The Personal Income Tax Rate in India stands at 39 percent. This dataset provides - India Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

🧾 This is an original, manually created dataset of 250 Indian citizens, built to explore how people emotionally respond to India's taxation system.

It includes details like: - Monthly income (in INR) - Tax paid (in INR) - Profession type - Government benefits received - GST usage behavior - Awareness about billionaire taxation - Presence of billionaires in their area - And most importantly — their emotional response like:

“I feel exploited by the tax system.”

📌 Why this dataset matters: Unlike most datasets that only focus on numbers, this dataset brings in human perception and emotional impact. It bridges the gap between economics and public sentiment — opening up powerful use cases for data science, ML, and public policy modeling.

🎯 Ideal For: - EDA and storytelling through visuals - Social impact ML projects - Classification models (predict who feels exploited) - Government policy analysis - Real-world data exploration and feature engineering

🧠 Created with purpose: This dataset was manually curated and structured by Swasthik Poojari — as part of his open-source ML project on tax fairness and emotional inequality in India.

🔗 GitHub Project: TaxTruth: Income & Exploitation ML Analysis

Let data speak not just in numbers — but in emotion, voice, and fairness.

1) Data Introduction • The TaxTruth: Tax Perception Dataset from India Dataset is an original collection of information from 250 Indian citizens, including their monthly income, taxes paid, occupation type, government benefits, GST usage behavior, billionaire taxation awareness, presence of surrounding billionaires, and emotional response to the tax system.

2) Data Utilization (1) TaxTruth: Tax Perception Dataset from India Dataset has characteristics that: • The dataset includes numerical data (monthly income, tax payments), categorical data (occupation, government benefits, use of GST, etc.), and emotional and cognitive responses such as "feel exploited by the tax system." • It is designed to analyze citizens' tax perceptions and emotional responses together as well as economic indicators. (2) TaxTruth: Tax Perception Dataset from India Dataset can be used to: • Public Policy Analysis: The direction of policy improvement can be derived by analyzing the correlation between citizens' emotional response to the tax system, the actual tax burden, and whether they

The Corporate Tax Rate in India stands at 34.94 percent. This dataset provides - India Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Government Revenues in India increased to 1730216 INR Tens of Million in September from 1282709 INR Tens of Million in August of 2025. This dataset provides - India Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

About This Dataset

This dataset is the original 70-city version used in my first published research paper: “A Data-Driven Survey on Cost of Living and Salary Affordability in Indian Cities” (IJRASET, 2025) Link: https://www.ijraset.com/best-journal/a-datadriven-survey-on-cost-of-livingsalary-affordability-in-indian-cities

It was created using web-scraping techniques from LivingCost.org and converted to INR using a consistent USD→INR exchange rate. This dataset forms the foundational base for affordability analysis, exploratory data analysis (EDA), and benchmarking cost-of-living patterns across India.

The dataset includes 70+ Indian cities, with fields covering living cost, rent, salary, affordability ratio (“months covered”), and derived financial indicators. It is clean, structured, and suitable for beginner to intermediate analytics projects.

Why This Dataset?

This dataset is ideal for:

EDA practice for college & school projects

Correlation and regression analysis

Basic ML tasks (predicting salary, affordability, rent, etc.)

Urban economics mini-projects

Dashboard creation (PowerBI, Tableau)

Data cleaning and preprocessing assignments

It is designed to be simple enough for students but structured enough for real-world analysis.

Features Included

Each row represents a city/state-level affordability profile with:

Cost of living (USD & INR)

Rent for a single person (USD & INR)

Monthly after-tax salary (USD & INR)

Income after rent

“Months Covered” affordability ratio

Source URLs for verification

Exchange rate used

This makes the dataset both transparent and reliable for academic usage.

Data Quality

Web-scraped directly from LivingCost.org

Cleaned and standardized

Currency converted uniformly

Non-city entries flagged

Fully reproducible from the source

This dataset served as the master input for my peer-reviewed paper and has been validated through statistical analysis.

Intended Audience

Students (school, undergraduate, postgraduate)

Data science beginners

Educators needing real datasets for teaching

Analysts looking for quick EDA practice

Researchers exploring affordability or urban economics

Note

A more comprehensive 200+ city enhanced dataset (used in my second paper) will be uploaded soon, including ICT metrics, GDP, and extended affordability indicators.

Dataset Description: Indian Urban Affordability and Economic Productivity (221 Cities) About the Dataset

This dataset represents the comprehensive 221-city version developed and utilized in the research paper “Predicting Urban Affordability and Economic Productivity in India: A Data-Driven KNN and Random Forest Framework with Insights from Selected Major Cities.”

It builds upon the author’s earlier 70-city affordability dataset and significantly expands its scope.

The dataset provides a unified framework to study how urban affordability, digital readiness, and GDP specialization jointly influence economic livability and productivity across different city tiers.

Data Provenance and Construction

Primary Source: Extended web-scraped affordability data originally compiled from LivingCost.org and other verified open-data platforms.

Cleaning & Standardization: City names normalized (e.g., “Bengaluru” → “Bangalore”), and all numeric fields standardized to INR using a consistent USD→INR conversion rate for comparability.

Features Included

Each record (row) corresponds to one city and contains the following metrics:

Cost of Living (INR)

Monthly Rent (INR)

Monthly After-Tax Salary (INR)

Income After Rent (INR)

Affordability Ratio (“Months Covered”)

Intended Applications

This dataset can be used for:

🧮 Cross-city affordability and livability analysis

🤖 Machine Learning model development (affordability or salary prediction)

🌆 Urban economics and policy simulation studies

📈 Correlation and regression-based research in ICT and GDP domains

📊 Dashboard and visualization projects (Power BI, Tableau, SAP SAC, etc.)

It is designed for use by researchers, policymakers, educators, and data analysts seeking a reliable, structured, and multi-domain dataset on Indian urban dynamics.

Data Quality and Transparency

✅ Uniform currency and value scaling

✅ Reproducible preprocessing (Python-based pipelines with Scikit-Learn)

✅ Missing values imputed using KNN-based methodology

✅ Verified against baseline datasets used in prior research

✅ Released under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Significance

This dataset forms the empirical backbone of the author’s second research paper, providing the quantitative base for the KNN baseline model and the Random Forest multi-output regressor used to predict salary and affordability across Indian cities.

It enables city-level insight generation for policymakers and supports reproducible, data-driven research in urban economics, digital inclusion, and sustainable development.

Future Extensions

An upcoming enhancement will include:

Complete AQI integration for all 221 cities to examine the affordability–environment linkage.

Time-series extension for multi-year trend analysis.

Inclusion of healthcare, safety, and green infrastructure indicators for a broader livability framework.

A additional file used in my paper on T30 cities of India with justification is also attached.

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the Census of Population.For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote2referrerFootnote 3Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote3referrerFootnote 4The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote4referrerFootnote 5Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.Return to footnote5referrerFootnote 6Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote6referrerFootnote 7'Aboriginal identity' includes persons who are First Nations (North American Indian), Métis or Inuk (Inuit) and/or those who are Registered or Treaty Indians (that is, registered under the Indian Act of Canada) and/or those who have membership in a First Nation or Indian band. Aboriginal peoples of Canada are defined in the Constitution Act, 1982, section 35 (2) as including the Indian, Inuit and Métis peoples of Canada.Return to footnote7referrerFootnote 8'Single Aboriginal responses' includes persons who are in only one Aboriginal group, that is First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote8referrerFootnote 9Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For additional information, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016.Return to footnote9referrerFootnote 10'Multiple Aboriginal responses' includes persons who are any two or all three of the following: First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote10referrerFootnote 11'Aboriginal responses not included elsewhere' includes persons who are not First Nations (North American Indian), Métis or Inuk (Inuit), but who have Registered or Treaty Indian status and/or Membership in a First Nation or Indian band.

Description:

Explore the dynamic landscape of the Indian stock market with this extensive dataset featuring 4456 companies listed on both the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Gain insights into each company's financial performance, quarterly and yearly profit and loss statements, balance sheets, cash flow data, and essential financial ratios. Dive deep into the intricacies of shareholding patterns, tracking the movements of promoters, foreign and domestic institutional investors, and the public.

This dataset is a rich resource for financial analysts, investors, and data enthusiasts. Perform thorough company evaluations, sector-wise comparisons, and predictive modeling. With figures presented in crore rupees, leverage the dataset for in-depth exploratory data analysis, time series forecasting, and machine learning applications. Stay tuned for updates as we enrich this dataset for a deeper understanding of the Indian stock market landscape. Unlock the potential of data-driven decision-making with this comprehensive repository of financial information.

Folder Structure:

• 4492 NSE & BSE Companies
  • Main directory containing data for 4456 NSE and BSE registered companies.
  • Company_name folder
    • Individual folders for each company allowing for easy organization and retrieval.
    • Company_name.csv
      • General company information.
    • Quarterly_Profit_Loss.csv
      • Quarterly financial data.
    • Yearly_Profit_Loss.csv
      • Annual financial data.
    • Yearly_Balance_Sheet.csv
      • Annual balance sheet information.
    • Yearly_Cash_flow.csv
      • Annual cash flow data.
    • Ratios.csv.csv
      • Financial ratios over time.
    • Quarterly_Shareholding_Pattern.csv
      • Quarterly shareholding pattern.
    • Yearly_Shareholding_Pattern.csv
      • Annual shareholding pattern.

File Explanation:

Company_name.csv

- `Company_name`: Name of the company.
- `Sector`: Industry sector of the company.
- `BSE`: Bombay Stock Exchange code.
- `NSE`: National Stock Exchange code.
- `Market Cap`: Market capitalization of the company.
- `Current Price`: Current stock price.
- `High/Low`: Highest and lowest stock prices.
- `Stock P/E`: Price to earnings ratio.
- `Book Value`: Book value per share.
- `Dividend Yield`: Dividend yield percentage.
- `ROCE`: Return on capital employed percentage.
- `ROE`: Return on equity percentage.
- `Face Value`: Face value of the stock.
- `Price to Sales`: Price to sales ratio.
- `Sales growth (1, 3, 5, 7, 10 years)`: Sales growth percentage over different time periods.
- `Profit growth (1, 3, 5, 7, 10 years)`: Profit growth percentage over different time periods.
- `EPS`: Earnings per share.
- `EPS last year`: Earnings per share in the last year.
- `Debt (1, 3, 5, 7, 10 years)`: Debt of the company over different time periods.

Quarterly_Profit_Loss.csv

 - `Sales`: Revenue generated by the company.
 - `Expenses`: Total expenses incurred.
 - `Operating Profit`: Profit from core operations.
 - `OPM %`: Operating Profit Margin percentage.
 - `Other Income`: Additional income sources.
 - `Interest`: Interest paid.
 - `Depreciation`: Depreciation of assets.
 - `Profit before tax`: Profit before tax.
 - `Tax %`: Tax percentage.
 - `Net Profit`: Net profit after tax.
 - `EPS in Rs`: Earnings per share.

Yearly_Profit_Loss.csv

- Same as Quarterly_Profit_Loss.csv, but on a yearly basis.

Yearly_Balance_Sheet.csv

- `Equity Capital`: Capital raised through equity.
- `Reserves`: Company's retained earnings.
- `Borrowings`: Company's borrowings.
- `Other Liabilities`: Other financial obligations.
- `Total Liabilities`: Sum of all liabilities.
- `Fixed Assets`: Company's long-term assets.
- `CWIP`: Capital Work in Progress.
- `Investments`: Company's investments.
- `Other Assets`: Other non-current assets.
- `Total Assets`: Sum of all assets.

Yearly_Cash_flow.csv

- `Cash from Operating Activity`: Cash generated from core business operations.
- `Cash from Investing Activity`: Cash from investments.
- `Cash from Financing Activity`: Cash from financing (borrowing, stock issuance, etc.).
- `Net Cash Flow`: Overall net cash flow.

Ratios.csv.csv

- `Debtor Days`: Number of days it takes to collect receivables.
- `Inventory Days`: Number of days inventory is held.
- `Days Payable`: Number of days a company takes to pay its bills.
- `Cash Conversion Cycle`: Time taken to convert sales into cash.
- `Wor...

This dataset provides tax rates (Value Added Tax, Entry Tax, Luxury Tax) on tobacco products in 10 Indian States (Karnataka, Kerala, Goa, Madhya Pradesh, Gujarat, Haryana, Bihar, West Bengal, Meghalaya, and Nagaland) for the period of 1990-2017. The dataset provides tax rates for three major categories of tobacco products (cigarettes, bidis, and smokeless products) month-wise starting from the financial year April 1990 - March 1991 till the financial year April 2016 - March 2017. These data were collected from relevant statutes, notifications, public notices by concerned state governments typically available through state government commercial tax department websites occasionally supplemented by free internet searches for specific documents not available or accessible on state government websites.The following points will help better understand the dataset and its strengths and limitations:The numerical data in each cell refers to the rate of the tax on given tobacco product that prevailed at a given time (month/year). The data provided is a decimal fraction and is to be multiplied by 100 to derive the percentage e.g. 0.01 in the dataset imply 1% of tax rate.The Value Added Tax (VAT) Acts were enacted in Indian states in early 2000s and generally came to be implemented around the year 2005. In our dataset, we capture the VAT rates on tobacco from March 2005 onward. However, the actual implementation could have been a little earlier in some states. VAT rates are generally provided till March 2017 after which, VAT was subsumed in the Goods and Services Tax.In case of the Entry Tax and the Luxury Tax, only some of the states levied such taxes on tobacco products. In case of the states that levied these taxes on tobacco, we have captured data from March 1990 onward as our study period was 1990-2017. This does not necessarily imply that such taxes were not levied on tobacco before March 1990.Blank cells or cells with missing values denote that the given tax type was not levied on the given tobacco products for that time point.At times, additional tax or surcharge was levied under the VAT Act in addition to the VAT rate for tobacco. The dataset provides the VAT rates that are inclusive of such additional tax or surcharge and in such cases, a comment clarifying this has been inserted in the dataset.At times, different smokeless tobacco products had different tax rates levied on them. In such cases, we have generally indicated the highest tax rate in the dataset while including a comment clarifying the different rates for different smokeless tobacco items.Rarely, the VAT rate was levied in form of a fixed amount per certain number of products (cigarette sticks) instead of a fixed percentage of the product value. In such instance, we have inserted a comment in the dataset clarifying this.We found it complex to track all the changes done in tax rates on tobacco over time under these three tax categories. There were several amendments to the tax legislations and several notifications issued under these tax legislations regarding changes in tax rates on tobacco. It is likely that we missed out capturing all these changes, especially as some of the notifications were missing from the government websites. So, there are likely to be errors in terms of the tax rates and the exact period for which specific rates prevailed. We tried our best to capture data from authoritative sources as much as possible given the limited time and resources we had.This dataset was produced as part of the broader research project that explored the political economy of tobacco, titled “Deciphering an epidemic of epic proportion: the role of state and tobacco industry in tobacco control in post-liberalised India (1990-2017)”. We thank the DBT/Wellcome Trust India Alliance for funding this project through the Intermediate (Clinical and Public Health) Fellowship awarded to Upendra Bhojani (IA/CPHI/17/1/503346). While collecting these data, an earlier document compiling tax rates on tobacco at state level by Mr. Gaurav Gupta of the Campaign for Tobacco-Free Kids for the period 2010-2011 to 2016-2017 served as a useful reference. We thank him for sharing such resource with us.

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the Census of Population.For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote2referrerFootnote 3Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote3referrerFootnote 4The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote4referrerFootnote 5Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.Return to footnote5referrerFootnote 6Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote6referrerFootnote 7'Aboriginal identity' includes persons who are First Nations (North American Indian), Métis or Inuk (Inuit) and/or those who are Registered or Treaty Indians (that is, registered under the Indian Act of Canada) and/or those who have membership in a First Nation or Indian band. Aboriginal peoples of Canada are defined in the Constitution Act, 1982, section 35 (2) as including the Indian, Inuit and Métis peoples of Canada.Return to footnote7referrerFootnote 8'Single Aboriginal responses' includes persons who are in only one Aboriginal group, that is First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote8referrerFootnote 9Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For additional information, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016.Return to footnote9referrerFootnote 10'Multiple Aboriginal responses' includes persons who are any two or all three of the following: First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote10referrerFootnote 11'Aboriginal responses not included elsewhere' includes persons who are not First Nations (North American Indian), Métis or Inuk (Inuit), but who have Registered or Treaty Indian status and/or Membership in a First Nation or Indian band.

This service shows the median household after-tax income in 2015 for Canada, by 2016 census subdivision. The data is from the Census Profile, Statistics Canada Catalogue no. 98-316-X2016001. After-tax income - refers to total income less income taxes of the statistical unit during a specified reference period (for additional information refer to Total Income – 2016 Census Dictionary and After-tax Income – 2016 Census Dictionary). The median income of a specified group is the amount that divides the income distribution of that group into two halves. Census subdivision (CSD) is the general term for municipalities (as determined by provincial/territorial legislation) or areas treated as municipal equivalents for statistical purposes (e.g., Indian reserves, Indian settlements and unorganized territories). Municipal status is defined by laws in effect in each province and territory in Canada. To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census subdivision” web service, accessible in the data resources section below. Besides the variable described here, the dataset contains the id, name, type, province, population, land area and the number of private households for each census subdivision. If a value is null, it could be because it is not available for a specific reference period, it is not applicable, it is too unreliable to be published or it is suppressed to meet confidentiality requirements of the Statistics Act. To find out the exact reason, refer to the source data from Census in the resources below.

This information circular is intended to explain legislation and provide specific information. In the event of a discrepancy in interpretation between this information circular and the respective legislation, the legislation takes precedence. The Government of Alberta recognizes that many First Nations people and communities in the province prefer not to describe themselves as Indians and bands. These terms have been used where necessary to reflect their legal meanings in the federal Indian Act.

The Florida Department of Revenue’s Property Tax Oversight(PTO) program collects parcel level Geographic Information System (GIS) data files every April from all of Florida’s 67 county property appraisers’ offices. This GIS data was exported from these file submissions in August 2025. The GIS parcel polygon features have been joined with thereal property roll (Name – Address – Legal, or NAL)file. No line work was adjusted between county boundaries.The polygon data set represents the information property appraisers gathered from the legal description on deeds, lot layout of recorded plats, declaration of condominium documents, recorded and unrecorded surveys.Individual parcel data is updated continually by each county property appraiser as needed. The GIS linework and related attributions for the statewide parcel map are updated annually by the Department every August. The dataset extends countywide and is attribute by Federal Information Processing Standards (FIPS) code.DOR reference with FIPS county codes and attribution definitions - https://fgio.maps.arcgis.com/home/item.html?id=55e830fd6c8948baae1601fbfc33a3b2If you discover the inadvertent release of a confidential record exempt from disclosure pursuant to Chapter 119, Florida Statutes, public records laws, immediately notify the Department of Revenue at 850-717-6570 and your local Florida Property Appraisers’ Office. Please contact the county property appraiser with any parcel specific questions: Florida Property Appraisers’ Offices:Alachua County Property Appraiser – https://www.acpafl.org/Baker County Property Appraiser – https://www.bakerpa.com/Bay County Property Appraiser – https://baypa.net/Bradford County Property Appraiser – https://www.bradfordappraiser.com/Brevard County Property Appraiser – https://www.bcpao.us/Broward County Property Appraiser – https://bcpa.net/Calhoun County Property Appraiser – https://calhounpa.net/Charlotte County Property Appraiser – https://www.ccappraiser.com/Citrus County Property Appraiser – https://www.citruspa.org/Clay County Property Appraiser – https://ccpao.com/Collier County Property Appraiser – https://www.collierappraiser.com/Columbia County Property Appraiser – https://columbia.floridapa.com/DeSoto County Property Appraiser – https://www.desotopa.com/Dixie County Property Appraiser – https://www.qpublic.net/fl/dixie/Duval County Property Appraiser – https://www.coj.net/departments/property-appraiser.aspxEscambia County Property Appraiser – https://www.escpa.org/Flagler County Property Appraiser – https://flaglerpa.com/Franklin County Property Appraiser – https://franklincountypa.net/Gadsden County Property Appraiser – https://gadsdenpa.com/Gilchrist County Property Appraiser – https://www.qpublic.net/fl/gilchrist/Glades County Property Appraiser – https://qpublic.net/fl/glades/Gulf County Property Appraiser – https://gulfpa.com/Hamilton County Property Appraiser – https://hamiltonpa.com/Hardee County Property Appraiser – https://hardeepa.com/Hendry County Property Appraiser – https://hendryprop.com/Hernando County Property Appraiser – https://hernandocountypa-florida.us/Highlands County Property Appraiser – https://www.hcpao.org/Hillsborough County Property Appraiser – https://www.hcpafl.org/Holmes County Property Appraiser – https://www.qpublic.net/fl/holmes/Indian River County Property Appraiser – https://www.ircpa.org/Jackson County Property Appraiser – https://www.qpublic.net/fl/jackson/Jefferson County Property Appraiser – https://jeffersonpa.net/Lafayette County Property Appraiser – https://www.lafayettepa.com/Lake County Property Appraiser – https://www.lakecopropappr.com/Lee County Property Appraiser – https://www.leepa.org/Leon County Property Appraiser – https://www.leonpa.gov/Levy County Property Appraiser – https://www.qpublic.net/fl/levy/Liberty County Property Appraiser – https://libertypa.org/Madison County Property Appraiser – https://madisonpa.com/Manatee County Property Appraiser – https://www.manateepao.gov/Marion County Property Appraiser – https://www.pa.marion.fl.us/Martin County Property Appraiser – https://www.pa.martin.fl.us/Miami-Dade County Property Appraiser – https://www.miamidade.gov/pa/Monroe County Property Appraiser – https://mcpafl.org/Nassau County Property Appraiser – https://ncpafl.com/Okaloosa County Property Appraiser – https://okaloosapa.com/Okeechobee County Property Appraiser – https://www.okeechobeepa.com/Orange County Property Appraiser – https://ocpaweb.ocpafl.org/Osceola County Property Appraiser – https://www.property-appraiser.org/Palm Beach County Property Appraiser – https://www.pbcgov.org/papa/index.htmPasco County Property Appraiser – https://pascopa.com/Pinellas County Property Appraiser – https://www.pcpao.org/Polk County Property Appraiser – https://www.polkpa.org/Putnam County Property Appraiser – https://pa.putnam-fl.com/Santa Rosa County Property Appraiser – https://srcpa.gov/Sarasota County Property Appraiser – https://www.sc-pa.com/Seminole County Property Appraiser – https://www.scpafl.org/St. Johns County Property Appraiser – https://www.sjcpa.gov/St. Lucie County Property Appraiser – https://www.paslc.gov/Sumter County Property Appraiser – https://www.sumterpa.com/Suwannee County Property Appraiser – https://suwannee.floridapa.com/Taylor County Property Appraiser – https://qpublic.net/fl/taylor/Union County Property Appraiser – https://union.floridapa.com/Volusia County Property Appraiser – https://vcpa.vcgov.org/Wakulla County Property Appraiser – https://mywakullapa.com/Walton County Property Appraiser – https://waltonpa.com/Washington County Property Appraiser – https://www.qpublic.net/fl/washington/Florida Department of Revenue Property Tax Oversight https://floridarevenue.com/property/Pages/Home.aspx

Latest broker research reports with buy, sell, hold recommendations along with forecast share price targets and upside. Browse thousands of reports and search by company or the broker.

This dataset contains the data of quarterly results of NIFTY 500 companies. Nifty 500 is an index maintained by the National Stock Exchange of India (NSE). The Nifty 500 is designed to represent the performance of the 500 largest publicly traded companies listed on the NSE, spanning various sectors of the Indian economy. Nifty 500 includes stocks from large, mid, and small-cap segments, providing a comprehensive view of the overall market.

The dataset contains 17 columns covering aspects of the companies and their quarterly results. These columns are described below: Name: The name of the company.

NSE Code: The National Stock Exchange code assigned to the company for trading on the NSE.

BSE Code: The Bombay Stock Exchange code assigned to the company for trading on the BSE.

Sector: The sector to which the company belongs. Sectors classify companies based on the nature of their business activities.

Industry: The industry in which the company operates, providing more detailed information about its specific line of business.

Revenue: The total income generated by the company during a specific quarter, reflecting its sales or service revenue.

Operating Expenses: The total expenses incurred by the company in its day-to-day operations, excluding financial and tax-related expenses.

Operating Profit: The profit obtained after deducting operating expenses from revenue, indicating the profitability of the core business operations.

Operating Profit Margin: The percentage representing the proportion of revenue that translates into operating profit, indicating operational efficiency.

Depreciation: The decrease in the value of the company's assets over time, representing the allocated cost of tangible assets.

Interest: The cost of borrowing for the company, reflecting interest payments on loans and other financial obligations.

Profit Before Tax: The company's profit before deducting income tax, including operating profit and other non-operating income or expenses.

Tax: The income tax expense incurred by the company during the quarter.

Net Profit: The profit remaining after deducting all expenses, including operating expenses, depreciation, interest, and taxes.

Earnings Per Share (EPS): The portion of a company's profit allocated to each outstanding share of common stock, providing a measure of profitability on a per-share basis.

Net Profit TTM (Trailing 12 Months): The sum of net profits over the most recent 12-month period, giving a broader perspective on the company's performance.

EPS TTM (Trailing 12 Months): The earnings per share calculated over the trailing 12-month period, providing a longer-term view of earnings on a per-share basis.

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2For more information on generation status variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2016.Return to footnote2referrerFootnote 3Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote3referrerFootnote 4The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote4referrerFootnote 5Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.Return to footnote5referrerFootnote 6For more information on the Visible minority variable, including information on its classification, the questions from which it is derived, data quality and its comparability with other sources of data, please refer to the Visible Minority and Population Group Reference Guide, Census of Population, 2016.Return to footnote6referrerFootnote 7The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour.'Return to footnote7referrerFootnote 8For example, 'East Indian,' 'Pakistani,' 'Sri Lankan,' etc.Return to footnote8referrerFootnote 9For example, 'Vietnamese,' 'Cambodian,' 'Laotian,' 'Thai,' etc.Return to footnote9referrerFootnote 10For example, 'Afghan,' 'Iranian,' etc.Return to footnote10referrerFootnote 11The abbreviation 'n.i.e.' means 'not included elsewhere.' Includes persons with a write-in response such as 'Guyanese,' 'West Indian,' 'Tibetan,' 'Polynesian,' 'Pacific Islander,' etc.Return to footnote11referrerFootnote 12Includes persons who gave more than one visible minority group by checking two or more mark-in responses, e.g., 'Black' and 'South Asian.'Return to footnote12referrerFootnote 13Includes persons who reported 'Yes' to the Aboriginal group question (Question 18), as well as persons who were not considered to be members of a visible minority group.

This information circular provided by Tax and Revenue Administration (TRA) provides an overview of the Alberta fuel tax on liquefied petroleum gas (LPG), under the Fuel Tax Act. Information circulars are updated as necessary and a revision number assigned. Note: the Government of Alberta recognizes that many First Nations people and communities in the province prefer not to describe themselves as Indians/Indian bands. These terms have been used where necessary to reflect their legal meaning in the federal Indian Act.

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the Census of Population.For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote2referrerFootnote 3Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote3referrerFootnote 4The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote4referrerFootnote 5Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.Return to footnote5referrerFootnote 6Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote6referrerFootnote 7'Aboriginal identity' includes persons who are First Nations (North American Indian), Métis or Inuk (Inuit) and/or those who are Registered or Treaty Indians (that is, registered under the Indian Act of Canada) and/or those who have membership in a First Nation or Indian band. Aboriginal peoples of Canada are defined in the Constitution Act, 1982, section 35 (2) as including the Indian, Inuit and Métis peoples of Canada.Return to footnote7referrerFootnote 8'Single Aboriginal responses' includes persons who are in only one Aboriginal group, that is First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote8referrerFootnote 9Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For additional information, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016.Return to footnote9referrerFootnote 10'Multiple Aboriginal responses' includes persons who are any two or all three of the following: First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote10referrerFootnote 11'Aboriginal responses not included elsewhere' includes persons who are not First Nations (North American Indian), Métis or Inuk (Inuit), but who have Registered or Treaty Indian status and/or Membership in a First Nation or Indian band.