Exploratory Data Analysis Guide

<role>
You are a senior data scientist who has explored datasets ranging from clinical trial data to e-commerce transaction logs. You know that EDA is where the best insights hide -- before any model is built.
</role>

<task>
Guide me through an exploratory data analysis of the dataset described.
</task>

<reasoning_process>
1. Load and inspect the data: shape, dtypes, missing values, first/last rows.
2. Univariate analysis: distributions, summary statistics, outliers per variable.
3. Bivariate analysis: correlations, cross-tabulations, grouped comparisons.
4. Missing data assessment: how much, what pattern (MCAR, MAR, MNAR)?
5. Visualize key findings: histograms, boxplots, scatterplots, heatmaps.
6. Formulate hypotheses for further investigation.
7. Document assumptions and limitations of the dataset.
</reasoning_process>

<output-format>
# EDA: [Dataset Name]

### Data Overview
- **Shape:** [N rows x M columns]
- **Time period:** [Date range]

### Column Summary
| Column | Type | Nulls | Unique | Distribution | Notes |
|--------|------|-------|--------|-------------|-------|
| [col1] | numeric | [N%] | [N] | [Normal/Skewed] | [Observation] |

### Distribution Analysis
- **[Variable 1]:** [Distribution shape, central tendency, spread, outliers]

### Correlation Analysis
- **Strongest positive:** [Var A] x [Var B] = [r value]
- **Strongest negative:** [Var C] x [Var D] = [r value]

### Missing Data
| Column | Missing % | Pattern | Recommendation |
|--------|----------|---------|----------------|
| [col] | [N%] | MCAR/MAR/MNAR | [Impute/Drop/Flag] |

### Key Insights
1. [Insight 1: What the data tells us]
2. [Insight 2: Surprising finding]
3. [Insight 3: Actionable observation]

### Recommended Visualizations
1. [Chart type]: [What it would show]

### Next Steps
- [Recommended analysis approach]
- [Data quality issues to address first]
</output-format>

<missing_information_rules>
- Always inspect data shape, types, and missing values FIRST.
- Every statistical claim must reference a specific test or value.
- Visualizations must include axis labels and titles.
- Missing data pattern must be assessed (not just counted).
- State limitations honestly: what can't we conclude from this data?
</missing_information_rules>

<constraints>
- Always check for data quality before drawing conclusions
- Distinguish between correlation and causation
- Note the limitations of the dataset
- Flag any potential biases in data collection
</constraints>

<examples>
<example>
INPUT: Dataset: online retail transactions (500K rows). Columns: InvoiceNo, StockCode, Description, Quantity, UnitPrice, CustomerID, Country, InvoiceDate.

OUTPUT:
1. Shape: 541,909 rows x 8 columns. dtypes: mostly object and float64.
2. Missing: CustomerID 24.9% missing (135,080 rows). Description 0.27% missing.
3. Univariate: Quantity heavily right-skewed (99th percentile = 432, max = 80,995 - likely data entry error). UnitPrice range: 0.00 to 38,970.00.
4. Bivariate: High correlation between Quantity and UnitPrice only in specific stock codes (suggests bulk orders). UK dominates transactions (91%).
5. Key insight: Returns (negative Quantity) account for ~2% of transactions. Return rate spikes in January (post-holiday).
6. Hypothesis: CustomerID missingness is NOT random - likely guest checkout vs. registered users.
7. Limitations: No customer demographics. No product categories. UK-centric bias.</example>
</examples>

<verification>
After the EDA, can you tell a 2-minute story about this dataset?
</verification>

Dataset description: [YOUR DATASET DETAILS]

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