Pip vs Conda: A Guide to Managing Python Packages for Data Scientists

Python is a popular language among data scientists due to its simplicity and the vast array of libraries available. However, managing these libraries can be a challenge. Two of the most popular tools for managing Python packages are pip and conda. In this blog post, we’ll compare these two tools and provide a guide for data scientists on when to use each one.

What is Pip?

Pip is a package manager for Python. It allows you to install and manage additional libraries that are not part of the Python standard library. Pip is the default package manager for Python and is included by default with most Python installations.

pip install numpy

What is Conda?

Conda is a cross-platform package manager that can install packages for multiple languages, including Python. It was developed by Anaconda, Inc., and is included with the Anaconda distribution of Python. Conda can also manage environments, which are isolated spaces where packages can be installed without interfering with each other.

conda install numpy

Pip vs Conda: Key Differences

1. Package Availability

Pip installs packages from the Python Package Index (PyPI), which hosts a vast array of Python libraries. Almost any Python library can be installed using pip.

On the other hand, conda installs packages from the Anaconda distribution and other channels. While the number of packages available through conda is smaller than pip, conda can install packages for multiple languages and not just Python.

2. Environment Management

While pip can be used in conjunction with virtualenv to create isolated environments, conda has this feature built-in. Conda environments can have different versions of Python and other languages, making it a powerful tool for managing complex projects.

3. Binary Packages

Conda installs binary packages, which means the packages include compiled code. This can make the installation process faster and more reliable, especially for packages with complex dependencies.

Pip, by contrast, often installs packages from source, which means the code is compiled during the installation process. This can be slower and more prone to errors, especially on Windows.

When to Use Pip or Conda?

So, when should you use pip or conda? Here are some guidelines:

  • Use pip if you are working with pure Python projects and need access to the vast array of libraries available on PyPI.
  • Use conda if you are working with projects that use multiple languages, need different versions of Python, or require complex binary dependencies.

In many cases, you can use both tools in the same project. For example, you can use conda to manage environments and install binary packages, and pip to install Python libraries that are not available through conda.


Both pip and conda are powerful tools for managing Python packages. The choice between them depends on your specific needs. By understanding the strengths and weaknesses of each tool, you can make an informed decision and manage your Python projects more effectively.

Remember, the best tool is the one that helps you get your work done efficiently. Whether that’s pip, conda, or a combination of both, the choice is yours.

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