Python can be very humble and does not require an advanced computer. In fact, you can run Python on a $10 Raspberry Pi or an Arduino board! The code and data we use in this book do not require any special computational power, any laptop, or any computer made after 2008. At least 2 GB of RAM, 20 GB of disk space, and an internet connection should suffice. Your operating system (OS) shouldn't be a problem either, as Python and all the tools we will use are cross-platform and work on Windows, macOS, and Linux.

Throughout the book, we'll use two main tools to write the code: Jupyter and VS Code. Both of them are free and aren't demanding.

All the code for the book is publicly available and free to access at https://github.com/PacktPublishing/Learn-Python-by-Building-Data-Science-Applications.

There are multiple Python distributions, starting with the original, vanilla Python, which is accessible at https://www.python.org/. Data analysis, however, adds unique requirements for packaging (https://www.youtube.com/watch?v=QjXJLVINsSA&feature=youtu.be&t=3555). In this book, we use Anaconda, which is an open source and free Python distribution, designed for data science and machine learning. Anaconda's main features include a smooth installation of data science packages (many of which run C and Fortran languages under the hood) and conda, which is a great package and environment manager (we will talk more about environments and conda later in Chapter 9, Shell, Git, Conda, and More – at Your Command). Conveniently, the Anaconda distribution installs all the packages (https://docs.anaconda.com/anaconda/packages/pkg-docs/) we need in this...

All code in this book is also available as a separate archive of files—either Python scripts or Jupyter notebooks. You can download the full archive and follow along with the book using the relevant code from GitHub (https://github.com/PacktPublishing/Learn-Python-by-Building-Data-Science-Applications). Everything is stored on GitHub, which is an online service for code storage with version control. We will discuss both Git and GitHub in Chapter 9, Shell, Git, Conda, and More – at Your Command, but in this case, you won't need version control, so it is easier to download everything as an archive. Just use the Clone or download button on the right side (1), and select Download ZIP (2):

Once the download is complete, unzip the file and move it to a convenient location. This folder will be our main workspace throughout...

VS Code is invaluable for Python development and experimentation. VS Code—not to be confused with Visual Studio, which is a commercial product—is a sophisticated, completely free, and open source text editor created by Microsoft. It is language-agnostic and will work perfectly with Python, JavaScript, Java, or any other language. VS Code has hundreds of built-in features and thousands of great plugins to expand its capabilities.

In order to install VS Code, head to its main web page, https://code.visualstudio.com/, and download the package for your OS. The installation is pretty straightforward; there is no need to change any of the default settings. Assuming you installed VS Code as part of the previous steps, you now need to open the VS Code application. Next, switch to the plugin marketplace menu (as shown in the following screenshot), type...

Another development environment we'll use is Jupyter. If you have installed Anaconda, then Jupyter is already on your machine, as it is one of the tools that come with Anaconda. To start using Jupyter, we need to run it from the Terminal (you might need to open a new Terminal to update the paths). The following code will run a newer version of the tool's frontend face, and that is what we'll use:

$ jupyter lab

Alternatively, it also supports an older version of the frontend via Jupyter Notebook. The two have their differences, but we'll stick with the lab.

The app's behavior depends on the folder from which it was started; it is more convenient to run it directly from the project's root folder. That's why it is so handy that VS Code's Terminal opens in a workspace folder by itself, as we don't need to navigate...

Before we proceed to the content of this book, let's ensure our code can actually be executed by running the simplest possible code in Jupyter. To do this, let's create a test notebook and run some code to ensure everything works as intended. Click on the Python 3 square in the Notebook section. A new tab should open, called Untitled.ipynb.

First, the blue line highlighted represents the selected cell in the notebook. Each cell represents a separate snippet of code, which is executed simultaneously in one step. Let's write our very first line of code in this cell:

print('Hello world')

Now, hit Shift + Enter. This shortcut executes the selected cells in Python and outputs the result on the next line. It also automatically creates a new input cell if there are none, as shown in the following screenshot. The number on the left gives a hint...

In this chapter, we prepared our working environment for the journey ahead. In particular, we installed the Anaconda Scientific Python Distribution with Python 3.7.2, which includes all the packages we'll need throughout the course of this book. We also installed and learned about the basics of VS Code, which is a sophisticated and interactive development environment that will be our primary tool for writing arbitrary code, and Jupyter, which we use for experimentation and analysis. Finally, we discussed and even ran some code already! We did this in Jupyter, which is a coding environment that is perfect for prototyping, experimentation, analysis, and educational purposes.

In the next chapter, we'll begin our introduction to Python, learning about variables, variable assignment, and Python's basic data types.

1. What version of Python do we use?
2. Will it work on a Windows PC?
3. Do I need to install any additional packages?
4. What is a Jupyter Notebook?
5. When and why should I use Jupyter Notebooks?
6. When should I switch to VS Code?
7. Can I run the code from this book on my smartphone/tablet?

• Python for Beginners: Learn Python Programming (Python 3) [Video] (https://www.packtpub.com/application-development/python-beginners-learn-python-programming-python-3-video)
• Data Science Projects with Python (https://www.packtpub.com/big-data-and-business-intelligence/data-science-projects-python)
• The Scientific Paper Is Obsolete (https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/)