pip is a standard package manager used to install & maintain packages for Python. The Python standard library comes with a collection of built-in functions & built-in packages.

Data science packages like scikit-learn & statsmodel are NOT part of the Python standard library. They can be installed through pip, the standard package manager for Python, via the command line.

Pip Documentation

We can print the pip version the same way we print the Python version. It is important that the pip version is compatible with the Python version. Here we see that pip 19.1.1 is compatible with Python 3.5.2.

Upgrading Pip

Upgrade pip using itself:

Viewing a Pip List

Installing the scikit-learn Package

We may notice from the logs that more then the scikit-learn package is being installed. This is because pip will install any other packages that scikit-learn depends on. These other packages are called dependencies.

Installing a Specific Package Version

Upgrading Packages

This upgrade will also upgrade any necessary dependency packages as well, automatically.

Installing & Upgrading the scikit-learn & statsmodel Package

We can also upgrade multiple packages in one line of code.

Installing Packages With requirements.txt

It’s conventional for Python package developers to create a requirements.txt file in their Github repositories listing all dependencies for pip to find & install.

The -r option flag in pip allows pip install to install packages from the file specified after the option flag. Keep in mind that naming this file requirements.txt is conventional but not required.

Using our examples, pip install -r requirements.txt will have the same effect as pip install scikit-learn statsmodel. Typing out each package could get messy if you needed to install ten packages. Using the requirements.txt file is much cleaner.

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