Box Office Mojo

It is the theatrical revenue ledger of the movies & entertainment business, drawn from Box Office Mojo by IMDbPro - the reference the trade press quotes whenever a weekend belongs to one film. The charts rank releases by gross across daily, weekend, weekly, monthly and yearly views for the United States/domestic market, per-territory international markets and worldwide aggregates, backed by all-time lifetime rankings, release schedules and a full gross history for each release: theater counts, per-theater averages, cumulative totals, days in release and distributor.

Within Datadory's fifteen-dataset movies & entertainment slice it plays the receipts role: metadata catalogs say what a film is, critic-score sets say what reviewers thought, and this one says what audiences paid - roughly 20,000+ titles with complete gross histories, scoring 8 out of 10 on Datadory's quality rubric. Where a rival such as The Numbers - Movie Financial Data pushes deeper into production budgets and ancillary economics, Box Office Mojo holds the cleanest recurring revenue curve in the catalog. Get a sample of this dataset cut to your titles and dates.

One row per release per chart period. Three straight off a recent weekend ranking:

# delivered grain: one row per release per chart period

rank                 : 1
release_title        : Spider-Man: Brand New Day
gross_usd            : $70,711,990      # Fri-Sun window, weekend 33 of 2026
change_vs_prior_pct  : -51%
theater_count        : 4539
theater_count_change : -122
per_theater_average  : $15,578
total_gross_usd      : $786,543,660     # 3 weeks into the run
distributor          : Sony Pictures Releasing

# two rows further down the same ranking
2  The Odyssey             $23,606,885   -25.6%   3217 thr   $505,087,255   Universal Pictures
3  The End of Oak Street   $21,010,488   new      3446 thr   $21,010,488    Warner Bros.

Read the top row as three facts already decided for you. The gross arrives as dollars with the window explicit, so "seventy million" is what the cell says without unit math. Saturation is priced in natively - per_theater_average separates a genuine hit from a wide-thrown mediocre one, which raw totals cannot. And because total_gross_usd rides every row rather than living on a separate title table, an opening-weekend-versus-final-cume comparison is one group-by, not a join project.

Eleven fields define the row, each definition verified against live output during the August 2026 research pass - a bar only 145 of the 1,744 datasets in Datadory's catalog clear outright. They split into three jobs:

• Identity: rank, release_title, prior_period_rank and distributor say who sold tickets, where it placed and who released it, with the prior-period rank carried natively so drop-off charts never require diffing two pulls.
• Measurement: gross_usd, total_gross_usd and change_vs_prior_pct carry the money; theater_count and theater_count_change carry footprint; per_theater_average_usd normalises the two so a 4,500-screen wide release and a 600-screen platform debut can sit in one analysis honestly.
• Time: period_in_release ages every row, turning a flat ranking into a decay curve once several periods stack up.

Chart-family variants travel beside the core row rather than bloating it - the full dictionary follows in tabular form, and anything chart-specific folds under additional fields on request below.

The eleven-column core covers every chart's spine. Beyond it, the catalog extends along chart families, delivered as extra columns on the same row shape when a sample or feed calls for them:

• Year-over-year comparators (% change versus the same day or weekend last year)
• Days-in-release counts and cumulative to-date totals on daily views
• Monthly, quarterly and yearly chart variants alongside the daily and weekend views
• Per-territory international charts and worldwide yearly rankings
• All-time top lifetime gross rankings, domestic and worldwide
• Franchise, brand, genre, season and holiday slice identifiers
• Release-schedule fields covering upcoming wide, limited and re-release dates plus date-change tracking

Naming the family you need at sample time pins the exact column set before anything ships, so downstream schemas never meet a surprise column mid-quarter.

• Geography - the United States/domestic market as the deepest cut, plus per-territory international charts and worldwide aggregates that combine the two. Domestic depth plus worldwide breadth in one catalog entry is the pairing equity analysts actually want; for country-level streaming availability rather than ticket sales, pair it with a catalog such as JustWatch Streaming Guide.
• Temporal - daily grosses reaching back to the early 1980s for major releases, with day, week, month, quarter and year views running through the current date, and all-time lifetime rankings spanning each title's entire run. Four decades of daily curves is what makes opening-weekend prediction models trainable rather than anecdotal.
• Granularity - one row per film release per chart period: the finest regular cut theatrical revenue gets. Tens of thousands of release pages and roughly 20,000+ titles with full gross histories, each row carrying its period explicitly so incremental deliveries append without double-counting.

Set against the wider catalog - 1,744 datasets averaging 7.81 - the 8/10 reflects verified field documentation against live chart output. Where it ranks: best movies & entertainment datasets.

API, files, or your warehouse. Daily, weekly, or hourly.

You pick the channel and cadence; chart parsing, window labelling and rank-history stitching stay our problem. Rows arrive normalised with the chart period explicit on every record, so a delta pull appends cleanly and a backfill joins history on title and period rather than guesswork.

Hourly suits opening weekends, where a launch trajectory moves fast enough to matter by Saturday afternoon. Daily suits distribution desks tracking holdovers against their own booking plans. Weekly suits equity research, because studio revenue lines move at quarterly grain anyway. Whichever you choose, the eleven-field dictionary travels unchanged. A sample ships first either way - real rows for your titles and dates before any commitment.

Six jobs the gross ledger settles outright:

1. Distribution and exhibition strategy - read holdover strength in week-over-week decay and per-theater averages before committing screens next frame.
2. Studio and slate competitive tracking - rivals' openings land on the ranking within the same cycle; watch whose platform strategy is working without waiting for quarterly reports; see competitor tracking.
3. Ad-sales and media planning - schedule around tentpole demand waves instead of seasonal folklore.
4. Equity research on media conglomerates - anchor theatrical revenue lines in four decades of gross curves; see market sizing.
5. ML model training - train opening-weekend and cume prediction on daily curves, not summary statistics; see ml model training.
6. Citation-grade verification - the number a headline quotes resolves to title, window and rank; see citation grade research.

Each job maps to a persona below, and the sample validates whichever one you came for.

Market Researchers & Consultants lead fit: theatrical revenue is the hardest demand signal in entertainment, and this is its reference series - see movies & entertainment data for market researchers. Investors & Quant Researchers get studio-level revenue proxies at weekly grain while the market waits on filings. Competitive Intelligence & Product Teams track rival slates frame by frame - see movies & entertainment data for competitive intel product teams. Journalists, Academics & Students get citation-grade answers to "what did people actually pay to see" - see movies & entertainment data for journalists, academics and students.

Persona fit has edges worth naming: this measures tickets sold as dollars, not attention. Streaming viewership, social sentiment and second-window economics live elsewhere in the catalog - pair accordingly rather than expecting one feed to cover the whole value chain. Persona-by-persona detail lives on the movies & entertainment data hub.

Three notes worth having upfront.

First, chart type changes the columns legitimately. Daily views carry days-in-release and to-date totals; weekend views carry prior-week ranks, weeks-in-release and total gross. Pick the cut your question needs before comparing, and keep it constant down a trend line.

Second, cumulative totals grow as a run continues - a title's total_gross_usd is a moving ceiling until the run closes, so snapshot dates matter when quoting lifetime figures. Every row carries its period, which makes the snapshot explicit rather than inferred.

Third, worldwide totals combine domestic and international, and international rows aggregate per territory - when joining external financial data denominated differently, decide the geography scope once and hold it. Start a sample with the domestic series if in doubt; it is the deepest and the cleanest. Then Get a sample of this dataset.

Because the raw artifact underneath is a set of browser charts built for one person reading one weekend, and every interesting question wants forty years, multiple territories and both daily and weekend cuts in one frame. Datadory hands over the corpus itself: the eleven-field dictionary kept verified against live output, prior-rank and cumulative columns delivered as fields so decay curves cost no diffing, chart families resolved into one stable row shape, and the result landed on the cadence your pipeline runs - with sample rows in hand before any commitment.

Browse the rest of the slice on the movies & entertainment data hub or the best movies & entertainment datasets ranking. Pair the receipts with budgets at The Numbers - Movie Financial Data, with review data at Rotten Tomatoes Movies & TV, with rating histories at GroupLens MovieLens Datasets, or read the source profile at IMDb.