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.

Dataset Card for Dataset Name

Includes Images for different Indian Cities.

  Dataset Details

Each city has 2500 images

  Dataset Description

This dataset contains 2500 images per Cities of popular indian Cities, City included are Ahmendabad, Mumbai, Delhi, Koklakta and A state Kerala.

Curated by: Divax Shah and Team

  Dataset Sources

Google

Demo: here

arXiv : https://arxiv.org/abs/2403.10912

As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

In India, the share of the population that earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms was ** percent. Hyderabad topped the list with the highest share of middle-class and above category of consumers. Cities from south India topped the list with the first four ranks, followed by the national capital, Delhi.

📌 Overview

"Living in India 2025" is a synthetic yet realistic dataset that explores the cost of living and quality of life across 200 Indian cities. It combines key indicators such as average rent, food cost, internet speed, healthcare rating, safety score, and happiness index to help analysts, students, and data enthusiasts perform in-depth comparisons and uncover meaningful insights. 📊 What’s Inside

The dataset contains 200 rows (one per city) and the following columns:

City – Name of the Indian city.

Average Rent (₹) – Estimated monthly rent for a standard apartment.

Food Cost (₹) – Average monthly food expenses per person.

Internet Speed (Mbps) – Typical broadband download speed.

Healthcare Rating (1-10) – Quality and accessibility of healthcare services.

Safety Score (1-10) – Perceived safety level in the city.

Happiness Index (1-10) – Overall life satisfaction rating.

💡 Potential Insights You Can Explore

Which Indian cities provide the best happiness for the least money?

How safety and happiness correlate across regions.

Which cities are most digital-nomad-friendly based on internet speed and cost.

Regional patterns in healthcare quality vs cost of living.

🛠 Ideal For

Exploratory Data Analysis (EDA)

Data Visualization Projects

Regression & Correlation Studies

Geospatial Mapping

Urban Economics & Policy Research

As of September 2025, Hyderabad was the leading Indian city in local purchasing power among other Indian cities, with an index score of over *****. It was followed by Bengaluru and Pune. The local purchasing power index depicts the relative purchasing power of goods and services in a city for the average net salary in that city.

Context

The dataset was created by keeping in mind the necessity of such historical weather data in the community. The datasets for top 8 Indian cities as per the population.

Content

The dataset was used with the help of the worldweatheronline.com API and the wwo_hist package. The datasets contain hourly weather data from 01-01-2009 to 01-01-2020. The data of each city is for more than 10 years. This data can be used to visualize the change in data due to global warming or can be used to predict the weather for upcoming days, weeks, months, seasons, etc. Note : The data was extracted with the help of worldweatheronline.com API and I can't guarantee about the accuracy of the data.

Acknowledgements

The data is owned by worldweatheronline.com and is extracted with the help of their API.

Inspiration

The main target of this dataset can be used to predict weather for the next day or week with huge amounts of data provided in the dataset. Furthermore, this data can also be used to make visualization which would help to understand the impact of global warming over the various aspects of the weather like precipitation, humidity, temperature, etc.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fcc0eb2fff946bf6e89ce8a5a1fcf2b57%2Fnewdelhi2.png?generation=1730130146299892&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, 2020 to October 27, 2024.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=2020-01-01&end_date=2024-10-27
• 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 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 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.

As of 2024, Mumbai had a gross domestic product of *** billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around *** billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

Actual value and historical data chart for India Population In Largest City

As per the Global Liveability Index of 2024, five Indian cities figured on the list comprising 173 across the world. Indian megacities Delhi and Mumbai tied for 141st place with a score of **** out of 100. They were followed by Chennai (****), Ahmedabad (****), and Bengaluru (****). What are indicators for livability The list was topped by Vienna for yet another year. The index measures cities on five broad indicators such as stability, healthcare, culture and environment, education, and infrastructure. As per the Economic Intelligence Unit’s suggestions, if a city’s livability score is between ** to ** then “livability is substantially constrained”. Less than ** means most aspects of living are severely restricted. Least Liveable cities on the index The least liveable cities were in Sub-Saharan Africa and the Middle East and North Africa regions. Damascus and Tripoli ranked the lowest. Tel Aviv also witnessed significant drop due to war with Hamas.

In the first half of 2024, the existing stock for warehousing in the Mumbai in India accounted for around **** million square meters. It ranks the top among major Indian cities. The Indian warehousing stock was at **** million square meters during the same period.

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.

The top 34 Airbnb markets in 2025 are: 1. Goa - Lenient regulations, 9,580 listings, 47% occupancy rate, ₹4,173 daily rate. See other 33 places.

Overview:

The India AQI dataset provides comprehensive information on air quality across various cities and regions in India. The dataset includes measurements of different air pollutants that contribute to the overall air quality index, enabling researchers, policymakers, and the public to understand and address air quality issues.

Key Components:

Parameters Measured:

PM2.5:

Particulate matter with a diameter of 2.5 micrometers or smaller, which can penetrate the respiratory system.

PM10:

Particulate matter with a diameter of 10 micrometers or smaller.

NO2:

Nitrogen dioxide, primarily produced from vehicle emissions and industrial processes.

SO2:

Sulfur dioxide, which results from burning fossil fuels and industrial processes.

CO:

Carbon monoxide, a colorless, odorless gas produced by burning carbon-based fuels.

O3:

Ozone, which can be beneficial in the upper atmosphere but harmful at ground level.

AQI Calculation:

The dataset may include the calculated AQI values based on the concentrations of the above pollutants, categorized into different levels (e.g., Good, Moderate, Unhealthy, Hazardous). Geographical Coverage:

Information on various states and cities across India, allowing for regional comparisons and analysis. Temporal Coverage:

The dataset may provide historical data over a specific time frame (e.g., daily, weekly, monthly), enabling trend analysis.

Data Sources:

Data collected from government agencies, environmental monitoring stations, and satellite data. Use Cases:

Useful for researchers studying environmental impacts on public health. Helps policymakers in formulating regulations to improve air quality. Provides valuable information for the public to make informed decisions regarding outdoor activities based on air quality levels. Format:

The dataset may be available in formats like CSV, JSON, or Excel, facilitating ease of use in data analysis tools.

Data Accessibility:

Information on how to access the dataset, including links to online repositories or APIs for real-time data retrieval.

Any Data is as good as its Description, so here's a brief explanation:

The following data set contains Temperature data (Minimum, Average, Maximum) in degrees Centigrade and Precipitation data in mm.

This data set contains daily Temperature and Precipitation data from 01/01/1990 to 20/07/2022. Data for the following cities is present : * Delhi * Bangalore * Chennai * Lucknow * Rajasthan * Mumbai * Bhubaneswar * Rourkela

The station Geolocation file will give you the approximate location from where these measurements are taken.

What Can you do with this Data Set ? * Can you Find the hottest/coldest years for each city? * Can you Find precipitation averages and tell when rainfall was abnormally less or abnormally more? * Can you Prove that temperature is increasing and if so at what rate (degree increase/ year)? * Can you create Effective Visualization to convey the same?

Note: This Data set is ideal for Beginners and college students to hone their data science and Visualization skills.

The size of the Dark Store Market market was valued at USD 19.86 billion in 2024 and is projected to reach USD 176.25 billion by 2033, with an expected CAGR of 36.6 % during the forecast period. Recent developments include: In August 2024, Blinkit, a subsidiary of Zomato Ltd. announced to open 2,000 dark stores by end of 2026 within top ten cities in India. The new dark stores would help the company to expand its presence primarily in top cities. , In July 2024, Flipkart Private Limited is planning to open 100 dark stores in India for Flipkart Minutes service. Through the service the company will deliver vegetables, groceries, fruits, and electronics in 10-15 minutes. .

📋 Dataset Overview

A comprehensive hourly dataset tracking atmospheric conditions and air quality across 29 major Indian cities (28 states + Delhi) from 2022 to 2025. This dataset provides detailed insights into India's environmental patterns, pollution trends, and meteorological conditions.

🗺️ Geographic Coverage

• Cities Covered: 29 major cities representing all 28 Indian states + Delhi
• Time Period: 2022 to 2025
• Records: 842,160 hourly observations
• Features: 63 parameters covering weather, pollution, and temporal data

📊 Dataset Specifications

• Total Records: 842,160
• Features: 63 columns
• Temporal Resolution: Hourly data
• Period: 2022-2025 (4 years)
• Memory Usage: ~405 MB

🏙️ Indian Cities Included

(Representing all 28 states + Union Territory of Delhi) - Delhi - Mumbai (Maharashtra) - Chennai (Tamil Nadu) - Kolkata (West Bengal) - Bengaluru (Karnataka) - Hyderabad (Telangana) - Ahmedabad (Gujarat) - Pune (Maharashtra) - Jaipur (Rajasthan) - Lucknow (Uttar Pradesh) - And other major state capitals...

🗂️ Data Columns

📍 Geographic & Temporal Context

🌡️ Meteorological Parameters

Temperature & Humidity - Temp_2m_C - Ambient temperature (°C) - Humidity_Percent - Relative humidity - Dew_Point_C - Dew point temperature - Humidity_Category - Comfort levels

Wind Patterns - Wind_Speed_10m_kmh - Surface wind speed - Wind_Dir_10m - Wind direction (critical for pollution dispersion) - Wind_Gusts_kmh - Wind gusts - Wind_Stagnation - Air stagnation events

Precipitation & Pressure - Precipitation_mm, Rain_mm - Monsoon rainfall tracking - Is_Raining, Heavy_Rain - Rain events - Pressure_MSL_hPa - Monsoon pressure systems

☀️ Solar & Cloud Data

• Solar_Radiation_Wm2 - Total solar radiation
• Direct/Diffuse_Radiation_Wm2 - Radiation components
• Cloud_Cover_Percent - Total cloud cover
• Cloud_Low/Mid/High_Percent - Cloud altitude distribution
• Sunshine_Seconds - Bright sunshine duration
• Is_Daytime - Day/night indicator

🏭 Air Quality Metrics (Critical for India)

Particulate Matter - PM2_5_ugm3 - Fine particulate matter (primary concern) - PM10_ugm3 - Coarse particulate matter - PM_Ratio - PM2.5/PM10 ratio (source identification) - Dust_ugm3 - Dust concentrations - AOD - Aerosol Optical Depth

Gaseous Pollutants - CO_ugm3 - Carbon monoxide (vehicular/industrial) - NO2_ugm3 - Nitrogen dioxide (traffic, industries) - SO2_ugm3 - Sulfur dioxide (industrial, power plants) - O3_ugm3 - Ozone (secondary pollutant)

📊 Air Quality Indices

US AQI System - US_AQI - Overall US AQI - US_AQI_PM25, US_AQI_PM10 - PM-specific indices - US_AQI_NO2, US_AQI_O3, US_AQI_CO - Gas-specific indices

EU AQI System - EU_AQI - European Air Quality Index - EU_AQI_PM25, EU_AQI_PM10 - European standards

India-Specific Categories - AQI_Category - Overall air quality category - PM25_Category_India - India-specific PM2.5 categorization

🌪️ Special Features

• Temp_Inversion - Temperature inversion events (critical for winter pollution in North India)

⚠️ Data Quality Notes

• High Completeness: Most columns have 842,160 non-null values
• Minor Missing Values:
  • US/EU AQI columns: ~145 missing records
  • AQI_Category: 2,516 missing
  • US_AQI_NO2: 2 missing
  • US_AQI_O3: 73 missing

🎯 India-Specific Analysis Opportunities

Seasonal Patterns

• Winter (Dec-Feb): Severe PM2.5 pollution in Indo-Gangetic plain
• Summer (Mar-May): Dust storms, high O3 levels
• Monsoon (Jun-Sep): Natural cleansing, improved AQI
• Post-Monsoon (Oct-Nov): Crop burning impacts in North India

Geographic Hotspots

• Northern Plains: Delhi, Lucknow - Winter smog, crop burning
• Coastal Cities: Mumbai, Chennai - Marine influence, humidity
• Southern Plateau: Bengaluru, Hyderabad - Moderate pollution
• Eastern India: Kolkata - Industrial and vehicular pollution

Event-Based Analysis

• Festival Impacts: Diwali fireworks, Holi bonfires
• Agricultural Cycles: Stubble burning seasons
• Meteorological Events: Western disturbances, monsoon progress

🛠️ Suggested Research Topics

Public Health

• C...

The India City Gas Distribution (CGD) Market Report is Segmented by Type (Pipeline Natural Gas (PNG) and Compressed Natural Gas (CNG)) and End User (Automotive, Residential, Commercial, and Industrial). The Market Size and Forecasts for the India City Gas Distribution Market in Terms of Revenue (USD) for all the Above Segments.