Alternatives to Panda logo

Alternatives to Panda

Pandas, NumPy, Grizzly, JavaScript, and Git 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. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • jQuery
    jQuery

    jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML. ...

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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PROS OF PANDAS
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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

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    Fundamental package for scientific computing with Python
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      related NumPy 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

      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

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      A framework for building scalable server applications
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          JavaScript logo

          JavaScript

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            Like it or not, JS is part of the web standard
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            Expansive community
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            Everyone use it
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            Can be used in backend, frontend and DB
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          • 3
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          related JavaScript posts

          Zach Holman

          Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

          But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

          But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

          Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

          See more
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M views

          How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

          Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

          Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

          https://eng.uber.com/distributed-tracing/

          (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

          Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

          See more
          Git logo

          Git

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          • 1
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          • 1
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          CONS OF GIT
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          • 1
            Doesn't scale for big data

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          Simon Reymann
          Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M views

          Our whole DevOps stack consists of the following tools:

          • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
          • Respectively Git as revision control system
          • SourceTree as Git GUI
          • Visual Studio Code as IDE
          • CircleCI for continuous integration (automatize development process)
          • Prettier / TSLint / ESLint as code linter
          • SonarQube as quality gate
          • Docker as container management (incl. Docker Compose for multi-container application management)
          • VirtualBox for operating system simulation tests
          • Kubernetes as cluster management for docker containers
          • Heroku for deploying in test environments
          • nginx as web server (preferably used as facade server in production environment)
          • SSLMate (using OpenSSL) for certificate management
          • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
          • PostgreSQL as preferred database system
          • Redis as preferred in-memory database/store (great for caching)

          The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

          • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
          • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
          • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
          • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
          • Scalability: All-in-one framework for distributed systems.
          • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
          See more
          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 8.7M views

          Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

          It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

          I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

          We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

          If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

          The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

          Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

          See more
          GitHub logo

          GitHub

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          • 4
            Loved by developers
          • 4
            Easy to use and collaborate with others
          • 3
            Ci
          • 3
            IAM
          • 3
            Nice to use
          • 3
            Easy deployment via SSH
          • 2
            Easy to use
          • 2
            Leads the copycats
          • 2
            All in one development service
          • 2
            Free private repos
          • 2
            Free HTML hostings
          • 2
            Easy and efficient maintainance of the projects
          • 2
            Beautiful
          • 2
            Easy source control and everything is backed up
          • 2
            IAM integration
          • 2
            Very Easy to Use
          • 2
            Good tools support
          • 2
            Issues tracker
          • 2
            Never dethroned
          • 2
            Self Hosted
          • 1
            Dasf
          • 1
            Profound
          CONS OF GITHUB
          • 54
            Owned by micrcosoft
          • 38
            Expensive for lone developers that want private repos
          • 15
            Relatively slow product/feature release cadence
          • 10
            API scoping could be better
          • 9
            Only 3 collaborators for private repos
          • 4
            Limited featureset for issue management
          • 3
            Does not have a graph for showing history like git lens
          • 2
            GitHub Packages does not support SNAPSHOT versions
          • 1
            No multilingual interface
          • 1
            Takes a long time to commit
          • 1
            Expensive

          related GitHub posts

          Johnny Bell

          I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

          I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

          I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

          Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

          Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

          With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

          If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

          See more
          Russel Werner
          Lead Engineer at StackShare · | 32 upvotes · 2.5M views

          StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

          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

          See more
          Python logo

          Python

          240.7K
          196.3K
          6.9K
          A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
          240.7K
          196.3K
          + 1
          6.9K
          PROS OF PYTHON
          • 1.2K
            Great libraries
          • 961
            Readable code
          • 846
            Beautiful code
          • 787
            Rapid development
          • 689
            Large community
          • 435
            Open source
          • 393
            Elegant
          • 282
            Great community
          • 272
            Object oriented
          • 220
            Dynamic typing
          • 77
            Great standard library
          • 59
            Very fast
          • 55
            Functional programming
          • 49
            Easy to learn
          • 45
            Scientific computing
          • 35
            Great documentation
          • 29
            Productivity
          • 28
            Easy to read
          • 28
            Matlab alternative
          • 23
            Simple is better than complex
          • 20
            It's the way I think
          • 19
            Imperative
          • 18
            Free
          • 18
            Very programmer and non-programmer friendly
          • 17
            Powerfull language
          • 17
            Machine learning support
          • 16
            Fast and simple
          • 14
            Scripting
          • 12
            Explicit is better than implicit
          • 11
            Ease of development
          • 10
            Clear and easy and powerfull
          • 9
            Unlimited power
          • 8
            It's lean and fun to code
          • 8
            Import antigravity
          • 7
            Print "life is short, use python"
          • 7
            Python has great libraries for data processing
          • 6
            Although practicality beats purity
          • 6
            Flat is better than nested
          • 6
            Great for tooling
          • 6
            Rapid Prototyping
          • 6
            Readability counts
          • 6
            High Documented language
          • 6
            I love snakes
          • 6
            Fast coding and good for competitions
          • 6
            There should be one-- and preferably only one --obvious
          • 6
            Now is better than never
          • 5
            Great for analytics
          • 5
            Lists, tuples, dictionaries
          • 4
            Easy to learn and use
          • 4
            Simple and easy to learn
          • 4
            Easy to setup and run smooth
          • 4
            Web scraping
          • 4
            CG industry needs
          • 4
            Socially engaged community
          • 4
            Complex is better than complicated
          • 4
            Multiple Inheritence
          • 4
            Beautiful is better than ugly
          • 4
            Plotting
          • 3
            If the implementation is hard to explain, it's a bad id
          • 3
            Special cases aren't special enough to break the rules
          • 3
            Pip install everything
          • 3
            List comprehensions
          • 3
            No cruft
          • 3
            Generators
          • 3
            Import this
          • 3
            It is Very easy , simple and will you be love programmi
          • 3
            Many types of collections
          • 3
            If the implementation is easy to explain, it may be a g
          • 2
            Batteries included
          • 2
            Should START with this but not STICK with This
          • 2
            Powerful language for AI
          • 2
            Can understand easily who are new to programming
          • 2
            Flexible and easy
          • 2
            Good for hacking
          • 2
            A-to-Z
          • 2
            Because of Netflix
          • 2
            Only one way to do it
          • 2
            Better outcome
          • 1
            Sexy af
          • 1
            Slow
          • 1
            Securit
          • 0
            Ni
          • 0
            Powerful
          CONS OF PYTHON
          • 53
            Still divided between python 2 and python 3
          • 28
            Performance impact
          • 26
            Poor syntax for anonymous functions
          • 22
            GIL
          • 19
            Package management is a mess
          • 14
            Too imperative-oriented
          • 12
            Hard to understand
          • 12
            Dynamic typing
          • 12
            Very slow
          • 8
            Indentations matter a lot
          • 8
            Not everything is expression
          • 7
            Incredibly slow
          • 7
            Explicit self parameter in methods
          • 6
            Requires C functions for dynamic modules
          • 6
            Poor DSL capabilities
          • 6
            No anonymous functions
          • 5
            Fake object-oriented programming
          • 5
            Threading
          • 5
            The "lisp style" whitespaces
          • 5
            Official documentation is unclear.
          • 5
            Hard to obfuscate
          • 5
            Circular import
          • 4
            Lack of Syntax Sugar leads to "the pyramid of doom"
          • 4
            The benevolent-dictator-for-life quit
          • 4
            Not suitable for autocomplete
          • 2
            Meta classes
          • 1
            Training wheels (forced indentation)

          related Python posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M views

          How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

          Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

          Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

          https://eng.uber.com/distributed-tracing/

          (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

          Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

          See more
          Nick Parsons
          Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.9M views

          Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

          We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

          We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

          Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

          #FrameworksFullStack #Languages

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          jQuery logo

          jQuery

          190.4K
          67K
          6.6K
          The Write Less, Do More, JavaScript Library.
          190.4K
          67K
          + 1
          6.6K
          PROS OF JQUERY
          • 1.3K
            Cross-browser
          • 957
            Dom manipulation
          • 809
            Power
          • 660
            Open source
          • 610
            Plugins
          • 459
            Easy
          • 395
            Popular
          • 350
            Feature-rich
          • 281
            Html5
          • 227
            Light weight
          • 93
            Simple
          • 84
            Great community
          • 79
            CSS3 Compliant
          • 69
            Mobile friendly
          • 67
            Fast
          • 43
            Intuitive
          • 42
            Swiss Army knife for webdev
          • 35
            Huge Community
          • 11
            Easy to learn
          • 4
            Clean code
          • 3
            Because of Ajax request :)
          • 2
            Powerful
          • 2
            Nice
          • 2
            Just awesome
          • 2
            Used everywhere
          • 1
            Improves productivity
          • 1
            Javascript
          • 1
            Easy Setup
          • 1
            Open Source, Simple, Easy Setup
          • 1
            It Just Works
          • 1
            Industry acceptance
          • 1
            Allows great manipulation of HTML and CSS
          • 1
            Widely Used
          • 1
            I love jQuery
          CONS OF JQUERY
          • 6
            Large size
          • 5
            Sometimes inconsistent API
          • 5
            Encourages DOM as primary data source
          • 2
            Live events is overly complex feature

          related jQuery posts

          Kir Shatrov
          Engineering Lead at Shopify · | 22 upvotes · 2M views

          The client-side stack of Shopify Admin has been a long journey. It started with HTML templates, jQuery and Prototype. We moved to Batman.js, our in-house Single-Page-Application framework (SPA), in 2013. Then, we re-evaluated our approach and moved back to statically rendered HTML and vanilla JavaScript. As the front-end ecosystem matured, we felt that it was time to rethink our approach again. Last year, we started working on moving Shopify Admin to React and TypeScript.

          Many things have changed since the days of jQuery and Batman. JavaScript execution is much faster. We can easily render our apps on the server to do less work on the client, and the resources and tooling for developers are substantially better with React than we ever had with Batman.

          #FrameworksFullStack #Languages

          See more
          Ganesa Vijayakumar
          Full Stack Coder | Technical Lead · | 19 upvotes · 4.7M views

          I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

          I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

          As per my work experience and knowledge, I have chosen the followings stacks to this mission.

          UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

          Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

          Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

          Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

          Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

          Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

          Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

          Happy Coding! Suggestions are welcome! :)

          Thanks, Ganesa

          See more