Data AnalysisMistral
Mistral Prompts for Data Analysis
Mistral follows structured output instructions reliably — making it practical for data analysis tasks where you need consistent JSON or table output that can be programmatically processed. Good for extraction and classification tasks.
8 copy-paste ready prompts — optimised for Mistral.
8 Mistral Data Analysis Prompts
Data Summary Narrative
Analyse this dataset and write a 300-word narrative summary for a non-technical [executive/stakeholder] audience. Data: [paste data or description]. Cover: the key trend, any anomalies or outliers, comparison to [benchmark/previous period], and 2-3 actionable recommendations. Avoid technical jargon.
SQL Query
Write a [database: PostgreSQL/MySQL/BigQuery] SQL query to [describe what you need]. Tables available: [table names and brief descriptions]. Relevant columns: [column names and types]. Requirements: [any specific conditions, joins, groupings]. Include comments explaining each step and an index recommendation if applicable.
Python Data Analysis
Write a Python script using pandas to analyse this dataset: [describe data structure]. Tasks: 1. Load from [CSV/JSON/database]. 2. Clean: handle nulls, remove duplicates, fix types. 3. Compute: [specific metrics or aggregations]. 4. Output: [format: print/CSV/chart]. Include comments and print intermediate results for debugging.
A/B Test Analysis
Analyse the results of this A/B test: [paste results including sample sizes, conversion rates, and confidence level]. Answer: 1. Is the result statistically significant? (show calculation). 2. What is the practical significance (effect size)? 3. Which variant should we ship and why? 4. Any caveats or confounding factors to note?
Dashboard KPI Brief
Design a KPI dashboard brief for a [type of team: marketing/product/sales/operations] team. Business goal: [goal]. Include: 5-7 key metrics with definitions, how each is calculated, data source, target/benchmark, update frequency, and the 1-2 most important metrics that should be prominently displayed.
Data Visualisation Recommendations
Recommend the best chart types to visualise the following data relationships: [describe your data and what you want to show]. For each recommendation: chart type, reason for choosing it, key design considerations (axes, colours, labels), and any pitfalls to avoid. Data: [paste or describe data].
Cohort Analysis Explanation
Analyse this cohort retention data and explain the key findings in plain English: [paste cohort table]. Identify: the strongest and weakest performing cohorts, whether retention is improving or declining over time, the period with the steepest drop-off, and 2 hypotheses for why certain cohorts perform better.
Forecast Summary
Given this historical data: [paste data], generate a 12-month forecast narrative. Include: the forecasting method used and why, key assumptions, the central forecast with a high/low range, factors that could cause the forecast to miss, and how to monitor performance against forecast monthly.
Tips for using Mistral for Data Analysis
- Mistral follows JSON output schemas precisely — use it for data extraction tasks where consistent output structure matters
- For multilingual data annotation or classification, Mistral's language coverage is a practical advantage
- Define your schema upfront in the system prompt for all API-based data analysis workflows
Get a prompt tailored to your exact data analysis task
PromptIt asks a few questions and builds a Mistral-optimised prompt for your specific use case — free.
✦ Try PromptIt Free