Install Python on Windows - HowTo

Note, we'll be using Python 3!

Download the Installer:

Python Releases for Windows

Select 32-bit and 64-bit versions according to your system

Customize installation:

installation location: C:/Python3 or whatever you like

Select "Add Python to PATH"

Windows Python Prerequisites

Windows Python needs Visual C++ libraries/SDK so make sure you have them.

Or install the 'Build Tools for Visual Studio 2019' from https://visualstudio.microsoft.com/downloads/#vstool-2019-family

Install Python on Windows - more info

How to Install Python on Windows @howtogeek.com

Using Python on Windows @docs.python.org

Install Python on Mac - more info

Using Python on a Macintosh @docs.python.org

Install Python on Linux

Using Python on Linux @docs.python.org

Verify installation:

Open a Command Prompt/Terminal and type:

          # check for python installed version:
          python --versions

          # check for pip installed version:
          pip --version
        

On Unix based systems, use python3 and pip3 instead.

The Python Package Index (PyPI) is a repository of software for the Python programming language.

pip is the recommended tool for installing Python packages from

Note, that by using pip, a package will be installed in your system/user python distribution.

Virtual environment - Why?

Python apps usually depends on many modules(packages), which depends on other packages, and very often - from a specific version of a package.

Many system tools also depends on specific versions of installed Python packages.

You don't want to pollute your system's Python environment!

Nor to pass through the dependency hell

Virtual environment - What?

Virtual environments create an isolated Python environment for your project.

Keep workflows between projects independent.

There are many tools for working with virtual environments in Python (virtualenv, pyenv, venv, pipenv, poetry, conda, etc.)

During this course we'll be using pipenv!

Higher-level tools (pipenv)

pipenv - package, dependency and environment manager, that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world. Windows is a first-class citizen.

Simplicity, Security, Portability (Linux, MacOS, Windows)

Easy to manage project's dependencies.Instead of pip's requirements.txt, pipenv creates two files: the Pipfile - for the packages you installed directly and Pipfile.lock for the dependencies and the specific version of the packages based on your Pipfile.

Higher-level tools (conda)

conda - package, dependency and environment management for any language—Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN

Portability (Linux, MacOS, Windows)

Included in all versions of Anaconda and Miniconda.

What?

A dependency and virtual environments manager combined into one.

Automatically creates and manages a virtual environments for your projects.

Functionally similar to npm and other modern package managers.

Useful/advanced readings:

Pipenv && virtual environments on Hitchhikers Guide to Python by Kenneth Reitz - the creator of pipenv

Simple Python Environments For Data Science

Install/Update/Remove

      pip install --user pipenv
    

      pip install --upgrade pipenv
    

      pip uninstall pipenv
    

Set up PATH

If pipenv isn’t available in your shell after installation, you'll need to add the user base's binary directory to your PATH.

find the user base binary directory, and:

Linux and macOS:

          $ python -m site --user-base
          # /home/username/.local

          # add it to your ~/.profile or ~/.bashrc file, or:
          $ export PATH=$PATH:/home/username/.local/bin
      

Windows:

        $ py -m site --user-site
        #C:\Users\Username\AppData\Roaming\Python36\site-packages

        # add to PATH:
        $ C:\Users\Username\AppData\Roaming\Python36\Scripts
      

Start again your Terminal, and write 'pipenv' - it should be present now.

On some Windows versions, you may need to log out for the PATH changes to take effect.

Notes

A virtual environment is associated with your project folder (and subfolders with max.depth 3, by default.)

If you need more - set PIPENV_MAX_DEPTH

You don't have to care where the virtualenv really lives.

But you should be sure that you execute next commands in your project folder.

Create Virtual Environment

pipenv can automatically creates a virtual environment (if there isn't any) and install a package in it, when you run:

        $ pipenv install packagename
      

The default(system) Python version will be used

Manage virtualenv

        ### Create virtualenv with system's Python3 version:
        $ pipenv --three

        ### activate the virtualenv
        $ pipenv shell

        ### Exit the virtualenv
        (...)$ exit

        ### Output virtualenv information
        $ pipenv --venv

        ### Remove the virtualenv
        $ pipenv --rm        
    

All options available with: pipenv --help

Video Resources

Intro to Pipenv - A Package Manager for Python by Pretty Printed

Useful video

Kenneth Reitz - Pipenv: The Future of Python Dependency Management - PyCon 2019

Create the file

create the file "simple_plot.py" in your project root folder.

Enter in it the code bellow:

      import matplotlib.pyplot as plt
      import numpy as np

      t = np.arange(0.0, 2.0, 0.01)
      s = 1 + np.sin(2*np.pi*t)
      plt.plot(t, s)

      plt.xlabel('time (s)')
      plt.ylabel('voltage (mV)')
      plt.title('About as simple as it gets, folks')
      plt.grid(True)
      plt.savefig("test.png")
      plt.show()
    

example from: matplotlib.org

Prepare Virtualenv

Open a terminal window and navigate to your project root folder.

      # navigate...
      $ cd your/project/folder

      # make sure you are in it - list your files:
      $ dir
      # you have to see your 'simple_plot.py' listed
      
    

Run the program

try to run the simple_plot.py program

      $ python simple_plot.py
      Traceback (most recent call last):
        File "simple_plot.py", line 1, in <module>
          import matplotlib.pyplot as plt
      ImportError: No module named matplotlib.pyplot
    

Yes, an error occurs, because we do not have the required module (matplotlib) installed

Install dependencies in a virtual environment

We will install the required modules in a safe virtual environment by pipenv!

      # install packages safely with pipenv:
      $ pipenv install matplotlib
        Creating a virtualenv for this project…
        ...
        To activate this project`s virtualenv, run the following
          pipenv shell
    

Activate the virtual environment

        $ pipenv shell
          Spawning environment shell...
      

Run the program within the virtual environment

Run the simple_plot.py program:

        $ python simple_plot.py
      

It should run now - a windows with simple plots wil be opened:

Note

In next slides we are going to install the all packages needed for the course with the pipenv command.

Make sure you that you are in your project folder with activated virtual environment

      # navigate to your project folder:
      $ cd your/project/foolder/path

      # activate the virtual environment:
      $ pipenv shell        
    

numpy

NumPy is the fundamental package for scientific computing with Python.

It contains among other things:

a powerful N-dimensional array object

sophisticated (broadcasting) functions

tools for integrating C/C++ and Fortran code

useful linear algebra, Fourier transform, and random number capabilities

Reference: numpy.org

      $ pipenv install numpy
    

pandas

The Python Data Analysis Library

Provides high-performance, easy-to-use data structures and data analysis tools

An open source, BSD-licensed library

Build on top of numpy

Lot's of Python ML tools are build on top of pandas (statsmodels and scikit-learn)

Reference: pandas.pydata.org:)

      $ pipenv install pandas
    

Matplotlib

A Python 2D plotting library

Produces publication quality figures in a variety of formats

Matplotlib tries to make easy things easy and hard things possible

Can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code

Reference: matplotlib.org

      $ pipenv install matplotlib
    

seaborn

Seaborn is a sofisticated Python data visualization library

Based on matplotlib

Closely integrated with pandas data structures

Features: An introduction to seaborn

Reference: seaborn.pydata.org

      $ pipenv install seaborn
    

The Jupyter Notebook

A Web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text

Supports for over 40 programming languages

Your code can produce rich, interactive output

Big data integration

Easily share your Notebooks using email, Dropbox, GitHub, the Jupyter Notebook Viewer and more

Reference: jupyter.org

Note that the classic Jupyter Notebook now is included in the Jupyter Lab package. Once you install jupyterlab you'll have both the classic Jupyter Notebook as well as the new Jupyter Lab Interface.

      $ pipenv install jupyterlab
    

What is a VCS and how it makes life easier?

VCSVersionControlSystem

a system that records changes to a file (or directory) over time so that you can recall specific versions later

You can version control all kind of work you do:

Poetry, Images, Music, ...

Programming

DataScience/ML Projects and the DataSets

Easily share your work with other team members

There are different types and implementations of VCS.

In this course we'll use git && GitHub

Installing and configuring git

Installing Git

Note for Windows Users - install from gitforwindows.org which also brings a nice features like git bash, shell integration and more

GitHub

github.com is Web hosting service for projects using git

Alternatives:

GitLab

BitBucket

Create a free account on GitHub Join GitHub

VC Your Project and share it on GitHub

NumPy on Youtube

Intro to Numerical Computing with NumPy (Beginner) | SciPy 2019 Tutorial | Alex Chabot-Leclerc

Pandas on Youtube

Brandon Rhodes - Pandas From The Ground Up - PyCon 2015

JupyterLab on YouTube

JupyterLab: The Next-Generation Jupyter Frontend