Working with CSV Files in Python (`csv` Module)

🟡 Intermediate

📖 Definition

CSV (Comma-Separated Values) is a universal, tabular text format used to store table data where each row represents a record and columns are separated by commas or delimiters. Python’s built-in csv module provides csv.reader, csv.writer, csv.DictReader, and csv.DictWriter for processing tabular spreadsheet datasets.

🇮🇳 Hindi

Tabular spreadsheet data (Excel files) process karne ke liye csv module ka use hota hai. Dictionaries ke saath kaam karne ke liye csv.DictReader aur csv.DictWriter sabse best aur clean tools hain. File open karte waqt newline="" compulsory set karein.

🚩 Marathi

Tabular data (Excel CSV) read aani write karnyasathi csv module vaparatat. DictReader aani DictWriter sope padtat.

📝 1. `csv.reader` vs `csv.DictReader`

csv.reader: Reads each CSV row as a list of strings (['101', 'Aarav', '95']).
csv.DictReader (Best Practice): Reads each CSV row as a dictionary mapping header column names to values ({"id": "101", "name": "Aarav", "marks": "95"}).

import csv

# Reading with DictReader
with open("students.csv", "r", encoding="utf-8") as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row["name"], row["marks"])

📝 2. Writing CSV Files (`newline=""`)

⚠️ CRITICAL RULE: Always specify newline="" when opening a CSV file for writing! Without newline="", Windows operating systems will insert blank extra newline rows between every single record line!

import csv

fieldnames = ["id", "name", "salary"]
data = [{"id": 1, "name": "Rahul", "salary": 85000}]

with open("output.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=fieldnames)
    writer.writeheader() # Writes "id,name,salary" header row
    writer.writerows(data)

💡 Complete Example: Student CSV Report Generator

import csv
import os

csv_file = "student_report.csv"

# Sample Student Dataset
students = [
    {"student_id": "STU-101", "name": "Aarav Mehta", "score": 85, "status": "PASS"},
    {"student_id": "STU-102", "name": "Sneha Kapoor", "score": 92, "status": "PASS"},
    {"student_id": "STU-103", "name": "Rohan Sharma", "score": 42, "status": "FAIL"}
]

headers = ["student_id", "name", "score", "status"]

print("=== CSV REPORT GENERATOR ===")

# 1. Writing Data using DictWriter
with open(csv_file, "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=headers)
    writer.writeheader()
    writer.writerows(students)

print(f"Successfully generated CSV file '{csv_file}'.")

# 2. Reading & Parsing Data using DictReader
print("\n--- PARSING CSV REPORT ---")

with open(csv_file, "r", encoding="utf-8") as file:
    reader = csv.DictReader(file)
    for row in reader:
        score_val = int(row["score"])
        print(f"ID: {row['student_id']} | Name: {row['name']:<15} | Score: {score_val} | Status: {row['status']}")

# Cleanup demo file
if os.path.exists(csv_file):
    os.remove(csv_file)

👀 Output

=== CSV REPORT GENERATOR ===
Successfully generated CSV file 'student_report.csv'.

--- PARSING CSV REPORT ---
ID: STU-101 | Name: Aarav Mehta     | Score: 85 | Status: PASS
ID: STU-102 | Name: Sneha Kapoor    | Score: 92 | Status: PASS
ID: STU-103 | Name: Rohan Sharma    | Score: 42 | Status: FAIL

⚠️ Common Mistakes

Omitting newline="" when writing CSV files on Windows, creating extra empty blank lines.
Treating numeric CSV column fields as integers automatically (DictReader returns all column values as strings; you must explicitly parse them using int() or float()).

🛡️ Safety / Important Notes

For advanced data analysis or multi-gigabyte CSV datasets with complex transformations, use the Pandas library (import pandas as pd).

🌍 Real-World Usage

Exporting database tables to Excel spreadsheets, downloading invoice reports, reading user batch import files, and data processing.

🧪 Try It Yourself

Create a script that writes 3 contacts (name, phone) to a contacts.csv file using DictWriter.
Read contacts.csv using DictReader and print each contact.

🎯 Mini Challenge

Write a program that reads a CSV file containing employee salaries, calculates the average salary across all records, and prints the result.

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Working with CSV Files in Python (csv Module)