Two major advancements happened in the field of data science and machine learning. One is the development of Anaconda and the next is the Python.
The development of these two programs has given rise to understanding the data clearly. Businesses these days seek the manpower who has skill sets in either of these or both.
Anaconda vs Python
The main difference between Anaconda and Python is that Anaconda is the distribution of Python and R programming languages mainly used for data science and machine learning whereas Python is a high-level general-purpose programming language used for data science and machine learning purposes.
Comparison Table Between Anaconda and Python (in Tabular Form)
|Parameter of Comparison||Anaconda||Python|
|Definition||Anaconda is the enterprise data science platform which distributes R and Python for machine learning and data science||Python is a high-level general-purpose programming language used for machine learning and data science|
|Category||Anaconda belongs to Data Science Tools||Python belongs to Computer Languages|
|Package Manager||Anaconda has conda has 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 not only used in data science and machine learning but also a variety of applications in embedded systems, web development, and networking program|
|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|
What is Anaconda?
Anaconda is a free open source data science tool that focusses on the distribution of 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 which manages the package versions.
Anaconda is a tool that offers all the required package 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 along with easy environment switching.
The installation of Anaconda is very easy and most preferred by non-programmers who are data scientists.
Anaconda is indeed a tool used for developing, testing and training in one single 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 combines with dynamic binding and typing makes 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 common 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 to develop complex and large software applications. The reason is behind the flexible programming paradigms and language features.
Python is extensively used because it is supported by most of the operating systems. 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 along with complex scientific numeric applications.
Python facilitates data analysis and 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 a wonderful find for the data science industry. The main difference between Anaconda and Python is, Anaconda is a distribution of Python and R programming languages for data science and Machine learning tasks whereas Python is a high-level general-purpose programming language whereas.
- The package manager in Anaconda is called Conda while for the Python it is 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 whereas the 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 the python is a programming language which also used in creating many web applications, networking programming, and desktop applications.
- Anaconda is a data science tool which means that it is not necessary for a person who works on it must be a programmer. However, to work in Python programming language, one must have learned the programming language completely
It is the necessity of the businesses to work on data to identify their prospects. Many business strategies can be developed using the analysis made on the data. Python and Anaconda are the best ones to facilitate the same.
The skill-set required to work on Python or anaconda is the same except for knowing what the language and the tool is. Anaconda is the best tool in processing a large amount of data for the required purpose. Python is versatile in creating the applications needed for the data science industry.
Though there are many shortcomings in practical applications of both, the up-gradation of the versions keep happening in the never-ending information technology world.