Prison Inmates In India by Kaggle | Demographics and Population Studies

About this Dataset

Prison Inmates In India

Prison Inmates in India

Demographics, Age, Education, Caste, Wages, Rehabilitation, Technical Info

By Rajanand Ilangovan [source]

About this dataset

This dataset provides a detailed view of prison inmates in India, including their age, caste, and educational background. It includes information on inmates from all states/union territories for the year 2019 such as the number of male and female inmates aged 16-18 years, 18-30 year old inmates and those above 50 years old. The data also covers total number of penalized prisoners sentenced to death sentence, life imprisonment or executed by the state authorities. Additionally, it provides information regarding the crimehead (type) committed by an inmate along with its grand total across different age groups. This dataset not only sheds light on India’s criminal justice system but also highlights prevelance of crimes in different states and union territories as well as providing insight into crime trends across Indian states over time

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How to use the dataset

This dataset provides a comprehensive look at the demographics, crimes and sentences of Indian prison inmates in 2019. The data is broken down by state/union territory, year, crime head, age groups and gender.

This dataset can be used to understand the demographic composition of the prison population in India as well as the types of crimes committed. It can also be used to gain insight into any changes or trends related to sentencing patterns in India over time. Furthermore, this data can provide valuable insight into potential correlations between different demographic factors (such as gender and caste) and specific types of crimes or length of sentences handed out.

To use this dataset effectively there are a few important things to keep in mind:
•State/UT - This column refers to individual states or union territories in India where prisons are located •Year – This column indicates which year(s) the data relates to •Both genders - Female columns refer only to female prisoners while male columns refers only to male prisoners •Age Groups – 16-18 years old = 21-30 years old = 31-50 years old = 50+ years old •Crime Head – A broad definition for each type of crime that inmates have been convicted for •No Capital Punishment – The total number sentenced with capital punishment No Life Imprisonment – The total number sentenced with life imprisonment No Executed– The total number executed from death sentence Grand Total–The overall totals for each category

By using this information it is possible to answer questions regarding topics such as sentencing trends, types of crimes committed by different age groups or genders and state-by-state variation amongst other potential queries

Research Ideas

Using the age and gender information to develop targeted outreach strategies for prisons in order to reduce recidivism rates.

Creating an AI-based predictive model to predict crime trends by analyzing crime head data from a particular region/state and correlating it with population demographics, economic activity, etc.

Analyzing the caste of inmates across different states in India in order to understand patterns of discrimination within the criminal justice system

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

You are free to:

Share - copy and redistribute the material in any medium or format for any purpose, even commercially.

Adapt - remix, transform, and build upon the material for any purpose, even commercially.

You must:

Give appropriate credit - Provide a link to the license, and indicate if changes were made.

ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: SLL_Crime_headwise_distribution_of_inmates_who_convicted.csv

Column name	Description
STATE/UT	Name of the state or union territory where the jail is located. (String)
YEAR	Year when the inmate population data was collected. (Integer)
CRIME HEAD	Type of offence/crime committed by the inmate. (String)
Male 16-18 years	Number of male inmates aged between 16-18. (Integer)
Female 16-18 years	Number of female inmates aged between 16-18. (Integer)
Total 16-18 years	Total number of inmates aged between 16-18. (Integer)
Male 18-30 years	Number of male inmates aged between 18-30. (Integer)
Female 18-30 years	Number of female inmates aged between 18-30. (Integer)
Total 18-30 years	Total number of inmates aged between 18-30. (Integer)
Male 30-50 years	Number of male inmates aged between 30-50. (Integer)
Female 30-50 years	Number of female inmates aged between 30-50. (Integer)
Total 30-50 years	Total number of inmates aged between 30-50. (Integer)
Male Above 50 years	Number of male inmates aged above 50. (Integer)
Female Above 50 years	Number of female inmates aged above 50. (Integer)
Total Above 50 years	Total number of inmates aged above 50. (Integer)
Total Male	Total number of male inmates across all age categories for a particular year/state/UT. (Integer)
Total Female	Total number of female inmates across all age categories for a particular year/state/UT. (Integer)
Grand Total	Sum of all age groups tallying up. (Integer)

File: Death_sentence.csv

Column name	Description
state_name	Name of the state or union territory where the jail is located. (String)
year	Year when the inmate population data was collected. (Integer)
no_capital_punishment	Number of capital punishment sentences handed out in each state/union territory. (Integer)
no_life_imprisonment	Number of life imprisonment sentences handed out in each state/union territory. (Integer)
no_executed	Number of executions carried out across India through the year. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Rajanand Ilangovan.

Tables

Age Group

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.age_group

75.93 KB
7152 rows
11 columns


CREATE TABLE age_group (
  "index" BIGINT,
  "state_name" VARCHAR,
  "is_state" BIGINT,
  "year" BIGINT,
  "category" VARCHAR,
  "type" VARCHAR,
  "gender" VARCHAR,
  "age_16_18" DOUBLE,
  "age_18_30" BIGINT,
  "age_30_50" BIGINT,
  "age_50_above" BIGINT
);

Caste

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.caste

47.7 KB
3568 rows
10 columns


CREATE TABLE caste (
  "index" BIGINT,
  "state_name" VARCHAR,
  "is_state" BIGINT,
  "year" BIGINT,
  "gender" VARCHAR,
  "caste" VARCHAR,
  "convicts" BIGINT,
  "under_trial" BIGINT,
  "detenues" BIGINT,
  "others" BIGINT
);

Death Sentence

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.death_sentence

8.74 KB
446 rows
6 columns


CREATE TABLE death_sentence (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "no_capital_punishment" BIGINT,
  "no_life_imprisonment" BIGINT,
  "no_executed" BIGINT
);

Details Of Inmates Parole During The Year

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.details_of_inmates_parole_during_the_year

19.15 KB
1341 rows
7 columns


CREATE TABLE details_of_inmates_parole_during_the_year (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "details" VARCHAR,
  "male" BIGINT,
  "female" BIGINT,
  "total" BIGINT
);

Domicile

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.domicile

37.7 KB
2676 rows
10 columns


CREATE TABLE domicile (
  "index" BIGINT,
  "state_name" VARCHAR,
  "is_state" BIGINT,
  "year" BIGINT,
  "gender" VARCHAR,
  "domicile" VARCHAR,
  "convicts" BIGINT,
  "under_trial" BIGINT,
  "detenues" BIGINT,
  "others" BIGINT
);

Education

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.education

63.04 KB
5346 rows
10 columns


CREATE TABLE education (
  "index" BIGINT,
  "state_name" VARCHAR,
  "is_state" BIGINT,
  "year" BIGINT,
  "gender" VARCHAR,
  "education" VARCHAR,
  "convicts" BIGINT,
  "under_trial" BIGINT,
  "detenues" BIGINT,
  "others" BIGINT
);

Education Facilities

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.education_facilities

13.6 KB
445 rows
7 columns


CREATE TABLE education_facilities (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "elementary_education" BIGINT,
  "adult_education" BIGINT,
  "higher_education" BIGINT,
  "computer_course" BIGINT
);

Inmates Death

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.inmates_death

33.31 KB
4023 rows
7 columns


CREATE TABLE inmates_death (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "details" VARCHAR,
  "male" BIGINT,
  "female" BIGINT,
  "total" BIGINT
);

Inmates Escapee

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.inmates_escapee

18.38 KB
1788 rows
7 columns


CREATE TABLE inmates_escapee (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "detail" VARCHAR,
  "male" BIGINT,
  "female" BIGINT,
  "total" BIGINT
);

Inmates Released

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.inmates_released

106.32 KB
8485 rows
7 columns


CREATE TABLE inmates_released (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "details" VARCHAR,
  "male" BIGINT,
  "female" BIGINT,
  "total" BIGINT
);

Inmates Suffering From Mental Ilness

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.inmates_suffering_from_mental_ilness

23.47 KB
442 rows
18 columns


CREATE TABLE inmates_suffering_from_mental_ilness (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "male_convicts" BIGINT,
  "female_convicts" BIGINT,
  "total_convicts" BIGINT,
  "male_under_trial" BIGINT,
  "female_under_trial" BIGINT,
  "total_under_trial" BIGINT,
  "male_detenues" BIGINT,
  "female_detenues" BIGINT,
  "total_detenues" BIGINT,
  "male_others" BIGINT,
  "female_others" BIGINT,
  "total_others" BIGINT,
  "total_male" BIGINT,
  "total_female" BIGINT,
  "grand_total" BIGINT
);

Ipc Crime Inmates Convicted

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.ipc_crime_inmates_convicted

173.14 KB
9391 rows
19 columns


CREATE TABLE ipc_crime_inmates_convicted (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "crime_head" VARCHAR,
  "male_16_18_years" DOUBLE,
  "female_16_18_years" BIGINT,
  "total_16_18_years" BIGINT,
  "male_18_30_years" BIGINT,
  "female_18_30_years" BIGINT,
  "total_18_30_years" BIGINT,
  "male_30_50_years" BIGINT,
  "female_30_50_years" BIGINT,
  "total_30_50_years" BIGINT,
  "male_above_50_years" BIGINT,
  "female_above_50_years" BIGINT,
  "total_above_50_years" BIGINT,
  "total_male" BIGINT,
  "total_female" BIGINT,
  "grand_total" BIGINT
);

Ipc Crime Inmates Under Trial

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.ipc_crime_inmates_under_trial

201.85 KB
9390 rows
19 columns


CREATE TABLE ipc_crime_inmates_under_trial (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "crime_head" VARCHAR,
  "male_16_18_years" BIGINT,
  "female_16_18_years" BIGINT,
  "total_16_18_years" BIGINT,
  "male_18_30_years" BIGINT,
  "female_18_30_years" BIGINT,
  "total_18_30_years" BIGINT,
  "male_30_50_years" BIGINT,
  "female_30_50_years" BIGINT,
  "total_30_50_years" BIGINT,
  "male_above_50_years" BIGINT,
  "female_above_50_years" BIGINT,
  "total_above_50_years" BIGINT,
  "total_male" BIGINT,
  "total_female" BIGINT,
  "grand_total" BIGINT
);

Jail Wise Population Of Prison Inmates

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.jail_wise_population_of_prison_inmates

117.72 KB
4023 rows
19 columns


CREATE TABLE jail_wise_population_of_prison_inmates (
  "index" BIGINT,
  "state_ut_name" VARCHAR,
  "year" BIGINT,
  "jail_type" VARCHAR,
  "male_convicts" BIGINT,
  "female_convicts" BIGINT,
  "total_convicts" BIGINT,
  "male_under_trial" BIGINT,
  "female_under_trial" BIGINT,
  "total_under_trial" BIGINT,
  "male_detenues" BIGINT,
  "female_detenues" BIGINT,
  "total_detenues" BIGINT,
  "male_others" BIGINT,
  "female_others" BIGINT,
  "total_others" BIGINT,
  "total_male" BIGINT,
  "total_female" BIGINT,
  "grand_total" BIGINT
);

Number Of Women Prisoners With Children

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.number_of_women_prisoners_with_children

20 KB
446 rows
13 columns


CREATE TABLE number_of_women_prisoners_with_children (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "no_of_convict_women_prisoners_with_children" BIGINT,
  "no_of_children_of_convict_women_prisoners" BIGINT,
  "no_of_under_trial_women_prisoners_with_children" BIGINT,
  "no_of_children_of_under_trial_women_prisoners" BIGINT,
  "no_of_detenues_women_prisoners_with_children" BIGINT,
  "no_of_children_of_detenues_women_prisoners" BIGINT,
  "no_of_other_women_prisoners_with_children" BIGINT,
  "no_of_children_of_other_women_prisoners" BIGINT,
  "total_no_of_women_prisoners_with_children" BIGINT,
  "total_no_of_children_of_women_prisoners" BIGINT
);

Period Of Detention Of Undertrials

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.period_of_detention_of_undertrials

52.9 KB
3576 rows
7 columns


CREATE TABLE period_of_detention_of_undertrials (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "period_of_detention" VARCHAR,
  "male" BIGINT,
  "female" BIGINT,
  "total" BIGINT
);

Population And Capacity Of Jails

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.population_and_capacity_of_jails

81.68 KB
4023 rows
11 columns


CREATE TABLE population_and_capacity_of_jails (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "type_of_jail" VARCHAR,
  "number_of_central_jails" BIGINT,
  "total_capacity_of_male_inmates" BIGINT,
  "total_capacity_of_female_inmates" BIGINT,
  "total_capacity_of_inmates" BIGINT,
  "male_inmate_population" BIGINT,
  "female_inmate_population" BIGINT,
  "total_inmate_population" BIGINT
);

Prison Details 2015

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.prison_details_2015

88.71 KB
1397 rows
15 columns


CREATE TABLE prison_details_2015 (
  "index" BIGINT,
  "category" VARCHAR,
  "state_ut_name" VARCHAR,
  "jail_type_sl_no" BIGINT,
  "jail_type" VARCHAR,
  "number_of_jails" BIGINT,
  "jail_sl_no" BIGINT,
  "jail_name" VARCHAR,
  "contact_no" VARCHAR,
  "vc_for_remand_purpose" VARCHAR,
  "vc_for_trial_purpose" VARCHAR,
  "vc_for_other_purpose" VARCHAR,
  "location1" VARCHAR,
  "location2" VARCHAR,
  "email_id" VARCHAR
);

Recidivism

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.recidivism

10.48 KB
447 rows
5 columns


CREATE TABLE recidivism (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "convicts_admitted" BIGINT,
  "habitual_offenders" BIGINT
);

Rehabilitation

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.rehabilitation

10.77 KB
445 rows
6 columns


CREATE TABLE rehabilitation (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "financial_assistance_provided" BIGINT,
  "rehabilitated" BIGINT,
  "legal_aid_provided" BIGINT
);

Religion

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.religion

52.68 KB
4460 rows
9 columns


CREATE TABLE religion (
  "index" BIGINT,
  "state_name" BIGINT,
  "is_state" BIGINT,
  "year" VARCHAR,
  "gender" VARCHAR,
  "convicts" BIGINT,
  "under_trial" BIGINT,
  "detenues" BIGINT,
  "others" BIGINT
);

Sentence Period

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.sentence_period

89.09 KB
8940 rows
10 columns


CREATE TABLE sentence_period (
  "index" BIGINT,
  "state_name" VARCHAR,
  "is_state" BIGINT,
  "year" BIGINT,
  "gender" VARCHAR,
  "sentence_period" VARCHAR,
  "age_16_18_years" BIGINT,
  "age_18_30_years" BIGINT,
  "age_30_50_years" BIGINT,
  "age_50_above" BIGINT
);

Sll Crime Headwise Distribution Of Inmates Who Convicted

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.sll_crime_headwise_distribution_of_inmates_who_convicted

142.36 KB
9458 rows
19 columns


CREATE TABLE sll_crime_headwise_distribution_of_inmates_who_convicted (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "crime_head" VARCHAR,
  "male_16_18_years" BIGINT,
  "female_16_18_years" BIGINT,
  "total_16_18_years" BIGINT,
  "male_18_30_years" BIGINT,
  "female_18_30_years" DOUBLE,
  "total_18_30_years" BIGINT,
  "male_30_50_years" BIGINT,
  "female_30_50_years" BIGINT,
  "total_30_50_years" BIGINT,
  "male_above_50_years" BIGINT,
  "female_above_50_years" BIGINT,
  "total_above_50_years" BIGINT,
  "total_male" BIGINT,
  "total_female" BIGINT,
  "grand_total" BIGINT
);

Sll Crime Head Wise Distribution Of Inmates Under Trial

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.sll_crime_head_wise_distribution_of_inmates_under_trial

173.35 KB
9457 rows
19 columns


CREATE TABLE sll_crime_head_wise_distribution_of_inmates_under_trial (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "crime_head" VARCHAR,
  "male_16_18_years" BIGINT,
  "female_16_18_years" BIGINT,
  "total_16_18_years" BIGINT,
  "male_18_30_years" BIGINT,
  "female_18_30_years" BIGINT,
  "total_18_30_years" BIGINT,
  "male_30_50_years" BIGINT,
  "female_30_50_years" BIGINT,
  "total_30_50_years" BIGINT,
  "male_above_50_years" BIGINT,
  "female_above_50_years" BIGINT,
  "total_above_50_years" BIGINT,
  "total_male" BIGINT,
  "total_female" BIGINT,
  "grand_total" BIGINT
);

Total Budget And Actual Expenditure During The Year 2015–2016

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.total_budget_and_actual_expenditure_during_the_year_2015_2016

11.38 KB
36 rows
10 columns


CREATE TABLE total_budget_and_actual_expenditure_during_the_year_2015_2016 (
  "index" BIGINT,
  "s_no_col_no_1" BIGINT,
  "state_ut_col_no_2" VARCHAR,
  "total_annual_budget_plan_col_no_3" DOUBLE,
  "total_annual_budget_non_plan_col_no_4" DOUBLE,
  "total_annual_budget_total_col_no_5" DOUBLE,
  "actual_expenditure_plan_col_no_6" DOUBLE,
  "actual_expenditure_non_plan_col_no_7" DOUBLE,
  "actual_expenditure_expenditure_on_account_of_funds_mad_cd57b885" DOUBLE,
  "actual_expenditure_total_col_no_9_col_6_7_8" DOUBLE
);

Tranquillity

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.tranquillity

16.25 KB
1336 rows
9 columns


CREATE TABLE tranquillity (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "type" VARCHAR,
  "incidence" BIGINT,
  "inmate_injured" BIGINT,
  "jail_personnel_injured" BIGINT,
  "inmate_killed" BIGINT,
  "jail_personnel_killed" BIGINT
);

Value Of Goods Produced By Inmates

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.value_of_goods_produced_by_inmates

37.29 KB
1954 rows
5 columns


CREATE TABLE value_of_goods_produced_by_inmates (
  "index" BIGINT,
  "state_ut" VARCHAR,
  "year" BIGINT,
  "vocation_activity" VARCHAR,
  "gross_value_of_sale_proceeds_earning_in_rs" DOUBLE
);

Vocational Training

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.vocational_training

55.37 KB
4122 rows
5 columns


CREATE TABLE vocational_training (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "vocational_trainings_program" VARCHAR,
  "inmates_trained" DOUBLE
);

Wages

@kaggle.thedevastator_prison_inmates_in_india_demographics_crimes_and.wages

9.32 KB
445 rows
6 columns


CREATE TABLE wages (
  "index" BIGINT,
  "state_name" VARCHAR,
  "year" BIGINT,
  "wages_skilled_convicts" DOUBLE,
  "wages_semi_skilled_convicts" DOUBLE,
  "wages_unskilled_convicts" DOUBLE
);