Data Dictionary

ID
id
Type
int4
Label
index

ID
year
Type
text
Label
Year

ID
season
Type
text
Label
Season

ID
scheme
Type
text
Label
Scheme name

ID
state_name
Type
text
Label
State

ID
state_code
Type
text
Label
State Code

ID
district_name
Type
text
Label
District Name

ID
district_code
Type
text
Label
District Code

ID
farmer_count
Type
numeric
Label
Percentage of the number of farmers

ID
loanee
Type
numeric
Label
Number of applications by lonees

ID
non_loanee
Type
numeric
Label
Number of applications by non-lonees

ID
area_insured
Type
numeric
Label
Area covered by them

ID
sum_insured
Type
numeric
Label
Sum insured by them

ID
farmer_share
Type
numeric
Label
Number of shares hold by farmer

ID
goi_share
Type
numeric
Label
Number of goi shares

ID
state_share
Type
numeric
Label
Number of state shares

ID
male
Type
numeric
Label
Percentage of males

ID
female
Type
numeric
Label
Percentage of females

ID
transgender
Type
numeric
Label
Percentage of transgender

ID
sc
Type
numeric
Label
Percentage of SC farmers

ID
st
Type
numeric
Label
Percentage of ST farmers

ID
obc
Type
numeric
Label
Percentage of OBC farmers

ID
gen
Type
numeric
Label
Percentage of farmers that belong to general caste

ID
marginal
Type
numeric
Label
Percentage of marginal farmers

ID
small
Type
numeric
Label
Percentage of small farmers

ID
other
Type
numeric
Label
Percentage of farmers that are neither small nor marginal

ID
iu_count
Type
numeric
Label
Number of Insurance Units

ID
gross_premium
Type
numeric
Label
Gross Premium

Additional Information

Field Value
Data last updated September 30, 2024
Metadata last updated September 30, 2024
Created September 26, 2024
Format CSV
License Open Data Commons Attribution License
Additional infonan
Data extraction pagehttps://pmfby.gov.in/adminStatistics/dashboard
Data insightsWhat highlights could be made from the loan insured and sum insured? What percentage of gender availed more loans? Account for that as graph. What differences could be made for Kharif and Ravi in terms of share taken in various districts?
Data last updated05-04-2023 ( updated daily)
Data retreival date2023-03-29 00:00:00
Datastore activeTrue
FrequencySeasonally
GranularityDistrict
Has viewsTrue
Idb867088e-2ceb-4274-a9ac-bcf3175e7e56
Idp readyFalse
MethodologyThe data is collected by the state governments and submitted to the Ministry of Agriculture and Farmers' Welfare for analysis and reporting.
Mimetypetext/csv
No indicators20
Package id7a1bc2c2-6702-43f6-94ac-5e3af6e63c79
Position1
Size1 MiB
Skumoafw-pmfby-dt-sn-syx
Stateactive
States uts no26
Tags['Rural', 'Crops', 'Insurance', 'Scheme', 'Farmers', 'District']
Url typeupload
Years covered2018-2022
Methodology The data is collected by the state governments and submitted to the Ministry of Agriculture and Farmers' Welfare for analysis and reporting.
Indicators
Similar Resources
Granularity Level District
Data Extraction Page https://pmfby.gov.in/adminStatistics/dashboard
Data Retreival Date 2023-03-29 00:00:00
Data Last Updated 05-04-2023 ( updated daily)
Sku moafw-pmfby-dt-sn-syx
Dataset Frequency Seasonally
Years Covered 2018-2022
No of States/UT(s) 26
No of Districts
No of Tehsils/blocks
No of Gram Panchayats
Additional Information nan
Number of Indicators 20
Insights from the dataset What highlights could be made from the loan insured and sum insured? What percentage of gender availed more loans? Account for that as graph. What differences could be made for Kharif and Ravi in terms of share taken in various districts?
IDP Ready No
LGD Mapping