Chapter 1. Getting Started

In this chapter, we will give a brief overview of the principal syntactical elements of Python. Readers who have just started learning programming are guided through the book in this chapter. Every topic is presented here in a how-to way and will be explained later in the book in a deeper conceptual manner and will also be enriched with many applications and extensions.

Readers who are already familiar with another programming language will come across, in this chapter, the Python way of doing classical language constructs. It offers them a quick start to Python programming.

Both types of readers are encouraged to take this chapter as a brief guideline when zigzagging through the book. However, before we start we have to make sure that everything is in place and you have the correct version of Python installed together with the main modules for Scientific Computing and tools, such as a good editor and a shell, which helps in code developing and testing.

Read the following section, even if you already have access to a computer with Python installed. You might want to adjust things to have a working environment conforming to the presentation in this book.

Installation and configuration instructions

Before diving into the subject of the book you should have all the relevant tools installed on your computer. We will give you some advice and recommend tools that you might want to use. We only describe public domain and free tools.

Installation

There are currently two major versions of Python; the 2.x branch and the new 3.x branch. There are language incompatibilities between these branches and one has to be aware of which one to use. This book is based on the 3.x branch, considering the language up to release 3.5.

For this book you need to install the following:

The interpreter: Python 3.5 (or later)
The modules for scientific computing: SciPy with NumPy
The module for graphical representation of mathematical results: matplotlib
The shell: IPython
A Python related editor: Spyder (refer to the following Figure 1.1, Spyder), Geany

The installation of these is eased by the so-called distribution packages. We recommend that you use Anaconda. The default screen of Spyder consists of an editor window on left, a console window in the lower right corner which gives access to an IPython shell and a help window in the upper right corner as shown in the following figure:

Figure 1.1: The default screen of Spyder consists of an editor window on left, a console window in the lower right corner which gives access to an IPython shell and a help window in the upper right corner.

Anaconda

Even if you have Python pre-installed on your computer, we recommend that you create your personal Python environment that allows you to work without the risk of accidentally affecting the software on which your computer's functionality might depend. With a virtual environment, such as Anaconda, you are free to change language versions and install packages without the unintended side-effects.

If the worst happens and you screw things up totally, just delete the Anaconda directory and start again. Running the Anaconda installer will install Python, a Python development environment and editor (Spyder), the shell IPython, and the most important packages for numerical computations, for example SciPy, NumPy, and matplotlib.

You can install additional packages with conda install within your virtual environment created by Anaconda (refer for official documentation from[2]).

Configuration

Most Python codes will be collected in files. We recommend that you use the following header in all your Python files:

from scipy import *
from matplotlib.pyplot import *

With this, you make sure that all standard modules and functions used in this book, such as SciPy, are imported. Without this step, most of the examples in the book would raise errors. Many editors, such as Spyder, provide the possibility to create a template for your files. Look for this feature and put the preceding header into a template.

Python Shell

The Python shell is good but not optimal for interactive scripting. We therefore recommend using IPython instead (refer to [26] for the official documentation). IPython can be started in different ways:

In a terminal shell by running the following command:ipython
By directly clicking on an icon called Jupyter QT Console

When working with Spyder you should use an IPython console (refer to Figure 1.1, Spyder).

Executing scripts

You often want to execute the contents of a file. Depending on the location of the file on your computer, it is necessary to navigate to the correct location before executing the contents of a file.

Use the command cd in IPython in order to move to the directory where your file is located.
To execute the contents of a file named myfile.py, just run the following command in the IPython shell

      run myfile

Getting Help

Here are some tips on how to use IPython:

To get help on an object, just type ? after the object's name and then return.
Use the arrow keys to reuse the last executed commands.
You may use the Tab key for completion (that is, you write the first letter of a variable or method and IPython shows you a menu with all the possible completions).
Use Ctrl+D to quit.
Use IPython's magic functions. You can find a list and explanations by applying %magic on the command prompt.

Tip

You can find out more about IPython in its online documentation, [15].

Jupyter – Python notebook

The Jupyter notebook is a fantastic tool for demonstrating your work. Students might want to use it to make and document homework and exercises and teachers can prepare lectures with it, even slides and web pages.

If you have installed Python via Anaconda, you already have everything for Jupyter in place. You can invoke the notebook by running the following command in the terminal window:

jupyter notebook

A browser window will open and you can interact with Python through your web browser.

Program and program flow

A program is a sequence of statements that are executed in a top-down order. This linear execution order has some important exceptions:

There might be a conditional execution of alternative groups of statements (blocks), which we refer to as branching.
There are blocks that are executed repetitively, which is called looping (refer to the following Figure 1.2, Program flow).
There are function calls that are references to another piece of code, which is executed before the main program flow is resumed. A function call breaks the linear execution and pauses the execution of a program unit while it passes the control to another unit-a function. When this gets completed, its control is returned to the calling unit.

Figure 1.2: Program flow

Python uses a special syntax to mark blocks of statements: a keyword, a colon, and an indented sequence of statements, which belong to the block (refer to the following Figure 1.3, Block command).

Figure 1.3: Block command

Comments

If a line in a program contains the symbol #, everything following on the same line is considered as a comment:

# This is a comment of the following statement
a = 3  # ... which might get a further comment here

Line joining

A backslash \ at the end of the line marks the next line as a continuation line, that is, explicit line joining. If the line ends before all the parentheses are closed, the following line will automatically be recognized as a continuation line, that is, implicit line joining.

Basic types

Let's go over the basic data types that you will encounter in Python.

Numbers

A number may be an integer, a real number, or a complex number. The usual operations are:

addition and subtraction, + and -
multiplication and division, * and /
power, **

Here is an example:

2 ** (2 + 2) # 16
1j ** 2 # -1
1. + 3.0j

Note

The symbol for complex numbers

j is a symbol to denote the imaginary part of a complex number. It is a syntactic element and should not be confused with multiplication by a variable. More on complex numbers can be found in section Numeric Types of Chapter 2, Variables and Basic Types.

Strings

Strings are sequences of characters, enclosed by simple or double quotes:

'valid string'
"string with double quotes"
"you shouldn't forget comments"
'these are double quotes: ".." '

You can also use triple quotes for strings that have multiple lines:

"""This is
 a long,
 long string"""

Variables

A variable is a reference to an object. An object may have several references. One uses the assignment operator = to assign a value to a variable:

x = [3, 4] # a list object is created
y = x # this object now has two labels: x and y
del x # we delete one of the labels
del y # both labels are removed: the object is deleted

The value of a variable can be displayed by the print function:

x = [3, 4] # a list object is created
print(x)

Lists

Lists are a very useful construction and one of the basic types in Python. A Python list is an ordered list of objects enclosed by square brackets. One can access the elements of a list using zero-based indexes inside square brackets:

L1 = [5, 6]
L1[0] # 5
L1[1] # 6
L1[2] # raises IndexError
L2 = ['a', 1, [3, 4]]
L2[0] # 'a'
L2[2][0] # 3
L2[-1] # last element: [3,4]
L2[-2] # second to last: 1

Indexing of the elements starts at zero. One can put objects of any type inside a list, even other lists. Some basic list functions are as follows:

list(range(n))} creates a list with n elements, starting with zero:

      print(list(range(5))) # returns [0, 1, 2, 3, 4]

len gives the length of a list:

      len(['a', 1, 2, 34]) # returns 4

append is used to append an element to a list:

      L = ['a', 'b', 'c']
      L[-1] # 'c'
      L.append('d')
      L # L is now ['a', 'b', 'c', 'd']
      L[-1] # 'd'

Operations on lists

The operator + concatenates two lists:

      L1 = [1, 2]
      L2 = [3, 4]
      L = L1 + L2 # [1, 2, 3, 4]

As one might expect, multiplying a list with an integer concatenates the list with itself several times:
n*L is equivalent to making n additions.
```
      L = [1, 2]
      3 * L # [1, 2, 1, 2, 1, 2]
```

Boolean expressions

A Boolean expression is an expression that may have the value True or False. Some common operators that yield conditional expressions are as follow:

Equal, ==
Not equal, !=
Less than, Less than or equal to, < , <=
Greater than, Greater than or equal to, > , >=

One combines different Boolean values with or and and. The keyword not , gives the logical negation of the expression that follows. Comparisons can be chained so that, for example, x < y < z is equivalent to x < y and y < z. The difference is that y is only evaluated once in the first example. In both cases, z is not evaluated at all when the first condition, x < y, evaluates to False:

2 >= 4  # False
2 < 3 < 4 # True
2 < 3 and 3 < 2 # False
2 != 3 < 4 or False # True
2 <= 2 and 2 >= 2 # True
not 2 == 3 # True
not False or True and False # True!

Note

Precedence rules

The <, >, <=, >=, !=, and == operators have higher precedence than not. The operators and, or have the lowest precedence. Operators with higher precedence rules are evaluated before those with lower.