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customer_id
string
phone_number
string
operator
string
month
string
customer_segment
string
voice_minutes
int64
data_gb
float64
sms_count
int64
city
string
network_type
string
account_type
string
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prepaid
CUST4856981
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MTN
2025-09
light
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Aba
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CUST8119831
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2025-09
heavy
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CUST9911770
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Airtel
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enterprise
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CUST1316822
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MTN
2025-09
medium
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CUST7247648
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Glo
2025-09
heavy
990
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Onitsha
4G
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CUST7060236
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MTN
2025-09
light
92
1.98
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Kano
3G
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9mobile
2025-09
enterprise
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Kaduna
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CUST2346762
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9mobile
2025-09
heavy
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Lagos
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CUST7446945
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Glo
2025-09
medium
315
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Lagos
4G
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MTN
2025-09
light
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Ibadan
4G
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CUST7568102
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9mobile
2025-09
medium
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Lagos
4G
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CUST2300802
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MTN
2025-09
medium
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Lagos
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CUST9801033
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MTN
2025-09
heavy
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Ilorin
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CUST7949389
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Glo
2025-09
medium
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Kano
4G
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MTN
2025-09
enterprise
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Lagos
4G
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CUST5401495
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MTN
2025-09
heavy
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Lagos
4G
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9mobile
2025-09
enterprise
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9mobile
2025-09
heavy
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Lagos
4G
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MTN
2025-09
light
49
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Onitsha
3G
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CUST7564369
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Airtel
2025-09
medium
482
12.86
91
Port Harcourt
4G
prepaid
CUST2885808
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MTN
2025-09
light
30
1.49
29
Lagos
4G
prepaid
CUST8319639
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MTN
2025-09
medium
153
8.7
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Lagos
4G
prepaid
CUST4989515
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MTN
2025-09
heavy
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Ibadan
4G
prepaid
CUST4693965
+2348038139879
MTN
2025-09
medium
396
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104
Lagos
4G
prepaid
CUST8085319
+2348059498483
Glo
2025-09
light
47
2.23
38
Calabar
4G
prepaid
CUST5985964
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MTN
2025-09
heavy
1,424
39.74
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Lagos
2G
prepaid
CUST3224956
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MTN
2025-09
heavy
1,841
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465
Ibadan
4G
postpaid
CUST5962486
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MTN
2025-09
medium
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Warri
4G
prepaid
CUST8823432
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Glo
2025-09
light
75
1.59
31
Lagos
4G
prepaid
CUST1359720
+2348163573593
MTN
2025-09
medium
444
5.23
78
Lagos
3G
prepaid
CUST4593658
+2348145095941
MTN
2025-09
heavy
567
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267
Abuja
4G
prepaid
CUST8769046
+2348137663597
MTN
2025-09
light
12
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Kano
3G
prepaid
CUST3441132
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9mobile
2025-09
light
40
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Lagos
4G
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CUST4078236
+2347012695080
Airtel
2025-09
light
49
1.9
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Ibadan
4G
postpaid
CUST4886027
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Airtel
2025-09
medium
194
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Lagos
4G
prepaid
CUST6110578
+2348039490554
MTN
2025-09
enterprise
7,306
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Ibadan
3G
prepaid
CUST6052755
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Airtel
2025-09
light
58
1.87
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4G
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CUST8033055
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Glo
2025-09
heavy
567
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Lagos
4G
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CUST4732574
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9mobile
2025-09
light
36
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Kano
4G
postpaid
CUST5090721
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Airtel
2025-09
enterprise
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595
Kano
2G
prepaid
CUST5003553
+2348027977520
Airtel
2025-09
medium
285
14.29
56
Abuja
3G
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CUST4844717
+2349029545602
Airtel
2025-09
light
40
2.87
30
Onitsha
4G
prepaid
CUST4899599
+2348106823931
MTN
2025-09
light
75
1.88
29
Jos
4G
postpaid
CUST3204442
+2348113063958
Glo
2025-09
heavy
1,385
41.23
219
Warri
4G
prepaid
CUST6323582
+2347059071495
Glo
2025-09
light
21
1.81
50
Ilorin
4G
prepaid
CUST5095156
+2349050078747
Glo
2025-09
medium
364
10.27
151
Ilorin
4G
prepaid
CUST2117207
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9mobile
2025-09
light
50
1.29
11
Ibadan
3G
prepaid
CUST9137062
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Airtel
2025-09
medium
133
4.18
150
Lagos
5G
prepaid
CUST3843772
+2348056759804
Glo
2025-09
light
93
2.84
6
Lagos
4G
prepaid
CUST9681060
+2348031219897
MTN
2025-09
medium
433
4.58
154
Kano
2G
prepaid
CUST9094827
+2349080992962
9mobile
2025-09
heavy
646
46.95
407
Kano
4G
prepaid
CUST6242497
+2347060302282
MTN
2025-09
heavy
1,180
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411
Kano
4G
prepaid
CUST9300999
+2347056746610
Glo
2025-09
light
94
1.72
35
Lagos
4G
prepaid
CUST4230637
+2348138044793
MTN
2025-09
heavy
1,833
37.71
234
Benin City
4G
prepaid
CUST2924803
+2348083129618
Airtel
2025-09
light
94
0.55
38
Lagos
3G
prepaid
CUST5888054
+2348126367074
Airtel
2025-09
heavy
988
38.39
226
Aba
5G
postpaid
CUST7770337
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Glo
2025-09
medium
463
5.68
141
Jos
4G
prepaid
CUST3367403
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Airtel
2025-09
light
75
2.82
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Abuja
4G
prepaid
CUST4644242
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Airtel
2025-09
light
47
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45
Abuja
4G
prepaid
CUST8824849
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MTN
2025-09
medium
387
5.41
67
Kano
3G
prepaid
CUST8547324
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Glo
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medium
392
5.72
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Jos
4G
prepaid
CUST2155285
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Glo
2025-09
heavy
511
49.14
300
Lagos
3G
prepaid
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Glo
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light
77
1.49
19
Abuja
4G
prepaid
CUST2898964
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Glo
2025-09
enterprise
8,900
97.43
1,749
Abuja
4G
prepaid
CUST5853980
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Airtel
2025-09
light
29
2.04
39
Lagos
4G
prepaid
CUST9074396
+2349014007969
Airtel
2025-09
heavy
1,304
20.82
216
Abuja
3G
prepaid
CUST1800548
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MTN
2025-09
medium
312
14.73
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Ibadan
3G
prepaid
CUST6943698
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Airtel
2025-09
heavy
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328
Benin City
3G
prepaid
CUST1853257
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Glo
2025-09
heavy
1,423
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336
Lagos
3G
prepaid
CUST7169613
+2349075558280
Airtel
2025-09
light
53
2.3
45
Kano
4G
prepaid
CUST8168606
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MTN
2025-09
light
80
0.71
49
Lagos
4G
postpaid
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MTN
2025-09
light
72
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Onitsha
2G
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CUST3485224
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9mobile
2025-09
light
94
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47
Lagos
3G
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CUST9566352
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9mobile
2025-09
light
39
1.83
14
Lagos
4G
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CUST9123175
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Glo
2025-09
light
85
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39
Lagos
4G
prepaid
CUST3409613
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9mobile
2025-09
light
20
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Lagos
4G
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CUST6560923
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9mobile
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light
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Kano
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Airtel
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heavy
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MTN
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light
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Calabar
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MTN
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medium
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Ibadan
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medium
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light
61
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Onitsha
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Benin City
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9mobile
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95
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medium
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Lagos
4G
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CUST4993460
+2348111360677
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heavy
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Ibadan
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Lagos
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2025-09
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Port Harcourt
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2025-09
medium
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Kaduna
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CUST4395004
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Airtel
2025-09
light
84
0.84
41
Warri
3G
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CUST4340247
+2348064087758
MTN
2025-09
enterprise
9,734
111.4
896
Lagos
4G
prepaid
CUST9694096
+2347036325687
MTN
2025-09
heavy
1,472
36.04
263
Lagos
4G
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CUST3421880
+2347014644668
Airtel
2025-09
heavy
1,010
45.04
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Aba
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CUST6855983
+2347085952103
Airtel
2025-09
light
58
2.61
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Lagos
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CUST4289456
+2348157979210
Glo
2025-09
light
70
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Kano
5G
prepaid
End of preview. Expand in Data Studio

Africa Synth Telecom Voice Data Sms Usage Patterns Nigeria | Africa (Electric Sheep Africa metadata inventory)

Size category: 100K<n<1M - Formats: parquet - Sector: technology_digital - 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

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Voice Data SMS Usage Patterns Dataset Description Monthly usage statistics per customer including voice minutes, data consumption, and SMS counts Dataset Information Category: Customer and User Behavior Format: CSV, Parquet Rows: 500,000 Columns: 11 Date Generated: 2025-10-05 Location: data/voice_data_sms_usage_patterns/ Schema Column Type Sample Values customer_id String CUST9540788 phone_number String +2349075150857 operator String… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-telecom-voice-data-sms-usage-patterns-nigeria.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-synth-telecom-voice-data-sms-usage-patterns-nigeria
Sector technology_digital
Topic tags telecom, voice, data, sms, usage
Modalities tabular, text
Formats parquet
Size category 100K<n<1M
Countries Nigeria
ISO3 coverage NGA
Last modified on HF 2025-10-05 20:11:59+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-synth-telecom-voice-data-sms-usage-patterns-nigeria")
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: 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_synth_telecom_voice_data_sms_usage_patterns_nigeria_2026,
  title        = {Africa Synth Telecom Voice Data Sms Usage Patterns Nigeria | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-telecom-voice-data-sms-usage-patterns-nigeria},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-telecom-voice-data-sms-usage-patterns-nigeria}}
}

License

Released under gpl.

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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