Exploratory Analysis Notebook Template (Reproducible)

Purpose

This notebook is a reproducible scaffold for exploratory data analysis (EDA). Use it to surface useful patterns, generate candidate hypotheses, perform quick validation checks, and produce a concise handoff-ready summary. The structure below favors clear provenance, repeatability, and easy handoff to analysts, engineers, reviewers, or decision-makers.

How to use this template

1. Copy the notebook into a feature branch and run the Environment & Setup cells first.
2. Keep data extraction code compact and parameterized so it can be re-run without manual edits.
3. Record findings in the Candidate hypotheses and Summary & Next Actions sections as you go.
4. Run the Quick validation tests before proposing experiments or production changes.

Notebook metadata & provenance (top cell)

Include a small metadata header that can be parsed automatically. Example (as a JSON or YAML dictionary):

{
  "title": "Exploratory Analysis: Customer Churn Signals",
  "author": "Jane Analyst",
  "date": "2026-08-01",
  "git_commit": "$(git rev-parse --short HEAD)",
  "data_snapshot_id": "customers_2026-07-31-v1",
  "notebook_version": "1.0",
  "purpose": "Surface leading indicators of churn for targeted outreach experiments"
}

Environment & dependencies

Pin your environment and record versions. Put this in an executable cell (or requirements.txt / environment.yml) and capture outputs in the notebook.

# Example: capture package versions
import sys
import pandas as pd
import numpy as np
import matplotlib
import seaborn as sns
import sklearn
print('python', sys.version)
print('pandas', pd.__version__)
print('numpy', np.__version__)
print('seaborn', sns.__version__)
print('sklearn', sklearn.__version__)

Recommendation: persist a copy of environment.yml or requirements.txt in the same git commit and reference it in metadata.

Configuration & secure data access

Centralize parameters in a single config cell or file. Never hardcode credentials; use environment variables, secrets manager, or token-based access. Example parameters:

• DATA_SOURCE (table, bucket, or file path)
• SNAPSHOT_ID or date range
• RANDOM_SEED for deterministic operations
• LIMIT for quick local runs

Data sources & extraction code

Provide one compact, re-runnable extraction cell for each source. Log the exact query, filters, and snapshot used.

# Example: parameterized extraction
from sqlalchemy import create_engine
engine = create_engine(DB_CONN_STRING)
query = f"SELECT * FROM customers WHERE snapshot_id = '{DATA_SNAPSHOT_ID}'"
df = pd.read_sql(query, engine)
print(df.shape)
df.head()

Data quality checks (executable checklist)

Run and save results for these common checks. Keep outputs visible and tag anything unexpected.

1. Row/column counts and schema validation
2. Missingness by column and by key
3. Duplicate keys or unexpected multiplicity
4. Value range checks (min/max) and anomalous outliers
5. Distribution snapshots for key numeric and categorical variables

# Simple missingness check
missing = df.isnull().sum().sort_values(ascending=False)
missing[missing>0]

# Duplicate key check
dupes = df.duplicated(subset=['customer_id'], keep=False)
df[dupes].shape

Exploratory plots & transformations

Include a compact, reproducible set of plots that reveal relationships rather than decoration. Each plot should have a short caption that states the question it addresses.

• Univariate distributions (histograms, value counts)
• Bivariate relationships (scatter, box, violin)
• Time-series trends with appropriate aggregations
• Heatmaps or correlation matrices for numeric features
• Small-multiple plots to compare cohorts

# Example: cohort comparison
import matplotlib.pyplot as plt
plt.figure(figsize=(8,4))
sns.boxplot(x='cohort', y='monthly_spend', data=df)
plt.title('Monthly spend by cohort')
plt.show()

Flagged anomalies (recorded observations)

Whenever you see an oddity, add a single-line entry to a running table with columns: id, location (cell or variable), issue, severity, and investigator note. This becomes an audit trail.

# Example schema
anomalies = pd.DataFrame(columns=['id','location','issue','severity','note'])
# anomalies.loc[len(anomalies)] = ['A1','df:age','age < 0','High','Negative ages found - check ingestion']
anomalies

Candidate hypotheses

Use a small table to state hypotheses in causal language and list the minimal experiment or check that would falsify each. Example columns: hypothesis, rationale, test/metric, priority.

# Example
hypotheses = pd.DataFrame([
  {'hypothesis': 'High early churn linked to long onboarding time', 'rationale': 'Users with >7 days to first success churn at higher rates', 'test': 'compare churn rate by onboarding_time bucket', 'priority':'High'}
])
hypotheses

Quick validation tests

Before presenting findings, run lightweight checks that reduce the risk of spurious conclusions. Examples:

• Holdout split or time-based split to confirm relationships persist
• Simple logistic or decision-tree baseline with cross-validation to test predictive signal
• Permutation tests for selected features to check significance
• Check for data leaks (features that are derived from the label)

# Example: quick baseline
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(max_iter=1000)
scores = cross_val_score(model, X, y, cv=5, scoring='roc_auc')
print('AUC mean', scores.mean())

Summary & recommended next actions (handoff-ready)

Write a concise summary (3–7 bullet sentences) that answers:

• What did we look for, and why?
• Which findings are robust enough to act on (and which are hypotheses needing more validation)?
• Recommended next steps (experiment, monitoring, pipeline change, deeper analysis).

Summary:
- Leading signal: increased support touches in week 1 correlate with churn (AUC 0.72 in baseline)
- Data caveat: onboarding_time contains inferred values for 8% of users; needs cleaning
Next steps:
1) Run a targeted A/B on an onboarding nudging workflow (2-week pilot)
2) Fix ingestion for onboarding_time and re-run checks
3) Add monitoring alert for sudden change in feature distributions

Reproducibility checklist (final cell)

1. Saved the notebook and committed to git with commit hash in metadata.
2. Captured environment/package versions.
3. Recorded data snapshot identifiers and queries.
4. Exported any derived datasets or artifacts (feature tables, pickled objects) with stable names/paths.
5. Updated the anomalies and hypotheses tables with actionable next steps.

Versioning & data lineage recommendations

When feasible, combine these practices:

• Persist data snapshots or use dataset versioning tools (DVC, lakehouse snapshots).
• Include git commit and notebook diff with reports.
• Store key notebook outputs (figures, small CSV summaries) alongside the notebook in the commit or an artifact store.

Handoff deliverables

When handing off, include:

• The notebook (committed) and metadata commit hash
• List of derived artifacts with locations
• Short report (1–2 page) with the Summary & Next Actions pasted at top
• Contact for follow-up and suggested reviewers/owners

Appendix: small-cell templates

Include ready-to-run code snippets for common tasks; keep them minimal and parameterized.

# set seed
import numpy as np
RANDOM_SEED = 42
np.random.seed(RANDOM_SEED)

# save artifact example
summary.to_csv(f"artifacts/summary_{DATA_SNAPSHOT_ID}.csv", index=False)

# record run metadata
run_info = {"git_commit": GIT_COMMIT, "data_snapshot": DATA_SNAPSHOT_ID, "ran_by": AUTHOR}
import json
open('artifacts/run_info.json','w').write(json.dumps(run_info))

Notes on responsible exploration

Be explicit about potential biases and avoid overclaiming. Record any pre-existing hypotheses you had when you started to help reviewers detect confirmation bias.