pandas-gpt 0.5.0

Power up your data science workflow with ChatGPT

pandas-gpt

Power up your data science workflow with ChatGPT.

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pandas-gpt is a Python library for doing almost anything with a pandas DataFrame based on natural language queries.

Installation

pip install pandas-gpt

Set the OPENAI_API_KEY environment variable to your OpenAI API key, or use the following code snippet:

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

If you are using another host like Azure:

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

import pandas_gpt
# pandas_gpt.model = '<Model>' # Default is 'gpt-3.5-turbo'
pandas_gpt.completion_config = {
  'engine': '<Engine>',
  # 'deployment_id': '<Deployment ID>',
}

Examples

Setup and usage examples are available in this Google Colab notebook.

import pandas as pd
import pandas_gpt

df = pd.DataFrame(...)

# Run Python code generated by ChatGPT
df.ask('plot x and y with nice colors')

# Return a value
model = df.ask('LightGBM model trained on the dataset')

# Specify a column or index
df['my_column'].ask('geometric mean')

# Show additional output
df.ask('clean the dataset', verbose=True)

# Print source code without running
df.ask.code('do something interesting with the dataset')

Alternatives

• GitHub Copilot: General-purpose code completion (paid subscription)
• Sketch: AI-powered data summarization and code suggestions (works without an API key)

Disclaimer

Please note that the limitations of ChatGPT also apply to this library. I would recommend using pandas-gpt in a sandboxed environment such as Google Colab, Kaggle, or GitPod.