Click here to download this page as a Jupyter Notebook.
pip install kxy
git clone https://github.com/kxytechnologies/kxy-python.git & cd ./kxy-python & pip install .
kxy package only supports Python 3. Replace
pip3 in the commands above, as needed.
All heavy computations are run on the KXY backend and require an API key. The API key is set by running the command
as a one-off, and following the instructions.
You may also set your API key as the
KXY_API_KEY environment variable, for instance by running
import os os.environ['KXY_API_KEY'] = '<API Key>'
To get an API key, you need to have an account with us. You can create an account here.
By default, you will start in the free
Starter plan, and you will be able to experience the KXY backend from the portal for free, and without providing a payment method, by uploading your data as csv files.
Once you have an account and are ready to use our API, you can find your API key on the KXY portal here.
API access to our backend is billed on a per-request basis, and requires that you provide a valid payment method in the KXY portal.
The public Docker image kxytechnologies/kxy has been built for your convenience, and comes with anaconda, the kxy package, and various popular machine learning packages (e.g. Tensorflow, XGBoost, LightGBM, scikit-learn, and more).
To start a Jupyter Notebook server from a sandboxed Docker, run
docker run -i -t -p 5555:8888 kxytechnologies/kxy /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
where you should replace
<YOUR API KEY> with your API key and navigate to http://localhost:5555 in your browser.
To start a Jupyter Notebook server from an existing directory of notebooks, run
docker run -i -t --mount src=</path/to/your/local/dir>,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
where you should replace
</path/to/your/local/dir> with the path to your local notebook folder and navigate to http://localhost:5555 in your browser.
For serverless applications running on AWS, we provide a kxy AWS Lambda layer so that you may simply import the
kxy package from within your AWS Python 3 lambda functions. No additional requirement is needed, other than specifying your API key as the environment variable
Working with Pandas DataFrames¶
The most convenient way of using the
kxy package is through pandas DataFrame objects. All our analyses are available as methods of pandas DataFrame objects, under the
kxy accessor (i.e. as
df.kxy.<method_name>). To access these, all you need is to import the
kxy package alongside
import pandas as pd import kxy
Checkout the Cheat Sheet section for code snippets, the Case Studies section for interesting applications, and the Kaggle & UCI Experiments section for experiments on dozens of UCI and Kaggle datasets.
Working with the Low Level RESTFul API¶