How to View Weather Data Map Tiles in Jupyter Notebooks Using Python

Jupyter Notebooks’ interactive capabilities make them an easy choice for visualizing weather data and interacting with weather maps, charts, and tables. With Python using simple syntax and offering an immense community of open-source libraries, it’s clear why the number of developers (or data scientists) who are using the tech for their weather visualization projects has grown. 

The standard Python mapping library used by devs is Folium. This library allows users to create dynamic maps in Jupyter Notebooks using Python and Leaflet.js.

Leaflet.js is “an open-source JavaScript library for mobile-friendly interactive maps.”

Utilizing folium and its Leaflet functionality, we can add data layers on top of satellite, terrain, or transit map tiles that automatically fetch data from providers like Tomorrow.io.

Consequently, we can overlay dozens of data layers onto an interactive map in a small amount of time. Let’s explore how to use folium with the Tomorrow.io Weather API to add data layers to interactive maps.

Prerequisites: What You Need to View Weather Data Map Tiles

Before we delve into the process, ensure that you have the following resources and knowledge base:

1. Access to Anaconda, Visual Studio Code, or another application that supports Jupyter Notebooks.
2. Knowledge of how to install Python packages.
3. Basic Python programming knowledge.
4. A Tomorrow.io API Key.

To get a Tomorrow.io weather API key, you need to sign up for an account at Tomorrow.io.

Once you’ve registered with Tomorrow.io and chosen the appropriate service tier for your needs, you can access your unique API key.

Simply log in to your account and navigate to your dashboard.

Here, you’ll find your API key. Remember, this key is sensitive information and should be kept confidential.

How to Interact with Maps and Weather Data in Jupyter Notebook Using Python

1. Install Python Packages

We’ll begin by installing the necessary Python packages into your local or global Python virtual environment. Open up a new Jupyter Notebook, and in the first cell, import the packages that we want for the tutorial.

from datetime import datetime
from folium import Map, LayerControl
from folium.raster_layers import TileLayer
from folium.plugins import FloatImage

Run the cell and wait for the packages to import.

2. Initialize the Map and API Variables

In the next cell, we define a few variables that help us build the map and call the API.

# Starting center of map
center = (47.000, -119.000) # somewhere in eastern Washington

# Tomorrow.io API Key
apikey = 'add your api key here'

# Current time
time = datetime.now().isoformat(timespec='milliseconds') + "Z" 
# '2021-06-02T10:02:06.828Z'

3. Create a Base Map

Then, we can initialize a base map, passing in configuration options.

pnw = Map(
   location=center,
   min_zoom=1,
   max_zoom=12,
   zoom_start=6,
   tiles="Stamen Terrain",
   height=500,
   width=1000,
   control_scale=True
)

# Show map
pnw

After running the cell, your output map should look like the image below.

4. Create a Temperature Layer with Tomorrow.io

Now we can overlay the temperature tiles.

temperature = TileLayer(
    name='Temperature',
tiles='https://api.tomorrow.io/v4/map/tile/{z}/{x}/{y}/temperature/{time}.png?apikey={apikey}',
    min_zoom=1,
    max_zoom=12,
    max_native_zoom=12,
    overlay=True,

Take a look at the tiles argument. We’re passing in a custom URL that matches the expected format in Tomorrow.io’s documentation. Also important, the x, y, and z variables are passed into the URL by folium. However, we are providing the time and API key variables as keyword arguments.

Run the cell. Then, adjust the zoom (or pan to a different area) and watch the temperature spectrum overlay the base map tiles! The folium library automatically makes calls to the API, retrieving the necessary tiles.

5. Add Layer Control

The last step is to add a layer control menu to control when to show the temperature tiles.

In a new cell, add the code:

LayerControl(position='topright').add_to(pnw)

# Show map
pnw

Again, run the cell. You should have a control menu in the top right of the map, giving you the option to toggle the temperature tiles.

Summary

In the article, we introduced the Python library folium and how it works with Leaflet to create interactive maps in Jupyter Notebooks.

Then, with only a few lines of code, we created a map, added detailed temperature data, and gave the map user controls.

Ready to give it a shot? Get your free Tomorrow.io Weather API key today. 

Want to learn more about using Python with Tomorrow.io?

See how to create daily forecasts with a Python weather API.