Need advice about which tool to choose?Ask the StackShare community!
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.
Pros of NumPy
- Great for data analysis8
- Faster than list2
Pros of PySpark
Sign up to add or upvote prosMake informed product decisions
What is NumPy?
What is PySpark?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with PySpark?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+7
+2
+11
+42