Python pip and Packages

Python’s standard library is big, but the real superpower is the ecosystem: over half a million free packages on PyPI (the Python Package Index) for web apps, data analysis, AI, automation and more. pip is the tool that installs them. In this lesson you’ll install packages the professional way, inside virtual environments.

What is a package?#

A package is code someone else wrote and published so you can use it with import. Examples:

Package	Used for
requests	Calling web APIs
pandas	Data analysis with tables
numpy	Fast maths on arrays
matplotlib	Charts
flask, django, fastapi	Web applications and APIs
beautifulsoup4	Reading data out of HTML pages
openpyxl	Excel files
pytest	Testing

Step 1: Create a virtual environment#

If you install every package into your main Python, different projects soon need different versions of the same package and break each other. A virtual environment (venv) is a private folder of packages for one project.

In your project folder:

python -m venv .venv

This creates a .venv folder containing a copy of Python and an empty set of packages. (Use python3 on macOS and Linux.)

Step 2: Activate it#

# Windows (Command Prompt or PowerShell)
.venv\Scripts\activate

# macOS / Linux
source .venv/bin/activate

Your prompt now starts with (.venv). While it’s active, python and pip refer to the environment’s versions, and packages install into .venv only. Type deactivate to leave.

VS Code detects .venv automatically and offers to use it; if not, press Ctrl+Shift+P → Python: Select Interpreter and choose the .venv one.

(On Windows PowerShell, if activation is blocked, run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once.)

Step 3: Install packages#

pip install requests

pip downloads requests and everything it depends on from PyPI. Then use it in your code:

import requests

response = requests.get("https://jsonplaceholder.typicode.com/todos/1", timeout=10)
print(response.status_code)
print(response.json()["title"])

Useful pip commands#

pip install requests pandas      # install several
pip install "requests==2.32.3"   # an exact version
pip install --upgrade requests   # upgrade to the latest
pip uninstall requests           # remove
pip list                         # what's installed in this environment
pip show requests                # details: version, dependencies, location

If pip isn’t found, use python -m pip install …, which always uses the pip belonging to the Python you’re running.

Step 4: Record your dependencies#

So others (or you, on another computer or a server) can recreate the environment:

pip freeze > requirements.txt

requirements.txt lists every installed package with its exact version:

certifi==2024.8.30
charset-normalizer==3.4.0
idna==3.10
requests==2.32.3
urllib3==2.2.3

To install everything from it in a fresh environment:

python -m venv .venv
source .venv/bin/activate          # or the Windows command
pip install -r requirements.txt

This is the standard way to share Python projects. Commit requirements.txt to Git, but not the .venv folder: add .venv/ to your .gitignore.

Modern projects often use a pyproject.toml file and tools like uv or Poetry, which do the same job faster with more features. The ideas (an isolated environment plus a list of dependencies) are the same.

Choosing packages safely#

Anyone can publish to PyPI, so choose carefully:

• Check the name exactly. Attackers publish look-alike packages (“requets”, “python-dateutils”) hoping for typos. This is called typosquatting.
• Look at the project page on pypi.org: recent releases, a link to source code, documentation, and how widely it’s used.
• Prefer well-known packages for common tasks.
• Pin versions in requirements.txt, so an update can’t suddenly change behaviour.
• **Never run sudo pip install** on Linux/macOS; it can break the system Python. Use a venv.

Example: a tiny weather-style script with requests#

import requests

def get_user(user_id):
    url = f"https://jsonplaceholder.typicode.com/users/{user_id}"
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()      # raises an error for 4xx/5xx responses
    except requests.RequestException as error:
        print("Request failed:", error)
        return None
    return response.json()

user = get_user(1)
if user:
    print(f"{user['name']} works at {user['company']['name']}")

You’ll go deeper into APIs and JSON in the next lesson.

Example: a quick chart with matplotlib#

pip install matplotlib

import matplotlib
matplotlib.use("Agg")               # draw to a file, no window needed
import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr"]
sales = [12000, 15500, 14200, 18900]

plt.bar(months, sales, color="#2563eb")
plt.title("Monthly sales (₹)")
plt.savefig("sales.png", dpi=120)
print("Saved sales.png")

A few lines to a chart image. This is why Python is so popular for data work.

Common mistakes#

• Installing without activating the venv, so packages land in the wrong Python.
• **ModuleNotFoundError** because VS Code uses a different interpreter than the one where you installed the package.
• **Committing .venv** to Git (it’s large and machine-specific).
• Typos in package names.
• **Naming your own file requests.py**, which hides the real package.

Exercise#

1. Create a folder api-practice, make a virtual environment and activate it.
2. Install requests and print the titles of the first 5 posts from https://jsonplaceholder.typicode.com/posts.
3. Run pip freeze > requirements.txt and look at the file.
4. Delete .venv, recreate it, and reinstall from requirements.txt.
5. Install matplotlib and make a line chart of your weekly study hours.

Summary#

• PyPI hosts hundreds of thousands of packages; pip install name installs them.
• Use a virtual environment per project: python -m venv .venv, then activate it.
• pip freeze > requirements.txt records versions; pip install -r requirements.txt restores them.
• Check package names and sources carefully, and never install with sudo.

Next, you’ll work with JSON data and web APIs.