Selecting columns by name in Pandas

Introduction

We select columns by name to focus on specific data we want to analyze or use. It helps us work only with the information we need.

You want to see only the 'age' and 'salary' columns from a large employee dataset.

You need to create a new table with just the 'name' and 'email' columns for sending newsletters.

You want to analyze sales data but only for the 'region' and 'sales_amount' columns.

You want to remove unnecessary columns before saving a smaller file.

You want to quickly check values in a specific column like 'date' or 'status'.

Syntax

Pandas

df['column_name']
df[['col1', 'col2']]
# df is your DataFrame

Use single brackets [] for one column, double brackets [[]] for multiple columns.

The result for one column is a Series; for multiple columns, it's a DataFrame.

Examples

Selects the column named 'age' as a Series.

Pandas

df['age']

Selects two columns 'name' and 'email' as a new DataFrame.

Pandas

df[['name', 'email']]

Stores selected columns in a new variable and prints the first 5 rows.

Pandas

selected = df[['region', 'sales_amount']]
print(selected.head())

Sample Program

This code creates a small table with people data. It then selects one column 'age' and prints it. Next, it selects two columns 'name' and 'email' and prints that smaller table.

Pandas

import pandas as pd

# Create a sample DataFrame
data = {
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'email': ['[e-mail]', '[e-mail]', '[e-mail]'],
    'salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Select the 'age' column
age_column = df['age']
print('Age column:')
print(age_column)

# Select multiple columns 'name' and 'email'
contact_info = df[['name', 'email']]
print('\nContact info DataFrame:')
print(contact_info)

Important Notes

If you try to select a column name that does not exist, pandas will give an error.

Using double brackets always returns a DataFrame, which is useful if you want to keep the table format.

Summary

Select columns by their names to work with specific data.

Use single brackets [] for one column (returns Series).

Use double brackets [[]] for multiple columns (returns DataFrame).

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