pip install pandas-gpt[openai]

import openai
openai.api_key = '<API Key>'

pip install pandas-gpt[litellm]

import pandas_gpt
pandas_gpt.completer = pandas_gpt.LiteLLM('gemini/gemini-1.5-pro', api_key='...')

import pandas as pd
import pandas_gpt

df = pd.DataFrame('https://gist.githubusercontent.com/bluecoconut/9ce2135aafb5c6ab2dc1d60ac595646e/raw/c93c3500a1f7fae469cba716f09358cfddea6343/sales_demo_with_pii_and_all_states.csv')

# Data transformation
df = df.ask('drop purchases from Laurenchester, NY')
df = df.ask('add a new Category column with values "cheap", "regular", or "expensive"')

# Queries
weekday = df.ask('which day of the week had the largest number of orders?')
top_10 = df.ask('what are the top 10 most popular products, as a table')

# Plotting
df.ask('plot monthly and hourly sales')
top_10.ask('horizontal bar plot with pastel colors')

# Allow changes to original dataset
df.ask('do something interesting', mutable=True)

# Show source code before running
df.ask('convert prices from USD to GBP', verbose=True)

import pandas_gpt

# Global default
pandas_gpt.completer = pandas_gpt.OpenAI('gpt-3.5-turbo')

# Custom completer for a specific request
df.ask('Do something interesting with the data', completer=pandas_gpt.LiteLLM('gemini/gemini-1.5-pro'))

df.ask('Do something important with the data', completer=pandas_gpt.OpenAI('gpt-4o', api_key='...'))

pandas_gpt.completer = pandas_gpt.OpenAI('gpt-4o')

pandas_gpt.completer = pandas_gpt.LiteLLM('gemini/gemini-1.5-pro')

pandas_gpt.completer = pandas_gpt.LiteLLM('huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct')

pandas_gpt.completer = pandas_gpt.OpenRouter('anthropic/claude-3.5-sonnet')

import openai
openai.api_type = 'azure'
openai.api_base = '<Endpoint>'
openai.api_version = '<Version>'
openai.api_key = '<API Key>'

import pandas_gpt
pandas_gpt.completer = pandas_gpt.AzureOpenAI(
  model='gpt-3.5-turbo',
  engine='<Engine>',
  deployment_id='<Deployment ID>',
)

def my_custom_completer(prompt: str) -> str:
  # Use an LLM or any other method to create a `process()` function that
  # takes a pandas DataFrame as a single argument, does some operations on it,
  # and return a DataFrame.
  return 'def process(df): ...'

pandas_gpt.completer = my_custom_completer