Python Libraries and Modules - Organising Code

Organising Classes with Modules and Packages

As the number of classes increases, it becomes essential to organize them effectively for accessibility and maintainability. Creating a well-structured codebase is crucial for good code.

Modular Programming

Modular programming involves dividing a large programming task into smaller, manageable subtasks or modules. This approach offers several advantages:

  • Simplicity: Modules simplify code by breaking it into smaller, understandable units.
  • Maintainability: Changes in one module are less likely to affect other parts of the code.
  • Reusability: Modules can be reused in different parts of the application or in other projects.
  • Scoping: Modules provide namespaces that help avoid naming conflicts.

In Python, modules are simple files. If two files are in the same folder, you can load a class from one module into another for use.

Packages

Packages are folders that contain modules. They help to organize projects by grouping related modules together, preventing a cluttered structure. To create a package, include a file named __init__.py in each folder that should be treated as a package. This file can be empty but must exist.

Using Python Modules

Example: Importing a Class

Consider a Point class defined in a file named point_docstrings.py. To use this class in another file, you can import it:

import point_docstrings

p1 = point_docstrings.Point()

Namespace

When you import point_docstrings into another program, point_docstrings becomes part of the calling program’s namespace. The namespace includes all methods and classes available to the current program.

Importing Specific Classes

To import only a specific class from a module, use the from keyword:

from point_docstrings import Point

p1 = Point()

Renaming Imported Classes

If a class with the same name already exists in the current namespace, you can use the as clause to rename the imported class:

from Pointdocstrings import Point as Pt

p1 = Pt()

Importing Multiple Classes

To import multiple classes from a module, list them after the import keyword:

from Pointdocstrings import Point, Line

To import all classes from a module, use the * wildcard:

from Pointdocstrings import *

Using Python Packages

Package Structure Example

parent_directory/
├── main.py
├── Drawing/
│   ├── __init__.py
│   ├── point_call.py
│   └── point_docstrings.py
└── Maths/
    ├── __init__.py
    └── Theorem.py

Absolute vs. Relative Imports

  • Absolute Imports: Specify the full path to the module or class.
  • Relative Imports: Specify the location relative to the current package.

Absolute Import Examples

# Method 1
import Drawing.point_call
P1 = Drawing.point_call.Point()

# Method 2
from Drawing.point_call import Point
P1 = Point()

# Method 3
from Drawing import point_call
P1 = point_call.Point()

Relative Import Examples

  • . refers to the current directory (package).
  • .. refers to the parent directory (package).

If Point-call.py wants to call Point-docstrings.py within the same Drawing package, you can use a relative import:

from .Point_docstrings import Point

If Theorem.py in the Maths package wants to call Point-docstrings.py in the Drawing package, you can use:

from ..Drawing.Point_docstrings import Point

Popular Libraries

Python has a vast collection of libraries available on the Python Package Index (PyPI): https://pypi.org/

Exercise 1: Matplotlib

Use the matplotlib library (https://pypi.org/project/matplotlib/) to create a line chart visualizing monthly sales data.

Instructions:

  1. Import the matplotlib.pyplot module.
  2. Create two lists:
    • Months: ['January', 'February', 'March', 'April', 'May', 'June']
    • Sales: [2500, 2700, 3000, 2800, 3200, 3500]
  3. Use plt.plot() to draw the line chart.
  4. Add chart elements:
    • Title: "Monthly Sales Overview"
    • X-axis label: "Month"
    • Y-axis label: "Sales ($)"
    • Grid for readability.
  5. Display the chart using plt.show().

Exercise 2: Faker

Use the faker library (https://pypi.org/project/Faker/) to generate synthetic customer data.

Instructions:

  1. Install the Faker library.
  2. Write a Python script to generate 100 fake customers with the following fields:
    • Full name
    • Email
    • Phone number
    • City
    • Date of birth
    • Job title
    • Company name
  3. Save the data in a file called customers.csv.

Exercise 3: PDF Text Extraction

Find a library that helps you read the contents of a PDF file and extract its text.

Instructions:

  1. Download or prepare a sample PDF file.
  2. Write a script that:
    • Opens the PDF file
    • Extracts the text from each page
    • Prints the content of each page to the console