Anaconda vs NumPy
Overview
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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. | A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. |
Powerful n-dimensional arrays;
Numerical computing tools;
Interoperable;
Performant;
Easy to use | Stay safe and secure;
Deliver on your data strategy;
Get to market faster;
Maximize flexibility and control |
Statistics | |
GitHub Stars 30.7K | GitHub Stars - |
GitHub Forks 11.7K | GitHub Forks - |
Stacks 4.3K | Stacks 439 |
Followers 799 | Followers 490 |
Votes 15 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
What are some alternatives to NumPy, Anaconda?
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
PyXLL
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SciPy
Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
Dataform
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PySpark
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.
Dask
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.
Pentaho Data Integration
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StreamSets
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KNIME
It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.
Denodo
It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.
