Getting started with Python and Pandas for analysis
Complete beginner guide to data analysis with Python Pandas, from data loading to first visualizations.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Je débute avec Python pour l'analyse de données et je veux analyser [DESCRIPTION_DONNEES] contenu dans un fichier [FORMAT_FICHIER] (CSV / Excel / JSON). Mon environnement : [ENVIRONNEMENT] (Jupyter Notebook / Google Colab / VS Code).
Mes données ont les caractéristiques suivantes :
- Nombre de lignes approximatif : [LIGNES]
- Colonnes principales : [COLONNES]
- Type d'analyse souhaitée : [TYPE_ANALYSE]
Guide-moi étape par étape pour mon analyse avec Python Pandas :
- Installation et import des bibliothèques nécessaires (pandas, numpy, matplotlib, seaborn)
- Chargement des données avec gestion des encodages et des valeurs manquantes
- Exploration initiale : shape, dtypes, head(), describe(), info()
- Nettoyage basique : gestion des NaN, doublons, types de colonnes
- Sélection, filtrage et tri des données (loc, iloc, query)
- Agrégations et groupby pour les statistiques par catégorie
- Création de 3 visualisations pertinentes pour mes données avec matplotlib/seaborn
- Sauvegarde des résultats (CSV, Excel)
Fournis le code complet commenté ligne par ligne pour un débutant.
Explique chaque concept la première fois qu'il apparaît.
Signale les erreurs courantes pour chaque étape.
Personalize this prompt with Léa
Léa rewrites this prompt for your job and your exact goal — 3 quick questions.
Why this prompt works
<p>This prompt is effective for beginners because it requires line-by-line comments and conceptual explanations, transforming code into learning. The request for common error handling prevents frustration.</p>
Use Cases
Expected Output
Complete line-by-line commented Python Pandas code with conceptual explanations and 3 visualizations.
Improve this prompt
Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
Improve this prompt with the OptimizerComments
- LéaAI
Ajoutez `parse_dates=['nom_colonne_date']` dans `pd.read_csv()` pour convertir automatiquement les dates. Cela évite des conversions manuelles et des erreurs de format. Très utile pour les chronologies ou séries temporelles.
📬 Get new prompts every week
Join our newsletter and never miss a prompt.
Go further
Similar Prompts
Correlation analysis between variables
Analyze correlations between dataset variables with business interpretation and heatmap visualization.
Gemini Prompt for Analyzing a Survey
Surveys are powerful tools for collecting data, but their true value lies in analyzing the results. Gemini, Google's artificial intelligence model, excels at interpreting qualitative and quantitative survey data. Whether you have collected responses via Google Forms, Typeform, or any other tool, Gemini can identify hidden trends, cross-reference demographic variables with responses, and produce actionable summaries in seconds. While manual analysis would take hours of sorting and categorizing, Gemini automatically structures verbatims, detects dominant sentiments, and quantifies recurring themes. This capability is particularly useful for marketing teams, researchers, product managers, and consultants who need to quickly transform raw data into strategic recommendations. In this guide, you will find an optimized prompt for Gemini as well as variants adapted to your expertise level and survey complexity.
Sora Prompt for Extracting Data Insights
Sora, the artificial intelligence model developed by OpenAI, is not limited to video generation. Used strategically, it can leverage visual and narrative data to extract actionable data insights. Whether you are a data analyst, growth marketer, or product manager, knowing how to formulate the right prompt to obtain an analytical synthesis from content generated or analyzed by Sora represents a major competitive advantage. Extracting data insights with Sora relies on the model's ability to interpret complex scenarios, identify visual patterns, and translate them into structured observations. By combining precise instructions on the output format, desired analysis dimensions, and expected level of granularity, you transform Sora into a true decision intelligence tool. This guide provides an optimized main prompt, its variants by expertise level, and practical tips to maximize the relevance of extracted insights. Each prompt is designed to produce actionable results, directly integrable into your dashboards and strategic presentations.
Perform cohort analysis
Analyze user retention by cohort