Of course! Let's break down easy_install, what it is, how to use it, and most importantly, why you should avoid it.
What is easy_install?
easy_install is a command-line tool that was the original package installer for Python. It was part of Setuptools, a library for packaging, distributing, and installing Python projects.
Its main job was to find packages on the Python Package Index (PyPI) and download/install them for you.
Key takeaway: easy_install is the predecessor to the modern and highly recommended tool, pip.
How to Use easy_install (The Old Way)
If you have an older Python installation or have Setuptools explicitly installed, you can use it like this.
Basic Installation
To install a package from PyPI:
# Install a single package easy_install requests # Install a specific version easy_install "requests==2.25.1" # Install from a different source (like a URL) easy_install http://example.com/my-package.tar.gz
Uninstalling a Package
This is one of easy_install's biggest weaknesses. It's not straightforward.
# You have to find the package and manually delete it # This command finds the path and then you delete it easy_install -m requests # This just marks it for uninstall, doesn't do it
You would then have to manually find the package in your Python's site-packages directory and delete its files. This is cumbersome and error-prone.
Why You Should NOT Use easy_install Anymore
While easy_install was revolutionary in its time, it has significant drawbacks that led to the creation of pip. Here’s a comparison to show why pip is vastly superior.
| Feature | easy_install |
pip |
Winner |
|---|---|---|---|
| Dependency Resolution | Poor. Installs dependencies without checking for conflicts. Can easily break your environment. | Excellent. Intelligently resolves and checks for conflicting dependencies. | pip |
| Uninstalling | Very Difficult. Requires manual deletion of files. No reliable command. | Easy. pip uninstall <package> |
pip |
| Virtual Environments | No built-in support. Works directly on the system Python, which is risky. | Fully Integrated. pip is the standard tool for use inside venv and virtualenv. |
pip |
| Package Management | Basic. Only installs. No way to list, upgrade, or check what's installed. | Comprehensive. list, show, freeze, upgrade. |
pip |
| Source Control | Limited. Can't install directly from Git, Mercurial, etc. | Excellent. pip install git+https://... is standard practice. |
pip |
| Default in Python | No. It was a separate install. | Yes! pip is included by default with Python 3.4+ and Python 2.7.9+. |
pip |
In short: easy_install is outdated, less reliable, and less powerful.
The Modern Way: Using pip
pip is now the standard and officially recommended package installer for Python. It's what you should use for all your Python projects.
How to Check if pip is Installed
Open your terminal or command prompt and run:
# For Python 3 pip --version # For Python 2 (if you still need it) pip2 --version
If you get an error, you may need to install it. For modern Python, it should already be there.
Basic pip Usage
# Install a package (this will also handle its dependencies correctly) pip install requests # Install a specific version pip install "requests==2.25.1" # Upgrade a package to the latest version pip install --upgrade requests # Uninstall a package (the easy_install way, but easy!) pip uninstall requests # List all installed packages pip list # Show details about a specific package pip show requests # Generate a requirements.txt file (crucial for sharing projects) pip freeze > requirements.txt # Install packages from a requirements.txt file pip install -r requirements.txt
Summary and Final Recommendation
| Tool | Status | Recommendation |
|---|---|---|
easy_install |
Legacy / Deprecated | Avoid. Use only if you are forced to maintain very old code. |
pip |
Current Standard | Use this for everything. It's more reliable, safer, and has all the features you need. |
Best Practice Workflow
-
Create a Virtual Environment: This isolates your project's dependencies from your system's Python.
# For Python 3 python -m venv my-project-env # Activate the environment # On Windows: my-project-env\Scripts\activate # On macOS/Linux: source my-project-env/bin/activate
-
Use
pipto Install Packages: Inside your active virtual environment, install your packages.pip install numpy pandas matplotlib
-
Save Your Dependencies: Create a
requirements.txtfile to share your project setup.pip freeze > requirements.txt
-
Collaborate: Others can set up the exact same environment by running:
pip install -r requirements.txt
