Recommender Ratings — user/item/rating (CSV, 240 rows)
A MovieLens-style ratings log — 240 user/item/rating events with Unix timestamps across 50 users and 40 items. A fixture for testing recommender pipelines, collaborative-filtering loaders, and sparse-matrix builders.
user_id,item_id,rating,timestamp
u043,i011,2.0,1749019461
u002,i015,3.5,1740485202
u015,i028,3.5,1751187822
u043,i034,3.5,1761299993
u027,i011,4.5,1738749759
u027,i005,5.0,1746535725
u028,i037,4.0,1754645426
u037,i010,3.5,1758376950
u010,i004,5.0,1761824880
u050,i012,3.5,1740905721
u042,i014,1.5,1764185001
u042,i027,4.5,1749221136
u014,i011,2.5,1747369986
u026,i011,3.0,1754435031
u041,i040,4.5,1759961100
u026,i001,4.5,1742850975
u026,i009,1.5,1745105854
u030,i040,4.0,1742814624
u024,i008,5.0,1765165327
u029,i004,3.0,1749465738
u002,i028,3.5,1748784810
u025,i016,3.5,1758592832
u038,i032,3.5,1757446950
u005,i001,2.0,1738905893
u050,i017,2.5,1755220290
u037,i019,4.0,1757269676
u019,i008,2.5,1762456883
u002,i003,5.0,1750310896
u017,i024,3.0,1740552369
u050,i016,3.0,1739450893
u025,i007,3.0,1737118668
u014,i020,3.5,1742186006
u020,i025,3.0,1752678222
u031,i020,4.0,1753854535
u024,i036,3.0,1738888708
u011,i017,3.5,1766086032
u042,i008,5.0,1747431188
u019,i036,2.0,1755996301
u038,i014,3.0,1758376991
u045,i033,3.5,1747890132
u035,i024,5.0,1745616877
u050,i019,3.0,1745342288
u009,i036,5.0,1740956998
u001,i031,4.0,1758661657
u041,i033,4.0,1741927516
u032,i001,3.5,1751814129
u025,i015,3.5,1751898271
u032,i031,3.5,1740970562
u011,i006,2.0,1754914438Specifications
- Rows
- 240
- Users
- 50
- Items
- 40
- Scale
- 0.5–5.0 (half-steps)
- Schema
- user_id, item_id, rating, timestamp
- Seed
- 910
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
Code examples
import pandas as pd
df = pd.read_csv("ratings.csv")
print(df.head())
print(df.dtypes)Related files
- csvSurvey Responses — Likert + NPS (CSV, 60 rows)A customer-survey response set — 60 respondents rating five statements on a 1–5 Likert scale plus a 0–10 NPS score and a segment. A fixture for testing survey analysis, aggregation, and NPS/CSAT calculations.
- jsonModel Benchmark Results (JSON)A model-evaluation summary in JSON — per-task scores for a fictional model across sentiment, NER, summarization, translation, and QA, each with its metric and sample size. A fixture for testing eval dashboards and leaderboard importers.
- jsonlSentiment Classification Dataset (JSONL)A labelled sentiment-classification dataset in JSON Lines — 24 short product-review-style sentences balanced across positive, negative, and neutral. Fully synthetic; a fixture for testing text-classification loaders, tokenizers, and JSONL parsers.
- csv10,000-Row CSVA CSV with 10,000 data rows — for testing streaming parsers, memory handling, and import performance.
- logApache Access Log — Combined Format (log)An Apache access log in the Combined Log Format — the Common fields plus referer and user-agent. A realistic web-server log for testing access-log parsers, analytics, and grok patterns.
- logApache Access Log — Common Format (log)An Apache access log in the Common Log Format (CLF) — client, timestamp, request line, status, and byte count. Deterministic, with reserved documentation IPs. Paired with a Combined-format twin for testing log parsers.
Generated by generation/data_graphs.py. Free for any use, no attribution required — license.