ComponentsAgents and toolkitsCSVOn this pageCSVThis notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.from langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.agents.agent_types import AgentTypefrom langchain.agents import create_csv_agentUsing ZERO_SHOT_REACT_DESCRIPTION​This shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type. Note that this is an alternative to the above.agent = create_csv_agent(    OpenAI(temperature=0),    "titanic.csv",    verbose=True,    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)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.agent = create_csv_agent(    ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),    "titanic.csv",    verbose=True,    agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows are there?")    Error in on_chain_start callback: 'name'        Invoking: `python_repl_ast` with `df.shape[0]`            891There are 891 rows in the dataframe.        > Finished chain.    'There are 891 rows in the dataframe.'agent.run("how many people have more than 3 siblings")    Error in on_chain_start callback: 'name'        Invoking: `python_repl_ast` with `df[df['SibSp'] > 3]['PassengerId'].count()`            30There are 30 people in the dataframe who have more than 3 siblings.        > Finished chain.    'There are 30 people in the dataframe who have more than 3 siblings.'agent.run("whats the square root of the average age?")    Error in on_chain_start callback: 'name'        Invoking: `python_repl_ast` with `import pandas as pd    import math        # Create a dataframe    data = {'Age': [22, 38, 26, 35, 35]}    df = pd.DataFrame(data)        # Calculate the average age    average_age = df['Age'].mean()        # Calculate the square root of the average age    square_root = math.sqrt(average_age)        square_root`            5.585696017507576The square root of the average age is approximately 5.59.        > Finished chain.    'The square root of the average age is approximately 5.59.'Multi CSV Example​This next part shows how the agent can interact with multiple csv files passed in as a list.agent = create_csv_agent(    ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),    ["titanic.csv", "titanic_age_fillna.csv"],    verbose=True,    agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows in the age column are different between the two dfs?")    Error in on_chain_start callback: 'name'        Invoking: `python_repl_ast` with `df1['Age'].nunique() - df2['Age'].nunique()`            -1There is 1 row in the age column that is different between the two dataframes.        > Finished chain.    'There is 1 row in the age column that is different between the two dataframes.'PreviousClickUp Langchiain ToolkitNextDocument ComparisonUsing ZERO_SHOT_REACT_DESCRIPTIONUsing OpenAI FunctionsMulti CSV Example