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How to Skip the Header Row when Reading CSV Files in Python

CSV (Comma Separated Values) files often have a header row containing column names. When processing the data, you usually want to skip this header row.

This guide demonstrates several methods for skipping the header row when reading CSV files in Python, using the csv module, focusing on csv.reader and csv.DictReader.

The most straightforward and Pythonic way to skip the header row when using csv.reader is to call the built-in next() function on the reader object before you start looping:

import csv

with open('employees.csv', 'r', encoding='utf-8') as csv_file:
    csv_reader = csv.reader(csv_file)  # Create the reader object

    next(csv_reader)  # Skip the first row (header)

    for row in csv_reader:
        print(row)

Output:

['Alice', 'Smith', '01/21/1995 14:32:44.042010']
['Carl', 'Lemon', '06/11/1994 15:52:43.016000']
['Tom', 'Nolan', '01/07/1999 12:34:56.025000']
employees.csv
first_name,last_name,date
Alice,Smith,01/21/1995 14:32:44.042010
Carl,Lemon,06/11/1994 15:52:43.016000
Tom,Nolan,01/07/1999 12:34:56.025000
  • csv.reader(csv_file): Creates a CSV reader object that will iterate over lines in the given CSV file.
  • next(csv_reader): This is the key line. It advances the iterator to the next row, effectively skipping the header. The next() function consumes the first row, so the subsequent for loop starts from the second row (the first data row).
  • The code will automatically handle the situation where the file is empty, by returning None.

Skipping the Header with csv.reader and islice()​

Another way to skip the header row when using csv.reader is to use the islice function:

import csv
from itertools import islice

with open('employees.csv', 'r', encoding='utf-8') as csv_file:
    csv_reader = csv.reader(csv_file)
    for row in islice(csv_reader, 1, None): # Skip the first row (header)
        print(row)

Output:

['Alice', 'Smith', '01/21/1995 14:32:44.042010']
['Carl', 'Lemon', '06/11/1994 15:52:43.016000']
['Tom', 'Nolan', '01/07/1999 12:34:56.025000']
  • The islice function skips the first line and returns an iterator with the other elements.
  • The second argument is the start index, and the third one is the end index. By providing 1 and None the slice starts at index 1 and returns the rest of the elements.

Skipping the Header with csv.DictReader (Automatic)​

If your CSV file has a header row, csv.DictReader is often a better choice than csv.reader. DictReader automatically uses the first row as the column names and returns each subsequent row as a dictionary, making your code more readable:

import csv

with open('employees.csv', 'r', encoding='utf-8') as csv_file:
    dict_reader = csv.DictReader(csv_file)  # No need for next()

    for row in dict_reader:
        # row is a dictionary now
        print(row['first_name'], row['last_name'], row['date'])

Output:

['Alice', 'Smith', '01/21/1995 14:32:44.042010']
['Carl', 'Lemon', '06/11/1994 15:52:43.016000']
['Tom', 'Nolan', '01/07/1999 12:34:56.025000']
  • csv.DictReader(csv_file): This creates a DictReader object. It automatically treats the first row as the header row and uses those values as keys for each subsequent row (which it returns as dictionaries).
  • No next() needed: You don't need to call next() with DictReader because it handles the header automatically.
  • Access by column name: Inside the loop, you access the data by column name (e.g., row['first_name']) instead of by index, which makes your code much more readable and less prone to errors.

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