Trading Economics Notebooks

The Trading Economics Python Jupyter Notebooks GitHub repository showcases examples on how one can easily interact with our data to make interesting data findings and insights. Trading Economics is a gateway to 20 million indicators from 196 countries. Trading Economics provides its subscribers with a near real-time economic calendar updated 24 hours a day, historical data time series sourced recently and directly from national statistics offices, quotes for thousands of financial markets, and active support. 

Getting started

Users also have the choice to fork or clone this repository into their local computer. Be aware that if you just clone the repository you will not be able to push changes into our master branch. Thus forking is probably better as it allows you to add new files and push changes.

git clone https://github.com/tradingeconomics/notebooks

Installation

JupyterLab allows users to do data science using a web-based user interface.

pip install jupyterlab

Credentials

Please subscribe to a plan at https://tradingeconomics.com/api/pricing.aspx to get an API key.

Protect your credentials! Please set your keys as environment variables before you launch your application.

# linux / mac 
export apikey=""

# windows
set apikey=""

Running

jupyter lab

Running with Docker

Trading Economics Notebooks Docker packages everything you need to run data science and analytics. Please subscribe to a plan at https://tradingeconomics.com/api/pricing.aspx to get an API key.

docker run --rm --name te-notebooks -p 8888:8888  -e apikey="" -v "${PWD}":/home/jovyan/work tradingeconomics/notebooks:latest  start.sh jupyter lab --LabApp.token=''

Documentation:

https://docs.tradingeconomics.com

Examples:

https://github.com/tradingeconomics/tradingeconomics

Learn more

https://tradingeconomics.com/api/

Support

Do you have any suggestions? Please let us know

https://tradingeconomics.com/contact.aspx?subject=notebooks