🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 10: Python Modules, Packages & File Handling

Welcome back! 👋

So far, you've learned Python fundamentals, functions, data structures, list comprehensions, and functional programming. In this lesson, you'll learn how to organize your code into modules and packages and how to read from and write to files.

These skills are essential for every Data Scientist because real-world projects involve working with multiple Python files, libraries, and datasets stored in files.

🔹 1. What is a Module?

A module is a Python file (".py") that contains functions, variables, or classes that can be reused in other Python programs.

Instead of writing the same code repeatedly, you can create a module once and import it wherever needed.

Example:

Suppose you have a file named calculator.py

def add(a, b):
    return a + b

def subtract(a, b):
    return a - b

Now use it in another file:

import calculator

print(calculator.add(10, 5))

Output: 15

🔹 2. Importing Modules

Python provides different ways to import modules.

Import the Entire Module

import math
print(math.sqrt(25))

Output: 5.0

Import Specific Functions

from math import sqrt
print(sqrt(49))

Output: 7.0

Import with an Alias

Aliases make long module names easier to use.

import math as m
print(m.pi)

Output: 3.141592653589793

🔹 3. Common Built-in Modules

Some commonly used Python modules are:

• "math" → Mathematical operations

• "random" → Generate random numbers

• "datetime" → Work with dates and times

• "os" → Interact with the operating system

• "sys" → Access system-specific information

• "statistics" → Perform statistical calculations

Example:

import random
print(random.randint(1, 10))

This generates a random integer between 1 and 10.

🔹 4. What is a Package?

A package is a collection of related modules organized into folders.

Example:

project/
│
├── main.py
├── utilities/
│   ├── init.py
│   ├── calculator.py
│   └── helper.py

Packages help organize large Python projects into manageable sections.

🔹 5. File Handling

Most Data Science projects involve reading data from files such as:

• CSV files

• Text files

• Excel files

• JSON files

Python provides built-in functions for file handling.

🔹 6. Opening a File

Syntax: open(file_name, mode)

Common modes:

Mode | Description

"r" | Read

"w" | Write (overwrites existing content)

"a" | Append

"x" | Create a new file

"rb" | Read binary files

"wb" | Write binary files

🔹 7. Reading a File

Suppose sample.txt contains:

Welcome to Python
Learning File Handling

Python code:

file = open("sample.txt", "r")
print(file.read())
file.close()

Output:

Welcome to Python
Learning File Handling

🔹 8. Writing to a File

file = open("sample.txt", "w")
file.write("Hello Data Science!")
file.close()

This replaces the previous contents of the file.

🔹 9. Appending to a File

file = open("sample.txt", "a")
file.write("\nPython is awesome!")
file.close()