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How to write Code Effectively in Python?
In the modern era of technology, coding has become one of the most popular practices. It is an essential skill that is highly prevalent and considered mandatory in many fields, especially those that involve software development or data analysis.
At its core, coding is the process of communicating your mental logic to a computer program, allowing it to execute a specific task. With advancements in programming, it has become increasingly important to write efficient code. This means not only creating code that produces the desired outcome but also optimising it to be as fast and resource−efficient as possible.
In addition to being a useful skill in many industries, coding is also a highly creative and rewarding pursuit. It allows individuals to create programs and applications that can help solve real−world problems, automate repetitive tasks, and bring their ideas to life.
Furthermore, learning to code can also improve one's critical thinking and problem−solving skills, as it requires breaking down complex problems into smaller, more manageable steps. It can also enhance one's ability to work collaboratively, as coding often involves working on projects with other programmers, designers, and developers.
Overall, coding has become an integral part of modern technology, and its importance is only expected to grow in the coming years. It offers a plethora of benefits and opportunities for those who are willing to learn and invest their time and effort into mastering this valuable skill.
Python is widely regarded as one of the easiest programming languages for beginner−level programmers to learn and get started with. Its simplicity and versatility make it an attractive option for a wide range of tasks, including data science, artificial intelligence, robotics, astronomy, and more.
However, despite its many advantages, programmers often fail to adopt the best practices for coding in Python. In this article, we will explore the essential elements required to write efficient code and why it is crucial to do so.
Why Code Effectively?
As a beginner programmer, we often develop habits that enable us to obtain solutions to problems or tasks in the easiest way possible. However, it is essential to question whether this easy approach is the most effective and efficient way to compute the task at hand.
The importance of writing effective and efficient code cannot be overstated. While it may not seem necessary when working on simpler projects or during the early stages of programming, as you become more advanced and experienced, you will need to debug and test your code systematically. This includes making your code more Pythonic and ensuring it meets the best requirements for space and time complexity.
To illustrate this point, let's consider a simple example of printing the statement "TutorialsPoint" five times using a print statement. There are numerous methods to achieve this task, and we will examine three approaches to see how effective and different each one is. The first approach involves simply printing the statement five times.
The first approach is to print the statement five times.
print("TutorialsPoint \n") print("TutorialsPoint \n") print("TutorialsPoint \n") print("TutorialsPoint \n") print("TutorialsPoint"
An alternative method is to print the statement just once and then apply a multiplier to obtain the intended output.
The last method we will examine involves utilizing a for loop to carry out this operation.
for i in range(5): print("Hello")
It is evident that there are several ways to write a program to achieve a specific goal. Nevertheless, a skilled programmer should aim to optimize their code for efficiency, readability, and producing the best results.
How to Code Effectively?
Having gained a basic understanding of the significance of writing efficient code, we will now shift our focus to exploring the methods that can help us achieve this goal in this section. We will examine both the technical and practical aspects of the key elements required to write the most optimal code for addressing a diverse range of programs, tasks, and projects.
Enhancing the Pythonic nature of your code.
To improve the performance and efficiency of your programs, it is recommended to make them more pythonic. This involves adopting a more Python−centric style of programming, which can enhance readability, presentation, and overall efficiency of your code.
For example, instead of hard coding an if statement with a particular condition, you can use variables to assign the statement and then perform the necessary operation. Consider the following code snippet:
# Non-Pythonic if x > 0: y = x * 2 else: y = x + 2 # Pythonic y = x * 2 if x > 0 else x + 2
In the second code block, the if statement is replaced with a single line using the ternary operator, making the code more concise and easier to read.
Another practice is to use default arguments for functions when the user doesn't want to pass any specific command, ensuring that the function still returns a value. Additionally, you can use the underscore variable (_) for throwaway variables to filter out unnecessary elements as required.
There are many other ways to make your code more pythonic, and the key is to focus on efficiency and adhering to Python's coding conventions. Future articles may delve into these concepts in further detail.
Avoid Declaring unnecessary variables
In Python, declaring unnecessary variables can lead to inefficient use of memory, slower performance, and more complex code. Therefore, it is important to only declare the variables that are necessary for a given task.
Consider the following example:
a = 5 b = 7 c = a + b print(c)
In this code, we declare three variables: a, b, and c. However, a and b are only used to calculate c, and are not used again in the code. This means that they are unnecessary variables that take up memory without contributing to the program's functionality.
We can simplify this code by eliminating the unnecessary variables:
c = 5 + 7 print(c)
In this code, we directly calculate c without declaring a and b. This code is simpler, more efficient, and easier to read.
Declaring unnecessary variables can also lead to naming conflicts and confusion in larger programs. For example, consider the following code:
def calculate_total(items): total = 0 for item in items: price = item["price"] total += price return total def calculate_discounted_total(items): total = 0 for item in items: price = item["price"] discount = item["discount"] discounted_price = price - discount total += discounted_price return total
In this code, we declare several variables that are only used within the respective functions. However, if we use similar variable names in different functions, it can lead to confusion and errors. For example, if we declare price in both functions with different values, it may be unclear which value is being used.
To avoid this issue, we can eliminate unnecessary variables and use more descriptive variable names:
def calculate_total(items): total_price = 0 for item in items: price = item["price"] total_price += price return total_price def calculate_discounted_total(items): total_discounted_price = 0 for item in items: price = item["price"] discount = item["discount"] discounted_price = price - discount total_discounted_price += discounted_price return total_discounted_price
In this code, we eliminate the unnecessary variables total and discounted_price and use more descriptive variable names to avoid naming conflicts.
In summary, it is important to only declare necessary variables in Python to avoid inefficient memory usage, slower performance, and naming conflicts. By simplifying and optimizing our code, we can create more efficient and readable programs.
Making use of Anonymous Functions
Functions play a crucial role in the development of most significant Python projects, providing repeatability and structure to your programs. They are defined using the 'def' keyword and can accept defined or undefined parameters. When called, the function's return value is interpreted by the Python compiler.
In Python, an anonymous function, also known as a lambda function, is defined without a name. While regular functions are defined using the 'def' keyword, anonymous functions are defined using the 'lambda' keyword. The primary advantage of using a lambda function is that it can evaluate its expression and automatically return the result.
Consider the following example, where we need to print only the even numbers from a list of elements. We will demonstrate both approaches, with and without anonymous functions, and compare their effectiveness.
# Without using lambda def get_even_numbers(lst): even_lst =  for num in lst: if num % 2 == 0: even_lst.append(num) return even_lst # Using lambda get_even_numbers_lambda = lambda lst: [num for num in lst if num % 2 == 0]
In the first approach, we define a function 'get_even_numbers' that accepts a list and returns a new list containing only the even numbers. In the second approach, we use a lambda function to achieve the same result. The lambda function takes a list and returns a new list with only the even numbers using list comprehension.
While both approaches achieve the desired result, the lambda function is more concise and eliminates the need for a separate function. The use of anonymous functions is particularly useful when dealing with simple tasks that do not require the full functionality of a regular function.
To start off with good programming practices, it is crucial to document your code. As you delve into coding and take up numerous projects, you will realize the importance of effective documentation. It is common to get lost in the world of coding while programming, and often, the focus is on obtaining a perfect solution rather than commenting on the different functions used throughout the program.
However, this lack of attention to documentation can lead to problems when you revisit the project after a few weeks or months. Without proper documentation and understanding of the code blocks used, it becomes challenging to determine the purpose of the code and how to accomplish the task.
Effective documentation not only helps you when revisiting the code, but it also helps others who want to read and understand your code. Therefore, it is good practice to add comments to explain the purpose of the code, especially when sharing it on a platform for others to view.
For example, let's say you are writing a function that calculates the area of a circle. A good way to document this function would be to include a comment at the beginning of the function that explains what the function does and what parameters it takes. Here is an example of how you can document the function:
# This function calculates the area of a circle given its radius # Parameters: # radius (float): the radius of the circle # Returns: # area (float): the area of the circle def calculate_area(radius): pi = 3.14159 area = pi * radius ** 2 return area
By including these comments, you can easily understand the purpose of the function, what parameters it takes, and what it returns. This makes it easier for you and others to read and understand the code, especially when you come back to it after some time.
Consider and experiment with alternatives.
After successfully completing a complex task in a program, it can be tempting to feel accomplished and move on to the next task. However, it is important to take a step back and reflect on your code to see if there are any areas that can be improved. By analyzing your programming more closely, you can identify potential changes that can make your program more efficient and better suited for the task at hand.
It is crucial to assess the changes that you can make, whether by yourself, with a friend, or with an expert. While it may be tempting to stick to conventional methods to save time, it is important to take the time to optimize your code for future reference. By doing so, you can improve the efficiency and functionality of your program and ensure that it is better suited for similar tasks in the future.
Additionally, testing out different alternatives can help you gain a better understanding of the problem at hand and how your code is performing in different scenarios. It can also help you identify any potential bugs or errors that may arise and allow you to fix them before they become major issues.
Furthermore, trying out different alternatives can also lead to new insights and ideas that you may not have considered before. It can open up new possibilities and lead to even more efficient and effective solutions.
Overall, taking the time to analyze your code and try out different alternatives can greatly improve the quality and efficiency of your programming. It can help you create more robust and reliable programs that are better suited for solving complex problems and achieving your goals.
After gaining an understanding of the basic principles and techniques of coding, it is essential to continually practice and apply these methods to every coding problem or project. Consistent practice will help you hone your skills and become more efficient in writing code.
In addition, it is crucial to incorporate new techniques and approaches as you learn them. By constantly upgrading your coding knowledge and integrating these techniques into your projects, you will see a significant improvement in your coding style.
To delve deeper into this topic, I have written an article that explains why daily coding practice is essential for data scientists. The article explores the benefits of daily coding practice and provides practical tips for incorporating it into your routine.
In conclusion, writing efficient Python code is not only a valuable skill for programmers, but it can also help to save time, increase productivity, and reduce the likelihood of errors. By following some of the techniques outlined in this article, such as optimizing loops, using list comprehension, avoiding unnecessary variables, and practicing regularly, programmers can improve the overall efficiency of their code.
It is essential to note that writing efficient code is not a one−time task but a continuous process. As new libraries and frameworks emerge, new techniques and best practices will come up, and programmers must keep learning and updating their knowledge to write more efficient code. With consistent practice, patience, and discipline, anyone can master the art of writing efficient Python code and become a more productive and effective programmer.
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