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

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.

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...

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.

Download seller's data & convert sellers into leads into potential clients for your business. Amazon sellers' data including their addresses, brands, ASINs, phone, and more. Amazon US, UK, India, Canada, Mexico, and Italy country data available All data went through QA process We are updating data every 6 months

Last Update - 9th FEB 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either money or any favor) for this dataset. RESEARCH PURPOSE ONLY

THIS IS THE LARGEST DATASET ON NIFTY 100 STOCKS WITH EACH MINUTES AND DAILY DATA (2015 to 2025)

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited.NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index is a free-float market capitalization-weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content This dataset contains Nifty 100 historical daily prices. The historical data are retrieved from the NSE India website. Each stock in this Nifty 500 and are of 1 minute itraday data.

Every dataset contains the following fields. Open - Open price of the stock High - High price of the stock Low - Low price of the stock Close - Close price of the stock Volume - Volume traded of the stock in this time frame

Inspiration

• Data is uploaded for Research and Educational purposes.
• The data scientists and researchers can download and can build EDA, find Correlations, and perform Regression analysis on it.
• Quant researchers can build strategies and backtest their strategies with this dataset.

Stock Names

| ACC | ADANIENT | ADANIGREEN | ADANIPORTS | AMBUJACEM | | -- | -- | -- | -- | -- | | APOLLOHOSP | ASIANPAINT | AUROPHARMA | AXISBANK | BAJAJ-AUTO | | BAJAJFINSV | BAJAJHLDNG | BAJFINANCE | BANDHANBNK | BANKBARODA | | BERGEPAINT | BHARTIARTL | BIOCON | BOSCHLTD | BPCL | | BRITANNIA | CADILAHC | CHOLAFIN | CIPLA | COALINDIA | | COLPAL | DABUR | DIVISLAB | DLF | DMART | | DRREDDY | EICHERMOT | GAIL | GLAND | GODREJCP | | GRASIM | HAVELLS | HCLTECH | HDFC | HDFCAMC | | HDFCBANK | HDFCLIFE | HEROMOTOCO | HINDALCO | HINDPETRO | | HINDUNILVR | ICICIBANK | ICICIGI | ICICIPRULI | IGL | | INDIGO | INDUSINDBK | INDUSTOWER | INFY | IOC | | ITC | JINDALSTEL | JSWSTEEL | JUBLFOOD | KOTAKBANK | | LICI | LT | LTI | LUPIN | M&M | | MARICO | MARUTI | MCDOWELL-N | MUTHOOTFIN | NAUKRI | | NESTLEIND | NIFTY 50 | NIFTY BANK | NMDC | NTPC | | ONGC | PEL | PGHH | PIDILITIND | PIIND | | PNB | POWERGRID | RELIANCE | SAIL | SBICARD | | SBILIFE | SBIN | SHREECEM | SIEMENS | SUNPHARMA | | TATACONSUM | TATAMOTORS | TATASTEEL | TCS | TECHM | | TITAN | TORNTPHARM | ULTRACEMCO | UPL | VEDL | | WIPRO | YESBANK | | | |

Afghanistan Import shipment dataset with 91% coverage and 2.6 million shipments.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination).

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

Snapshot img

The electric two-wheeler motor market share in India is expected to increase by 2446.03 thousand units from 2021 to 2026, and the market’s growth momentum will decelerate at a CAGR of 32.28%.

This electric two-wheeler motor market in India research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers electric two-wheeler motor market segmentation in India by product (in-wheel motor and mid-drive motor) and vehicle type (e-scooter and e-motorcycle). The electric two-wheeler motor market in India report also offers information on several market vendors, including Compage Automation Systems Pvt. Ltd., Dana Inc., EMF Innovations Pte Ltd., Entuple E-Mobility Pvt. Ltd., Konmos Technologies Pvt. Ltd., Physics Motors Technology Pvt. Ltd., Rotomotive Powerdrives India Ltd., SONA BLW Precision Forgings Ltd., Tata Sons Pvt. Ltd., and TSUYO Manufacturing Pvt. Ltd. among others.

What will the Electric Two-wheeler Motor Market Size in India be During the Forecast Period?

Download the Free Report Sample to Unlock the Electric Two-wheeler Motor Market Size in India for the Forecast Period and Other Important Statistics

Electric Two-wheeler Motor Market in India: Key Drivers, Trends, and Challenges

The increasing sales of electric two-wheelers in India is notably driving the electric two-wheeler motor market growth in India, although factors such as lack of operational infrastructure in India may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the electric two-wheeler motor market industry in India. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Electric Two-wheeler Motor Market Driver in India

One of the key factors driving growth in the electric two-wheeler motor market in India is the increasing sales of electric two-wheelers in India. The demand for electric two-wheelers in India is growing at a fast pace. Companies such as Ola Electric, Simple Energy, Bounce Infinity, Tork Motors and Ultraviolette have entered the electric two-wheeler market in India. The two-wheeler market witnessed a 132% jump in the sales of electric two-wheelers in India in 2021 as compared to 2020. In 2021, the total number of electric two-wheelers sold in India were 2,33,977 as compared to 1,00,736 electric two-wheelers sold in 2020. The rising price of oil in the country is driving the demand for electric two-wheelers, which in turn is boosting the demand for electric two- wheeler motor market during the forecast period.

Key Electric Two-wheeler Motor Market Trend in India

The growing movement of multi-speed transmission in electric two-wheelers is a electric two-wheeler motor market trend in India that is expected to have a positive impact in the coming years. Until 2016, most of the electric two-wheeler, were using single-speed transmission technology. However, certain drivetrain manufactures, such as Ather Energy, have plans to develop multi-gear transmission as the single-speed transmission does not perform equally well at low as well as high speeds. Moreover, the multi-gear transmission would deliver greater efficiency as compared with single gear transmission on the suburban driving and in highways. However, for city driving, the single or dual-speed transmission will suffice. EVs with multi-gear transmission technology will have increased vehicle range in terms of distance traveled on one charge. In addition, it will also help in reducing the battery pack size. It will even improve the low-speed pull away and the high-speed driving. All these benefits will lead the OEMs to the adoption of multi-gear transmission in electric two-wheelers.

Key Electric Two-wheeler Motor Market Challenge in India

The lack of operational infrastructure in India will be a major challenge for the electric two-wheeler motor market in India during the forecast period. The lack of proper road infrastructure in India hampers the wide-scale expansion of EV charging infrastructure, as the availability of electricity on roads and highways is essential to set up an EV charging station. Individuals and freight operators using electric two-wheeler vehicles for commercial purposes limit their operations to major cities because the operational infrastructure required for setting up an EV charging station is available mostly in major cities, not in tier-2 and tier-3 cities. Governing bodies are developing charging stations in their respective regions to promote the use of electric two-wheelers for commercial purposes. However, most of them are slow-charging stations that require a considerable amount of time for charging a vehicle, which dissuades the use of electric two-wheelers for comme

The number of Youtube users in India was forecast to continuously increase between 2024 and 2029 by in total ***** million users (+***** percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach ****** million users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Sri Lanka and Nepal.

In 2023, the market size of the beauty and personal care industry was valued at 21 billion U.S. dollars in India. The market size for this industry is likely to increase to 34 billion dollars by 2028. Online personal care market In India, beauty and personal care is a competitive market with international brands competing with local ones. The market size of the online beauty and personal care industry was worth 4.9 billion U.S. dollars. Due to the pandemic, the manufacturers in the online personal care sector are continually redefining the sector by being better equipped to deal with growing consumer expectations. In order to succeed in the personal care sector, brands need to develop strategies that cater to consumer appeal. Market segmentation The beauty and personal care market has grown significantly as a result of changing lifestyles and increased awareness. The market segment consists of hair care products, skin care products, oral care, cosmetics and makeup, beauty tools, bath, and shower products. Due to increasing adoption of herbal cosmetics and homemade products, the segment is expected to see a growth trend in the coming years. are driving growth, and the segment is expected to grow in the coming years. Local Indian brands such as MamaEarth, Khadi Essentials Plum, Kama Ayurveda, and Forest Essentials are trending because of their better suitability for Indian skin and hair types.  

Indian Patent Dataset

A comprehensive dataset of all the patents filed in India for the years 2010, 2011, and 2019.

Overview

The Indian Patent Dataset provides detailed information about each patent application filed in India, including application number, title, application date, inventor and applicant information, patent status, and more. The dataset covers patents filed during the years 2010, 2011, and 2019.

Motivation

The dataset was created to provide researchers, policymakers, businesses, legal professionals, and academics with valuable insights into the patent landscape in India. It aims to facilitate research and analysis, inform policy decisions, support business intelligence efforts, ensure legal and regulatory compliance, and enable academic research on innovation and technology transfer.

Composition

The dataset comprises individual instances representing patent applications filed in India. Each instance includes various features, such as:

• Application Number: Unique identifier for each patent application.
• Title: Title of the patent application.
• Application Date: Date when the patent application was filed.
• Status: Current status of the patent application.
• Publication Number: Number assigned to the publication of the patent application.
• Publication Date (U/S 11A): Date of publication under Section 11A.
• Publication Type: Type of publication.
• Application Filing Date: Date when the patent application was filed.
• Priority Number: Priority number of the patent application.
• Priority Country: Country of priority.
• Priority Date: Priority date of the patent application.
• Field Of Invention: Field of invention of the patent application.
• Classification (IPC): International Patent Classification of the patent application.
• Inventor Information: Information about the inventor(s) of the patent application.
• Applicant Information: Information about the applicant(s) of the patent application.
• Application Type: Type of patent application.
• E-mail (As Per Record): Email associated with the patent application record.
• Additional E-mail (As Per Record): Additional email associated with the patent application record.
• E-mail (Updated Online): Updated email associated with the patent application record.
• Request for Examination Date: Date of request for examination.
• First Examination Report Date: Date of the first examination report.
• Date Of Certificate Issue: Date of issue of the certificate.
• Post Grant Journal Date: Date of post-grant journal publication.
• Reply to FER Date: Date of reply to the first examination report.
• PCT International Application Number: International application number for PCT applications.
• PCT International Filing Date: International filing date for PCT applications.
• Application Status: Current status of the patent application.

Collection Process

The data associated with each instance was acquired by scraping directly from the Indian Patent Advanced Search System using automation tools such as Selenium and Python. The scraping script scraping_main.py is provided in the repository for reference.

To run the scraping code download the scraping_main.py and chromedriver-win64.zip.

Preprocessing

The dataset underwent some basic preprocessing, including cleaning strings and removing unnecessary symbols, to ensure data quality and consistency.

Uses

The dataset can be used for various purposes, including:

• Research and Analysis
• Policy Making
• Business Intelligence
• Legal and Regulatory Compliance
• Academic Research

Distribution

This dataset is distributed under the Creative Commons Attribution-ShareAlike (CC-BY-SA) license. Please refer to the LICENSE file for more information. Copyright is retained by the dataset owner.

Maintenance

The dataset will be periodically updated to correct labeling errors, add new instances, or delete instances as necessary. Updates will be communicated through the repository.

Contact

For questions or inquiries regarding the dataset, please contact [Aryan Singh] at aryansinghmain09@gmail.com.

GitHub Repository

Find the dataset repository on GitHub: Indian Patent Dataset Repository

In 2021, WhatsApp's user base in India amounts to approximately ****** million users. The number of WhatsApp users in India is projected to reach ****** million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Real Estate / Housing Dataset

This dataset is web scrapped from a real estate website, collecting all the necessary infos on the resale and new properties. It has around 14000+ rows of data having properties from various Indian cities like Chennai, Mumbai, Bangalore, Delhi, Pune, Kolkata and Hyderabad. Columns:

Name: Property Name, Property Title: Property Ad Title, Price: Property Price Location: Property Located Locality and Region Total Area: Total SQFT of the property Price Per SQFT: Price of Per SQFT of the property Description: Small paragraph about the property Baths: Number of baths in the property Balcony: Whether the Property has balcony or not

This dataset contains detailed specifications and official launch prices of various mobile phone models from different companies. It provides insights into smartphone hardware, pricing trends, and brand competitiveness across multiple countries. The dataset includes key features such as RAM, camera specifications, battery capacity, processor details, and screen size.

One important aspect of this dataset is the pricing information. The recorded prices represent the official launch prices of the mobile phones at the time they were first introduced in the market. Prices vary based on the country and the launch period, meaning older models reflect their original launch prices, while newer models include their most recent launch prices. This makes the dataset valuable for studying price trends over time and comparing smartphone affordability across different regions.

Features:

• Company Name: The brand or manufacturer of the mobile phone.
• Model Name: The specific model of the smartphone.
• Mobile Weight: The weight of the mobile phone (in grams).
• RAM: The amount of Random Access Memory (RAM) in the device (in GB).
• Front Camera: The resolution of the front (selfie) camera (in MP).
• Back Camera: The resolution of the primary rear camera (in MP).
• Processor: The chipset or processor used in the device.
• Battery Capacity: The battery size of the smartphone (in mAh).
• Screen Size: The display size of the smartphone (in inches).
• Launched Price: (Pakistan, India, China, USA, Dubai): The official launch price of the mobile in the respective country at the time of its release. Prices vary based on the year the mobile was launched.
• Launched Year: The year the mobile phone was officially launched.

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

This dataset contains records of motorcycle sales across various Indian states, covering top brands like Honda, Royal Enfield, TVS, Yamaha, Hero, Bajaj, KTM, and Kawasaki. The dataset includes key attributes such as average daily distance traveled, engine capacity, fuel type, mileage, price, resale value, insurance status, and seller type. It provides insights into bike sales trends, market demand, and resale values across different city tiers.

🔹 Use Cases:

Market Analysis: Understand the sales trend of different brands and models.

Resale Price Estimation: Analyze depreciation trends.

Consumer Behavior: Study how owner type, insurance, and mileage impact pricing.

Geographical Trends: Identify demand patterns in different Indian states and city tiers.

State (Random Indian states)

Average Daily Distance (in km, between 5-80 km)

Bike Brand (One of the top 10 brands you listed)

Model Name (Random bike models per brand)

Price (INR) (Based on brand & model)

Year of Manufacture (2015-2024)

Engine Capacity (cc) (100cc - 1000cc)

Fuel Type (Petrol, Electric, Hybrid)

Mileage (km/l) (Varies by brand)

Owner Type (First, Second, Third)

Registration Year (Varies based on Year of Manufacture)

Insurance Status (Active, Expired, Not Available)

Seller Type (Dealer, Individual)

Resale Price (INR) (Based on depreciation formula)

City Tier (Metro, Tier 1, Tier 2, Tier 3)

This dataset consists of India's export in US$ Thousand from 1988 to March-2022. The data is collected from the official government website. India's goods export amount to various countries is given in this dataset in yearwise format.

India Import Dataset can be visited from given link.

Please give credit to this dataset if you download it.

Greetings , fellow analysts !

(NOTE : This is a random dataset generated using python. It bears no resemblance to any real entity in the corporate world. Any resemblance is a matter of coincidence.)

REC-SSEC Bank is a govt-aided bank operating in the Indian Peninsula. They have regional branches in over 40+ regions of the country. You have been provided with a massive excel sheet containing the transaction details, the total transaction amount and their location and total transaction count.

The dataset is described as follows :

1. Date - The date on which the transaction took place. 2.Domain - Where or which type of Business entity made the transaction. 3.Location - Where the data is collected from 4.Value - Total value of transaction
2. Count of transaction .

For example , in the very first row , the data can be read as : " On the first of January, 2022 , 1932 transactions of summing upto INR 365554 from Bhuj were reported " NOTE : There are about 2750 transactions every single day. All of this has been given to you.

The bank wants you to answer the following questions :

1. What is the average transaction value everyday for each domain over the year.
2. What is the average transaction value for every city/location over the year
3. The bank CEO , Mr: Hariharan , wants to promote the ease of transaction for the highest active domain. If the domains could be sorted into a priority, what would be the priority list ?
4. What's the average transaction count for each city ?