Two significant advancements happened in the field of data science and machine learning. One is the development of Anaconda, and the next is Python.
The development of these two programs has given rise to an understanding of the data. Businesses seek manpower with skill sets in either of these or both.
- Anaconda is a distribution of the Python programming language, which includes pre-installed packages, libraries, and tools that simplify the setup and management of Python environments for data science and machine learning applications.
- Python is a versatile, high-level programming language emphasizing code readability and allowing developers to write clear, logical code for small and large-scale projects.
- The primary difference between Anaconda and Python is that Anaconda is a distribution of Python specifically designed for data science and machine learning tasks. At the same time, Python is a general-purpose programming language.
Anaconda vs Python
Anaconda is a distribution of Python language used for scientific computing and data science. Python is a high-level, general-purpose data science and machine learning language.
|Parameter of Comparison||Anaconda||Python|
|Definition||Anaconda is the enterprise data science platform that distributes R and Python for machine learning and data science.||Python is a high-level, general-purpose programming language for machine learning and data science.|
|Category||Anaconda belongs to Data Science Tools||Python belongs to Computer Languages|
|Package Manager||Anaconda has conda as its package manager||Python has pip as the package manager|
|User Applications||Anaconda is primarily developed to support data science and machine learning tasks.||Python is used in data science and machine learning and in various applications in embedded systems, web development, and networking programs.|
|Package Management||Package manager conda allows Python as well as Non-Python library dependencies to install.||Package manager pip allows all the Python dependencies to install|
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What is Anaconda?
Anaconda is a free, open-source data science tool that focuses on distributing R and Python programming languages for data science and machine learning tasks. Anaconda aims at simplifying the data management and deployment of the same.
Anaconda is a powerful data science platform for data scientists. The package manager of Anaconda is the conda that manages the package versions.
Anaconda is a tool that offers all the required packages involved in data science at once. The programmers choose Anaconda for its ease of use.
Anaconda is written in Python, and the worthy information on Conda is unlike pip in Python; this package manager checks for the requirement of the dependencies and installs it if it is required.
More importantly, warning signs are given if the dependencies already exist.
Conda very quickly installs the dependencies along with frequent updates. It facilitates creation and loading with equal speed and easy environment switching.
The installation of Anaconda is easy to use and most preferred by non-programmers who are data scientists.
Anaconda is pre-built with more than 1500 Python or R data science packages. Anaconda has specific tools to collect data using Machine learning and Artificial Intelligence.
Anaconda is a tool used to develop, test, and train in one system. The tool can be managed with any project as the environment is easily manageable.
What is Python?
Python is a high–level interpreted, object-oriented high-level programming language named for its dynamic semantics.
The data structures are built-in high-level combined with dynamic binding and typing, making it more convenient for rapid application development.
Python is widely used in developing GUI applications, websites, and applications. It also takes care of the core functionality of the application by constant monitoring and execution of everyday programming tasks.
Code readability in Python is the best feature of the language. The syntax of the code is relatively simple. At times common English words can be used as a command.
Python is so versatile that one can build a customized application without overdoing the code: meaning not writing additional code. This saves time and effort from the programmer’s point of view.
Python is a reliable programming language for developing complex and prominent software applications. The reason is behind the flexible programming paradigms and language features.
Python is extensively used because most operating systems support it. The same code can be run on multiple platforms without recompilation.
Complex software development is simplified using Python. It can be used for desktop and web applications and complex scientific numeric applications.
Python facilitates data analysis and is thus remarkably used in the data science and machine learning industry. Data analysis features of Python help create customized bug data solutions without taking much time.
Main Differences Between Anaconda and Python
- Anaconda and Python are best used for the data science industry. Anaconda is a distribution of Python and R programming languages used for data science and Machine learning tasks. In comparison, Python is a high-level, general-purpose programming language.
- The package manager in Anaconda is called Conda, while for Python, it is a pip.
- Anaconda is written in Python. However, it is to be noted Conda is the package manager of any software which can be used in virtual system environments. In contrast, pip, the package of the manager of Python, facilitates installation, up-gradation, and also uninstallation of python packages only.
- Anaconda is only used for data science and machine learning tasks, whereas python is a programming language used to create many web applications, networking programming, and desktop applications.
- Anaconda is a data science tool, which means it is unnecessary for a person who works on it to be a programmer. However, to work in Python programming language, one must have learned the programming language completely.
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Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.