Selecting rows (loc, iloc) in Data Analysis Python - Mini Project: Build & Apply

Selecting Rows with loc and iloc in Pandas

📖 Scenario: You work in a small bookstore. You have a list of books with their details. You want to find specific books by their position or by their label.

🎯 Goal: Learn how to select rows from a table using loc and iloc in pandas.

📋 What You'll Learn

Create a pandas DataFrame with book data

Create variables to select specific rows

Use loc to select rows by label

Use iloc to select rows by position

Print the selected rows

💡 Why This Matters

🌍 Real World

Selecting rows by label or position is common when working with data tables, like filtering customer records or finding specific entries in a dataset.

💼 Career

Data analysts and scientists often need to extract specific rows from large datasets to analyze or visualize data effectively.

1

Create the books DataFrame

Import pandas as pd and create a DataFrame called books with these exact entries: index labels 101, 102, 103, 104; columns 'Title', 'Author', 'Year'; and rows: ['The Alchemist', 'Paulo Coelho', 1988], ['1984', 'George Orwell', 1949], ['To Kill a Mockingbird', 'Harper Lee', 1960], ['The Great Gatsby', 'F. Scott Fitzgerald', 1925].

Data Analysis Python

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

Hint

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

2

Create variables for row selection

Create a variable called label_to_select and set it to 102. Create another variable called position_to_select and set it to 2.

Data Analysis Python

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'To Kill a Mockingbird', 'The Great Gatsby'],
    'Author': ['Paulo Coelho', 'George Orwell', 'Harper Lee', 'F. Scott Fitzgerald'],
    'Year': [1988, 1949, 1960, 1925]
}, index=[101, 102, 103, 104])
# Create 'label_to_select' with value 102
# Create 'position_to_select' with value 2
# Your code here

Hint

Just assign the numbers 102 and 2 to the variables.

3

Select rows using loc and iloc

Use loc on books with label_to_select to select the row by label and save it in row_by_label. Use iloc on books with position_to_select to select the row by position and save it in row_by_position.

Data Analysis Python

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'To Kill a Mockingbird', 'The Great Gatsby'],
    'Author': ['Paulo Coelho', 'George Orwell', 'Harper Lee', 'F. Scott Fitzgerald'],
    'Year': [1988, 1949, 1960, 1925]
}, index=[101, 102, 103, 104])

label_to_select = 102
position_to_select = 2
# Select row by label using loc and save to 'row_by_label'
# Select row by position using iloc and save to 'row_by_position'
# Your code here

Hint

Use books.loc[label_to_select] and books.iloc[position_to_select].

4

Print the selected rows

Print row_by_label and then print row_by_position to show the selected rows.

Data Analysis Python

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'To Kill a Mockingbird', 'The Great Gatsby'],
    'Author': ['Paulo Coelho', 'George Orwell', 'Harper Lee', 'F. Scott Fitzgerald'],
    'Year': [1988, 1949, 1960, 1925]
}, index=[101, 102, 103, 104])

label_to_select = 102
position_to_select = 2

row_by_label = books.loc[label_to_select]
row_by_position = books.iloc[position_to_select]
# Print 'row_by_label'
# Print 'row_by_position'
# Your code here

Hint

Use two print() statements, one for each selected row.

Practice

1. Which of the following statements correctly describes the difference between loc and iloc in pandas?

easy

A. loc selects rows by label, iloc selects rows by position.

B. loc selects columns by position, iloc selects rows by label.

C. loc selects rows by position, iloc selects columns by label.

D. loc and iloc both select rows by position.

Solution

1. Step 1: Understand loc usage

   loc selects rows using the row labels or names, not their numeric position.

2. Step 2: Understand iloc usage

   iloc selects rows based on their integer position, starting at zero.

3. Final Answer:

   loc selects rows by label, iloc selects rows by position. -> Option A

4. Quick Check:

   loc = label, iloc = position [OK]

Hint: Remember: loc = label, iloc = integer position [OK]

Common Mistakes:

• Confusing label with position
• Using iloc with labels
• Assuming loc uses integer positions

2. Which of the following is the correct syntax to select the row with label '2023-01-01' from a DataFrame df using loc?

easy

A. df.loc[2023-01-01]

B. df.iloc[2023-01-01]

C. df.iloc['2023-01-01']

D. df.loc['2023-01-01']

Solution

1. Step 1: Identify correct method for label selection

   To select by label, use loc with the label as a string if it is a string label.

2. Step 2: Check syntax for string label

   The label '2023-01-01' must be in quotes inside loc brackets.

3. Final Answer:

   df.loc['2023-01-01'] -> Option D

4. Quick Check:

   Use quotes for string labels with loc [OK]

Hint: Use quotes around string labels inside loc brackets [OK]

Common Mistakes:

• Omitting quotes around string labels
• Using iloc with labels
• Using loc without brackets

3. Given the DataFrame df below:

import pandas as pd
data = {'Name': ['Anna', 'Bob', 'Cara', 'Dave'], 'Age': [23, 35, 45, 29]}
df = pd.DataFrame(data, index=['a', 'b', 'c', 'd'])

What is the output of df.loc['b']?

medium

A. Name Bob
Age 35
Name: 1, dtype: object

B. Name Cara
Age 45
Name: c, dtype: object

C. Name Bob
Age 35
Name: b, dtype: object

D. Error: KeyError

Solution

1. Step 1: Understand DataFrame index labels

   The DataFrame has index labels 'a', 'b', 'c', 'd'. The row with label 'b' corresponds to Bob, age 35.

2. Step 2: Use loc to select row by label

   df.loc['b'] returns the row with label 'b' as a Series with Name and Age values.

3. Final Answer:

   Name Bob Age 35 Name: b, dtype: object -> Option C

4. Quick Check:

   loc selects row 'b' with Bob's data [OK]

Hint: loc uses index labels exactly as given [OK]

Common Mistakes:

• Using iloc instead of loc
• Confusing label 'b' with position 1
• Expecting a DataFrame instead of Series

4. Consider this code snippet:

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

What is wrong with this code if the intention was to select the second row by position?

medium

A. It selects the row with label 2, not the second row by position.

B. It raises a KeyError because 2 is not a label.

C. It correctly selects the second row by position.

D. It raises a TypeError because loc needs a string.

Solution

1. Step 1: Check DataFrame index labels

   The DataFrame index labels are [1, 2, 3]. Using loc[2] selects the row with label 2, not the second row by position.

2. Step 2: Understand difference between label and position

   The second row by position is at index 1 (zero-based), so iloc[1] should be used to select by position.

3. Final Answer:

   It selects the row with label 2, not the second row by position. -> Option A

4. Quick Check:

   loc uses labels, iloc uses positions [OK]

Hint: Use iloc for position, loc for label [OK]

Common Mistakes:

• Using loc to select by position
• Confusing index labels with positions
• Expecting loc to be zero-based

5. You have a DataFrame df with a non-numeric index:

import pandas as pd
data = {'Score': [88, 92, 95, 70]}
labels = ['a', 'b', 'c', 'd']
df = pd.DataFrame(data, index=labels)

How would you select the first two rows by position and the rows labeled 'b' and 'c' by label, then combine these selections into one DataFrame?

hard

A. df.loc[:2] + df.loc[['b', 'c']]

B. pd.concat([df.iloc[:2], df.loc[['b', 'c']]])

C. df.iloc[['a', 'b']] + df.loc[1:2]

D. df.loc[['a', 'b']] + df.iloc[1:2]

Solution

1. Step 1: Select first two rows by position

   Use df.iloc[:2] to select rows at positions 0 and 1.

2. Step 2: Select rows labeled 'b' and 'c'

   Use df.loc[['b', 'c']] to select rows by their labels.

3. Step 3: Combine selections into one DataFrame

   Use pd.concat to join the two selections vertically.

4. Final Answer:

   pd.concat([df.iloc[:2], df.loc[['b', 'c']]]) -> Option B

5. Quick Check:

   Combine iloc and loc selections with concat [OK]

Hint: Use pd.concat to join iloc and loc selections [OK]

Common Mistakes:

• Trying to add DataFrames with + operator
• Mixing labels and positions incorrectly
• Using loc with positions or iloc with labels