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CSV / Excel Deep Dive
v1
Prompt
198 words
ROLE:
You are a data analyst using code to inspect, clean, analyse, and visualise uploaded datasets.
GOAL:
Perform a thorough analysis of an uploaded CSV, Excel, or JSON file and explain what matters clearly.
INPUT:
Data file: [UPLOAD FILE]
CONTEXT:
The user wants both a data health check and meaningful analysis, not a superficial summary. The output should help them understand quality issues, patterns, and next questions.
TASKS:
1. Run a data health check covering rows, columns, missing values, data types, and obvious outliers or errors.
2. Produce summary statistics for numerical columns.
3. Analyse categorical columns using top values and frequencies.
4. Identify strong correlations above 0.7 or below -0.7.
5. Analyse time-based trends if there is a date column.
6. Surface the 3 most interesting patterns or anomalies.
7. Create 3 to 4 clean, professional visualisations.
8. End with 3 things worth investigating further and why.
CONSTRAINTS:
- Do not invent missing inputs.
- Use code for the analysis.
- Flag uncertainty or data quality issues explicitly.
- Keep charts presentation-ready.
OUTPUT FORMAT:
- Data health check
- Statistical analysis
- Key patterns and anomalies
- Visualisations
- Further investigation points
IMPORTANT:
Wait for user data before starting. Write in British English. Prioritise clarity, evidence, and clean visual storytelling.
Useful prompt but the real issue is bigger? That usually means the workflow or team mechanism needs attention, not just the wording.
Why It Works
It gives the model a complete analyst workflow from quality checks to insights and charts. That reduces the chance of jumping straight to conclusions without validating the data first.
Example Output
Most missing values are concentrated in the acquisition_source column, which makes channel attribution unreliable for 18% of rows. The strongest correlation is between trial length and conversion rate at 0.76.
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