Alternatives to NumPy logo

Alternatives to NumPy

Pandas, MATLAB, R Language, SciPy, and Panda are the most popular alternatives and competitors to NumPy.
807
584
+ 1
6

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 17.4K GitHub stars and 5.6K GitHub forks. Here鈥檚 a link to NumPy's open source repository on GitHub

Top Alternatives to NumPy

  • Pandas

    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. ...

  • MATLAB

    MATLAB

    Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. ...

  • R Language

    R Language

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. ...

  • SciPy

    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. ...

  • Panda

    Panda

    Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br> ...

  • TensorFlow

    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • PyTorch

    PyTorch

    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...

  • Anaconda

    Anaconda

    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. ...

NumPy alternatives & related posts

Pandas logo

Pandas

1.1K
921
19
High-performance, easy-to-use data structures and data analysis tools for the Python programming language
1.1K
921
+ 1
19
PROS OF PANDAS
  • 18
    Easy data frame management
  • 1
    Extensive file format compatibility
CONS OF PANDAS
    Be the first to leave a con

    related Pandas posts

    Server side

    We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

    • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

    • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

    • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

    Client side

    • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

    • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

    • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

    Cache

    • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

    Database

    • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

    Infrastructure

    • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

    Other Tools

    • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

    • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

    See more
    Guillaume Simler

    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
    MATLAB logo

    MATLAB

    553
    485
    27
    A high-level language and interactive environment for numerical computation, visualization, and programming
    553
    485
    + 1
    27
    PROS OF MATLAB
    • 14
      Simulink
    • 5
      Functions, statements, plots, directory navigation easy
    • 3
      S-Functions
    • 2
      Model based software development
    • 1
      Simple variabel control
    • 1
      REPL
    • 1
      Solve invertible matrix
    CONS OF MATLAB
    • 1
      Parameter-value pairs syntax to pass arguments clunky
    • 0
      Does not support named function arguments
    • 0
      Doesn't allow unpacking tuples/arguments lists with *

    related MATLAB posts

    R Language logo

    R Language

    2K
    1.4K
    368
    A language and environment for statistical computing and graphics
    2K
    1.4K
    + 1
    368
    PROS OF R LANGUAGE
    • 77
      Data analysis
    • 60
      Graphics and data visualization
    • 51
      Free
    • 40
      Great community
    • 36
      Flexible statistical analysis toolkit
    • 25
      Access to powerful, cutting-edge analytics
    • 24
      Easy packages setup
    • 17
      Interactive
    • 10
      R Studio IDE
    • 9
      Hacky
    • 5
      Preferred Medium
    • 4
      Shiny interactive plots
    • 4
      Shiny apps
    • 3
      Cutting-edge machine learning straight from researchers
    • 3
      Automated data reports
    CONS OF R LANGUAGE
    • 4
      Very messy syntax
    • 2
      Tables must fit in RAM
    • 1
      Messy character encoding
    • 0
      Messy syntax for string concatenation
    • 0
      Messy syntax for array/vector combination
    • 0
      No push command for vectors/lists
    • 0
      Poor syntax for classes
    • 0
      Arrays indices start with 1

    related R Language posts

    Eric Colson
    Chief Algorithms Officer at Stitch Fix | 21 upvotes 路 1.9M views

    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
    Maged Maged Rafaat Kamal
    Shared insights
    on
    Python
    R Language

    I am currently trying to learn R Language for machine learning, I already have a good knowledge of Python. What resources would you recommend to learn from as a beginner in R?

    See more
    SciPy logo

    SciPy

    181
    112
    0
    Scientific Computing Tools for Python
    181
    112
    + 1
    0
    PROS OF SCIPY
      Be the first to leave a pro
      CONS OF SCIPY
        Be the first to leave a con

        related SciPy posts

        Panda logo

        Panda

        8
        16
        0
        Dedicated video encoding in the cloud
        8
        16
        + 1
        0
        PROS OF PANDA
          Be the first to leave a pro
          CONS OF PANDA
            Be the first to leave a con

            related Panda posts

            TensorFlow logo

            TensorFlow

            2.5K
            2.7K
            76
            Open Source Software Library for Machine Intelligence
            2.5K
            2.7K
            + 1
            76
            PROS OF TENSORFLOW
            • 24
              High Performance
            • 16
              Connect Research and Production
            • 13
              Deep Flexibility
            • 9
              Auto-Differentiation
            • 9
              True Portability
            • 2
              Easy to use
            • 2
              High level abstraction
            • 1
              Powerful
            CONS OF TENSORFLOW
            • 8
              Hard
            • 5
              Hard to debug
            • 1
              Documentation not very helpful

            related TensorFlow posts

            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

            Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

            At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

            TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

            Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

            https://eng.uber.com/horovod/

            (Direct GitHub repo: https://github.com/uber/horovod)

            See more

            In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

            Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

            !

            See more
            PyTorch logo

            PyTorch

            872
            967
            33
            A deep learning framework that puts Python first
            872
            967
            + 1
            33
            PROS OF PYTORCH
            • 11
              Easy to use
            • 9
              Developer Friendly
            • 8
              Easy to debug
            • 5
              Sometimes faster than TensorFlow
            CONS OF PYTORCH
            • 2
              Lots of code

            related PyTorch posts

            Server side

            We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

            • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

            • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

            • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

            Client side

            • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

            • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

            • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

            Cache

            • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

            Database

            • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

            Infrastructure

            • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

            Other Tools

            • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

            • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

            Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

            At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

            TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

            Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

            https://eng.uber.com/horovod/

            (Direct GitHub repo: https://github.com/uber/horovod)

            See more
            Anaconda logo

            Anaconda

            315
            345
            0
            The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
            315
            345
            + 1
            0
            PROS OF ANACONDA
              Be the first to leave a pro
              CONS OF ANACONDA
                Be the first to leave a con

                related Anaconda posts

                Shared insights
                on
                Java
                Anaconda
                Python

                I am going to learn machine learning and self host an online IDE, the tool that i may use is Python, Anaconda, various python library and etc. which tools should i go for? this may include Java development, web development. Now i have 1 more candidate which are visual studio code online (code server). i will host on google cloud

                See more
                Guillaume Simler

                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