Exploratory Data Analysis — Starter Notebook Template

Purpose and how to use this starter notebook

This notebook is built for discovery-focused Exploratory Data Analysis (EDA). Use it to surface patterns, check data quality, test simple causal leads, and translate insights into concrete experiments or actions. Keep the notebook readable, reproducible, and minimal: capture decisions, code, rationale, and artifacts so others can reproduce and follow up.

Quick notes

• Recommended environment: Python (pandas, numpy, matplotlib/seaborn, plotly, scikit-learn) or SQL-first workflows when appropriate. R users can map the steps to tidyverse and ggplot2.
• Keep exploratory cells focused and mark provisional analysis clearly (e.g., PROTOTYPE or TENTATIVE tags).
• Prefer small, testable findings over long lists of correlations. Each finding should suggest a next step (experiment, deeper analysis, or data collection).

Notebook sections (template)

1) Goal & hypothesis

State the high-level discovery goal and one or more concrete hypotheses that you will look for evidence of. Link to stakeholders, metric(s) of interest, and the decision this analysis will inform.

Template:

2) Data sources & quality checks

List each data source, owner, ingestion timestamp, row counts, freshness, and any known caveats. Run quick quality checks and log the results.

Example checks:

• Row counts by source and recent partition.
• Missingness per column and per key (percent nulls).
• Duplicate keys and referential integrity between joins.
• Out-of-range values and obvious encoding issues.

Sample SQL to preview a table:

SELECT COUNT(*) AS rows, MIN(dt) AS first_date, MAX(dt) AS last_date FROM dataset.table;

Sample Python (pandas) checks:

import pandas as pd
df = pd.read_csv('data.csv')
print(len(df))
print(df.dtypes)
print(df.isna().mean().sort_values(ascending=False).head(10))
print(df.duplicated(subset=['user_id']).sum())

3) Data profiling & missingness (light profiling)

Produce summary statistics and simple distributions for key numeric, categorical, and datetime fields. Note suspicious patterns that might be data-collection artifacts.

Suggested outputs:

• Descriptive table: count, unique, mean, median, std, min, max, percentiles.
• Frequency tables for top categorical values (and long-tail handling).
• Time-series coverage heatmap (by date vs. source) if applicable.

4) Key aggregations & segmentations

Define and compute the primary aggregations tied to your decision or metric. Build segmentations that matter to stakeholders (e.g., channel, cohort, geography, product tier, device type).

Example SQL aggregation:

SELECT channel, COUNT(DISTINCT user_id) as users,
       SUM(CASE WHEN churned_within_30_days THEN 1 ELSE 0 END) / COUNT(DISTINCT user_id) AS churn_30d
FROM events
WHERE event_date BETWEEN '2025-01-01' AND '2025-03-31'
GROUP BY channel
ORDER BY churn_30d DESC;

In Python/pandas, use groupby with agg and reset_index for readable tables.

5) Visualizations to inspect distributions and relationships

Choose simple, interpretable visuals that answer specific questions. Annotate charts with short captions describing the insight and confidence level.

Suggested visuals:

• Histograms or density plots for numeric distributions (log-transform when skewed).
• Boxplots by segment to compare spreads and outliers.
• Time series plots for trends and sudden shifts; add smoothing and seasonal decomposition when relevant.
• Scatter plots with regression lines for bivariate relationships; color by segment to reveal interactions.
• Heatmaps or correlation matrices for variable relationships (be cautious: correlation != causation).

Python example (seaborn):

import seaborn as sns
sns.histplot(df['time_to_first_purchase'], bins=50, log_scale=(True, False))
sns.boxplot(x='channel', y='time_to_first_purchase', data=df)

6) Anomaly detection & possible causal signals

Look for abrupt changes, persistent outliers, or subgroup behavior that deviates from the baseline. Use simple methods first: control charts, rolling z-scores, or isolation forest for high-dimensional anomalies.

Checklist for anomalies:

• Was the anomaly caused by data collection changes or true behavior change?
• Do anomalies align with external events (campaigns, releases, outages)?
• Is the signal consistent across related metrics?

Basic rolling z-score example (pandas):

df['rolling_mean'] = df['metric'].rolling(window=7).mean()
df['rolling_std'] = df['metric'].rolling(window=7).std()
df['z'] = (df['metric'] - df['rolling_mean']) / df['rolling_std']
# flag points where |z| > 3 as candidate anomalies

7) Candidate experiments arising from patterns

Convert each actionable pattern into an experiment concept with a clear metric, population, and success criterion. Use the template below to keep ideas testable.

Experiment candidate template (table or short card):

Reproducibility & export checklist

Before finalizing the notebook, ensure others can reproduce and validate your work. Save these artifacts and document them:

1. Data snapshot or clear query (SQL) used to build analysis tables; record dataset version and timestamp.
2. Notebook file exported (nbconvert, HTML, or PDF) and a link to the version-controlled notebook.
3. Environment specification: requirements.txt or environment.yml, plus language and package versions.
4. Seeds and random states used for any sampling or models.
5. Key parameters and filters recorded in a single Parameters cell at top of notebook.
6. Short decisions log: what was kept, what was discarded, and why (one-paragraph rationale per major finding).
7. Privacy and compliance notes: make sure outputs do not expose PII; note any masking applied.

Practical tips & common pitfalls

• Avoid over-interpreting correlations; prefer experiments or quasi-experimental designs for causal claims.
• Be explicit about multiple comparisons when scanning many segments; consider adjusted thresholds or holdout validation.
• Keep exploratory notebooks separate from production pipelines; move validated queries into reproducible ETL only after confirmation.
• Annotate the notebook liberally—future you (and other teams) will thank you.

Optional: Quick starter code block (Python) to populate structure

# Parameters
DATA_PATH = 'data/events.parquet'
DATE_MIN = '2025-01-01'
DATE_MAX = '2025-03-31'

# Load and quick profile
import pandas as pd
from pathlib import Path

df = pd.read_parquet(DATA_PATH)
print('rows, cols', df.shape)
print(df.describe(include='all'))

# Example aggregation
agg = df.groupby('channel').agg(users=('user_id', 'nunique'),
                                 churn_30d=('churn_30d', 'mean'))
agg = agg.reset_index().sort_values('churn_30d', ascending=False)
print(agg.head())

Notes on ethics, privacy, and safety

Whenever analysis involves people data, document data-minimization choices, anonymization or aggregation steps, and any regulatory constraints. Avoid publishing small-cell tables that could re-identify individuals.

Suggested follow-ups and handoff

When findings suggest an experiment or deeper causal analysis, prepare a short handoff packet: the experiment candidate card(s), a reproducible query or dataset, suggested timeline, sample size estimate, and the owner. Consider moving validated analytical queries into a shared analytics repo or a dashboard for stakeholders.