Anaconda vs Python: Exploring Their Key Differences

If you're just getting started with Python programming, you would have come across two terms - Anaconda and Python. While they may seem like synonyms, they are quite different in their functionality and purpose.

In this article, we will explore the key differences between Anaconda and Python and when each of them is used.

What is Python?

If you’re curious to learn more about Python, check out our full guide - What is Python? Below, we’ve summarised the key points.

Overview of Python

Python programming language - source

Python is a versatile, high-level, and interpreted programming language that is widely used for various applications such as web development, data analysis, artificial intelligence, machine learning, and scientific computing.

To help you understand Python more, here's our Python cheat sheet:

Python cheat sheet - source

Features and Advantages of Python

Easy to Learn and Use

One of the major advantages of Python is its simple syntax.

This makes it easy for beginners to learn Python compared to other programming languages. It also has a large standard library that provides ready-to-use functions for various tasks, saving developers time and effort.

Platform Independent

Python is an interpreted language, meaning it can run on different operating systems without the need for any changes in the code. This makes it highly versatile and portable.

Object-Oriented Programming

Python supports object-oriented programming (OOP), which allows developers to create reusable code and build complex applications by breaking them down into smaller, manageable objects. Our course on Object-Oriented Programming in Python will provide you with a good foundation.

Large Community Support

Python has a large and active community of developers who contribute to its growth and development. This community provides support, resources, and tools for beginners and experienced developers alike.

Thinking of finding a good community of beginners in Python? Come join our DataCamp Slack Community!

Extensive Libraries

Python has a vast collection of libraries and frameworks that provide solutions for various tasks.

Such tasks include:

• Data manipulation and cleaning (pandas)
• Scientific computing (NumPy & SciPy)
• Web development (Django & Flask)
• Machine learning (TensorFlow & PyTorch)

These libraries make programming in Python efficient and convenient.

Are you curious to know more about some common tasks possible with Python? Here’s a cheat sheet of how pandas can be used in Python for data manipulation:

Pandas cheat sheet - source

For a more detailed guide on data manipulation and cleaning, I recommend our Data Manipulation with pandas course.

What is Anaconda?

Overview of Anaconda

Anaconda is an open-source distribution of the Python and R programming languages that is used for data science, machine learning, and artificial intelligence applications.

It includes over 250 popular data science packages and management tools for simplifying package installation and deployment.

Features and Advantages of Anaconda

Package Management

Anaconda comes with its own package, environment, and dependency management system called "conda".

Here’s a helpful cheat sheet to help you get started with using conda:

Conda cheat sheet - source

This makes it easier to install, update, and remove packages and their dependencies.

Cross-Platform Support

Similar to Python, Anaconda is also cross-platform compatible, allowing users to develop and deploy applications on different operating systems without any changes in the code.

Data Science-Focused Tools

Anaconda includes popular data science tools and IDEs such as Jupyter Notebook, Spyder, and RStudio. These tools provide an interactive environment for data analysis and visualization, making it a preferred choice among data scientists.

Easy Collaboration

Anaconda allows users to easily share their code and collaborate with others through its cloud-based platform Anaconda Cloud.

Anaconda Cloud - source

This makes it convenient for teams or individuals working on the same project.

Key Differences between Anaconda and Python

While both Anaconda and Python are programming languages used for similar purposes, there are some key differences between the two.

1. Package Management

One of the major differences between Anaconda and Python is in their package management systems. While Python uses "pip" for managing packages, Anaconda uses its own system called "conda".

Conda offers more advanced features, such as creating isolated environments for different projects, making it easier to manage dependencies.

2. Environment Setup

Anaconda comes with its own set of pre-installed data science packages and tools, making it convenient for beginners to set up their environment quickly. Python, on the other hand, requires users to manually install the required libraries and tools.

3. Use Cases and Suitability

While Python is a general-purpose language, Anaconda is more focused on data science and machine learning applications. Therefore, it may not be the best choice for developers working on projects that do not involve data analysis or scientific computing.

When to Use Each Tool

Choosing Between Anaconda and Python

The decision to use either Anaconda or Python highly depends on the specific requirements and goals of a project.

If the project involves data analysis, machine learning, or scientific computing, then Anaconda may be the more suitable choice due to its pre-installed packages and data science tools.

If the project is more general-purpose, like requiring web development or building a backend, and does not require specialized tools, then Python may be a better choice.

Ready-to-Use Pre-Installed Packages in Anaconda

Anaconda comes with a wealth of pre-installed packages that streamline data analysis and scientific computing. These packages, over 250 in total, cover a wide range of functionalities and eliminate the need for manual installation. Notable examples include:

NumPy

NumPy, short for Numerical Python, is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Pandas

Pandas data analytics library - source

Pandas provides flexible data structures designed to make working with structured data fast, easy, and expressive. It is a fundamental high-level building block for doing practical, real-world data analysis.

Matplotlib

Matplotlib is a plotting library used for 2D graphics in Python programming language. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK. Learn more about matplotlib in our Introduction to Data Visualization with Matplotlib course.

SciPy

SciPy is a free and open-source Python library used for scientific and technical computing. It is a collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. This SciPy tutorial provides a good overview of using SciPy to learn linear algebra.

Scikit-Learn

Scikit-learn machine learning library - source

Scikit-learn is a machine learning library for Python. It features various machine learning algorithms like support vector machine, random forests, and k-nearest neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy.

Some algorithms included in scikit-learn are:

• Supervised Learning (Classification, Regression),
• Unsupervised Learning (Clustering, Dimensionality Reduction)
• Semi-Supervised Learning

Do check out our Supervised Learning with scikit-learn course for a good introduction.

Flexibility

Python's flexibility is another aspect that sets it apart. It is a dynamically typed language, which means variables can be defined and changed as needed, allowing developers to write and modify code more quickly and easily.

Additionally, Python supports both procedural and object-oriented programming paradigms, giving developers the flexibility to choose the approach that best fits their project.

This flexibility, combined with its readability and simplicity, makes Python an excellent choice for both beginners and experienced developers.

Learning Curve Considerations

The learning curve for both Anaconda and Python may differ for individuals based on their prior experience and expertise.

However, in general, Python is considered to have a more accessible learning curve due to its simple syntax and ease of use. On the other hand, Anaconda may require some additional knowledge of data science tools and concepts for effective usage.

Wrap Up

To wrap things up, let's go over what we've covered so far. Anaconda and Python are two powerful tools for data analysis, machine learning, scientific computing, and general-purpose programming.

Anaconda offers a convenient all-in-one solution with its pre-installed packages and development environment management through the Anaconda Navigator. On the other hand, Python's flexibility and simplicity make it an excellent choice for a wide range of projects.

Interested in learning more about Python? You'll like our courses on Python, such as the Introduction to Python course, or our Python Programming Skill Track. If you’re thinking about becoming a data scientist, our Data Scientist with Python career track is for you.