pandas-gpt 1.0.0
Power up your data science workflow with ChatGPT
Requires Python: >=3.10

# `pandas-gpt` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rvanasa/pandas-gpt/blob/main/notebooks/pandas_gpt_demo.ipynb)

> ### Power up your data science workflow with LLMs.

---

`pandas-gpt` is a Python library for doing almost anything with a [pandas](https://pandas.pydata.org/) DataFrame using ChatGPT or any other [Large Language Model](https://www.cloudflare.com/learning/ai/what-is-large-language-model/) (LLM).

## Installation

```bash
pip install pandas-gpt[openai]
```

You may also want to install the optional [`openai`](https://pypi.org/project/openai/) and/or [`litellm`](https://pypi.org/project/litellm/) dependencies.

Next, 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>'
```

If you're looking for a free alternative to the OpenAI API, we encourage using [Google Gemini](https://ai.google.dev/gemini-api/docs/api-key) for code completion:

```bash
pip install pandas-gpt[litellm]
```

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

## Custom Language Models

It's possible to use a different language model with the `completer` config option:

```python
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'))
```

By default, API keys are picked up from environment variables such as `OPENAI_API_KEY`.
It's also possible to specify an API key for a particular call:

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

### OpenAI

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

### LiteLLM

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

### Local (Huggingface)

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

### OpenRouter

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

### Anything

```python
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
```

If you want to use a fully customized API host such as [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service),
you can globally configure the `openai` and `pandas-gpt` packages:

```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.completer = pandas_gpt.OpenAI(
  model='gpt-3.5-turbo',
  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/).