Code Interpreter Data Analysis
GPT-4o's Code Interpreter runs Python in a sandboxed environment, so it can actually execute analysis code and return real results. Specifying the exact charts and analysis steps prevents generic exploratory output and gets you to insights faster.
I'm uploading a {{fileType}} file containing {{dataDescription}}. Perform the following analysis: 1. **Data quality check:** Missing values, outliers, data type issues, duplicate rows 2. **Summary statistics:** Key metrics with distributions for numeric columns 3. **Analysis:** {{specificAnalysis}} 4. **Visualization:** Create {{chartCount}} charts that best tell the story of this data: {{chartDescriptions}} Use Python with pandas and matplotlib/seaborn. Show your code and explain each step. At the end, provide 3 key insights and 2 recommended next steps for further analysis.
Variables to customize
Why this prompt works
GPT-4o's Code Interpreter runs Python in a sandboxed environment, so it can actually execute analysis code and return real results. Specifying the exact charts and analysis steps prevents generic exploratory output and gets you to insights faster.
Save this prompt to your library
Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.
Related prompts
Forcing the agent to plan before acting prevents premature execution and wasted steps. Explicit dependency mapping enables parallel execution and catches logical gaps early.
Tool Selection AgentThe ReAct pattern (Reason + Act) creates an explicit reasoning trace that improves tool selection accuracy. The error-handling rule prevents infinite retry loops.
Prompt CompressorExplicitly requiring all functional requirements to be preserved prevents the model from over-compressing and losing critical instructions.
Memory Management AgentExplicit memory read/write instructions create agents that improve over time. Categorization keeps memories organized, and the deduplication rule prevents context bloat.