# Xorbits

This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.

**NOTE: this agent calls the Python agent under the hood, which
executes LLM generated Python code - this can be bad if the LLM
generated Python code is harmful. Use cautiously.**

## Pandas examples

`import xorbits.pandas as pd`

from langchain_experimental.agents.agent_toolkits import create_xorbits_agent

from langchain_openai import OpenAI

`data = pd.read_csv("titanic.csv")`

agent = create_xorbits_agent(OpenAI(temperature=0), data, verbose=True)

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`agent.run("How many rows and columns are there?")`

> Entering new chain...

Thought: I need to count the number of rows and columns

Action: python_repl_ast

Action Input: data.shape

Observation: (891, 12)

Thought: I now know the final answer

Final Answer: There are 891 rows and 12 columns.

> Finished chain.

`'There are 891 rows and 12 columns.'`

`agent.run("How many people are in pclass 1?")`

> Entering new chain...

Thought: I need to count the number of people in pclass 1

Action: python_repl_ast

Action Input: data[data['Pclass'] == 1].shape[0]

Observation: 216

Thought: I now know the final answer

Final Answer: There are 216 people in pclass 1.

> Finished chain.

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`'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()

Observation: 29.69911764705882

Thought: I now know the final answer

Final Answer: The mean age is 29.69911764705882.

> Finished chain.

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`'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()

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.

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`'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...

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.

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`'20'`

## Numpy examples

`import xorbits.numpy as np`

from langchain.agents import create_xorbits_agent

from langchain_openai import OpenAI

arr = np.array([1, 2, 3, 4, 5, 6])

agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True)

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`agent.run("Give the shape of the array ")`

> Entering new chain...

Thought: I need to find out the shape of the array

Action: python_repl_ast

Action Input: data.shape

Observation: (6,)

Thought: I now know the final answer

Final Answer: The shape of the array is (6,).

> Finished chain.

`'The shape of the array is (6,).'`

`agent.run("Give the 2nd element of the array ")`

> Entering new chain...

Thought: I need to access the 2nd element of the array

Action: python_repl_ast

Action Input: data[1]

Observation: 2

Thought: I now know the final answer

Final Answer: 2

> Finished chain.

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`'2'`

`agent.run(`

"Reshape the array into a 2-dimensional array with 2 rows and 3 columns, and then transpose it"

)

> Entering new chain...

Thought: I need to reshape the array and then transpose it

Action: python_repl_ast

Action Input: np.reshape(data, (2,3)).T

Observation: [[1 4]

[2 5]

[3 6]]

Thought: I now know the final answer

Final Answer: The reshaped and transposed array is [[1 4], [2 5], [3 6]].

> Finished chain.

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`'The reshaped and transposed array is [[1 4], [2 5], [3 6]].'`

`agent.run(`

"Reshape the array into a 2-dimensional array with 3 rows and 2 columns and sum the array along the first axis"

)

> Entering new chain...

Thought: I need to reshape the array and then sum it

Action: python_repl_ast

Action Input: np.sum(np.reshape(data, (3,2)), axis=0)

Observation: [ 9 12]

Thought: I now know the final answer

Final Answer: The sum of the array along the first axis is [9, 12].

> Finished chain.

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`'The sum of the array along the first axis is [9, 12].'`

`arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])`

agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True)

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`agent.run("calculate the covariance matrix")`

> Entering new chain...

Thought: I need to use the numpy covariance function

Action: python_repl_ast

Action Input: np.cov(data)

Observation: [[1. 1. 1.]

[1. 1. 1.]

[1. 1. 1.]]

Thought: I now know the final answer

Final Answer: The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].

> Finished chain.

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`'The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].'`

`agent.run("compute the U of Singular Value Decomposition of the matrix")`

> Entering new chain...

Thought: I need to use the SVD function

Action: python_repl_ast

Action Input: U, S, V = np.linalg.svd(data)

Observation:

Thought: I now have the U matrix

Final Answer: U = [[-0.70710678 -0.70710678]

[-0.70710678 0.70710678]]

> Finished chain.

`'U = [[-0.70710678 -0.70710678]\n [-0.70710678 0.70710678]]'`