Tidy dataset
6. Family and domestic violence_1 — Table 40
A tidy Recorded Crime – Offenders table with 2,695 rows. It reports recorded values by measure, year and state or territory.
- Status
- approved
- Publisher
- Australian Bureau of Statistics
- Release
- 2023-24
- License
- CC-BY-4.0
Data preview
| 42 | Number | 2016–17 | New South Wales(e) | Males | 01 Homicide and related offences |
| 31 | Number | 2017–18 | New South Wales(e) | Males | 01 Homicide and related offences |
| 52 | Number | 2018–19 | New South Wales(e) | Males | 01 Homicide and related offences |
| 37 | Number | 2019–20 | New South Wales(e) | Males | 01 Homicide and related offences |
| 43 | Number | 2020–21 | New South Wales(e) | Males | 01 Homicide and related offences |
| 28 | Number | 2021–22 | New South Wales(e) | Males | 01 Homicide and related offences |
| 40 | Number | 2022-23 | New South Wales(e) | Males | 01 Homicide and related offences |
| 41 | Number | 2023-24 | New South Wales(e) | Males | 01 Homicide and related offences |
| 1.3 | Offender rate(b) | 2016–17(d) | New South Wales(e) | Males | 01 Homicide and related offences |
| 0.9 | Offender rate(b) | 2017–18(d) | New South Wales(e) | Males | 01 Homicide and related offences |
| 1.5 | Offender rate(b) | 2018–19(d) | New South Wales(e) | Males | 01 Homicide and related offences |
| 1.1 | Offender rate(b) | 2019–20(d) | New South Wales(e) | Males | 01 Homicide and related offences |
| 1.2 | Offender rate(b) | 2020–21(d) | New South Wales(e) | Males | 01 Homicide and related offences |
| 0.8 | Offender rate(b) | 2021–22 | New South Wales(e) | Males | 01 Homicide and related offences |
| 1.1 | Offender rate(b) | 2022-23 | New South Wales(e) | Males | 01 Homicide and related offences |
| 1.1 | Offender rate(b) | 2023-24 | New South Wales(e) | Males | 01 Homicide and related offences |
| 15245 | Number | 2016–17 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 15070 | Number | 2017–18 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 16032 | Number | 2018–19 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 16781 | Number | 2019–20 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 17703 | Number | 2020–21 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 17955 | Number | 2021–22 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 19001 | Number | 2022-23 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 19428 | Number | 2023-24 | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 455.1 | Offender rate(b) | 2016–17(d) | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 442.5 | Offender rate(b) | 2017–18(d) | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 463.3 | Offender rate(b) | 2018–19(d) | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 478.8 | Offender rate(b) | 2019–20(d) | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 503.8 | Offender rate(b) | 2020–21(d) | New South Wales(e) | Males | 02 Acts intended to cause injury |
| 510.6 | Offender rate(b) | 2021–22 | New South Wales(e) | Males | 02 Acts intended to cause injury |
Get the data
CSV
334.0 KiB
Download tidy.csvJSON
1.1 MiB
Download tidy.jsonPARQUET
29.2 KiB
Download tidy.parquetOpen in Query with this immutable dataset version and a starter SQL query.
Copy-paste a stable URL
import pandas as pd
df = pd.read_csv("https://data.tidybank.net/data/abs/recorded-crime-offenders/2023-24/table-40/tidy.csv")data <- read.csv("https://data.tidybank.net/data/abs/recorded-crime-offenders/2023-24/table-40/tidy.csv")SELECT *
FROM read_parquet('https://data.tidybank.net/data/abs/recorded-crime-offenders/2023-24/table-40/tidy.parquet');Schema and semantics
| Column | Type | Unit | Semantic role | Join key | Distinct / range |
|---|---|---|---|---|---|
| row | number | — | attribute | No | — |
| col | number | — | attribute | No | — |
| address | string | — | attribute | No | — |
| .value | string | — | measure | No | — |
| measure | string | — | dimension | No | — |
| measure_source | string | — | attribute | No | — |
| year | string | — | time | Yes | — |
| year_source | string | — | attribute | No | — |
| state_or_territory | string | — | geography | Yes | — |
| state_or_territory_source | string | — | attribute | No | — |
| sex | string | — | dimension | Yes | — |
| sex_source | string | — | attribute | No | — |
| principal_offence | string | — | dimension | No | — |
| principal_offence_source | string | — | attribute | No | — |
How it was tidied
Source overlay
See value and header roles on the original worksheet grid.
Open overlay evidenceReconstructed table
Compare the tidy output with a human-readable reconstruction.
Open reconstructed evidenceRecipe
The auditable RecipeV01 transformation used for this asset.
Download recipe.jsonQuality checks
| Check | Status | Detail |
|---|---|---|
| recipe-validation | pass | — |
| execution-warnings | warn | AMBIGUOUS_HEADER: 2358 |
| non-empty-output | pass | — |
| header-name-uniqueness | pass | — |
Generated by seeded ; pipeline version 0.1.0.
Coverage and lineage
- Release position
- 2 of 3
- Retrieved
- 2026-07-17T12:29:54.070Z
- Availability
- unchecked
- Workbook checksum
- sha256:91db681481764fc676f68e4bff041e97f144b087f3b9657085d1189a3502de5f
Other approved releases
Related tables
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- Asset ID
- abs/recorded-crime-offenders/2023-24/table-40
- Release
- 2023-24
Prefer to verify or report manually? View the recorded source or open a public GitHub issue.