Pip vs Conda: Which One to Use for Your Machine Learning and Data Science Projects

As a developer diving into Machine Learning (ML) or Data Science, you’ll often face the challenge of managing libraries, dependencies, and environments. Two of the most popular tools for doing this are Pip and Conda. But which one should you use, and when?

In this blog, we’ll break down the differences between Pip and Conda, explore their use cases, and help you decide which is best suited for your ML and Data Science projects.

What are Pip and Conda?

Before diving into the comparison, let’s clarify what Pip and Conda are.

Pip: The Python Package Installer

Pip (short for “Pip Installs Packages”) is the standard package manager for Python. It’s included with Python installations and used to install, upgrade, and manage Python libraries from PyPI (Python Package Index), the central repository for Python packages.

Conda: The Cross-Language Package and Environment Manager

Conda is both a package manager and an environment manager. It was created by Anaconda specifically to handle not only Python libraries but also other languages like R, Ruby, Lua, and even system-level dependencies like BLAS (used in deep learning) or HDF5 (for data storage).

Key Differences Between Pip and Conda

While Pip and Conda serve similar purposes, they have distinct differences that impact how and when you should use them in your machine learning and data science projects.

1. Scope of Packages

• Pip: Pip exclusively installs Python packages from the PyPI repository. This makes it highly effective when you only need Python-based libraries like TensorFlow, scikit-learn, or matplotlib.
• Conda: Conda can install packages from its own Anaconda Repository as well as from conda-forge (a community-driven repository). It can manage libraries and dependencies across different languages and environments, including non-Python libraries like OpenCV, CUDA, or libjpeg.

Takeaway: If you’re only working with Python libraries, Pip may be sufficient. But if you need to install non-Python dependencies, Conda becomes much more versatile.

2. Environment Management

• Pip: Pip by itself doesn’t manage environments. To create isolated environments with Pip, you would typically use virtualenv or venv in combination with Pip. These tools allow you to isolate your dependencies per project but don’t handle system-level dependencies.
• Conda: Conda has environment management built-in. You can easily create isolated environments where each environment has its own version of Python and any other necessary packages or libraries. Conda environments can also include non-Python dependencies like C libraries or compilers, making it a powerful tool for complex ML projects.

Takeaway: Conda shines when you need to create isolated environments with complex dependencies, particularly in data science and ML, where projects often require non-Python libraries.

3. Speed and Performance

• Pip: Installing packages with Pip can be slower, especially for libraries with complex dependencies that may need to be compiled (like scipy or pandas). Since Pip downloads and installs Python packages one by one from PyPI, it can sometimes result in dependency conflicts (also called the “dependency hell”).
• Conda: Conda is generally faster when creating environments and installing packages, especially large data science and ML libraries. Conda uses pre-compiled binaries, so you don’t have to compile libraries from source. This can drastically reduce the time it takes to set up environments.

Takeaway: If speed of environment creation is crucial, or you need to set up environments often (as is common in experimentation-heavy fields like ML), Conda may be the better choice.

4. Package Availability

• Pip: Pip taps into the enormous PyPI repository, which contains a vast selection of Python libraries. However, Pip can’t install non-Python dependencies like CUDA for GPUs or system packages.
• Conda: The Anaconda and conda-forge repositories have a smaller number of Python packages compared to PyPI. However, they are specifically curated and often optimized for performance in data science and ML tasks. Conda packages often include compiled system libraries (e.g., optimized BLAS versions) that Pip can’t handle on its own.

Takeaway: For Python-only packages, Pip offers the largest selection. But for ML/data science projects that often need non-Python dependencies (e.g., CUDA for GPU-accelerated deep learning), Conda is far more effective.

Pros and Cons of Pip

Pros

• Wide availability of packages: With PyPI, Pip provides access to the most extensive collection of Python packages.
• Lightweight and standard: Pip is included with Python, so you don’t need to install anything extra.
• Simple to use: The syntax is straightforward, and it’s designed for pure Python developers.

Cons

• No built-in environment management: You need to combine Pip with other tools like virtualenv or venv to create isolated environments.
• No non-Python dependencies: Pip can only install Python packages, so you’ll have to manually install system-level dependencies.

Pros and Cons of Conda

Pros

• Cross-language support: Conda can handle packages for languages other than Python (e.g., R or C++), making it versatile for ML projects with complex dependencies.
• Built-in environment management: Conda lets you easily create, clone, and manage isolated environments with Python and non-Python dependencies.
• Speed: Conda installs pre-compiled binaries, so installations and environment setups are faster, especially for data-heavy libraries.

Cons

• Smaller repositories: Anaconda and conda-forge have fewer packages than PyPI.
• Larger installation footprint: The Conda tool itself, especially with Anaconda, comes with many pre-installed packages, which can be overkill for some projects.

When to Use Pip

• Pure Python projects: If your project only requires Python libraries, Pip is lightweight and easy to use.
• Standard Python environments: When you don’t need non-Python dependencies or system libraries, Pip provides the simplest solution.

Example: Developing a small web scraping tool with libraries like BeautifulSoup and requests.

When to Use Conda

• Complex data science or ML projects: When your project has both Python and non-Python dependencies (e.g., working with GPUs or C libraries), Conda simplifies the setup.
• Environment management: If you need isolated environments for different projects with different versions of Python or libraries, Conda’s built-in environment management makes life easier.

Example: Building a deep learning model using TensorFlow, CUDA, and HDF5, where you need GPU acceleration and optimized binaries.Can You Use Pip and Conda Together?

Yes! You can mix Pip and Conda, but it requires caution. The general rule is to:

1. Create environments with Conda.
2. Install core libraries with Conda (especially non-Python dependencies).
3. Install Python-only packages with Pip.

This ensures compatibility and minimizes the risk of breaking your environment.

Final Thoughts: Pip or Conda for Machine Learning and Data Science?

Both Pip and Conda have their strengths. If you’re working on a straightforward Python project with minimal external dependencies, Pip is fast and efficient. On the other hand, Conda’s powerful environment management and ability to handle complex dependencies make it ideal for large-scale machine learning and data science projects.

In most machine learning and data science workflows, Conda will often be the better choice, as it simplifies managing complex environments and ensures smoother installations.

However, for Python-only environments or developers who need the widest possible package selection, Pip is still a great option.

In summary:

• Use Pip for simple, Python-only projects.
• Use Conda for complex environments and cross-language dependencies, particularly in ML and Data Science.

What has your experience been using Pip or Conda in your projects? Let’s discuss in the comments below!