Since Anaconda comes with Conda’s graphical user interface (GUI) known as the Anaconda Navigator it helps one to quickly launch some of the applications that can be integrated with Conda’s package and environment management system. Amongst the features that set Anaconda’s Navigator as a package, when installed as part of the Anaconda Distribution, more than 300 data science and machine learning packages are loaded and Navigator is a Calbind accompanied by a desktop GUI.
How often do respondents use Python?
Python development services is also becoming more and more popular among data scientists and programmers.
- In the 2021 State of Data Science study from Anaconda, the most popular language among those surveyed was Python, with 63% of respondents saying they use it regularly or always.
- Data scientists are shifting from more conventional, proprietary languages like MATLAB and SAS to the open-source environment in tandem with Python’s growing popularity.
Around 5 billion packages were downloaded from the Anaconda repository in 2021.
Being the backbone of contemporary machine learning and the most widely used data science platform globally, Anaconda is utilized by over 25 million users to fuel their data science and AI operations. Anaconda has been in the forefront of utilizing Python for data science, supporting its thriving community, and maintaining open-source projects that enable innovation in the future. Corporate, academic, and research organizations may use the power of open source for competitive advantage, ground-breaking research, and a better world with our enterprise-grade solutions.
There are some fundamental differences between these two languages and we should know when and where to use both of them. This blog will help you analyse why Anaconda is widely preferred to Python, analysing the key differences and benefits.
What is Anaconda?
We all know about the Python language, but now what is Anaconda? Why is it commonly heard?
Anaconda is a software platform that is made for data science and scientific computing in general. So, in the beginning, it was actually made for the scientific community and now it is particularly used for data science platforms. There are many additions to the Anaconda. Some of them are the
- Individual edition
- Commercial edition
- Team edition
- Enterprise edition
These are popular among the users when the Individual edition and mini conda are free to use. The commercial and team editions are paid services usually opted for small, medium or large-sized companies.
The free edition is the Individual edition is a distribution over Python with a collection of 250+ open-source packages including
Python Vs Anaconda
Capabilities of Python:
The first thing is Python is a software programming language designed towards multiple purposes, for example, you can create your web apps with Python. You can build machine learning data and also apply to Artificial Intelligence models. Within mobile app networking, you can perform multi-tasking with Python.
To be more specific, Python is an object-oriented and multi-purpose programming language that can be used for building a lot of applications from machine learning to web design whereas Anaconda is a distribution system that is built on top of programming languages like Python or Julia.
Python is specifically built for machine learning, data science and Artificial Intelligence Tasks so it is called as a multi-purpose language for programming. On the other side, Anaconda is a distribution or a wrapper on top of Python which will in turn help to perform Machine Learning and data management tasks easily.
Pip or conda:
Secondly, this is more like a technical difference where both – Anaconda and Python require packages to be installed from external environments. So to manage these packages, python comes with pip. pip stands for “Pip-Installs Packages” or “Pip Installs Python”. It is the official package manager for Python which manages automated installation, update and package removal whereas Anaconda has its own package management system namely “conda package manager”.
So, in case if there is a package that is not installed and you are using a Python environment you will be using pip install whereas Anaconda is a distribution of Python, R or other languages.
Pre-installed Libraries:
The next difference is that Python is a crore programming language or module, where we have to explicitly install libraries one by one, Python comes with the standard libraries and is also called as the “batteries-included” language.
Some of the standard libraries found to be installed in Python are:
- pandas
- NumPy & SciPy
- Django & Flask
- TensorFlow & PyTorch
Meanwhile, Anaconda comes with pre-installed libraries, specifically nearly 250+ libraries are installed. It is actually a plug-and-play environment, ready to use with pre-installed languages such as:
- Jupyter Lab
- Jupyter Notebook
- R studio
- Orange
- Glueviz
- Spyder
- Visual Studio Code
Space Required:
In terms of space, Anaconda is not going to occupy more space because it also installs a lot of packages, so the minimum requirement for Anaconda is 4 GB whereas Python is 1 GB of space.
It can be inferred that Python is best for building applications and Anacondas is best for building machine learning-related tasks.
Graphical User Interface (GUI):
The specific reason Python does not have GUI is that it does not come with one out of the box.
The popularity of Anaconda is attributed to its interface for Jupyter Notebook and RStudio.
In contrast, with Anaconda you get Python, R, 250+ pre-installed packages, data science tools, and the graphical user interface Anaconda Navigator.
So, when you install Python, you get a programming language and pip (available in Python 3.4+ and Python 2.7.9+), which enables a user to install additional packages available on the Python Package Index (or PyPi).
A Major Drawback of Python:
This is especially true on the higher levels of abstraction like having Python work on several operating systems at the same time, or different runs on the same hardware, which again also adds another layer of abstractness and is not supported well by Python’s package management system. Most of the projects in the open-source Python software system use many other projects to run their applications; this brings about a chain-like structure known as the “dependency graph. ” Despite being open source, each software is independently controlled and distributed at various intervals.
In Nutshell
Anaconda is a well-known pioneer in Python package management with commitment and responsibility. The entire Python ecosystem trusts Anaconda. -Jarrod LaFon, Vice President, Cloud Development, OpenEye Scientific Anaconda:
Amidst the fact that Conda is most useful for reproducibility and for deploying into different environments. Anatomy Models and applications that have been developed on one computer are relatively simple to transfer to another machine regardless of type. This type of interoperability is still alive today in managing some of the values that created this notion including adoption, sharing and collaboration. Currently, Conda has over 25 million active users, the popularity of which increased by 34% within the December 2020 – December 2021 period.