Time Series with Python & MongoDB

Learn the fundamental techniques for analyzing time-series data with Python, MongoDB, PyMongo, Pandas, & Matplotlib

References

• Blog post (coming soon)
• Video (coming soon)

Prerequisites

• Python 3.8+ Installed
• Docker Desktop Installed (for local MongoDB instance)
• Terminal or PowerShell experience

Getting Started

1. Make a project directory

mkdir -p ~/dev/ts-pymongo
cd ~/dev/ts-pymongo

2. Clone this repo:

git clone https://github.com/codingforentrepreneurs/time-series-mongodb-pymongo .

3. Make and activate a virtual environment:

python3.10 -m venv venv

macOS/Linux activation

source venv/bin/activate

Windows activation

./venv/Scripts/activate

4. Upgrade Virtual Environment Pip

(venv) python -m pip install pip --upgrade

5. Move src/example.env to src/.env

mv src/example.env src/.env

6. Change MONGO_INITDB_ROOT_PASSWORD in .env Create a new password with:

(venv) python -c "import secrets;print(secrets.token_urlsafe(32))"

So .env looks like:

MONGO_INITDB_ROOT_USERNAME="root"
MONGO_INITDB_ROOT_PASSWORD="wlke0lL-v7FkGFn5Cl0brfxHJqhDPImBmg-MRfCIXx4" 

7. Install requirements

(venv) python -m pip install -r src/requirements.txt

8. Run Docker Compose Don't have docker? Install Docker Desktop

cd ~/dev/ts-pymongo
docker compose up

9. Checkout the Final Results If you want to see the final code changes, checkout the final branch.

git checkout final