Explore a dataset with explicit row definitions, missingness checks, plots and paired comparisons, then preserve a reproducible findings record.
Record what one row represents, the population covered, collection dates, units, identifiers and how the data was obtained. Separate questions you brought to the data from patterns discovered during exploration. Keep the source snapshot and a data dictionary in an approved analysis workspace; record filters and permissions before creating a working copy.
In your analysis environment, count rows and distinct entities; check column types, duplicate keys, units, ranges and missing values. Define the eligible denominator for each missingness rate. Use Percentage Calculator for a copied count comparison, such as 120 missing values among 1,000 eligible records = 12%. It does not inspect a file or infer why values are missing.
Create labeled histograms, category counts, scatterplots and time or group summaries in your analysis environment. State units, scales, sample sizes, filters and binning choices. Investigate unusual values against collection records before excluding them. If a sample is necessary, record its selection and separately check rare events, small groups and overall totals.
For a specific numeric pair, align observations by the same identifier and time window; document which incomplete pairs were excluded. Paste the matched X and Y sequences into Pearson Correlation and retain the pair count with the result. Inspect the scatterplot for curvature, clusters and influential values. This tool calculates one coefficient, not a correlation matrix, significance test or causal effect.
Write a cleaning and follow-up plan that distinguishes verified data errors from analysis choices. If the next task is predictive modeling, reserve evaluation data before choosing features or fitting transformations; keep repeated entities and time ordering in the split design. Explore the development partition for those choices and leave the evaluation partition for the planned assessment.
Summarize the original questions, unexpected findings, quality issues, exclusions, denominators and unresolved explanations. Include links to approved source snapshots, scripts and labeled figures, plus a concrete next check for each important finding. Use Markdown Preview to review and download your written record; it does not execute the analysis or embed external data automatically.
Check a copied missingness or exclusion count against its explicitly eligible denominator.
Calculate one Pearson coefficient from aligned numeric observation pairs after inspecting their plot.
Review and download the authored EDA findings record with source and analysis references.
Loading your checklist…
NIST describes EDA as a way to investigate structure, unusual observations and assumptions, often using graphics. A coefficient alone does not replace this inspection.
pandas distinguishes missing-value representations and documents detection and handling. The correct action still depends on the meaning of the field and how values were collected.
Pearson correlation describes a linear relationship between paired observations. The coefficient is undefined for constant input; confidence intervals and hypothesis tests require additional assumptions and methods beyond this site’s coefficient mode.
scikit-learn shows how preprocessing and feature selection can leak test information. Fit these choices on the appropriate training partition, including inside cross-validation.
Keep observations and interpretations distinguishable.
| Record | Include |
|---|---|
| Dataset | Source/version, row unit, dates, identifiers and units |
| Quality finding | Count, denominator, affected groups and source check |
| Pattern | Figure or paired data, sample size, filters and uncertainty |
| Next decision | Cleaning proposal, validation step and owner |
Record newly noticed patterns as exploratory findings and plan how they could be checked with suitable additional evidence.
Unusual values may be errors, rare valid observations or a different population. Record the reason and sensitivity of any exclusion.
Start with the question, row unit and collection process, then choose summaries that can expose relevant failures.