Software engineering and personal development
Python is known for giving developers the ability to do a lot of things in a single line of code.
This can get quite messy from time to time and can make the code unreadable, but there can be some exceptions. Today’s trick is going to be one.
Let us say that we want to find the most frequent element in a list.
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You have probably had the chance to iterate through a list of elements in one way or another, or through elements of a set, or a dictionary. We can go through a list, a set, or a dictionary and access their elements because they are iterable objects.
An iterator is an object that contains a countable number of objects. This means that you can iterate through elements that an iterator contains.
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Dictionaries also known as maps are data structures that are used a lot in different scenarios. The process of getting an element from a dictionary can be done using an element that is not part of the dictionary which results in an error.
For example, let us take this scenario where we have a
dictionary that has an element with the key
name and another one with
the element surname. If we
want to access it using another element, such as
age, we are going to see an
error like the following:
my_dictonary = {"name": "Name", "surname": "Surname"}
print(my_dictonary["age"])
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When you have a list of elements, there can be cases when you need to check whether there an element is part of a list or not.
You can do this using
in checker.
One of the common things that you can notice in many tasks is the fact that you need to sort elements in a list. In fact, this can also be part of an interview that you may go through while applying for a job. Maybe not only sorting elements, but sorting elements fulfilling certain conditions.
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One way of introducing potential bugs is the lack of information about the way scopes work inside functions in Python.
When we declare global variables, their value may not change as we may expect.
Let us see this in an example to understand how a simple example can be misleading and confusing for a lot of people.
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Similar to the case of using
any(), we can also use a
method that allows us to check whether all conditions are met.
This can also greatly reduce the complexity of the code since
you do not need to use multiple and checks.
Let us see this with an example.
Let us assume that we have the following conditions where we are checking whether we have more than 50 points in each school course:
math_points = 51
biology_points = 78
physics_points = 56
history_points = 72
my_conditions = [math_points > 50, biology_points > 50,
physics_points > 50, history_points > 50]
Now passing all of them means that each condition should be
met. To help us with that, we can simply use
all(), as can be seen in
the following snippet:
In many cases, we may have multiple conditions that we want to check, and going through each one of them can be a clutter.
First and foremost, we should keep in mind that we write code for other humans to read it. These are our colleagues that work with us, or people who use our open source projects.
As such, checking whether at least one condition is met can be done using a very quick way by using the method any().
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One of the most common ways to debug is by using print() functions in Python.
They are so commonly used all over the place that you may even end up seeing them in code that is included in production.
One thing that not a lot of people know is that you can include conditions in print() functions.
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A lambda function is a small anonymous function.
A lambda function can take any number of arguments, but can only have one expression.
Let us see a quick example of a lambda expression being used to square a number:
sqr = lambda x: x * xContinue reading
sqr(10) # 100
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