Sure, currently it supports aggregation and visualization, I am working to add more functionality to it.
The core process of Auto-Analyst consists of several steps:

1. ⁠Parsing the data, description, and question: The tool takes your data and a plain English question as input, then parses and understands the context.
2. ⁠Basic data cleaning: Before diving into the analysis, Auto-Analyst cleans the data to ensure it's ready for processing.
3. ⁠Determining the answer type: Based on the input question, Auto-Analyst figures out if the answer can be provided through aggregation or visualization.
4. ⁠Aggregation: If the question requires an aggregated answer, Auto-Analyst leverages the OpenAI API to generate an SQL query. It then tries running the query on the data. If it fails, the OpenAI API is used to correct the query. This process continues until a working query is obtained or the user-defined maximum number of tries is reached. The aggregation results are then returned to the user.
5. ⁠Visualization: If the question calls for a plot, Auto-Analyst first identifies the aggregated data needed for the visualization. It uses the aggregation steps described above to obtain this data. Next, it employs the OpenAI API to generate Python code for the plot and returns the visualization to the user.

Demo - https://github.com/aadityaubhat/auto-analyst/blob/main/auto-analyst/auto-analyst-demo.gif