NumPy vs PySpark: What are the differences?
Developers describe NumPy as "Fundamental package for scientific computing with Python". 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. On the other hand, PySpark is detailed as "The Python API for Spark". It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
NumPy and PySpark can be primarily classified as "Data Science" tools.
NumPy is an open source tool with 11.4K GitHub stars and 3.76K GitHub forks. Here's a link to NumPy's open source repository on GitHub.
According to the StackShare community, NumPy has a broader approval, being mentioned in 87 company stacks & 251 developers stacks; compared to PySpark, which is listed in 8 company stacks and 6 developer stacks.
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