Getting started with Python
Contents
Getting started with Python¶
This brief introduction will give you an idea of Python syntax.
You will learn about key concepts such as variables, what they are, and how they are created and updated.
You will also learn about various types of objects defined in Python and how the type of an object determines its behaviour.
Variables¶
Understanding the concept of a variable is crucial when getting started with Python and other programming languages.
To put it simply, variables are unique names for objects defined in the program. If an object does not have a name, it cannot be referred to elsewhere in the program.
In Python, variables are assigned on the fly using a single equal sign =.
The name of the variable is positioned left of the equal sign, while the object that the variable refers to is placed on the right-hand side.
Let’s create a variable named var containing a string object and call this object by its name.
Note that string objects are always surrounded by single or double quotation marks!
var = "This is a variable."
The following cell simply calls the variable, returning the object that the variable refers to.
var
'This is a variable.'
As the notion of a variable suggests, the value of a variable can be changed or updated.
var = "Yes, the variable name stays the same but the contents change."
var
'Yes, the variable name stays the same but the contents change.'
If you happen to need a placeholder for some object, you can also assign the value None to a variable.
var = None
var
Variable names can be chosen freely and thus the names should be informative.
Variable names are case sensitive, which means that var and Var are interpreted as different variables.
Var
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Input In [8], in <cell line: 1>()
----> 1 Var
NameError: name 'Var' is not defined
Calling the variable Var raises a NameError, because a variable with this name has not been defined.
Naming variables is only limited by keywords that are part of Python’s syntax.
Running the following cell prints out these keywords.
import keyword
keywords = keyword.kwlist
print(keywords)
Printing out a list of keywords introduces several important aspects of Python: the import command can be used to load additional modules and make their functionalities available in Python.
We will frequently use the import command to import various external libraries and/or their parts for natural language processing and other tasks.
In this case, the module keyword has an attribute called kwlist, which contains a list of keywords. We assign this list to the variable keywords and print out its contents using the print() function.
Objects¶
A list is just one type of object defined in Python. More specifically, a list is one kind of data structure in Python.
We can use the type() function to check the type of an object. To get the type of an object assigned to some variable, place its name within parentheses.
type(keywords)
Remember our variable var? Let’s check its type as well.
type(var)
The type() function is essential when hunting for errors in code.
Knowing the type of a Python object is useful, because it determines what can be done with the object.
For instance, brackets that follow the variable name can be used to access items contained in a list.
Note that Python lists are zero-indexed, which means that counting starts from zero, not one.
keywords[3]
This returns the fourth item in the keywords list.
Can we do the same with the variable var?
var[3]
This will not work, since we set var to None, which is a special type of object called NoneType.
Python raises a TypeError, because unlike a list object, a NoneType object cannot contain any other objects.
Let’s return to the list of Python keywords under the variable keywords and check the type of the fourth item in the list.
type(keywords[3])
As you can see, a list can contain other types of objects.
Both strings and lists are common types when working with textual data.
Let’s define a toy example consisting of a string with some HTML (Hypertext Markup Language, the language used for creating webpages) tags.
text = "<p>This is an <b>example</b> string with some HTML tags thrown in.</p>"
text
Python provides various methods for manipulating strings such as the one stored under the variable text.
The split() method, for instance, splits a string into a list.
The sep argument defines the character that is used as the boundary for a split.
By default, the separator is a whitespace or empty space.
Let’s use the split() method to split the string under text at empty space.
tokens = text.split(sep=' ')
We assign the result to the varible tokens. Calling the variable returns a list.
tokens
We can just as easily define some other separator, such as the less than symbol (<) marking the beginning of an HTML tag.
text.split('<')
As you can see, the split() method is destructive: the character that we defined as the boundary is deleted from each string in the list.
Note that we do not necessarily have to give the arguments such as sep explicitly: a correct type (string, ':') at the correct position (as the first argument) is enough.
What if we would like to remove the HTML tags from our example string?
Let’s go back to our original string stored under the variable text.
text
Python strings also have a replace() method, which allows replacing specific characters or their sequences in a string.
Let’s begin by replacing the initial tag <p> in text by providing '<p>' as input to its replace method.
Note that the tag <p> is in quotation marks, as the replace method requires the input to be a string.
The replace method takes two inputs: the string to be replaced (<p>) and the replacement (''). By providing an empty string as input to the second argument, we essentially remove any matches from the string.
text = text.replace('<p>', '')
text
Success! The first tag <p> is no longer present in the string. The other strings, however, remain in place.
Although the replace method allowed us to easily replace parts of a string, it is not the most effective way to do so. What if the data contains dozens of HTML tags or other kind of markup? For this reason, we will explore more efficient ways of manipulating text data in Part II.
This introduction should have given you a first taste of Python and its syntax. We will continue to learn more Python while working with actual examples.
