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electricsheepafrica/africa-morocco-ventilation-croisee-des-credits-bancaires-par-objet-et-par-f3e92375

Ventilation Croisee Des Credits Bancaires Par Objet Et Par | Africa (Morocco Open Data) 15 rows - 1 Africa country/area - time not specified - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 15 rows from Morocco Open Data, covering Ventilation Croisee Des Credits Bancaires Par Objet Et Par. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-morocco-ventilation-croisee-des-credits-bancaires-par-objet-et-par-f3e92375.

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Dataset Card

Ventilation Croisee Des Credits Bancaires Par Objet Et Par | Africa (Morocco Open Data)

15 rows - 1 Africa country/area - time not specified - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 15 rows from Morocco Open Data, covering Ventilation Croisee Des Credits Bancaires Par Objet Et Par. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.

Source-provided context: Ce fichier présente la ventilation croisée des crédits bancaires par objet et par terme pour la période de décembre 2006 à septembre 2023

How To Read This Dataset

  • —One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
  • —Primary geography column: country_iso3.
  • —Best time column: not detected.
  • —Time coverage basis: not detected.
  • —Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

DimensionValue
Rows15
Countries/areas1
First periodn/a
Last periodn/a
Indicators0
Columns81
Source formatXLSX

Geographic Coverage

Top areas shown below, sorted by row count when available:

AreaRowsFirst yearLast yearName
MAR15n/an/aMorocco

Indicators, Variables, Or Resource Contents

  • —This repo preserves one source tabular resource with its usable columns kept together.

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.4253e55c-2ea3-4cfd-9161-ced6a6c4454e:feuil1:0
country_iso3stringISO3 country or area code.MAR
country_namestringCountry or area name.Morocco
source_sheetstringSource column from the original resource.Feuil1
comptes_debiteurs_et_credits_de_tresoreriestringSource column from the original resource.Crédits à l'équipement
d_101340_81044466216doubleSource column from the original resource.58411.4818080652
d_95966_80838969898doubleSource column from the original resource.61161.53045626678
d_104621_52997076433doubleSource column from the original resource.63274.27359157759
d_105025_67281032451doubleSource column from the original resource.67058.38657309106
d_112840_0799370537doubleSource column from the original resource.76851.74904586804
d_113186_60391786945doubleSource column from the original resource.81999.47733002361
d_128227_0109229889doubleSource column from the original resource.84002.21911538579
d_131767_28038424868doubleSource column from the original resource.85312.48508195273
d_134535_34866792752doubleSource column from the original resource.91708.9349726734
d_132553_84739072813doubleSource column from the original resource.97469.21908105849
d_134779_0825584518doubleSource column from the original resource.104499.4544533278
d_136497_1596843356doubleSource column from the original resource.111093.39085445748
d_135100_15962587335doubleSource column from the original resource.114967.06153923752
d_134039_5811457165doubleSource column from the original resource.120866.81337578314
d_145682_75725831444doubleSource column from the original resource.122731.52785959405
d_141723_47279124596doubleSource column from the original resource.130078.51275883384
d_143031_7977972467doubleSource column from the original resource.134516.1346781277
d_146527_97167359112doubleSource column from the original resource.132866.156707404
d_165078_78582218027doubleSource column from the original resource.135745.73693865634
d_165819_94187983082doubleSource column from the original resource.136129.50781149295
d_172323_729567837doubleSource column from the original resource.140096.84302259856
d_169962_11032464306doubleSource column from the original resource.135929.52984567484
d_180663_57703714728doubleSource column from the original resource.137414.16379885477
d_181461_09585088206doubleSource column from the original resource.134896.1859264736
d_185713_00303420107doubleSource column from the original resource.137251.61921136806
d_171457_92650507198doubleSource column from the original resource.134113.00596730824
d_183718_89388752973doubleSource column from the original resource.137348.86205286754
d_180683_06894126724doubleSource column from the original resource.134609.5704454166
d_175284_5356437572doubleSource column from the original resource.139374.2277828868
d_171824_81415731666doubleSource column from the original resource.136308.6988002353
d_187501_35663907597doubleSource column from the original resource.140715.53700281138
d_183970_71094794793doubleSource column from the original resource.139861.13253237883
d_180657_35175899768doubleSource column from the original resource.142533.83225171018
d_172130_67096523446doubleSource column from the original resource.142390.4910410606
d_180507_13309552806doubleSource column from the original resource.139469.46733030153
d_174057_11774255976doubleSource column from the original resource.137674.56756950807
d_171830_39475702826doubleSource column from the original resource.141514.7337700669
d_170060_5678884659doubleSource column from the original resource.142981.96076938763
d_179589_15879950707doubleSource column from the original resource.143989.53650244157
d_177104_20173427073doubleSource column from the original resource.147441.45067194462
d_172728_16302306292doubleSource column from the original resource.152917.94923839445
d_164557_04910663728doubleSource column from the original resource.151924.78967163042
d_182527_16665398632doubleSource column from the original resource.157481.69666309888
d_171350_75674382068doubleSource column from the original resource.165233.5592218985
d_167383_2484630037doubleSource column from the original resource.170582.24430347772
d_163445_7263247729doubleSource column from the original resource.171044.65709301567
d_175737_7627191416doubleSource column from the original resource.172749.83154538574
d_176704_44760463887doubleSource column from the original resource.171659.29101202814
d_177725_05906188482doubleSource column from the original resource.173613.89347140983
d_180001_72601093067doubleSource column from the original resource.174572.65886687147
d_186496_92853924702doubleSource column from the original resource.176500.58175163704
d_188665_39227273737doubleSource column from the original resource.177198.41491161476
d_189744_65218062687doubleSource column from the original resource.183612.9668768919
d_194393_46942613725doubleSource column from the original resource.189570.868996428
d_206649_2607680624doubleSource column from the original resource.182993.17109141464
d_209535_16419441366doubleSource column from the original resource.180145.3267779212
d_206088_47248915094doubleSource column from the original resource.179811.00534445047
d_211098_13941836162doubleSource column from the original resource.179654.92604521185
d_227262_30385095064doubleSource column from the original resource.178702.14698768247
d_228326_18002730384doubleSource column from the original resource.175637.16874219442
d_225741_086944977doubleSource column from the original resource.169070.55613332317
d_226887_28113384932doubleSource column from the original resource.172809.16807904342
d_249581_11762249042doubleSource column from the original resource.173109.38839055353
d_266650_1678412406doubleSource column from the original resource.174923.81039683384
d_264033_596688312doubleSource column from the original resource.179705.7978650348
d_249127_83017700925doubleSource column from the original resource.180539.4968760741
d_256577_9620684064doubleSource column from the original resource.187252.03464804025
d_250453_14720100656doubleSource column from the original resource.190335.01429983176
source_providerstringPublishing organization.Bank Al-Maghrib
source_datasetstringSource dataset or package title.Ventilation croisée des crédits bancaires par objet et par terme (déc...
source_resourcestringSource resource title, table name, or file name.18-Ventilation croisée des crédits bancaires par objet et par terme T...
source_package_idstringSource package identifier.293ccb53-e9b5-4b62-8827-ceae7daed5cf
source_resource_idstringSource resource identifier.4253e55c-2ea3-4cfd-9161-ced6a6c4454e
source_urlstringOriginal source URL or download URL.https://data.gov.ma/data/fr/dataset/293ccb53-e9b5-4b62-8827-ceae7daed...
license_idstringSource license identifier.odc-odbl
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.``

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-morocco-ventilation-croisee-des-credits-bancaires-par-objet-et-par-f3e92375")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

python
print(df.info())
print(df.head())

Filter By Geography

python
if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MAR"]

Time-Series Pattern

python
if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

python
if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

  • —No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
  • —Missing values are preserved rather than silently imputed.
  • —Column names are standardized for machine use; source meanings are preserved where known.
  • —Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

Transformations Applied

  • —Converted the source table to Parquet for efficient analytics and ML workflows.
  • —Added or preserved source provenance columns where available.
  • —Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
  • —Preserved source-reported values without analytical imputation.

Suggested Analyses

  • —Build time-series dashboards
  • —Compare economic indicators
  • —Join with population or sector data
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_morocco_ventilation_croisee_des_credits_bancaires_par_objet_et_par_f3e923_2026,
  title        = {Ventilation Croisee Des Credits Bancaires Par Objet Et Par | Africa (Morocco Open Data)},
  author       = {Bank Al-Maghrib},
  year         = {2026},
  url          = {https://data.gov.ma/data/dataset/ventilation-croisee-des-credits-bancaires-par-objet-et-par-terme-dec-2006-a-sept-2023},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-morocco-ventilation-croisee-des-credits-bancaires-par-objet-et-par-f3e92375}}
}

License

Released under Open Data Commons Open Database License.

Original data is published by Bank Al-Maghrib. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

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


Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.gov.ma/data/dataset/ventilation-croisee-des-credits-bancaires-par-objet-et-par-terme-dec-2006-a-sept-2023