Python Modules and Packages

Understanding Python modules, packages, and imports

What are Modules?

A module is a file containing Python definitions and statements. The file name is the module name with the suffix .py added.

# Example: Using built-in modules
import math
import datetime

# Using math module
print(f"Pi value: {math.pi}")
print(f"Square root of 16: {math.sqrt(16)}")

# Using datetime module
now = datetime.datetime.now()
print(f"Current time: {now}")

Pi value: 3.141592653589793
Square root of 16: 4.0
Current time: 2025-09-18 11:56:02.592034

Import Statements

There are several ways to import modules in Python:

# 1. Import entire module
import numpy

# 2. Import with alias
import numpy as np

# 3. Import specific functions
from math import sqrt, pi

# 4. Import all (not recommended)
# from math import *

# Examples
arr = np.array([1, 2, 3, 4, 5])
print(f"Array: {arr}")
print(f"Mean: {np.mean(arr)}")

print(f"Using imported sqrt: {sqrt(25)}")
print(f"Using imported pi: {pi}")

Array: [1 2 3 4 5]
Mean: 3.0
Using imported sqrt: 5.0
Using imported pi: 3.141592653589793

Creating Custom Modules

You can create your own modules by saving Python code in a .py file.

# Example: Creating a simple utility module (conceptually)
# This would be saved as utils.py

def greet(name):
    """Greet a person by name"""
    return f"Hello, {name}!"

def calculate_area(radius):
    """Calculate area of a circle"""
    import math
    return math.pi * radius ** 2

# Constants
VERSION = "1.0.0"
AUTHOR = "Mohammed Adil Siraju"

# Test the functions
print(greet("Adil"))
print(f"Area of circle with radius 5: {calculate_area(5):.2f}")
print(f"Module version: {VERSION}")

Hello, Adil!
Area of circle with radius 5: 78.54
Module version: 1.0.0

Packages

A package is a collection of modules organized in directories. Packages help organize related modules together.

# Example package structure:
# mypackage/
#     __init__.py
#     module1.py
#     module2.py
#     subpackage/
#         __init__.py
#         submodule.py

# Importing from packages
# import mypackage.module1
# from mypackage import module2
# from mypackage.subpackage import submodule

print("Package structure example shown above")

Package structure example shown above

Popular Python Packages for Data Science

Let’s explore some essential packages for AI/ML work:

# Data manipulation and analysis
import pandas as pd
import numpy as np

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Machine Learning
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Create sample data
data = {
    'x': [1, 2, 3, 4, 5],
    'y': [2, 4, 6, 8, 10]
}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)

# Simple linear regression example
X = df[['x']]
y = df['y']

model = LinearRegression()
model.fit(X, y)

predictions = model.predict(X)
mse = mean_squared_error(y, predictions)

print(f"\nLinear Regression Results:")
print(f"Slope: {model.coef_[0]:.2f}")
print(f"Intercept: {model.intercept_:.2f}")
print(f"MSE: {mse:.2f}")

Sample DataFrame:
   x   y
0  1   2
1  2   4
2  3   6
3  4   8
4  5  10

Linear Regression Results:
Slope: 2.00
Intercept: -0.00
MSE: 0.00

Best Practices

1. Use descriptive names for modules and packages
2. Keep modules focused on a single responsibility
3. Use __init__.py to control package imports
4. Document your modules with docstrings
5. Follow PEP 8 naming conventions
6. Avoid circular imports

Key Takeaways

• Modules help organize and reuse code
• Packages group related modules together
• Import statements control what code is available
• Python’s standard library provides many useful modules
• Third-party packages extend Python’s capabilities

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Next: We’ll explore Python functions and classes in detail.