Anaconda vs Python

If you venture into the world of programming with Python, you will inevitably meet the terms «Anaconda» and «Python». Although they may seem similar at first sight, these two technologies have distinct functions and purposes. To choose the right tool for your projects, you must understand what differentiates them.

Python is known for being a programming language, interpreted, with a simple and intuitive syntax, which makes it a popular choice for beginners. On the other hand, Anaconda is an open source distribution of Python and R programming languages, specialized in data science applications, automatic learning and artificial intelligence.

If you want to find out more, stay with us, on the Newtech Academy blog, to understand the main differences between Anaconda and Python. You will discover in which situations the use of each is recommended and how to choose the most appropriate tool for your projects.

What is Python?

Python is a high -level programming language, known for its versatility and ease of use. Created by Guido Van Rossum and launched for the first time in 1991, Python has evolved over the years in one of the most popular programming languages ​​in the world. It is widely used in various sectors, from the development of web and games applications, to the complex analysis of data and the development of automatic learning and artificial intelligence algorithms.

Advantages of the use of Python

• Easy to learn and use: It is known for its clear and simple syntax, which makes it accessible to beginners. This function reduces the programming barrier and allows developers to write efficient and easy to understand code.

• The independence of the platform: Being an interpreted language, Python can be performed on a variety of operating systems, including Windows, Macos and Linux, without requesting significant changes in the code. This makes it very versatile and suitable for the development of portable applications.

• Object -oriented programming (OOP): It fully supports object -oriented programming, offering the possibility of creating complex and modular applications. Oop allows developers to structure the code in reusable and manageable components, facilitating the maintenance and downsizing of applications.

• Large community and active support: Benefits from a vast and active global community of developers and enthusiasts who continually contribute to the expansion and improvement of language. This community offers an abundance of resources, including documentation, tutorials and support forums.

• Extended libraries and framework: It is provided with an impressive collection of libraries and framework that cover almost every need for development. For example, for data analysis and their management, bookcases such as Panda and number are recommended. For the development of web applications, Django and Ballon. And in the field of automatic learning and artificial intelligence between the most used framework are Tensorflow and Pytorch.

Python is also highly appreciated for its flexibility, being able to resist both procedural and object -oriented programming.

As mentioned at the beginning of the article, Anaconda is an open source distribution, special designed for data science, automatic learning and artificial intelligence applications. First launched in 2012 by Continuum Analytics (now Anaconda, Inc.), Anaconda has quickly become a standard science standard thanks to its complete package of tools and bookstores.

Advantages of the use of Anaconda

• Package management: It comes with its package and the medium management system, called «Conda». Consions simplifies the installation, updating and elimination of their packages and addictions, while offering the possibility of creating isolated environments for various projects. This allows the efficient management of addictions and avoid conflicts between the packages.

• Multiplatform support: Like Python, Anaconda is compatible with more platforms, allowing developers to develop and implement applications on different operating systems without changes to the code.

• Specialized data science tools: Includes a series of popular tools and ideas (integrated development environment) for data science, such as Jupyter Notebook, Spyder and RStudio. These tools provide an interactive environment for the analysis and display of data, which is made by the favorite choice of many scientists in the data field.

• Easy collaboration: Facilitates the sharing of the code and collaborates with others through its cloud platform, Anaconda Cloud. This allows teams or individuals to work together on the same project, simplifying the management and distribution of packages and environments.

• Pre -installed packages for data science: They benefit from over 250 pre -installed packages for data science and scientific calculation, eliminating the need for manual installation. These include number, Panda, Matplotlib, Scipy and Scikit-Learn.

Here are 3 of the most remarkable differences between these two platforms:

1. Package management: Python USA «Pip«For the management of the packaging, while Anaconda USA»conda«Conda offers advanced features, such as the creation of isolated environments for different projects, thus facilitating the management of addiction and avoiding conflicts between the packages.
2. Installation and setting of the environment: Anaconda is provided with a predefined set of packages and data science tools, which makes it convenient for beginners quickly configure their work environment. On the contrary, Python requires manual installation of the necessary bookstores and tools.
3. Use and combine: Python is a generalist language, suitable for a wide range of applications, including web development and back -End. On the contrary, Anaconda is more suitable for data science applications, automatic learning and scientific calculation thanks to its packages and specialized tools.