How to Sort a CSV File by Multiple Columns in Python

Sorting CSV data by multiple columns is a common task in data analysis: for example, sorting sales records first by region and then by revenue within each region, or ordering student data by grade and then by name. Python's Pandas library makes this straightforward by loading the CSV into a DataFrame and using the sort_values() method.

This guide covers sorting by one or more columns with mixed ascending and descending orders.

Understanding `sort_values()` Syntax

DataFrame.sort_values(
    by,
    axis=0,
    ascending=True,
    inplace=False,
    kind='quicksort',
    na_position='last',
    ignore_index=False
)

Parameter	Description	Default
`by`	Column name or list of column names to sort by	Required
`axis`	`0` to sort rows	`0`
`ascending`	`True` for ascending, `False` for descending. Pass a list for mixed orders	`True`
`inplace`	If `True`, modifies the DataFrame directly	`False`
`na_position`	Where to place `NaN` values: `'first'` or `'last'`	`'last'`
`ignore_index`	If `True`, resets the index after sorting	`False`

Sample CSV File

The examples use a CSV file called products.csv with the following content:

products.csv
Product,Category,Price,Rating
Laptop,Electronics,999.99,4.5
Mouse,Electronics,29.99,4.2
Desk,Furniture,249.99,4.7
Chair,Furniture,199.99,4.3
Keyboard,Electronics,79.99,4.6
Bookshelf,Furniture,149.99,4.1
Monitor,Electronics,399.99,4.8
Lamp,Furniture,49.99,3.9

Sorting by a Single Column

Start by loading the CSV and sorting by one column:

import pandas as pd

data = pd.read_csv('products.csv')

# Sort by Price in ascending order
sorted_data = data.sort_values(by='Price')
print(sorted_data)

Output:

     Product     Category   Price  Rating
    Mouse  Electronics   29.99     4.2
     Lamp    Furniture   49.99     3.9
 Keyboard  Electronics   79.99     4.6
Bookshelf    Furniture  149.99     4.1
    Chair    Furniture  199.99     4.3
     Desk    Furniture  249.99     4.7
  Monitor  Electronics  399.99     4.8
   Laptop  Electronics  999.99     4.5

tip

When sorting by a single column, you can pass the column name directly as a string: sort_values(by='Price'). For multiple columns, pass a list: sort_values(by=['col1', 'col2']).

Sorting by Multiple Columns (Ascending)

To sort by more than one column, pass a list of column names. The DataFrame is sorted by the first column in the list, and ties are broken by the second column, and so on:

import pandas as pd

data = pd.read_csv('products.csv')

# Sort by Category first, then by Price within each category
sorted_data = data.sort_values(by=['Category', 'Price'], ignore_index=True)
print(sorted_data)

Output:

     Product     Category   Price  Rating
    Mouse  Electronics   29.99     4.2
 Keyboard  Electronics   79.99     4.6
  Monitor  Electronics  399.99     4.8
   Laptop  Electronics  999.99     4.5
     Lamp    Furniture   49.99     3.9
Bookshelf    Furniture  149.99     4.1
    Chair    Furniture  199.99     4.3
     Desk    Furniture  249.99     4.7

Products are grouped by Category (Electronics first, then Furniture), and within each category, they are sorted by Price from lowest to highest.

Sorting with Mixed Ascending and Descending Orders

Pass a list of booleans to the ascending parameter to control the direction for each column independently:

import pandas as pd

data = pd.read_csv('products.csv')

# Sort by Category ascending, then by Price descending within each category
sorted_data = data.sort_values(
    by=['Category', 'Price'],
    ascending=[True, False],
    ignore_index=True
)
print(sorted_data)

Output:

     Product     Category   Price  Rating
   Laptop  Electronics  999.99     4.5
  Monitor  Electronics  399.99     4.8
 Keyboard  Electronics   79.99     4.6
    Mouse  Electronics   29.99     4.2
     Desk    Furniture  249.99     4.7
    Chair    Furniture  199.99     4.3
Bookshelf    Furniture  149.99     4.1
     Lamp    Furniture   49.99     3.9

Within each category, products are now sorted from most expensive to least expensive.

Sorting by Three or More Columns

You can sort by as many columns as needed. Each additional column acts as a further tiebreaker:

import pandas as pd

data = pd.read_csv('products.csv')

# Sort by Category (asc), Rating (desc), then Price (asc)
sorted_data = data.sort_values(
    by=['Category', 'Rating', 'Price'],
    ascending=[True, False, True],
    ignore_index=True
)
print(sorted_data)

Output:

     Product     Category   Price  Rating
  Monitor  Electronics  399.99     4.8
 Keyboard  Electronics   79.99     4.6
   Laptop  Electronics  999.99     4.5
    Mouse  Electronics   29.99     4.2
     Desk    Furniture  249.99     4.7
    Chair    Furniture  199.99     4.3
Bookshelf    Furniture  149.99     4.1
     Lamp    Furniture   49.99     3.9

Within Electronics, products are sorted by highest rating first. If two products had the same rating, the cheaper one would appear first.

Saving the Sorted Data Back to CSV

After sorting, you can write the result to a new CSV file:

import pandas as pd

data = pd.read_csv('products.csv')

sorted_data = data.sort_values(
    by=['Category', 'Price'],
    ascending=[True, True],
    ignore_index=True
)

# Save to a new CSV file
sorted_data.to_csv('products_sorted.csv', index=False)
print("Sorted CSV saved successfully.")

note

Setting index=False prevents the DataFrame index from being written as an extra column in the CSV.

Handling NaN Values During Sorting

CSV files often contain missing data. The na_position parameter controls where NaN values are placed:

import pandas as pd
import numpy as np

data = pd.DataFrame({
    'Product': ['Laptop', 'Mouse', 'Desk', 'Chair', 'Keyboard'],
    'Category': ['Electronics', 'Electronics', 'Furniture', None, 'Electronics'],
    'Price': [999.99, 29.99, np.nan, 199.99, 79.99]
})

# NaN values placed at the beginning
sorted_first = data.sort_values(by='Price', na_position='first')
print("NaN first:")
print(sorted_first)

# NaN values placed at the end (default)
sorted_last = data.sort_values(by='Price', na_position='last')
print("\nNaN last:")
print(sorted_last)

Output:

NaN first:
    Product     Category   Price
    Desk    Furniture     NaN
   Mouse  Electronics   29.99
Keyboard  Electronics   79.99
   Chair         None  199.99
  Laptop  Electronics  999.99

NaN last:
    Product     Category   Price
   Mouse  Electronics   29.99
Keyboard  Electronics   79.99
   Chair         None  199.99
  Laptop  Electronics  999.99
    Desk    Furniture     NaN

Common Mistake: Mismatched `ascending` List Length

When sorting by multiple columns, the ascending parameter must have the same number of elements as the by parameter:

import pandas as pd

data = pd.read_csv('products.csv')

# WRONG: 2 columns but only 1 ascending value (as a list)
try:
    sorted_data = data.sort_values(
        by=['Category', 'Price'],
        ascending=[True]  # Should have 2 values
    )
except ValueError as e:
    print(f"ValueError: {e}")

Output:

ValueError: Length of ascending (1) != length of by (2)

The correct approach:

# CORRECT: match the number of ascending values to the number of columns
sorted_data = data.sort_values(
    by=['Category', 'Price'],
    ascending=[True, False]  # One value per column
)

warning

If you want the same direction for all columns, pass a single boolean (not a list): ascending=True or ascending=False. Use a list only when columns need different sort directions.

Complete Workflow: Load, Sort, and Save

Here is a complete example combining all steps:

import pandas as pd

# Step 1: Load the CSV
data = pd.read_csv('products.csv')
print("Original data:")
print(data)

# Step 2: Sort by multiple columns
sorted_data = data.sort_values(
    by=['Category', 'Rating', 'Price'],
    ascending=[True, False, True],
    ignore_index=True
)
print("\nSorted data:")
print(sorted_data)

# Step 3: Save the sorted data
sorted_data.to_csv('products_sorted.csv', index=False)
print("\nSaved to products_sorted.csv")

Output:

Original data:
     Product     Category   Price  Rating
   Laptop  Electronics  999.99     4.5
    Mouse  Electronics   29.99     4.2
     Desk    Furniture  249.99     4.7
    Chair    Furniture  199.99     4.3
 Keyboard  Electronics   79.99     4.6
Bookshelf    Furniture  149.99     4.1
  Monitor  Electronics  399.99     4.8
     Lamp    Furniture   49.99     3.9

Sorted data:
     Product     Category   Price  Rating
  Monitor  Electronics  399.99     4.8
 Keyboard  Electronics   79.99     4.6
   Laptop  Electronics  999.99     4.5
    Mouse  Electronics   29.99     4.2
     Desk    Furniture  249.99     4.7
    Chair    Furniture  199.99     4.3
Bookshelf    Furniture  149.99     4.1
     Lamp    Furniture   49.99     3.9

Saved to products_sorted.csv

Quick Reference

Goal	Code
Sort by one column (ascending)	`df.sort_values(by='col')`
Sort by one column (descending)	`df.sort_values(by='col', ascending=False)`
Sort by multiple columns (same direction)	`df.sort_values(by=['col1', 'col2'])`
Sort by multiple columns (mixed directions)	`df.sort_values(by=['col1', 'col2'], ascending=[True, False])`
Sort and reset index	`df.sort_values(by='col', ignore_index=True)`
Sort and modify original DataFrame	`df.sort_values(by='col', inplace=True)`
Control NaN placement	`df.sort_values(by='col', na_position='first')`
Save sorted DataFrame to CSV	`df.to_csv('output.csv', index=False)`

Sorting a CSV file by multiple columns in Python is a three-step process: load with read_csv(), sort with sort_values(), and optionally save with to_csv(). The key to multi-column sorting is providing a list of column names in priority order and matching ascending values for each column when mixed directions are needed.

Understanding sort_values() Syntax​

Sample CSV File​

Sorting by a Single Column​

Sorting by Multiple Columns (Ascending)​

Sorting with Mixed Ascending and Descending Orders​

Sorting by Three or More Columns​

Saving the Sorted Data Back to CSV​

Handling NaN Values During Sorting​

Common Mistake: Mismatched ascending List Length​

The correct approach:​

Complete Workflow: Load, Sort, and Save​

Quick Reference​

Table of Contents

Understanding `sort_values()` Syntax

Sample CSV File

Sorting by a Single Column

Sorting by Multiple Columns (Ascending)

Sorting with Mixed Ascending and Descending Orders

Sorting by Three or More Columns

Saving the Sorted Data Back to CSV

Handling NaN Values During Sorting

Common Mistake: Mismatched `ascending` List Length

The correct approach:

Complete Workflow: Load, Sort, and Save

Quick Reference