About the Dataset: Pharmaceutical Products Pricing and Availability Data in Pakistan

This dataset contains information about pharmaceutical product pricing and availability in Pakistan. The data was collected from various sources and compiled into a structured format for analysis. The dataset consists of 1630 entries with 7 columns, including:

Name: The name of the pharmaceutical product. Company: The company manufacturing or distributing the product. Price_before: The product's price before any discount is applied. Discount: The discount offered on the product, if applicable. Price_After: The price of the product after applying any discount. Pack_Size: The size or quantity of the product's packaging. Availability: The availability status of the product.

The dataset provides insights into the pricing trends and availability of pharmaceutical products in Pakistan, which can be valuable for various stakeholders including consumers, healthcare professionals, and policymakers. It can be used for analysis, research, and decision-making in the pharmaceutical industry.

Data Overview: Entries: 1630 Missing Values: Some columns have missing values, such as 'Name', 'Company', 'Price_before', 'Discount', 'Price_After', 'Pack_Size', and 'Availability'. Data Types: The dataset consists of object types for textual data and one float type for numerical data.

Potential Uses: This dataset can be used for a variety of purposes, including:

• Analyzing pricing trends and patterns of different drugs in Pakistan.
• Studying the affordability and accessibility of essential medicines in Pakistan.
• Tracking the performance of different pharmaceutical companies in the Pakistani market.
• Identifying potential areas for improving access to affordable medicines in Pakistan.

Limitations: It is important to note that this dataset only includes data on the maximum retail prices of pharmaceutical products. The actual price consumers pay may vary depending on the pharmacy and other factors. Additionally, the dataset does not include information on the quality of the pharmaceutical products.

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Every year, many people migrate to different countries from Pakistan, and a lot of them migrate to Pakistan as emigrants of refugees. Pakistan ranks 2nd, according to UNHCR, among the countries to host the most refugees. Thus this is a tribute to Pakistan and information to the world that Pakistan is quite different than you think!

The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)

Climate change is affecting the world and Pakistan is no different. This is the first of its kind dataset for average temperature in the country for the last 116 years.

Money Supply M2 in Pakistan increased to 41409456 PKR Million in October from 41372563 PKR Million in September of 2025. This dataset provides - Pakistan Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Government Spending in Pakistan increased to 1426525 PKR Million in the second quarter of 2025 from 1005783 PKR Million in the first quarter of 2025. This dataset provides - Pakistan Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Supreme Court of Pakistan Judgments DatasetThis dataset contains almost 1200 judgments made by the Supreme Court of Pakistan up to May 2025.This dataset includes the judgments made by

Context: The 2019–20 coronavirus pandemic was confirmed to have reached Pakistan on 26 February 2020, when a student in Karachi tested positive upon returning from Iran. By 18 March, cases had been registered in all four provinces, the two autonomous territories, and the federal territory of Islamabad. The dataset is completely acquired from NIH Publications, Governmental resources and extra mile contacts. The dataset reflects at provincial level and details from all the aspects. Complete details can be visualized at hyperurl.co/pakcovid Content: The dataset contains chronological seven tabs and 80+ columns with data ranging from Suspected Cases Last Date Suspected Cases Last 24 Hrs Suspected Cases Cumulative Lab Tests Last 24 Hrs Lab Tests Cumulative Confirmed Cases Last Date Confirmed Cases Last 24 Hrs Confirmed Cases Cumulative Deaths Last Date Deaths Last 24 Hrs Deaths Cumulative Transmission Total Transmission Foreign - Iran Transmission Foreign - Iran % Transmission Foreign - Other Transmission Foreign - Other % Transmission Local - Tableegh Transmission Local % - Tableegh Transmission Local - Others Transmission Local % - Others Transmission Local Transmission Local % Total Hospitals Beds for COVID Total Admitted Admitted Stable Admitted Critical Admitted Ventilator Home Quarantine Recovered Death Quarantine Facilities Last 24 Hrs Arrival Last 24 Hrs (Location) Last 24 Hrs Departure Cumulative Quarantined Number of Tests Results Achieved Test Positive Cases Test Positive Cases % Confirmed HW - Active Doctors Confirmed HW - Active Nurses Confirmed HW - Active Others Confirmed HW - Active Total Confirmed HW - Active Isolation Confirmed HW - Active Hospital Confirmed HW - Active Hospital Stable Confirmed HW - Active Hospital Ventilator Confirmed HW - Active Recovered Confirmed HW - Active Deaths all at provincial level The first version has the data from first case of February 26 2020 to April 19, 2020. We intend to publish weekly updates Data Source: National Institute of Health website Daily publication Processed via Python Camelot Package. Visit https://github.com/MesumRaza for details on scripting. Acknowledgements: Users are allowed to use, copy, distribute and cite the dataset as follows: “Mesum Raza Hemani, Corona Virus Pakistan Dataset 2020, Kaggle Dataset Repository”

Since the data we are using is sampled from questionnaire responses, it would only be logical to classify mental health disorders based on questionnaire responses the users provide when using the platform. The platform would incorporate an intelligent algorithm trained on the data we have right now and would resultantly diagnose users with the different mental health disorders they exhibit. These disorders can vary from depression and anxiety related issues to serious issues like schizophrenia and dementia. The dataset obtained from The Fountain House Mental Health Institute Pakistan comprised diverse patient records, encompassing illnesses such as mood disorders, anger disorders, and sleep disorders, as well as more severe conditions like obsessive-compulsive disorder, paranoid psychosis, and schizophrenia. This extensive range of data enabled us to examine the contrasting patterns and markers exhibited by patients with significantly impactful mental health illnesses in comparison to those with less severe mental health conditions.

Graph and download economic data for World Uncertainty Index for Pakistan (WUIPAK) from Q1 1952 to Q3 2025 about Pakistan, uncertainty, World, and indexes.

Access Pakistan trade data with updated export-import records. Discover major products, top buyers and suppliers, HS codes, and real-time shipment data.

Monthly and long-term Pakistan Current Account data: historical series and analyst forecasts curated by FocusEconomics.

Pakistan: Government accountability: The latest value from 2024 is 0.183 index points, a decline from 0.316 index points in 2023. In comparison, the world average is 0.532 index points, based on data from 170 countries. Historically, the average for Pakistan from 1960 to 2024 is 0.062 index points. The minimum value, -0.722 index points, was reached in 1979 while the maximum of 0.657 index points was recorded in 2013.

Key information about Pakistan Number of Subscriber Fixed Line

• Pakistan Number of Subscriber Fixed Line was reported at 2,573,430.000 Unit in Dec 2023
• This records a decrease from the previous number of 2,799,360.000 Unit for Dec 2022
• Pakistan Number of Subscriber Fixed Line data is updated yearly, averaging 1,179,841.000 Unit from Dec 1960 to 2023, with 64 observations
• The data reached an all-time high of 6,370,860.000 Unit in 2013 and a record low of 66,776.000 Unit in 1964
• Pakistan Number of Subscriber Fixed Line data remains active status in CEIC and is reported by World Bank
• The data is categorized under World Trend Plus’s Association: Telecommunication Sector – Table PK.World Bank.WDI: Telecommunication

Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.;International Telecommunication Union (ITU) World Telecommunication/ICT Indicators Database;Sum;Please cite the International Telecommunication Union for third-party use of these data.

Pakistan Stock Symbols & Company Metadata

This dataset contains stock symbols and basic company metadata for all listed companies in Pakistan.It is updated weekly if new changes are there.

  📊 Dataset Contents

The dataset is provided as a CSV file with the following columns:

Column Description

name Full company name

ticker Stock ticker symbol (e.g., AAPL, MSFT)

market The exchange/market where the stock is listed

sector The primary business sector of the… See the full description on the dataset page: https://huggingface.co/datasets/ThunderDrag/Pakistan-Stock-Symbols-and-Metadata.

The ratio of national debt to gross domestic product (GDP) of Pakistan stood at 70.39 percent in 2024. Between 1994 and 2024, the ratio rose by 11.86 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The ratio is forecast to decline by 10.16 percentage points from 2024 to 2030, fluctuating as it trends downward.The general government gross debt consists of all liabilities that require payment or payments of interest and/or principal by the debtor to the creditor at a date or dates in the future. Here it is depicted in relation to the country's GDP, which refers to the total value of goods and services produced during a year.

Pakistan: Government spending, billion USD: The latest value from 2024 is 31.71 billion U.S. dollars, a decline from 34.83 billion U.S. dollars in 2023. In comparison, the world average is 78.89 billion U.S. dollars, based on data from 100 countries. Historically, the average for Pakistan from 1960 to 2024 is 11.54 billion U.S. dollars. The minimum value, 0.38 billion U.S. dollars, was reached in 1960 while the maximum of 39.33 billion U.S. dollars was recorded in 2022.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Did not include Azad Jammu and Kashmir (AJK) and Gilgit-Baltistan. The excluded area represents approximately 5 percent of the total population. Gender-matched sampling was used during the final stage of selection.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Pakistan is 1002.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Graph and download economic data for Value of Exports to Pakistan from Mississippi (MSPAKA052SCEN) from 1997 to 2022 about Pakistan, MS, and exports.