Python Packages and pip
Learn what packages are, how pip installs them, and how requirements files keep projects repeatable.
Why it matters: AI projects depend on packages such as NumPy, pandas, scikit-learn, PyTorch, and FastAPI.
Basics
Modules and Packages
A module is one Python file. A package is a folder of related modules.
Python includes standard-library packages such as json. Third-party packages add tools such as NumPy and pandas.
Use import package_name after a package is available.
Tiny example
Check whether Python can find a package.
import importlib.util
name = "json"
found = importlib.util.find_spec(name) is not None
print(name, "available:", found)Basics
Install with pip
pip is Python's package installer.
- Install:
python -m pip install pandas - Upgrade:
python -m pip install --upgrade pandas - List:
python -m pip list - Remove:
python -m pip uninstall pandas
The browser lab loads supported packages automatically, so students can practise here without setup.
Tiny example
See the common pip commands as a small list.
commands = [
"python -m pip install pandas",
"python -m pip list",
]
for command in commands:
print(command)Basics
Requirements Files
A requirements.txt file lists the packages a project needs.
Pin versions with == when repeatable builds matter. Install the full list with python -m pip install -r requirements.txt.
Common mistake: installing packages without recording their versions.
Tiny example
Create a short repeatable package list.
requirements = [
"numpy==2.1.3",
"pandas==2.2.3",
]
print("\n".join(requirements))Then AI / ML
AI / ML examples
Same idea, used in real AI work. Press Try in lab to run it.
AI / ML example
AI / ML: check project dependencies
Check which common data packages are available.
In AI / ML: Training and data projects usually depend on NumPy, pandas, and scikit-learn.
example.py
Python
import importlib.util
packages = ["numpy", "pandas", "sklearn"]
for name in packages:
available = importlib.util.find_spec(name) is not None
print(name, "available:", available)AI / ML example 2
AI / ML: build a requirements list
Keep one readable list of project dependencies.
In AI / ML: Model services pin data, model, and API libraries before deployment.
example.py
Python
project_packages = {
"numpy": "2.1.3",
"pandas": "2.2.3",
"scikit-learn": "1.5.2",
}
for name, version in project_packages.items():
print(f"{name}=={version}")Takeaways
- 1A module is one file; a package groups modules.
- 2pip installs and manages third-party packages.
- 3Requirements files make projects easier to rebuild.