NumPy vs Dask: What are the differences?
What is NumPy? 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.
What is Dask? A flexible library for parallel computing in Python. It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers. .
NumPy and Dask can be categorized as "Data Science" tools.
Some of the features offered by NumPy are:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
On the other hand, Dask provides the following key features:
- Supports a variety of workloads
- Dynamic task scheduling
- Trivial to set up and run on a laptop in a single process
NumPy is an open source tool with 11.5K GitHub stars and 3.79K GitHub forks. Here's a link to NumPy's open source repository on GitHub.
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What is Dask?
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