Exploratory Data Analysis Starter Notebook

Welcome — Purpose and hunger

This notebook is a practical starting point for discovering patterns, signals, and testable hypotheses in tabular data. It helps you move from wondering "what's in this data?" to producing concrete experiments, measurements, and decisions you can act on. It focuses on quick, repeatable checks that reduce noise, avoid spurious conclusions, and create clear next steps.

Quick setup & prerequisites

Recommended environment: Python 3.8+, pandas, numpy, matplotlib, seaborn, statsmodels, scikit-learn. Install as needed:

pip install pandas numpy matplotlib seaborn statsmodels scikit-learn yellowbrick

When running in a managed notebook, confirm package versions and note them in a cell so others can reproduce results.

Notebook sections

1. Data ingestion (safe loading & initial checks)
2. Basic profiling (types, missingness, ranges, cardinality)
3. Autocorrelation & seasonality checks (time series basics)
4. Segmentation & pivot exploration (quick group insights)
5. Key visualizations (distributions, relationships, heatmaps)
6. Anomaly / outlier quick checks
7. Hypothesis extraction checklist & suggested next experiments
8. Save, share, and instrument results

1) Data ingestion — load safely

Load the data defensively. Inspect shape, types, preview rows, and capture provenance (source, pull timestamp, any filters applied).

# Example Python (pandas)
import pandas as pd
from datetime import datetime

SOURCE = 'data/my_dataset.csv'
df = pd.read_csv(SOURCE, parse_dates=['event_ts'], low_memory=False)
print('Loaded', df.shape)
print('Preview:')
print(df.head())
print('Columns and dtypes:')
print(df.dtypes)
meta = {'source': SOURCE, 'loaded_at': datetime.utcnow().isoformat()}

2) Basic profiling — what stands out

Compute compact profile metrics: missingness, unique counts, basic descriptive stats, and cardinality warnings for supposed categorical fields.

# Basic profile
profile = {}
profile['missing'] = df.isnull().mean().sort_values(ascending=False)
profile['n_unique'] = df.nunique().sort_values(ascending=False)
profile['describe'] = df.describe(include='all').T

print('Top missing fields:')
print(profile['missing'].head())
print('\nTop cardinality:')
print(profile['n_unique'].head())

Flag columns with >30% missing or extremely high cardinality where a categorical assumption may be wrong.

3) Autocorrelation & seasonality checks (for time-indexed data)

If you have a timestamp, check for periodic patterns and autocorrelation before using simple cross-sectional assumptions.

from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphics.tsaplots import plot_acf
import matplotlib.pyplot as plt

# Resample to regular frequency if appropriate
ts = df.set_index('event_ts').resample('D').size()
plt.figure(); ts.plot(title='Daily event counts');

# Decompose (additive) if series is long enough
decomp = seasonal_decompose(ts.dropna(), period=7, model='additive', extrapolate_trend='freq')
plt.figure(); decomp.plot();

# Autocorrelation
plt.figure(); plot_acf(ts.dropna(), lags=30); plt.title('ACF')

Look for weekly or monthly cycles, and persistent autocorrelation. If present, incorporate time-series techniques in experiments.

4) Segmentation and pivot exploration

Explore group-level signals; often aggregate patterns reveal opportunities invisible at the row level.

# Example: conversion rate by channel and country
agg = (
    df.assign(converted=lambda d: d['event'] == 'purchase')
      .groupby(['channel', 'country'])
      .agg(events=('event', 'count'), conversions=('converted', 'mean'))
      .sort_values('events', ascending=False)
)
print(agg.head(20))

Sort by volume to avoid chasing tiny noisy segments. Add minimum-sample-size filters before comparing rates.

5) Key visualizations (fast, interpretable)

Favor simple, interpretable plots. Examples:

• Distribution: histograms, density plots, boxplots
• Relationships: scatter with lowess, hexbin for dense clouds
• Pairs: pairplot limited to small feature sets
• Correlation heatmap for numeric features

import seaborn as sns
sns.histplot(df['amount'].dropna(), kde=True)
sns.scatterplot(data=df.sample(1000), x='age', y='amount', hue='converted')
plt.figure(); sns.heatmap(df.select_dtypes('number').corr(), annot=True, fmt='.2f')

6) Anomaly & outlier quick checks

Use robust statistics and simple models to find suspicious values that deserve attention:

# IQR rule for outliers
num = df['amount'].dropna()
q1, q3 = num.quantile([0.25, 0.75])
iqr = q3 - q1
outliers = num[(num < q1 - 1.5*iqr) | (num > q3 + 1.5*iqr)]
print('Outliers count:', outliers.count())

# Optional: lightweight ML-based anomaly scoring (isolation forest)
from sklearn.ensemble import IsolationForest
clf = IsolationForest(random_state=0)
sample = df[['amount','age']].dropna().sample(min(5000, len(df)))
clf.fit(sample)
scores = clf.decision_function(sample)

Document anomalies: are they data issues, rare-but-valid cases, or early warning signals?

7) Hypothesis extraction checklist

Convert observations into crisp, testable hypotheses. Use this checklist for each signal you want to act on:

• What is the observed signal? (metric, segment, direction, magnitude)
• How confident are we? (sample size, variance, multiple comparisons)
• Could this be an artifact? (missingness, duplication, timezone, encoding)
• What is the plausible causal story? (why would A affect B?)
• What is the measurable outcome for an experiment? (conversion rate lift, cost reduction, response time)
• What minimum detectable effect (MDE) matters to stakeholders?
• What action or treatment can we apply, and how will we randomize or compare?
• What instrumentation or logging must be added before running the experiment?

8) Suggested next experiments

Turn a hypothesis into a concrete experiment idea. Example patterns:

• If Channel X shows low conversion but high traffic, test a tailored landing flow for Channel X.
• When weekday peaks are observed, schedule promotions for slower days and measure lift.
• For a suspicious outlier cohort, verify data pipeline and if valid, run a controlled offer limited to that cohort and compare outcomes.
• Instrument additional events (funnel steps, errors) where gaps in observability limit causal inference.

9) Save, share, and instrument results

Record the hypotheses, evidence, and chosen next steps. Reproducibility matters — commit the notebook and any derived datasets. The platform supports storing structured findings so your team can track experiments and reuse insights.

Example: serialize a short findings JSON and submit it to the content collection endpoint (replace authentication details with your environment's method):

curl -X POST -F "ContentItemID=1941" \
     -F "findings={\"hypotheses\": [\"Channel X low conversion due to landing flow\"], \"signals\": [\"channel=organic, conversion=1.2%\"], \"next_steps\": [\"A/B test new landing for Channel X\"]}" \
     https://your.the.instance/content/1941/submit

# Note: include required auth headers for your deployment.

Using the platform's submission capability makes it easier to produce a searchable, auditable record of EDA outcomes that other teams can act on.

Reproducibility & reporting tips

• Pin library versions in a requirements file.
• Log data source, extraction query, and any filters used.
• Prefer deterministic sampling when showing example visualizations.
• Attach representative charts and a one-paragraph executive summary to each hypothesis record.

When to stop and move to experiment design

Stop exploratory analysis and move to experiment planning when you have: a clear hypothesis, a measurable primary outcome, an actionable treatment, and instrumented metrics that will reliably detect the expected effect.

Appendix — quick checklist before publication

1. Have you ruled out simple data quality issues?
2. Are sample sizes sufficient for the segments you compare?
3. Have you corrected for obvious confounders or stratified appropriately?
4. Are you candid about uncertainty and next steps?
5. Have you saved the notebook and findings to the shared content system?

Use this notebook as a living template: copy it into a project-specific notebook, adapt the checks and thresholds to your domain, and record every EDA run's findings so your organization builds reliable institutional knowledge.