/7.1/guide-tags/visualization/Recent content in Visualization on Kinetica DocsHugo -- gohugo.ioen/7.1/guides/quickstart-guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/quickstart-guide/Install Kinetica There are several different options for installing Kinetica on-premises or in the cloud (Azure or AWS). However, to get started within minutes (and for free) we recommend either of the following two routes: Kinetica Cloud Free: This is a free managed version with 10 GB of storage, which is hosted in the cloud. Follow the instructions for Kinetica Cloud Free to create an account and launch Kinetica./7.1/guides/isochrones/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/isochrones/The following is a complete example, using the Python API, of generating images containing isochrones via the /visualize/isochrone and /wms endpoints using an existing graph. Prerequisites Python API Installation PyPI Git Data File Script Detail Connections Constants Graph Creation Visualizing Isochrones Visualizing Isochrones Using WMS Download & Run Prerequisites The prerequisites for running the isochrones example are listed below: Graph server enabled Python API Isochrones example script DC Shape data CSV file Python API Installation The native Kinetica Python API is accessible through the following means:/7.1/guides/graph_video_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/graph_video_guide/In this video series, Hari Subhash walks through graphs in Kinetica, from high-level concepts to practical examples./7.1/guides/jupyterlab/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/jupyterlab/Introduction Prerequisites Docker Install Setting Permissions Entering the License Key Pulling the Image Managing the Container Starting Using Stopping Exploring the Environment JupyterLab Contents Mounted Volumes Example Notebooks SVD Recommender and Visualization Example KJIO Utility Library Conclusion References Introduction JupyterLab is an integrated environment that can streamline the development of Python code and Machine Learning (ML) models in Kinetica. Jupyter notebooks integrate code execution, debugging, documentation, and visualization in a single document for consumption by multiple audiences.