While Pandas is great for big data analysis, sometimes you just need to read a simple CSV file. For this, the Python csv module is perfect—it’s fast, light, and requires no installation.
1. Reading a CSV File
This is the most common task. We use csv.reader to loop through the file row by row.
import csv
with open('data.csv', 'r') as file:
csv_reader = csv.reader(file)
# Skip the header row
next(csv_reader)
for row in csv_reader:
# Each 'row' is a list of strings
print(f"Name: {row[0]}, Age: {row[1]}")2. Reading a CSV as a Dictionary
This is even better. csv.DictReader uses the header row to create a dictionary for each line.
import csv
with open('data.csv', 'r') as file:
csv_reader = csv.DictReader(file)
for row in csv_reader:
# Now you can use column names!
print(f"Name: {row['Name']}, Age: {row['Age']}")3. Writing to a CSV File
import csv
# Data to write
header = ['Name', 'Score']
data = [
['Alice', 95],
['Bob', 80]
]
with open('scores.csv', 'w', newline='') as file:
csv_writer = csv.writer(file)
csv_writer.writerow(header) # Write a single header row
csv_writer.writerows(data) # Write all data rowsNote: The newline='' is a required step to prevent extra blank lines on Windows.
