Sorting Data

Sorting Data

Let me give you the quickest way to organize your data. Sorting in Pandas is intuitive once you know the basics.

Sort by a Single Column

The most common operation — sort by one column:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
        'Age': [25, 30, 35, 28],
        'Score': [88, 92, 79, 95]}
df = pd.DataFrame(data)

print(df.sort_values('Age'))
    

This sorts by Age in ascending order (lowest to highest). Think of it like clicking the sort button in Excel.

Descending Order

Want highest to lowest? Set `ascending=False`:

print(df.sort_values('Score', ascending=False))
    

Multiple Sort Keys

Need to sort by more than one column? Pass a list:

print(df.sort_values(['Age', 'Score'], ascending=[True, False]))
    

This sorts by Age first, then by Score within each age group. The `ascending` list controls each column separately.

Handling Missing Values

One thing that confused me at first was where NaN values end up. Use `na_position` to control this:

print(df.sort_values('Age', na_position='last'))
    

By default, NaN goes to the bottom. You can set it to 'first' if needed.

Key Takeaways

• `sort_values()` sorts by column values
• Use `ascending=False` for descending order
• Pass lists to sort by multiple columns
• `na_position` controls where missing values appear