The number of Apple iPhone unit sales dramatically increased between 2007 and 2024. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around **** million smartphones. By 2024, this number reached over ***** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2025, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A19 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

This dataset offers a comprehensive overview of the iPhone's journey in the global smartphone market from 2010 to 2024 . It includes:

📊 Number of iPhone Users: Total users worldwide and within the USA. 📈 Sales Figures: Yearly iPhone sales data. 🏆 Market Share: Comparison of iOS and Android market shares across years. This dataset is perfect for:

Market forecasting and trend analysis. Competitive landscape studies between iOS and Android. Consumer behavior research in the tech industry. Whether you're a data scientist, market analyst, or tech enthusiast, this dataset provides valuable insights to support your research and projects.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

Percentage of smartphone users by selected smartphone use habits in a typical day.

This dataset has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

This repository contains four datasets about the number of active users of selected mobile apps purchased from Selectivv company (https://selectivv.com/). Details regarding the data may be found below:

How data was collected: Selectivv uses programmatic advertisements systems that collect information on about 24 mln smartphone users in Poland

Apps:

Transportation: Uber, Bolt Driver, FREE NOW, iTaxi,

Delivery: Glover, Takeaway, Bolt Courier, Wolt;

Unit: an active user of a given app. Active = used given app at least 1 minute in a given period (e.g. 1 unit during whole month, half-year).

Period: 2018-2018; monthly and half-year data

Spatial aggregation: country level, city level, functional area level, voivodeship level. Functional area is defined as here https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/

Activity time: measured by activity time of given app (in hours; average and standard deviation)

Datasets:

gig-table1-monthly-counts-stats.csv -- the monthly number of active users;

gig-table2-halfyear-demo-stats.csv -- the half-year number of active users by socio-demographic variables;

gig-table3-halfyear-region-stats.csv -- the half-year number of active users by spatial aggregation;

gig-table4-halfyear-activity-stats.csv -- the half-year activity time by working week, weekend, day (8-18) and night (18-8).

Detailed description:

1. gig-table1-monthly-counts-stats.csv

Structure:

month - YYYY-MM-DD -- we set all dates to 15th of given month but actually the data is about the whole month (active users in whole period); 2018-01-15 to 2021-12-15

app -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

category -- Transportation, Deliver

1. gig-table2-halfyear-demo-stats.csv

Structure:

gender -- men, women

age -- 18-30, 31-50, 51-64

country -- Poland, Ukraine, Other

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

students -- the share of students within a given row

parents_of_children_0_4_years -- the share of parents of 0-4 years children in a given row

parents_of_children_5_10_years -- the share of parents of 5-10 years children in a given row

women_planning_a_baby -- the share of women planing a baby in a given row

standard -- the share of standard smartphones in a given row

premium_i_phone -- the share of iPhone smartphones in a given row

other_premium -- the share of other premium smartphones in a given row

category -- Transportation, Delivery

1. gig-table3-halfyear-region-stats.csv

Structure:

group -- Voivodeship, Functional Area, Cities

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

region_name:

Cities -- Białystok, Bydgoszcz, Gdańsk, Gdynia, Gorzów Wielkopolski, Katowice, Kielce, Kraków, Łódź, Lublin, Olsztyn, Opole, Poznań, Rzeszów, Sopot, Szczecin, Toruń, Warszawa, Wrocław, Zielona Góra

Functional Area -- Functional area - Białystok, Functional area - Bydgoszcz, Functional area - Gorzów Wielkopolski, Functional area - GZM, Functional area - GZM2, Functional area - Kielce, Functional area - Kraków, Functional area - Łódź, Functional area - Lublin, Functional area - Olsztyn, Functional area - Opole, Functional area - Poznań, Functional area - Rzeszów, Functional area - Szczecin, Functional area - Toruń, Functional area - Trójmiasto, Functional area - Warszawa, Functional area - Wrocław, Functional area - Zielona Góra

Voivodeship -- dolnośląskie, kujawsko-pomorskie, łódzkie, lubelskie, lubuskie, małopolskie, mazowieckie, opolskie, podkarpackie, podlaskie, pomorskie, śląskie, świętokrzyskie, warmińsko-mazurskie, wielkopolskie, zachodniopomorskie

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

category -- Transportation, Delivery

Please note that:

the number of active users in a given functional area = number of active users in a city and a functional area of this city

the number of active users in voivodeship = number of active users in a city, its functional area and the rest of the voivodeship where this city and functional area is located

More details here: https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/

1. gig-table4-halfyear-activity-stats.csv

Structure:

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

day -- Mondays-Thursdays, Fridays-Sundays

hour -- day (8-18), night (18-8)

activity_time -- in hours

statistic -- Average, Std.Dev. (standard deviation)

category -- Transportation, Delivery

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. 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 150 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 mobile broadband connections in countries like Vietnam and Laos.