LEARN COMPLETE PYTHON IN 24 HOURS

8. File Handling & Data Formats

8.1 Reading/Writing Text & Binary Files

Python provides the built-in open() function to work with files.

Modes you will use most often:

• 'r' → read text (default)

• 'w' → write text (overwrites if exists)

• 'a' → append text

• 'rb' → read binary

• 'wb' → write binary

• 'r+' → read + write (file must exist)

Best practice: Always use with statement (automatically closes file)

Text file examples

Python

# Reading entire file with open("notes.txt", "r", encoding="utf-8") as file: content = file.read() # → one big string print(content) # Reading line by line (memory efficient) with open("log.txt", "r", encoding="utf-8") as file: for line in file: print(line.strip()) # process each line # Writing text with open("output.txt", "w", encoding="utf-8") as file: file.write("Hello, Anshuman!\n") file.write("This is line 2.\n") # Appending with open("log.txt", "a", encoding="utf-8") as file: file.write(f"New entry at {datetime.now()}\n")

Binary file example (copy image/video)

Python

with open("photo.jpg", "rb") as src: data = src.read() with open("backup.jpg", "wb") as dest: dest.write(data)

Important flags:

• encoding="utf-8" → almost always use for text files (handles Hindi, emojis, etc.)

• newline="" → use when writing CSV on Windows to avoid extra blank lines

8.2 with Statement Best Practices

The with statement is the safest and cleanest way to handle files (and other resources).

Correct & safe

Python

with open("data.txt", "r", encoding="utf-8") as f: content = f.read() # file is automatically closed here – even if exception occurs

Multiple files in one with

Python

with open("input.txt", "r") as src, open("copy.txt", "w") as dest: dest.write(src.read())

Nested with (when needed)

Python

with open("config.json") as cfg: with open("backup.log", "a") as log: log.write("Config loaded successfully\n")

Never do this (risk of file not closing)

Python

f = open("file.txt") try: data = f.read() finally: f.close() # easy to forget finally

8.3 CSV – csv module

The csv module handles commas, quotes, delimiters, and newlines correctly — never use split(',') for real CSV.

Reading CSV

Python

import csv # Simple reader with open("students.csv", "r", encoding="utf-8") as f: reader = csv.reader(f) header = next(reader) # ['name', 'age', 'city'] for row in reader: print(row) # ['Anshuman', '25', 'Muzaffarpur'] # DictReader – most useful with open("students.csv", "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: print(row["name"], row["age"]) # Anshuman 25

Writing CSV

Python

import csv data = [ {"name": "Rahul", "age": 24, "city": "Patna"}, {"name": "Priya", "age": 23, "city": "Delhi"} ] with open("output.csv", "w", encoding="utf-8", newline="") as f: writer = csv.DictWriter(f, fieldnames=["name", "age", "city"]) writer.writeheader() # writes header row writer.writerows(data)

Tip: newline="" prevents extra blank lines on Windows.

8.4 JSON – json module & serialization

JSON is the most common format for APIs, config files, web data.

Reading JSON

Python

import json with open("config.json", "r", encoding="utf-8") as f: config = json.load(f) # directly gets dict/list print(config["api"]["key"]) # your-api-key-here

Writing JSON (pretty print)

Python

import json person = { "name": "Anshuman", "age": 25, "skills": ["Python", "FastAPI", "SQL"], "address": {"city": "Muzaffarpur", "state": "Bihar"}, "active": True } with open("person.json", "w", encoding="utf-8") as f: json.dump(person, f, indent=4, ensure_ascii=False) # indent=4 → beautiful formatting # ensure_ascii=False → allows Hindi/Unicode without escaping

String conversion (very common in APIs)

Python

json_string = json.dumps(person, indent=2, ensure_ascii=False) print(json_string) back_to_dict = json.loads(json_string)

8.5 Pickle – Serializing Python Objects

pickle can save almost any Python object (lists, dicts, classes, functions, models, etc.) — but only use it for trusted data.

Important warning: Never load pickle files from untrusted sources → security risk (can execute arbitrary code)

Basic usage

Python

import pickle data = { "model_weights": some_large_array, "training_history": [0.92, 0.85, 0.89], "timestamp": datetime.now() } # Save with open("model.pkl", "wb") as f: pickle.dump(data, f) # Load with open("model.pkl", "rb") as f: loaded = pickle.load(f)

Use cases:

• Save ML models (scikit-learn, PyTorch state_dict)

• Cache expensive computations

• Save game state, user sessions internally

Safer alternatives for sharing data: JSON, CSV, Parquet, HDF5

8.6 Working with Large Files (chunk reading)

Never load huge files (GBs) into memory at once.

Line-by-line (best for text/CSV)

Python

with open("very_large_log.txt", "r", encoding="utf-8") as f: for line in f: # process each line if "ERROR" in line: print(line.strip())

Chunk reading (binary or text)

Python

def process_in_chunks(filename, chunk_size=1024*1024): # 1 MB chunks with open(filename, "rb") as f: while chunk := f.read(chunk_size): # process chunk (e.g., hash, search bytes, upload) print(f"Processed {len(chunk)} bytes") process_in_chunks("big_video.mp4")

Memory-efficient CSV processing

Python

import csv with open("million_rows.csv", "r", encoding="utf-8") as f: reader = csv.DictReader(f) total = 0 for row in reader: total += float(row["sales"]) # no need to store all rows print(f"Total sales: ₹{total:,.2f}")

Mini Project – Simple Log Analyzer (JSON Lines + chunks)

Python

import json def analyze_logs(filename): error_count = 0 with open(filename, "r", encoding="utf-8") as f: for line in f: try: log = json.loads(line.strip()) if log.get("level") == "ERROR": error_count += 1 print(f"Error: {log['message']}") except json.JSONDecodeError: print("Skipping invalid JSON line") print(f"Total errors: {error_count}") analyze_logs("server_logs.jsonl")

This completes the full File Handling & Data Formats section — now you can confidently handle any kind of file, from small configs to massive logs and datasets!

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