Selecting multiple columns in Pandas - Mini Project: Build & Apply

Selecting multiple columns

📖 Scenario: You work in a small bookstore. You have a list of books with details like title, author, year, and price. You want to look at only the title and price columns to decide which books to promote.

🎯 Goal: Learn how to select multiple columns from a pandas DataFrame to focus on specific information.

📋 What You'll Learn

Create a pandas DataFrame with book data

Create a list of column names to select

Select multiple columns from the DataFrame using the list

Print the selected columns

💡 Why This Matters

🌍 Real World

Selecting specific columns helps you focus on important data, like prices and titles, when working with large datasets.

💼 Career

Data analysts and scientists often select multiple columns to prepare data for reports, visualizations, or further analysis.

1

Create the book DataFrame

Import pandas as pd and create a DataFrame called books with these exact columns and rows:
'Title': 'The Alchemist', '1984', 'Python 101'
'Author': 'Paulo Coelho', 'George Orwell', 'Michael Driscoll'
'Year': 1988, 1949, 2019
'Price': 10.99, 8.99, 29.99

Pandas

# Your code here

Hint

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

2

Create a list of columns to select

Create a list called cols_to_select containing the exact strings 'Title' and 'Price'.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'Python 101'],
    'Author': ['Paulo Coelho', 'George Orwell', 'Michael Driscoll'],
    'Year': [1988, 1949, 2019],
    'Price': [10.99, 8.99, 29.99]
})

# Create a list called cols_to_select with 'Title' and 'Price'
# Your code here

Hint

Use square brackets to create a list with the two column names as strings.

3

Select the columns from the DataFrame

Create a new DataFrame called selected_books by selecting the columns in cols_to_select from the books DataFrame.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'Python 101'],
    'Author': ['Paulo Coelho', 'George Orwell', 'Michael Driscoll'],
    'Year': [1988, 1949, 2019],
    'Price': [10.99, 8.99, 29.99]
})

cols_to_select = ['Title', 'Price']

# Select the columns in cols_to_select from books and save to selected_books
# Your code here

Hint

Use books[cols_to_select] to select multiple columns by passing the list inside square brackets.

4

Print the selected columns

Print the selected_books DataFrame to see only the Title and Price columns.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'Python 101'],
    'Author': ['Paulo Coelho', 'George Orwell', 'Michael Driscoll'],
    'Year': [1988, 1949, 2019],
    'Price': [10.99, 8.99, 29.99]
})

cols_to_select = ['Title', 'Price']

selected_books = books[cols_to_select]

# Print the selected_books DataFrame
# Your code here

Hint

Use print(selected_books) to display the DataFrame with only the selected columns.

Practice

1. What is the correct way to select multiple columns 'A' and 'B' from a pandas DataFrame df?

easy

A. df.loc['A', 'B']

B. df['A', 'B']

C. df[['A', 'B']]

D. df['A' & 'B']

Solution

1. Step 1: Understand pandas column selection syntax

   To select multiple columns, pandas requires a list of column names inside double brackets.

2. Step 2: Apply correct syntax for multiple columns

   Using df[['A', 'B']] correctly selects columns 'A' and 'B'. Other options are invalid syntax or select rows instead.

3. Final Answer:

   df[['A', 'B']] -> Option C

4. Quick Check:

   Multiple columns = list inside double brackets [OK]

Hint: Use double brackets with a list for multiple columns [OK]

Common Mistakes:

• Using single brackets with comma-separated names
• Using bitwise operators like & between column names
• Using .loc incorrectly for column selection

2. Which of the following is the correct syntax to select columns 'X' and 'Y' from DataFrame df?

easy

A. df.loc[:, 'X', 'Y']

B. df.loc['X', 'Y']

C. df['X', 'Y']

D. df[['X', 'Y']]

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Using double brackets with a list of column names is the standard way: df[['X', 'Y']].

2. Step 2: Check other options for correctness

   df.loc[:, 'X', 'Y'] causes TypeError (invalid arguments to loc); df['X', 'Y'] raises KeyError (tuple not a column); df.loc['X', 'Y'] is incorrect syntax.

3. Final Answer:

   df[['X', 'Y']] -> Option D

4. Quick Check:

   Double brackets with list = correct syntax [OK]

Hint: Remember: double brackets with list select multiple columns [OK]

Common Mistakes:

• Using single brackets with multiple column names
• Confusing .loc row and column selection syntax
• Using tuple instead of list inside brackets

3. Given the DataFrame df below:

   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

What is the output of df[['B', 'C']]?

medium

A. B C
0 4 7
1 5 8
2 6 9

B. A B
0 1 4
1 2 5
2 3 6

C. A C
0 1 7
1 2 8
2 3 9

D. KeyError

Solution

1. Step 1: Identify columns requested

   The code selects columns 'B' and 'C' from the DataFrame.

2. Step 2: Extract columns 'B' and 'C' values

   Columns 'B' and 'C' have values [[4,7],[5,8],[6,9]] for rows 0,1,2 respectively.

3. Final Answer:

   B C 0 4 7 1 5 8 2 6 9 -> Option A

4. Quick Check:

   Selected columns 'B' and 'C' output matches

      B  C
   0  4  7
   1  5  8
   2  6  9

   [OK]

Hint: Selected columns show only those columns with all rows [OK]

Common Mistakes:

• Confusing columns selected and expecting all columns
• Getting KeyError by misspelling column names
• Selecting rows instead of columns

4. What error will occur with this code?

df = pd.DataFrame({'X':[1,2], 'Y':[3,4]})
selected = df['X', 'Y']

medium

A. TypeError

B. KeyError

C. SyntaxError

D. No error, returns both columns

Solution

1. Step 1: Analyze the indexing syntax

   The code uses single brackets with a comma, creating a tuple key ('X', 'Y').

2. Step 2: Understand pandas error type

   This raises KeyError because pandas looks for a column named ('X', 'Y'), which doesn't exist.

3. Final Answer:

   KeyError -> Option B

4. Quick Check:

   Tuple key in single brackets causes KeyError [OK]

Hint: Use list inside double brackets, not comma in single brackets [OK]

Common Mistakes:

• Using comma inside single brackets for multiple columns
• Expecting no error with wrong syntax
• Confusing TypeError with KeyError

5. You have a DataFrame df with columns ['Name', 'Age', 'City', 'Salary']. You want to create a new DataFrame df_selected with only 'Name' and 'Salary'. Which code correctly does this and also resets the index?

hard

A. df_selected = df[['Name', 'Salary']].reset_index(drop=True)

B. df_selected = df.loc[:, ['Name', 'Salary']].reset_index()

C. df_selected = df[['Name', 'Salary']].reset_index()

D. df_selected = df[['Name', 'Salary']].reset_index(drop=False)

Solution

1. Step 1: Select columns 'Name' and 'Salary'

   Use double brackets with list: df[['Name', 'Salary']] to select these columns.

2. Step 2: Reset index without adding old index as column

   Use reset_index(drop=True) to reset index and drop old index column.

3. Step 3: Check other options

   df.loc[:, ['Name', 'Salary']].reset_index() resets index but keeps old index as column. Using reset_index() without drop=True (default behavior) keeps the old index as a new column.

4. Final Answer:

   df_selected = df[['Name', 'Salary']].reset_index(drop=True) -> Option A

5. Quick Check:

   Select columns + reset index drop old index = df_selected = df[['Name', 'Salary']].reset_index(drop=True) [OK]

Hint: Select columns with double brackets, reset index with drop=True [OK]

Common Mistakes:

• Forgetting to use double brackets for multiple columns
• Not using drop=True in reset_index to avoid old index column
• Using .loc without understanding index reset effect