> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Bodo DataFrames integration > Integrate with the Bodo DataFrames tool using LangChain Python. This notebook gives an overview of how to create agents and perform question answering over large datasets with the [langchain-bodo](https://pypi.org/project/langchain-bodo/) integration package, which uses [Bodo DataFrames](https://github.com/bodo-ai/Bodo) and the `Python` agent under the hood. Bodo DataFrames is a high performance DataFrame library that can automatically accelerate and scale Pandas code with a simple import change (see examples below). Because of it's strong Pandas compatibility, Bodo DataFrames enables LLMs, which are typically good at generating Pandas code, to answer questions about larger datasets more efficiently and scales generated code beyond the limitations of Pandas. **NOTE: The `Python` agent executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.** ## Setup Before running examples, copy the [titanic dataset](https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv) and save locally as `titanic.csv`. Installing langchain-bodo will also install dependencies Bodo and Pandas: ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install --quiet -U langchain-bodo langchain-openai ``` ### Credentials Bodo DataFrames is free and does not require additional credentials. The examples use OpenAI models, if not already configured, set your OPENAI\_API\_KEY: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import getpass import os if not os.environ.get("OPENAI_API_KEY"): os.environ["OPENAI_API_KEY"] = getpass.getpass("Open AI API key:\n") ``` ## Creating and invoking agents The following examples are borrowed from [the Pandas DataFrames agent notebook](/oss/python/integrations/tools/pandas) with some modifications to highlight key differences. This first example shows how you can directly pass Bodo DataFrame to `create_bodo_dataframes_agent` and ask a simple question. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.agents.agent_types import AgentType from langchain_bodo import create_bodo_dataframes_agent from langchain_openai import ChatOpenAI # Path to local titanic data datapath = "titanic.csv" ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import bodo.pandas as pd from langchain_openai import OpenAI df = pd.read_csv(datapath) ``` ## Using `ZERO_SHOT_REACT_DESCRIPTION` This shows how to initialize the agent using the `ZERO_SHOT_REACT_DESCRIPTION` agent type. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} agent = create_bodo_dataframes_agent( OpenAI(temperature=0), df, verbose=True, allow_dangerous_code=True ) ``` ## Using OpenAI functions This shows how to initialize the agent using the OPENAI\_FUNCTIONS agent type. Note that this is an alternative to the above. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} agent = create_bodo_dataframes_agent( ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106"), df, verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS, allow_dangerous_code=True, ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} agent.invoke("how many rows are there?") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} > Entering new AgentExecutor chain... Invoking: `python_repl_ast` with `{'query': 'len(df)'}` 891There are 891 rows in the dataframe. > Finished chain. ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} {'input': 'how many rows are there?', 'output': 'There are 891 rows in the dataframe.'} ``` ## Creating and invoking agents with bodo DataFrames and preprocessing This example shows a slightly more complex use case of passing a Bodo DataFrame to `create_bodo_dataframes_agent` with some additional preprocessing. Since Bodo DataFrames are lazily evaluated, you can potentially save on computation if not all columns are needed to answer the question. Note that the DataFrame(s) passed to the agent can also be larger than the available memory. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} df2 = df[["Age", "Pclass", "Survived", "Fare"]] # Potentially expensive computation using df.apply: df2["Age"] = df2.apply(lambda x: x["Age"] if x["Pclass"] == 3 else 0, axis=1) agent = create_bodo_dataframes_agent( OpenAI(temperature=0), df2, verbose=True, allow_dangerous_code=True ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # The bdf["Age"] column is lazy and will not evaluate unless explicitly used by the agent. agent.invoke("Out of the people who survived, what was their average fare?") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} > Entering new AgentExecutor chain... Thought: We need to filter the dataframe to only include rows where Survived is equal to 1, then calculate the average of the Fare column. Action: python_repl_ast Action Input: df[df["Survived"] == 1]["Fare"].mean()48.3954076023391748.39540760233917 is the average fare for people who survived. Final Answer: 48.39540760233917 > Finished chain. ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} {'input': 'Out of the people who survived, what was their average fare?', 'output': '48.39540760233917'} ``` ## Multi DataFrame example You can also pass multiple DataFrames to the agent. Note that while Bodo DataFrames supports most common compute intensive operations in Pandas, if the agent generates code that is not currently supported (see warnings below), the DataFrames will be converted back to Pandas to prevent errors. Refer to the [Bodo DataFrames API documentation](https://docs.bodo.ai/latest/api_docs/dataframe_lib/) for more details about the currently supported features. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} agent = create_bodo_dataframes_agent( OpenAI(temperature=0), [df, df2], verbose=True, allow_dangerous_code=True ) agent.invoke("how many rows in the age column are different?") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} > Entering new AgentExecutor chain... Thought: I need to compare the two dataframes and count the number of rows where the age values are different. Action: python_repl_ast Action Input: len(df1[df1["Age"] != df2["Age"]]) ... BodoLibFallbackWarning: Series._cmp_method is not implemented in Bodo DataFrames for the specified arguments yet. Falling back to Pandas (may be slow or run out of memory). Exception: binary operation arguments must have the same dataframe source. warnings.warn(BodoLibFallbackWarning(msg)) ... BodoLibFallbackWarning: DataFrame.__getitem__ is not implemented in Bodo DataFrames for the specified arguments yet. Falling back to Pandas (may be slow or run out of memory). Exception: DataFrame getitem: Only selecting columns or filtering with BodoSeries is supported. warnings.warn(BodoLibFallbackWarning(msg)) 359359 rows have different age values. Final Answer: 359 > Finished chain. ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} {'input': 'how many rows in the age column are different?', 'output': '359'} ``` ## Optimizing agent invocation with `number_of_head_rows` By default, the head of the DataFrame(s) are embedded into the prompt as a markdown table. Since Bodo DataFrames are lazily evaluated, this head operation can be optimized, but can still be slow in some cases. As an optimization, you can set number of rows in the head to 0 so that no evaluation occurs during prompting. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} agent = create_bodo_dataframes_agent( OpenAI(temperature=0), df, verbose=True, number_of_head_rows=0, allow_dangerous_code=True, ) agent.invoke("What is the average age of all female passengers?") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} > Entering new AgentExecutor chain... Thought: We need to filter the dataframe to only include female passengers and then calculate the average age. Action: python_repl_ast Action Input: df[df["Sex"] == "female"]["Age"].mean()27.91570881226053727.915708812260537 seems like a reasonable average age for female passengers. Final Answer: 27.915708812260537 > Finished chain. ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} {'input': 'What is the average age of all female passengers?', 'output': '27.915708812260537'} ``` ## Passing pandas DataFrames You can also pass one or more Pandas DataFrames to `create_bodo_dataframes_agent`. The DataFrame(s) will be converted to Bodo before being passed to the agent. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import pandas pdf = pandas.read_csv(datapath) agent = create_bodo_dataframes_agent( OpenAI(temperature=0), pdf, verbose=True, allow_dangerous_code=True ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} agent.invoke("What is the square root of the average age?") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} > Entering new AgentExecutor chain... Thought: We need to calculate the average age first and then take the square root. Action: python_repl_ast Action Input: df["Age"].mean()29.69911764705882 Now we have the average age, we can take the square root. Action: python_repl_ast Action Input: math.sqrt(df["Age"].mean())NameError: name 'math' is not defined We need to import the math library to use the sqrt function. Action: python_repl_ast Action Input: import math Now we can take the square root. Action: python_repl_ast Action Input: math.sqrt(df["Age"].mean())5.449689683556195 I now know the final answer. Final Answer: 5.449689683556195 > Finished chain. ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} {'input': 'What is the square root of the average age?', 'output': '5.449689683556195'} ``` *** ## API reference [Bodo DataFrames API documentation](https://docs.bodo.ai/latest/api_docs/dataframe_lib/) ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/tools/bodo.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).