Datasets:

electricsheepafrica
/

africa-synth-trade-port-throughput-performance-all

record_id stringlengths 36 36	port_name stringclasses 15 values	country stringclasses 13 values	year int64 2.02k 2.03k	quarter stringclasses 4 values	container_throughput_teu int64 150k 7.42M	vessel_turnaround_hours float64 8.63 96	berth_occupancy_rate float64 0.33 0.99	crane_moves_per_hour float64 6.49 50	dwell_time_days float64 1.12 27	cost_per_container_usd float64 107 586	connectivity_index float64 30.3 100	customs_integration_score float64 29.9 100	congestion_index float64 0.13 0.99	port_efficiency_class stringclasses 4 values
2a021b73-7771-4d00-8343-ed1be64f5604	Abidjan	Côte d'Ivoire	2,022	Q4	898,919	26.87	0.6905	23.52	4.86	212.19	67.84	58.52	0.4951	moderate
e2986332-4be4-4eaf-b369-272fab298b66	Tema	Ghana	2,021	Q3	778,482	32.36	0.6132	22.27	6.57	204.84	56.46	66.06	0.5834	inefficient
618cd1dc-723f-46ca-8819-cfaebe1b56e7	Douala	Cameroon	2,024	Q3	646,531	39.42	0.7764	14.38	8.53	263.21	54.08	50.29	0.7932	congested
7516242f-7e0f-4e56-9f08-d937d41f8286	Dar es Salaam	Tanzania	2,020	Q4	814,102	42.16	0.8771	14.28	9.57	318.08	57.8	54.97	0.8089	congested
0af08d6e-4dfa-4636-88f6-bd018cd65819	Djibouti	Djibouti	2,019	Q4	1,109,042	17.47	0.5975	29.26	2.69	178.88	77.67	65.59	0.2754	efficient
1fff20cd-3998-434b-9fe8-740dba5db285	Mombasa	Kenya	2,020	Q2	1,491,666	36.84	0.8301	17.48	7.5	268.37	62.34	60.21	0.6968	inefficient
84cbcde1-3b77-458f-8bbd-61f0e8d0bcfa	Maputo	Mozambique	2,020	Q1	364,727	32.64	0.6987	22.3	6.45	209.98	50.17	50.38	0.5284	moderate
59225ea2-f5b0-431b-8fa7-94f7796cd0c0	Mombasa	Kenya	2,021	Q3	1,628,123	35.38	0.887	16.24	8.54	251.48	68.7	57.34	0.6917	inefficient
047e3e94-5e31-43a6-9717-4bcfe1b576ac	Durban	South Africa	2,019	Q1	4,633,651	19.44	0.7412	31.32	5.04	204.29	75.06	80.53	0.4392	moderate
b0d51a5e-fc04-4452-9253-3bad834bd571	Lagos/Apapa	Nigeria	2,025	Q1	1,712,494	47.06	0.8834	15.79	12.21	340.4	60.85	45.17	0.8652	congested
5d68ab77-c2bb-4aa0-8190-07e26e4f5502	Douala	Cameroon	2,019	Q1	582,421	43.78	0.8013	16.37	8.68	257.99	56.33	49.38	0.6944	inefficient
cb3b4721-b8dd-4e4f-b7d6-165760ca47d0	Beira	Mozambique	2,025	Q1	278,697	32.59	0.5824	17.95	6.41	236.66	40.1	50.95	0.6208	inefficient
a1c8f4d5-ff3c-4b6a-94e1-81e1f91bed0b	Tema	Ghana	2,025	Q4	694,293	26.32	0.6872	21.05	5.96	221.07	61.04	65.24	0.5513	inefficient
2dfabc9f-d58c-4480-90ec-06e1255c9d7b	Lagos/Apapa	Nigeria	2,025	Q3	1,848,339	44.63	0.8662	15.03	13.09	373.46	60.09	46.67	0.8649	congested
32c36afc-9d1f-4098-a353-298b047db727	Djibouti	Djibouti	2,023	Q4	1,180,350	19.43	0.5887	27.51	2.62	183.04	67.57	66.61	0.245	efficient
71197e71-0556-43ad-ae0b-e37df3a85bdd	Lagos/Apapa	Nigeria	2,022	Q4	1,821,997	50.46	0.939	13.76	9.75	369.49	59.18	51.22	0.7189	inefficient
7bce75c8-40a3-4d54-838c-558fc3f334c0	Beira	Mozambique	2,023	Q2	228,805	30.77	0.5847	16.08	7.83	236.27	40.83	51.84	0.6162	inefficient
d9660809-309e-44cb-a037-1958e1524492	Walvis Bay	Namibia	2,020	Q3	445,138	16.41	0.5281	31.09	2.87	155.64	48.42	69.02	0.2877	efficient
e1a35b58-641f-4a22-b81a-d13592c21431	Cape Town	South Africa	2,025	Q2	924,705	22.23	0.5977	28.62	3.81	215.76	68.9	65.67	0.3841	moderate
bdd6bdae-c929-4835-84fe-291ea0505642	Mombasa	Kenya	2,019	Q4	1,517,340	31.4	0.7736	19.09	6.92	230.82	69.24	59.5	0.8493	congested
6b0fef27-13c1-47a1-b476-8a38f5416642	Cape Town	South Africa	2,019	Q1	841,481	22.61	0.4574	24.03	2.95	213.35	82.7	76.34	0.35	moderate
e6041697-3f3c-4be6-bcf9-7842667f6f47	Lagos/Apapa	Nigeria	2,019	Q4	1,987,115	47.4	0.8749	13.19	12.91	288.28	52.18	45.73	0.8579	congested
1ca3f8e1-e55f-49a9-9490-09bcce83bf3c	Douala	Cameroon	2,021	Q1	526,488	32.62	0.8354	17.2	6.81	261.27	50.66	54.86	0.7049	inefficient
7f6fae73-ec47-4155-a4cf-412657c679c0	Dakar	Senegal	2,025	Q4	678,234	25.41	0.6787	19.83	7.33	232.59	50.81	56.56	0.4487	moderate
3ed474ce-0878-468f-92b4-2b4d1c8e1107	Pointe Noire	Republic of Congo	2,019	Q4	401,599	32.3	0.8499	18.55	8.15	265.28	47.25	41	0.5506	inefficient
980b2c24-fe66-4555-a4b7-e3f235bcedc2	Douala	Cameroon	2,025	Q4	592,327	33.98	0.817	14.31	8.22	262.15	54.07	48.68	0.7591	congested
42cfa1e4-2a60-4d31-b280-292c10429089	Lome	Togo	2,019	Q1	1,143,219	18.46	0.6516	21.61	3.94	173.8	59.51	64.04	0.3626	moderate
bb14046a-0fcb-4a46-aea2-6bde87a00e6f	Dakar	Senegal	2,024	Q4	696,237	28.67	0.6306	19.66	5.74	238.35	52.26	55.79	0.49	moderate
8e6694e8-9ce5-4cbe-854b-01f765124dbb	Dar es Salaam	Tanzania	2,024	Q1	720,284	40.72	0.8445	17.33	8.47	246.64	52.41	51.76	0.6887	inefficient
25b56c60-0449-4cda-bd62-9e5266367048	Walvis Bay	Namibia	2,024	Q4	432,585	18.13	0.5674	35.7	2.69	156.05	54.49	69.03	0.2176	efficient
6e09e99b-b010-4e37-bcf4-b4d643169ad6	Lome	Togo	2,025	Q2	1,128,908	20.58	0.6699	24.61	4.19	206.06	59.47	66.61	0.4045	moderate
8f3d4d97-1b77-4a38-be83-afefc1c1ea86	Abidjan	Côte d'Ivoire	2,024	Q2	936,240	28.81	0.6258	26.22	5.45	219.14	67.24	52.17	0.4635	moderate
3570654b-c5d5-42c6-a8e1-13b8aa29fe23	Douala	Cameroon	2,022	Q3	754,572	37.89	0.7902	13.82	9.46	276.31	51.11	53.89	0.6203	inefficient
04144e59-0816-40d9-bd8d-44e8303ad811	Cape Town	South Africa	2,024	Q3	941,833	25.65	0.5719	25.18	3.41	203.33	73.59	76.12	0.2739	efficient
5a5ac313-e848-41d4-8c9e-7eca154aea06	Dakar	Senegal	2,019	Q1	701,731	26.68	0.6726	20.75	4.79	198.94	54.36	60.19	0.5364	moderate
29897605-30a1-4642-bec5-147e81a17a16	Cape Town	South Africa	2,024	Q3	919,345	16.93	0.5711	21.72	3.85	200.54	80.3	83.7	0.3238	efficient
4280b3ba-3262-4278-867f-cdff65e8fa89	Pointe Noire	Republic of Congo	2,025	Q1	420,695	27.49	0.7313	18.45	7.13	286.23	47.97	45.62	0.6944	inefficient
8f2a5d15-9fff-4188-bead-042e93a97f09	Lome	Togo	2,020	Q2	984,571	19.48	0.6849	28.75	3.79	179.2	63.74	64.22	0.4099	moderate
dcd86e3a-d4de-40b6-8db9-8e4ea1375317	Mombasa	Kenya	2,024	Q1	1,599,507	31.44	0.8912	16.91	6.94	244.8	65.75	53.19	0.6711	inefficient
d33580c7-8048-42f9-8f63-8d446ddf0e8c	Cape Town	South Africa	2,020	Q1	870,714	21.78	0.5653	25.02	3.35	208.11	79.75	69.95	0.3929	moderate
0793411f-7afb-4ae0-80cb-b847a88eafe7	Dakar	Senegal	2,025	Q4	719,204	23.82	0.6577	20.74	5.48	244.11	55.59	56.82	0.4957	moderate
6ef1b837-24bf-4869-8766-b5b07bd01962	Abidjan	Côte d'Ivoire	2,025	Q2	895,143	25.54	0.7649	20.06	4.25	192.28	62.56	55.25	0.4903	moderate
2bb86da2-d93a-41e4-86de-38e48b6ba1a5	Abidjan	Côte d'Ivoire	2,024	Q3	1,039,221	24.14	0.6805	18.21	5.29	190.99	63.38	55.03	0.5574	inefficient
bde87cdb-d013-4a6f-b4ad-8db53722cdbd	Maputo	Mozambique	2,025	Q2	370,409	28.62	0.6821	19.64	4.97	187.41	48.47	47.17	0.413	moderate
3582e613-a390-4d7b-9264-e9650542de45	Cape Town	South Africa	2,021	Q3	928,084	18.42	0.5949	24.94	3.59	188.54	73.54	78.2	0.3683	moderate
e24b252a-0884-4520-91a1-4749a27f457c	Lagos/Apapa	Nigeria	2,019	Q3	1,752,622	46.84	0.9567	11.05	13.02	320.25	66.84	42.07	0.8585	congested
0ab643eb-0980-4f9b-b0ac-f0b2a170a138	Djibouti	Djibouti	2,023	Q4	961,075	17.72	0.5624	28.3	2.54	161.88	66.4	70.16	0.2696	efficient
42f65f54-a818-4629-bf47-1c3b60d81977	Durban	South Africa	2,020	Q3	5,117,055	20.99	0.6786	30.7	4.81	206.95	72.37	75.41	0.4687	moderate
876b1aa6-5037-477c-8183-de5a032f9481	Dakar	Senegal	2,024	Q3	823,796	30.96	0.6497	20.04	5.86	208.52	58.38	60.4	0.4729	moderate
d53236b2-5ce8-420d-afee-d66b229a5119	Dar es Salaam	Tanzania	2,023	Q3	848,234	41.28	0.8025	13.47	8.6	273.47	55.08	52.35	0.6619	inefficient
24470420-64b8-404a-9864-1a32c707e587	Lome	Togo	2,021	Q1	1,068,321	19.6	0.5802	25.32	4.16	177.32	62.37	62.85	0.4504	moderate
7e4bae70-c87b-43ca-a074-ff6aad877cd3	Durban	South Africa	2,019	Q2	4,107,000	23.78	0.745	24.33	4.43	175.68	76.64	82.27	0.4781	moderate
4d000b1c-c6a9-4f33-9067-198fee5dbf1f	Lome	Togo	2,021	Q4	1,029,246	20.93	0.5526	22.99	3.68	192.54	63.85	65.33	0.4321	moderate
4aa46b21-6075-4e41-b2e2-42abaf738eb4	Lome	Togo	2,019	Q4	1,097,800	22.58	0.5472	24.27	3.86	143.79	58.83	64.21	0.3556	moderate
4d703627-37ab-462d-b334-caba181c1b0d	Abidjan	Côte d'Ivoire	2,022	Q4	900,675	26.22	0.5845	20.71	5.14	212.9	67.57	64.41	0.5161	moderate
94ae0d59-7b09-423d-a931-cddef3a503b1	Durban	South Africa	2,019	Q1	4,367,412	22.79	0.6827	25.68	4.69	188.24	74.2	85.4	0.4699	moderate
bd75e967-d79e-464f-a792-1c9871742a40	Mombasa	Kenya	2,024	Q1	1,290,882	39.09	0.8355	19.1	6.7	247	65.29	60.89	0.7064	inefficient
666cfc59-d9be-484c-a592-0a12e698fda0	Beira	Mozambique	2,023	Q1	257,980	34.18	0.5811	16.3	4.86	245.92	43.11	47.72	0.5729	inefficient
f694c090-9370-48ec-94c1-4789b5bd3c27	Cape Town	South Africa	2,022	Q3	988,811	19.72	0.531	26.81	3.7	201.46	82.04	68.96	0.2714	efficient
51d15950-b82a-4c05-b964-7f434d77f517	Cape Town	South Africa	2,025	Q2	896,630	20.9	0.5853	26.64	3.42	215.81	70.08	73.63	0.3655	moderate
861b4360-88d3-4571-80e2-2280814609a7	Mombasa	Kenya	2,025	Q4	1,389,476	42.24	0.7383	19.69	7.67	287.67	69.09	53.37	0.6779	inefficient
1c30a012-3f6f-40ea-af1c-2ebe186deeeb	Tema	Ghana	2,023	Q2	770,231	30.56	0.6667	20.1	5.84	228.96	57.9	67.27	0.5552	inefficient
c4ad1687-6fe0-4b94-97d0-8146f0da5a8c	Djibouti	Djibouti	2,024	Q3	1,297,251	17.51	0.57	32.73	3.06	162	69.05	66.64	0.3274	efficient
124ae269-1114-4b33-94da-e8ab5c4129a6	Cape Town	South Africa	2,020	Q1	902,673	24.57	0.578	25.88	3.95	186.58	75.81	83	0.3767	moderate
01e28487-dead-409e-b06d-8773a31f59c3	Dar es Salaam	Tanzania	2,023	Q3	782,237	45.61	0.838	17.48	10.39	280.46	55.12	51.35	0.6817	inefficient
af8ebb70-f63b-4ef3-9153-2b8a64f6167e	Pointe Noire	Republic of Congo	2,021	Q4	369,660	36.51	0.8084	18.68	6.98	273.33	44.34	45.27	0.6155	inefficient
459e91f0-c692-4106-9904-e1f22390dcb6	Walvis Bay	Namibia	2,020	Q3	466,918	14.9	0.488	29.54	2.5	163.56	53.34	70.78	0.2218	efficient
d8f88d6e-9c01-4a58-b6c5-6466af92f14a	Pointe Noire	Republic of Congo	2,025	Q2	443,168	27.2	0.7313	18.03	7.63	260.19	41.72	47.53	0.553	inefficient
0525de9e-0f60-41ec-b3e3-c0fdc3075c4b	Pointe Noire	Republic of Congo	2,024	Q3	402,936	33.25	0.6808	17.63	6.22	243.14	44.39	47.22	0.6249	inefficient
f99db055-758e-41e4-a1a7-d94782a6f8b2	Lome	Togo	2,022	Q1	1,053,844	20.27	0.6561	27.94	4.32	161.01	59.98	60.15	0.4398	moderate
75ea2ece-b202-44ff-bb1a-f5b9fa3bb84a	Mombasa	Kenya	2,024	Q3	1,515,561	36.84	0.7905	17.08	6.15	282.2	71.78	54.01	0.5144	moderate
530699cd-6c38-4613-81a1-5c4f17fbdc0f	Durban	South Africa	2,022	Q4	4,247,814	26.79	0.7817	30.03	5.5	182.38	78.03	75.53	0.4509	moderate
d298aa4a-b5ed-4f18-848c-a0b7cad83844	Beira	Mozambique	2,025	Q4	259,072	33.78	0.5286	19.73	5.72	222.47	43.01	47.43	0.4632	moderate
f0a54f0f-2cec-4f60-a3d2-e8b571c81bc9	Pointe Noire	Republic of Congo	2,019	Q1	393,867	31.49	0.6731	19.1	6.81	301.09	41.02	43	0.6325	inefficient
1672b1aa-342d-4924-9da1-826e9ff9a3ee	Cape Town	South Africa	2,022	Q1	982,396	25.56	0.5177	23.02	4.6	178.06	67.18	82.31	0.361	moderate
00d12650-da2e-4c7f-894d-87f4f1edd850	Maputo	Mozambique	2,024	Q4	347,105	28.15	0.6707	20.29	5.88	221.56	48.4	49.49	0.5198	moderate
5d554c53-38b6-45ba-ab92-b1d97bcaf21f	Mombasa	Kenya	2,020	Q1	1,358,079	31.48	0.7614	23.95	5.7	300.18	71.31	54.5	0.6595	inefficient
2a1a04ec-707e-443e-b916-b2f3a2d31e69	Walvis Bay	Namibia	2,022	Q4	464,750	15.12	0.4848	30.04	2.43	144.04	53.13	73.07	0.2959	efficient
58df7df2-1271-4ea0-a3b1-1b0495956f1e	Pointe Noire	Republic of Congo	2,021	Q1	401,431	33.71	0.7117	20.64	8.08	269.13	42.22	46.52	0.789	congested
89741c22-d434-4e43-864e-28f52d7effaa	Lagos/Apapa	Nigeria	2,024	Q1	1,862,656	56.84	0.99	14.48	12.08	383.08	60.76	43.46	0.7682	congested
e4cdf98b-7b4f-4b07-a914-c3eba6af0cf7	Lagos/Apapa	Nigeria	2,025	Q4	1,965,041	50.27	0.7885	14.39	10.07	344.72	62.94	43.23	0.9199	congested
b1c29aaa-c358-46ff-a2df-79d375db0f21	Abidjan	Côte d'Ivoire	2,019	Q2	955,107	29.27	0.7699	20.92	5.33	159.28	58.78	55.93	0.4352	moderate
59fc7940-0b5a-44a5-bcb6-5f45039f8fbd	Durban	South Africa	2,022	Q3	4,608,135	23	0.6046	30.23	3.59	174.56	76.46	74.21	0.499	moderate
60a01f76-2c56-4165-aee9-9fd825b11255	Douala	Cameroon	2,023	Q3	619,427	31.78	0.6834	16.02	8.99	245.19	42.07	50.34	0.6695	inefficient
6402a63f-e5dc-4da5-ad16-d3adf7518b92	Dakar	Senegal	2,019	Q1	715,628	27.42	0.6665	24.02	5.62	180.28	62.56	60.16	0.5491	moderate
2d9d7a8b-5854-4e9b-987b-e6d0c4a6ed7e	Walvis Bay	Namibia	2,021	Q1	431,278	15.11	0.4646	33.92	2.71	155.44	54.66	63.4	0.2136	efficient
19f5cdac-21d0-4c73-98ea-905f6120d6bb	Abidjan	Côte d'Ivoire	2,025	Q3	939,766	28.41	0.6738	24.7	4.59	202.76	70.99	63.71	0.591	inefficient
c3fb861f-a369-4655-879e-ec927b4099a5	Beira	Mozambique	2,019	Q3	283,992	30.42	0.6325	19.17	6.72	261.36	43.34	52.45	0.5977	inefficient
c31afdef-7e3f-402d-8808-3cb3cf623c4b	Maputo	Mozambique	2,021	Q1	345,998	29	0.6579	17.83	5.48	179.63	44.57	52.16	0.4277	moderate
29dfade1-99e9-4d19-b4d4-ef4ce4fbf3db	Cape Town	South Africa	2,021	Q3	953,902	20.2	0.5194	22.49	3.72	218.99	73.5	75.97	0.3944	moderate
777fee2b-f8ff-4061-8ce2-6ba26a3edb2d	Pointe Noire	Republic of Congo	2,022	Q4	392,501	29.19	0.7545	19.08	9.89	252.43	45.56	42.5	0.6183	inefficient
a585adf3-e064-48a3-8888-a8995750033a	Abidjan	Côte d'Ivoire	2,020	Q2	975,158	28.95	0.6119	17.21	5.42	199.87	63.31	62.64	0.5037	moderate
9db77c18-708a-4bca-abf5-30bd044ee271	Djibouti	Djibouti	2,025	Q2	1,279,813	17.66	0.602	32.54	2.79	171.61	74.48	73.91	0.2773	efficient
3411c85d-743c-4376-aa69-5a2a195c7ffd	Pointe Noire	Republic of Congo	2,019	Q1	370,182	37.84	0.7314	20	7.45	270.4	46.42	42.37	0.6937	inefficient
c90b7c5e-51f0-4982-8b90-a9ee469a0acb	Pointe Noire	Republic of Congo	2,023	Q4	383,098	35.49	0.7286	16.61	6.64	249.8	40.4	47.19	0.6542	inefficient
a640d453-820d-4d7c-b308-3b5928e6a19b	Dakar	Senegal	2,021	Q2	811,098	27.9	0.6458	20.43	4.86	217.14	54.06	57.87	0.5528	inefficient
b8865cae-ab6c-44d4-8010-c68fc98b91e7	Beira	Mozambique	2,022	Q4	228,688	31.17	0.5821	16.8	6.77	253.63	44.03	49.34	0.4642	moderate
da382b4b-21d7-4f48-b011-282f3f9d303e	Beira	Mozambique	2,020	Q2	263,342	32.39	0.6004	18.65	7.12	247.16	45.12	47.37	0.5152	moderate
f6d3de41-ccbc-4aa0-bf29-9e6e1efed7f5	Cape Town	South Africa	2,020	Q4	884,463	19.93	0.6436	21.9	3.32	211.66	70.25	76.44	0.3356	efficient
1343c94a-3558-4d28-8c69-6c19647bd545	Lome	Togo	2,021	Q1	1,152,299	21.54	0.6705	21.66	4.07	172.89	57.49	62.26	0.4082	moderate

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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

African Port Throughput Performance

Synthetic dataset modelling container throughput and operational performance for 15 major African seaports across three scenarios.

Scenarios

Scenario	Description
`baseline`	Current operational conditions
`capacity_expansion`	Investment in cranes, berths, and digital customs
`congestion_crisis`	Surge in demand with infrastructure bottlenecks

Each scenario contains 10 000 records covering 2019–2025.

Ports

Mombasa (Kenya), Dar es Salaam (Tanzania), Lagos/Apapa (Nigeria), Durban (South Africa), Tema (Ghana), Djibouti (Djibouti), Maputo (Mozambique), Lome (Togo), Abidjan (Côte d'Ivoire), Dakar (Senegal), Walvis Bay (Namibia), Beira (Mozambique), Douala (Cameroon), Pointe Noire (Republic of Congo), Cape Town (South Africa).

Variables

Column	Type	Description
`record_id`	string	Unique identifier (UUID)
`port_name`	string	Port name
`country`	string	Country
`year`	int	Calendar year (2019–2025)
`quarter`	string	Quarter (Q1–Q4)
`container_throughput_teu`	int	Twenty-foot equivalent units
`vessel_turnaround_hours`	float	Hours from arrival to departure
`berth_occupancy_rate`	float	0–1
`crane_moves_per_hour`	float	Container moves per crane-hour
`dwell_time_days`	float	Average container dwell time
`cost_per_container_usd`	float	USD per TEU handled
`connectivity_index`	float	0–100 liner connectivity score
`customs_integration_score`	float	0–100 digital customs score
`congestion_index`	float	0–1
`port_efficiency_class`	string	efficient / moderate / inefficient / congested

Generation

pip install -r requirements.txt
python generate_dataset.py --output-dir data --records-per-scenario 10000
python validate_dataset.py data

License

CC BY 4.0

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