Calling Your First API Using Its OpenAPI Spec — A Python Walkthrough

Iman — Fri, 15 May 2026 10:26:02 +0000

Every public API comes with documentation. But some APIs go further — they ship an OpenAPI spec. It's a single JSON file that describes every endpoint, every parameter, and every possible response. Read it once and you know exactly how to use the API before writing a single line of code.
Let's do that right now.

What Is an OpenAPI Spec?
It's a standardized JSON (or YAML) file that describes an API completely. Endpoints, required parameters, response shapes — all in one place. Tools like Swagger UI turn it into interactive documentation, but the raw file is what matters here.

Step 1 — Get the Spec
We're using the Swagger Petstore. It's a fake pet store API built purely for learning. Open this in your browser:
https://petstore3.swagger.io/api/v3/openapi.json
Save it locally. Don't try to memorize anything — just scan the structure. You'll notice three things that matter:

paths — all available endpoints
parameters — what each endpoint accepts
responses — what comes back

That's the whole mental model.

Step 2 — Find Your Endpoint
Search for /pet/findByStatus in the spec. Here's what it looks like:
json"/pet/findByStatus": {
"get": {
"summary": "Finds Pets by status",
"parameters": [
{
"name": "status",
"in": "query",
"required": false,
"schema": {
"type": "string",
"enum": ["available", "pending", "sold"]
}
}
]
}
}
The spec tells you everything. One query parameter called status, three allowed values. No guessing.

Step 3 — Call It in Python
pythonimport requests

response = requests.get(
"https://petstore3.swagger.io/api/v3/pet/findByStatus",
params={"status": "available"}
)

data = response.json()
print(f"Status: {response.status_code}")
print(f"Pets returned: {len(data)}")
print(data[0]) # peek at the first result
Run it. Then try swapping available for pending or sold and see what changes.

Step 4 — Read the Response
You'll get back a list of pet objects. Something like:
json{
"id": 1,
"name": "doggie",
"status": "available",
"photoUrls": []
}
The spec told you this was coming — check the responses section for /pet/findByStatus. It describes the exact shape of what you just received.

Why This Actually Matters
Most developers go straight to tutorial blog posts when they want to use an API. The spec is better. It's always up to date, it's authoritative, and it tells you things blog posts skip — like which parameters are optional, what the valid enum values are, and what error responses look like.
Get comfortable reading specs now. As your projects grow more complex, this habit saves hours.

Stop Breaking Your System Python: A Practical Guide to Managing Multiple Python Versions

Iman — Fri, 15 May 2026 10:17:08 +0000

Every Python developer eventually hits the same wall: one project needs Python 3.9, another requires 3.11, and the new one won't run on anything below 3.12. The instinct is to upgrade globally — which promptly breaks something else.
There's a better way. Two tools worth knowing: pyenv and conda. They solve the same problem differently, and knowing which to reach for matters.

pyenv — Lightweight, Automatic Version Switching
pyenv lets you install and switch between Python versions at the system, user, or project level. Critically, it never touches your system Python.
Installing pyenv
Linux / Mac:
bashcurl https://pyenv.run | bash
Add this to your ~/.bashrc or ~/.zshrc:
bashexport PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"
Windows — use pyenv-win:
powershellInvoke-WebRequest -UseBasicParsing -Uri "https://raw.githubusercontent.com/pyenv-win/pyenv-win/master/pyenv-win/install-pyenv-win.ps1" -OutFile "./install-pyenv-win.ps1"; &"./install-pyenv-win.ps1"
Basic Usage
bash# See available versions
pyenv install --list

Install a specific version

pyenv install 3.11.9

Set global default

pyenv global 3.11.9

Set version for current project only

pyenv local 3.10.14
pyenv local creates a .python-version file in your project directory. Every time you cd into that folder, pyenv switches automatically — no manual toggling.
Per-Project Workflow
bashcd my-project
pyenv local 3.10.14
python --version # Python 3.10.14

cd ../other-project
pyenv local 3.12.0
python --version # Python 3.12.0
Clean and automatic.

conda — Full Environment Management
conda handles Python versions and packages together. It's heavier than pyenv but earns its weight when dependencies get complex — particularly in data science and ML.
Installing conda
Download Miniconda — the minimal install — for Windows, Linux, or Mac.
Basic Usage
bash# Create an environment with a specific Python version
conda create -n myenv python=3.10

Activate it

conda activate myenv

Install packages

conda install numpy pandas

Deactivate

conda deactivate
Per-Project Workflow
bashcd my-project
conda activate project-39 # Python 3.9 environment

cd ../other-project
conda activate project-312 # Python 3.12 environment
Unlike pyenv, switching isn't automatic — you activate manually. Small trade-off for what you get in return.

pyenv vs conda — Which One?
pyenvcondaPython version switchingAutomatic (per directory)Manual activationPackage managementNo — use pip + venvYes, built-inBest forGeneral / backend / web devData science / MLWindows supportVia pyenv-win (limited)ExcellentOverheadLightweightHeavier
Use pyenv if you want lightweight, automatic version switching per project.
Use conda if you're in data science, need environment and package management together, or are primarily on Windows.

The Ideal Setup: pyenv + venv Together
For most developers, this combination covers everything:
bash# Set Python version with pyenv
pyenv local 3.11.9

Create a virtual environment

python -m venv .venv

Activate it

source .venv/bin/activate # Linux/Mac
.venv\Scripts\activate # Windows

Install dependencies

pip install -r requirements.txt
pyenv handles the version. venv handles dependency isolation. Best of both.

Common Issues Worth Knowing
pyenv: command not found after install
Your shell config wasn't reloaded. Run source ~/.bashrc (or ~/.zshrc) after editing it, or close and reopen your terminal.
conda: environment not activating in scripts
Run conda init once after installing, then restart your terminal. This adds the necessary hooks to your shell profile.
python --version still shows system Python after pyenv local
Make sure the pyenv init lines are actually in your shell config and that you've reloaded it. Running pyenv versions should list your installed versions.

Quick Reference
bash# pyenv
pyenv install 3.11.9
pyenv local 3.11.9 # project-level
pyenv global 3.11.9 # system-level
pyenv versions # list installed versions

conda

conda create -n myenv python=3.11
conda activate myenv
conda deactivate
conda env list # list all environments

Managing Python versions correctly is one of those things that saves you hours of debugging later. Set it up once, stop thinking about it.
If you're running into an issue not covered here, drop it in the comments.

DEV Community: Iman

Calling Your First API Using Its OpenAPI Spec — A Python Walkthrough

Stop Breaking Your System Python: A Practical Guide to Managing Multiple Python Versions

Install a specific version

Set global default

Set version for current project only

Activate it

Install packages

Deactivate

Create a virtual environment

Activate it

Install dependencies

conda