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Sample OpenAPI query

This code can be used to test a sample API query. It retrieves the data catalogue of this dataset. For a complete guide on query parameters and syntax, please refer to the API documentation. Try it out on your browser to see the response schema.

import requests
          
dataset_id = "d_3c51b2e39181d7b5f0ec3f67a5928681"
url = "https://data.gov.sg/api/action/datastore_search?resource_id="  + dataset_id 
        
response = requests.get(url)
print(response.json())

Citation

This dataset can be reused and cited in research publications.

Singapore Department of Statistics. (2024). Employed Residents Aged 15 Years and Over by Usual Mode of Transport to Work, Monthly Household Income from Work and Sex (Census of Population 2020) (2025) [Dataset]. data.gov.sg. Retrieved December 22, 2025 from https://data.gov.sg/datasets/d_3c51b2e39181d7b5f0ec3f67a5928681/view

Data explorer

EmployedResidentsAged15YearsandOverbyUsualModeofTransporttoWorkMonthlyHouseholdIncomefromWorkandSexCensusofPopulation2020.csv (12 rows, 6.3 KB)

Number Text	(Total) Total Numeric	(Total) Males Numeric	(Total) Females Numeric	(Below $1,000) Total Numeric	(Below $1,000) Males Numeric	(Below $1,000) Females Numeric	($1,000 - $1,999) Total Numeric	($1,000 - $1,999) Males Numeric	($1,000 - $1,999) Females Numeric	($2,000 - $2,999) Total Numeric	($2,000 - $2,999) Males Numeric	($2,000 - $2,999) Females Numeric	($3,000 - $3,999) Total Numeric	($3,000 - $3,999) Males Numeric	($3,000 - $3,999) Females Numeric	($4,000 - $4,999) Total Numeric	($4,000 - $4,999) Males Numeric	($4,000 - $4,999) Females Numeric	($5,000 - $5,999) Total Numeric	($5,000 - $5,999) Males Numeric	($5,000 - $5,999) Females Numeric	($6,000 - $6,999) Total Numeric	($6,000 - $6,999) Males Numeric	($6,000 - $6,999) Females Numeric	($7,000 - $7,999) Total Numeric	($7,000 - $7,999) Males Numeric	($7,000 - $7,999) Females Numeric	($8,000 - $8,999) Total Numeric	($8,000 - $8,999) Males Numeric	($8,000 - $8,999) Females Numeric	($9,000 - $9,999) Total Numeric	($9,000 - $9,999) Males Numeric	($9,000 - $9,999) Females Numeric	($10,000 - $10,999) Total Numeric	($10,000 - $10,999) Males Numeric	($10,000 - $10,999) Females Numeric	($11,000 - $11,999) Total Numeric	($11,000 - $11,999) Males Numeric	($11,000 - $11,999) Females Numeric	($12,000 - $12,999) Total Numeric	($12,000 - $12,999) Males Numeric	($12,000 - $12,999) Females Numeric	($13,000 - $13,999) Total Numeric	($13,000 - $13,999) Males Numeric	($13,000 - $13,999) Females Numeric	($14,000 - $14,999) Total Numeric	($14,000 - $14,999) Males Numeric	($14,000 - $14,999) Females Numeric	($15,000 - $17,499) Total Numeric	($15,000 - $17,499) Males Numeric	($15,000 - $17,499) Females Numeric	($17,500 - $19,999) Total Numeric	($17,500 - $19,999) Males Numeric	($17,500 - $19,999) Females Numeric	($20,000 & Over) Total Numeric	($20,000 & Over) Males Numeric	($20,000 & Over) Females Numeric
(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%
Total	2177456	1163601	1013856	27203	14139	13064	75725	42714	33012	85571	48957	36614	100056	55621	44435	104126	57019	47107	115691	63537	52154	116525	63142	53383	119565	64565	54999	118512	64062	54450	117838	62725	55113	109893	57920	51973	101201	53530	47671	92751	49355	43397	88837	45696	43141	78620	41515	37104	171676	90346	81330	125767	66085	59683	427898	222672	205226
Public Bus Only	325779	142175	183603	6469	2803	3666	20162	9451	10710	19947	9603	10344	21550	9659	11891	20895	9047	11848	21912	10185	11727	21232	9458	11774	20275	8691	11584	19451	8313	11138	18656	7930	10726	17427	7458	9969	14323	6389	7933	12474	5096	7378	11041	4512	6529	10341	4371	5970	20587	8653	11934	13760	5825	7934	35277	14731	20547
MRT/LRT Only	287426	123164	164262	2566	1014	1552	7275	3374	3901	8437	3795	4642	10658	4894	5764	11865	5059	6806	14165	6201	7964	13741	5606	8135	14957	6501	8456	13759	5801	7958	15392	6546	8846	14486	6039	8447	14327	6174	8153	13159	5598	7561	12943	5345	7598	10796	4515	6281	25746	10813	14933	19592	8468	11124	63562	27420	36142
MRT/LRT & Public Bus Only	559336	258897	300440	5654	2637	3017	16927	8176	8751	21291	10703	10588	28010	13880	14130	29089	14123	14966	32151	15219	16931	33655	15679	17976	34448	15668	18780	34851	16355	18496	34263	15459	18803	29060	13201	15859	29146	13236	15911	26072	11878	14194	25530	11491	14039	21671	9594	12077	45428	20668	24760	30754	14166	16588	81335	36762	44572
Other combinations of MRT/LRT or Public Bus	84893	42562	42330	603	272	331	1472	732	739	2584	1451	1133	3135	1709	1426	3706	1967	1739	4492	2485	2008	4944	2661	2283	4677	2466	2211	5034	2664	2370	4883	2447	2436	4909	2429	2479	4893	2432	2461	4413	2323	2090	3868	1776	2092	3723	1742	1981	7368	3508	3860	5786	2830	2956	14402	6668	7734
Taxi/Private Hire Car Only	65118	28941	36178	361	196	165	936	370	566	1102	611	491	1830	774	1056	2636	1137	1499	2579	1281	1298	2319	986	1333	3065	1378	1687	2998	1500	1498	3346	1541	1805	3328	1363	1965	2966	1336	1630	3067	1264	1803	3021	1267	1754	2333	1001	1332	6296	2784	3512	3949	1489	2460	18987	8662	10325
Car Only	459796	299847	159949	1440	910	531	4014	2655	1360	6490	4285	2205	8285	5980	2305	8826	6005	2821	12800	9101	3700	13227	9120	4107	15905	11192	4714	18339	12450	5889	19013	13101	5912	19863	13420	6443	18542	12687	5855	18283	12898	5384	18752	12426	6326	18653	12851	5801	43091	29221	13870	37112	24368	12744	177161	107180	69981
Private Chartered Bus/Van Only	44883	28361	16522	437	203	234	1452	988	464	2208	1645	564	2244	1656	588	3466	2419	1047	3399	2264	1135	3201	2192	1009	3034	2048	987	3043	1978	1064	2597	1780	817	2271	1501	770	2202	1225	977	2128	1412	716	2027	1154	873	1501	778	723	3077	1773	1304	2082	1003	1079	4514	2342	2172
Lorry/Pickup Only	28894	25879	3016	331	290	41	1520	1377	143	1864	1742	122	2451	2316	135	2560	2269	291	2423	2131	292	2222	2022	200	2318	2009	309	2344	1982	362	1754	1511	243	1538	1402	137	1212	1089	123	790	756	35	1131	985	145	922	772	151	1293	1214	79	847	807	40	1372	1205	167
Motorcycle/Scooter Only	73487	67155	6332	477	473	5	1642	1527	115	2792	2598	193	4045	3687	359	5000	4552	448	5338	4940	397	6258	5843	416	6312	5857	455	5327	4925	401	5638	5136	502	4728	4240	487	4183	3808	375	3835	3396	439	3216	2966	249	2578	2312	266	4591	4111	480	2955	2638	317	4572	4146	426

Column legend

Title	Column name	Data type	Unit of measure	Description
Number	Number	Text	Number	-
(Total) Total	Total_Total	Numeric	Number	-
(Total) Males	Total_Males	Numeric	Number	-
(Total) Females	Total_Females	Numeric	Number	-
(Below $1,000) Total	Below_1_000_Total	Numeric	Number	-
(Below $1,000) Males	Below_1_000_Males	Numeric	Number	-
(Below $1,000) Females	Below_1_000_Females	Numeric	Number	-
($1,000 - $1,999) Total	1_000_1_999_Total	Numeric	Number	-
($1,000 - $1,999) Males	1_000_1_999_Males	Numeric	Number	-
($1,000 - $1,999) Females	1_000_1_999_Females	Numeric	Number	-

Employed Residents Aged 15 Years and Over by Usual Mode of Transport to Work, Monthly Household Income from Work and Sex (Census of Population 2020)

Sample OpenAPI query

Citation

About this dataset

Contact

Created on

Licence

Agency

Employed Residents Aged 15 Years and Over by Usual Mode of Transport to Work, Monthly Household Income from Work and Sex (Census of Population 2020)

Employed Residents Aged 15 Years and Over by Usual Mode of Transport to Work, Monthly Household Income from Work and Sex (Census of Population 2020)

Sample OpenAPI query

Citation

About this dataset

Contact

Created on

Licence

Agency

Data explorer

EmployedResidentsAged15YearsandOverbyUsualModeofTransporttoWorkMonthlyHouseholdIncomefromWorkandSexCensusofPopulation2020.csv (12 rows, 6.3 KB)

Column legend

Analyse this dataset with Colab Notebook