Follow Anthony @

Software & Coding Art & Design Social Media Schedule A Meeting Send A Message Support Me Emergency Only

TLDR: I participated in a MongoDB Hackathon and share what I learned about MongoDB and Django.


On Dev Community, I saw a post for a Hackathon sponsored by MongoDB and thought I’d give it a shot. I have no experience with MongoDB so I thought it’d be fun. Unfortunately, I saw the post only a few days before the due date, so I didn’t have enough time to finish it. But I still learned a lot!

I picked the Time Series, category because I wanted to work with time series data, basically a collection of data in different time intervals. Think about a stock tracker or climate tracking. Time series data has some sort of final variable (unchanging) alongside data that frequently changes. For stock tracking the unchanged variable is the stock symbol (i.e. AAPL) and the data that changes frequently is the stock price.

Since I was using MongoDB for the first time, I also thought it’d be fun to use Django, a web framework in Python, for the first time.

My Idea

I looked for publicly available data and found an animal tracker, so I thought it’d be fun to track Bat movements and plot it on Google Maps.

Movebank, is a public database, where scientists track animal movements by attaching a GPS device to the animal. Based on a certain frequencies, that data is recorded by the device and stored in the database.

There is a public API which gets this data, so developers can use it in various projects.

My idea for this was to:

  1. Get European free-tailed bat tracking data from Movebank’s API.
  2. Store that data in MongoDB.
  3. Convert it to Time Series data.
  4. Display that data on a Map to track their movement history.

Unfortunately, I saw this Hackathon only a few days before the due date and did not have enough time to complete my idea. But I submitted it as is.

What I completed:

  • ✅ Get European free-trailed bat tracking data from Movebank’s API
  • ✅ Store that data in MongoDB.
  • ⛔ Convert it to Time Series data.
  • ⛔ Display that data on a Map to track their movement history.

I plan on finishing this project later on, follow me for more on that.

Sharing What I Learned

Although I didn’t complete my idea, I still learned a lot and wanted to share that info.

Virtual Environments in Python

Before installing Django, its good to understand venv, a virtual environment for Python. venv is a self-contained directory that has the Python installation plus external packages. Think of it as any other virtual environment. They should NOT be committed to your repo (you can put it outside the directory or just add it to .gitignore)!

Create a venv with:

python3 -m venv .name-of-virtual-env-directory

This will create the name-of-virtual-env-directory as well as the Python installation and any packages your project needs.

A best practice for handling virtual environments is to store them all in the same place. So, you might want to store them in your home directory or wherever your Git repos are stored. i.e.:

python3 -m venv ~/.virtualenvs/.project-virtual-env

After that, you can activate the virtual environment with:

source ~./virtualenvs/.project-virtual-env/bin/activate

Anytime you install a package with pip, it will be installed in the active virtual environment you’re in.

pip list

Gives a list of all the installed packages within the venv.

After installing dependencies into the virtual environment, make sure to deactivate it when you’re done. Use command:


pip freeze is similar to pip list, but it also give specific version numbers. Copy the contents of that list into a reqirements.txt file (which is committed into version control AKA Git). Then, when a user clones your project, they run the following, to install all of the project dependencies.

pip install -r requirements.txt

Think of requirements.tx as package.json used in NPM.

| Another option: virtualenv is an even more popular option than venv. Look into using it here.


MongoDB is a NoSQL, non-relational, schema-less, document-based database. Document-based means that the data is stored in key-value pairs, think about JSON. Check out the table below for a comparison between SQL and NoSQL.

Type Table based databases Document based, like key-value pairs
Schema Uses a schema No need for a schema
Scaling Not preferred (vertical) Preferred for scaling (horizontal)
ACID Best for ACID properties Not ACID compliant


This shows how you need to create a schema with relational databases, and how it’s not needed in MongoDB.


MongoDB isn’t officially supported by Django, a popular web framework with Python. PostresSQL, MySQL, Oracle, and SQLite are. But it is possible to do this per the MongoDB docs. This is because, usually, you’ll want to use a relational database with Django.

Flask is another popular web framework in Python. It’s a better choice to use with NoSql databases. You can also use django-nonrel, which is a community fork to add NoSQL support to Django. However, it is not supported and uses an old Django version.

There are 3 options to connect to MongoDB with Django. Pymongo, MongoEngine, and Djongo. But by using PyMongo, a driver, you can connect MongoDB and Django. Instructions for this are in the subsequent section.

After activating the virtual env (from above), installing Django can be done with:

python -m install Django

View the Django version with:

python -m django --version

cd into a directory and enter the command to create a project:

django-admin startproject name-of-project

To start the server & application:

python runserver

Earlier, we created a project named name-of-project. We can also create apps within the project. Projects can have multiple apps (think of apps as modules). Use this command:

python startapp name-of-app
Directory Summary
name-of-project The root directory (can be named anything)
  » CLI utility
  » name-of-project Directory for the Python package (e.g. name-of-project.settings)
    » Tells Python this directory is a package
    » Settings for Django
    » Table of contents and URL routing
    » Entry-point for ASGI web servers
    » Entry-point for WSGI web servers


According to MongoDB docs, Pymongo is the preferred way to use MongoDB with Django. But as mentioned above, there are some other options using MongoEngine and Djongo.

Install PyMongo with:

python3 -m pip install pymongo

For installation with MongoDB, it might be good to install some optional dependencies with:

pip install pymongo[snappy,gssapi,srv,tls]

As well as this (for using mongodb+srv://):

pip install dnspython

There’s a couple of ways to connect to our database session. One of which involves creating a file in the PROJECT folder (where is).

from pymongo import MongoClient

def get_db_handle(db_name, host, port, username, password):

 client = MongoClient(host=host,
    db_handle = client['db_name']
 return db_handle, client

Use env variables to hide the username and password from being exposed. That’s out of scope of this tutorial, but if you have questions, just ask.

Then, in the app directory, find From here, the function get_db_handle() can be accessed.

Now, our MongoDB client is connected! If you’d like to check out how to set this up with MongoEngine or Djongo, just check out this tutorial.

For more information:

Thanks for reading! 🙌

Follow me for more content:
Please react or comment to this article, down below!