Packages and Import Statements

Learn how Python organizes reusable code into packages, and how to import it correctly.

As Python projects grow, keeping code in a single file becomes unmanageable. Packages solve this by letting you group related modules into folders, while the import statement is how you pull that code into the file you're working in.

Modules vs Packages

A module is simply a single .py file. A package is a folder containing multiple modules, along with an __init__.py file that tells Python to treat the folder as an importable package.

my_project/
  analysis/
    __init__.py
    cleaning.py
    visualization.py
  main.py

Different Ways to Import

Python gives you several import styles depending on how much of a module you need:

import pandas
import pandas as pd
from pandas import DataFrame
from analysis.cleaning import remove_nulls

Installing Third-Party Packages

Not every package comes pre-installed. Tools like pip let you add external packages such as pandas, matplotlib, or scikit-learn to your environment with a single command: pip install pandas.

Common Import Mistakes to Avoid

• Circular imports — two modules importing each other
• Using wildcard imports (from module import *) which pollute the namespace
• Forgetting __init__.py in older Python versions when creating packages

Once imports feel natural, you're ready to structure real, multi-file data science projects with confidence.