PandasAI offers the flexibility to handle chat responses in a customized manner. By default, PandasAI includes a ResponseParser class that can be extended to modify the response output according to your needs.

You have the option to provide a custom parser, such as StreamlitResponse, to the configuration object like this:

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Example Usage



import os

import pandas as pd

from pandasai import SmartDatalake

from pandasai.responses.response_parser import ResponseParser



# This class overrides default behaviour how dataframe is returned

# By Default PandasAI returns the SmartDataFrame

class PandasDataFrame(ResponseParser):



    def __init__(self, context) -> None:

        super().__init__(context)



    def format_dataframe(self, result):

        # Returns Pandas Dataframe instead of SmartDataFrame

        return result["value"]





employees_df = pd.DataFrame(

    {

        "EmployeeID": [1, 2, 3, 4, 5],

        "Name": ["John", "Emma", "Liam", "Olivia", "William"],

        "Department": ["HR", "Sales", "IT", "Marketing", "Finance"],

    }

)



salaries_df = pd.DataFrame(

    {

        "EmployeeID": [1, 2, 3, 4, 5],

        "Salary": [5000, 6000, 4500, 7000, 5500],

    }

)



agent = SmartDatalake(

    [employees_df, salaries_df],

    config={"llm": llm, "verbose": True, "response_parser": PandasDataFrame},

)



response = agent.chat("Return a dataframe of name against salaries")

# Returns the response as Pandas DataFrame

​
Streamlit Example



import os

import pandas as pd

from pandasai import SmartDatalake

from pandasai.responses.streamlit_response import StreamlitResponse



employees_df = pd.DataFrame(

    {

        "EmployeeID": [1, 2, 3, 4, 5],

        "Name": ["John", "Emma", "Liam", "Olivia", "William"],

        "Department": ["HR", "Sales", "IT", "Marketing", "Finance"],

    }

)



salaries_df = pd.DataFrame(

    {

        "EmployeeID": [1, 2, 3, 4, 5],

        "Salary": [5000, 6000, 4500, 7000, 5500],

    }

)



agent = SmartDatalake(

    [employees_df, salaries_df],

    config={"verbose": True, "response_parser": StreamlitResponse},

)



agent.chat("Plot salaries against name")