Anaconda vs. Python: What’s the Difference?
Updated Jan 9, 2025
Anaconda vs. Python: What’s the Difference?
When data science teams start a new project, they need to determine which programming languages or tools would be most suitable. Every technology — including Anaconda and Python — has a different learning curve, core capabilities, potential performance, and other factors to consider.
Python is one of the most popular programming languages for data science, machine learning, web development, and more. Anaconda is the leading data science platform and an open-source distribution of Python and R. The key distinction is that Python is a programming language, while Anaconda is a distribution of Python tailored for data science.
Here’s a quick overview of the differences:
- Anaconda is a distribution of the Python language with additional tools and packages.
- Standalone Python is better suited to lightweight projects or web development.
- Anaconda is ideal for most data science, AI, and machine learning projects.
- Anaconda supports R and other programming languages besides Python.
In this article, we’ll explore Python and Anaconda in more depth so that you can understand their differences, how they relate to each other, and which is right for your projects.
What Is Python?
Python is a leading general-purpose programming language that was invented in the 1980s and released in 1991 as an easy-to-use option for a variety of projects. Its simplicity, readability, and extensive library ecosystem have led to the widespread adoption of Python for web development, data science, and more.
More specifically, Python is a high-level, interpreted language that supports multiple programming paradigms, such as object-oriented and functional programming. Python is versatile and highly portable, allowing developers to accomplish a broad range of functions without needing to add many external libraries. As the most popular programming language, it is often praised for its readability, ease of use, and adaptability across different applications.
What Is Anaconda?
Anaconda is a distribution that hosts essential Python packages, tools like Jupyter and RStudio, and a package manager called Conda. This simplifies package management and deployment for data science, machine learning, and scientific computing.
Anaconda, while primarily focused on making data science accessible, also offers solutions that enhance security and support AI-driven development. For example, its Package Security Manager helps users ensure the integrity of their software environments, which can be difficult when leveraging AI models in production.
In addition, Anaconda provides enterprise-level solutions that extend Python’s open-source capabilities with features like team collaboration, security, and scalability. These solutions are designed to support large organizations that use Python for data science, AI, and machine learning.
Anaconda vs. Python: Core Differences
The primary difference between Python and Anaconda is that Python is a programming language and Anaconda is a platform for AI and data science that uses the Python language. Let’s explore other key differences between Python and Anaconda, focusing on their key features, integrations, performance, and other criteria.
- Installation and Setup
When you install Python, you get the programming language, standard library, and pip package manager. You’d then need to install additional packages before getting started on most projects. Anaconda comes ready to go with Python, R, hundreds of pre-installed packages, and more. - Package Management
Python’s native package manager pip can install packages from the Python Package Index. However, pip is limited to Python libraries, sometimes requires a compatible compiler, and installs dependencies without verifying all requirements are met.
Anaconda comes with the Conda package manager, which simplifies the process of installing, verifying, managing, and updating Python packages. Conda can manage both Python packages and non-Python dependencies in isolated environments, making it more versatile than Python’s native package manager for data science projects. - Integrated Tools and Libraries
Along with the Python Standard Library, hundreds of thousands of open-source packages are available on PyPI. This vast ecosystem of libraries allows data scientists to tackle complex problems easily.
Anaconda includes Python and a wide range of pre-installed libraries, such as NumPy, SciPy, pandas, and scikit-learn, making it easy for users to get started with data science without having to install these packages separately. Each package in the Anaconda repository also comes with signature verification, ensuring that dependencies are secure and thoroughly vetted. This layer of security gives organizations confidence in using these libraries at scale, knowing they are working with reliable and safe tools backed by Anaconda’s support.
Other tools like Jupyter Notebooks, Spyder, and RStudio — which provide an interactive environment ideal for creating and sharing code — come with Anaconda as well. These built-in tools offer smoother workflows for data science than the limited core features of standalone Python. - Performance and Resource Usage
The performance of a standalone Python project varies depending on the type and complexity of the application, the number of dependencies, and many other factors. In general, Python might achieve slightly better performance for very small projects with few libraries because there’s less overhead. While Anaconda might be heavier due to the bundled tools and libraries, it also optimizes performance for data-intensive tasks. - Usability in Data Science and Machine Learning
Since Python is a general-purpose programming language, users might need to install and configure several packages manually before they have an environment suitable for data science and machine learning projects.
In contrast, Anaconda is tailored for data science, AI, and machine learning, making it easier to get started with these disciplines. Anaconda’s pre-configured environments help data scientists avoid manually installing libraries, dealing with compatibility issues, and other obstacles to setting up a new data project. - Community and Support
Python and Anaconda both have massive and thriving communities, with millions of Python users across various industries and over 45 million Anaconda users. This means there are a large amount of tutorials, documentation, and forums available, such as Anaconda Learning. Additionally, enterprise Anaconda customers benefit from dedicated technical support including access to a professional services team that can assist with custom builds that are tailored to unique project needs.
Anaconda vs. Python: When to Use Anaconda
Anaconda is ideal for large-scale data science projects that require multiple packages and libraries along with reproducibility and consistency across teams. The platform’s environment management features and built-in package manager make it easier to set up and maintain complex data projects.
Anaconda vs. Python: When to Use Python Alone
Python might be enough for lightweight projects or situations where users need more control over the specific packages and versions they use. In addition, Python is better suited for web development than Anaconda.
Try Anaconda Today
In short, Anaconda and Python are both popular tools for data science, machine learning, and AI use cases. Anaconda leverages Python at its core and has additional capabilities to provide the best experience for data science teams.
Anaconda offers powerful tools and frameworks such as Panel that allow data scientists to share interactive applications with ease. With one-click deployment capabilities and simplified environment management, Anaconda streamlines collaboration across large teams and makes it easy to develop, deploy, and maintain Python projects.
Request a demo to see if Anaconda is right for your data science and machine learning projects. Or, if you’re curious to experiment with Anaconda on your own, you can get started for free.