electricsheepafrica/africa-qlik-sense-template-cod-mli

Mali Qlik Sense Template-COD

Publisher: ITOS (inactive) · Source: HDX · License: cc-by · Updated: 2025-05-22

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Abstract

(Beta) Qlik Sense COD template app and its source data including the administrative hierarchy table and geographical data (KML). The KML files in this data set are optimized for Qlik Sense and are not the substitute for the original COD data sets used in the GIS applications. To see the differences, refer CAVEATS/COMMENTS in the Metadata. The data set is currently under the user acceptance testing phase.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the date_start column(s). Geographic scope: MLI.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	830
Columns	17 (2 numeric, 14 categorical, 1 datetime)
Train split	664 rows
Test split	166 rows
Geographic scope	MLI
Publisher	ITOS (inactive)
HDX last updated	2025-05-22

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Variables

Geographic — country_i_fr_name (Mali), country_code_v_pcode (ML), country_code_v_pcode_alpha3 (MLI).

Temporal — date_start.

Identifier / Metadata — adm1_i_fr_name (Sikasso, Kayes, Segou), adm1_code_v_pcode (ML03, ML01, ML04), adm1_code_v_pcode_alpha3 (MLI03, MLI01, MLI04), adm2_i_fr_name (Bamako, Sikasso, Kati), adm2_code_v_pcode (ML0901, ML0307, ML0205) and 6 others.

Other — shape_length (range 0.011–70.3383), shape_area (range 0.0–106.824).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-qlik-sense-template-cod-mli")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
country_i_fr_name	object	0.0%	Mali
country_code_v_pcode	object	0.0%	ML
shape_length	float64	0.0%	0.011 – 70.3383 (mean 1.8163)
shape_area	float64	0.0%	0.0 – 106.824 (mean 0.512)
country_code_v_pcode_alpha3	object	0.0%	MLI
adm1_i_fr_name	object	0.1%	Sikasso, Kayes, Segou
adm1_code_v_pcode	object	0.1%	ML03, ML01, ML04
adm1_code_v_pcode_alpha3	object	0.1%	MLI03, MLI01, MLI04
adm2_i_fr_name	object	1.1%	Bamako, Sikasso, Kati
adm2_code_v_pcode	object	1.1%	ML0901, ML0307, ML0205
adm2_code_v_pcode_alpha3	object	1.1%	MLI0901, MLI0307, MLI0205
adm3_i_fr_name	object	7.0%	Commune III, Commune VI, Commune II
adm3_code_v_pcode	object	7.0%
adm3_code_v_pcode_alpha3	object	7.0%
date_start	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
shape_length	0.011	70.3383	1.8163	0.9379
shape_area	0.0	106.824	0.512	0.037

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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. 3 column(s) with >80% missing values were removed: adm4_code_v_pcode, adm4_code_v_pcode_alpha3, adm4_i_fr_name. 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from ITOS (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.

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Citation

@dataset{hdx_africa_qlik_sense_template_cod_mli,
  title     = {Mali Qlik Sense Template-COD},
  author    = {ITOS (inactive)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/qlik-sense-template-cod-mli},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.