Large Transactions Dataset (CSV, 50k rows)
A 50,000-row transactions dataset — an in-repo 'large' fixture for testing streaming CSV parsers, import performance, and pagination. Deterministic (fixed seed).
txn_id,timestamp,account,amount,currency,category
1,2025-11-07T22:13:25Z,ACCT-2610,60.14,EUR,transfer
2,2025-01-30T03:17:37Z,ACCT-4334,159.57,CAD,healthcare
3,2025-09-15T02:47:03Z,ACCT-9145,75.96,CAD,travel
4,2025-02-05T22:34:05Z,ACCT-3684,180.02,GBP,dining
5,2025-04-14T02:41:32Z,ACCT-6722,-21.37,USD,entertainment
6,2025-08-24T06:11:55Z,ACCT-8432,-100.94,EUR,salary
7,2025-02-27T09:25:08Z,ACCT-3500,-343.61,CAD,entertainment
8,2025-02-03T10:25:15Z,ACCT-4877,-58.07,CAD,groceries
9,2025-06-20T00:34:05Z,ACCT-5028,456.15,USD,utilities
10,2025-06-24T07:21:15Z,ACCT-6353,-307.5,GBP,salary
11,2025-12-19T03:04:28Z,ACCT-2884,337.86,USD,salary
12,2025-06-19T01:09:32Z,ACCT-9520,-221.38,GBP,transfer
13,2025-06-03T02:22:00Z,ACCT-9103,-114.35,EUR,travel
14,2025-05-31T08:12:55Z,ACCT-3824,51.11,USD,travel
15,2025-06-06T03:44:01Z,ACCT-3050,361.08,EUR,entertainment
16,2025-08-17T17:25:21Z,ACCT-2699,545.87,CAD,entertainment
17,2025-06-14T13:00:17Z,ACCT-6592,787.73,GBP,transfer
18,2025-11-09T08:38:06Z,ACCT-5229,-165.96,CAD,entertainment
19,2025-12-09T04:48:05Z,ACCT-4746,-5.41,USD,healthcare
20,2025-11-15T18:34:25Z,ACCT-5673,322.02,GBP,transfer
21,2025-04-21T19:19:30Z,ACCT-1840,-138.9,EUR,dining
22,2025-05-09T19:11:16Z,ACCT-1528,-242.07,GBP,travel
23,2025-09-26T23:47:53Z,ACCT-3827,323.32,GBP,transfer
24,2025-03-11T10:11:37Z,ACCT-7967,-304.22,CAD,dining
25,2025-12-17T16:14:56Z,ACCT-6587,-174.69,USD,dining
26,2025-04-09T16:06:10Z,ACCT-1212,279.99,CAD,salary
27,2025-02-07T06:29:15Z,ACCT-7045,-163.03,USD,groceries
28,2025-05-12T10:36:42Z,ACCT-1115,381.86,CAD,transfer
29,2025-07-22T23:40:15Z,ACCT-6411,-157.75,GBP,dining
30,2025-04-21T17:33:06Z,ACCT-9264,93.29,EUR,dining
31,2025-03-31T08:10:11Z,ACCT-8088,-49.0,USD,travel
32,2025-01-26T08:49:53Z,ACCT-5913,147.0,CAD,utilities
33,2025-10-08T04:31:00Z,ACCT-6393,-320.39,EUR,utilities
34,2025-10-15T21:16:09Z,ACCT-6538,-95.59,USD,healthcare
35,2025-05-03T04:36:27Z,ACCT-1609,-261.71,GBP,healthcare
36,2025-05-12T18:13:05Z,ACCT-6654,236.8,USD,travel
37,2025-08-08T05:14:11Z,ACCT-4043,49.46,CAD,utilities
38,2025-06-22T01:05:19Z,ACCT-3519,121.84,EUR,groceries
39,2025-12-05T01:56:32Z,ACCT-5992,-572.01,CAD,entertainment
40,2025-12-26T19:58:58Z,ACCT-6935,-6.79,GBP,salary
41,2025-10-11T18:50:03Z,ACCT-3867,191.2,USD,groceries
42,2025-06-15T18:44:02Z,ACCT-6967,279.63,EUR,transfer
43,2025-05-03T14:50:51Z,ACCT-6024,259.75,CAD,travel
44,2025-07-13T17:12:35Z,ACCT-7704,53.07,USD,utilities
45,2025-06-16T15:46:28Z,ACCT-6850,-265.08,CAD,healthcare
46,2025-12-01T05:00:29Z,ACCT-4093,204.63,GBP,entertainment
47,2025-07-11T15:51:24Z,ACCT-4683,526.65,USD,salary
48,2025-05-16T20:56:40Z,ACCT-9670,-247.89,USD,groceries
49,2025-08-29T00:03:57Z,ACCT-4308,-354.24,CAD,salarySpecifications
- Rows
- 50000
- Schema
- txn_id, timestamp, account, amount, currency, category
- Note
- in-repo large fixture
- Domain
- finance
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.
How to use this file for testing
“Large Transactions Dataset (CSV, 50k rows)” is a deterministic Novus Examples fixture for Data import, Performance testing, Conversion testing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 50,000 rows · schema: txn_id, timestamp, account, amount, currency, category. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import pandas as pd
df = pd.read_csv("transactions-50k.csv")
print(df.head())
print(df.dtypes)Related files
- csvBank Transactions (CSV, 60 rows)A bank-transaction statement — 60 debits and credits across three accounts (masked numbers) with running balances, categories, and merchants. Synthetic data for testing statement parsers, categorisation, and reconciliation.
- jsonBank Transactions (JSON, 60 records)The bank transactions as a JSON array — the format twin of the CSV, for import and reconciliation testing.
- jsonColumnar Nulls Schema (JSON)JSON description of nullable columns in the null-heavy columnar fixtures.
- gexfDirected Acyclic Pipeline - GEXF 1.3A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.
- graphmlDirected Acyclic Pipeline - GraphMLA six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.
- jsonDirected Acyclic Pipeline - JSON edge listA six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.
Generated by generation/data_realworld.py. Free for any use, no attribution required — license.