Monthly state sales tax collections is an experimental dataset published by the U.S. Census Bureau. It provides data for collections from sales taxes including motor fuel taxes. Data reported for a specific month generally represent sales taxes collected on sales made during the prior month. Tax collections primarily rely on unaudited data collected from existing state reports or state data sources available from and posted on the Internet. Secondarily, states report the data via the Quarterly Survey of State and Local Tax Revenue. Data are updated monthly, but due to differing reporting cycles data for some states may lag.

The main data files of 2010-2020 US state + DC corporate (top/max), personal (top/max), and sales tax rates are State_Taxes.dta in Stata dta format, and State_Taxes.csv, which is the same, but converted to a csv file.

For more details concerning variables and sources, see Readme.md.

HitHorizons Dataset of VAT and Tax Numbers gives access to aggregated data on 80M+ companies from the whole of Europe and beyond.

Company registration data: company name national identifier and its type registered address: street, postal code, city, state / province, country business activity: SIC code, local activity code with classification system year of establishment company type location type

Sales and number of employees data: sales in EUR, USD and local currency (with local currency code) total number of employees sales and number of employees accuracy local number of employees (in case of multiple branches) companies’ sales and number of employees market position compared to other companies in a country / industry / region

Industry data: size of the whole industry size of all companies operating within a particular SIC code benchmarking within a particular country or industry regional benchmarking (EU 27, state / province)

Contact details: company website company email domain (without person’s name)

Invoicing details available for selected countries: company name company address company VAT number

Dataset

This dataset was created by sibmike

Contents

📊 Coffee Shop Financial Dataset (Synthetic, 2022–2023)

📝 Overview

This dataset simulates the financial records of a small-town coffee shop over a two-year period (Jan 2022 – Dec 2023).
It was designed for data science, bookkeeping, and analytics projects — including financial dashboards, revenue forecasting, and expense tracking.

The dataset contains 5 CSV files representing different business accounts:
1. checking_account_main.csv - Daily sales deposits (hot drinks, cold drinks, pastries, sandwiches) + operating expenses
2. checking_account_secondary.csv - Monthly transfers between accounts + payroll funding
3. credit_card_account.csv - Weekly credit card expenses (supplies, utilities, vendor charges) and payments
4. gusto_payroll.csv - Payroll data for 3 employees + 1 contractor
5. gusto_payroll_bc.csv - Payroll data for 3 full-time employees + 1 contractor + 1 seasonal employee, with actual tax breakdown for the province of British Columbia, Canada

📂 File Details

checking_account_main.csv

• date
• description
• category (Sales, Utilities, Rent, Supplies, etc.)
• amount (positive = inflow, negative = outflow)
• balance

checking_account_secondary.csv

• date
• description
• amount
• balance

credit_card_account.csv

• date
• vendor
• category (Supplies, Marketing, Utilities, etc.)
• amount (negative = charge, positive = payment)
• balance

gusto_payroll.csv

• date
• employee_id
• employee_name (Owner, Barista 1, Barista 2, Contractor)
• role (Owner, Barista, Manager, Contractor)
• gross_pay

gusto_payroll_bc.csv

This file simulates bi-weekly payroll data for a small coffee shop in British Columbia, Canada, covering January 2022 – December 2023.
It reflects realistic Canadian payroll structure with federal and provincial tax breakdowns, CPP, EI, and additional factors.

Columns: - date → Pay date (bi-weekly schedule)
- employee_id → Unique identifier for each employee
- employee_name → Owner, Barista 1, Barista 2, Manager, Contractor, plus a seasonal Barista (June–Aug 2022)
- role → Role within the coffee shop (Owner, Barista, Manager, Contractor)
- gross_pay → Total earnings before deductions (wages + tips + reimbursements)
- federal_tax → Federal income tax withheld
- provincial_tax → British Columbia income tax withheld
- cpp_employee → Employee CPP contribution
- ei_employee → Employee EI contribution
- other_deductions → Placeholder for possible deductions (e.g., garnishments, union dues)
- net_pay → Take-home pay after deductions
- tips → Declared tips (taxable, included in gross pay)
- travel_reimbursement → Non-taxable reimbursement for travel expenses (if applicable)
- cpp_employer → Employer portion of CPP contributions
- ei_employer → Employer portion of EI contributions

Notes: - Payroll data is synthetic but modeled on Canadian payroll rules (2022–2023 rates).
- A seasonal barista employee is included (employed June 1 – Aug 31, 2022).
- Travel reimbursements are non-taxable and recorded separately.
- This file allows users to practice payroll accounting, deductions analysis, and tax reconciliation.

📈 Business Context

• The coffee shop experiences higher sales September–February (holiday season & winter drinks).
• Sales dip March–June due to seasonality in a small town.
• Pastries are sourced from a local bakery, while sandwiches are made in-house.
• Payroll includes 3 employees (baristas, manager) and 1 independent contractor.

🎯 Possible Use Cases

• Build a financial health dashboard
• Forecast revenue and expenses
• Create a profit & loss statement
• Test SQL queries for accounting workflows
• Explore data visualization with Python, R, or BI tools
• Educational projects for small business analytics

📜 License

This dataset is released under the MIT License, free to use for research, learning, or commercial purposes.

⭐ If you use this dataset in your project or notebook, please credit and share your work, it helps the community!

📷 Photo Credits: freepik

Comprehensive Federal Tax Lien Data by CompCurve Unlock unparalleled insights into tax lien records with CompCurve Federal Tax Lien Data, a robust dataset sourced directly from IRS records. This dataset is meticulously curated to provide detailed information on federal tax liens, unsecured liens, and tax-delinquent properties across the United States. Whether you're a real estate investor, financial analyst, legal professional, or data scientist, this dataset offers a treasure trove of actionable data to fuel your research, decision-making, and business strategies. Available in flexible formats like .json, .csv, and .xls, it’s designed for seamless integration via bulk downloads or API access, ensuring you can harness its power in the way that suits you best.

IRS Tax Lien Data: Unsecured Liens in Focus At the heart of this offering is the IRS Tax Lien Data, capturing critical details about unsecured federal tax liens. Each record includes key fields such as taxpayer full name, taxpayer address (broken down into street number, street name, city, state, and ZIP), tax type (e.g., payroll taxes under Form 941), unpaid balance, date of assessment, and last day for refiling. Additional fields like serial number, document ID, and lien unit phone provide further granularity, making this dataset a goldmine for tracking tax liabilities. With a history spanning 5 years, this data offers a longitudinal view of tax lien trends, enabling users to identify patterns, assess risk, and uncover opportunities in the tax lien market.

Detailed Field Breakdown for Precision Analysis The Federal Tax Lien Data is structured with precision in mind. Every record includes a document_id (e.g., 2025200700126004) as a unique identifier, alongside the IRS-assigned serial_number (e.g., 510034325). Taxpayer details are comprehensive, featuring full name (e.g., CASTLE HILL DRUGS INC), and, where applicable, parsed components like first name, middle name, last name, and suffix. Address fields are equally detailed, with street number, street name, unit, city, state, ZIP, and ZIP+4 providing pinpoint location accuracy. Financial fields such as unpaid balance (e.g., $15,704.43) and tax period ending (e.g., 09/30/2024) offer a clear picture of tax debt, while place of filing and prepared_at_location tie the data to specific jurisdictions and IRS offices.

National Coverage and Historical Depth Spanning the entire United States, this dataset ensures national coverage, making it an essential resource for anyone needing a coast-to-coast perspective on federal tax liens. With 5 years of historical data, users can delve into past tax lien activity, track refiling deadlines (e.g., 01/08/2035), and analyze how tax debts evolve over time. This historical depth is ideal for longitudinal studies, predictive modeling, or identifying chronic tax delinquents—key use cases for real estate professionals, lien investors, and compliance experts.

Expanded Offerings: Secured Real Property Tax Liens Beyond unsecured IRS liens, CompCurve enhances its portfolio with the Real Property Tax Lien File, focusing on secured liens tied to real estate. This dataset includes detailed records of property tax liens, featuring fields like tax year, lien year, lien number, sale date, interest rate, and total due. Property-specific data such as property address, APN (Assessor’s Parcel Number), FIPS code, and property type ties liens directly to physical assets. Ownership details—including owner first name, last name, mailing address, and owner-occupied status—add further context, while financial metrics like face value, tax amount, and estimated equity empower users to assess investment potential.

Tax Delinquent Properties: A Wealth of Insights The Real Property Tax Delinquency File rounds out this offering, delivering a deep dive into tax-delinquent properties. With fields like tax delinquent flag, total due, years delinquent, and delinquent years, this dataset identifies properties at risk of lien escalation or foreclosure. Additional indicators such as bankruptcy flag, foreclosure flag, tax deed status, and payment plan flag provide a multi-dimensional view of delinquency status. Property details—property class, building sqft, bedrooms, bathrooms, and estimated value—combined with ownership and loan data (e.g., total open loans, estimated LTV) make this a powerhouse for real estate analysis, foreclosure tracking, and tax lien investment.

Versatile Formats and Delivery Options CompCurve ensures accessibility with data delivered in .json, .csv, and .xls formats, catering to a wide range of technical needs. Whether you prefer bulk downloads for offline analysis or real-time API access for dynamic applications, this dataset adapts to your workflow. The structured fields and consistent data types—such as varchar, decimal, date, and boolean—ensure compatibility with databases, spreadsheets, and programming environments, making it easy to integrate into your existing systems.

...

I generated a database for sales data of a supermarket in order to practice determining KPI's and make data visualizations.

This data set includes: - Unique sales id for each row. - Branch of the supermarket (New York, Chicago, and Los Angeles). - City of the supermarket (New York, Chicago, and Los Angeles). - Customer Type (Member or Normal). Members receive reward points. - Gender (Male or Female) - Product name of the product sold. - Product category of the product sold. - Unit price of each product sold. - Quantity of the product sold. - 7% sales tax of each product. - Total price of the product after tax. - Reward points for only members customer type.

The Creation Queries.sql file will have the creation query for the Sales table and Insert queries. The data provided here is the same as what is found in the sales.csv file.

The Sales and Revenue KPIs.sql file will have the queries I used to perform my analysis on key performance indicators relating to sales and revenue of this fictional company.

The Customer Behavior KPIs.sql file will have the queries I used to perform my analysis on key performance indicators relating to customer behavior of this fictional company.

The Product Performance KPIs.sql file will have the queries I used to perform my analysis on key performance indicators relating to product performance of this fictional company.

This is a collection of CSV files that contain assessment data. The files in this extract are: Primary Parcel file containing primary owner and land information; Addn file containing drawing vectors for dwelling records; Additional Address file containing any additional addresses that exist for a parcel; Assessment file containing assessed value-related data; Appraisal file containing appraised value-related data; Commercial file containing primary commercial data; Commercial Apt containing commercial apartment data; Commercial Interior Exterior data Dwelling file Entrance data containing data from appraisers' visits; Other Buildings and Yard Improvements Sales File Tax Rate File for the current billing cycle by taxing district authority and property class; and, Tax Payments File containing tax charges and payments for current billing cycle.In addition to the CSV files, the following are included: Data Dictionary PDF; and, St Louis County Rate Book for the current tax billing cycle.

 Primary Parcel file containing primary owner and land information; Addn file containing drawing vectors for dwelling records; Additional Address file containing any additional addresses that exist for a parcel; Assessment file containing assessed value-related data; Appraisal file containing appraised value-related data; Commercial file containing primary commercial data; Commercial Apt containing commercial apartment data; Commercial Interior Exterior data Dwelling file Entrance data containing data from appraisers' visits; Other Buildings and Yard Improvements Sales File Tax Rate File for the current billing cycle by taxing district authority and property class; and, Tax Payments File containing tax charges and payments for current billing cycle.In addition to the CSV files, the following are included: Data Dictionary PDF; and, St Louis County Rate Book for the current tax billing cycle.

This dataset is the continuation of the Sample Dataset "Supermarket sales". It is a version of the year 2020. It is an example dataset for the purpose of practicing data analysis.

You can also work independently without having the "Supermarket sales" dataset

Attribute information

Invoice id: Computer generated sales slip invoice identification number Branch: Branch of supercenter (3 branches are available identified by A, B and C). City: Location of supercenters Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card. Gender: Gender type of customer Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel Unit price: Price of each product in $ Quantity: Number of products purchased by customer Tax: 5% tax fee for customer buying Total: Total price including tax Date: Date of purchase (Record available from January 2019 to March 2019) Time: Purchase time (10am to 9pm) Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet) COGS: Cost of goods sold Gross margin percentage: Gross margin percentage Gross income: Gross income Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10)

US Industry Data by State, by Industry

Number of Establishments, Sales, Payroll, and Employees

By Gary Hoover [source]

About this dataset

This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.

Research Ideas

• Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
• Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
• Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...

Description:

• This dataset contains information about real estate property transactions.
• Each row represents a single property transaction.
• The dataset includes various attributes such as transaction dates, property locations, estimated and sale prices, property characteristics (e.g., type, residential status, number of rooms and bathrooms, carpet area), and property tax rates.
• The dataset provides insights into the real estate market, including trends in property prices, property types, and tax rates across different localities and years.
• It can be used for various analytical purposes such as market analysis, predictive modeling, and decision-making in the real estate industry.
• Researchers, analysts, and stakeholders in the real estate sector can utilize this dataset to understand market dynamics, assess property values, and make informed investment decisions.

Features:

Date: The date when the property transaction occurred. Year: The year of the property transaction. Locality: The locality or area where the property is located. Estimated Value: The estimated value of the property. Sale Price: The actual sale price of the property. Property: The type of property (e.g., Single Family). Residential: Indicates whether the property is residential or not. Num_rooms: The number of rooms in the property. Num_bathrooms: The number of bathrooms in the property. Carpet Area: The carpet area of the property. Property Tax Rate: The property tax rate applicable to the property. Face: The facing direction of the property (e.g., North, South, East).

The ATO (Australian Tax Office) made a dataset openly available (see links) showing all the Australian Salary and Wages (2002, 2006, 2010, 2014) by detailed occupation (around 1,000) and over 100 SA4 regions. Sole Trader sales and earnings are also provided. This open data (csv) is now packaged into a database (*.sql) with 45 sample SQL queries (backupSQL[date]_public.txt).See more description at related Figshare #datavis record. Versions:V5: Following #datascience course, I have made main data (individual salary and wages) available as csv and Jupyter Notebook. Checksum matches #dataTotals. In 209,xxx rows.Also provided Jobs, and SA4(Locations) description files as csv. More details at: Where are jobs growing/shrinking? Figshare DOI: 4056282 (linked below). Noted 1% discrepancy ($6B) in 2010 wages total - to follow up.#dataTotals - Salary and WagesYearWorkers (M)Earnings ($B) 20028.528520069.4372201010.2481201410.3584#dataTotal - Sole TradersYearWorkers (M)Sales ($B)Earnings ($B)20020.9611320061.0881920101.11122620141.19630#links See ATO request for data at ideascale link below.See original csv open data set (CC-BY) at data.gov.au link below.This database was used to create maps of change in regional employment - see Figshare link below (m9.figshare.4056282).#packageThis file package contains a database (analysing the open data) in SQL package and sample SQL text, interrogating the DB. DB name: test. There are 20 queries relating to Salary and Wages.#analysisThe database was analysed and outputs provided on Nectar(.org.au) resources at: http://118.138.240.130.(offline)This is only resourced for max 1 year, from July 2016, so will expire in June 2017. Hence the filing here. The sample home page is provided here (and pdf), but not all the supporting files, which may be packaged and added later. Until then all files are available at the Nectar URL. Nectar URL now offline - server files attached as package (html_backup[date].zip), including php scripts, html, csv, jpegs.#installIMPORT: DB SQL dump e.g. test_2016-12-20.sql (14.8Mb)1.Started MAMP on OSX.1.1 Go to PhpMyAdmin2. New Database: 3. Import: Choose file: test_2016-12-20.sql -> Go (about 15-20 seconds on MacBookPro 16Gb, 2.3 Ghz i5)4. four tables appeared: jobTitles 3,208 rows | salaryWages 209,697 rows | soleTrader 97,209 rows | stateNames 9 rowsplus views e.g. deltahair, Industrycodes, states5. Run test query under **#; Sum of Salary by SA4 e.g. 101 $4.7B, 102 $6.9B#sampleSQLselect sa4,(select sum(count) from salaryWageswhere year = '2014' and sa4 = sw.sa4) as thisYr14,(select sum(count) from salaryWageswhere year = '2010' and sa4 = sw.sa4) as thisYr10,(select sum(count) from salaryWageswhere year = '2006' and sa4 = sw.sa4) as thisYr06,(select sum(count) from salaryWageswhere year = '2002' and sa4 = sw.sa4) as thisYr02from salaryWages swgroup by sa4order by sa4

Exploring Potential Tax and Water Liens in NYC

Investigating Geographic Distribution and Effects of Local Policies

By data.world's Admin [source]

About this dataset

This dataset captures properties in New York City that have tax and/or water liens potentially eligible to be included in the next lien sale. Explore the city's fiscal landscape with information about borough, lot, tax class code, building classes, community board, council district, house number street name and zip code. This data is updated monthly with new liens being added from the most current month back to 12 months prior. By analyzing this data you can gain greater insight into New York City’s financial conditions over time as well as how this affects individual properties throughout the city. This data is provided by the New York City open data portal is subject to terms of use outlined on its website so please refer to it for any additional information regarding usage rights

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Featured Notebooks

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How to use the dataset

This data is sourced from New York City's Open Data portal. By exploring this dataset you can search for properties with possible tax and/or water liens located in a particular area or neighborhood or by a specific attribute such as the house number or street name. You can also take a look at particular subsections of potential eligible lien sale records over time – for example you could look at all potential water debt liens only during a certain month – to get some more specific insights into what tax and water liens may be available at certain points in time. To use this dataset please note the following important tips: • Start by familiarizing yourself with each column’s field meaning (using our table above); • When searching for records use quotation marks if you are looking up something which is two words (e.g “construction”) ;
• Use an underscore _for replacing spaces if necessary e.g “west_village”;
• Be aware that Boroughs are referenced by their full names (e.g Manhattan, Queens, etc);
• If using wild cards (*) make sure not to put them on either side of your query - e.g instead of Lastname * use Lastname*.
We hope this guide was helpful and good luck exploring!

Research Ideas

• Real Estate Investment Analysis: Create a platform or tool using this dataset to assist individuals looking to invest in properties with potential tax and water liens. The platform/tool should provide insights into the best locations for purchasing real estate based on location, tax class code, building class and council district data points from this dataset.
• Tax Foreclosure Notifications: Use this dataset to create an automated notification system which informs registered users when a property they are interested in is coming up for sale with a lien in the next sale date.
• Local Planning Solutions: Leverage data from this dataset to identify areas where there might be concentration of properties with tax liens that could potentially benefit from local planning solutions such as community grants, affordable housing initiatives etc.. This could help municipalities deploy resources more effectively towards restoring distressed properties instead of letting them slip out of their control via foreclosure at lien sales

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: tax-lien-sale-lists-1.csv | Column name | Description | |:---------------------|:-----------------------------------------------------------------| | Month | The month in which the lien sale is eligible. (String) | | Borough | The borough in which the property is located. (String) | | Lot | The lot number of the property. (Integer) | | Tax Class Code | The tax class code of the property. (Integer) | | Building Class | The building class of the property. (String) | | Community Board | The community board in which the property is located. (Integer) | | Council District | The council district in which the property is located. (Integer) | | House Number | The house number of the property. (Integer) | | Street Name | The street name of the property. (String) | | Zip Code | The zip code of the p...

Aquest conjunt conté les dades dels ingressos generats per la modalitat de compravenda d’un vehicle usat o d'una embarcació usada (model 620) de l’impost sobre transmissions patrimonials i actes jurídics documentats. Es faciliten les dades des del 2022 fins al darrer període anual de liquidació vençut. El conjunt de dades conté la informació de l’exercici; la subjecció de l’operació (subjecta, no subjecta o exempta); el tipus de mitjà de transport; la mitjana de potència, cilindrada, eslora (embarcacions), superfície de vela (embarcacions), nombre de motors (embarcacions i aeronaus) i pes màxim d'enlairament en kg (aeronaus), i la suma de la base imposable i la suma de la quota tributària. Les dades estan agregades territorialment per comarca i municipi. Fora de Catalunya, es distribueix per la resta de l’Estat i la resta del món. Per tal de preservar la confidencialitat de la informació tributària que estableix l’article 95 de la Llei 58/2003 general tributària, només es donen les dades a escala municipal per als municipis de més de 10.000 habitants. Per a la resta de municipis que no compleixen aquest criteri, les dades s’agreguen a escala municipal sota el concepte “Resta de municipis”. En el cas que un conjunt inclogui menys de 4 ocurrències (p. ex.: Aeronaus transmeses a la resta de municipis de l’Alt Camp el 2022), no es faciliten les dades.

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...

The dataset provides comprehensive information on sales transactions conducted by Walmart, one of the leading retail chains globally. It encompasses various attributes including Invoice ID, Branch, City, Customer Type, Gender, Product Line, Unit Price, Quantity, Tax (5%), Total Price, Date, Time, Payment Method, Cost of Goods Sold (COGS), Gross Margin Percentage, Gross Income, and Rating. This rich dataset facilitates detailed analysis and insights into sales patterns, customer preferences, revenue generation, and performance evaluation, empowering businesses to make informed decisions and strategies to enhance their operational efficiency and customer satisfaction.

Context

Along with their core mission of counting the US population, the United States Census Bureau gathers a wide range of economic data. This dataset covers 16 of their economic reports and surveys:

• Advance Monthly Sales for Retail and Food Services
• Construction Spending
• Housing Vacancies and Homeownership
• Manufactured Housing Survey (1980-2013)
• Manufactured Housing Survey (Current)
• Manufacturers' Shipments, Inventories, and Orders
• Manufacturing and Trade Inventories and Sales
• Monthly Retail Trade and Food Services
• Monthly Wholesale Trade: Sales and Inventories
• New Home Sales
• New Residential Construction
• Quarterly Financial Report
• Quarterly Services Survey
• Quarterly Summary of State & Local Taxes
• Quarterly Survey of Public Pensions
• U.S. International Trade in Goods and Services

Content

• The data csv is arranged in a long format, with the time_series_code column tying it back to the metadata csv. If you're trying to figure out what data is available, you'll want to start with the metadata.
• Just over a third of the time series store error codes, usually confidence intervals, rather than actual values. The metadata for these time series will have values in the columns et_code, et_desc, and et_unit.
• All of the dates are stored as complete beginning of the period dates, but all of the time series are at either monthly, quarterly, or annual resolution. Exact days and months are provided for convenience when aligning time series and so that you don't have to unpack period codes like 'Q22009'.
• There may be many time series bundled under a given data category or description. For example, the largest category (taxes) contains dozens of types of tax categories, and each of those contains a separate time series for each state in the country.
• Two of the error code time series have non-numeric values. To convert the values column into reasonable units you'll need to drop all entries equal to the string Less than .05 percent.
• The data have been substantially reformatted from how they are provided by the Census Bureau. You can find the script I used to prepare the data here.

Acknowledgements

This data was kindly made available by the United States Census. You can find the original data here. If you enjoyed this dataset you might also like one of the other US Census datasets available on Kaggle.

Inspiration

• The National Bureau of Economic Research's macroeconomic history of the United States covers many similar time series, but before the census data was reported. Can you integrate it with this census data? This should allow you to generate many time series stretching from the present back to the 19th century.