Intelligent strategies and techniques for enhancing your Python projects!
Prologue
Python’s surge in the realm of Data Science has led to its dominance in the programming sphere, and this is no accident. There are several reasons why this language is beneficial, including its seamless integration with C and a function system well-suited for solving a majority of issues. Amongst its advantages is Python’s simplicity in terms of writing, learning, and reading, making it the go-to, advanced scripting language for fundamental computing tasks.
Python’s user-friendly nature and accessibility make it an attractive option, particularly for Scientists who prefer to concentrate on data or budding coders who wish to steer clear of complex syntax. Nevertheless, these conveniences come with a trade-off in performance. Also, scripting languages like Python often provide more leeway for mistakes without immediate realization or necessary correction. Luckily for Python beginners, its widespread popularity means that there’s a large community of users for support. Over time, Python has accumulated a range of features to overcome the limitations of an intuitive, dynamically-typed scripting language. By incorporating good coding practices with Python’s type system and comprehensive resources, we can offset many of Python’s shortcomings, resulting in improved programming in this language.
Abstraction
When delving into any object-oriented programming language, the term that comes to the forefront is abstraction. This broad programming notion empowers us to construct extensive functionality using minimal code via inheritance. A method by which abstraction is enabled, inheritance involves class types containing Methods that cascade down to their sub-classes. This foundational element of abstraction underpins the entire concept of object-oriented programming. Here, a “Method” represents a Function exclusive to a class that is specifically tailored to that type.
Abstraction is the key to crafting generic functions that can be applied across multiple types – a determination made possible through hierarchical sub-classing or polymorphism. This term denotes the capability to “morph” various – poly – data structure types using a single subroutine. Within Python, classes are made up of methods and attributes. A subsequent class may become a subclass of an existing class, gaining its attributes and methods through inheritance – this process, initially introduced in the Simula simulation language, is the birthplace of abstraction.
A common piece of wisdom shared among programmers is the encouragement to “compose generic functions”. This counsel holds weight; the more versatile a function is, the lesser the code required for the project. This not only reduces the potential for errors but also simplifies the troubleshooting process when issues arise. Crucially though, this advice often overlooks the need for generic functions to be structured for hierarchical abstraction levels. Simplified, we create our function for all cars, and only when necessary, design a function specifically for a truck.
Python’s Function and Method potentials follow a natural pattern – generic calls find their best fit in functions, while methods are apt for type-specific calls, especially those intended for inheritance. To employ the abstraction technique in Python, it all begins with the creation of a class:
class Automobile:
def __init__(self, brand, paint_color: str):
self.brand = brand
self.paint_color = paint_color
def activate_horn(self):
print(self.brand + " honked its horn, it's a " + self.paint_color + " automobile")
This basic class possesses characteristics such as name and color, along with a method dubbed activate_horn. Upon creating a vehicle and invoking the activate_horn method, a concise message is produced.
my_vehicle = Automobile("The Mystery Machine", "green with flowers")
my_vehicle.activate_horn()
#Output: The Mystery Machine honked its horn, it's a green with flowers automobile
Certainly, I’m a country girl and I own a truck, which needs representation as well. If we extend the Truck class with our Automobile class within a bracket, all the characteristics and methods of Automobile become available to Truck. Though the data stored – brand and paint_color remains unchanged, the sound_horn method requires revision since it currently asserts the vehicle as a Automobile. This can be rectified by overriding the inherited method with a fresh one in our subclass.
class Truck(Automobile):
def sound_horn(self):
print(self.brand + " honked its horn, it's a " + self.paint_color + " truck")
In the absence of abstraction, the code appears as follows:
class Truck(Automobile):
def __init__(self, brand, paint_color: str):
super().__init__(brand, paint_color)
def activate_horn(self):
print(self.brand + " honked its horn, it's a " + self.paint_color + " truck")
my_truck = Truck("My Big Rig", "red")
my_truck.activate_horn()
# Output: My Big Rig honked its horn, it's a red truck
We have the capability to conceptualize a completely unique Car solely through the use of abstraction. The pivotal factor here is leveraging the ‘pass’ keyword, enabling us to conform to Python’s indentation syntax and define a constructor merely by its title.
class Sedan(Automobile):
pass
The application of abstraction in Python coding, particularly in larger projects, is undeniably crucial for superior results. Luckily, implementing this method is straightforward, as is the subsequent strategy that we are about to delve into.
Comprehensions
Having utilized an array of scripting languages akin to Python, I am poised to testify that comprehensions significantly enhance your work in these languages. Comprehensions offer a succinct and potent method to formulate generators, lists, and other types that would typically be constructed using a rudimentary for loop. Take for instance, this example where we generate the .^ of the range 1:30:
values = []
for i in range(1, 20):
values.append(i ** 2)
The technique of using a for loop is a classic method when dealing with iterables in Python. It necessitates us to kick-start a list and in every iteration, we append fresh elements. While this yields results, utilizing a generator to fabricate the elements is deemed superior. The latter not only provides a succinct code but also outperforms the former in execution. The illustrative code above can be simplified to a single line using list comprehension. Essentially, creating a generator involves scripting an inverse for loop enclosed within list delimiters.
values = [i ** 2 for i in range(1, 20)]
The reasoning for this lies in the fact that a comprehension or generator specifically fabricates a Function, particularly mapping its return to the execution of the subsequent iteration step. To put it in layman’s terms, a generator is designed to create return values for a Function throughout an iterator, as opposed to a for loop that generates an iterator in an open-ended manner. This approach drastically boosts the speed in Python and makes our coding much more succinct. However, there could be instances where we do not anticipate a return for each element, in which case a different approach might be preferred.
Lambda & Map
A straightforward method to significantly enhance your Python code is by amalgamating lambda and map to evoke functions across iterable elements. Given that Python is predominantly used in Scientific Computing in today’s world, the application of lambda and map is astonishingly high. Besides, these tools demonstrate their versatility not only in tandem but individually in numerous fields — therefore, mastering this technique becomes crucial, especially for individuals utilizing Python for Data Science.
Lambda is implemented to concisely transform expressions into callable function types. This facilitates in crafting straightforward closure functions that can be supplied as arguments, opening the door to a multitude of possibilities. The utilization of lambda in Python is fairly direct; we furnish our arguments to the lambda keyword, succeeded by a colon and the logic of our Function.
g = lambda y: y + 5
g(5)
#Output: 10
This notion is immensely potent in its own right, given its capability to formulate functions. However, it truly excels when these functions are mapped to an iterable. This facilitates modifications to an array equivalent to a comprehension’s approach, yet devoid of the generation component inherent in a generator. In this instance, the elements are directly offered to a function as an argument, delivering functionality akin to a generator but with unique variances.
g = lambda y: y ** 2
n = map(g, values)
After applying the function to our data points with the help of mapping, we effortlessly transform the map into a list, hence obtaining the squared array.
square = list(n)
[6,
9,
14,
21,
30,
41,
54,
69,
86,
105,
....
Clearly, the amalgamation of these two elements can substantially streamline your coding process!
Extraction
Enhancing your software can also be achieved through an exceptional coding principle. The process of extraction implies removing code from bigger subroutines and repackaging them into an array of smaller ones. Instead of penning a single function to manage a whole operation, the steps of this operation are delegated to different functions within the project. I have penned numerous pieces detailing this procedure, underscoring its criticality and impact in the creation of outstanding software engineering projects.
def quiz_user():
scores = []
print("What is your name?")
user_name = input()
print("What is 2 + 2?")
answer = input()
if answer == "4":
print("Correct!")
scores.append(True)
else:
print("Incorrect.")
scores.append(False)
print("What color is the sky?")
answer = input()
if answer.lower() == "blue":
print("Correct!")
scores.append(True)
else:
print("Incorrect.")
scores.append(False)
return scores
The operation in question is more complex than its title suggests, performing numerous tasks rather than focusing on its primary purpose. This can be seen as a flaw since each operation should concentrate on its specific purpose, leaving minor non-related tasks to separate functions. The whole procedure of extraction comprises several essential stages. Initially, we examine various components of our operation and their roles in terms of data processing. Once our operations are classified, we extract them from the main function and execute the primary function. Although this scenario might benefit from broader, more adaptable question-asking functions, for now, we must assume that each print action is a unique step in an algorithmic process which transforms our function’s input into its output.
def ask_username():
print("What is your name?")
username = input()
print("Hi " + username)
return username
def ask_question1():
print("What is 2 + 2?")
answer = input()
if answer == "4":
print("Correct!")
return True
else:
print("Incorrect.")
return False
def ask_question2():
print("What color is the sky?")
answer = input()
if answer.lower() == "blue":
print("Correct!")
return True
else:
print("Incorrect.")
return False
def quiz_user():
correct_answers = []
username = ask_username()
correct_answers = [question_func() for question_func in (ask_question1, ask_question2)]
return correct_answers
Employing the method of extraction significantly enhances your code’s functionality. By crafting a Function, we can redeploy the code contained within it. If that particular algorithm was kept within a larger function, it might restrict us from accessing a minor part of the code that has a distinct function. Another advantage of extraction is the orderly structure it gives to our project. Functions ought to be concise with minimal nesting. Essentially, compartmentalizing logical code into its unique scope is often a wise decision.
del
The Python characteristic that I’d like to bring to the forefront is ‘del’. This specific function aids in eradicating a Python item from the computer’s memory. At first look, del might not appear to hold much importance, but given Python’s user-friendly scripting language nature, it is undoubtedly a feature that we should seize the opportunity of using frequently. Although ‘del’ easily slips under the radar, its potential in memory conservation can be monumental.
del square
With programming languages such as Python, typically considered a scripting language, we often have restricted control over memory management and performance elements. Thus, it becomes essential to leverage any available tools to minimize computation duration and free up memory. What distinguishes Python from its scripting language counterparts is the presence of a unique keyword, del, that enables rapid elimination of an item from the heap and garbage collection. Therefore, it becomes imperative to utilize this characteristic proficiently.
Break and Continue
Essential tools for your repertoire include the pivotal terms break and continue. Primarily utilized within the framework of a repetitive loop, they enable us to enhance the efficiency of these loops by determining the precise juncture for halting iteration with break or advancing to the subsequent component using continue.
data = [None, None, 55, 22, 33, 44, None, 2, None, 73, 22, None, None, None, 36, "stop here please", 23]
filtered_values = []
for item in data:
if item is None:
continue
elif item == "stop here please":
break
filtered_values.append(item)
filtered_values
Undoubtedly, both break and continue commands have distinct purposes. Picture a scenario where we want to perform a task on specific elements – maybe tied with a condition. The potential uses for these commands are innumerable, making it beneficial to retain these fundamental tools in your toolkit.
Fewer Else
In terms of refining your code, one crucial point I’d like to stress is the minimized usage of else. In my opinion, excessive and complex conditional statements, particularly nested ones, should be sidestepped whenever practicable. The else statement is essentially a subroutine, adding another distinctly unique scope layer within our function. Despite the numerous situations in which the else keyword proves useful, there are instances where its use should be curtailed to enhance code readability and functionality.
class PumpSystem:
def __init__(self):
self.is_pumping = False
def switch_lights():
print("The lights are now switched on.")
main_valve = PumpSystem()
def start_pump():
main_valve.is_pumping = True
def toggle_switch(has_electricity: bool):
if not has_electricity:
print("There's no electricity available.")
else:
switch_lights()
start_pump()
if not main_valve.is_pumping:
print("Error encountered while turning on the pump.")
else:
print("The pump is now running!")
In this situation, we are fundamentally maintaining our operation under the else condition. This method, however, has various flaws. Primarily, the probability of coding the same feature twice significantly increases — a major concept this write-up seeks to avoid.
def toggle_switch(has_power: bool):
if not has_power:
print("There's no power supply.")
else:
activate_lights()
start_pump()
if not main_valve.is_pumping:
print("Error encountered while starting the pump.")
else:
print("The pump is now operational!")
def toggle_switch(has_electricity: bool):
if not has_electricity:
print("Power is unavailable.")
return
switch_lights()
start_pump()
if not main_valve.is_pumping:
print("Error occurred while activating the pump.")
return
print("The pump is now active!")
Moreover, during the evolution of the aforementioned scenario, we might need to designate something to be employed within our conditions. It’s more appropriate and logical to maintain this value within the private domain of the function, especially if our plans include utilization outside the conditional. It’s crucial to remember that we cannot assign new variables within a conditional unless it is designed to persist within that conditional. This is because, similar to a loop, a conditional introduces another degree of lexical scope. Keeping this in mind, the construction and inter-scope data translation of these scopes consume significant time. While a small number of inefficient conditionals may not trigger considerable issues, further complexities and increased calls will invariably result in degraded performance. Maximizing performance becomes even more crucial in Python-like languages. It would be ideal if a significant portion of the Python code we author is at the peak of optimization since we can’t solely depend on the speed constancy as with languages such as C.
While there are several valid reasons to avoid using ‘else’ frequently, this doesn’t imply that ‘else’ lacks utility. It is essential to indicate that ‘else’ should be employed when it is the most suitable option, rather than being the default option post every conditional – an unfortunately prevalent coding habit. The former option indeed reflects improvement.
To conclude
The popularity of Python can be largely credited to its user-friendly nature and approachability. Even though the language has its limitations, like any other language, it’s clear to see why its usage has skyrocketed. This is particularly evident when observing sectors that predominantly make use of Python. A downside to its approachability, however, is that it’s quite easy to overlook certain features due to lack of knowledge. For example, an individual could use Python for an extended period and not know how to deploy pipenv, or even be unaware of pipenv’s existence, depending on their tasks.
Yet, over time and through experience, programming skills enhance and knowledge expands, which leads to improved code. Though Python is a straightforward language to learn, it does possess numerous interesting features that require some investigation to truly unearth. Hence, it’s beneficial to continually enhance coding skills to achieve consistent improvement. As developers, we should always be advancing. We have unlimited creativity but limited time for learning, teaching, and crafting the things we want to actualize, no matter how revolutionary they may be. The ability to express oneself in a way that benefits all is one of the key highlights of being a remarkable programmer, akin to a street performer surrounded solely by admirers. Given the present landscape, possessing profound knowledge in Python is highly valuable, so I am pleased to have shared some of my specialized and specific programming advice on this language!
