By default, PandasAI only allows to run code that uses some whitelisted modules. This is to prevent malicious code from being executed on the server or locally.

The whitelisted modules are:

  • pandas
  • numpy
  • matplotlib
  • seaborn
  • datetime
  • json
  • base64

These libraries are sandboxed for security reasons, so that malicious code cannot be executed on the server or locally.

However, it is possible to add custom modules to the whitelist. This can be done by passing a list of modules to the custom_whitelisted_dependencies parameter when instantiating the Agent class.

Note: PandasAI cannot sandbox arbitrary code execution for custom libraries that are whitelisted. If you add a custom library to the whitelist, arbitrary code execution will be possible for that library. Whitelisting a custom library means that the library is “trusted” and can be used without any limitations. Only whitelist libraries that are under your control or that you trust.

For example, to add the scikit-learn module to the whitelist:

from pandasai import Agent
agent = Agent("data.csv", config={
    "custom_whitelisted_dependencies": ["scikit-learn"]
})

The custom_whitelisted_dependencies parameter accepts a list of strings, where each string is the name of a module. The module must be installed in the environment where PandasAI is running.

Please, make sure you have installed the module in the environment where PandasAI is running. Otherwise, you will get an error when trying to run the code.

By default, PandasAI only allows to run code that uses some whitelisted modules. This is to prevent malicious code from being executed on the server or locally.

The whitelisted modules are:

  • pandas
  • numpy
  • matplotlib
  • seaborn
  • datetime
  • json
  • base64

These libraries are sandboxed for security reasons, so that malicious code cannot be executed on the server or locally.

However, it is possible to add custom modules to the whitelist. This can be done by passing a list of modules to the custom_whitelisted_dependencies parameter when instantiating the Agent class.

Note: PandasAI cannot sandbox arbitrary code execution for custom libraries that are whitelisted. If you add a custom library to the whitelist, arbitrary code execution will be possible for that library. Whitelisting a custom library means that the library is “trusted” and can be used without any limitations. Only whitelist libraries that are under your control or that you trust.

For example, to add the scikit-learn module to the whitelist:

from pandasai import Agent
agent = Agent("data.csv", config={
    "custom_whitelisted_dependencies": ["scikit-learn"]
})

The custom_whitelisted_dependencies parameter accepts a list of strings, where each string is the name of a module. The module must be installed in the environment where PandasAI is running.

Please, make sure you have installed the module in the environment where PandasAI is running. Otherwise, you will get an error when trying to run the code.