eda-checklist

name	eda-checklist
description	Systematic exploratory data analysis checklist covering structure, quality, distributions, relationships, and target analysis. Use when starting EDA on any dataset.

EDA Checklist

Systematic checklist for exploratory data analysis. Work through each section in order. Check off items as you complete them.

Role in ds plugin: This skill provides the systematic methodology for tabular EDA -- what to check and in what order. It is invoked by /ds:eda at step 6 (Data Quality Checks). The exploratory-data-analysis skill handles file type detection and format-specific analysis for non-tabular scientific formats. These skills are complementary: eda-checklist answers "what to check in tabular data" while exploratory-data-analysis answers "how to analyze any file format."

Structure

• Shape and types -- Row count, column count, dtypes, memory usage
• Index structure -- Is the index meaningful (datetime, ID) or just a range?
• Column naming -- Consistent naming convention? Any ambiguous names?

Missing Data

• Missing rates per column -- Compute df.isnull().mean() and sort descending
• Missing patterns -- Use a missing data heatmap to check if missingness is random (MCAR), conditional (MAR), or structural (MNAR)
• Columns >50% missing -- Flag for potential removal or imputation investigation

Duplicates

• Exact duplicate rows -- df.duplicated().sum()
• Near-duplicate detection -- Check key columns for records that differ only in minor fields

Target Analysis

• Target distribution -- Class balance (classification) or distribution shape (regression)
• Target-feature correlations -- Rank features by correlation with target
• Point-biserial correlation -- For categorical features vs. continuous target

Numeric Distributions

• Summary statistics -- Mean, median, std, min, max, percentiles (p5, p25, p75, p95)
• Skewness -- Flag features with |skewness| > 2 for potential log transform
• Outlier detection -- IQR method (below Q1-1.5IQR or above Q3+1.5IQR) and z-score method

Categorical Distributions

• Value counts -- Top values and their frequencies
• Rare categories -- Categories with <1% frequency
• High cardinality -- Columns with >50 unique values (consider encoding strategy)

Temporal Patterns

• Time range -- Earliest and latest dates
• Gaps -- Missing time periods or irregular frequency
• Seasonality -- Weekly, monthly, or yearly patterns
• Trend -- Increasing or decreasing over time

Correlations

• Numeric correlation matrix -- Pearson correlation heatmap
• Top correlated pairs -- Pairs with |r| > 0.8 (potential multicollinearity)
• Multicollinearity check -- VIF (variance inflation factor) for features >10
• Cross-feature patterns -- Scatter matrix for top features

Red Flags

• Constant/near-constant -- Columns with single value or >99% same value
• ID-like columns -- Columns with all unique values (potential identifiers, not features)
• Leakage suspects -- Features with suspiciously high target correlation (>0.95)
• Suspicious distributions -- Features that look derived from the target