Topic: Python File Handling — Reading, Writing, and Managing Files (Beginner to Advanced)

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What is File Handling?

• File handling allows Python programs to read from and write to external files — such as .txt, .csv, .json, etc.

• Python uses built-in functions like open(), read(), and write() to interact with files.

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Opening a File

file = open("example.txt", "r")  # "r" = read mode
content = file.read()
file.close()

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Using with Statement (Best Practice)

• Automatically handles file closing:

with open("example.txt", "r") as file:
    content = file.read()

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File Modes

• "r" — read (default)
• "w" — write (creates or overwrites)
• "a" — append (adds to the end)
• "x" — create (fails if file exists)
• "b" — binary mode
• "t" — text mode (default)

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Writing to Files

with open("output.txt", "w") as file:
    file.write("Hello, world!")

• Note: "w" overwrites existing content.

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Appending to Files

with open("output.txt", "a") as file:
    file.write("\nNew line added.")

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Reading Line by Line

with open("example.txt", "r") as file:
    for line in file:
        print(line.strip())

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Working with File Paths

• Use os.path or pathlib for platform-independent paths.

from pathlib import Path

file_path = Path("folder") / "file.txt"
with open(file_path, "r") as f:
    print(f.read())

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Advanced Tip: Reading and Writing CSV Files

import csv

with open("data.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["name", "age"])
    writer.writerow(["Alice", 30])

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

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Summary

• Use open() with correct mode to read/write files.

• Prefer with statement to manage files safely.

• Use libraries like csv, json, or pickle for structured data.

• Always handle exceptions like FileNotFoundError for robust file operations.

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Exercise

• Write a Python program that reads a list of names from names.txt, sorts them alphabetically, and saves the result in sorted_names.txt.

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#Python #FileHandling #ReadWrite #DataProcessing #ProgrammingTips

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👩‍💻 heapq.merge(): Combining sorted data!

If you have multiple sources of data that are already sorted, you don't need to collect them into a single collection and sort them again. heapq.merge() combines such sources into a single, ordered iterator.

In this guide:
• We will combine multiple sorted sequences;
• We will explore lazy processing of large data sources;
• We will configure comparison using the key argument;
• We will combine data sorted in reverse order.

This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order.

#Python #heapq #DataProcessing #CodingTips #Programming #Algorithms

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