Python & SQL
Research answer
What AppTrendKit can say about Python & SQL today
AppTrendKit estimates Python & SQL's App Store USD gross consumer spend at $198–$1.4K and downloads at 223–1.8K during 2026-08-09 to 2026-09-07 (30-day window) with storefront contributor coverage unavailable.
These are modeled public App Store estimates, not official publisher-reported figures. Storefront scope is stated only when persisted contributor lineage identifies it.
Performance intelligence
Observed trajectory and estimates
Store ranks are observations. Downloads and gross store consumer spend are AppTrendKit estimates and only appear when persisted evidence exists.
Estimated downloads · 30D
Modeled223–1.8K
Likely range · modeled estimate
Estimated App Store gross consumer spend · 30D
Modeled$198–$1.4K
Likely range · modeled estimate
Estimated downloads · 7D
Modeled223–1.8K
Likely range · modeled estimate
Estimated App Store gross consumer spend · 7D
Modeled$198–$1.4K
Likely range · modeled estimate
Evidence history
Collected daily performance
1 observed day · missing days are left blank
| Date | Downloads | Gross store revenue | Confidence |
|---|---|---|---|
| Sep 7, 2026 | 631 | $534 | 16% |
Rank history
Observed chart positions
Lower rank is better. Only collected observations are shown.
Coverage note
What the ranks mean
A missing rank is not treated as zero downloads or zero revenue. Apple chart feeds are bounded and may return censored or not-ranked observations outside the visible chart depth.
Read the methodStorefront mix
Top countries by estimated contribution
Country estimates are aggregated only after country-level predictions exist.
| Storefront | Revenue | Share | Downloads | Rank | Confidence |
|---|---|---|---|---|---|
| United KingdomGB | $534 | 100.0% | 631 | — | 1% |
Monetization
Observed store economics signals
These are listing and product observations. They are not company revenue, subscriber counts, or advertising revenue.
App details
Store metadata
- Version
- 3.01
- Released
- Apr 4, 2018
- Last updated
- Jul 30, 2025
- Content rating
- 4+
- Default storefront
- GB
Public IAP observations
Validated in-app purchase prices
Only persisted rows with an explicit public publication boundary are shown. Missing price, currency, country, or date values stay unknown.
Listing detail
What the store says
• Learn programming with the best code recipes. • Python: - How to get a substring? - How to declare a generalized method? - How to perform a database query? • SQL: - How to combine multiple tables in a single query? - How to sort the result of grouping data? - What types of data are there in SQL Server? • The best examples of code from books and specific internet resources are selected in the app. Finding the best solution on the internet can take hours of work. Ready-to-use and tested "recipes" for code are included in the app. • The app is a great tool for passing exams or preparing for interviews. It helps you study the typical tasks you might face. • One app for iOS and macOS. Buy once and use it on your phone, tablet, or computer. • Quick search of the correct example by code or name of topic. • All examples in the app are available offline. The app can be used when an internet connection is not available. • As a developer, I use this app when I need to quickly remember how to solve a given task in SQL or Python. If you can help to translate the app into your language, please contact me by email. In the application, some examples are available for an additional fee (approximately 45%).
Publisher websiteStore creative
Screenshots
Competitive set
Similar apps
Deterministic links to real stored apps matched by category, publisher, and comparable performance bands.
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Methodology and provenance
Read every number with its evidence
Metadata source: Apple iTunes Search API; observed Sep 7, 2026, 8:37 PM.
Rankings: collected chart positions from the listed storefront. Missing ranks are not zeros.
Economics: downloads and gross store consumer spend are estimated, not official Apple or publisher figures.
Model: No persisted model version.
Confidence: 1%; intervals widen when evidence or history is limited.
Last observation: Sep 7, 2026, 10:18 PM.
See how AppTrendKit separates observations, estimates, coverage, and confidence.
Open methodology