Alternatives to NumPy logo

Alternatives to NumPy

MATLAB, Pandas, R Language, SciPy, and Panda are the most popular alternatives and competitors to NumPy.
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What is NumPy and what are its top alternatives?

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.
NumPy is a tool in the Data Science Tools category of a tech stack.
NumPy is an open source tool with 13K GitHub stars and 4.3K GitHub forks. Here鈥檚 a link to NumPy's open source repository on GitHub

NumPy alternatives & related posts

MATLAB logo

MATLAB

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A high-level language and interactive environment for numerical computation, visualization, and programming
MATLAB logo
MATLAB
VS
NumPy logo
NumPy
Pandas logo

Pandas

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High-performance, easy-to-use data structures and data analysis tools for the Python programming language
Pandas logo
Pandas
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NumPy logo
NumPy

related Pandas posts

Guillaume Simler
Guillaume Simler
at Velchanos.io | 4 upvotes 59.4K views
Jupyter
Jupyter
Anaconda
Anaconda
Pandas
Pandas
IPython
IPython

Jupyter Anaconda Pandas IPython

A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

See more

related R Language posts

Eric Colson
Eric Colson
Chief Algorithms Officer at Stitch Fix | 19 upvotes 622.7K views
atStitch FixStitch Fix
Kafka
Kafka
PostgreSQL
PostgreSQL
Amazon S3
Amazon S3
Apache Spark
Apache Spark
Presto
Presto
Python
Python
R Language
R Language
PyTorch
PyTorch
Docker
Docker
Amazon EC2 Container Service
Amazon EC2 Container Service
#AWS
#Etl
#ML
#DataScience
#DataStack
#Data

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

See more
SciPy logo

SciPy

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Scientific Computing Tools for Python
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    SciPy logo
    SciPy
    VS
    NumPy logo
    NumPy
    Panda logo

    Panda

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    Dedicated video encoding in the cloud
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      Panda logo
      Panda
      VS
      NumPy logo
      NumPy
      Anaconda logo

      Anaconda

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      The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
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        Anaconda logo
        Anaconda
        VS
        NumPy logo
        NumPy

        related Anaconda posts

        Guillaume Simler
        Guillaume Simler
        at Velchanos.io | 4 upvotes 59.4K views
        Jupyter
        Jupyter
        Anaconda
        Anaconda
        Pandas
        Pandas
        IPython
        IPython

        Jupyter Anaconda Pandas IPython

        A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

        The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

        See more
        PySpark logo

        PySpark

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        The Python API for Spark
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          PySpark logo
          PySpark
          VS
          NumPy logo
          NumPy
          Pentaho Data Integration logo

          Pentaho Data Integration

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          Easy to Use With the Power to Integrate All Data Types
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            Pentaho Data Integration logo
            Pentaho Data Integration
            VS
            NumPy logo
            NumPy