Alternatives to Panda logo

Alternatives to Panda

Pandas, NumPy, Grizzly, Google Drive, and CloudFlare are the most popular alternatives and competitors to Panda.
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What is Panda and what are its top alternatives?

Panda is a popular data manipulation library in Python that offers data structures and functions for data analysis tasks. It provides easy-to-use data structures like DataFrame which allows users to manipulate and analyze data effectively. Panda's key features include data cleaning, reshaping, merging, slicing, and groupby operations. However, some limitations of Panda include slower performance with larger datasets and a steeper learning curve for beginners.

  1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
  2. Dask: Dask is a flexible parallel computing library in Python that enables efficient parallel computing and handling of larger than memory datasets. It provides dynamic task scheduling and parallel computing capabilities.
  3. Modin: Modin is a scalable and fast distributed dataframe library in Python that aims to optimize data processing tasks using parallel computing techniques. It provides seamless integration with Pandas syntax.
  4. Vaex: Vaex is a high-performance Python library for lazy and out-of-core data processing. It is designed to handle large datasets efficiently through memory mapping and provides various advanced data manipulation functions.
  5. Datarah: Datarah is a data manipulation library in Python that focuses on simplifying data cleaning, manipulation, and analysis tasks in a user-friendly manner. It offers an intuitive interface for handling complex data operations.
  6. Koalas: Koalas is an open-source Python library that provides a familiar Pandas API on top of Apache Spark for scalable data processing. It allows users to leverage Spark's distributed computing capabilities with Pandas syntax.
  7. Pyspark: PySpark is the Python API for Apache Spark, a popular distributed computing framework. It enables faster data processing and analysis on large datasets using Spark's parallel computing architecture.
  8. Cudf: Cudf is a Python GPU DataFrame library built on top of the RAPIDS ecosystem. It leverages GPU acceleration for data processing tasks, providing significant speedups compared to CPU-based processing.
  9. DolphinDB: DolphinDB is a distributed analytical processing database system that offers efficient and scalable data processing capabilities for big data analytics. It provides high-performance data manipulation functions for in-memory and distributed computing.
  10. Arrow: Apache Arrow is a cross-language development platform for in-memory data processing. It provides a standardized columnar memory format for efficient data interchange between different systems and languages.

Top Alternatives to Panda

  • 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. ...

  • NumPy
    NumPy

    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. ...

  • Grizzly
    Grizzly

    Writing scalable server applications in the Java™ programming language has always been difficult. Before its advent, thread management issues made it impossible for a server to scale to thousands of users. This framework has been designed to help developers to take advantage of the Java™ NIO API. ...

  • Google Drive
    Google Drive

    Keep photos, stories, designs, drawings, recordings, videos, and more. Your first 15 GB of storage are free with a Google Account. Your files in Drive can be reached from any smartphone, tablet, or computer. ...

  • CloudFlare
    CloudFlare

    Cloudflare speeds up and protects millions of websites, APIs, SaaS services, and other properties connected to the Internet. ...

  • Dropbox
    Dropbox

    Harness the power of Dropbox. Connect to an account, upload, download, search, and more. ...

  • Amazon CloudFront
    Amazon CloudFront

    Amazon CloudFront can be used to deliver your entire website, including dynamic, static, streaming, and interactive content using a global network of edge locations. Requests for your content are automatically routed to the nearest edge location, so content is delivered with the best possible performance. ...

  • Akamai
    Akamai

    If you've ever shopped online, downloaded music, watched a web video or connected to work remotely, you've probably used Akamai's cloud platform. Akamai helps businesses connect the hyperconnected, empowering them to transform and reinvent their business online. We remove the complexities of technology, so you can focus on driving your business faster forward. ...

Panda alternatives & related posts

Pandas logo

Pandas

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

    Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?

    See more
    NumPy logo

    NumPy

    3K
    14
    Fundamental package for scientific computing with Python
    3K
    14
    PROS OF NUMPY
    • 10
      Great for data analysis
    • 4
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    CONS OF NUMPY
      Be the first to leave a con

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      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

      Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?

      See more
      Grizzly logo

      Grizzly

      8
      0
      A framework for building scalable server applications
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      PROS OF GRIZZLY
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          I created a simple upload/download functionality for a web application and connected it to Mongo, now I can upload, store and download files. I need advice on how to create a SPA similar to Dropbox or Google Drive in that it will be a hierarchy of folders with files within them, how would I go about creating this structure and adding this functionality to all the files within the application?

          Intuitively creating a react component and adding it to a File object seems like the way to go, what are some issues to expect and how do I go about creating such an application to be as fast and UI-friendly as possible?

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          CloudFlare logo

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          Johnny Bell

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          One of the issues I had with Netlify was I wanted to gzip my JavaScript files, I had this setup in my #Webpack file, however Netlify didn't offer an easy way to set this.

          Over the weekend I decided I wanted to know more about how #DevOps worked so I decided to switch from Netlify to Amazon S3. Instead of creating any #Git Webhooks I decided to use Buddy for my pipeline and to run commands. Buddy is a fantastic tool, very easy to setup builds, copying the files to my Amazon S3 bucket, then running some #AWS console commands to set the content-encoding of the JavaScript files. - Buddy is also free if you only have a few pipelines, so I didn't need to pay anything 🤙🏻.

          When I made these changes I also wanted to monitor my code, and make sure I was keeping up with the best practices so I implemented Code Climate to look over my code and tell me where there code smells, issues, and other issues I've been super happy with it so far, on the free tier so its also free.

          I did plan on using Amazon CloudFront for my SSL and cacheing, however it was overly complex to setup and it costs money. So I decided to go with the free tier of CloudFlare and it is amazing, best choice I've made for caching / SSL in a long time.

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          Intuitively creating a react component and adding it to a File object seems like the way to go, what are some issues to expect and how do I go about creating such an application to be as fast and UI-friendly as possible?

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          Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

          #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

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          Julien DeFrance
          Principal Software Engineer at Tophatter · | 16 upvotes · 3.2M views

          Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

          I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

          For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

          Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

          Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

          Future improvements / technology decisions included:

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          Akamai

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          The leading platform for cloud, mobile, media and security across any device, anywhere.
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