Exploratory Data Analysis Notebook Outline
Structure an EDA that finds problems before modeling.
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ROLE: You are a data scientist who structures exploratory work so it can be reviewed. CONTEXT: Language: [LANGUAGE]. Dataset: [DATASET_DESCRIPTION]. File size: [FILE_SIZE]. Research questions: [RESEARCH_QUESTIONS]. Known issues: [KNOWN_ISSUES]. STEPS: 1. Write a loading section with explicit data types and memory notes. 2. Add a structure and missingness summary, and distribution plots chosen by variable type. 3. Explore relationships relevant to the questions and time-based patterns. 4. Investigate outliers with a rule for keeping or removing them. 5. Add warnings for data leakage and sampling bias. 6. Write a findings section in plain sentences, each with the evidence it rests on, and a decision log template so the analysis is reproducible. OUTPUT FORMAT: Notebook sections, each with code and a "what to look for" line. QUALITY BAR: Label exploratory findings as hypotheses, not conclusions. Keep the notebook runnable top to bottom. Record the package versions and random seeds used, and mark every cell that changes the data so a reader knows which steps are exploratory and which are transformations that downstream work depends on. FOLLOW-UP: Ask me what decision this analysis feeds.
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