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What are the differences between Python and an Anaconda?
In this article, we will learn the differences between Python and Anaconda.
What is Python?
Python is an open-source language that places a high value on making code easy to read and understand by indenting lines and providing white space. Python's flexibility and ease of use make it well-suited for a wide range of applications, including but not limited to scientific computing, AI, and data science, as well as the creation and development of online applications. When Python is put through its paces, it is immediately translated into machine language since it is an interpreted language. Some languages, like C++, require compilation before they can be understood.
Proficiency in Python is a major benefit because of how easy it is to understand, develop, execute, and read. This makes Python the most popular and accessible programming language for many applications in the computer industry, including cybersecurity.
What Is Anaconda?
Anaconda is a free and open-source distribution of the Python and R programming languages. Data science, machine learning, predictive analytics, big data processing, and deep learning applications use it to improve package management and deployment.
In 2012, Peter Wang and Travis Oliphant founded Anaconda Inc (Continuum Analytics) to take responsibility for the development and maintenance of Anaconda. Aside from being an Anaconda product, it goes by the names Anaconda Distribution and Anaconda Individual Edition.
There are more than 8 million people using the Anaconda distribution, which offers more than 300 data science programs for Windows, Linux, and macOS.
Some of the packages are as follows −
Jupyter Notebook − It is a collaborative(shareable) notebook that combines live code, visualizations, and text.
Visualization libraries − Bokeh, Datashader, Matplotlib, and Holoviews are several visualization libraries.
Data science libraries − Pandas, NumPy, and Dask are some examples of data science libraries.
Machine learning libraries − TensorFlow, Scikit-learn, and Theano are examples of machine learning libraries.
Installing and updating packages and setting up new environments are both made easier using Conda, an open-source package and environment management system.
Key Differences Between Anaconda and Python
The data science community has greatly benefited from the creation of Anaconda and Python. The main difference between Python and Anaconda is that it is also a high-level general-purpose programming language and the former is a distribution of the Python and R programming languages for data science and machine learning applications.
In contrast to the Python package manager, pip, the Anaconda package manager is known as conda.
While Python is used to create Anaconda, it is important to note that Conda is a package manager for any program that can be used in virtual system environments, whereas pip is a package manager for only Python.
Python is a general-purpose programming language that can be used to make both web and desktop apps, whereas Anaconda is limited to data science and machine learning.
As a data science tool, Anaconda doesn't require its contributors to be programmers. Python programming language is powerful, but it takes a solid grasp of the language to use it effectively.
Difference Between Anaconda and Python
| Comparison Factors | Anaconda | Python |
|---|---|---|
| Description | Anaconda is an open-source Python and R distribution that aims to make scientific computing easier by improving package management and deployment. | Python is a high-level, interpreted, and free programming language that may be used for a wide variety of projects. |
| Uses | In particular, Anaconda was developed to facilitate deep learning, machine learning, and data science projects. | Beyond the areas of data science and machine learning, Python finds use in a wide variety of other fields as well, including embedded systems, computer vision, web development, and networking software. |
| Developed by | The firm founded in 2012 by Peter Wang and Travis Oliphant is responsible for the ongoing development and maintenance of Anaconda. | Guido van Rossum first designed the Python programming language, and the Python Software Foundation has continued the language's development. |
| Package Manager | Conda is the package manager provided by Anaconda. | pip is the package manager provided by the Python programming language. |
| Community | When compared to Python's large user base, Anaconda's is far smaller. | When compared to Anaconda, Python's user base is considerably greater. |
| Support Element | Numerous packages and libraries, like NumPy, SciPy, Panda, Scikit learn, nltk, and Jupiter, are already installed in Anaconda. | Python may be run on any operating system. Numeric numbers, strings, lists, tuples, and dictionaries are all valid inputs. Python code runs properly on a wide variety of systems. |
| Other Programming Language Support | The R and Python programming languages are supported by Anaconda. As a sub-program of Anaconda, Spyder is the Python tool of choice. | Python may be used for both procedural and object-oriented programming, making it a versatile language. |
| Popularity | Anaconda is preferred by the data science community over Python because it solves several common issues both at the beginning and during the development process. | As a general-purpose language with an approachable syntax, it has a high degree of popularity among both beginners and experienced programmers. |
| Package Manager Functioning | The package manager in Anaconda (Conda) may be used to set both Python and non-Python libraries. | The pip package manager will only let you install Python-related packages. |
Conclusion
Data analysis assists businesses in identifying their prospects. The evolution of technology has simplified data management and analysis.
If you have a lot of data that you need to analyze, Anaconda is the ideal program to use. However, Python's flexibility makes it a good choice for programmers creating data science applications.
Anaconda programming employs the conda package manager, while Python programming often makes use of the pip package manager.
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