Selecting multiple columns in Pandas

Introduction

We select multiple columns to look at or work with specific parts of a table of data. This helps us focus on the information we need.

When you want to see only a few columns from a big table.

When you need to analyze or change data in certain columns.

When you want to create a smaller table with just the columns you need.

When preparing data for charts or reports that use specific columns.

Syntax

Pandas

df[["column1", "column2", "column3"]]

Use double square brackets [[ ]] to select multiple columns.

The columns inside must be in a list (inside [ ]).

Examples

Selects the columns named 'Name' and 'Age' from the DataFrame.

Pandas

df[["Name", "Age"]]

Selects three columns: 'City', 'Salary', and 'Department'.

Pandas

df[["City", "Salary", "Department"]]

Sample Program

This code creates a table with four columns. Then it selects only the 'Name' and 'City' columns and prints them.

Pandas

import pandas as pd

# Create a simple DataFrame
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"],
    "Salary": [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Select multiple columns
selected_columns = df[["Name", "City"]]
print(selected_columns)

Important Notes

Remember to use double brackets [[ ]] when selecting multiple columns, not single brackets.

If you use single brackets with a list, it will cause an error.

The order of columns in the list decides the order in the result.

Summary

Select multiple columns by passing a list of column names inside double brackets.

This helps you focus on the data you want to see or work with.

Always check your column names to avoid errors.