Data Dictionary

ID
id
Type
int4
Label
index

ID
date
Type
text
Label
Date

ID
state_name
Type
text
Label
State Name

ID
state_code
Type
text
Label
State Code

ID
commodity
Type
text
Label
Commodity

ID
price
Type
numeric
Label
Price

Additional Information

Field Value
Data last updated September 16, 2024
Metadata last updated September 16, 2024
Created September 16, 2024
Format CSV
License 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
Additional Information 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