Exploratory Data Analysis Notebook Template (reproducible)

Purpose

This notebook is a reproducible scaffold for exploratory data analysis (EDA). It helps analysts surface meaningful patterns, log hypotheses and anomalies, validate early findings, and hand off work to engineering or stakeholders with clear context and reproducibility artifacts.

How to use this notebook

Copy this notebook as a project-specific file, fill the metadata header, run cells in sequence, and capture observations in the Hypothesis & Anomaly Log. Use the Validation Checklist before promoting any finding.

Recommended metadata header (example)

# Metadata (fill before running)
# Project: CustomerChurn-Exploration
# Author: Your Name
# Date: 2026-08-27
# DataSource: s3://bucket/path/customers-v1.csv
# DataVersion: 2026-08-20-commit-abc123
# NotebookVersion: 1.0
# Environment: python=3.10; pandas=1.5.3; numpy=1.24.1; scikit-learn=1.2.0
# RandomSeed: 42
# Purpose: surface candidate signals for churn prediction and validate initial hypotheses
# Notes: include any access, privacy, or sensitivity constraints
  

Commit / Git notes template

When committing, use a message structure that preserves context:

git commit -m "EDA: customers-v1 | hypothesis: high_usage -> lower churn | records: 1.2M | env: py3.10/pd1.5 | notebook: exploratory/eda_customer_churn_v1.ipynb"

Cell scaffold (run in order)

1. Environment & imports: lock package versions, set random seed.
2. Metadata header: confirm values above and record runtime environment (python, package versions).
3. Data provenance & quick sanity check: load sample, check schema, record row counts, sample hashes, and data timestamp.
4. Missing / duplicate detection: per-column missing rates, duplicates, null patterns, and row-level completeness score.
5. Basic descriptive stats: numeric summaries, categorical frequencies, and distribution snapshots.
6. Canonical visualizations: distributions, relationships, time-series overview, correlation heatmap.
7. Initial anomaly & hypothesis log: record observations with tags and confidence levels.
8. Candidate features table: candidate feature, rationale, derivation notes, expected direction, transformation ideas, risk/quality score.
9. Simple modeling sanity checks (optional): quick baseline (e.g., logistic regression) to test signal presence, with train/validation split and fixed seed.
10. Validation checklist: steps to increase confidence before handing off or acting.
11. Next steps & handoff: precise asks for engineering, product, or experiments team.

Suggested minimal imports (example)

import os
import hashlib
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
np.random.seed(42)

Key checks and example code snippets

Provenance & sample hashing (quick):

df = pd.read_csv('data/customers-v1.csv')
row_count = len(df)
sample_hash = hashlib.sha256(pd.util.hash_pandas_object(df.head(100)).values).hexdigest()
print(row_count, sample_hash)

Missing rates and duplicates:

missing = df.isna().mean().sort_values(ascending=False)
dup_count = df.duplicated().sum()
print(missing.head(10))
print('duplicates:', dup_count)

Canonical visual checklist

• Distribution: histograms / density plots (numeric)
• Outliers: boxplots with per-segment breakdown
• Relationship: scatterplots for numeric pairs (or pairplot for small feature sets)
• Correlation: correlation matrix / heatmap (include Spearman for rank relationships)
• Time series: aggregate and seasonal decomposition for time-indexed data
• Category: bar charts for frequency and stacked bars for target rates

Hypothesis & Anomaly Log (template)

Keep this table live in the notebook so reviewers can read findings in context.

Candidate features table (example columns)

• feature_name — short id
• source_columns — source fields used
• derivation — short formula or window
• expected_direction — positive/negative/unknown
• quality_issues — missingness, leakage risk
• priority — low/medium/high

Validation checklist

1. Confirm data provenance and exact slice used (data version/hash logged).
2. Reproduce key aggregations & visuals from raw file without manual edits.
3. Check confounders: stratify by important segments (region, plan, cohort).
4. Holdout / temporal validation: do patterns hold on a subsequent time window or held-out cohort?
5. Statistical sanity: test effect sizes and uncertainty (confidence intervals where applicable).
6. Assess leakage risk: ensure candidate features wouldn't leak target in production.
7. Quick baseline model: confirm improvement over naive baseline is non-trivial and consistent across splits.
8. Peer review: add a reviewer note and have at least one colleague replicate the key chart or aggregation.

Handoff & next steps

When handing off, include:

• Notebook file with metadata header completed.
• Data version, sample hash, and exact query used to extract the dataset.
• Hypothesis log exported as CSV / JSON.
• Candidate features table exported with derivation pseudocode.
• Validation results and short summary: which hypotheses passed, which require more data, and suggested experiments.
• Clear action: e.g., "Engineering: implement feature X calculation in ETL and add unit tests; Product: approve A/B test for pricing change based on H003".

Guardrails & common pitfalls

• Avoid overfitting by not cherry-picking splits or hyperparameters in exploratory phase.
• Beware spurious correlations: prefer experiments or holdout validation to claim causality.
• Record multiple comparisons when scanning many features—adjust expectations and plan validation.
• Data leakage: explicitly note any fields that may leak future information.

Quick checklist for reproducibility

• Metadata header filled and saved.
• Environment pinned (requirements.txt or environment.yml) and recorded.
• Random seed set and logged.
• Data source, version, and sample hash recorded.
• Key cells have short descriptions and rationale comments.
• Hypothesis log exported and committed with the notebook.

Notes on privacy and sensitivity

If data contains PII or sensitive attributes, cancel any sharing until required transformations (masking, hashing, aggregation) are implemented and approvals obtained. Record any approvals in the metadata header.

Where this notebook sits in a larger workflow

This scaffold is intended to be an early-stage, shareable artifact that helps teams move from "what happened" to "what to try next" while retaining reproducibility. Use it to generate candidate features, prioritized hypotheses, and validation-ready analyses for experimentation or productionization.