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indicator_code
stringclasses
1 value
country_iso3
stringclasses
40 values
who_region
stringclasses
1 value
year
int64
1.99k
2.02k
dim1_type
stringclasses
6 values
dim1
stringlengths
18
30
value_numeric
float64
0
95
value_low
float64
0
93.7
value_high
float64
0
97
value_display
stringlengths
1
16
last_updated
stringdate
2022-05-26 10:59:09+0200
2022-05-26 14:54:10+0200
fps
LSO
AFR
2,009
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE07
67.89977
61.47771
73.70908
67.9 [61.5-73.7]
2022-05-26T12:16:38+02:00
fps
GIN
AFR
2,012
RESIDENCEAREATYPE
RESIDENCEAREATYPE_URB
25.27621
20.85298
30.27874
25.3 [20.9-30.3]
2022-05-26T11:35:19+02:00
fps
MDG
AFR
2,003
DHSMICSGEOREGION
DHSMICSGEOREGION_MDGDHS200306
47.25397
37.2933
57.43816
47.3 [37.3-57.4]
2022-05-26T14:32:15+02:00
fps
KEN
AFR
2,014
RESIDENCEAREATYPE
RESIDENCEAREATYPE_RUR
73.18844
71.36539
74.93619
73.2 [71.4-74.9]
2022-05-26T11:36:19+02:00
fps
ETH
AFR
2,000
WEALTHQUINTILE
WEALTHQUINTILE_WQ2
8.5135
5.99566
11.95423
8.5 [6.0-12.0]
2022-05-26T12:51:31+02:00
fps
MWI
AFR
2,000
DHSMICSGEOREGION
DHSMICSGEOREGION_MWIDHS200002
49.09325
46.11698
52.07596
49.1 [46.1-52.1]
2022-05-26T14:32:43+02:00
fps
COD
AFR
2,017
DHSMICSGEOREGION
DHSMICSGEOREGION_CODMICS201718
32.05932
22.73472
43.07621
32.1 [22.7-43.1]
2022-05-26T14:19:47+02:00
fps
UGA
AFR
2,016
WEALTHQUINTILE
WEALTHQUINTILE_WQ4
66.48472
63.39676
69.43776
66.5 [63.4-69.4]
2022-05-26T13:06:23+02:00
fps
SWZ
AFR
2,006
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE03
58.27973
48.27021
67.65045
58.3 [48.3-67.7]
2022-05-26T12:07:51+02:00
fps
GMB
AFR
2,018
DHSMICSGEOREGION
DHSMICSGEOREGION_GMBMICS201807
43.38838
37.08876
49.90913
43.4 [37.1-49.9]
2022-05-26T14:23:05+02:00
fps
GAB
AFR
2,012
DHSMICSGEOREGION
DHSMICSGEOREGION_GABDHS201201
55.84068
50.80568
60.75839
55.8 [50.8-60.8]
2022-05-26T14:22:46+02:00
fps
COD
AFR
2,013
DHSMICSGEOREGION
DHSMICSGEOREGION_CODDHS201307
33.33587
26.08665
41.46936
33.3 [26.1-41.5]
2022-05-26T14:19:37+02:00
fps
GHA
AFR
2,014
RESIDENCEAREATYPE
RESIDENCEAREATYPE_RUR
46.92779
43.2995
50.58886
46.9 [43.3-50.6]
2022-05-26T11:35:08+02:00
fps
GNB
AFR
2,006
DHSMICSGEOREGION
DHSMICSGEOREGION_GNBMICS200603
31.12301
26.02814
36.7201
31.1 [26.0-36.7]
2022-05-26T14:24:54+02:00
fps
TCD
AFR
2,019
WEALTHQUINTILE
WEALTHQUINTILE_WQ4
21.94259
18.3615
25.99968
21.9 [18.4-26.0]
2022-05-26T12:48:45+02:00
fps
ZMB
AFR
2,018
EDUCATIONLEVEL
EDUCATIONLEVEL_NHLM
60.91225
55.5227
66.04807
60.9 [55.5-66.0]
2022-05-26T11:26:19+02:00
fps
ZMB
AFR
2,018
DHSMICSGEOREGION
DHSMICSGEOREGION_ZMBDHS201804
60.36134
53.19262
67.111
60.4 [53.2-67.1]
2022-05-26T14:53:28+02:00
fps
NGA
AFR
2,011
DHSMICSGEOREGION
DHSMICSGEOREGION_NGAMICS201126
36.43149
27.42231
46.504
36.4 [27.4-46.5]
2022-05-26T14:37:39+02:00
fps
MLI
AFR
2,018
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE05
40.23691
33.72946
47.10741
40.2 [33.7-47.1]
2022-05-26T12:19:10+02:00
fps
GHA
AFR
2,008
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE01
25.8064
18.6063
34.60804
25.8 [18.6-34.6]
2022-05-26T12:09:55+02:00
fps
COG
AFR
2,005
DHSMICSGEOREGION
DHSMICSGEOREGION_COGDHS200503
69.70274
65.33589
73.74058
69.7 [65.3-73.7]
2022-05-26T14:18:14+02:00
fps
SLE
AFR
2,008
AGEGROUP
AGEGROUP_YEARS20-49
23.71946
21.22011
26.41448
23.7 [21.2-26.4]
2022-05-26T11:06:23+02:00
fps
COD
AFR
2,010
EDUCATIONLEVEL
EDUCATIONLEVEL_SHLM
46.29213
42.30784
50.32434
46.3 [42.3-50.3]
2022-05-26T11:16:42+02:00
fps
UGA
AFR
2,011
AGEGROUP
AGEGROUP_YEARS15-19
30.79327
23.69493
38.9331
30.8 [23.7-38.9]
2022-05-26T11:07:24+02:00
fps
SWZ
AFR
2,010
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE10
84.83686
78.45017
89.5821
84.8 [78.5-89.6]
2022-05-26T12:07:57+02:00
fps
CIV
AFR
2,006
DHSMICSGEOREGION
DHSMICSGEOREGION_CIVMICS200610
25.76853
16.41449
38.02794
25.8 [16.4-38.0]
2022-05-26T14:19:04+02:00
fps
LBR
AFR
2,013
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE02
32.79317
27.15438
38.97639
32.8 [27.2-39.0]
2022-05-26T12:17:01+02:00
fps
CAF
AFR
2,010
AGEGROUP
AGEGROUP_YEARS15-19
33.28353
27.93171
39.10439
33.3 [27.9-39.1]
2022-05-26T11:00:27+02:00
fps
SEN
AFR
2,016
DHSMICSGEOREGION
DHSMICSGEOREGION_SENDHS201614
49.53488
38.82155
60.2911
49.5 [38.8-60.3]
2022-05-26T14:46:17+02:00
fps
BFA
AFR
2,010
RESIDENCEAREATYPE
RESIDENCEAREATYPE_RUR
30.96898
28.87902
33.13972
31.0 [28.9-33.1]
2022-05-26T11:33:17+02:00
fps
MLI
AFR
2,015
DHSMICSGEOREGION
DHSMICSGEOREGION_MLIMICS201507
10.18411
6.40329
15.81998
10.2 [6.4-15.8]
2022-05-26T14:33:44+02:00
fps
MOZ
AFR
2,011
WEALTHQUINTILE
WEALTHQUINTILE_WQ5
52.53798
49.67542
55.38397
52.5 [49.7-55.4]
2022-05-26T12:58:32+02:00
fps
ETH
AFR
2,000
DHSMICSGEOREGION
DHSMICSGEOREGION_ETHDHS200009
42.71247
36.00119
49.70322
42.7 [36.0-49.7]
2022-05-26T14:22:12+02:00
fps
RWA
AFR
2,005
DHSMICSGEOREGION
DHSMICSGEOREGION_RWADHS200505
22.05133
17.6612
27.17261
22.1 [17.7-27.2]
2022-05-26T14:45:10+02:00
fps
CMR
AFR
2,004
DHSMICSGEOREGION
DHSMICSGEOREGION_CMRDHS200409
65.15775
59.23607
70.64549
65.2 [59.2-70.6]
2022-05-26T14:16:01+02:00
fps
TZA
AFR
1,999
AGEGROUP
AGEGROUP_YEARS20-49
54.36299
50.39029
58.28096
54.4 [50.4-58.3]
2022-05-26T11:07:34+02:00
fps
COG
AFR
2,005
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE01
60.21471
49.83516
69.75024
60.2 [49.8-69.8]
2022-05-26T12:03:55+02:00
fps
GIN
AFR
2,012
WEALTHQUINTILE
WEALTHQUINTILE_WQ1
14.40907
9.49912
21.2607
14.4 [9.5-21.3]
2022-05-26T12:53:02+02:00
fps
MLI
AFR
2,001
AGEGROUP
AGEGROUP_YEARS15-19
13.53598
10.35731
17.49976
13.5 [10.4-17.5]
2022-05-26T11:03:57+02:00
fps
ZMB
AFR
2,013
WEALTHQUINTILE
WEALTHQUINTILE_WQ1
60.47256
56.70977
64.11526
60.5 [56.7-64.1]
2022-05-26T13:07:40+02:00
fps
RWA
AFR
2,005
WEALTHQUINTILE
WEALTHQUINTILE_WQ3
28.47762
24.76694
32.50406
28.5 [24.8-32.5]
2022-05-26T13:02:31+02:00
fps
CIV
AFR
1,994
WEALTHQUINTILE
WEALTHQUINTILE_WQ5
52.52184
46.9545
58.0272
52.5 [47.0-58.0]
2022-05-26T12:49:44+02:00
fps
GMB
AFR
2,018
RESIDENCEAREATYPE
RESIDENCEAREATYPE_RUR
33.60565
30.65089
36.69452
33.6 [30.7-36.7]
2022-05-26T11:34:56+02:00
fps
ZWE
AFR
2,015
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE07
85.2391
80.22283
89.15504
85.2 [80.2-89.2]
2022-05-26T12:39:22+02:00
fps
MOZ
AFR
2,003
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE01
57.71869
51.34365
63.84661
57.7 [51.3-63.8]
2022-05-26T12:20:27+02:00
fps
CIV
AFR
2,006
WEALTHQUINTILE
WEALTHQUINTILE_WQ3
24.03251
19.11182
29.7541
24.0 [19.1-29.8]
2022-05-26T12:49:51+02:00
fps
GMB
AFR
2,018
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE08
47.68753
41.20795
54.2459
47.7 [41.2-54.2]
2022-05-26T12:09:08+02:00
fps
GAB
AFR
2,012
AGEGROUP
AGEGROUP_YEARS20-49
55.15785
51.68477
58.58133
55.2 [51.7-58.6]
2022-05-26T11:01:46+02:00
fps
GHA
AFR
2,003
DHSMICSGEOREGION
DHSMICSGEOREGION_GHADHS200310
23.33203
12.973
38.32051
23.3 [13.0-38.3]
2022-05-26T14:23:38+02:00
fps
LSO
AFR
2,018
AGEGROUP
AGEGROUP_YEARS15-19
60.55312
51.40592
69.01627
60.6 [51.4-69.0]
2022-05-26T11:03:34+02:00
fps
TCD
AFR
1,996
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE07
15.17838
8.38768
25.91186
15.2 [8.4-25.9]
2022-05-26T12:02:53+02:00
fps
MDG
AFR
1,997
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE08
54.79533
47.79678
61.60923
54.8 [47.8-61.6]
2022-05-26T12:17:13+02:00
fps
BDI
AFR
2,016
DHSMICSGEOREGION
DHSMICSGEOREGION_BDIDHS201607
53.98918
45.62146
62.13791
54.0 [45.6-62.1]
2022-05-26T14:14:54+02:00
fps
GMB
AFR
2,019
WEALTHQUINTILE
WEALTHQUINTILE_WQ4
50.64231
45.45961
55.81124
50.6 [45.5-55.8]
2022-05-26T12:52:06+02:00
fps
TZA
AFR
1,996
DHSMICSGEOREGION
DHSMICSGEOREGION_TZADHS199613
38.4
29.28566
48.40908
38.4 [29.3-48.4]
2022-05-26T14:50:54+02:00
fps
SEN
AFR
2,019
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE03
47.03291
40.32302
53.8518
47.0 [40.3-53.9]
2022-05-26T12:30:46+02:00
fps
NGA
AFR
2,003
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE01
24.57823
16.17472
35.49881
24.6 [16.2-35.5]
2022-05-26T12:22:53+02:00
fps
SEN
AFR
2,019
EDUCATIONLEVEL
EDUCATIONLEVEL_SHLM
61.44437
55.93848
66.67216
61.4 [55.9-66.7]
2022-05-26T11:24:02+02:00
fps
ETH
AFR
2,011
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE06
50.36938
43.58528
57.1399
50.4 [43.6-57.1]
2022-05-26T12:08:20+02:00
fps
RWA
AFR
2,019
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE05
82.89585
79.43399
85.87858
82.9 [79.4-85.9]
2022-05-26T12:29:04+02:00
fps
TZA
AFR
1,999
WEALTHQUINTILE
WEALTHQUINTILE_WQ2
41.79265
35.61924
48.2344
41.8 [35.6-48.2]
2022-05-26T13:06:41+02:00
fps
KEN
AFR
2,008
WEALTHQUINTILE
WEALTHQUINTILE_WQ3
69.02279
63.89752
73.7197
69.0 [63.9-73.7]
2022-05-26T12:55:36+02:00
fps
COD
AFR
2,017
EDUCATIONLEVEL
EDUCATIONLEVEL_NHLM
47.1402
41.09392
53.27157
47.1 [41.1-53.3]
2022-05-26T11:16:46+02:00
fps
COD
AFR
2,017
WEALTHQUINTILE
WEALTHQUINTILE_WQ5
70.36216
64.00623
76.01629
70.4 [64.0-76.0]
2022-05-26T12:50:11+02:00
fps
MLI
AFR
1,995
WEALTHQUINTILE
WEALTHQUINTILE_WQ5
42.56689
38.40669
46.83506
42.6 [38.4-46.8]
2022-05-26T12:57:19+02:00
fps
CMR
AFR
2,014
DHSMICSGEOREGION
DHSMICSGEOREGION_CMRMICS201412
63.24372
55.5936
70.28046
63.2 [55.6-70.3]
2022-05-26T14:16:23+02:00
fps
ZWE
AFR
2,014
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE03
86.15299
83.1204
88.71472
86.2 [83.1-88.7]
2022-05-26T12:39:16+02:00
fps
BDI
AFR
2,016
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE05
51.66853
47.16726
56.14289
51.7 [47.2-56.1]
2022-05-26T12:01:19+02:00
fps
GIN
AFR
2,012
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE02
12.37782
6.82866
21.40062
12.4 [6.8-21.4]
2022-05-26T12:10:57+02:00
fps
STP
AFR
2,008
DHSMICSGEOREGION
DHSMICSGEOREGION_STPDHS200801
48.91199
42.04805
55.8172
48.9 [42.0-55.8]
2022-05-26T14:45:27+02:00
fps
MRT
AFR
2,015
RESIDENCEAREATYPE
RESIDENCEAREATYPE_URB
44.91016
41.43677
48.4339
44.9 [41.4-48.4]
2022-05-26T11:37:12+02:00
fps
CIV
AFR
1,998
WEALTHQUINTILE
WEALTHQUINTILE_WQ5
63.68371
56.2093
70.55075
63.7 [56.2-70.6]
2022-05-26T12:49:48+02:00
fps
CIV
AFR
1,994
AGEGROUP
AGEGROUP_YEARS20-49
30.18121
27.53787
32.96286
30.2 [27.5-33.0]
2022-05-26T11:00:57+02:00
fps
CAF
AFR
2,018
DHSMICSGEOREGION
DHSMICSGEOREGION_CAFMICS201806
20.80353
15.2455
27.72501
20.8 [15.2-27.7]
2022-05-26T14:16:59+02:00
fps
GHA
AFR
2,003
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE04
41.14957
33.38407
49.38238
41.1 [33.4-49.4]
2022-05-26T12:09:44+02:00
fps
BDI
AFR
2,010
RESIDENCEAREATYPE
RESIDENCEAREATYPE_URB
57.91498
52.04657
63.56792
57.9 [52.0-63.6]
2022-05-26T11:33:20+02:00
fps
STP
AFR
2,008
WEALTHQUINTILE
WEALTHQUINTILE_WQ4
55.77831
48.12582
63.16592
55.8 [48.1-63.2]
2022-05-26T13:02:51+02:00
fps
RWA
AFR
2,000
DHSMICSGEOREGION
DHSMICSGEOREGION_RWADHS200002
27.18894
21.91053
33.19836
27.2 [21.9-33.2]
2022-05-26T14:45:03+02:00
fps
CMR
AFR
1,998
WEALTHQUINTILE
WEALTHQUINTILE_WQ4
59.98547
54.2949
65.41879
60.0 [54.3-65.4]
2022-05-26T12:48:02+02:00
fps
SEN
AFR
2,012
DHSMICSGEOREGION
DHSMICSGEOREGION_SENDHS201214
29.90471
22.3713
38.70994
29.9 [22.4-38.7]
2022-05-26T14:45:53+02:00
fps
ZWE
AFR
2,005
DHSMICSGEOREGION
DHSMICSGEOREGION_ZWEDHS200501
76.67391
71.68589
81.0159
76.7 [71.7-81.0]
2022-05-26T14:53:47+02:00
fps
GAB
AFR
2,012
RESIDENCEAREATYPE
RESIDENCEAREATYPE_RUR
39.71932
35.97827
43.58454
39.7 [36.0-43.6]
2022-05-26T11:34:51+02:00
fps
BEN
AFR
2,006
WEALTHQUINTILE
WEALTHQUINTILE_WQ4
37.73944
34.65112
40.9306
37.7 [34.7-40.9]
2022-05-26T12:46:50+02:00
fps
NGA
AFR
2,007
DHSMICSGEOREGION
DHSMICSGEOREGION_NGAMICS200715
11.40351
6.70083
18.74351
11.4 [6.7-18.7]
2022-05-26T14:37:14+02:00
fps
ETH
AFR
2,005
DHSMICSGEOREGION
DHSMICSGEOREGION_ETHDHS200502
32.9668
20.90424
47.7848
33.0 [20.9-47.8]
2022-05-26T14:22:18+02:00
fps
NER
AFR
1,998
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE04
20.86976
14.22961
29.54128
20.9 [14.2-29.5]
2022-05-26T12:22:26+02:00
fps
CMR
AFR
2,011
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE03
34.39968
28.23957
41.13329
34.4 [28.2-41.1]
2022-05-26T12:02:16+02:00
fps
RWA
AFR
2,005
DHSMICSGEOREGION
DHSMICSGEOREGION_RWADHS200504
25.60282
20.81189
31.064
25.6 [20.8-31.1]
2022-05-26T14:45:10+02:00
fps
LBR
AFR
2,019
RESIDENCEAREATYPE
RESIDENCEAREATYPE_RUR
44.59713
41.51661
47.71972
44.6 [41.5-47.7]
2022-05-26T11:36:41+02:00
fps
UGA
AFR
2,006
WEALTHQUINTILE
WEALTHQUINTILE_WQ4
42.40553
38.05186
46.88037
42.4 [38.1-46.9]
2022-05-26T13:06:16+02:00
fps
NGA
AFR
2,018
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE04
33.57647
29.7915
37.58487
33.6 [29.8-37.6]
2022-05-26T12:23:35+02:00
fps
ZMB
AFR
2,013
RESIDENCEAREATYPE
RESIDENCEAREATYPE_RUR
64.53969
62.43437
66.59017
64.5 [62.4-66.6]
2022-05-26T11:40:54+02:00
fps
NGA
AFR
2,016
DHSMICSGEOREGION
DHSMICSGEOREGION_NGAMICS201620
17.65103
11.80454
25.55415
17.7 [11.8-25.6]
2022-05-26T14:38:03+02:00
fps
ZWE
AFR
1,999
AGEGROUP
AGEGROUP_YEARS15-19
77.56794
69.87609
83.75242
77.6 [69.9-83.8]
2022-05-26T11:08:01+02:00
fps
UGA
AFR
2,006
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE02
23.24445
18.2425
29.12924
23.2 [18.2-29.1]
2022-05-26T12:35:53+02:00
fps
CIV
AFR
2,006
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE05
23.60679
17.03826
31.73882
23.6 [17.0-31.7]
2022-05-26T12:05:10+02:00
fps
GHA
AFR
1,993
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE09
47.44186
41.29528
53.66695
47.4 [41.3-53.7]
2022-05-26T12:09:33+02:00
fps
TZA
AFR
2,004
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE04
48.68927
41.88578
55.54167
48.7 [41.9-55.5]
2022-05-26T12:36:47+02:00
fps
MLI
AFR
2,001
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE01
13.18386
9.4781
18.0496
13.2 [9.5-18.0]
2022-05-26T12:18:38+02:00
fps
BEN
AFR
1,996
WEALTHDECILE
WEALTHDECILE_WEALTHDECILE01
30.14266
20.9114
41.31996
30.1 [20.9-41.3]
2022-05-26T11:59:10+02:00
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Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%) | Africa (World Health Organization)

Size category: 1K<n<10K - Formats: parquet - Sector: health - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.

Dataset context from the existing Hugging Face card: Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%) Indicator code: fps HuggingFace slug: electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional Source: WHO Global Health Observatory License: CC BY 4.0 — WHO Open Data Dataset Description This dataset contains country-level observations for the WHO GHO indicator "Demand for family planning satisfied - modern and traditional methods (%)" (fps)… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional
Sector health
Topic tags health, who, gho, fps
Modalities tabular, text
Formats parquet
Size category 1K<n<10K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2026-05-02 13:17:26+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: country, upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_who_demand_for_family_planning_satisfied_modern_and_traditional_2026,
  title        = {Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%) | Africa (World Health Organization)},
  author       = {WHO public data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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