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PACKAGES-ONBOARDING-WIN.md

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Python3 Packages

Packages are extensions, which you can import into your python code to gain powerful functionality.

Installation

There are different options how to install packages. You can install them via the command line, via PyCharm or the Anaconda navigator.

PyCharm

To install packages in PyCharm, select
File > Settings > Project > Project Interpreter.
On the page, you can see a list of the packages you have installed. Make sure that the correct venv is selected.
To install a new package, click on the + icon on the right side of the page. Then, search for the package and click Install Package in the bottom left corner. Optionally, you can specify a version before installing, which may be required sometimes.

Anaconda Navigator

Open the Anaconda Navigator, click on Environments, then select the correct environment from the list.
Select All packages from the dropdown menu, and search for the desired package. Select the package you need and click apply in the bottom right corner.

Terminal

Alternatively, you can use the terminal to install packages. Therefore, make sure that the correct venv is activated and then type
pip install package-name
into the terminal. If you have Anaconda, you can also type
conda install package-name.
Both commands should install the package correctly, the difference is that conda install performs additional checks after installing (compatibility with other packages etc.).
NOTE: Some packages can only be installed via pip.

TASK: Install the packages numpy and matplotlib

requirements.txt

To make collaboration on a project easier, it is common practice to write down all the packages you are using in a requirements.txt file which is located in the root of the project.
TASK: Take a look at the requirements.txt file.

You can also automatically install all packages in the requirements.rxt file by typing
pip install -r requirements.txt.
TASK: Install all packages in the requirements.txt file.

Importing packages into your code

Importing packages into your Python code is done via the import keyword. Examples:
Imagine that we want to use the function numpy.zeros((3,3)) that creates a 3x3 matrix with zeros.

  • import numpy -> Imports entire numpy package. We can then use the function numpy.zeros((3, 3))
  • import numpy as np -> Imports entire numpy package, but under another name (np). We can then use the function np.zeros((3, 3))
  • from numpy import zeros -> Imports only the zeros(...) function. We can then use zeros((3, 3)) without numpy or np

This means that the way you import the package also influences the way you can use it.

TASK: Take a look at and run the python file 4-packages/packages.py. Try to understand what is happening in the file and especially understand where and how the packages are imported.

Package Overview

An overview with lots of great and powerful packages can be found under
4-packages/Cool_Python_Packages.pdf

End

You have now installed packages in your virtual environment and imported and used them in some code.
The next and last part of this tutorial runs a small machine learning example in a jupyter notebook.