Analyze a CSV file and generate a dashboard
This Cowork prompt transforms any CSV file into a structured analysis. Claude explores the data, cleans it, applies relevant analyses based on detected data type (sales, customers, marketing) and generates actionable insights. The report includes statistics, trends, and recommendations. Multiple exports (Markdown, cleaned CSV, JSON) facilitate follow-up work.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Analyze the data file [path to the CSV, e.g., ~/Data/sales-data.csv] and generate a complete analysis report.
Step 1: Data exploration
- Read the file and display the first rows
- Identify the columns, data types, and missing values
- Calculate descriptive statistics (mean, median, min, max, standard deviation)
- Identify anomalies and outliers
- State the period covered (if there is a date column)
Step 2: Cleaning (if needed)
- Suggest the data corrections to make
- Flag problematic rows (duplicates, inconsistent values)
- Ask for my approval before making changes
Step 3: In-depth analysis
Depending on the nature of the data detected, run the relevant analyses:
If sales data:
- Total revenue and change over time
- Top products/services by revenue
- Seasonality identified
- Average order value and trend
If customer data:
- RFM segmentation (Recency, Frequency, Monetary value)
- Estimated retention/churn rate
- Typical profiles identified
If traffic/marketing data:
- Best-performing sources
- Conversion rate by channel
- Engagement trends
Universal analysis (always included):
- Correlations between variables
- Trends over time (if there are dates)
- Distribution of key values
- Comparison by category/segment
Step 4: Insights and recommendations
- Top 5 most important insights
- 3 actionable recommendations based on the data
- Points that need further investigation
- Limitations of the analysis (missing data, potential bias)
Step 5: Export
- Save the report to [path, e.g., ~/Data/reports/analysis-{name}.md]
- Generate a cleaned CSV (if there are changes) in [path]-clean.csv
- Create a summary file of key figures (JSON) for programmatic use
Ask me for clarification if the file's contents are unclear.
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Why this prompt works
The prompt is adaptive: instead of asking for data type, Cowork detects it and applies the most relevant analysis. The cleanup step with validation prevents analyzing corrupted data. Multiple exports serve different needs (presentation, analysis, code).
Use Cases
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Pour accélérer l'exploitation, demandez explicitement la génération de code Python (pandas, plotly) en sortie, pas seulement du texte : vous pourrez recréer le dashboard dynamiquement au lieu de le lire.
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