What is the difference between create_pandas_dataframe_agent & PythonAstREPLTool?

[mraguth]

I like to query two dataframes together. What is the better approch?

References:
PythonAstREPLTool = https://api.python.langchain.com/en/latest/tools/langchain_experimental.tools.python.tool.PythonAstREPLTool.html
create_pandas_dataframe_agent = https://api.python.langchain.com/en/latest/agents/langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent.html

[dosubot[bot]]

The create_pandas_dataframe_agent is better suited for querying two dataframes together. It allows an LLM to iteratively execute code while maintaining the inputs and outputs of previous executions, which is beneficial for handling complex interactions with multiple dataframes. In contrast, the PythonAstREPLTool is more appropriate for executing specific Python code snippets, typically for simpler, one-off tasks ^[1].

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[mraguth]

@dosu If I like to use 'create_pandas_dataframe_agent' with langgraph agent? is that possible? do you have some sample code please?

[dosubot[bot]]

Yes, it is possible to use create_pandas_dataframe_agent with a LangChain agent. Here is a sample code for this integration:

from langchain_experimental.agents import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
import pandas as pd

# Load your DataFrame
df = pd.read_csv("titanic.csv")

# Initialize the language model
llm = ChatOpenAI()

# Create the pandas dataframe agent
agent = create_pandas_dataframe_agent(llm, df, agent_type="openai-tools", verbose=True)

# Use the agent to perform a query
result = agent.invoke({
    "input": "What's the correlation between age and fare? is that greater than the correlation between fare and survival?"
})

print(result)

This code demonstrates how to create a pandas dataframe agent using the create_pandas_dataframe_agent function and a language model, and then use the agent to perform a query on the dataframe ^[1].

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[mraguth]

@dosu... which one is more powerful method if I like to RAG via Python/Pandas between create_pandas_dataframe_agent & PythonAstREPLTool regardless of one or multiple dataframes?

[dosubot[bot]]

The create_pandas_dataframe_agent is generally more powerful for retrieval-augmented generation (RAG) tasks involving Python/Pandas, especially when working with one or multiple dataframes. It is specifically designed to handle dataframe operations and can iteratively execute code while maintaining the context of previous executions, which is beneficial for complex interactions. On the other hand, the PythonAstREPLTool is more suited for executing specific Python code snippets and may not provide the same level of integration and efficiency for dataframe-centric tasks. However, both tools have security implications due to their ability to execute arbitrary Python code, so appropriate safeguards should be implemented ^[1][2].

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[mraguth]

@dosu ... So, can I safely assume that my data frames are individual and completely separate datasets, and that this agent will never attempt to merge them? It will provide answers based solely on the individual data frames. Is that correct? Please confirm.

[dosubot[bot]]

Yes, you can safely assume that the create_pandas_dataframe_agent will treat your dataframes as individual and separate datasets. It will not attempt to merge them unless explicitly instructed to do so through your queries. The agent provides answers based solely on the operations you specify for each dataframe.

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[mraguth]

@dosu in create_pandas_dataframe_agent I added below extra_tools. However, the invoke or run function is simply skipping the 'extra_tools'. How I force to use extra tools? any thoughts?

extra_tools (Sequence[[BaseTool](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.base.BaseTool.html#langchain_core.tools.base.BaseTool)]) – Additional tools to give to agent on top of a PythonAstREPLTool.

https://api.python.langchain.com/en/latest/agents/langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent.html

[dosubot[bot]]

Could you provide more details about how you are instantiating and passing the extra_tools to the create_pandas_dataframe_agent? Additionally, are there any specific errors or logs that indicate the tools are being skipped? This information would help in diagnosing the issue.

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[mraguth]

extra_tools = [retriever_tool_n, tavily,add, subtract, multiply, divide, modulus, exponentiate, integer_divide]

from langchain_openai import ChatOpenAI
from langchain_experimental.agents import create_pandas_dataframe_agent
import pandas as pd

agent_executor = create_pandas_dataframe_agent(
    llm,
    [df1,df2],
    verbose=True,
    extra_tools=extra_tools,    
    early_stopping_method="force",    ##### Changed from generate due to Generate error.
    allow_dangerous_code=True,
    allow_dangerous_requests=True,
    handle_parsing_errors=True,
    agent_type='tool-calling',
#    engine = 'modin',
)

[mraguth]

@dosu Can I convert this 'create_pandas_dataframe_agent' as a tool and add in langgraph agent?

agent_executor = create_pandas_dataframe_agent(
    llm,
    [df1,df2],
    verbose=True,
    extra_tools=extra_tools,    
    early_stopping_method="force",    ##### Changed from generate due to Generate error.
    allow_dangerous_code=True,
    allow_dangerous_requests=True,
    handle_parsing_errors=True,
    agent_type='tool-calling',
#    engine = 'modin',
)

[dosubot[bot]]

I couldn't find specific information on converting create_pandas_dataframe_agent into a tool for integration with a langgraph agent. However, you can generally wrap the functionality of an agent as a tool by creating a custom tool class that encapsulates the agent's logic. This involves defining a class that implements the necessary interface for a tool, such as a run method that invokes the agent's functionality.

Here's a basic outline of how you might approach this:

1. Define a Custom Tool Class: Create a class that implements the tool interface, including methods like run or invoke.

2. Encapsulate the Agent Logic: Within this class, instantiate and manage the create_pandas_dataframe_agent as needed.

3. Integrate with Langgraph Agent: Add this custom tool to the langgraph agent's toolset, allowing it to be used as part of the agent's operations.

This approach allows you to leverage the capabilities of create_pandas_dataframe_agent within a broader agent framework like langgraph.

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