With 19.7 Million Businesses in India , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all cities and regions in India ( example ) :

Mumbai Maharashtra Delhi Delhi Bangalore Karnataka Hyderabad Telangana Ahmedabad Gujarat Chennai Tamil Nadu Kolkata West Bengal Surat Gujarat Pune Maharashtra Jaipur Rajasthan Lucknow Uttar Pradesh Kanpur Uttar Pradesh Nagpur Maharashtra Indore Madhya Pradesh Thane Maharashtra Bhopal Madhya Pradesh Visakhapatnam[4] Andhra Pradesh Pimpri-Chinchwad Maharashtra Patna Bihar Vadodara Gujarat Ghaziabad Uttar Pradesh Ludhiana Punjab Agra Uttar Pradesh Nashik Maharashtra Ranchi Jharkhand Faridabad Haryana Meerut Uttar Pradesh Rajkot Gujarat Kalyan-Dombivli Maharashtra Vasai-Virar Maharashtra Varanasi Uttar Pradesh Srinagar Jammu and Kashmir Aurangabad Maharashtra Dhanbad Jharkhand Gurgaon[5] Haryana Amritsar Punjab Navi Mumbai Maharashtra Allahabad Uttar Pradesh[6] Howrah West Bengal Gwalior Madhya Pradesh Jabalpur Madhya Pradesh Coimbatore Tamil Nadu Vijayawada Andhra Pradesh Jodhpur Rajasthan Madurai Tamil Nadu Raipur Chhattisgarh Kota[8] Rajasthan Chandigarh Chandigarh Guwahati Assam Solapur Maharashtra Hubli–Dharwad Karnataka Mysore[9][10][11] Karnataka Tiruchirappalli[12] Tamil Nadu Bareilly Uttar Pradesh Aligarh Uttar Pradesh Tiruppur Tamil Nadu Moradabad Uttar Pradesh Jalandhar Punjab Bhubaneswar Odisha Salem Tamil Nadu Warangal[13][14] Telangana Mira-Bhayandar Maharashtra Jalgaon Maharashtra Guntur[15] Andhra Pradesh Thiruvananthapuram Kerala Bhiwandi Maharashtra Tirupati Andhra Pradesh Saharanpur Uttar Pradesh Gorakhpur Uttar Pradesh Bikaner Rajasthan Amravati Maharashtra Noida Uttar Pradesh Jamshedpur Jharkhand Bhilai Chhattisgarh Cuttack Odisha Firozabad Uttar Pradesh Kochi Kerala Nellore[16][17] Andhra Pradesh Bhavnagar Gujarat Dehradun Uttarakhand Durgapur West Bengal Asansol West Bengal Rourkela Odisha Nanded Maharashtra Kolhapur Maharashtra Ajmer Rajasthan Akola Maharashtra Gulbarga Karnataka Jamnagar Gujarat Ujjain Madhya Pradesh Loni Uttar Pradesh Siliguri West Bengal Jhansi Uttar Pradesh Ulhasnagar Maharashtra Jammu[18] Jammu and Kashmir Sangli-Miraj & Kupwad Maharashtra Mangalore Karnataka Erode[19] Tamil Nadu Belgaum Karnataka Kurnool[20] Andhra Pradesh Ambattur Tamil Nadu Rajahmundry[21][22] Andhra Pradesh Tirunelveli Tamil Nadu Malegaon Maharashtra Gaya Bihar Udaipur Rajasthan Karur Tamilnadu Kakinada Andhra Pradesh Davanagere Karnataka Kozhikode Kerala Maheshtala West Bengal Rajpur Sonarpur West Bengal Bokaro Jharkhand South Dumdum West Bengal Bellary Karnataka Patiala Punjab Gopalpur West Bengal Agartala Tripura Bhagalpur Bihar Muzaffarnagar Uttar Pradesh Bhatpara West Bengal Panihati West Bengal Latur Maharashtra Dhule Maharashtra Rohtak Haryana Sagar Madhya Pradesh Korba Chhattisgarh Bhilwara Rajasthan Berhampur Odisha Muzaffarpur Bihar Ahmednagar Maharashtra Mathura Uttar Pradesh Kollam Kerala Avadi Tamil Nadu Kadapa[23] Andhra Pradesh Anantapuram[24] Andhra Pradesh Kamarhati West Bengal Bilaspur Odisha Sambalpur Odisha Shahjahanpur Uttar Pradesh Satara Maharashtra Bijapur Karnataka Rampur Uttar Pradesh Shimoga Karnataka Chandrapur Maharashtra Junagadh Gujarat Thrissur Kerala Alwar Rajasthan Bardhaman West Bengal Kulti West Bengal Nizamabad Telangana Parbhani Maharashtra Tumkur Karnataka Khammam Telangana Uzhavarkarai Puducherry Bihar Sharif Bihar Panipat Haryana Darbhanga Bihar Bally West Bengal Aizawl Mizoram Dewas Madhya Pradesh Ichalkaranji Maharashtra Karnal Haryana Bathinda Punjab Jalna Maharashtra Eluru[25] Andhra Pradesh Barasat West Bengal Kirari Suleman Nagar Delhi Purnia[26] Bihar Satna Madhya Pradesh Mau Uttar Pradesh Sonipat Haryana Farrukhabad Uttar Pradesh Durg Chhattisgarh Imphal Manipur Ratlam Madhya Pradesh Hapur Uttar Pradesh Arrah Bihar Anantapur Andhra Pradesh Karimnagar Telangana Etawah Uttar Pradesh Ambarnath Maharashtra North Dumdum West Bengal Bharatpur Rajasthan Begusarai Bihar New Delhi Delhi Gandhidham Gujarat Baranagar West Bengal Tiruvottiyur Tamil Nadu Pondicherry Puducherry Sikar Rajasthan Thoothukudi Tamil Nadu Rewa Madhya Pradesh Mirzapur Uttar Pradesh Raichur Karnataka Pali Rajasthan Ramagundam[27] Telangana Silchar Assam Haridwar Uttarakhand Vijayanagaram Andhra Pradesh Tenali Andhra Pradesh Nagercoil Tamil Nadu Sri Ganganagar Rajasthan ...

Context

List of companies available in India with some additional details.

Content

This dataset contain a list of companies along with additional details like (name, type, average rating, review count, company age, company headquarters and number of employee working on that company). The whole list of company is web scrapped from the website AmbitionBox.com.

Acknowledgements

Data Source: https://www.ambitionbox.com/list-of-companies This dataset wouldn't be made without data available at ambitionbox.com. So a big thanks to the whole team of ambitionbox from the whole kaggle community.

Inspiration

My intension to create this dataset was to enlist the companies available in India and do some analysis on that.

Comprehensive dataset containing 2,446 verified Duty free store businesses in India with complete contact information, ratings, reviews, and location data.

The company level data includes 1. Company Name 2. Website 3. Industry segmentation and sub-segmentation 4. Sector 5. Revenue Range 6. Employee size 7. Address line 8. City 9. State 10. Pin-code 11. Company Landline

POC Level Information 1. Name (Mapped to relevant company) 2. Position (CEO, MD, HR) 3. Exact Designation 4. Email ID

Comprehensive dataset containing 1,701 verified Free clinic businesses in India with complete contact information, ratings, reviews, and location data.

This dataset contains information about companies in India that offer financial services and are currently hiring for various roles. The data is web scraped from a popular career advisory platform called AmbitionBox.

The csv file lists almost 9000 such companies and describes their attributes such as rating, reviews on the website, type of the company, their operating locations, the age of the company and the number of employees that work for them.

Feel free to go through the data and use it for any projects that you wish to do.

Comprehensive dataset containing 86 verified Free clinic businesses in West Bengal, India with complete contact information, ratings, reviews, and location data.

📊 Kaggle Dataset: Comprehensive Company Information as of August 2023 📊

Embark on an insightful journey through the corporate landscape with this meticulously curated dataset. Collected and compiled in August 2023, this dataset offers an extensive array of company information, making it an invaluable resource for in-depth analysis and sophisticated research.

📦 Dataset Overview:

Features: This dataset encompasses crucial attributes, including Company Name, Rating, Reviews, Company Type, Headquarter & Branch Locations, Age, and Employee Count. Examples: Explore concrete instances of corporate profiles, each delineating key details such as company nomenclature, rating, review statistics, industry classification, geographical presence, and workforce size. 🧐 Data Dimensions: Comprising 2,800 meticulously sourced records and spanning across 8 meticulously structured columns, this dataset provides an extensive canvas for your analytical pursuits. Harness this wealth of data to unveil intricate trends, conduct meticulous sectoral analyses, and glean actionable insights.

🌐 Data Authenticity: The dataset draws from live sources, ensuring its fidelity as an up-to-the-minute snapshot of the corporate ecosystem as of August 2023.

🚀 Applications: Unlock a multitude of applications, including market intelligence, competitive intelligence, and trend delineation within the multifaceted corporate sphere.

📈 Data Analysis: Harness the power of advanced data analytics and visualization techniques to extract nuanced insights, fostering a profound understanding of diverse corporate entities and industry segments.

🔗 Access the Dataset: Explore the Company Information Dataset on Kaggle

Leverage this invaluable resource to conduct comprehensive corporate research and data-driven decision-making. If you seek additional insights or possess specific inquiries regarding the dataset, kindly feel free to initiate a dialogue.

Description

This dataset contains a list of new startups in India along with their incubation center, location, sector, and company profile. The dataset can be used to analyze the startup ecosystem in India and to identify trends and patterns in the industry.

Key Features

The incubation center column contains the name of the incubation center where the startup is located. An incubation center is a facility that provides resources and support to startups in their early stages of development.
The location column contains the city where the startup is located.
The sector column contains the industry sector that the startup operates in.
The company profile column contains a brief description of the startup and its products or services. The dataset has one file, Listofstartups.csv, with five columns and 238 rows.

Comprehensive dataset containing 189 verified Gluten-free restaurant businesses in India with complete contact information, ratings, reviews, and location data.

This vast repository houses crucial information on international trade transactions, capturing the intricate details of both export and import activities of India. The Export Database contains meticulous records of outbound shipments, offering valuable insights into the products, exporters, and destinations involved in each transaction. On the other hand, the Import Database provides a comprehensive view of inbound shipments, shedding light on the importers, origins, and details of the products acquired. Together, these two databases present a holistic perspective on global trade dynamics, encompassing critical metadata such as dates, product descriptions, quantities, values, and transportation specifics. Whether you are an analyst, researcher, or business professional, this comprehensive database will undoubtedly prove to be an invaluable resource for gaining a deep understanding of international trade patterns and market dynamics. Explore the wealth of information within and unlock new opportunities in the world of trade and commerce. The Export Database contains information related to export transactions. Each entry in the database represents a specific export event. The metadata fields in this database hold crucial details about the exported products and the transaction itself. The "DATE" field indicates the date of the export. "EXPORTER NAME" refers to the name of the entity or company responsible for exporting the goods. "DESTINATION COUNTRY" indicates the country to which the products are being shipped. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the exported item. The "BRAND" field specifies the brand associated with the product. "QUANTITY" indicates the total quantity of the product being exported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main exported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the exported products. "SUBITEM FOB VALUE" and "TOTAL FOB VALUE" denote the Free on Board (FOB) value of the subitem and the total FOB value of the export, respectively. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "ITEM NUMBER" is a unique identifier for each product item. "TRANSPORT TYPE" specifies the mode of transportation used for the export. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "CUSTOMS" indicates the customs information related to the export. "VARIETY" and "ATTRIBUTES" hold additional details about the product. The "OPERATION TYPE" field indicates the type of export operation, such as direct export or re-export. "MONTH" and "YEAR" represent the month and year when the export occurred. The Import Database contains information related to import transactions. Each entry in the database represents a specific import event. The metadata fields in this database hold crucial details about the imported products and the transaction itself. The "DATE" field indicates the date of the import. "IMPORTER NAME" refers to the name of the entity or company responsible for importing the goods. "SALES COUNTRY" indicates the country from which the products are being purchased. "ORIGIN COUNTRY" denotes the country where the imported products originate. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the imported item. "QUANTITY" indicates the total quantity of the product being imported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main imported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the imported products. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "TOTAL FREIGHT VALUE" and "TOTAL INSURANCE VALUE" represent the respective values for freight and insurance. "ITEM FOB VALUE," "SUBITEM FOB VALUE," and "ITEM CIF VALUE" denote the Free on Board (FOB) value of the item, subitem, and the cost, insurance, and freight value of the item, respectively. "ORIGIN PORT" specifies the port from which the products were shipped. "TRANSPORT TYPE" specifies the mode of transportation used for the import. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "ITEM NUMBER" is a unique identifier for each product item. "CUSTOMS" indicates the customs information related to the import. "OPERATION TYPE" field indicates the type of import operation, such as direct import...

Snapshot img

Business Information Market Size 2025-2029

The business information market size is forecast to increase by USD 79.6 billion, at a CAGR of 7.3% between 2024 and 2029.

The market is characterized by the increasing demand for customer-centric solutions as enterprises adapt to evolving customer preferences. This shift necessitates the provision of real-time, accurate, and actionable insights to facilitate informed decision-making. However, this market landscape is not without challenges. The threat of data misappropriation and theft looms large, necessitating robust security measures to safeguard sensitive business information. As businesses continue to digitize their operations and rely on external data sources, ensuring data security becomes a critical success factor. Companies must invest in advanced security technologies and implement stringent data protection policies to mitigate these risks. Navigating this complex market requires a strategic approach that balances the need for customer-centric solutions with the imperative to secure valuable business data.

What will be the Size of the Business Information Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In today's data-driven business landscape, the continuous and evolving nature of market dynamics plays a pivotal role in shaping various sectors. Data integration solutions enable seamless data flow between different systems, enhancing cloud-based business applications' functionality. Data quality management ensures data accuracy and consistency, crucial for strategic planning and customer segmentation. Data infrastructure, data warehousing, and data pipelines form the backbone of business intelligence, facilitating data storytelling and digital transformation. Data lineage and data mining reveal valuable insights, fueling data analytics platforms and business intelligence infrastructure. Data privacy regulations necessitate robust data management tools, ensuring compliance and protecting sensitive information.

Sales forecasting and business intelligence consulting offer valuable industry analysis and data-driven decision making. Data governance frameworks and data cataloging maintain order and ethics in the vast expanse of big data analytics. Machine learning algorithms, predictive analytics, and real-time analytics drive business intelligence reporting and process modeling, leading to business process optimization and financial reporting software. Sentiment analysis and marketing automation cater to customer needs, while lead generation and data ethics ensure ethical business practices. The ongoing unfolding of market activities and evolving patterns necessitate the integration of various tools and frameworks, creating a dynamic interplay that fuels business growth and innovation.

How is this Business Information Industry segmented?

The business information industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  BFSI
  Healthcare and life sciences
  Manufacturing
  Retail
  Others


Application

  B2B
  B2C


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW). 

By End-user Insights

The bfsi segment is estimated to witness significant growth during the forecast period.

In the dynamic business landscape, data-driven insights have become essential for strategic planning and decision-making across various industries. The market caters to this demand by offering solutions that integrate and manage data from multiple sources. These include cloud-based business applications, data quality management tools, data warehousing, data pipelines, and data analytics platforms. Data storytelling and digital transformation are key trends driving the market's growth, enabling businesses to derive meaningful insights from their data. Data governance frameworks and policies are crucial components of the business intelligence infrastructure. Data privacy regulations, such as GDPR and HIPAA, are shaping the market's development.

Data mining, predictive analytics, and machine learning algorithms are increasingly being used for sales forecasting, customer segmentation, and churn prediction. Business intelligence consulting and industry analysis provide valuable insights for organizations seeking competitive advantage. Data visualization dashboards, market research databases, and data discovery tools facilitate data-driven decision making. Sentiment analysis and predictive analytics are essential for marketing automation and business process

Access updated Company Name import data India with HS Code, price, importers list, Indian ports, exporting countries, and verified Company Name buyers in India.

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

Comprehensive dataset containing 108 verified Free clinic businesses in Tamil Nadu, India with complete contact information, ratings, reviews, and location data.

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

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Stonks by unknown memer.

Quarterly dataset of the India - Business Expectations Index, including historical data, latest releases, and long-term trends from 2015-12-31 to 2025-12-31. Available for free download in CSV format.

Free-Cash-Flow-To-Equity Time Series for The Indian Hotels Company Limited. The Indian Hotels Company Limited, together with its subsidiaries, owns, operates, and manages hotels, palaces and resorts in India and internationally. It operates through two segments: Hotel Services and Air and Institutional Catering. The company operates hotels under the Taj, Claridges Collection, SeleQtions, GATEWAY, Vivanta, Ginger, Tree of Life, amã Stays & Trails, Qmin, and Taj Sats brand names. It also provides trails, stays, restaurants, bars, clubs, spas, salons, food and beverages, and boutique services. The Indian Hotels Company Limited was founded in 1868 and is headquartered in Mumbai, India.

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 a detailed overview of the Environmental, Social, and Governance (ESG) performance of the top 50 companies listed on the National Stock Exchange of India (NSE). It includes essential metrics such as ESG risk scores, controversy levels, and sectoral breakdowns. Ideal for investors, researchers, and policymakers seeking to understand the sustainability landscape of India's corporate giants.