Major Difference Between Anaconda And Python Programming

What Is Anaconda Programming?

Anaconda is a free and open source distribution of the Python and R programming languages and it is used in data science, machine learning, predictive analytics, large-scale data processing and deep learning-related applications aiming to simplify package management and deployment.

Anaconda is developed and maintained by Anaconda Inc (Continuum Analytics), which was founded by Peter Wang and Travis Oliphant in 2012. As an Anaconda product, it is also referred to as Anaconda Distribution or Anaconda Individual Edition.

Anaconda distribution is used by over 8 million users and includes more than 300 data science packages suitable for Windows, Linux and MacOS. Some of the packages include:

• Jupyter Notebook, a shareable notebook that combines live code, visualizations and text.
• Visualization libraries such as Bokeh, Datashader, Matplotlib and Holoviews.
• Data science libraries such as pandas, NumPy and Dask.
• Machine learning libraries such as TensorFlow, Scikit-learn and Theano.
• An open-source package and environment management system referred to as Conda which makes it easy to install/update packages and create/load environments.

What You Need To know About Anaconda Programming

1. Anaconda is a free and open source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment.  
2. Anaconda is mainly developed to support data science, deep learning and machine learning tasks.
3. Anaconda is developed and maintained by Anaconda Inc (Continuum Analytics), which was founded by Peter Wang and Travis Oliphant in 2012.
4. Anaconda provides Conda as the package manager.
5. Package manager of Anaconda (Conda) allows installation of python and non-python library dependencies.  
6. Generally Anaconda has a small community than Python.
7. Anaconda provides a bunch of pre-installed libraries and packages such as NumPy, SciPy, Panda, Scikit learn, nltk and Jupiter.
8. Anaconda allows business organizations to develop enterprise level, scalable and secure applications.
9. Generally with availability of more than 300 libraries for data science, Anaconda makes data science and machine learning tasks easier.
10. Data science community prefers Anaconda to Python as it solves a lot of common issues at the initial stage as well as throughout the development process.
11. Anaconda comes with a wide variety of tools to easily collect data from various sources using various machine learning and AI algorithms.
12. Anaconda works for R and python programming language. Spyder (sub-application of Anaconda) is used for Python.

Also Read: Difference Between Object Code And Source Code

What Is Python Programming?

Python is an open source interpreted, high-level and general purpose programming language. Python is not only used in creating software prototypes, data science and machine learning but also in a variety of applications such as  embedded systems, computer vision, web development and networking program. Created by Guido Van Rossum and first released in 1991, Python’s design Philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is cross-platform. It supports data types such as numerical values, strings, lists, tuples and dictionaries.  You can move Python programs from one platform to another and run it without any changes. Python is design to be highly readable. It uses English keywords frequently whereas other languages use punctuation and it has fewer syntactical constructions than other languages.

Python is a multi-paradigm programming language and supports procedural programming and object-oriented programming. Python is often described as a ‘’batteries included’’ language due to its comprehensive standard library.  You can add low-level modules to the Python Interpreter. These modules enable programmers to add to or customize their tools to be more efficient.

Python provides interface to all major commercial databases. Python supports GUI applications that can be created and ported to many system calls, libraries and windows systems such as Windows MFC, Macintosh and the X Window system of Unix.

What You Need To Know About Python Programming

1. Python is a high-level, interpreted, interactive and object-oriented scripting language.
2. Python is not only used in data science and machine learning but also in a variety of applications such as  embedded systems, computer vision, web development and networking program.
3. Guido van Rossum designed Python language and Python Software Foundation further developed the language.
4. Python language provides pip as the package manager.
5. Package manager of python (pip) only allows installation of python dependencies.  
6. Python has a large community than Anaconda.
7. Python is cross-platform. It supports data types such as numerical values, strings, lists, tuples and dictionaries.  You can move Python programs from one platform to another and run it without any changes.
8. Python is a multi-paradigm programming language and supports procedural programming and object-oriented programming.
9. Python is an interpreter-based language. The interpreter reads the source code line by line and in this regard, python is a slow language when compared to compiler-based languages such as C, C++.
10. Python is simple and easy to learn. The simplicity of this language helps to develop algorithms and solve problems within a minimum time. It is easier to read and write Python programs compared to other languages like C++, Java, C#.
11. With python, it is easier to build powerful graphical User Interfaces. It also supports databases such as MySQL and MMSQL.
12. Being a general-purpose language with simple easy-to-use syntax, it is popular among beginners as well as a developer.

Also Read: Difference Between Procedural And Object Oriented Programming

Difference Between Anaconda And Python Programming In Tabular Form

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