Dataset Python
Website Events Dataset
Synthetic clickstream-style events for practicing timestamp parsing, anonymous activity, funnels and customer behavior analysis with pandas.
- Type
- Dataset
- Level
- Intermediate
- Updated
In short
Three hundred timestamped website events with repeated customers, anonymous activity, six event types, six pages and three device classes.
Downloads and links
Who it is for
- Data engineers learning event-data preparation
- Readers practicing timestamp and funnel analysis
What it helps you do
- Parse and derive features from UTC timestamps
- Retain anonymous events while enriching known customers
- Summarize behavior by event, page, day, and device
On this page
Dataset shape
The file contains 300 data rows and six columns.
| Column | Meaning |
|---|---|
event_id |
Unique event identifier |
customer_id |
Customer key; blank for anonymous activity |
event_time |
UTC timestamp in ISO 8601 form |
event_type |
Page view, login, search, add-to-cart, checkout, or purchase |
page |
Simplified website path |
device |
Desktop, mobile, or tablet |
What to practice
Use this dataset for timestamp parsing, daily and hourly features, event counts, device comparisons, session-oriented thinking, and simple funnel analysis. Each day contains several timestamps, and customers recur across the file. Blank customer IDs model anonymous browsing rather than damaged records, so dropping every null would discard meaningful traffic.
A left join to customers.csv can enrich known activity while retaining anonymous events. Grouping by event type and device provides a compact first aggregation; sorting by customer and time supports behavioral sequences. The deterministic pattern keeps expected examples reproducible.
This is intentionally a learning-scale clickstream, not a claim that production event processing belongs entirely in memory. Later comparisons can use the same shape to discuss chunking, Parquet, and PySpark when volume outgrows a single pandas process.
Go deeper: related articles
Python
Reading CSV Files with pandas
Load a real CSV into pandas deliberately, validate its shape and types, and avoid the ingestion assumptions that cause downstream data problems.
Python
Inspecting a DataFrame
Profile a pandas DataFrame systematically with shape, schema, samples, null counts, uniqueness and distribution checks before transforming it.
Python
GroupBy and Aggregations
Build trustworthy pandas summaries by defining grain, revenue rules, null behavior and joins before grouping real order data.
Python
Joining and Merging DataFrames
Join pandas DataFrames safely with explicit cardinality, unmatched-key checks, indicators and row-count reconciliation.