pandas-gpt-0.6.0.tar.gz

# `pandas-gpt` [](https://colab.research.google.com/github/rvanasa/pandas-gpt/blob/main/notebooks/pandas_gpt_demo.ipynb) > ### Power up your data science workflow with ChatGPT. --- `pandas-gpt` is a Python library for doing almost anything with a [pandas](https://pandas.pydata.org/) DataFrame using ChatGPT prompts. ## Installation ```bash pip install pandas-gpt ``` Set the `OPENAI_API_KEY` environment variable to your [OpenAI API key](https://platform.openai.com/account/api-keys), or use the following code snippet: ```python import openai openai.api_key = '<API Key>' ``` ## Examples Setup and usage examples are available in this **[Google Colab notebook](https://colab.research.google.com/github/rvanasa/pandas-gpt/blob/main/notebooks/pandas_gpt_demo.ipynb)**. ```python 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) ``` ## Other Hosts If you want to use a different API host such as [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service): ```python 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>', } ``` ## Alternatives - [GitHub Copilot](https://github.com/features/copilot): General-purpose code completion (paid subscription) - [Sketch](https://github.com/approximatelabs/sketch): AI-powered data summarization and code suggestions (works without an API key) ## Disclaimer Please note that the [limitations](https://github.com/openai/gpt-3/blob/master/model-card.md#limitations) of ChatGPT also apply to this library. I would recommend using `pandas-gpt` in a sandboxed environment such as [Google Colab](https://colab.research.google.com), [Kaggle](https://www.kaggle.com/docs/notebooks), or [GitPod](https://www.gitpod.io/).