Data Analysis with ChatGPT

Introduction

The ChatGPT API can be used to analyze and interpret data in a variety of ways. In this tutorial, we will explore how to use ChatGPT for data analysis, visualization, and interpretation of results.

Types of Analysis

1. Descriptive analysis

   • Basic statistics
   • Trends and patterns
   • Data summaries

2. Predictive analysis

   • Forecasting
   • Anomaly detection
   • Classification

3. Text analysis

   • Sentiment analysis
   • Entity extraction
   • Text classification

Integration with Python

1. Basic Configuration

import openai
import pandas as pd
import numpy as np
from typing import List, Dict, Any

class ChatGPTAnalyzer:
    def __init__(self, api_key: str):
        openai.api_key = api_key
        
    def analyze_dataframe(self, df: pd.DataFrame, analysis_type: str) -> str:
        """
        Analyse un DataFrame pandas en utilisant ChatGPT
        """
        # Convertir le DataFrame en format texte
        data_description = df.describe().to_string()
        data_sample = df.head().to_string()
        
        prompt = f"""
        Analysez les données suivantes de type {analysis_type}:
        
        Description statistique:
        {data_description}
        
        Échantillon de données:
        {data_sample}
        
        Veuillez fournir:
        1. Un résumé des observations clés
        2. Les tendances principales
        3. Des recommandations basées sur les données
        """
        
        try:
            response = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "system", "content": "Vous êtes un expert en analyse de données."},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.3
            )
            
            return response.choices[0].message.content
        except Exception as e:
            print(f"Erreur lors de l'analyse: {str(e)}")
            return None