Browser Market Share History Csv Data: Dataset Structure and Field Coverage

Granularity is per entity per period: one row per browser (or version, or operating system) per day, week, month, quarter or year, holding its share value. Aggregation levels are switchable on the site before you export, so one workflow produces both a smoothed monthly series for presentation and a noisy daily series for event analysis.

Geographic scope is selectable the same way: worldwide, seven continents, or country-level breakdowns for an extensive list of countries — roughly 250 geographic scopes in total across the stat families. Each scope-and-stat pair exports separately, which keeps single files small but means a full country-by-browser matrix is assembled by looping over scopes.

Two limits belong in your data dictionary. The sample underlying the shares is page-view traffic — billions of views per month, 5.3 billion in July 2022 — not installs or users, so shares measure browsing activity rather than installed base. And the raw page-view data is not distributed; only aggregated percentages are published, so recomputing a share from counts is impossible.

A share series answers "which browser dominates" but not "what do those users install", and the rest of this industry's catalog covers the second question. The comparison below lines the adjacent sources up against StatCounter on exactly the dimensions a buyer cares about: what each one measures, how deep its history runs, how often it refreshes, and what terms attach to reuse.

Pairing choices follow from that asymmetry. If your deliverable is a platform-shift thesis, StatCounter supplies the time axis while store-side records supply the installed-base cross-check; if your deliverable is a competitive screen, run them the other way around.

First, match the aggregation to the claim. Monthly worldwide shares support trend statements; a single country's daily series supports launch-impact claims. Mixing the two is the most common citation error.

Second, state the denominator. These are shares of tracked page views, so a browser gaining share among high-activity users moves the number more than the same browser gaining casual users — different from install-count measures like Google Play's brackets or the exact install minima and maxima recorded for 2.3 million Play apps in the Kaggle extended snapshot.

The application software data guide is the pillar for this industry. It maps all 25 pooled records Datadory catalogs for application software — 24 primary datasets plus one related record — and situates this usage-share question inside the wider three-layer stack described there: platform stores led by the 3,194,924-row AppGoblin table, open-source infrastructure such as GitHub's 200M+ indexed repositories, and commercial directories including Capterra's 86,267 product profiles.