File Handling - Binary and CSV Files

1. Introduction to Binary Files

A binary file stores data in the same format as it is stored in memory. Unlike text files, binary files are not human-readable. They store data as a sequence of bytes.

Characteristics:

• Data is stored in binary format (0s and 1s).
• Not human-readable without special tools.
• More efficient for storing complex data structures.
• Faster to read and write compared to text files.
• No newline character conversion.

Common uses: Images (.jpg, .png), audio (.mp3), video (.mp4), executable files (.exe), and Python object serialization (.dat, .pkl).

2. Binary File Modes

To work with binary files, append "b" to the file mode:

3. The Pickle Module

The pickle module in Python is used for serializing and deserializing Python objects. Serialization converts a Python object into a byte stream, which can be stored in a binary file.

import pickle

Key functions:

4. Writing with dump()

import pickle

# Storing a list in a binary file
students = ["Riya", "Amit", "Priya", "Rahul"]

with open("students.dat", "wb") as f:
    pickle.dump(students, f)

print("Data written successfully.")

You can store multiple objects by calling dump() multiple times.

with open("data.dat", "wb") as f:
    pickle.dump(100, f)        # Integer
    pickle.dump(3.14, f)       # Float
    pickle.dump("Hello", f)    # String
    pickle.dump([1, 2, 3], f)  # List

5. Reading with load()

import pickle

with open("students.dat", "rb") as f:
    data = pickle.load(f)
    print(data)  # Output: ["Riya", "Amit", "Priya", "Rahul"]

When multiple objects are stored, call load() in the same order as dump() was called. An EOFError is raised when the end of file is reached.

with open("data.dat", "rb") as f:
    print(pickle.load(f))  # 100
    print(pickle.load(f))  # 3.14
    print(pickle.load(f))  # Hello
    print(pickle.load(f))  # [1, 2, 3]

6. Binary File Example Program

import pickle

def add_student():
    students = []
    n = int(input("Enter number of students: "))
    for i in range(n):
        roll = int(input("Enter roll number: "))
        name = input("Enter name: ")
        marks = float(input("Enter marks: "))
        students.append({"roll": roll, "name": name, "marks": marks})
    with open("students.dat", "wb") as f:
        pickle.dump(students, f)

def display_students():
    try:
        with open("students.dat", "rb") as f:
            data = pickle.load(f)
            for s in data:
                print(f"Roll: {s['roll']}, Name: {s['name']}, Marks: {s['marks']}")
    except FileNotFoundError:
        print("File not found.")

7. Searching and Updating in Binary Files

def search_student(roll_no):
    try:
        with open("students.dat", "rb") as f:
            data = pickle.load(f)
            for s in data:
                if s["roll"] == roll_no:
                    print(f"Found: {s['name']}, Marks: {s['marks']}")
                    return
        print("Student not found.")
    except FileNotFoundError:
        print("File not found.")

Updating: To update a record, read all data, modify the required record, and write back the entire data.

8. Introduction to CSV Files

A CSV (Comma Separated Values) file stores tabular data in plain text. Each line represents a row, and values are separated by commas.

Roll,Name,Marks
101,Riya,95
102,Amit,87
103,Priya,92

CSV files can be opened in any text editor or spreadsheet application like Excel.

9. The CSV Module

Python provides the csv module for working with CSV files.

import csv

Key functions:

10. Reading CSV Files with reader()

import csv

with open("students.csv", "r") as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)

To skip the header row:

with open("students.csv", "r") as f:
    reader = csv.reader(f)
    next(reader)  # Skip header
    for row in reader:
        print(f"Roll: {row[0]}, Name: {row[1]}, Marks: {row[2]}")

11. Writing CSV Files with writer()

import csv

data = [
    ["Roll", "Name", "Marks"],
    [101, "Riya", 95],
    [102, "Amit", 87],
    [103, "Priya", 92]
]

with open("students.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerows(data)

The newline="" parameter prevents extra blank rows on Windows.

12. Using DictReader

import csv

with open("students.csv", "r") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["Name"], row["Marks"])

DictReader uses the first row as header to create dictionary keys.

13. Using DictWriter

import csv

students = [
    {"Roll": 101, "Name": "Riya", "Marks": 95},
    {"Roll": 102, "Name": "Amit", "Marks": 87}
]

with open("students.csv", "w", newline="") as f:
    fieldnames = ["Roll", "Name", "Marks"]
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(students)

14. CSV File Example Program

import csv

def add_record():
    with open("records.csv", "a", newline="") as f:
        writer = csv.writer(f)
        roll = int(input("Enter roll: "))
        name = input("Enter name: ")
        marks = float(input("Enter marks: "))
        writer.writerow([roll, name, marks])

def display_all():
    try:
        with open("records.csv", "r") as f:
            reader = csv.reader(f)
            for row in reader:
                print(", ".join(row))
    except FileNotFoundError:
        print("File not found.")

15. Text vs Binary vs CSV - Comparison