Selecting columns by name in Pandas - Mini Project: Build & Apply

Selecting columns by name

📖 Scenario: You work in a small shop and keep track of sales data in a table. You want to look at only some parts of the data to understand sales better.

🎯 Goal: Learn how to select specific columns by their names from a table of data using pandas.

📋 What You'll Learn

Create a pandas DataFrame with given sales data

Create a list of column names to select

Select columns from the DataFrame using the list of column names

Print the selected columns

💡 Why This Matters

🌍 Real World

Selecting specific columns helps you focus on important parts of your data, like sales numbers or product names, without extra details.

💼 Career

Data scientists often select columns by name to prepare data for analysis or visualization, making their work clearer and faster.

1

Create the sales data table

Create a pandas DataFrame called sales_data with these exact columns and rows:
Product: ['Apple', 'Banana', 'Carrot', 'Dates']
Price: [1.2, 0.5, 0.8, 3.0]
Quantity: [10, 20, 15, 7]
Date: ['2024-06-01', '2024-06-01', '2024-06-02', '2024-06-02']

Pandas

import pandas as pd
# Create the DataFrame sales_data with the given data
# 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 columns_to_select with these exact column names: 'Product' and 'Quantity'

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Dates'],
    'Price': [1.2, 0.5, 0.8, 3.0],
    'Quantity': [10, 20, 15, 7],
    'Date': ['2024-06-01', '2024-06-01', '2024-06-02', '2024-06-02']
})
# Create the list columns_to_select with 'Product' and 'Quantity'
# Your code here

Hint

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

3

Select columns from the DataFrame

Create a new DataFrame called selected_data by selecting the columns in columns_to_select from sales_data

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Dates'],
    'Price': [1.2, 0.5, 0.8, 3.0],
    'Quantity': [10, 20, 15, 7],
    'Date': ['2024-06-01', '2024-06-01', '2024-06-02', '2024-06-02']
})
columns_to_select = ['Product', 'Quantity']
# Select the columns from sales_data using columns_to_select
# Your code here

Hint

Use square brackets with the list of column names to select columns from the DataFrame.

4

Print the selected columns

Print the selected_data DataFrame to see only the selected columns

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Dates'],
    'Price': [1.2, 0.5, 0.8, 3.0],
    'Quantity': [10, 20, 15, 7],
    'Date': ['2024-06-01', '2024-06-01', '2024-06-02', '2024-06-02']
})
columns_to_select = ['Product', 'Quantity']
selected_data = sales_data[columns_to_select]
# Print the selected_data DataFrame
# Your code here

Hint

Use print(selected_data) to show the selected columns.

Practice

1.

Which syntax correctly selects a single column named 'Age' from a pandas DataFrame df?

easy

A. df.Age[]

B. df['Age']

C. df[['Age']]

D. df('Age')

Solution

1. Step 1: Understand single column selection syntax

   Using single brackets with the column name as a string selects one column and returns a Series.

2. Step 2: Compare options

   df['Age'] is the correct syntax. Double brackets return a DataFrame, and other options are invalid syntax.

3. Final Answer:

   df['Age'] -> Option B

4. Quick Check:

   Single brackets select one column [OK]

Hint: Use single brackets for one column, double for many [OK]

Common Mistakes:

• Using double brackets for single column returns DataFrame, not Series
• Trying to use parentheses instead of brackets
• Using dot notation with brackets incorrectly

2.

Which of the following is the correct syntax to select multiple columns 'Name' and 'Salary' from a DataFrame df?

easy

A. df[['Name', 'Salary']]

B. df['Name', 'Salary']

C. df['Name' & 'Salary']

D. df.Name, df.Salary

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Multiple columns must be passed as a list inside double brackets.

2. Step 2: Evaluate options

   Only df[['Name', 'Salary']] correctly uses a list inside double brackets. Others are invalid or return separate Series.

3. Final Answer:

   df[['Name', 'Salary']] -> Option A

4. Quick Check:

   Double brackets + list select multiple columns [OK]

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

Common Mistakes:

• Using single brackets with multiple column names separated by comma
• Using bitwise AND (&) instead of list
• Trying to select columns separately without combining

3.

Given the DataFrame df below, what is the output of df[['City', 'Age']]?

import pandas as pd
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30], 'City': ['NY', 'LA']}
df = pd.DataFrame(data)

medium

A. City Name
0 NY Alice
1 LA Bob

B. Name Age
0 Alice 25
1 Bob 30

C. City Age
0 NY 25
1 LA 30

D. Age City
0 25 NY
1 30 LA

Solution

1. Step 1: Understand the selection

   The code selects columns 'City' and 'Age' in that order from the DataFrame.

2. Step 2: Check the output order and values

   The output DataFrame has columns 'City' then 'Age' with values from original data.

3. Final Answer:

   City Age 0 NY 25 1 LA 30 -> Option C

4. Quick Check:

   Selected columns match output order [OK]

Hint: Output columns appear in the order listed inside double brackets [OK]

Common Mistakes:

• Mixing column order in output
• Selecting wrong columns
• Confusing Series output with DataFrame output

4.

What is wrong with this code to select columns 'A' and 'B' from DataFrame df?

df['A', 'B']

medium

A. It raises a KeyError because tuple ('A', 'B') is not a column

B. It returns a DataFrame with columns 'A' and 'B'

C. It returns a Series with combined values of 'A' and 'B'

D. It raises a SyntaxError due to wrong brackets

Solution

1. Step 1: Analyze the indexing syntax

   Using single brackets with multiple comma-separated strings creates a tuple key, not a list.

2. Step 2: Understand pandas column selection behavior

   Since ('A', 'B') is a tuple, pandas looks for a column with that exact tuple name, which does not exist, causing KeyError.

3. Final Answer:

   It raises a KeyError because tuple ('A', 'B') is not a column -> Option A

4. Quick Check:

   Single brackets with comma create tuple key, causing KeyError [OK]

Hint: Use double brackets with list for multiple columns to avoid KeyError [OK]

Common Mistakes:

• Using single brackets with multiple column names separated by comma
• Expecting a DataFrame but getting error
• Confusing tuple with list in indexing

5.

You have a DataFrame df with columns 'Name', 'Age', 'Salary', and 'Department'. You want to create a new DataFrame with only 'Name' and 'Salary', but also want to rename 'Salary' to 'Income'. Which code correctly does this?

hard

A. new_df = df[['Name', 'Salary']].rename(columns=['Name', 'Income'])

B. new_df = df['Name', 'Salary'].rename(columns={'Salary': 'Income'})

C. new_df = df[['Name', 'Salary']].rename({'Salary': 'Income'})

D. new_df = df[['Name', 'Salary']].rename(columns={'Salary': 'Income'})

Solution

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

   Use double brackets with a list: df[['Name', 'Salary']] to get a DataFrame with those columns.

2. Step 2: Rename 'Salary' to 'Income'

   Use rename(columns={'Salary': 'Income'}) to rename the column correctly.

3. Final Answer:

   new_df = df[['Name', 'Salary']].rename(columns={'Salary': 'Income'}) -> Option D

4. Quick Check:

   Select columns then rename with columns dict [OK]

Hint: Select columns first, then rename with columns dict [OK]

Common Mistakes:

• Using single brackets for multiple columns
• Passing rename argument without 'columns=' keyword
• Passing list instead of dict to rename