Python Fundamentals#

In this chapter, we will introduce you to the very basics of computer programming with Python. We will first see some general notions that will make you understand how a Python program looks. Then we will go through how to write basic instructions for reading and writing inside a computer program. We will also explain the types of variables we can use.

Introduction#

Python is a high-level programming language, like others you may have heard about, including Fortran, C or C++. High-level languages differ from low-level languages in that the former can be easily understood by people, while the latter are those that can be understood by machines. So when writing in a high-level language we normally need to translate the actions in our code to machine-readable format.

If you have ever done any programming before using languages like Fortran, C or C++, you surely adopted a workflow where you first wrote code and then compiled it using another program called compiler, wich generated a binary, machine-readable file. It would look something like this

compiling

That will not be the case with Python. One of the key differences between Python and those other programming languages is that Python is an interpretive language. Hence you will not have to compile the code as you modify it. The Python interpreter performs those actions for you.

All of these comes with a caveat. Because Python is not compiled, your programs written in Python will not be as fast as they could be when written in Fortran, C or C++. Below you can see a comparison between speeds of various programming languages

performance-speed

Hint

Clearly, Python is not the fastest language in town. There are workarounds for this, like linking Python and C or Fortran code, or using Cython.

Writing your first Python program#

Let’s see how running a simple Python program works in practice. Using your IDE or terminal, open a text editor and write the following:

#!/usr/bin/env python
# Our first Python program
print ("Hello World!")

Now let’s examine what we have just written. You will notice that the first couple of lines that we have written start with a hash symbol (#). This means that whatever follows will not be read by the Python interpreter. This is what we usually regard as a comment. Then there is a print statement followed by a parentheses () with a piece of text inside. And that is all there is to our first Python program.

Now close the file, save it as hello_world.py and run the following command on your terminal

foo@bar:~$ python hello_world.py

Now check whether the program has done as you intended it to.

Names and cases#

In your programs you will usually be defining lots of different variables. Variables are names for values and the naming of variables follow some conventions. For example, they can be formed by letters, numbers and only one symbol, the underscore _.

Something else you must remember is that Python is case sensitive, so when you try to use a variable without regard to the case you used to assign it a value, things will not go well.

a = 10
print (A)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[1], line 2
      1 a = 10
----> 2 print (A)

NameError: name 'A' is not defined

As we see, Python returns an error (specifically, a NameError), as a variable named A does not exist. The only variable that we assigned a value to is a, and in Python a and A are different things.

Hint

Always try to read error messages from Python. They are highly informative and often direct you to the exact line where the problem is.

Also, in Python some possibilities are forbidden, like starting a variable with a number. If you write code including something like:

77david = 'myname'

and try to run it, you will get a SyntaxError. The same thing will happen if you try to use one of the many keywords in Python. These include

and     del     from        not     while
as      elif    global      or      with
assert  else    if          pass    yield
break   except  import      print   class
in      raise   continue    is      return
def     for     lambda      try

It is convenient to choose variable names that will be meaningful to you and any other potential user of your program (often your future self). For example, if you are calculating the area of a circle, it would make sense to write something like this:

area = pi*radius**2

Here we are using some stuff that you we have not covered yet. Also, we are assuming that you have given values to both pi and radius. But you surely understand why we are using the code snippet as an example of using meaningful variable names. Using cryptic variable names will make everyone miserable, so it is a good investment to use names that seem sensible choices.

Additionally, in Python there are naming conventions that you should follow. These are described in PEP8. For example, variables are typically written in lowercase, and so are functions. On the other hand, classes, which we will learn about in the future, use the so-called CapWords style.

Input / Output#

In our first program hello_world.py above, we have already written to something we call the “standard output”, i.e. the display, using the print function. The print function displays values as text.

In Python there are many other ways to write your output and also to read input into a program. You can, for example, make your program write the results of a calculation to a file instead of to your screen, or prompt the user to write a set of input variables using the keyword for the program to read (this would be the “standard input”). Here we explore the basics of Input / Output operations.

Opening and closing files#

Suppose that there is a file in your working directory called myfile.txt from which you want to read its contents. In order to do that, you will have to write

f = open("myfile.txt", "r")

In this statement, we are using a built-in function called open. The first argument of this function is the external filename (Python will assume that the file does exist). The second argument indicates the processing mode. There are different modes available:

  • r: read only mode

  • w: write only mode

  • a: append mode

  • r+: read and write mode

  • t: text mode

  • b: binary mode

Depending on the type of data in the file and what you intend to use the file for, you will choose one of these ways of opening the file. As a good programming habbit, remember to close files once you no longer need to access them:

f.close()

Another way of opening files in Python is using file iterators. You are using them when you open files using the with statement, which works as

with open("myfile.txt", "r") as f:
    ...

In this case you do not need to close the file yourself.

Reading and writing data into files#

Once you have opened a file, you will many times want to read its contents. You can do that line by line for the myfile.txt which you have opened and named as f in your program doing

f.readline()

You can bulk-read all the file using readlines() instead.

Alternatively, if you want to write into your file, you will first have to open it for writing. Then, you will be able to write into the file using the following statement

f = open("test.txt", "w")
f.write("hello world!\n")
f.close()

where we are simply writing a text string. As we go along, we will greatly expand our understanding of Input/Output operations.

Data Types#

Variables in Python can be of many different types, including text strings, lists, integers, floats and Boolean. In Python variables take the form of objects, although we will not explain what this highly consequential fact entails just yet. For now, we will just say that objects are pieces of memory, with values and sets of associated operations.

In Python, a variable is created when you assign a value to it. Assignment of a value to a variable is simply made using the equal operator (=) in statements like

a = 10

Because Python has dynamic typing, you do not have to declare variables. Python will assign a type to each variable in your code. In order to know the type of a given variable, you can use the intrinsic function type. For the variable you just created you can find the type using

type(a)

int

In the case above, you might want a to be defined as a float instead of an integer. If that is the case, you can assign the type explicitly

a = float(10)

Alternatively, you can add a point at the end

a = 10.

The result will be the same.

In Python, you can sometimes combine different types to perform arithmetic operations

a = 10; b = 1.
c = a + b
print (c)
print (type(c))

11.0
<class 'float'>

Note

In the past, there were problems with integer divisions, as you would always recover an int, but that is no longer the case in Python 3, where they are converted into a float. Try the following:

a = 10; b = 3
c = a/b
print (c)
print (type(c))

Numeric types#

In the few lines of code above, we have used the most straightforward types in Python, integers, which lack a decimal part, and floating-point numbers. Using floats and integers you can do all sorts of computations in Python, very much like you would in a regular calculator. You can for example use operators like + and -, which are called unary operators, because they act on a single numeric value. You can perform multiplications, using the operator *, divisions with /, and integer divisions with //. Using % you will obtain the remainder of a division. Also you can exponentiate using the operator **.

There are also some additional operations that you may want to know of, like the += addition assignment or -= substraction assignment. You can also use relational operators, like <, >, <=, >= or ==, to compare the values of two variables. For example,

a = 10; b = 3
a < b

False

These operators will return either True or False. This is what we call a Boolean. We will discuss Booleans and logical operations in the next chapter.

Something you must bear in mind when you are programming is the finite machine precision with which your computer works. A case in point is the following:

print (0.1 + 0.05)

0.15000000000000002

Discouragingly, the result is not what one would expect. There is, however, nothing wrong with that result. It is the consequence of the way real numbers are stored in your machine, which results in round-off or truncation errors. Specifically, floats are written using 53 bits of precision. Because 0.1 and 0.5 are truncated in floating-point representation, the result is not exactly what you would expect. When writing a program, you should think about whether this actually matters for the problem at hand.

Another type of numeric that you may need are complex numbers. In Python, you can define them using the character j.

type(1 + 1j)

complex

Text strings#

The variable type we normally use to store text characters in Python are strings. In fact, we have already written one such variable in our hello_world.py program above. Strings are generated using single ' or double quotes " around a set of characters. It is usually good to pick a rule and stick to either single or double quotes.

Python provides a number of intrinsic functions and operators that are specific to string manipulation. For example, if you want to know the length of a string, you can use the len function.

myname = 'david'
print (len(myname))

5

Additionally, addition and multiplication work in a special way when applied to strings. For example,

string1 = "hello"
string2 = "world"
space = " "
print (string1 + space + string2)

hello world

Also,

verse1 = "Good morning\n"
verse2 = "Nothing to do to save his life call his wife in"
print (5*verse1 + verse2)

Good morning
Good morning
Good morning
Good morning
Good morning
Nothing to do to save his life call his wife in

Hence, addition for strings means concatenation, while multiplication means repetition.

You can also perform membership operators, to check whether an element is present in a string.

'a' in 'Antonio'

False

Another interesting thing you can do with strings that was not possible with int or float is indexing. You can access the i-th element of a string using

string[i]

where you must remember that Python uses zero-based indexing. Hence, the 1st element in a string is accessed using the index 0, and so on. You can also access characters in a string backwards, using negative numbers. We can also perform slicing operations, which extract sections (or slices) of the string

val = 'spam'
print (val[2:4])

am

There is an interesting property of strings, which is called immutability. While you can redefine a string, you cannot change it. For example, you can do

s = 'spam'
s = 'ham'

but if you try

s = 'spam'
s[0] = 'h'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[12], line 2
      1 s = 'spam'
----> 2 s[0] = 'h'

TypeError: 'str' object does not support item assignment

you will obtain a TypeError. What you can do instead is

s = 'h' + s[2:]
print (s)

ham

This construction generates a new string using the old string, but importantly, it does not change the old string.

There are many other interesting manipulations that we can do with text strings, using type-specific methods. Strings are, for example, searchable

s = 'david'
s.find('d')

0

and you can generate new strings making intuitive changes like making uppercase what was lowercase

s = 'peter'
s.upper()

'PETER'

You can also split a string into parts, based on a given element. hable

s = 'bread, butter, flour, milk'
s.split(',')

['bread', ' butter', ' flour', ' milk']

But this takes us directly into the next type of variable we want to consider.

Lists, tuples and dictionaries#

Lists are a very important type of sequence, which in Python is a type of data structures that contain a collection of elements. In the last section, we have generated one such object

['bread', ' butter', ' flour', ' milk']

which is of course a list of strings.

Lists are defined when the list of elements is written inside square brackets ([ ]). If you were using parenthesis instead, (( )) then you would be defining what we call a tuple. Finally, there are dictionaries, which are defined using curly brackets ({ }).

Lists and tuples can contain all sorts of data types. You can for example combine numerics and strings.

mylist = ["I", "am", 30, "years", "old"]

Also, you can easily convert between lists and tuples

mytuple = tuple(mylist)

The main difference between lists and tuples is that the latter are immutable, while you are allowed to change lists.

Operators work with lists and tuples like they did with strings. Addition is concatenation and multiplication is repetition. For example,

['one', 'two'] + ['three', 'four']

['one', 'two', 'three', 'four']

or

(1, 2)*3

(1, 2, 1, 2, 1, 2)

Membership operators are also similar to what we saw for lists

1 in [10, 20, 30]

False

As are the slicing operations, which are again accessing elements of the list using zero-based indexing

decades = list(range(10, 110, 10))
print (decades[2:-2])

[30, 40, 50, 60, 70, 80]

Here we are using something called a range object, which we are using to build a list. The syntax is worth discussing: using range(start, stop, step) we enumerate integers from the first argument (start), to the second argument (stop), in steps of size given by the third argument (step).

You can interrogate lists using intrinsic functions. For example, you can find how many elements there are in a list using len()

names = ['pedro', 'jose', 'felipe', 'mariano']
print (len(names))

4

and you can sort its contents with sorted()

names = ['pedro', 'jose', 'felipe', 'mariano']
print (sorted(names))
print (names)

['felipe', 'jose', 'mariano', 'pedro']
['pedro', 'jose', 'felipe', 'mariano']

which, as you sure have noticed, sorts strings alphabetically.

To finalize we will say that as objects, lists have associated methods that are particularly useful. For example, you can easily add an element to a list

names = ['pedro', 'jose', 'felipe', 'mariano']
names.append('yolanda')
print (names)

['pedro', 'jose', 'felipe', 'mariano', 'yolanda']

remove elements from a list

names = ['pedro', 'jose', 'felipe', 'mariano']
names.remove('pedro')
print (names)

['jose', 'felipe', 'mariano']

reverse the order of the elements

names = ['pedro', 'jose', 'felipe', 'mariano']
names.reverse()
print (names)

['mariano', 'felipe', 'jose', 'pedro']

or sort them. Remember that before we have used the function sorted(). There is an equivalent method:

numbers = [30, 1,  100]
numbers.sort()
print (numbers)

[1, 30, 100]

Dictionaries are a more sofisticated type of sequence, with elements having multiple entries called keys and their corresponing values. Their structure is hence

mydict = {key1: val1, key2: val2, key3: val3}

There is great flexibility in terms of what can be a key or a value

presidents = {'Adolfo': [1976, 1977, 1979], 'Leopoldo': [1981], \
              'Felipe': [1982, 1986, 1989, 1993]}

You can access dictionaries in many different ways, for example using the items(), keys() or values() method

presidents = {'Adolfo': [1976, 1977, 1979], 'Leopoldo': [1981], \
              'Felipe': [1982, 1986, 1989, 1993]}
print (presidents.items())
print (presidents.keys())
print (presidents.values())

dict_items([('Adolfo', [1976, 1977, 1979]), ('Leopoldo', [1981]), ('Felipe', [1982, 1986, 1989, 1993])])
dict_keys(['Adolfo', 'Leopoldo', 'Felipe'])
dict_values([[1976, 1977, 1979], [1981], [1982, 1986, 1989, 1993]])

In the next few lessons we will put all these data structures to good use.