ChartFactor Py Overview¶
ChartFactor Py enables you to visually interact with your pandas dataframes in JupyterLab and Jupyter Notebook without having to write code and without moving your data.
With ChartFactor Py, you can easily create beautiful web-based visualizations and interact with them using the ChartFactor Studio canvas in your Jupyter Lab or Jupyter Notebook environment. You can visually examine column statistics, create multiple charts, filter between them, and use the "Copy code" function to copy the code of any of your charts and paste it on a separate cell. The video below shows you how to quickly start with ChartFactor Py.
The lightweight architecture of ChartFactor means visual analytics without the cost and complexity of integrating a traditional BI tool. ChartFactor's edge computing design means that your data application can scale to tens of thousands of users and petabytes of data, only limited by the scalability of your data engine.
ChartFactor Studio enables you to point to modern data engines such as BigQuery and Elasticsearch to create technology-agnostic data applications that you can publish to the web server of your choice. This powerful combination brings the work product of data scientists closer to end users for faster exploration and explotation of their work.
The next sections will walk you through the ChartFactor Py installation and will get you quickly started visualizing and exploring your dataframes.