Learn how to analyze CSV datasets with pandas and the OpenAI API to extract structured, point-form insights.Documentation Index
Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
Use this file to discover all available pages before exploring further.
Prerequisites
| Requirement | Version / Link |
|---|---|
| Python | 3.7+ |
| OpenAI API Key | https://platform.openai.com/account/api-keys |
| pandas | https://pandas.pydata.org/ |
| OpenAI SDK | https://pypi.org/project/openai/ |
Installation
Install both pandas and the OpenAI Python client:If you already have these packages installed, pip will confirm that the requirements are satisfied.
Configuration
Import the necessary modules and initialize your OpenAI client.Warning: Never commit your API key to version control.
"YOUR_API_KEY" with your actual key or load it from an environment variable.
Loading Your CSV Dataset
Download a CSV (for example, from Kaggle) and load it into a pandas DataFrame:Defining the Analysis Function
This function converts the DataFrame to CSV text, invokes the GPT-4 model, and returns the AI-generated insights:API Call Parameters
| Parameter | Description | Example |
|---|---|---|
| model | OpenAI model to use | "gpt-4" |
| max_tokens | Maximum tokens in the response | 500 |
| temperature | Controls randomness; lower is more deterministic | 0.2 |
Running the Assistant
Invoke the function and print the summary:Focusing on Demographics
To target only demographic columns (e.g., age, gender, country), filter before sending:Conclusion
You’ve now built an AI research assistant that:- Installs and imports pandas & OpenAI SDK.
- Loads a CSV into a DataFrame.
- Sends your data to GPT-4.
- Returns structured, point-form insights.