Conda and Pip are frequently thought to be practically identical. Despite the fact that some of the functionality of these two tools overlaps, they were created for and should be used for different purposes. Pip is the Python Packaging Authority’s preferred tool for installing Python Package Index (PyPI) packages. Pip installs Python software that comes in the form of wheels or source releases. To succeed, the latter may require that compatible compiler and possibly libraries be installed on the system before invoking pip.
Pip:
Pip is an easy command-line utility for installing Python packages. It is the official and preferred method of installing packages from the Python Package Index, which contains all of the released python packages (PyPI). If you are using Python 3.4 (or higher), pip is already installed and may be obtained from python.org, or if you are working in a Virtual Environment established by virtualenv or venv.
Conda:
Conda is a cross-platform package and environment manager that installs and manages Anaconda packages from the Anaconda repository and the Anaconda Cloud. Binaries are what Conda packages are. It is never necessary to have compilers on hand to install them. Conda packages aren’t just for Python apps. They might also include C or C++ libraries, R packages, or other software.
Differences Between Conda and Pip
| Â Â Â Â Â Â Â Â Differences | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Pip | Â Â Â Â Â Â Â Â Â Â Â Conda |
| Multi-Language Dependency | Â Â Â Â Â Â Â Does Not support | Â Â Â Â Â Â Â Supports |
| Package Installation | Â Build on wheels or source | Â Â Download binary |
| Package Types | Â Â Â Â Â Â Only Python | Â Â Â Any Type |
| Availability of Packages |  235,000 packages are        available | Approximately 1,500+ packages are available |
| Virtual Environment Management |  No in-built Virutal    management | In-built virtual management system |
| Do Compilers Required | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Yes | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â No |
| Dependency Management | Â No SAT test | Conda performs SAT test |
| Minimalistic | Â Â Â Â Â Â Â Â Â Â Â Â Yes | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â No |
Now that we have a fundamental understanding of the two package management systems, we will look at the key distinctions that distinguish these two managers.
Non-Python Dependencies Handling
Conda, as previously stated, supports languages other than Python. This may appear to be a minor feature, but it is a highly strong and much-needed tool when it comes to dependency management.
Python packages may be dependent on programs/packages written in languages other than Python. Pip is unable to correctly handle non-Python dependencies such as LLVM, HDF5, and others. Certain packages may be broken as a result of this.
As we can see, Conda is a step ahead of pip when it comes to dealing with dependencies.
Installation of package
There is a significant variation in how these two packages are installed.
PyPI’s python packages are distributed as wheel or source distributions. This means that before we can utilize the package, we must first compile it on our local workstation. Before running the pip command, we must first install appropriate compilers and libraries on our local PC.
Conda, on the other hand, relies on built binaries obtained from the Anaconda repository and cloud. This method eliminates any compiler or library dependency issues from the installation process.
Availability of Packages
Given the similarity between conda and pip, it’s not unexpected that some people try to integrate these tools to build data science environments. When one or more packages are exclusively accessible for installation via pip, combining pip with conda is a good idea. The Anaconda repository contains over 1,500 packages, including the most popular data science, machine learning, and AI frameworks.
Conda may be used to install these, as well as thousands of more packages accessible on Anaconda cloud from channels, such as conda-forge and bioconda. Despite its size, the library of packages is relatively limited in comparison to the nearly 150,000 packages accessible on PyPI. Occasionally, a package that is not accessible as a conda package but is available on PyPI and can be installed with pip is required. It makes sense in these circumstances to try to use both conda and pip.
Dependency Management
The most significant distinction between pip and conda is how they approach the dependence problem.
Pip installs dependencies using a recursive serial loop. Pip does not check to see if all of the dependencies of all packages are met at the same time.
If the packages installed earlier in the order have incompatible dependencies with versions that are different from the ones installed later in the order, the environment is broken, and most importantly, this problem goes undiscovered until you notice some unusual errors.
Conda uses a satisfiability (SAT) solver to ensure that all requirements of all packages loaded in an environment are met. This check may take a little longer, but it helps to avoid the development of faulty situations. Conda will reliably generate functional environments as long as the package metadata regarding dependencies is correct.
In general, conda is a preferable solution for dependency management.
Management of Virtual Environments
Pip is a small tool for package maintenance. Conda provides a lot more than that. It includes a built-in virtual environment manager.
To create virtual environments using pip, you’ll need programs like pipenv and virutalenv. This is a design decision to keep pip focused solely on package management and to keep it from becoming bloated. Pip and one of these environment managers can be used to effectively create and manage virtual environments.
Conda has an out-of-the-box virtual environment manager. It not only provides virtual environment functionalities such as virutalenv and pipenv, but it also allows us to select the Python version of each virtual environment. This feature makes it easier for users to deal with outdated packages or packages that are only available in older versions of Python.
Minimalism
Pip is a simple command-line utility with a single purpose. It is designed to be basic, modular, and minimalist.
Conda, on the other hand, was created to be a simple, all-in-one solution. It was intended to offer a different approach to pip. Its approach is far from modest. Conda has a slew of pre-installed packages and apps.
For some users, the non-minimalist approach may be an unwelcome aspect. Conda attempts to overcome this by offering the Miniconda, a smaller version of Conda. Miniconda has all of the capabilities of conda but just instals the packages required to set up conda.