Historical dataset of population level and growth rate for the Mumbai, India metro area from 1950 to 2025.

🇮🇳 Indian Housing Datasets — Ready-to-use for ML & Data Analysis

This dataset provides clean, ready-to-use Indian housing data for: - 🏙️ Ahmedabad - 🏙️ Gurugram - 🏙️ Mumbai

Each dataset includes features like: - Property size (sqft) - Location & locality - Price - Number of bedrooms - Furnishing details - Property type (apartment, villa, etc.) - Age of property

All datasets are formatted in CSV for quick loading and analysis in Python, Pandas, or any ML pipeline.

📦 Python Package (for easy access)

You can directly load these datasets using my PyPI library:

pip install india-housing-datasets

ObjectiveA focus on bacterial contamination has limited many studies of water service delivery in slums, with diarrheal illness being the presumed outcome of interest. We conducted a mixed methods study in a slum of 12,000 people in Mumbai, India to measure deficiencies in a broader array of water service delivery indicators and their adverse life impacts on the slum’s residents.MethodsSix focus group discussions and 40 individual qualitative interviews were conducted using purposeful sampling. Quantitative data on water indicators—quantity, access, price, reliability, and equity—were collected via a structured survey of 521 households selected using population-based random sampling.ResultsIn addition to negatively affecting health, the qualitative findings reveal that water service delivery failures have a constellation of other adverse life impacts—on household economy, employment, education, quality of life, social cohesion, and people’s sense of political inclusion. In a multivariate logistic regression analysis, price of water is the factor most strongly associated with use of inadequate water quantity (≤20 liters per capita per day). Water service delivery failures and their adverse impacts vary based on whether households fetch water or have informal water vendors deliver it to their homes.ConclusionsDeficiencies in water service delivery are associated with many non-health-related adverse impacts on slum households. Failure to evaluate non-health outcomes may underestimate the deprivation resulting from inadequate water service delivery. Based on these findings, we outline a multidimensional definition of household “water poverty” that encourages policymakers and researchers to look beyond evaluation of water quality and health. Use of multidimensional water metrics by governments, slum communities, and researchers may help to ensure that water supplies are designed to advance a broad array of health, economic, and social outcomes for the urban poor.

Census: Population: City: Mumbai data was reported at 12,442.373 Person th in 03-01-2011. This records a decrease from the previous number of 16,368.000 Person th for 03-01-2001. Census: Population: City: Mumbai data is updated decadal, averaging 12,596.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 16,368.000 Person th in 03-01-2001 and a record low of 12,442.373 Person th in 03-01-2011. Census: Population: City: Mumbai data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB004: Census: Population: by Selected Cities.

This dataset presents the estimated population for 736 districts and 543 parliamentary constituencies (PCs) in India in 2020. Population estimates were calculated by summing the population count across 100m × 100m pixels over the shapefile boundaries using the WorldPop raster data (https://www.worldpop.org/geodata/summary?id=6527). Both PC and District shapefiles were downloaded from the Community Created Maps of India (CCMA) project published by Data {Meet}. The district shapefile was edited in alignment with the latest district boundary of 2020. Districts of Mumbai and Mumbai Suburban were both reported under Mumbai

Number of cases , age standardised (per 100 000) cancer incidence rates and number of person-years of observation for White & Indian children in Leicester, and for children in Mumbai & Ahmedabad, India. (All rates are standardised to the age distribution of the Segi standard population).

## TNA_NETS

Terrorist Attack Network Datasets

This collection consists of annotated networks developed from documents related to various terrorist attacks in India. Each dataset is named after a specific case and visualizes relationships and interactions among individual entities involved in these incidents. These networks are instrumental for analyzing key actors, hierarchies, and patterns within terrorist organizations. Below is an overview of each file:

• TerroristData1 : Captures the network associated with the 2001 Parliament attack in India. This dataset outlines connections between key individuals, detailing both direct and inferred associations critical for understanding the network structure behind this event.
• TerroristData2: Represents the network surrounding the 1996 Dausa blast, in which a bomb exploded in a Rajasthan State Transport Corporation (RSTC) bus traveling from Agra to Bikaner. The explosion occurred at Samleti village in Dausa district, Rajasthan, killing 14 people and injuring 37. The incident took place a day after a similar blast in Delhi’s Lajpat Nagar. This dataset captures the interactions among involved individuals and entities, shedding light on the logistical and operational aspects of this tragic event.
• TerroristData3 : Documents the network based on the 2008 Mumbai 26/11 attacks. It highlights the coordination among individuals and entities involved in the planning and execution, giving insights into the operational dynamics of the group.
• TerroristData4: Visualizes the network behind the assassination of former Prime Minister of India, Mr. Rajiv Gandhi. The dataset details the connections and hierarchy within the network involved in the plot, showcasing the organizational structure and chain of command.
• TerroristData5: Maps the network involved in the 1993 Bombay blasts, displaying connections between perpetrators, facilitators, and key locations involved in the coordinated attacks across Mumbai.

These datasets are a resource for studying the structure and influence of clandestine networks in terrorist operations. They can support research on network centrality, influence analysis, and resilience, offering insights into the organizational dynamics of terror groups

Abstract

The 2015-16 National Family Health Survey (NFHS-4), the fourth in the NFHS series, provides information on population, health, and nutrition for India and each state and union territory. For the first time, NFHS-4 provides district-level estimates for many important indicators. All four NFHS surveys have been conducted under the stewardship of the Ministry of Health and Family Welfare (MoHFW), Government of India. MoHFW designated the International Institute for Population Sciences (IIPS), Mumbai, as the nodal agency for the surveys. Funding for NFHS-4 was provided by the United States Agency for International Development (USAID), the United Kingdom Department for International Development (DFID), the Bill and Melinda Gates Foundation (BMGF), UNICEF, UNFPA, the MacArthur Foundation, and the Government of India. Technical assistance for NFHS-4 was provided by ICF, Maryland, USA. Assistance for the HIV component of the survey was provided by the National AIDS Control Organization (NACO) and the National AIDS Research Institute (NARI), Pune.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Kind of data

Sample survey data [ssd]

Sampling procedure

The NFHS-4 sample was designed to provide estimates of all key indicators at the national and state levels, as well as estimates for most key indicators at the district level (for all 640 districts in India, as of the 2011 Census). The total sample size of approximately 572,000 households for India was based on the size needed to produce reliable indicator estimates for each district and for urban and rural areas in districts in which the urban population accounted for 30-70 percent of the total district population. The rural sample was selected through a two-stage sample design with villages as the Primary Sampling Units (PSUs) at the first stage (selected with probability proportional to size), followed by a random selection of 22 households in each PSU at the second stage. In urban areas, there was also a two-stage sample design with Census Enumeration Blocks (CEB) selected at the first stage and a random selection of 22 households in each CEB at the second stage. At the second stage in both urban and rural areas, households were selected after conducting a complete mapping and household listing operation in the selected first-stage units.

The figures of NFHS-4 and that of earlier rounds may not be strictly comparable due to differences in sample size and NFHS-4 will be a benchmark for future surveys. NFHS-4 fieldwork for Bihar was conducted in all 38 districts of the state from 16 March to 8 August 2015 by the Academic Management Studies (AMS) and collected information from 36,772 households, 45,812 women age 15-49 (including 7,464 women interviewed in PSUs in the state module), and 5,872 men age 15-54.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires - household, woman's, man's, and biomarker, were used to collect information in 19 languages using Computer Assisted Personal Interviewing (CAPI).

Context

Mumbai is the capital city of the Indian state of Maharashtra. It is a major financial and cultural hub in India, with a population of over 20 million people. The real estate market in Mumbai is known to be one of the most expensive in India. This house price dataset for Mumbai contains information on the sale prices of residential properties in the city, such as apartments and houses. It also includes information on the location, size, and age of the properties.

Columns

bhk

contains the number of bedroom, hall, kitchen collectively.

type

contains the type of house, type can be one of the following:

• apartment • villa • independent house • studio apartment

Note: the majority of data is based on one of the above types.

locality

contains information about the locality of house.

area

contains the area of house, unit of measurement is sq ft.

price

contains the price value for the house.

price_unit

contains the price unit for the house which can be from given below:

• L -> represents Lakh (1 Lakh = 1,00,000 and 10 Lakhs = 1 Million) • Cr -> represents Crore (1 Crore = 1,00,00,000 and 10 Million = 1 Crore)

Note: the currency of price here is Indian Rupee.

region

contains the region of the house.

status

contains information about the status of the house which can be either of given below:

• Ready to move -> the house is ready to use • Under Construction -> the house is currently under construction

age

contains the information regarding age of house which can any of below:

• New: a new house for sale • Resale: an old house for resale

Note: the ‘unknown’ values in age represents that no data available for that instance, unknown is placeholder for null values in age.

Please share your valuable review. If you like the dataset, kindly upvote😃 .

other kaggle work - DravidVaishnav

Thank you

In the first half of 2023, south-central Mumbai had the highest average real estate price rate of around **** thousand Indian rupees per square foot in the Mumbai metropolitan region. Peripheral central suburbs recorded the cheapest property rates.

Mumbai’s real estate market

India’s financial capital, Mumbai’s increasing population, rapid urbanization, and employment opportunities have contributed to skyrocketing property rates in the city. Its real estate market is the most expensive in India. Since the onset of the pandemic, the demand for larger units has been on the rise. In 2023, the realty market saw a surge, especially in luxury and high-end properties catering to the affluent of the city.

New metro connectivity with suburbs

As per realtors, the inauguration of new metro lines connecting the suburbs of Mumbai will provide enhanced connectivity, ease of traveling, and reduced travel time. As a result, a rise in homebuying sentiment in the city, especially in the western suburban areas is expected. The area is considered to be one of the most preferred home-buying destinations in Mumbai with Bollywood actors, non-resident Indians, and industrialists vying for properties.

Net-Income-From-Continuing-Operations Time Series for LIC HOUSING FINANCE LTD.. LIC Housing Finance Limited, a housing finance company, provides loans for the purchase, construction, repair, and renovation of houses/buildings in India. It operates through Loans and Other segments. The company offers public and corporate deposits; home loans to residents and non-residents, as well as to pensioners; plot loans, home improvement and construction loans, home extension, and top up loans; refinance; construction finance and term loans for builders and developers; and loans for staff quarters and other lines of credit for corporates. It also provides loans against properties for companies and individuals; loans against securities; loans under rental securitization; and loans to professionals. In addition, the company develops, establishes, and operates assisted living community centers for elderly citizens; manages, advises, and administers private equity funds, including venture capital and alternate investment funds; offers asset management and trusteeship services; and markets housing loan, life and general insurance products, mutual funds, fixed deposits, and credit cards. It serves salaried/self-employed/professionals/SME customers, retired government employees, and retail customers through home loan agents, direct sales agents, and customer relation associates. LIC Housing Finance Limited was incorporated in 1989 and is based in Mumbai, India.

Stock Price Time Series for LIC HOUSING FINANCE LTD.. LIC Housing Finance Limited, a housing finance company, provides loans for the purchase, construction, repair, and renovation of houses/buildings in India. It operates through Loans and Other segments. The company offers public and corporate deposits; home loans to residents and non-residents, as well as to pensioners; plot loans, home improvement and construction loans, home extension, and top up loans; refinance; construction finance and term loans for builders and developers; and loans for staff quarters and other lines of credit for corporates. It also provides loans against properties for companies and individuals; loans against securities; loans under rental securitization; and loans to professionals. In addition, the company develops, establishes, and operates assisted living community centers for elderly citizens; manages, advises, and administers private equity funds, including venture capital and alternate investment funds; offers asset management and trusteeship services; and markets housing loan, life and general insurance products, mutual funds, fixed deposits, and credit cards. It serves salaried/self-employed/professionals/SME customers, retired government employees, and retail customers through home loan agents, direct sales agents, and customer relation associates. LIC Housing Finance Limited was incorporated in 1989 and is based in Mumbai, India.

Abstract

The National Family Health Survey (NFHS) is a large-scale, multi-round survey conducted in a representative sample of households throughout India. Four rounds of the survey have been conducted in 1992-93, 1998-99, 2005-06, and 2015-16. The fifth round of the survey (2019-2020) is currently in the field. All of the surveys are part of the Demographic and Health Surveys (DHS) Program. The surveys provide information on population, health, and nutrition at the national and state level. Since 2015-16, the surveys have also provided information at the district level. Some of the major topics included in NFHS-4 (2015-16) are fertility, infant and child mortality, family planning, maternal and reproductive health, child vaccinations, prevalence and treatment of childhood diseases, nutrition, women’s empowerment, domestic violence, marriage, sexual activity, employment, anemia, anthropometry, HIV/AIDS knowledge and testing, tobacco and alcohol use, biomarker tests (anthropometry, anemia, HIV, blood pressure, and blood glucose), and water, sanitation, and hygiene. The primary objective of the NFHS surveys is to provide essential data on health and family welfare, as well as emerging issues in these areas. The information collected through the NFHS surveys is intended to assist policymakers and program managers in setting benchmarks and examining progress over time in India’s health sector. The Ministry of Health and Family Welfare (MOHFW), Government of India, designated the International Institute for Population Sciences (IIPS), Mumbai, as the agency responsible for providing coordination and technical guidance for all of the surveys. IIPS has collaborated with a large number of field agencies for survey implementation. The Demographic and Health Surveys Program has provided technical assistance for all of the surveys.

Documentation

You can access the data through the DHS website. Data files are available in the following five formats:

• Hierarchical CSPro file
• Flat files: ASCII data with syntax, Stata, SPSS, SAS

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All datasets are distributed in archived ZIP files that include the data file and its associated documentation. The DHS Program is authorized to distribute, at no cost, unrestricted survey data files for legitimate academic research. Registration is required to access the data.

Additional information about the surveys is available on the India page on the DHS Program website. This page provides a list of surveys and reports, plus Country Quickstats for India, and it is the gateway to accessing more information about the India surveys and datasets.

Methodology

2015-16 National Family Health Survey (NFHS-4): Fieldwork for NFHS-4 was conducted in two phases, from January 2015 to December 2016. The fieldwork was conducted by 14 field agencies, including three Population Research Centers. Laboratory testing for HIV was done by seven laboratories throughout India. NFHS-4 collected information from a nationally representative sample of 601,509 households, 699,686 women age 15-49, and 112,122 men age 15-54. The survey covered all 29 states, 7 Union Territories, and 640 districts in India.

Funding for the survey was provided by the Ministry of Health and Family Welfare, Government of India; the United States Agency for International Development (USAID); UKAID/DFID; the Bill & Melinda Gates Foundation; UNICEF; the United Nations Population Fund (UNFPA); and the MacArthur Foundation. Technical Assistance for NFHS-4 was provided by Macro International, Maryland, USA.

2005-06 National Family Health Survey (NFHS-3): Fieldwork for NFHS-3 was conducted in two phases, from November 2005 to August 2006. The fieldwork was conducted by 18 field agencies, including six Population Research Centers. Laboratory testing for HIV was done by the SRL Ranbaxy laboratory in Mumbai. NFHS-3 collected information from a nationally representative sample of 109,041 households, 124,385 women age 15-49, and 74,369 men age 15-54. The survey covered all 29 states. Only the Union Territories were not included.

Funding for the survey was provided by the United States Agency for International Development (USAID); United Kingdom Department for International Development (DFID); the Bill & Melinda Gates Foundation; UNICEF; the United Nations Population Fund (UNFPA); and the Government of India. Technical assistance for NFHS-3 was provided by Macro International, Maryland, USA.

1998-99 National Family Health Survey (NFHS-2): Fieldwork for NFHS-2 was conducted in two phases, from November 1998 to December 1999. The fieldwork was conducted by 13 field agencies, including five Population Research Centers. NFHS-2 collected information from a nationally representative sample of 91,196 households and 89,188 ever-married women age 15-49. Male interviews were not included in the survey. The survey cover

Context

This dataset contains swiggy registered restaurants details of major metropoliton cities of India. I have considered only metropoliton cities with population 4.5 million. As per the Census of India 2011 definition of more than 4 million population, some of the major Metropolitan Cities in India are:

Mumbai (more than 18 Million) Delhi (more than 16 Million) Kolkata (more than 14 Million) Chennai (more than 8.6 million) Bangalore (around 8.5 million) Hyderabad (around 7.6 million) Ahmedabad (around 6.3 million) Pune (around 5.05 million) Surat (around 4.5 million)

Content

I have scrapped the data using python. It may not have all the restaurants of a particular city because if during webscrapping any restaurant has not enabled swiggy as their delivery partner, that restaurant's details will not be scrapped. Though I have scrapped same cities multiple times, to include maximum restaurant details. The data is collected on 12th Jan 2022.

Acknowledgements

Thank you swiggy for the dataset.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the disaster risk and vulnerability of storm surge in each unit are extracted and calculated using100 meter grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, population density, land cover type, etc. The comprehensive index of storm surge disaster risk is constructed, and the risk index of storm surge is obtained by using the weighted method. Finally, the storm surge risk index is normalized to 0-1, which can be used to evaluate the risk level of storm surge in each assessment unit.At the same time, the data set includes the corresponding risk index, exposure index and vulnerability assessment results.The key nodes data set only contains 11 nodes which have risks ((Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).

On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the vulnerability of storm surge in each unit are extracted and calculated using 100 meter grid as evaluation unit, such as population density, land cover type, etc. The comprehensive index of storm surge vulnerability is constructed, and the vulnerability index of storm surge is obtained by using the weighted method. Finally, the storm surge vulnerability index is normalized to 0-1, which can be used to evaluate the vulnerability level of storm surge in each assessment unit. The key nodes data set only contains 11 nodes which have risks (Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

Nashik is 4th largest city in Maharastra ,Located at an approximate distance of 200 kms from Mumbai and Pune, Nashik gained traction as a vacation hotspot and a location for investing in one’s retirement home .As real estate prices in Pune and Mumbai soared, wine capital of India started being considered as a viable option for living. Social infrastructure gradually improved, with the economic growth of the city and with people choosing the region for their permanent homes.

Inspiration 💡: I am always curious about real estate market in nashik ,many of my friend and relatives are looking for buying house in Nashik .They are always asking me, if you could find any nearby apartment within our budget then let us know , finding your dream house is real struggle. Then I thought with my python and data science skills , I can help them and make their job little easy.

Context

The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.

With the help of NSE, you can trade in the following segments:

• Equities

• Indices

• Mutual Funds

• Exchange Traded Funds

• Initial Public Offerings

• Security Lending and Borrowing Scheme

https://cdn6.newsnation.in/images/2019/06/24/Sharemarket-164616041_6.jpg" alt="Stock image">

Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .

Content

The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.

Timeline of Data recording : 1-1-2015 to 31-12-2015.

Source of Data : Official NSE website.

Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.

Shape of Dataset:

INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15

• Colum Descriptors:

• Date: date on which data is recorded

• Symbol: NSE symbol of the stock

• Series: Series of that stock | EQ - Equity

OTHER SERIES' ARE:

EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.

BE: It stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.

BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.

GC: This series allows Government Securities and Treasury Bills to be traded under this category.

IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.

• Prev Close: Last day close point

• Open: current day open point

• High: current day highest point

• Low: current day lowest point

• Last: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.

• Close: Closing point for the current day

• VWAP: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon

• Volume: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each transaction contributes to the count of total volume.

• Turnover: Total Turnover of the stock till that day

• Trades: Number of buy or Sell of the stock.

• Deliverable: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).

• %Deliverble: percentage deliverables of that stock

Acknowledgements

I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.

Inspiration

I have also built a starter kernel for this dataset. You can find that right here .

I am so excited to see your magical approaches for the same dataset.

THANKS!

Context

Air is a vital part of human life. Monitoring and understanding its quality over time is crucial for our health and well-being.

Content

The dataset contains Air Quality Data, AQI (Air Quality Index) and Metrological Data at hourly and daily level of various stations across multiple cities in India.

States and Union Territories

Andhra Pradesh: Amravati, Anantpur, Chitoor, Kadapa, Rajamahendravaram, Tirupati, Vijayawada, Visakhapatnam. Arunachal Pradesh: Naharlagun. Assam: Guwahati, Nagaon, Nalbari, Silchar. Bihar: Araria, Arrah, Aurangabad, Begusarai, Bettiah, Bhagalpur, Buxar, Chappra, Darbhanga, Gaya, Hajipur, Katihar, Kishanganj, Manguraha, Motihari, Munger, Muzaffarpur, Patna, Purnia, Rajgir, Saharsa, Samastipur, Sasaram, Siwan. Chandigarh: Chandigarh. Chhattisgarh: Bhilai, Bilaspur, Chhai, Korba, Milupara, Raipur, Tumidih. Delhi: Delhi. Gujarat: Ahmedabad, Ankleshwar, Gandhinagar, Nandesari, Surat, Vapi. Haryana: Ambala, Bahadurgarh, Bailabgarh, Bhiwani, Faridabad, Fatehpur, Gurugram, Hisar, Jind, Kaithal, Karnal, Manesar, Narnaul, Panchkula, Panipat, Rohtak, Sirsa, Sonipat. Himachal Pradesh: Baddi. Jammu and Kashmir: Srinagar. Jharkhand: Dhanbad, Jorapokhar, Pathardih. Karnataka: Bagalkot, Belgaum, Bengaluru, Bidar, Chikkamagaluru, Dharwad, Gadag, Hassan, Haveri, Hubballi, Kalaburagi, Karwar, Koppal, Madikeri, Mangalore, Mysuru, Raichur, Ramanagara, Shivamogga, Tumakuru, Udupi, Vijayapura, Yadgir. Kerala: Kannaur, Kollam, Thiruvananthapuram, Thrissur. Madhya Pradesh: Bhopal, Damoh, Dewas, Gwalior, Indore, Jabalpur, Katni, Maihar, Mandideep, Pithampur, Ratlam, Sagar, Satna, Singrauli, Ujjain. Maharashtra: Ahmednagar, Akola, Amravati, Aurangabad, Belapur, Bhiwandi, Boisar, Chandrapur, Dhule, Jalgaon, Jalna, Kalyan, Kolhapur, Latur, Mahad, Malegaon, Mumbai, Nagpur, Nanded, Nashik, Navi Mumbai, Parbhani, Pune, Sangli, Solapur, Thane, Ulhasnagar, Virar. Manipur: Imphal. Meghalaya: Shillong. Mizoram: Aizawl. Nagaland: Kohima. Odisha: Angul, Balasore, Barbil, Baripada, Bhubaneswar, Byasanagar, Cuttack, Keonjhar, Nayagarh, Rairangpur, Rourkela, Suakati, Talcher, Tensa. Puducherry: Puducherry. Punjab: Amritsar, Bathinda, Jalandhar, Khanna, Ludhiana, Patiala, Rupnagar. Rajasthan: Ajmer, Alwar, Banswara, Baran, Barmer, Bharatpur, Bhilwara, Bhiwadi, Bikaner, Bundi, Chittorgarh, Churu, Dausa, Dungarpur, Hanumangarh, Jaipur, Jaisalmer, Jhalawar, Jhunjhunu, Jodhpur, Karauli, Kota, Nagaur, Pali, Pratapgarh, Rajsamand, Sikar, Sirohi, Tonk, Udaipur. Sikkim: Gangtok. Tamil Nadu: Ariyalur, Chengalpattu, Chennai, Coimbatore, Cuddalore, Dindigul, Gummidipoondi, Hosur, Kanchipuram, Karur, Madurai, Nagapattinam, Ooty, Perundurai, Pudukottai, Ramanathapuram, Ranipet, Salem, Thanjavur, Thoothukudi, Tiruchirappalli, Tirunelveli, Tirupur, Vellore. Telangana: Hyderabad. Tripura: Agartala. Uttar Pradesh: Agra, Baghpat, Bareilly, Bulandshahr, Firozabad, Ghaziabad, Gorakhpur, Hapur, Jhansi, Kanpur, Khurja, Lucknow, Meerut, Moradabad, Muzaffarnagar, Noida, Prayagraj, Varanasi, Vrindavan. Uttarakhand: Dehradun, Kashipur, Rishikesh. West Bengal: Asansol, Durgapur, Haldia, Howrah, Kolkata, Siliguri.

Acknowledgements

The data has been made publicly available by the Central Pollution Control Board: https://cpcb.nic.in/ which is the official portal of Government of India and Open Meteo: https://open-meteo.com.