Observation: 216    Thought: I now know the final answer    Final Answer: There are 216 people in pclass 1.        > Finished chain.    'There are 216 people in pclass 1.'agent.run("whats the mean age?")            > Entering new  chain...    Thought: I need to calculate the mean age    Action: python_repl_ast    Action Input: data['Age'].mean()      0%|          |   0.00/100 [00:00<?, ?it/s]        Observation: 29.69911764705882    Thought: I now know the final answer    Final Answer: The mean age is 29.69911764705882.        > Finished chain.    'The mean age is 29.69911764705882.'agent.run("Group the data by sex and find the average age for each group")            > Entering new  chain...    Thought: I need to group the data by sex and then find the average age for each group    Action: python_repl_ast    Action Input: data.groupby('Sex')['Age'].mean()      0%|          |   0.00/100 [00:00<?, ?it/s]        Observation: Sex    female    27.915709    male      30.726645    Name: Age, dtype: float64    Thought: I now know the average age for each group    Final Answer: The average age for female passengers is 27.92 and the average age for male passengers is 30.73.        > Finished chain.    'The average age for female passengers is 27.92 and the average age for male passengers is 30.73.'agent.run(    "Show the number of people whose age is greater than 30 and fare is between 30 and 50 , and pclass is either 1 or 2")            > Entering new  chain...      0%|          |   0.00/100 [00:00<?, ?it/s]    Thought: I need to filter the dataframe to get the desired result    Action: python_repl_ast    Action Input: data[(data['Age'] > 30) & (data['Fare'] > 30) & (data['Fare'] < 50) & ((data['Pclass'] == 1) | (data['Pclass'] == 2))].shape[0]    Observation: 20    Thought: I now know the final answer    Final Answer: 20        > Finished chain.    '20'Numpy examples‚Äãimport xorbits.numpy as npfrom langchain.agents import create_xorbits_agentfrom langchain.llms import
This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.
This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering. ->: Observation: 216    Thought: I now know the final answer    Final Answer: There are 216 people in pclass 1.        > Finished chain.    'There are 216 people in pclass 1.'agent.run("whats the mean age?")            > Entering new  chain...    Thought: I need to calculate the mean age    Action: python_repl_ast    Action Input: data['Age'].mean()      0%|          |   0.00/100 [00:00<?, ?it/s]        Observation: 29.69911764705882    Thought: I now know the final answer    Final Answer: The mean age is 29.69911764705882.        > Finished chain.    'The mean age is 29.69911764705882.'agent.run("Group the data by sex and find the average age for each group")            > Entering new  chain...    Thought: I need to group the data by sex and then find the average age for each group    Action: python_repl_ast    Action Input: data.groupby('Sex')['Age'].mean()      0%|          |   0.00/100 [00:00<?, ?it/s]        Observation: Sex    female    27.915709    male      30.726645    Name: Age, dtype: float64    Thought: I now know the average age for each group    Final Answer: The average age for female passengers is 27.92 and the average age for male passengers is 30.73.        > Finished chain.    'The average age for female passengers is 27.92 and the average age for male passengers is 30.73.'agent.run(    "Show the number of people whose age is greater than 30 and fare is between 30 and 50 , and pclass is either 1 or 2")            > Entering new  chain...      0%|          |   0.00/100 [00:00<?, ?it/s]    Thought: I need to filter the dataframe to get the desired result    Action: python_repl_ast    Action Input: data[(data['Age'] > 30) & (data['Fare'] > 30) & (data['Fare'] < 50) & ((data['Pclass'] == 1) | (data['Pclass'] == 2))].shape[0]    Observation: 20    Thought: I now know the final answer    Final Answer: 20        > Finished chain.    '20'Numpy examples‚Äãimport xorbits.numpy as npfrom langchain.agents import create_xorbits_agentfrom langchain.llms import