by position 1; df.iloc['Jane'] selects by label 'Jane'; both return the same row

C. df.loc['Jane'] selects by label 'Jane'; df.iloc[1] selects by position 1; both return the same row

D. df.loc[1] and df.iloc[1] both select by position 1; output differs

Solution

1. Step 1: Understand row labels and positions

   Rows are labeled 'John', 'Jane', 'Doe' at positions 0, 1, 2 respectively.

2. Step 2: Use loc and iloc correctly

   df.loc['Jane'] selects row with label 'Jane'. df.iloc[1] selects row at position 1, which is also 'Jane'. Both return the same row.

3. Final Answer:

   df.loc['Jane'] selects by label 'Jane'; df.iloc[1] selects by position 1; both return the same row -> Option C

4. Quick Check:

   loc uses label, iloc uses position; same row here [OK]

Hint: loc needs label, iloc needs position; same row if label matches position [OK]

Common Mistakes:

• Using integer with loc
• Using label with iloc
• Assuming loc and iloc always differ