electricsheepafrica/africa-daily-summaries-of-precipitation-indicators-for-congo-the-democratic-republic-of-the

Daily Summaries of Precipitation Indicators for Congo, the Democratic Republic of the

Publisher: HDX · Source: HDX · License: other-pd-nr · Updated: 2025-03-10

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Abstract

This dataset contains the daily summaries on base stations across Congo, the Democratic Republic of the. The four indicators included are:

• TPCP: Total precipitation
  
• MXSD: Maximum snow depth
  
• TSNW: Total snow fall
  
• EMXP: Extreme maximum daily precipitation
  
  Indicators are compiled by the National Centers for Environmental Information (NCEI), which is administrated by National Oceanic and Atmospheric Administration (NOAA) an organization part of the United States government. NOAA has access to data collected from thousands of base stations around the world, which collect data periodically on weather and climate conditions.
  
  This dataset contains the latest **5 years of available data**.

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

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Country-level aggregates
Rows (total)	216
Columns	10 (3 numeric, 6 categorical, 1 datetime)
Train split	172 rows
Test split	43 rows
Geographic scope	COD
Publisher	HDX
HDX last updated	2025-03-10

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Variables

Geographic — datatype (TPCP, EMXP), country (Congo, DRC).

Temporal — date.

Outcome / Measurement — value (range 0.0–5258.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-07).

Other — station (GHCND:CGM00064210, GHCND:CFM00064463, GHCND:CGM00064360), fl_miss (range 0.0–0.0), fl_cmiss (range 0.0–0.0), indicator (TPCP, EMXP).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-daily-summaries-of-precipitation-indicators-for-congo-the-democratic-republic-of-the")
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
date	datetime64[ns]	0.0%
datatype	object	0.0%	TPCP, EMXP
station	object	0.0%	GHCND:CGM00064210, GHCND:CFM00064463, GHCND:CGM00064360
value	int64	0.0%	0.0 – 5258.0 (mean 420.7824)
fl_miss	int64	0.0%	0.0 – 0.0 (mean 0.0)
fl_cmiss	int64	0.0%	0.0 – 0.0 (mean 0.0)
country	object	0.0%	Congo, DRC
indicator	object	0.0%	TPCP, EMXP
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-07

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Numeric Summary

Column	Min	Max	Mean	Median
value	0.0	5258.0	420.7824	180.0
fl_miss	0.0	0.0	0.0	0.0
fl_cmiss	0.0	0.0	0.0	0.0

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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. 756 exact duplicate rows were removed. 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 HDX 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_daily_summaries_of_precipitation_indicators_for_congo_the_democratic_republic_of_the,
  title     = {Daily Summaries of Precipitation Indicators for Congo, the Democratic Republic of the},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/daily-summaries-of-precipitation-indicators-for-congo--the-democratic-republic-of-the},
  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.