Google Play Store Dataset: What's Actually Inside It

You found a Google Play Store dataset. You downloaded it. You opened it.

Now you're staring at 20+ columns wondering which ones actually matter and which ones are noise.

That confusion is more common than people admit. Most resources tell you the dataset "contains app metadata." That's not an answer. This post is.

Here's a complete, field-by-field breakdown of what a Google Play Store dataset includes, what varies by source, and how to decide what's worth your time.

The Core Data Fields You'll Find in Almost Every Dataset

Regardless of where you get the data, whether it's Kaggle, a third-party provider, or a custom scrape, these fields show up consistently.

App Identity Fields

These are the basics. Every row in the dataset maps to a single app.

• App Name. The display name as shown on the Play Store.
• App ID / Package Name. The unique identifier (e.g., com.whatsapp). This is the most reliable field for deduplication and API lookups.
• Category. The primary category the developer listed the app under. One app, one category.
• Developer Name / Developer ID. Who published the app. Developer ID is the more stable identifier.

Performance and Popularity Signals

These fields tell you how an app is performing in the market.

• Installs. Usually a range (e.g., "10M+"), not an exact number. More on why this matters later.
• Ratings Count. The total number of user ratings submitted.
• Average Rating. Typically on a 0–5 scale.
• Reviews Count. Distinct from ratings. Some datasets include the actual review text; most just include the count.

Monetization and Pricing Fields

• Price. Free or paid. If paid, the listed price in USD (or local currency, depending on the scrape).
• In-App Purchases. A boolean or flag indicating whether the app offers purchases beyond the base price.
• Content Rating. Age classification. Everyone, Teen, Mature 17+, etc.

Fields That Vary by Dataset Source

Here's what most guides won't tell you: not all Google Play Store datasets are built the same.

Kaggle datasets are usually static snapshots. A third-party data provider scrapes continuously. A raw scrape you run yourself gives you the most control but requires the most cleaning.

The fields that vary the most across sources:

If your use case depends on any of the bottom rows, a Kaggle dataset likely won't cut it.

The Install Count Problem (And Why It Matters More Than You Think)

This is the counter-intuitive insight most analysts skip past.

Google does not publish exact install numbers. What you get is a bucketed range. 1M+, 5M+, 10M+. That means an app with 1.1 million installs and an app with 4.9 million installs are indistinguishable in the dataset.

Why does this matter?

• For market sizing, install ranges create a wide margin of error. A category with "50 apps at 1M+ installs" could represent anywhere from 50M to 249M total installs.
• For ranking models, using install count as a continuous variable will break your model. It's ordinal data, not ratio data. Treat it accordingly.
• For competitive analysis, relative comparisons (App A is in the 10M+ bucket, App B is in the 1M+ bucket) are valid. Absolute comparisons are not.

Most junior analysts treat install ranges as exact figures. That's a data quality error that compounds through every downstream insight.

Structured vs. Raw Scraped Data: What's the Difference?

This question comes up constantly, especially from developers and students building projects for the first time.

Structured datasets (Kaggle, Crawl Feeds) come pre-cleaned. Column names are consistent. Missing values are handled. You can load them straight into pandas or a BI tool with minimal friction.

Raw scraped data is messier but more current and more complete. You'll deal with:

• Inconsistent encoding (special characters in app names and descriptions)
• Mixed data types in the same column
• Null values where the Play Store returned no data
• Duplicates from scrape retries

For learning projects and academic work, structured datasets are fine. For production use cases, competitive intelligence, or building a data product, you want raw scraped data with a cleaning pipeline you control.

What You Can Extract Beyond the Raw Fields

A Google Play dataset isn't just the columns it ships with. It's also the derived signals you can build from it.

From the Description Field (If You Have It)

• Keyword extraction. What terms do top-rated apps in a category use most?
• Feature mapping. What capabilities are mentioned most often in high-install apps vs. low-install apps?
• Sentiment proxy. App descriptions that use urgency language vs. feature-led language. A useful signal for positioning analysis.

From Ratings + Reviews Count Together

A single rating score means very little. Combine it with review volume and you get real signal:

• High rating + low reviews. Promising but statistically thin. Don't over-index on it.
• High rating + high reviews. Validated quality. Use this as your benchmark tier.
• Low rating + high reviews. Known problems at scale. Either a formulation issue or a review manipulation problem.

From Category + Price + Rating

Cross these three fields and you get a competitive positioning map. It's one of the fastest ways to identify underserved segments in a category before building or investing.

Play Store Dataset vs. Play Store API: Which One Do You Need?

If you're a developer wondering whether to use the dataset or hit the API directly, here's the honest answer.

Use the dataset if:

• You need a broad, horizontal view of many apps at once
• You're doing market analysis, trend research, or building ML models
• You don't need real-time data

Use the API (or a scraping pipeline) if:

• You need data on specific apps, updated frequently
• You're building a monitoring tool or alerting system
• You need fields that bulk datasets don't include (permissions, changelogs, version history)

The dataset is a snapshot. The API is a feed. They solve different problems.

Do All Google Play Store Datasets Include the Same Fields?

No. Not even close.

The most commonly included fields across all public datasets are: app name, category, rating, installs, price, and content rating. That's the floor.

Everything beyond that, including description text, last updated date, app size, version info, and developer contact details, depends entirely on who built the dataset, when they scraped it, and what their use case was.

Before you download any dataset, check the column list first. Match it against the specific questions you're trying to answer. If the fields aren't there, the dataset won't answer your question, no matter how large it is.

The Field That Most People Ignore (But Shouldn't)

Last Updated Date.

It's often missing from older Kaggle datasets, which is exactly why you should care about it.

An app's update frequency is a strong proxy for developer activity, user retention investment, and product health. Apps that haven't been updated in 18+ months are either abandoned, deprecated, or running on pure organic traffic.

For market analysis, filtering out stale apps changes your category-level averages significantly. For competitive intelligence, it's the fastest way to separate active competitors from legacy ones.

If your dataset doesn't include this field, it's worth building a pipeline to enrich it.

What to Do With This Information

Now that you know what's in the dataset, the next step is matching your question to the right fields.

Here's a quick mapping:

Final Word

A Google Play Store dataset is only as useful as your understanding of what's in it.

The fields aren't complicated. But the assumptions people make about them, especially around install counts, rating reliability, and data freshness, are where most analyses go wrong.