Pandas 数据框
本笔记本展示了如何使用代理与Pandas DataFrame进行交互。它主要针对问答进行了优化。
注意：此代理在底层调用了Python代理，该代理执行由LLM生成的Python代码 - 如果LLM生成的Python代码有害，这可能会很糟糕。请谨慎使用。
注意：由于langchain已迁移到v0.3版本，您应该升级langchain_openai和langchain。这将避免导入错误。
pip install --upgrade langchain_openai pip install --upgrade langchain
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
import pandas as pd
from langchain_openai import OpenAI
df = pd.read_csv(
"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"
)
API Reference:OpenAI
使用 ZERO_SHOT_REACT_DESCRIPTION
这展示了如何使用ZERO_SHOT_REACT_DESCRIPTION代理类型初始化代理。
agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)
使用 OpenAI 函数
这展示了如何使用OPENAI_FUNCTIONS代理类型初始化代理。请注意，这是上述方法的替代方案。
agent = create_pandas_dataframe_agent(
ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),
df,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
)
agent.invoke("how many rows are there?")
[1m> Entering new chain...[0m
[32;1m[1;3m
Invoking: `python_repl_ast` with `df.shape[0]`
[0m[36;1m[1;3m891[0m[32;1m[1;3mThere are 891 rows in the dataframe.[0m
[1m> Finished chain.[0m
'There are 891 rows in the dataframe.'
agent.invoke("how many people have more than 3 siblings")
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mThought: I need to count the number of people with more than 3 siblings
Action: python_repl_ast
Action Input: df[df['SibSp'] > 3].shape[0][0m
Observation: [36;1m[1;3m30[0m
Thought:[32;1m[1;3m I now know the final answer
Final Answer: 30 people have more than 3 siblings.[0m
[1m> Finished chain.[0m
'30 people have more than 3 siblings.'
agent.invoke("whats the square root of the average age?")
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mThought: I need to calculate the average age first
Action: python_repl_ast
Action Input: df['Age'].mean()[0m
Observation: [36;1m[1;3m29.69911764705882[0m
Thought:[32;1m[1;3m I now need to calculate the square root of the average age
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())[0m
Observation: [36;1m[1;3mNameError("name 'math' is not defined")[0m
Thought:[32;1m[1;3m I need to import the math library
Action: python_repl_ast
Action Input: import math[0m
Observation: [36;1m[1;3m[0m
Thought:[32;1m[1;3m I now need to calculate the square root of the average age
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())[0m
Observation: [36;1m[1;3m5.449689683556195[0m
Thought:[32;1m[1;3m I now know the final answer
Final Answer: The square root of the average age is 5.449689683556195.[0m
[1m> Finished chain.[0m
'The square root of the average age is 5.449689683556195.'
多数据框示例
下一部分展示了代理如何与作为列表传递的多个数据框进行交互。
df1 = df.copy()
df1["Age"] = df1["Age"].fillna(df1["Age"].mean())
agent = create_pandas_dataframe_agent(OpenAI(temperature=0), [df, df1], verbose=True)
agent.invoke("how many rows in the age column are different?")
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mThought: I need to compare the age columns in both dataframes
Action: python_repl_ast
Action Input: len(df1[df1['Age'] != df2['Age']])[0m
Observation: [36;1m[1;3m177[0m
Thought:[32;1m[1;3m I now know the final answer
Final Answer: 177 rows in the age column are different.[0m
[1m> Finished chain.[0m
'177 rows in the age column are different.'