The total population in Pakistan was estimated at 251.3 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Pakistan Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Pakistan Drone Attacks (2004-2016)

The United States has targeted militants in the Federally Administered Tribal Areas [FATA] and the province of Khyber Pakhtunkhwa [KPK] in Pakistan via its Predator and Reaper drone strikes since year 2004. Pakistan Body Count (www.PakistanBodyCount.org) is the oldest and most accurate running tally of drone strikes in Pakistan. The given database (PakistanDroneAttacks.CSV) has been populated by using majority of the data from Pakistan Body Count, and building up on it by canvassing open source newspapers, media reports, think tank analyses, and personal contacts in media and law enforcement agencies. We provide a count of the people killed and injured in drone strikes, including the ones who died later in hospitals or homes due to injuries caused or aggravated by drone strikes, making it the most authentic source for drone related data in this region.

We will keep releasing the updates every quarter at this page.

Content

Geography: Pakistan

Time period: 2004-2016

Unit of analysis: Attack

Dataset: The dataset contains detailed information of 397 drone attacks in Pakistan that killed an estimated 3,558 and injured 1,333 people including 2,539 civilians.

Variables: The dataset contains Serial No, Incident Day & Date, Approximate Time of the attack, Specific Location, City, Province, Number of people killed who claimed to be from Al-Qaeeda, Number of people killed who claimed to be from Taliban, minimum and maximum count of foreigners killed, minimum and maximum count of civilians killed, minimum and maximum count of civilians injured, special mention (more details) and comments about the attack, longitude and latitude of the location. Sources: Unclassified media articles, hospital reports, think tank analysis and reports, and government official press releases.

Acknowledgements & References

Pakistan Body Count has been leveraged extensively in scholarly publications, reports, media articles and books. The website and the dataset has been collected and curated by the founder Zeeshan-ul-hassan Usmani. Users are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Pakistan Body Count, Drone Attacks Dataset, Kaggle Dataset Repository, Jan 25, 2017.”

Past Research

Zeeshan-ul-hassan Usmani and Hira Bashir, “The Impact of Drone Strikes in Pakistan”, Cost of War Project, Brown University, December 16, 2014

Inspiration

Some ideas worth exploring:

• How many people got killed and injured per year in last 12 years?

• How many attacks involved killing of actual terrorists from Al-Qaeeda and Taliban?

• How many attacks involved women and children?

• Visualize drone attacks on timeline

• Find out any correlation with number of drone attacks with specific date and time, for example, do we have more drone attacks in September?

• Find out any correlation with drone attacks and major global events (US funding to Pakistan and/or Afghanistan, Friendly talks with terrorist outfits by local or foreign government?)

• The number of drone attacks in Bush Vs Obama tenure?

• The number of drone attacks versus the global increase/decrease in terrorism?

• Correlation between number of drone strikes and suicide bombings in Pakistan

Questions?

For detailed visit www.PakistanBodyCount.org

Or contact Pakistan Body Count staff at info@pakistanbodycount.org

Techsalerator’s Location Sentiment Data for Pakistan

Techsalerator’s Location Sentiment Data for Pakistan provides an extensive collection of real-time and historical sentiment insights, crucial for businesses, researchers, and analysts. This dataset helps in understanding public opinion, market trends, and regional sentiment variations across Pakistan.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Top 5 Key Data Fields

• Geographic Location – Captures sentiment data at the city, district, and regional levels, enabling localized analysis.
• Sentiment Score – Provides a numerical sentiment value (positive, neutral, negative) based on language processing algorithms.
• Topic Categorization – Classifies sentiment by industries such as politics, retail, healthcare, and technology.
• Source of Data – Aggregates sentiment from social media, news articles, customer reviews, and forums.
• Time-Stamped Sentiment Trends – Tracks how sentiment evolves over time for better trend analysis and forecasting.

Top 5 Location Sentiment Trends in Pakistan

• Political Sentiment Shifts – Regional differences in political sentiment vary widely, influencing election campaigns and policymaking.
• Consumer Market Insights – Retail sentiment analysis helps businesses understand consumer preferences and brand reputation.
• Tourism and Hospitality Sentiment – Feedback from travelers and locals provides insights into the evolving perception of tourist destinations.
• Economic and Business Confidence – Corporate sentiment analysis aids in investment decisions and market predictions.
• Social Issues and Public Opinion – Monitoring public discourse on key social issues helps organizations respond effectively to societal concerns.

Top 5 Applications of Location Sentiment Data in Pakistan

• Market Research & Consumer Behavior – Businesses can track consumer sentiment to adjust strategies and improve customer satisfaction.
• Political Campaigning & Governance – Governments and political parties can assess public perception to shape policies and campaigns.
• Crisis Management & Public Relations – Sentiment tracking helps brands and organizations respond effectively to crises and reputation risks.
• Investment & Economic Forecasting – Investors and analysts use sentiment trends to predict economic shifts and business performance.
• Tourism & City Planning – Authorities can use sentiment insights to enhance city infrastructure and promote tourism initiatives.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Pakistan, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographic Location
• Sentiment Score
• Topic Categorization
• Source of Data
• Time-Stamped Sentiment Trends
• Emotion Analysis (Joy, Anger, Sadness, etc.)
• Industry-Specific Sentiment Insights
• Social Media and News Sentiment
• Customer Feedback Analysis
• Contact Information

For in-depth insights into sentiment trends across Pakistan, Techsalerator’s dataset is an invaluable resource for market researchers, businesses, policymakers, and analysts.

Explore the nuanced evolution of Pakistan's population through this comprehensive dataset. Covering annual demographics from 1951 to 2021, it delineates the urban-rural divide and gender-specific population trends. While census data anchors the counts for pivotal years like 1951, 1961, 1972, 1981, and 1998, estimated figures bridge the gaps for 1991-1997 and 1999-2015. Unravel the societal fabric with insights into the distribution shifts and gender demographics, derived from Ministry of Planning, Development, and Special Initiatives records. Ideal for examining historical patterns and projecting future trends in Pakistan's populace.

Dataset Description This dataset consists of annual position of population of Pakistan. This data is bifurcated on the basis of urban/rural and male/female population. Data is on calendar year basis.

Note: 1. Data for 1951, 1961, 1972, 1981 and 1998 is based on Census. 2. Estimated figures for period 1991-1997 and 1999-2015. 3. Population distribution of urban and rural is not available for 1951,1961, 1991-1997 and 1999-2015. Data Source Ministry of Planning, Development and Special Initiatives Data Frequency Occasionally Annual Available Since 1951 Available Upto 2021

About this dataset

Context

This list ranks the 1,430 cities in the New York by Pakistani population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Pakistani Population: This column displays the rank of city in the New York by their Pakistani population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Pakistani Population: The Pakistani population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Pakistani. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total New York Pakistani Population: This tells us how much of the entire New York Pakistani population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 238 cities in the Washington by Pakistani population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Pakistani Population: This column displays the rank of city in the Washington by their Pakistani population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Pakistani Population: The Pakistani population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Pakistani. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Washington Pakistani Population: This tells us how much of the entire Washington Pakistani population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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!

Pakistan number dataset is a very significant element for telemarketing campaigns. Likewise, the Pakistan number dataset is the most effective in offering all B2C contacts. Around 251.26 million people live here and we have their active contacts list. Additionally, List To Data is very popular worldwide for delivering 95% accurate contact numbers. However, this Pakistan number dataset can now be a potential tool for SMS marketing. Also, the website gives you many genuine sales leads at a reasonable price. Moreover, the seller will get more profit than expenses from their business. Thus, the country’s economic growth is rising day by day in the country. Most notably, dealers can get all authentic databases from our website. Pakistan phone data will give many potential contacts for direct marketing. Our skilled team always collects all these contact leads from very genuine sites. On the other hand, it takes less time to express with many new clients. Possibly, Pakistan phone data creates huge opportunities for the company to increase sales. Mainly, we do not compromise on protection so we maintain the proper rules of GDPR. So, every person can take it without any doubt. Above all, this Pakistan phone data is very effective for business publicity through cold calls. Also, this mobile lead helps share your trade info by sending text messages to the buyers. For this reason, they will understand about this instant and give you feedback. After taking this directory, we show it to you in a CSV or Excel format. In other words, anybody can use this in CRM software anytime. Pakistan phone number list benefits in multiple ways to run a business. However, this country is very well known for its better economic condition. Anyway, you can add our List To Data website to your preference to get verified contacts. Accordingly, it will give more returns than before if you utilize it properly. Hence, take it now and create better opportunities in business. In addition, the Pakistan phone number list develops your telemarketing strategy more easily. Previously, we updated the database every month to give you all the up-to-date contact leads. Thus, it will be your excellent decision to buy essential data from us. Indeed, without delay purchase the Pakistan phone number list now and achieve the sales target.

This comprehensive dataset provides detailed population statistics for major cities across Pakistan, spanning multiple census years from 1972 to 2023. The dataset includes population figures for each city as recorded in the 1972, 1981, 1998, 2017, and 2023 censuses, along with the percentage change in population between consecutive censuses. The data is organized by city and province, offering valuable insights into urban growth trends, demographic shifts, and regional development over the past five decades.

Features

• City: Name of the city.

• Pop (2023 Census): Population as per the 2023 census, with percentage change from the 2017 census.

• Pop (2017 Census): Population as per the 2017 census, with percentage change from the 1998 census.

• Pop (1998 Census): Population as per the 1998 census, with percentage change from the 1981 census.

• Pop (1981 Census): The Population as of the 1981 census, with a percentage change from the 1972 census.

• Pop (1972 Census): Population as per the 1972 census.

• Province: The province or administrative region where the city is located.

Potential Use Cases

• Urban Planning: Analyze population growth trends to inform infrastructure development and resource allocation.

• Demographic Studies: Study the demographic changes in different regions of Pakistan over time.

• Policy Making: Support evidence-based policy decisions related to housing, education, healthcare, and transportation.

• Academic Research: Utilize the dataset for research in urban studies, sociology, and economics.

Data Source

This dataset's data was collected and compiled from the Wikipedia page titled "List of cities in Pakistan by population." The information on Wikipedia is based on publicly available census data and government records, which have been aggregated and presented in a structured format. While Wikipedia serves as a secondary source, the original data is derived from official census reports conducted by the Pakistan Bureau of Statistics and other governmental bodies.

Acknowledgments We acknowledge Wikipedia for providing a consolidated and accessible source of information on city-wise population data in Pakistan. Additionally, we extend our gratitude to the Pakistan Bureau of Statistics and other government agencies responsible for conducting and publishing the census data, which forms the foundation of this dataset. Their efforts in collecting and maintaining accurate demographic records have made this dataset possible.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development. Indicators: Access to electricity, Agricultural irrigated land, Agricultural land, Agriculture, Annual freshwater withdrawals, Arable land, Average precipitation in depth, CO2 emissions, CO2 emissions from gaseous fuel consumption, CO2 emissions from liquid fuel consumption, CO2 emissions from solid fuel consumption, CO2 intensity, CPIA public sector management and institutions cluster average, Cereal yield, Community health workers, Disaster risk reduction progress score, Droughts, Ease of doing business index, Electric power consumption, Electricity production from coal sources, Electricity production from hydroelectric sources, Electricity production from natural gas sources, Electricity production from nuclear sources, Electricity production from oil sources, Electricity production from renewable sources, Energy use, Foreign direct investment, Forest area, GHG net emissions/removals by LUCF, HFC gas emissions, Land area where elevation is below 5 meters, Marine protected areas, Methane emissions, Mortality rate, Nitrous oxide emissions, Other greenhouse gas emissions, PFC gas emissions, Population, Population growth, Population in urban agglomerations of more than 1 million, Population living in areas where elevation is below 5 meters, Poverty headcount ratio at $1.90 a day, Prevalence of underweight, Primary completion rate, Renewable electricity output, Renewable energy consumption, Rural land area where elevation is below 5 meters, Rural population living in areas where elevation is below 5 meters, SF6 gas emissions, School enrollment, Terrestrial and marine protected areas, Terrestrial protected areas, Total greenhouse gas emissions, Urban land area where elevation is below 5 meters, Urban population, Urban population growth, Urban population living in areas where elevation is below 5 meters

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers. The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters. The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules. The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

This dataset presents a comprehensive survey of ride-hailing app users in Pakistan, capturing their experiences, preferences, and behavior regarding these services. With the increasing reliance on digital transportation solutions, ride-hailing apps have transformed urban mobility in the country. This dataset aims to provide insights into how users interact with these services, what factors influence their choices, and how satisfied they are with their overall experience.

The dataset includes key variables such as demographic details (age, gender, occupation), ride frequency, preferred ride-hailing apps, pricing perceptions, and service quality evaluations. Additionally, it explores factors like waiting time, ride availability, safety concerns, and customer support satisfaction. Understanding these elements is crucial for identifying gaps in service and improving user experience.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F24002135%2Fc3d44ebc9e78fc4ffd60bdfce2dc261a%2FDiscovering-Essential-Travel-and-Transport-Android-Apps-in-Pakistan.jpg?generation=1742482903424771&alt=media" alt="">

Researchers, data analysts, and industry professionals can leverage this dataset to study market trends, assess customer satisfaction, and explore areas for service enhancement. It can also be used for predictive modeling, sentiment analysis, and business strategy development in the ride-hailing industry. Policymakers and urban planners may find it useful for transportation planning and infrastructure development.

This dataset is ideal for exploring consumer behavior, evaluating competition among ride-hailing services, and identifying the key drivers behind customer retention and loyalty. Whether you're conducting academic research, working on a business case study, or developing a machine-learning model, this dataset offers valuable insights into the evolving landscape of ride-hailing in Pakistan.

Context This is the largest retail e-commerce orders dataset from Pakistan. It contains half a million transaction records from March 2016 to August 2018. The data was collected from various e-commerce merchants as part of a research study. I am releasing this dataset as a capstone project for my data science course at Alnafi (alnafi.com/zusmani). There is a dire need for such dataset to learn about Pakistan’s emerging e-commerce potential and I hope this will help many startups in many ways.

Content Geography: Pakistan

Time period: 03/2016 – 08/2018

Unit of analysis: E-Commerce Orders

Dataset: The dataset contains detailed information of half a million e-commerce orders in Pakistan from March 2016 to August 2018. It contains item details, shipping method, payment method like credit card, Easy-Paisa, Jazz-Cash, cash-on-delivery, product categories like fashion, mobile, electronics, appliance etc., date of order, SKU, price, quantity, total and customer ID. This is the most detailed dataset about e-commerce in Pakistan that you can find in the Public domain.

Columns: The dataset contains Item ID, Order Status (Completed, Cancelled, Refund), Date of Order, SKU, Price, Quantity, Grand Total, Category, Payment Method and Customer ID, Market Value, Customer Since.

Size: 101 MB

File Type: CSV

Disclaimer: This dataset is a preprocessed version of the dataset which was uploaded by Zeeshan-ul-hassan Usmani. The original dataset link : https://www.kaggle.com/datasets/zusmani/pakistans-largest-ecommerce-dataset/data

I used this dataset for my own data analysis project so I decided why not share it?

The dataset consists of alot of missing values, confusing values, along with some invalid values which was filtered out. All of the preprocessing steps are listed below:

drop_unnecessary_columns: - Dropped several irrelevant date/time columns and unnamed columns to streamline the data. - This reduces noise, enhances computation efficiency, and ensures focus on meaningful features.

drop_null_values: - Dropped rows with null values to ensure that data quality remains high. - This avoids errors during analysis or model training.

convert_to_datetime: - Converted 'created_at' and 'Customer Since' into datetime format. - This allows for temporal calculations, time-series comparisons, and feature extraction.

replace_payment_method_values: - Grouped payment methods into broader categories like 'Bank or Card', 'JazzCash', 'Easypaisa', 'Cash_on_delivery', and 'Others'. -This grouping simplifies the data and ensures categories are meaningful for downstream analysis.

replace_status_values: - Replaced redundant and fragmented statuses into logical categories: 'completed', 'canceled', 'refund', and 'others'. - This ensures better categorical grouping and reduces complexity for machine learning models.

replace_missing_category_name_1: - Any missing or invalid value in the 'category_name_1' was replaced with the default category 'Others'. - This ensures no missing categorical values in the dataset.

filter_negative_values: - Negative values from financial metrics such as 'grand_total', 'discount_amount', 'price', and 'qty_ordered' were removed. - This ensures logical financial consistency for analysis and model processing.

market_value_handling: - The column 'MV' was cleaned or processed if present. If absent, a warning was logged. - This ensures robustness in column processing without errors during missing column handling.

renaming_columns: - Renamed columns ('MV', 'sku', 'status', and 'category_name_1') to clearer names like 'market_value', 'stock_keeping_unit', 'order_status', and 'product_category'. - Clearer naming improves interpretability and prevents confusion in downstream analysis.

clean_bi_status: - Invalid entries like '#REF!' in the 'BI Status' column were replaced with NaN and dropped to ensure data integrity. - This avoids invalid or incorrect data affecting analysis and model training.

Quoting from the original dataset:

"" I’d like to call the attention of my fellow Kagglers to use Machine Learning and Data Sciences to help me explore these ideas:

• What is the best-selling category? • Visualize payment method and order status frequency • Find a correlation between payment method and order status • Find a correlation between order date and item category • Find any hidden patterns that are counter-intuitive for a layman • Can we predict number of orders, or item category or number of customers/amount in advance? ""

Hazy conditions make computer vision tasks difficult, especially when working with real-world videos. Although many video dehazing algorithms have been proposed, their progress is limited by the lack of large, real-world hazy video datasets. To fill this gap, we introduce HazeBench, a dataset compiled from real-world footage captured under various environmental conditions. Unlike many existing datasets, HazeBench does not use ground-truth clear videos, making it more representative of real haze situations. The dataset includes 153 videos (1-15 seconds each) and 65,078 images, grouped into five scene categories: Indoor, Mountains, Night, Road, and Rural Areas. We describe how the dataset was collected, highlight its main features, and show its usefulness through benchmark experiments with video dehazing algorithms. HazeBench provides a valuable resource for developing and testing dehazing methods and supports more reliable computer vision applications in real-world environments.

The Pakistan Demographic and Health Survey (PDHS) was fielded on a national basis between the months of December 1990 and May 1991. The survey was carried out by the National Institute of Population Studies with the objective of assisting the Ministry of Population Welfare to evaluate the Population Welfare Programme and maternal and child health services. The PDHS is the latest in a series of surveys, making it possible to evaluate changes in the demographic status of the population and in health conditions nationwide. Earlier surveys include the Pakistan Contraceptive Prevalence Survey of 1984-85 and the Pakistan Fertility Survey of 1975. The primary objective of the Pakistan Demographic and Health Survey (PDHS) was to provide national- and provincial-level data on population and health in Pakistan. The primary emphasis was on the following topics: fertility, nuptiality, family size preferences, knowledge and use of family planning, the potential demand for contraception, the level of unwanted fertility, infant and child mortality, breastfeeding and food supplementation practices, maternal care, child nutrition and health, immunisations and child morbidity. This information is intended to assist policy makers, administrators and researchers in assessing and evaluating population and health programmes and strategies. The PDHS is further intended to serve as a source of demographic data for comparison with earlier surveys, particularly the 1975 Pakistan Fertility Survey (PFS) and the 1984-85 Pakistan Contraceptive Prevalence Survey (PCPS). MAIN RESULTS Until recently, fertility rates had remained high with little evidence of any sustained fertility decline. In recent years, however, fertility has begun to decline due to a rapid increase in the age at marriage and to a modest rise in the prevalence of contraceptive use. The lotal fertility rate is estimated to have fallen from a level of approximately 6.4 children in the early 1980s to 6.0 children in the mid-1980s, to 5.4 children in the late 1980s. The exact magnitude of the change is in dispute and will be the subject of further research. Important differentials of fertility include the degree ofurbanisation and the level of women's education. The total fertility rate is estimated to be nearly one child lower in major cities (4.7) than in rural areas (5.6). Women with at least some secondary schooling have a rate of 3.6, compared to a rate of 5.7 children for women with no formal education. There is a wide disparity between women's knowledge and use of contraceptives in Pakistan. While 78 percent of currently married women report knowing at least one method of contraception, only 21 percent have ever used a method, and only 12 percent are currently doing so. Three-fourths of current users are using a modem method and one-fourth a traditional method. The two most commonly used methods are female sterilisation (4 percent) and the condom (3 percent). Despite the relatively low level of contraceptive use, the gain over time has been significant. Among married non-pregnant women, contraceptive use has almost tripled in 15 years, from 5 percent in 1975 to 14 percent in 1990-91. The contraceptive prevalence among women with secondary education is 38 percent, and among women with no schooling it is only 8 percent. Nearly one-third of women in major cities arc current users of contraception, but contraceptive use is still rare in rural areas (6 percent). The Government of Pakistan plays a major role in providing family planning services. Eighty-five percent of sterilised women and 81 percent of IUD users obtained services from the public sector. Condoms, however, were supplied primarily through the social marketing programme. The use of contraceptives depends on many factors, including the degree of acceptability of the concept of family planning. Among currently married women who know of a contraceptive method, 62 percent approve of family planning. There appears to be a considerable amount of consensus between husbands and wives about family planning use: one-third of female respondents reported that both they and their husbands approve of family planning, while slightly more than one-fifth said they both disapprove. The latter couples constitute a group for which family planning acceptance will require concerted motivational efforts. The educational levels attained by Pakistani women remain low: 79 percent of women have had no formal education, 14 percent have studied at the primary or middle school level, and only 7 percent have attended at least some secondary schooling. The traditional social structure of Pakistan supports a natural fertility pattern in which the majority of women do not use any means of fertility regulation. In such populations, the proximate determinants of fertility (other than contraception) are crucial in determining fertility levels. These include age at marriage, breastfeeding, and the duration of postpartum amenorrhoea and abstinence. The mean age at marriage has risen sharply over the past few decades, from under 17 years in the 1950s to 21.7 years in 1991. Despite this rise, marriage remains virtually universal: among women over the age of 35, only 2 percent have never married. Marriage patterns in Pakistan are characterised by an unusually high degree of consangninity. Half of all women are married to their first cousin and an additional 11 percent are married to their second cousin. Breasffeeding is important because of the natural immune protection it provides to babies, and the protection against pregnancy it gives to mothers. Women in Pakistan breastfeed their children for an average of20months. Themeandurationofpostpartumamenorrhoeais slightly more than 9 months. After tbebirth of a child, women abstain from sexual relations for an average of 5 months. As a result, the mean duration of postpartum insusceptibility (the period immediately following a birth during which the mother is protected from the risk of pregnancy) is 11 months, and the median is 8 months. Because of differentials in the duration of breastfeeding and abstinence, the median duration of insusceptibility varies widely: from 4 months for women with at least some secondary education to 9 months for women with no schooling; and from 5 months for women residing in major cities to 9 months for women in rural areas. In the PDHS, women were asked about their desire for additional sons and daughters. Overall, 40 percent of currently married women do not want to have any more children. This figure increases rapidly depending on the number of children a woman has: from 17 percent for women with two living children, to 52 percent for women with four children, to 71 percent for women with six children. The desire to stop childbearing varies widely across cultural groupings. For example, among women with four living children, the percentage who want no more varies from 47 percent for women with no education to 84 percent for those with at least some secondary education. Gender preference continues to be widespread in Pakistan. Among currently married non-pregnant women who want another child, 49 percent would prefer to have a boy and only 5 percent would prefer a girl, while 46 percent say it would make no difference. The need for family planning services, as measured in the PDHS, takes into account women's statements concerning recent and future intended childbearing and their use of contraceptives. It is estimated that 25 percent of currently married women have a need for family planning to stop childbearing and an additional 12 percent are in need of family planning for spacing children. Thus, the total need for family planning equals 37 percent, while only 12 percent of women are currently using contraception. The result is an unmet need for family planning services consisting of 25 percent of currently married women. This gap presents both an opportunity and a challenge to the Population Welfare Programme. Nearly one-tenth of children in Pakistan die before reaching their first birthday. The infant mortality rate during the six years preceding the survey is estimaled to be 91 per thousand live births; the under-five mortality rate is 117 per thousand. The under-five mortality rates vary from 92 per thousand for major cities to 132 for rural areas; and from 50 per thousand for women with at least some secondary education to 128 for those with no education. The level of infant mortality is influenced by biological factors such as mother's age at birth, birth order and, most importantly, the length of the preceding birth interval. Children born less than two years after their next oldest sibling are subject to an infant mortality rate of 133 per thousand, compared to 65 for those spaced two to three years apart, and 30 for those born at least four years after their older brother or sister. One of the priorities of the Government of Pakistan is to provide medical care during pregnancy and at the time of delivery, both of which are essential for infant and child survival and safe motherhood. Looking at children born in the five years preceding the survey, antenatal care was received during pregnancy for only 30 percent of these births. In rural areas, only 17 percent of births benefited from antenatal care, compared to 71 percent in major cities. Educational differentials in antenatal care are also striking: 22 percent of births of mothers with no education received antenatal care, compared to 85 percent of births of mothers with at least some secondary education. Tetanus, a major cause of neonatal death in Pakistan, can be prevented by immunisation of the mother during pregnancy. For 30 percent of all births in the five years prior to the survey, the mother received a tetanus toxoid vaccination. The differentials are about the same as those for antenatal care generally. Eighty-five percent of the births occurring during the five years preceding the survey were delivered

Gold Reserves in Pakistan increased to 64.76 Tonnes in the third quarter of 2025 from 64.75 Tonnes in the second quarter of 2025. This dataset provides - Pakistan Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Pakistan’s largest ecommerce data – Power BI Report

Dataset Link: pakistan’s_largest_ecommerce_dataset Cleaned Data: Cleaned_Pakistan’s_largest_ecommerce_dataset

Raw Data:

Rows: 584525 **Columns: **21

Process:

All the raw data transformed and saved in new Excel file Working – Pakistan Largest Ecommerce Dataset

Processed Data:

Rows: 582250 Columns: 22 Visualization: Here is the link of Visualization report link: Pakistan-s-largest-ecommerce-data-Power-BI-Data-Visualization-Report

Conclusion:

In categories Mobiles & Tables make more money by selling highest no of products and also providing highest amount of discount on products. On the other side Men’s Fashion Category has sell second highest no of products but it can’t generate money with that ratio, may be the prices of individual products is a good reason behind that. And in orders details we experience Mobiles & Tablets have highest no of canceled orders but completed orders are almost same as Men’s Fashion. We have mostly completed orders but have huge no of canceled orders. In payment methods cod has most no of completed order and mostly canceled orders have payment method Easyaxis.

Pakistan phone number database is a collection of phone numbers that are always correct and valid. Therefore, it can give you the most recent and up-to-date contact number list. Also, it can give you a 95% accuracy rate. However, you can get a high response rate. Similarly, we can give you an Excel or CSV file. Thus, buy our mobile number list and put it on any CRM software. Moreover, Pakistan phone number database is a helpful tool for reaching people in Pakistan. Pakistan mobile number data can help you in multiple ways to boost your business. This data is a well-known service in this segment so you can get it now. Furthermore, the List to Data website is a famous database service provider in this world. In addition, it can develop your business in a very short period. On the other hand, if you want to get a huge return on investment (ROI), buy our service right now. Also, it is the fastest way to get in touch with the right people. Most importantly, we strictly follow the GDPR rules and restrictions.

Description:

This dataset provides a comprehensive overview of the overseas Pakistani population across different continents and countries, along with their remittance statistics. The dataset includes information about the country, article, continent, and year, as well as the number of overseas Pakistani residents and their remittance amounts in US dollars. The dataset also contains additional information on the overseas Pakistani population by continent and their overall population.

Country: The name of the country where the Pakistani expatriates are residing. Article:Any relevant article or information associated with the country, such as its political or economic situation. Continent:The name of the continent where the country is located. Overseas Pakistani population (By Country): The estimated number of Pakistani expatriates residing in the country. Extra Information: Any additional information that is relevant to the Pakistani expatriate population in the country. Overseas Pakistani Population by Continent:The estimated number of Pakistani expatriates residing in the continent. Population (by Continent): The estimated population of the continent. Year: The year to which the data corresponds Remittance received. Remittance ($ billion): The amount of remittance in US dollars sent by the Pakistani expatriate population from the country to Pakistan during the corresponding year.

This dataset is an essential resource for researchers, policymakers, and anyone interested in studying or analyzing the trends and patterns of the Pakistani diaspora around the world. The data can help researchers identify areas of high remittance inflows and study their economic impact on the country of origin. It can also be used by policymakers to formulate policies that cater to the needs and challenges of Pakistani expatriates in different countries. Finally, this dataset can also be used to understand the social and cultural dynamics of the Pakistani community in different parts of the world.

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.