Exploratory Data Analysis Notebook Template

Exploratory Data Analysis Notebook Template

Purpose: Help analysts, product teams, and stakeholders discover patterns, surface plausible causal leads, and convert findings into concrete experiments and decisions. This scaffold is intended to be copy-pasted into a notebook (e.g., Jupyter) and adapted to your project context.

Quick guidance

Keep the notebook reproducible and communication-ready: record data lineage, environment, key assumptions, tests run, and a concise one-page summary for non-technical stakeholders. End each run with suggested next experiments and owners.

Notebook header / metadata (example)

# Project: Customer Churn Exploratory Analysis
# Data snapshot: customers_2026-07-01.csv
# Source: internal-billing-db.replica.customers
# Analyst: Ana Perez
# Version: v0.1
# Environment: python 3.10, pandas 2.1, matplotlib 3.x
# Purpose: Surface signals that might explain increased churn in Q2 and propose experiments

Sections (copy these headings into your notebook)

1. 1) Data sources & lineage
2. 2) Data quality checks
3. 3) Variable profiling (univariate & missingness)
4. 4) Initial visualizations (bivariate & multi-variate)
5. 5) Hypothesis tests and quick statistical checks
6. 6) Anomalies & exceptions log
7. 7) Suggested next experiments (ranked)
8. 8) One-page summary for non-technical stakeholders
9. 9) Export & reproducibility checklist

1) Data sources & lineage

Record precise sources, extraction scripts/queries, snapshot time, filters, and joins. This prevents "magic data" and enables reproducibility.

DataSourceTable = {
  'name': 'customers',
  'database': 'billing_replica',
  'query': 'SELECT * FROM customers WHERE created_at < "2026-07-01"',
  'snapshot_filename': 'customers_2026-07-01.parquet',
  'notes': 'Excluded test accounts; merged with payments table on customer_id'
}

2) Data quality checks

Run automated checks and record outcomes. Save results as a small table in the notebook.

• Row count sanity (expected vs actual)
• Missingness by column (percent)
• Duplicated keys (primary key collisions)
• Out-of-range / invalid values (dates, negative amounts)
• Schema drift checks (expected columns present)

# Example (pandas)
import pandas as pd
df = pd.read_parquet('customers_2026-07-01.parquet')
quality = {
  'rows': len(df),
  'missing_pct': df.isna().mean().round(3).to_dict(),
  'duplicates': df.duplicated(subset=['customer_id']).sum()
}
quality

3) Variable profiling

For each variable (or a prioritized subset), capture:

• Type (categorical / numeric / datetime / text)
• Cardinality (unique counts)
• Basic stats (mean, median, std, IQR, percentiles)
• Missing rate and patterns (is missingness informative?)
• Top values for categoricals and examples for free text

# Example profiling helper (pandas)
def profile_col(s):
    return {
        'dtype': str(s.dtype),
        'n_missing': int(s.isna().sum()),
        'pct_missing': float(s.isna().mean()),
        'unique': int(s.nunique(dropna=True)),
        'top': s.dropna().value_counts().head(3).to_dict() if s.dtype=='object' else None,
        'describe': s.describe().to_dict() if pd.api.types.is_numeric_dtype(s) else None
    }

col_profiles = {c: profile_col(df[c]) for c in ['age','zipcode','last_login']}
col_profiles

4) Initial visualizations (with intent)

Choose visuals to answer a question, not to decorate. Examples with intent:

• Distribution plot (numeric) — look for skew, multi-modality, and outliers
• Missingness heatmap — discover correlated gaps
• Bar chart (categorical vs target) — check signal strength
• Time series of key metrics — spot trends and changepoints
• Pairwise scatter / correlation matrix — surface linear relationships

# Example plotting (pandas + seaborn)
import seaborn as sns
sns.histplot(df['tenure_months'].dropna(), kde=True)
sns.barplot(x='plan_type', y='churned', data=df.groupby('plan_type').churned.mean().reset_index())

5) Hypothesis checks and quick statistical tests

Convert interesting patterns into testable hypotheses and run appropriate quick checks. Record test, assumptions, result, p-value, effect size, and practical interpretation.

# Example hypothesis: "Customers on plan B churn more than plan A"
from scipy import stats
groupA = df[df.plan_type=='A'].churned.dropna()
groupB = df[df.plan_type=='B'].churned.dropna()
# two-sample proportion test (or t-test on rates)
stat, p = stats.ttest_ind(groupA, groupB, equal_var=False)
{'test': 'ttest_ind','stat': stat, 'p_value': p}

6) Anomalies & exceptions log (structured)

Keep a small table of discovered anomalies so they are visible to stakeholders and future runs.

| id | discovered_by | date | description | root_cause_guess | severity (1-5) | status | follow_up_owner |
|----|---------------|------|-------------|------------------|----------------|--------|-----------------|
| A001 | Ana Perez | 2026-07-12 | Spike in cancellations on 2026-06-29 | Possible billing outage | 4 | open | ops-team |

7) Suggested next experiments (actionable and ranked)

Translate insights into experiments. For each suggestion include:

• Hypothesis
• Experiment design (A/B, cohort study, log change)
• Primary metric and success criterion
• Estimated sample size / time to run
• Owner

- Experiment 1: Pause billing retries for 2% of customers and measure churn delta over 30 days.
  - Hypothesis: Retry policy causes frustrated customers to churn.
  - Metric: 30-day churn rate difference; success if delta < -2pp.
  - Owner: billing-product

8) One-page summary for non-technical stakeholders (template)

Keep it one page. Use simple language and clear next steps.

9) Export & reproducibility checklist

• Save a data snapshot (filename + checksum)
• Save the notebook (with version) and environment.yml / pip freeze
• Record random seeds used for sampling
• Attach query text or notebook cell that extracts data
• Update anomalies log and tag owners
• Produce one-page summary and send to stakeholders with clear decision request

Helpful patterns & tips

• Prioritize variables that are actionable or instrumentable (can trigger an experiment)
• Track effect sizes, not just p-values — small but statistically significant effects may be practically irrelevant
• Use stratified views to check whether a signal is driven by a small subgroup
• Record decisions: every notebook run should end with a visible "Decision / Ask" cell

Example export artifacts

• CSV: data_snapshot.csv (used to run notebook)
• Notebook: exploratory_churn_v0.1.ipynb
• Summary: churn_onepage_v0.1.pdf
• Anomalies log entry: A001 (linked)

Where this fits in the Discovery & Innovation Hub

This notebook template is intended to be a repeatable starting point for discovery experiments. Consider bundling it with an "Experiment Planner" worksheet and an "Anomalies Tracker" audit so teams can move quickly from analysis to action.