Anaconda is a Python distribution. It includes a set of pre-installed libraries and packages that are meant for data science, scientific computing, and other tasks.
Python is a programming language. It’s one of the most popular languages used in data science, second only to R. Python has a simple syntax similar to the English language.
Anaconda vs Python
The main difference between Anaconda and Python is that Anaconda is a distribution of Python and R programming languages that are mostly used for data science and machine learning. On the other hand, Python is a high-level general-purpose programming language that can be used for a variety of tasks.
Anaconda is a freemium open source Python and R programming language distribution that seeks to ease package management and deployment for large-scale data processing, predictive analytics, and scientific computing. It is the most popular, free and open-source data science software distribution used by over 6 million users worldwide. Anaconda includes over 1,500 Python packages and the conda package and virtual environment manager for Windows, Linux, and MacOS.
Python is a high-level programming language that can be used on any modern computer operating system. It was created in 1991 by Guido van Rossum and released in 1994. Python is a programming language with an easy to learn syntax that emphasizes readability. Python is a versatile programming language that may be used for a variety of tasks. It’s utilised in things like web development, data science, and software prototyping.
Comparison Table Between Anaconda and Python
|Parameters of Comparison||Anaconda||Python|
|Applications by Users||Anaconda was created primarily to assist with data science and machine learning activities.||Python is utilised in a range of applications, including embedded devices, web development, and networking programmes, in addition to data research and machine learning.|
|Management of Packages||Conda is a package manager that allows you to install Python and non-Python library requirements.||All Python requirements may be installed using the package manager pip.|
|Definition||Anaconda is an industrial data science platform for machine learning and data science that distributes R and Python.||Python is a high-level general-purpose programming language that is frequently used in machine learning and data research.|
|Cataegory||Anaconda is part of the Data Science Tools category.||Python is a programming language that belongs to the category of computer languages.|
|Package manager||Anaconda features its own package manager, conda.||The package manager for Python is pip.|
What is Anaconda?
Anaconda is available in two editions: an open source edition with a community of users, contributors, and companies; and an enterprise edition that comes with enterprise-grade support of Anaconda Inc’s “Anaconda Enterprise” platform. Continuum Analytics was founded in 2011 by Travis Oliphant.
The company’s focus was to develop commercial products around the NumPy project. In 2012 Continuum Analytics hired Peter Wang as co-founder, who led the development of the SciPy library. In 2014 Continuum Analytics raised $6 million in Series A funding from General Catalyst Partners.
Anaconda includes over 250 packages carefully selected to support large-scale data processing, predictive analytics, and scientific computing. Over 15 million users globally have used Anaconda Distribution to simplify package management and deployment. Whether you use Python, R, or Scala, Anaconda Distribution provides optimized binaries of the most popular packages for each language, including NumPy, SciPy, scikit-learn, LightGBM, TensorFlow and many more.
Anaconda Enterprise 2.2 is a platform that lets you automate AI/ML pipelines and manage models across your team in an enterprise setting. It can be deployed on-premise or in the cloud. The company claimed that enterprises of all sizes can use Anaconda Enterprise to harness the power of data science by enabling teams to collaborate on projects and access shared resources.
Anaconda Enterprise extends Anaconda Distribution with collaboration and deployment capabilities that empower organizations to govern their data science assets and models from exploration through production.
What is Python?
Python is taking over the world and is used in everything from web development to machine learning! And if you’re looking for a job in this space, it’s one of the most sought-after skills. The language is relatively easy to learn, and it has a very clean style that makes it appealing to developers of all backgrounds and levels of experience. The fact that it’s a general purpose language means that it can be used in many industries, such as finance and education.
Developers use it to create software prototypes quickly, which then forms the foundation for more complex languages such as Java or CPython is an interpreted language, which means that it is executed line by line at runtime — as opposed to other languages like C and its variants, which need to be compiled before they are run.
This can mean an increase in overall execution time since the code must be parsed each time it runs. But it also gives Python a number of advantages over compiled languages.
The Python community has developed a number of libraries that are useful for machine learning. These libraries include NumPy, SciPy, and Pandas. NumPy is an excellent toolset for performing mathematical operations on large arrays. You can use it to create multidimensional arrays and perform various mathematical operations on them.
Main Differences Between Anaconda and Python
- Although Anaconda is developed in Python, it should be emphasised that Conda is a package manager for any programme that can be used in virtual system environments, whereas pip, the Python package manager, only allows for the installation, upgrade, and removal of Python packages.
- Anaconda is only used for machine learning and data science projects. Python, on the other hand, is a programming language that is used to create a wide range of online applications, networking programmes, and desktop applications.
- Anaconda is a data science and machine learning package that includes Python and R programming languages. Python, on the other hand, is a high-level programming language that can be used for a variety of tasks.
- Conda is Anaconda’s package management, whereas pip is Python’s package manager.
- Anaconda is a data science tool, which implies that anyone who works with it does not need to be a coder. To operate with the Python programming language, however, one needs have a thorough understanding of the language.
The fundamental distinction between Anaconda and Python is that Anaconda is a distribution of the Python and R programming languages for data science and machine learning while Python includes only the Python language.
Python programming language was developed in 1991 by Guido van Rossum. It’s a widely used high-level programming language used for general-purpose programming, created to emphasize on code readability.
Anaconda also includes more than 1,000 data packages as well as the Conda package and virtual environment manager for Windows, Linux, and MacOS. It has been downloaded over 4 million times each month with an active community of contributors.