Datasets:
type_of_personnel_over_the_years stringclasses 8
values | 2000 int64 746 55.7k | 2001 int64 763 57.2k | 2002 int64 761 59k | 2003 int64 772 60.6k | 2004 int64 841 68k | 2005 int64 871 65.9k | 2006 int64 898 67.2k | 2007 int64 931 73.2k | 2008 int64 974 76.9k | 2009 int64 859 95.4k | 2010 int64 898 100k | 2011 int64 930 96k | 2012 int64 985 105k | 2013 int64 1.05k 113k | esa_source stringclasses 1
value | esa_processed stringdate 2026-04-07 00:00:00 2026-04-07 00:00:00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Total | 55,732 | 57,208 | 59,049 | 60,599 | 67,993 | 65,914 | 67,175 | 73,236 | 76,883 | 95,390 | 100,411 | 95,960 | 104,913 | 112,576 | HDX | 2026-04-07 |
Pharmacists | 1,682 | 1,758 | 1,866 | 1,881 | 2,570 | 2,637 | 2,697 | 2,775 | 2,860 | 2,921 | 3,097 | 2,432 | 2,076 | 2,202 | HDX | 2026-04-07 |
Registered Nurses | 9,211 | 9,392 | 9,753 | 9,869 | 10,210 | 10,657 | 10,905 | 12,198 | 14,073 | 26,988 | 29,678 | 31,719 | 35,148 | 37,907 | HDX | 2026-04-07 |
Public Health Officers | 929 | 1,042 | 1,174 | 1,216 | 1,314 | 1,388 | 1,457 | 6,728 | 6,960 | 7,192 | 7,429 | 7,584 | 8,069 | 8,637 | HDX | 2026-04-07 |
Clinical officers | 4,492 | 4,610 | 4,778 | 4,804 | 4,953 | 5,059 | 5,285 | 4,182 | 5,035 | 7,816 | 8,708 | 9,793 | 11,185 | 13,216 | HDX | 2026-04-07 |
Dentists | 746 | 763 | 761 | 772 | 841 | 871 | 898 | 931 | 974 | 859 | 898 | 930 | 985 | 1,045 | HDX | 2026-04-07 |
Enrolled Nurses | 27,902 | 28,420 | 29,094 | 30,212 | 30,562 | 31,895 | 31,917 | 31,917 | 31,917 | 34,032 | 34,282 | 24,375 | 26,621 | 26,841 | HDX | 2026-04-07 |
Public Health Technicians | 5,032 | 5,272 | 5,484 | 5,627 | 5,861 | 5,938 | 5,969 | 5,969 | 5,969 | 5,969 | 5,969 | 5,969 | 5,969 | 5,969 | HDX | 2026-04-07 |
National Registered Medical Personnel: 2000 to 2013
Publisher: Kenya National Bureau of Statistics (inactive) · Source: HDX · License: other-pd-nr · Updated: 2025-02-06
Abstract
This dataset shows the Nationally Registered Medical Personnel: 2000 to 2013
Each row in this dataset represents time-series observations. Data was last updated on HDX on 2025-02-06. Geographic scope: KEN.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Public health |
| Unit of observation | Time-series observations |
| Rows (total) | 11 |
| Columns | 17 (14 numeric, 3 categorical, 0 datetime) |
| Train split | 8 rows |
| Test split | 2 rows |
| Geographic scope | KEN |
| Publisher | Kenya National Bureau of Statistics (inactive) |
| HDX last updated | 2025-02-06 |
Variables
Geographic — type_of_personnel_over_the_years (PharmTechnologist, Registered Nurses, Pharmacists).
Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-07).
Other — 2000 (range 0.0–55732.0), 2001 (range 0.0–57208.0), 2002 (range 0.0–59049.0), 2003 (range 0.0–60599.0), 2004 (range 280.0–67993.0) and 9 others.
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-national-registered-medical-personnel-2000-to-2013")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
type_of_personnel_over_the_years |
object | 0.0% | PharmTechnologist, Registered Nurses, Pharmacists |
2000 |
int64 | 0.0% | 0.0 – 55732.0 (mean 10133.0909) |
2001 |
int64 | 0.0% | 0.0 – 57208.0 (mean 10401.4545) |
2002 |
int64 | 0.0% | 0.0 – 59049.0 (mean 10736.1818) |
2003 |
int64 | 0.0% | 0.0 – 60599.0 (mean 11018.0) |
2004 |
int64 | 0.0% | 280.0 – 67993.0 (mean 11929.0909) |
2005 |
int64 | 0.0% | 367.0 – 65914.0 (mean 11984.3636) |
2006 |
int64 | 0.0% | 478.0 – 67175.0 (mean 12213.6364) |
2007 |
int64 | 0.0% | 585.0 – 73236.0 (mean 13315.6364) |
2008 |
int64 | 0.0% | 657.0 – 76883.0 (mean 13978.7273) |
2009 |
int64 | 0.0% | 859.0 – 95390.0 (mean 17343.6364) |
2010 |
int64 | 0.0% | 898.0 – 100411.0 (mean 18256.5455) |
2011 |
int64 | 0.0% | 930.0 – 95960.0 (mean 17447.2727) |
2012 |
int64 | 0.0% | 985.0 – 104913.0 (mean 19075.0909) |
2013 |
int64 | 0.0% | 1045.0 – 112576.0 (mean 20468.3636) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-07 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
2000 |
0.0 | 55732.0 | 10133.0909 | 4492.0 |
2001 |
0.0 | 57208.0 | 10401.4545 | 4610.0 |
2002 |
0.0 | 59049.0 | 10736.1818 | 4740.0 |
2003 |
0.0 | 60599.0 | 11018.0 | 4804.0 |
2004 |
280.0 | 67993.0 | 11929.0909 | 4953.0 |
2005 |
367.0 | 65914.0 | 11984.3636 | 5059.0 |
2006 |
478.0 | 67175.0 | 12213.6364 | 5285.0 |
2007 |
585.0 | 73236.0 | 13315.6364 | 5969.0 |
2008 |
657.0 | 76883.0 | 13978.7273 | 5969.0 |
2009 |
859.0 | 95390.0 | 17343.6364 | 6800.0 |
2010 |
898.0 | 100411.0 | 18256.5455 | 7129.0 |
2011 |
930.0 | 95960.0 | 17447.2727 | 7549.0 |
2012 |
985.0 | 104913.0 | 19075.0909 | 8069.0 |
2013 |
1045.0 | 112576.0 | 20468.3636 | 8637.0 |
Curation
Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
Limitations
- Data originates from Kenya National Bureau of Statistics (inactive) and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.
Citation
@dataset{hdx_africa_national_registered_medical_personnel_2000_to_2013,
title = {National Registered Medical Personnel: 2000 to 2013},
author = {Kenya National Bureau of Statistics (inactive)},
year = {2025},
url = {https://data.humdata.org/dataset/national-registered-medical-personnel-2000-to-2013},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.
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