Iteratively vs NumPy
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. | Iteratively helps teams capture reliable product analytics they can trust. It eliminates the most common causes of error during the definition and implementation of tracking plans, and cuts down on the time it takes to correctly instrument the product. As a result, folks that consume product analytics get exactly what they spec'd out and can rely on the incoming data knowing it is trustworthy and accurate. |
Powerful n-dimensional arrays;
Numerical computing tools;
Interoperable;
Performant;
Easy to use | Single source of truth for your product analytics events;
Generates a strongly typed library for your analytics with cross-platform support (Typescript, Kotlin, Swift, etc..);
Integrates with the 3rd party and custom backends (Amplitude, Mixpanel, Segment, Intercom, etc...);
Established best practices for taxonomy and naming convention;
Quality assurance built in with code linting and runtime analysis;
Generate automatic & beautiful documentation |
Statistics | |
GitHub Stars 30.7K | GitHub Stars - |
GitHub Forks 11.7K | GitHub Forks - |
Stacks 4.3K | Stacks 6 |
Followers 799 | Followers 16 |
Votes 15 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
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