The north-western state of Rajasthan was the largest in terms of land area in India in 2021 with over 342 thousand square kilometers. Central Madhya Pradesh and south-western Maharashtra followed, while the union territory of Lakshadweep recorded an area of 30 square kilometers. Overall, India's geographical area amounted to about 3.3 million square kilometers.

📊 India States & Union Territories Dataset 🇮🇳

This dataset provides a comprehensive overview of India's States and Union Territories, offering detailed information about their administrative structure, demographics, and linguistic diversity. It serves as a valuable resource for understanding the vastness and complexity of India's regional distribution.

📑 Dataset Contents:

• Name: Name of the State or Union Territory 🏙️
• Type: Administrative type (State or Union Territory) 🏛️
• ISO: Official ISO Code 🌍
• Vehiclecode: Unique Vehicle Registration Code 🚗
• Zone: Geographical Zone 🌏
• Capital: Administrative Capital 🏛️
• Largest City: The most populous city within the state 🏙️
• Statehood: Year the state gained full statehood 🎉
• Population: Total population of the state or UT 👥
• Area: Geographic area in square kilometers 🌍
• Official Languages: Primary official languages of the state 🗣️
• Additional Official Languages: Secondary official languages 📚

🔍 Purpose:

This dataset is designed to provide an in-depth look at the various States and Union Territories across India. It combines vital information on governance, geography, population, and language diversity, making it an essential tool for researchers, analysts, and anyone interested in India's regional landscape.

🚀 Applications:

• Data Analysis: Understand regional distribution and demographics 📊
• Geography Research: Explore geographical zones and area details 🌍
• Linguistic Studies: Study the diversity of languages spoken across India 🗣️
• Governance Comparison: Compare States and Union Territories based on statehood and type ⚖️

📝 Contributing:

Feel free to fork this repository and contribute! 💡 Pull requests are welcome for improvements or additional information.

📬 Contact:

For any questions or suggestions, raise suggestions! 📧

Comprehensive dataset containing statistics about all 29 Indian states and 8 union territories including population, area, capitals, languages, and cultural information

In 2022, the union territory of Delhi had the highest urban population density of over ** thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

This data contains all the essential data in the form of % with respect to rural and urban Indian states . This dataset is highly accurate as this is taken from the Indian govt. it is updated till 2021 for all states and union territories. source of data is data.gov.in titled - ******All India and State/UT-wise Factsheets of National Family Health Survey******

it is advised to you pls search the data keywords you need by using (Ctrl+f) , as it will help to avoid time wastage. States/UTs

Different columns it contains are Area

Number of Households surveyed Number of Women age 15-49 years interviewed Number of Men age 15-54 years interviewed

Female population age 6 years and above who ever attended school (%)

Population below age 15 years (%)

Sex ratio of the total population (females per 1,000 males)

Sex ratio at birth for children born in the last five years (females per 1,000 males)

Children under age 5 years whose birth was registered with the civil authority (%)

Deaths in the last 3 years registered with the civil authority (%)

Population living in households with electricity (%)

Population living in households with an improved drinking-water source1 (%)

Population living in households that use an improved sanitation facility2 (%)

Households using clean fuel for cooking3 (%) Households using iodized salt (%)

Households with any usual member covered under a health insurance/financing scheme (%)

Children age 5 years who attended pre-primary school during the school year 2019-20 (%)

Women (age 15-49) who are literate4 (%)

Men (age 15-49) who are literate4 (%)

Women (age 15-49) with 10 or more years of schooling (%)

Men (age 15-49) with 10 or more years of schooling (%)

Women (age 15-49) who have ever used the internet (%)

Men (age 15-49) who have ever used the internet (%)

Women age 20-24 years married before age 18 years (%)

Men age 25-29 years married before age 21 years (%)

Total Fertility Rate (number of children per woman) Women age 15-19 years who were already mothers or pregnant at the time of the survey (%)

Adolescent fertility rate for women age 15-19 years5 Neonatal mortality rate (per 1000 live births)

Infant mortality rate (per 1000 live births) Under-five mortality rate (per 1000 live births)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any modern method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Female sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Male sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - IUD/PPIUD (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Pill (%)  

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Condom (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Injectables (%)

Total Unmet need for Family Planning (Currently Married Women Age 15-49 years)7 (%)

Unmet need for spacing (Currently Married Women Age 15-49 years)7 (%)

Health worker ever talked to female non-users about family planning (%)

Current users ever told about side effects of current method of family planning8 (%)

Mothers who had an antenatal check-up in the first trimester (for last birth in the 5 years before the survey) (%)

Mothers who had at least 4 antenatal care visits (for last birth in the 5 years before the survey) (%)

Mothers whose last birth was protected against neonatal tetanus (for last birth in the 5 years before the survey)9 (%)

Mothers who consumed iron folic acid for 100 days or more when they were pregnant (for last birth in the 5 years before the survey) (%)

Mothers who consumed iron folic acid for 180 days or more when they were pregnant (for last birth in the 5 years before the survey} (%)

Registered pregnancies for which the mother received a Mother and Child Protection (MCP) card (for last birth in the 5 years before the survey) (%)

Mothers who received postnatal care from a doctor/nurse/LHV/ANM/midwife/other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)

Children born at home who were taken to a health facility for a check-up within 24 hours of birth (for last birth in the 5 years before the survey} (%)

Children who received postnatal care from a doctor/nurse/LHV/ANM/midwife/ other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Institutional births (in the 5...

The dataset contains Year and State wise Density of Population

Note: 1. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 2. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. 3. For working out the density of India and Jammu & Kashmir for 1991,2001, the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account.

The dataset contains year- and state-wise compiled data on the total area of land under mangroves in India.

I have compiled this dataset from different sources, listing the sources below -

1) For States and UT's - https://igod.gov.in/sg/states 2) For Cities - https://simplemaps.com/data/in-cities, https://github.com/thatisuday/indian-cities-database/blob/master/cities.json 3) For Villages - https://villageinfo.in/, https://github.com/pranshumaheshwari/indian-cities-and-villages/tree/master, https://data.gov.in/catalog/complete-villages-directory-indiastatedistrictsub-district-level-census-2011

All the credit goes to the government census and various people who have complied this data before, I have just brought these 3 things in a place.

The open forest area in the state of Madhya Pradesh was the largest among the other Indian states which amounted to over ** thousand square kilometers as of 2021. Odisha had the second largest open forest area of about ** thousand square kilometers.

Rajasthan had the largest area for cultivation of Kharif pulses at about ** percent between financial years 2018 and 2022 compared to other Indian states. Madhya Pradesh ranked second during the same measured time period.

As of financial year 2019, the state of Punjab had the highest share of surfaced rural roads in India. About **** percent of rural roads in Punjab was surfaced. It was followed by Rajasthan, with over ** percent of surfaced rural roads. About **** percent of the total *** million kilometers of rural roads were surfaced in the country.

The dataset contains year- and state-wise historically compiled data on the total area of tree cover in India, along with percentage of geographical area under tree cover.

Note:

The term tree cover denotes to the area of land under tree cover, excluding the area of land under forest cover in India

This dataset encompasses agricultural data for multiple crops cultivated across various states in India from the year 1997 till 2020. The dataset provides crucial features related to crop yield prediction, including crop types, crop years, cropping seasons, states, areas under cultivation, production quantities, annual rainfall, fertilizer usage, pesticide usage, and calculated yields.

Columns Description:

1. Crop: The name of the crop cultivated.
2. Crop_Year: The year in which the crop was grown.
3. Season: The specific cropping season (e.g., Kharif, Rabi, Whole Year).
4. State: The Indian state where the crop was cultivated.
5. Area: The total land area (in hectares) under cultivation for the specific crop.
6. Production: The quantity of crop production (in metric tons).
7. Annual_Rainfall: The annual rainfall received in the crop-growing region (in mm).
8. Fertilizer: The total amount of fertilizer used for the crop (in kilograms).
9. Pesticide: The total amount of pesticide used for the crop (in kilograms).
10. Yield: The calculated crop yield (production per unit area).

Use Cases:

This comprehensive dataset is valuable for agricultural analysts, researchers, and data scientists interested in crop yield prediction and agricultural analysis. It offers insights into the relationship between various agronomic factors (e.g., rainfall, fertilizer, pesticide usage) and crop productivity across different states and crop types. Researchers can utilize this data to develop robust machine learning models for crop yield prediction and identify trends in agricultural production.

Note:

Given the diversity of crops, states, and years covered in this dataset, users are encouraged to exercise caution when drawing generalizations or making predictions for specific regions or timeframes outside the scope of the dataset. They are further advised to apply various feature engineering and feature selection techniques on this dataset, so as to make the dataset more robust and suitable for the ML model.

The dataset contains year-, month-, state- and gender-wise compiled data on urban population of India from the year 2011 to 2036. The figures of population given for different years are the projected figures, except for the census year of 2011.

The dataset contains State wise Gross Fixed Capital Formation from Handbook of Statistics on Indian States

Note: 1. Even though erstwhile Andhra Pradesh was divided into Andhra Pradesh and Telangana w.e.f June 2, 2014, ASI data published for the years 2012-13 and 2013-14 contained break-up figures for the two states, separately. 2. Data for Arunachal Pradesh has been included in ASI from 2014-15. 3. Reference period for ASI 2018-19 is the accounting year of the factory, ending on any day during the fiscal year 2018-19. The field work of ASI 2018-19 was adversely affected by the pandemic situation prevailing in the country, which stretched the survey period beyond its stipulated time. The survey was conducted during November 2019 to November 2020 through ASI Web Portal. 4. Separate estimates for UTs Jammu & Kashmir and Ladakh were available in ASI 2019-20 as the erstwhile State of Jammu & Kashmir was divided in two UTs viz., Jammu & Kashmir and Ladakh w.e.f Oct 31, 2019. 5. Mizoram was included for the first time in ASI 2019-20. 6. Combined estimates for Dadra & Nagar Haveli and Daman & Diu were available in ASI 2020-21 onwards as the erstwhile separate UTs Dadra & Nagar Haveli and Daman & Diu were merged into single UT 7. Data as of the end of March 8. Data is for each 3-digit industry group (NIC-2008)

The Indian state of Gujarat had the highest continental shelf area, amounting to about ******* square kilometers, followed by the state of Maharashtra as of 2020. The union territory of Puducherry had the smallest continental shelf of ************ square kilometers.

The statistic displays the main states and union territories with the highest number of people living in urban areas in India in 2011. In that year, the state of Maharashtra had the highest population with over 50 million people living in urban areas. The population density in India from 2004 to 2014 can be seen here.

This dataset provides the State and Union Territory level administrative boundaries for India in a Shape file format. Rendered in the industry-standard coordinate reference system, EPSG:4326 (WGS84), this dataset ensures precision and compatibility.

India Pin/Zip Code (City, Area, District, State) Contain pincode/zipcode with city, area, district, state information Source: India post gov in website

The statistic displays the Indian states with the smallest area of national parks in 2016. During the measured time period, the smallest area of national parks belonged to the state of Telangana with about **** square kilometers, followed by Tripura in the north-east with about **** square kilometers for national parks.