Dicionario de datos

ID
id
Tipo
int4
Etiqueta
index

ID
date
Tipo
text
Etiqueta
Date

ID
state_name
Tipo
text
Etiqueta
State Name

ID
state_code
Tipo
text
Etiqueta
State Code

ID
commodity
Tipo
text
Etiqueta
Commodity

ID
price
Tipo
numeric
Etiqueta
Price

Información adicional

Campo Valor
Última actualización de datos 16 de setembro de 2024
Última actualización de metadatos 16 de setembro de 2024
Creado 16 de setembro de 2024
Formato CSV
Licenza Open Data Commons Attribution License
Additional infonan
Data extraction pagenan
Data insightsInsights from the "State Level Daily Wholesale Price Data" include identifying price trends and volatility for various commodities over time. By analyzing the average, maximum, minimum, and modal prices, one can assess market stability, detect unusual price spikes or drops, and determine the most frequent price points. This data can help in forecasting price movements, understanding the impact of seasonal factors, supply-demand fluctuations, and regional variations, and assist policymakers, traders, and businesses in making informed decisions regarding commodity pricing and procurement.
Data last updated16-09-2024
Data retreival date16-09-2024
Datastore activeTrue
District no0
FrequencyDaily
Gp no0
GranularityState
Has viewsTrue
Ide6b8a69f-2701-4017-b24a-d16614c62000
Idp readyTrue
Lgd mappingyes
Methodologynan
No indicators1
Package id1a59d7e5-baf1-4c4a-b10f-55a6367f82f7
Position2
Size116 MiB
Skumocafpd-wholesale_price-st-dl-rrr
Stateactive
States uts no34
Tehsil no0
Url typeupload
Years covered2015 - 2024
Methodology nan
Indicators []
Similar Resources
Granularity Level State
Data Extraction Page nan
Data Retreival Date 16-09-2024
Data Last Updated 16-09-2024
Sku mocafpd-wholesale_price-st-dl-rrr
Dataset Frequency Daily
Years Covered 2015 - 2024
No of States/UT(s) 34
No of Districts 0
No of Tehsils/blocks 0
No of Gram Panchayats 0
Información adicional nan
Number of Indicators 1
Insights from the dataset Insights from the "State Level Daily Wholesale Price Data" include identifying price trends and volatility for various commodities over time. By analyzing the average, maximum, minimum, and modal prices, one can assess market stability, detect unusual price spikes or drops, and determine the most frequent price points. This data can help in forecasting price movements, understanding the impact of seasonal factors, supply-demand fluctuations, and regional variations, and assist policymakers, traders, and businesses in making informed decisions regarding commodity pricing and procurement.
IDP Ready Yes
LGD Mapping Yes
Mapping Status %