This is a dataset of highly populated cities of india. It contains details of about top 500 cities according to population. I scraped this dataset from census2011.co.in website and It is a very nice dataset for exloratory data analysis.

About Dataset:

There are 7 columns in this dataset and 498 rows. The column names are city,state,population,metropolitan,sexratio and literacy rate.

Real Estate / Housing Dataset

This dataset is web scrapped from a real estate website, collecting all the necessary infos on the resale and new properties. It has around 14000+ rows of data having properties from various Indian cities like Chennai, Mumbai, Bangalore, Delhi, Pune, Kolkata and Hyderabad. Columns:

Name: Property Name, Property Title: Property Ad Title, Price: Property Price Location: Property Located Locality and Region Total Area: Total SQFT of the property Price Per SQFT: Price of Per SQFT of the property Description: Small paragraph about the property Baths: Number of baths in the property Balcony: Whether the Property has balcony or not

The dataset contains air quality information for various cities across India. It includes parameters such as Air Quality Index (AQI), concentrations of particulate matter (PM2.5 and PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), as well as geographical coordinates and time stamps. This dataset enables analysis and comparison of air quality levels among different cities, aiding in understanding environmental health impacts and informing policy decisions.

The dataset contains daily temperature for four major cities in India namely : Kolkata , Mumbai , Chennai , Delhi .

The dataset has been curated from : academic.udayton.edu The blanks or not available data has been marked : na

Population in the largest city (% of urban population) in India was reported at 6.3201 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

This dataset includes survey responses from 1,200 urban residents across four cities in India, focusing on urban foraging practices. It provides socio-demographic details such as city, gender, age, education, occupation, and length of residence. Respondents shared their views on the importance of urban blue spaces (such as water bodies and green spaces) for foraging, rated on a Likert scale from 1 ("not important") to 5 ("very important").

Analysis of ‘Swiggy Restaurants Dataset of Metro Cities’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aniruddhapa/swiggy-restaurants-dataset-of-metro-cities on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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?

--- Original source retains full ownership of the source dataset ---

About this dataset

Context

The dataset tabulates the median household income in Indian Village. It can be utilized to understand the trend in median household income and to analyze the income distribution in Indian Village by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Indian Village, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Indian Village, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Indian Village, IN
• Indian Village, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Indian Village median household income. You can refer the same here

Population in largest city in India was reported at 33807403 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fc0393227e715926755b2bcd49da447dc%2Fganges1.png?generation=1730219826318502&alt=media" alt="">

Context

Hourly and Daily Weather Dataset of Top 50 Most populous Indian cities. Weather data from https://open-meteo.com/ from January 01, 1980 to December 31, 1989.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=1980-01-01&end_date=1989-12-31
• Datetime is in GMT

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)
• Another way to visualize the coordinates https://www.ncei.noaa.gov/maps/radar/

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

India's Largest Cities Weather Data Weather Datasets

• 🇮🇳 India's Largest Cities Weather Data 1980-1989
• 🇮🇳 India's Largest Cities Weather Data 1990-1999
• 🇮🇳 India's Largest Cities Weather Data 2000-2009
• 🇮🇳 India's Largest Cities Weather Data 2010-2019
• 🇮🇳 India's Largest Cities Weather Data 2020-YTD

Note

Image generated with Bing Image Generator

The datasets contains date- and state-wise historically compiled data on air quality (by pollution level) in rural and urban areas of India from the year 2015 , as measured by Central Pollution Board (CPCB) through its daily (24 hourly measurements, taken at 4 PM everyday) Air Quality Index (AQI) reports.

The CPCB measures air quality by continuous online monitoring of various pollutants such as Particulate Matter10 (PM10), Particulate Matter2.5 (PM2.5), Sulphur Dioxide (SO2), Nitrogen Oxide or Oxides of Nitrogen (NO2), Ozone (O3), Carbon Monoxide (CO), Ammonic (NH3) and Lead (Pb) and calculating their level of pollution in the ambient air. Based on the each pollutant load in the air and their associated health impacts, the CPCB calculates the overall Air Pollution in Air Quality Index (AQI) value and publishes the data. This AQI data is then used by CPCB to report the air quality status i.e good, satisfactory, moderate, poor, very poor and severe, etc. of a particular location and their related health impacts because of air pollution.

Smart City Mission is an initiative of Govt. of India. This layer provides boundary of 100 smart cities announced till fourth round. Source of information is http://smartcities.gov.in/content/innerpage/city-wise-projects-under-smart-cities-mission.phpBoundary data has been extracted from OpenStreetMap. Please refer OSM website for more details on data contributors.

Smart City Market Outlook

The global smart city market size was estimated at $500 billion in 2023 and is projected to reach $3 trillion by 2032, growing at a compound annual growth rate (CAGR) of 23%. This remarkable growth is driven by rapid urbanization, technological advancements, and increasing government initiatives aimed at sustainable development. The convergence of IoT, AI, and data analytics is playing a pivotal role in transforming urban landscapes into interconnected, efficient ecosystems.

One of the primary growth factors of the smart city market is the accelerated pace of urbanization. With more than half of the world’s population now residing in urban areas, cities face increasing pressure to improve infrastructure and services. Smart city technologies offer solutions for efficient resource management, enhanced public safety, and improved quality of life. The need for effective urban planning and sustainable development is pushing governments to adopt smart city initiatives at an unprecedented rate.

Advancements in technology, particularly in IoT, AI, and big data, are significantly contributing to the smart city market's expansion. IoT sensors and devices facilitate real-time data collection, enabling cities to monitor and manage resources such as water, electricity, and waste more efficiently. AI and data analytics are used to interpret this data, providing actionable insights that help in optimizing urban operations, reducing costs, and enhancing citizen services. The integration of these technologies is creating a symbiotic relationship between the digital and physical worlds, driving the evolution of smart cities.

Government support and initiatives are also major catalysts for the growth of the smart city market. Various governments around the world are investing heavily in smart city projects to address urban challenges such as traffic congestion, pollution, and energy consumption. For instance, the European Union has earmarked substantial funding for smart city projects under its Horizon 2020 program. Similarly, countries like China and India have launched extensive smart city missions aimed at transforming urban areas into technologically advanced, sustainable habitats.

Regionally, North America and Europe are leading the smart city market, owing to their advanced technological infrastructure and significant government investments. However, Asia Pacific is expected to exhibit the highest growth rate during the forecast period. Rapid urbanization, coupled with increasing government initiatives in countries like China, India, and Japan, is driving the smart city market in this region. Latin America and the Middle East & Africa are also showing promising growth, supported by improving economic conditions and increasing focus on sustainable development.

Component Analysis

The smart city market is segmented into three primary components: hardware, software, and services. Each of these components plays a crucial role in enabling and enhancing the various functionalities of a smart city. Hardware components include sensors, smart meters, and communication devices, among others. These devices are essential for collecting real-time data from various urban environments, which is then used to monitor and manage city operations.

Software solutions are integral to the smart city market as they provide the platforms and applications needed to analyze and interpret the data collected by hardware devices. These software solutions enable various functions such as traffic management, energy management, and public safety. They also offer predictive analytics capabilities, which help city administrators anticipate and mitigate potential issues before they escalate. The increasing complexity and volume of data generated by smart cities necessitate robust software solutions to manage and analyze this data effectively.

Services are another critical component of the smart city market. These include consulting services, system integration, and managed services, which are essential for the successful implementation and operation of smart city projects. Consulting services help cities identify their specific needs and design customized smart city solutions. System integration services ensure that various hardware and software components work seamlessly together, while managed services provide ongoing support and maintenance to ensure the smooth functioning of smart city systems.

The hardware segment is expected to account for a significant share of the smart city market, driv

Analysis of ‘Daily Air Pollution Data - India & USA’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sumandey/daily-air-quality-dataset-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Air Pollution is a major health concern of many. However, the COVID-19 pandemic might have some role to play in bringing some changes to the overall quality of air.

Content

The dataset consists of pm2.5 measurements from Jan 2019 to May 2021 of the Major Cities of India & the United States. You also need to understand how pm2.5 classifies Air Quality.

Acknowledgements

Special thanks go to https://aqicn.org for making the data open-source and use it for research purposes.

Inspiration

This data could be used to answer several questions -

• How the air quality been pre and post-Covid.
• How Air Quality varies across different cities.
• Can you forecast the values for the next 1 month.

You are open to coming up with your own analysis as well.

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for Geographical Outreach: Number of Automated Teller Machines (ATMs) in 3 Largest Cities for India (INDFCACLNUM) from 2007 to 2015 about ATM, India, banks, and depository institutions.

Delhi was the largest city in terms of number of inhabitants in India in 2023.The capital city was estimated to house nearly 33 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

This horizontal bar chart displays male population (people) by capital city using the aggregation sum in India. The data is about countries per year.

This dataset and codebook correspond to the second round of survey data gathered in Delhi and Mumbai (India) in 2024, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.

As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained.

In order to consider sufficiency-oriented lifestyles not only in Europe but also in the Global South, we conducted a similar survey in India. More specifically, we adjusted the survey to fit the context (e.g., including cooling) and, due to the large size and diversity within India, we focused data collection on two Mega Cities (>10Mio inhabitants), namely Mumbai and Delhi. Due to the different cultural context and in exchange with Indian researchers and the supporting market research institute, we decided to change the methodology for data collection from an online survey to face-to-face interviews. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.

dataplor's India location intelligence dataset provides detailed information on a wide range of POIs, making it a valuable resource for businesses and organizations looking to understand and utilize spatial data in the region. It includes extensive data, but is not limited to the following use cases:

• Third-party logistics (3PL): Optimize delivery routes across India's diverse terrain, identify strategic warehouse locations in key cities, and streamline last-mile delivery in densely populated urban areas.

• Economic Development Planning: Identify high-growth potential areas, target investment in emerging markets, and allocate resources effectively based on regional data.

• Retail and Chain Location Site Selection: Analyze demographics and competition to pinpoint ideal locations for new stores in bustling metropolises and underserved rural areas.

• Resource Allocation: Direct resources to where they will have the most impact, ensuring equitable distribution across India's vast and diverse landscape.

• Investment Opportunities: Identify untapped markets and evaluate investment potential in various sectors and regions across India.

• Tourist Attraction Mapping: Enhance tourism experiences by mapping attractions, identifying hidden gems, and understanding tourist movements in popular destinations.

• Infrastructure Improvement: Optimize transportation networks, identify areas for infrastructure development, and plan for efficient resource allocation.

• Market Access: Expand market reach by identifying untapped customer bases in emerging cities and rural areas.

• Conservation Efforts: Monitor environmental impact, identify areas for conservation efforts, and protect India's rich biodiversity.

• Smart City Initiatives: Develop data-driven smart city initiatives, optimize urban planning, and improve quality of life in urban areas.

• Data-Driven Decisions: Empower decision-makers with accurate, reliable, and granular data for evidence-based policymaking and planning.

• Monitoring and Evaluation: Track progress, measure outcomes, and evaluate the effectiveness of various initiatives and programs across India.

dataplor’s Point of Interest (POI) data and location intelligence offers a rich set of 55+ attributes that provide in-depth insights into each location. Key data attributes include:

• Business name
• Business Category
• Product Category
• Address
• Latitude and longitude
• NAIC
• SIC
• H3 Index
• Phone number
• Website
• Opening hours
• Visitation metrics
• Popularity Indices
• Sentiment Indices and more

India IESH: RBI: Inflation Expectations: Kolkata: Three Months Ahead: Standard Deviation data was reported at 4.600 % in Jun 2018. This records an increase from the previous number of 4.400 % for May 2018. India IESH: RBI: Inflation Expectations: Kolkata: Three Months Ahead: Standard Deviation data is updated monthly, averaging 4.100 % from Sep 2008 (Median) to Jun 2018, with 44 observations. The data reached an all-time high of 5.440 % in Sep 2008 and a record low of 1.300 % in Jun 2012. India IESH: RBI: Inflation Expectations: Kolkata: Three Months Ahead: Standard Deviation data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC005: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Major Cities.