Analyse CSV

A tool designed to interpret and answer queries about data contained within a CSV file by converting it to JSON, analyzing its contents, and generating Python scripts to extract insights.


Analyse CSV is a sophisticated tool that streamlines the process of extracting insights from CSV files. It begins by converting the data into a JSON format for easier manipulation. Then, it employs a Python script to analyze the data structure, identifying data types and unique values within each column. The tool intelligently determines which columns are relevant to a user's query and prompts an AI model to generate a Python script tailored to retrieve the necessary information. If errors occur, the tool iteratively generates corrective scripts until the desired result is achieved. Finally, it synthesizes the findings into a natural language answer, providing users with clear and actionable insights.

Use cases

Analyse CSV can be used by market researchers to identify consumer trends from sales data, by HR professionals to analyze employee performance metrics, or by financial analysts to extract insights from transactional data. It's also valuable for anyone needing to quickly answer data-driven questions without delving into manual data analysis.


The tool offers a seamless experience for users looking to derive specific insights from CSV datasets without deep technical expertise. It automates the data analysis process, reduces the potential for human error, and provides clear, actionable answers to complex data-related questions. This efficiency saves time and resources, allowing users to focus on decision-making rather than data processing.

How it works

Upon receiving a CSV file URL and a user query, Analyse CSV converts the file into JSON and uses a Python script to analyze the data's structure. It then identifies relevant columns and leverages an AI model to create a Python script that extracts the requested information. The tool executes the script, handles any errors by generating new scripts if necessary, and consolidates the results. An AI model is used once more to translate the data analysis into a comprehensible answer to the user's query.

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