Lesson Objectives
By the end of this lesson, you will be able to:
- Import and use Python modules
- Understand the difference between modules and packages
- Read from and write to text files
- Organize code into reusable components
This lesson introduces scalability:
code stops living in a single file and becomes a structured system.
1️⃣ What Is a Module
A module is a Python file that contains:
- functions
- variables
- classes
Every .py file is a module.
Example:
# math.py
def square(x):
return x * x
You can use it in another file.
2️⃣ Importing Modules
Import the Entire Module
import math
print(math.sqrt(16))
You access module content using dot notation.
Import Specific Objects
from math import sqrt
print(sqrt(16))
Use this carefully to avoid name conflicts.
Aliasing Imports
import numpy as np
This is extremely common in data analysis.
3️⃣ Built-in vs External Modules
Built-in Modules
Python ships with many built-in modules:
mathossysrandom
Example:
import random
random.randint(1, 10)
External Packages
External packages must be installed.
Example (in terminal):
pip install numpy
Then (in code editor):
import numpy as np
This is how the scientific ecosystem works.
4️⃣ What Is a Package
A package is a collection of modules.
Example structure:
my_project/
│
├── analysis/
│ ├── __init__.py
│ ├── stats.py
│ └── preprocessing.py
Packages allow:
- logical grouping
- reusable pipelines
- clean project structure
5️⃣ Reading Files in Python
Reading files is fundamental in data analysis.
Basic File Reading
file = open("data.txt", "r")
content = file.read()
file.close()
This works, but it is not recommended.
Using with (Best Practice)
with open("data.txt", "r") as file:
content = file.read()
Why with?
- automatically closes the file
- safer and cleaner
6️⃣ Writing Files in Python
Writing Text
with open("output.txt", "w") as file:
file.write("Hello Python")
Mode "w" overwrites existing files.
Appending Text
with open("output.txt", "a") as file:
file.write("\\nNew line")
7️⃣ Reading Files Line by Line
Useful for large files.
with open("data.txt", "r") as file:
for line in file:
print(line.strip())
This pattern is common when processing logs or datasets.
8️⃣ Why This Matters for Data Analysis
Modules and files allow you to:
- separate logic (clean code)
- reuse functions across projects
- load datasets from disk
- save results and reports
Every serious analytical workflow depends on these concepts.
FAQ — Frequently Asked Questions
Q: What happens if the file does not exist?
A: Python raises a FileNotFoundError.
Q: Should I always use with when opening files?
A: Yes, almost always.
Q: Where should my modules live?
A: Inside the project folder, organized by purpose.
Q: Are CSV and Excel files read this way?
A: Technically yes, but libraries like pandas are preferred.
Exercises
Exercise 1
Import the math module and compute the square root of 25.
Exercise 2
Import only pi from math and print it.
Exercise 3
Create a module utils.py with a function cube(x).
Exercise 4
Import cube in another file and use it.
Exercise 5
Write a file called hello.txt containing "Hello Python".
Exercise 6
Append a second line to the same file.
Exercise 7
Read and print the content of hello.txt.
Exercise 8
Read a file line by line.
Exercise 9
Explain why with is safer than open() + close().
Exercise 10
Explain why modules are essential in large projects.
Solutions
Exercise 1
import math
math.sqrt(25)
Exercise 2
from math import pi
print(pi)
Exercise 3
# utils.py
def cube(x):
return x ** 3
Exercise 4
from utils import cube
cube(3)
Exercise 5
with open("hello.txt", "w") as f:
f.write("Hello Python")
Exercise 6
with open("hello.txt", "a") as f:
f.write("\\nSecond line")
Exercise 7
with open("hello.txt", "r") as f:
print(f.read())
Exercise 8
with open("hello.txt", "r") as f:
for line in f:
print(line.strip())
Exercise 9
# Automatic file closing, fewer errors
Exercise 10
# Modules improve structure, reuse, and maintainability
🚀 Next Lesson Preview
In Lesson 9, we will cover:
- runtime errors
- exceptions
try / exceptblocks- writing robust and fault-tolerant code