Census: Population: City: Hyderabad data was reported at 3,943.323 Person th in 03-01-2011. This records a decrease from the previous number of 5,534.000 Person th for 03-01-2001. Census: Population: City: Hyderabad data is updated decadal, averaging 4,344.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 5,534.000 Person th in 03-01-2001 and a record low of 3,943.323 Person th in 03-01-2011. Census: Population: City: Hyderabad 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.

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

Census: Population: Andhra Pradesh: Hyderabad: Male data was reported at 3,928,408.000 Person in 03-01-2011. This records an increase from the previous number of 2,973,472.000 Person for 03-01-2001. Census: Population: Andhra Pradesh: Hyderabad: Male data is updated decadal, averaging 608,272.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 3,928,408.000 Person in 03-01-2011 and a record low of 209,513.000 Person in 03-01-1921. Census: Population: Andhra Pradesh: Hyderabad: Male 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.GAC033: Census: Population: By Towns and Urban Agglomerations: Telangana.

Dataset

This dataset was created by SaiSrinivasaBotta

Contents

Census: Population: Andhra Pradesh: Hyderabad: Female data was reported at 3,748,610.000 Person in 03-01-2011. This records an increase from the previous number of 2,768,564.000 Person for 03-01-2001. Census: Population: Andhra Pradesh: Hyderabad: Female data is updated decadal, averaging 581,556.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 3,748,610.000 Person in 03-01-2011 and a record low of 196,117.000 Person in 03-01-1921. Census: Population: Andhra Pradesh: Hyderabad: Female 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.GAC033: Census: Population: By Towns and Urban Agglomerations: Telangana.

Census: Population: Andhra Pradesh: Hyderabad data was reported at 7,677,018.000 Person in 03-01-2011. This records an increase from the previous number of 5,742,036.000 Person for 03-01-2001. Census: Population: Andhra Pradesh: Hyderabad data is updated decadal, averaging 1,189,828.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 7,677,018.000 Person in 03-01-2011 and a record low of 405,630.000 Person in 03-01-1921. Census: Population: Andhra Pradesh: Hyderabad 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.GAC033: Census: Population: By Towns and Urban Agglomerations: Telangana.

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.

​The dataset you've uploaded to Kaggle, sourced from the Hyderabad Metropolitan Development Authority (HMDA), comprises 954 rows and 80 columns, detailing loan application records from 2017. Each row represents a unique loan application, while the columns encompass various attributes related to the loan, the applicant, and the property involved.​

Key Columns and Their Descriptions:

• as_of_year: The year the loan application was recorded, which is 2017 for all entries.​

• respondent_id: A unique identifier for the financial institution that processed the loan application.​

• agency_name: The name of the agency overseeing the financial institution.​

• agency_abbr: Abbreviation of the agency name.​

• agency_code: A numerical code representing the agency.​

• loan_type_name: The type of loan, such as Conventional, FHA-insured, VA-guaranteed, etc.​

• loan_type: A numerical code corresponding to the loan type.​ HMDA

• property_type_name: Description of the property type, e.g., One-to-four family dwelling.​

• property_type: A numerical code representing the property type.​

• loan_purpose_name: The purpose of the loan, such as Home purchase or Refinancing.​

• sequence_number: A unique identifier for each loan application record.​

• population: The population of the area where the property is located.​

• minority_population: Percentage of minority population in the area.​

• hud_median_family_income: Median family income for the area as determined by the Department of Housing and Urban Development (HUD).​

• tract_to_msamd_income: Ratio of the tract median income to the metropolitan statistical area median income.​

• number_of_owner_occupied_units: Count of owner-occupied housing units in the area.​

• number_of_1_to_4_family_units: Number of housing units designed for one to four families.​

• application_date_indicator: Indicator of the application date; specific coding may require further clarification.​

This dataset provides a comprehensive view of the loan application landscape within the Hyderabad Metropolitan Region for 2017, offering valuable insights for analysis in areas such as loan approval patterns, demographic influences on lending, and property-related financial assessments.​

Census: Population: Uttar Pradesh: Hyderabad: Female data was reported at 3,694.000 Person in 03-01-2011. This records an increase from the previous number of 3,334.000 Person for 03-01-2001. Census: Population: Uttar Pradesh: Hyderabad: Female data is updated decadal, averaging 3,018.000 Person from Mar 1981 (Median) to 03-01-2011, with 4 observations. The data reached an all-time high of 3,694.000 Person in 03-01-2011 and a record low of 2,154.000 Person in 03-01-1981. Census: Population: Uttar Pradesh: Hyderabad: Female 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.GAC035: Census: Population: By Towns and Urban Agglomerations: Uttar Pradesh.

Census: Population: Uttar Pradesh: Hyderabad: Male data was reported at 4,003.000 Person in 03-01-2011. This records an increase from the previous number of 3,583.000 Person for 03-01-2001. Census: Population: Uttar Pradesh: Hyderabad: Male data is updated decadal, averaging 3,342.000 Person from Mar 1981 (Median) to 03-01-2011, with 4 observations. The data reached an all-time high of 4,003.000 Person in 03-01-2011 and a record low of 2,348.000 Person in 03-01-1981. Census: Population: Uttar Pradesh: Hyderabad: Male 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.GAC035: Census: Population: By Towns and Urban Agglomerations: Uttar Pradesh.

Dataset

This dataset was created by Jatinjindal322

Released under MIT

Contents

Abstract

The National Family Health Surveys (NFHS) programme, initiated in the early 1990s, has emerged as a nationally important source of data on population, health, and nutrition for India and its states. The 2005-06 National Family Health Survey (NFHS-3), the third in the series of these national surveys, was preceded by NFHS-1 in 1992-93 and NFHS-2 in 1998-99. Like NFHS-1 and NFHS-2, NFHS-3 was designed to provide estimates of important indicators on family welfare, maternal and child health, and nutrition. In addition, NFHS-3 provides information on several new and emerging issues, including family life education, safe injections, perinatal mortality, adolescent reproductive health, high-risk sexual behaviour, tuberculosis, and malaria. Further, unlike the earlier surveys in which only ever-married women age 15-49 were eligible for individual interviews, NFHS-3 interviewed all women age 15-49 and all men age 15-54. Information on nutritional status, including the prevalence of anaemia, is provided in NFHS3 for women age 15-49, men age 15-54, and young children.

A special feature of NFHS-3 is the inclusion of testing of the adult population for HIV. NFHS-3 is the first nationwide community-based survey in India to provide an estimate of HIV prevalence in the general population. Specifically, NFHS-3 provides estimates of HIV prevalence among women age 15-49 and men age 15-54 for all of India, and separately for Uttar Pradesh and for Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu, five out of the six states classified by the National AIDS Control Organization (NACO) as high HIV prevalence states. No estimate of HIV prevalence is being provided for Nagaland, the sixth high HIV prevalence state, due to strong local opposition to the collection of blood samples.

NFHS-3 covered all 29 states in India, which comprise more than 99 percent of India's population. NFHS-3 is designed to provide estimates of key indicators for India as a whole and, with the exception of HIV prevalence, for all 29 states by urban-rural residence. Additionally, NFHS-3 provides estimates for the slum and non-slum populations of eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur. NFHS-3 was conducted under the stewardship of the Ministry of Health and Family Welfare (MOHFW), Government of India, and is the result of the collaborative efforts of a large number of organizations. The International Institute for Population Sciences (IIPS), Mumbai, was designated by MOHFW as the nodal agency for the project. Funding for NFHS-3 was provided by the United States Agency for International Development (USAID), DFID, the Bill and Melinda Gates Foundation, UNICEF, UNFPA, and MOHFW. Macro International, USA, provided technical assistance at all stages of the NFHS-3 project. NACO and the National AIDS Research Institute (NARI) provided technical assistance for the HIV component of NFHS-3. Eighteen Research Organizations, including six Population Research Centres, shouldered the responsibility of conducting the survey in the different states of India and producing electronic data files.

The survey used a uniform sample design, questionnaires (translated into 18 Indian languages), field procedures, and procedures for biomarker measurements throughout the country to facilitate comparability across the states and to ensure the highest possible data quality. The contents of the questionnaires were decided through an extensive collaborative process in early 2005. Based on provisional data, two national-level fact sheets and 29 state fact sheets that provide estimates of more than 50 key indicators of population, health, family welfare, and nutrition have already been released. The basic objective of releasing fact sheets within a very short period after the completion of data collection was to provide immediate feedback to planners and programme managers on key process indicators.

Geographic coverage

• National (29 states )
• Regional (for HIV Prevalence : Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu)
• Local (population and health indicators for slum and non-slum populations for eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur)

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Universe

The population covered by the 2005 DHS is defined as the universe of all ever-married women age 15-49, NFHS-3 included never married women age 15-49 and both ever-married and never married men age 15-54 as eligible respondents.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE

Since a large number of the key indicators to be estimated from NFHS-3 refer to ever-married women in the reproductive ages of 15-49, the target sample size for each state in NFHS-3 was estimated in terms of the number of ever-married women in the reproductive ages to be interviewed.

The initial target sample size was 4,000 completed interviews with ever-married women in states with a 2001 population of more than 30 million, 3,000 completed interviews with ever-married women in states with a 2001 population between 5 and 30 million, and 1,500 completed interviews with ever-married women in states with a population of less than 5 million. In addition, because of sample-size adjustments required to meet the need for HIV prevalence estimates for the high HIV prevalence states and Uttar Pradesh and for slum and non-slum estimates in eight selected cities, the sample size in some states was higher than that fixed by the above criteria. The target sample was increased for Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland, Tamil Nadu, and Uttar Pradesh to permit the calculation of reliable HIV prevalence estimates for each of these states. The sample size in Andhra Pradesh, Delhi, Maharashtra, Tamil Nadu, Madhya Pradesh, and West Bengal was increased to allow separate estimates for slum and non-slum populations in the cities of Chennai, Delhi, Hyderabad, Indore, Kolkata, Mumbai, Meerut, and Nagpur.

The target sample size for HIV tests was estimated on the basis of the assumed HIV prevalence rate, the design effect of the sample, and the acceptable level of precision. With an assumed level of HIV prevalence of 1.25 percent and a 15 percent relative standard error, the estimated sample size was 6,400 HIV tests each for men and women in each of the high HIV prevalence states. At the national level, the assumed level of HIV prevalence of less than 1 percent (0.92 percent) and less than a 5 percent relative standard error yielded a target of 125,000 HIV tests at the national level.

Blood was collected for HIV testing from all consenting ever-married and never married women age 15-49 and men age 15-54 in all sample households in Andhra Pradesh, Karnataka, Maharashtra, Manipur, Tamil Nadu, and Uttar Pradesh. All women age 15-49 and men age 15-54 in the sample households were eligible for interviewing in all of these states plus Nagaland. In the remaining 22 states, all ever-married and never married women age 15-49 in sample households were eligible to be interviewed. In those 22 states, men age 15-54 were eligible to be interviewed in only a subsample of households. HIV tests for women and men were carried out in only a subsample of the households that were selected for men's interviews in those 22 states. The reason for this sample design is that the required number of HIV tests is determined by the need to calculate HIV prevalence at the national level and for some states, whereas the number of individual interviews is determined by the need to provide state level estimates for attitudinal and behavioural indicators in every state. For statistical reasons, it is not possible to estimate HIV prevalence in every state from NFHS-3 as the number of tests required for estimating HIV prevalence reliably in low HIV prevalence states would have been very large.

SAMPLE DESIGN

The urban and rural samples within each state were drawn separately and, to the extent possible, unless oversampling was required to permit separate estimates for urban slum and non-slum areas, the sample within each state was allocated proportionally to the size of the state's urban and rural populations. A uniform sample design was adopted in all states. In each state, the rural sample was selected in two stages, with the selection of Primary Sampling Units (PSUs), which are villages, with probability proportional to population size (PPS) at the first stage, followed by the random selection of households within each PSU in the second stage. In urban areas, a three-stage procedure was followed. In the first stage, wards were selected with PPS sampling. In the next stage, one census enumeration block (CEB) was randomly selected from each sample ward. In the final stage, households were randomly selected within each selected CEB.

SAMPLE SELECTION IN RURAL AREAS

In rural areas, the 2001 Census list of villages served as the sampling frame. The list was stratified by a number of variables. The first level of stratification was geographic, with districts being subdivided into contiguous regions. Within each of these regions, villages were further stratified using selected variables from the following list: village size, percentage of males working in the nonagricultural sector, percentage of the population belonging to scheduled castes or scheduled tribes, and female literacy. In addition to these variables, an external estimate of HIV prevalence, i.e., 'High', 'Medium' or 'Low', as estimated for all the districts in high HIV prevalence states, was used for stratification in high HIV prevalence states. Female literacy was used for implicit stratification (i.e., villages were

Context

Everyone wants to compare their own place with neighbors. So, In order to compare, we require some data points. Country performs better when States perform well. A State will perform well, only if the Local Governance bodies work well. So, How do you compare local Governance bodies with others? This Data might help citizens to question the Local Governance bodies and make their own wards/corporations healthy.

Content

Data set consists of 1. A shape file of GHMC(Greater Hyderabad Municipal Corporation) : Credits to https://github.com/datameet/Municipal_Spatial_Data/tree/master/Hyderabad for consolidating the shape files/geoJson files of smart cities of INDIA. 2. A csv file consisting of Ward Names, Circle, Zones, Amenities and address of those Amenities within Hyderabad(GHMC). Credits to Telangana govt. for making it public. Source: https://data.telangana.gov.in/dataset/geo-locations-amenities-present-ghmc-hyderabad

Acknowledgements

Thanks to datameet for consolidating the shape files/geoJson files of smart cities of INDIA. Thanks to Telangana govt for Amenities Data within GHMC

Inspiration

Apart from solving the business purpose as a Data Science enthusiast, I would like to challenge the Data Science Community to solve the social life problems by gathering the open source data as it makes a huge impact in real life.

Additional file 4. Table S3: Subset analysis (30%, 50%, 70%) of CAD cases and controls to check for the internal consistency and replicability of significant SNPs.

Census: Population: Uttar Pradesh: Hyderabad data was reported at 7,697.000 Person in 03-01-2011. This records an increase from the previous number of 6,917.000 Person for 03-01-2001. Census: Population: Uttar Pradesh: Hyderabad data is updated decadal, averaging 6,360.000 Person from Mar 1981 (Median) to 03-01-2011, with 4 observations. The data reached an all-time high of 7,697.000 Person in 03-01-2011 and a record low of 4,502.000 Person in 03-01-1981. Census: Population: Uttar Pradesh: Hyderabad 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.GAC035: Census: Population: By Towns and Urban Agglomerations: Uttar Pradesh.

Additional file 3. Table S2: Minor allele and Genotype frequencies of 61 SNPs in CAD cases and controls

Additional file 1: Table S1. Comparison of baseline characteristics of T2DM cases and controls

Abstract

The Enterprise Surveys of Micro firms (ESM) conducted by the World Bank Group's (WBG) Enterprise Analysis Unit (DECEA) in India. The survey covers nine cities: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

The primary objectives of the ESM are to: i) understand demographics of the micro enterprises in the covered cities, ii) describe the environment within which these enterprises operate, and iii) enable data analysis based on the samples that are representative at each city level.

Geographic coverage

Nine cities in India: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

Analysis unit

• Firms

Universe

The universe of ESM includes formally registered businesses in the sectors covered by the ES and with less than five employees. The definition of formal registration can vary by country. The universe table for each of the nine cities covered by ESM in India was obtained from the 6th Economic Census (EC) of India (conducted between January 2013 and April 2014), which has its own well-defined definition of registration. Generally, this entails registration with any central/government agency, under Shops & Establishment Act, Factories Act etc.

In terms of sectors, the survey covers all non-agricultural and non-extractive sectors. In particular, according to the group classification of ISIC Revision 4.0, it includes: all manufacturing sectors (group D), construction (group F), wholesale and retail trade (group G), transportation and storage (group H), accommodation and food service activities (group I), a subset of information and communications (group J), some administrative and support service activities (codes 79) and other service activities (codes 95). Notably, the ESM universe excludes the following sectors: financial and insurance activities (group K), real estate activities (group L), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Enterprise Survey of Micro firms in India 2022 was selected using stratified random sampling, following the methodology explained in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling was preferred over simple random sampling for several reasons, including: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision, along with the unbiased estimates for the whole population. b. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. c. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) d. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. e. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Two levels of stratification were used in this survey: industry and region. For stratification by industry, two groups were used: Manufacturing (combining all the relevant activities in ISIC Rev. 4.0 codes 10-33) and Services (remainder of the universe, as outlined above). Regional stratification was done across nine cities included in the study, namely: Hyderabad, Jaipur, Kochi, Ludhiana, Mumbai, Sehore, Surat, Tezpur and Varanasi.

Mode of data collection

Face-to-face [f2f]

As per the Census data dated 2011, the slum dwellers population in Mumbai was the highest among all other major metropolitan cities of India, at around ************. Hyderabad and Delhi followed it. A total of about ** million people were estimated to be living in slums across the country.

The above dataset consists of 20 rows and 5 columns that represent sales data for a company. Each row in the dataset represents a single sale, and the columns provide information about the sale, including:

Order ID: A unique identifier for each sale, assigned by the company. Product Name: The name of the product that was sold. Price: The price of the product, represented in dollars. Quantity: The number of units of the product that were sold. Sales Amount: The total sales amount for the sale, calculated as the product of the price and the quantity. This dataset can be used to analyze various aspects of the company's sales performance, such as the total revenue generated, the most popular products, and the average order size. The data could be further analyzed using various statistical techniques and visualization tools to gain insights into the company's sales trends and performance.