Anaconda vs Python

Overview

Python

Stacks262.9K

Followers205.4K

Votes6.9K

GitHub Stars69.7K

Forks33.3K

Anaconda

Stacks440

Followers490

Votes0

Anaconda vs Python: What are the differences?

Anaconda is a specialized version of Python that comes with pre-installed tools and packages for data science and scientific computing. Python, on the other hand, is a versatile programming language and is known for its simplicity. Here are the key differences between Anaconda and Python:

Distribution and Package Management: Python refers to the programming language itself, while Anaconda is a distribution of Python. Anaconda includes the Python interpreter along with a comprehensive collection of pre-installed packages and tools for scientific computing and data analysis. It also provides its package manager called Conda, which simplifies the installation and management of packages, environments, and dependencies. Python, on the other hand, requires manual installation of packages using tools like pip, which is the default package manager for Python.
Package Ecosystem: Python has a vast ecosystem of packages and libraries available through the Python Package Index (PyPI). These packages cover a wide range of domains, including web development, data analysis, machine learning, and more. Anaconda, in addition to PyPI packages, offers its curated package repository called Anaconda Repository. It hosts a collection of packages specifically focused on scientific computing and data science. Anaconda also provides an easy way to create isolated environments, known as conda environments, which enable better package and dependency management.
Platform Support: Python is platform-independent, meaning it can run on various operating systems such as Windows, macOS, and Linux. Anaconda, as a distribution of Python, also supports multiple platforms and provides pre-compiled packages that are optimized for each platform.
Target Audience: Python caters to a broad audience, including developers from various domains, hobbyists, and beginners. Anaconda, on the other hand, is specifically designed for data scientists, researchers, and developers in the field of scientific computing and data analysis. It provides a streamlined environment with pre-installed packages and tools that are commonly used in these domains.

In summary, Python is the programming language itself, while Anaconda is a distribution of Python that focuses on scientific computing and data analysis. Anaconda includes its package manager (Conda) and a curated set of packages, providing a convenient and robust environment for working in data-intensive domains. Python, on the other hand, is a versatile language with a vast ecosystem of packages that extends beyond scientific computing.

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Advice on Python, Anaconda

Thomas

Talent Co-Ordinator at Tessian

Mar 11, 2020

Decided

In December we successfully flipped around half a billion monthly API requests from our Ruby on Rails application to some new Python 3 applications. Our Head of Engineering has written a great article as to why we decided to transition from Ruby on Rails to Python 3! Read more about it in the link below.

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Comments

Avy

Apr 8, 2020

Needs adviceon

React Native

Python

Flutter

I've been juggling with an app idea and am clueless about how to build it.

A little about the app:

Social network type app ,
Users can create different directories, in those directories post images and/or text that'll be shared on a public dashboard .

Directory creation is the main point of this app. Besides there'll be rooms(groups),chatting system, search operations similar to instagram,push notifications

I have two options:

@{React Native}|tool:2699|, @{Python}|tool:993|, AWS stack or
@{Flutter}|tool:7180|, @{Go}|tool:1005| ( I don't know what stack or tools to use)

722k views722k

Comments

Davit

Apr 11, 2020

Needs advice

Hi everyone, I have just started to study web development, so I'm very new in this field. I would like to ask you which tools are most updated and good to use for getting a job in medium-big company. Front-end is basically not changing by time so much (as I understood by researching some info), so my question is about back-end tools. Which backend tools are most updated and requested by medium-big companies (I am searching for immediate job possibly)?

Thank you in advance Davit

390k views390k

Comments

Detailed Comparison

Python	Anaconda
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.	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.
-	Stay safe and secure; Deliver on your data strategy; Get to market faster; Maximize flexibility and control
Statistics
GitHub Stars 69.7K	GitHub Stars -
GitHub Forks 33.3K	GitHub Forks -
Stacks 262.9K	Stacks 440
Followers 205.4K	Followers 490
Votes 6.9K	Votes 0
Pros & Cons
Pros 1186 Great libraries 966 Readable code 848 Beautiful code 789 Rapid development 692 Large community Cons 53 Still divided between python 2 and python 3 28 Performance impact 26 Poor syntax for anonymous functions 22 GIL 20 Package management is a mess	No community feedback yet
Integrations
Django	PyCharm Visual Studio Code Atom-IDE Visual Studio

What are some alternatives to Python, Anaconda?

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.

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Swift

Writing code is interactive and fun, the syntax is concise yet expressive, and apps run lightning-fast. Swift is ready for your next iOS and OS X project — or for addition into your current app — because Swift code works side-by-side with Objective-C.

Related Comparisons

Distribution and Package Management: Python refers to the programming language itself, while Anaconda is a distribution of Python. Anaconda includes the Python interpreter along with a comprehensive collection of pre-installed packages and tools for scientific computing and data analysis. It also provides its package manager called Conda, which simplifies the installation and management of packages, environments, and dependencies. Python, on the other hand, requires manual installation of packages using tools like pip, which is the default package manager for Python.
Package Ecosystem: Python has a vast ecosystem of packages and libraries available through the Python Package Index (PyPI). These packages cover a wide range of domains, including web development, data analysis, machine learning, and more. Anaconda, in addition to PyPI packages, offers its curated package repository called Anaconda Repository. It hosts a collection of packages specifically focused on scientific computing and data science. Anaconda also provides an easy way to create isolated environments, known as conda environments, which enable better package and dependency management.
Platform Support: Python is platform-independent, meaning it can run on various operating systems such as Windows, macOS, and Linux. Anaconda, as a distribution of Python, also supports multiple platforms and provides pre-compiled packages that are optimized for each platform.
Target Audience: Python caters to a broad audience, including developers from various domains, hobbyists, and beginners. Anaconda, on the other hand, is specifically designed for data scientists, researchers, and developers in the field of scientific computing and data analysis. It provides a streamlined environment with pre-installed packages and tools that are commonly used in these domains.

Anaconda vs Python