Selecting Columns

Selecting Columns

Let me show you how to grab specific columns from a DataFrame. This is fundamental — you don't always need every column, just the ones that matter.

Single Column with Bracket Notation

The most common way to select a column:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

print(df['Name'])
    

This returns a Series — basically a single column. Think of it like extracting one column from a spreadsheet.

Multiple Columns

Need more than one column? Use double brackets:

print(df[['Name', 'City']])
    

Double brackets return a DataFrame instead of a Series. This matters when you want to keep working with a table structure.

Dot Notation

Here is the thing — you can also use dot notation:

print(df.Name)
    

It works, but I don't recommend it. If your column name has spaces or special characters, it breaks. Stick with bracket notation to be safe. I definitely did when I started, and it saved me from many headaches.

Key Takeaways

• `df['column']` selects a single column (returns Series)
• `df[['col1', 'col2']]` selects multiple columns (returns DataFrame)
• Dot notation works but is risky with special column names
• Bracket notation is the safest and most common approach