Coding can be a lot of fun! It can be very exciting to obtain the solutions to a problem you are working on or crack a complex project by yourself. However, you might find yourself stuck at several intervals during the course of the completion of your particular task.
Even when you manage to successfully build the project you have worked so hard to complete, there are high chances that there are several improvements that can be made to improve the quality of code, the readability of the code, reduce space and time complexity, and other essential fixes to achieve overall high productivity.
As the significance of the Python programming language in the modern-day increases due to its enormous usage in data science, Artificial Intelligence, IoT, and so much more, it is not sufficient to just know the language. Expertise in coding in this language is a necessary requirement for achieving the best results. Hence, there are some best practices that one should aim to follow while developing complex projects.
This article assumes that the reader has a basic understanding of Python programming. If you do not have a decent knowledge of Python, I would recommend checking out some of my previous articles where I cover this topic more handily. I would also recommend checking out one of my preceding works, where we discuss the ten best websites to learn programming from the link provided below.
7 Best Programming Practices In Python To Improve Productivity:
In this section of the article, I will break down seven of the most significant practices that I feel programmers should utilize while Coding to get the most beneficial results while programming in Python. These tips will not only aid you in making your coding simpler and life easier but will also enable you to get the highest performance boost for your Python projects.
1. Utilize F-Strings:
One of the most useful and essential utilities offered by the Python programming language is the f-string. It eases out the process of string concatenation with minimal amounts of syntax. In simpler words, with these f-strings, you can easily combine the usage of both variables and string characters together with ease. Let us understand the significance of this concept with an example.
fruit = "Apple"
color = "Red"print("%s is tasty. It is %s in color." %(fruit, color))
print("{0} is tasty. It is {1} in color." .format(fruit, color))Output:
Apple is tasty. It is Red in color.
Apple is tasty. It is Red in color.Let us try to print the exact same statement with the help of f-strings. Below is the code snippet for performing a similar action.
print(f"{fruit} is tasty. It is {color} in color.")Output:
Apple is tasty. It is Red in color.Note that f-strings are available in versions greater than Python 3.6. All the other versions of Python do not support f-strings, and hence, more mediocre methods of string concatenation must be used. While the other two mentioned methods of using the "%s" symbol and the format method are usable in certain cases, as the length and readability of statements increases, programmers must start adapting to f-strings for better results.
2. Making use of Try and Except Statements:
Another lack of utility of major conditions I find is the use of try and except statements, which can come in handy in a number of situations. Let us say the programmer wanted to continue running an execution after a certain condition was reached, but they couldn’t do so because an error message would be displayed. Once the error message is displayed, the run-time of the program will be terminated.
To avoid these situations, we can make use of a try and except code block. When the condition is satisfied, the code block in the try statement is executed to produce the desired results within the code lines. However, when the code doesn’t yield a satisfactory result or leads to the occurrence of an error, the except statements catch these exceptions. Let us look at a code snippet to understand an example.
a = 10
b = 0try:
c = a/b
print(f"Division Successful, The Quotient Is {c}")
except:
print("Zero Division Error Has Most Likely Occurred.")Output:
Zero Division Error Has Most Likely Occurred.The code block above is quite simple to understand. The process of normal division is carried out when the division task is possible. However, a zero error exception is returned for the particular failed case. This option is also useful when you are reading an audio or image input from the user and want to wait until the user enters their input and not terminate the program if no input is given yet. I would also recommend checking out specific exception cases and the "finally" statement that you can add to these code blocks.
3. List Comprehensions:
We know that the list data structures in Python are a significant aspect of this programming language. Lists are one of the most important data structures in Python as they are mutable, and you can construct numerous projects with the right functionality of lists. They are a type of sequenced data structure with each element in the list being assigned a specific index number by which it can be accessed.
List comprehensions help to simplify more complex blocks of code with the utility of a single for loop within a list. List comprehensions provide a concise way to create lists. Their most common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable or to create a subsequence of those elements that satisfy a certain condition. Let us look at an example of the successful utility of these list comprehensions.
squares = []
for x in range(10):
squares.append(x**2)squaresOutput:
Now, let us look at the code snippet that allows us to receive the same output with greater ease just by utilizing list comprehensions.
squares = [i**2 for i in range(10)]
print(squares)If you are interested in learning more about the lists data structure in Python and mastering all of the essential concepts related to this topic, I would recommend checking one of my previous articles where I cover this topic in immense detail. Below is the link for the blog to learn more about this concept.
4. Getting familiar with mathematical operations:
When dealing with any kind of programming, there is a high probability that you might encounter some problem that will require you to use your mathematical skills to solve. Regardless of what field you choose, it is essential to know some of the mathematical utilities offered to its users by Python. Most of the mathematical operations are simplified with Python programming. Let us explore some of the basic functionalities of math that every Python programmer must know how to use. Below is a code snippet that covers most of the basic concepts.
a = 10
b = 5add = a + b
sub = a - b
mult = a * b
div = a / b
floor_div = a//b
mod = a % b
exp = a ** bOutput:
Sum = 15
Difference = 5
Product = 50
Quotient = 2.0
Floor Division = 2
Reminder = 0
Exponential Operation = 100000Apart from these basic mathematical operations, Python also allows its programmers with exclusive access to the math library through which several mathematical computations are possible. Let us look at a simple example code snippet where we utilize a few math functions on an int-type variable after importing the math library module.
import matha = 5.6print("The rounded value is:", round(a))
print("The ceiled value is:", math.ceil(a))
print("The floored value is:", math.floor(a))Output:
The rounded value is: 6
The ceiled value is: 6
The floored value is: 5I would recommend checking out all the other basic concepts of the math library that allow you to perform most of the required mathematical computations. Math is significant even if you are not working on machine learning or deep learning projects while coding. If you are interested in learning more about simplifying mathematical topics such as differentiation and integration for machine learning, check out one of my previous articles from the link provided below.
5. Making Use of Multiple Python files:
When working on larger projects, it becomes evident that the number of lines required for completing the project might be humungous. Sometimes these lines can range from a few hundred to a few thousand code statements. While working on such projects, it is often better to divide the content into separate files, similar to how you make use of functions and classes to reduce the reputability and increase the overall efficiency.
Let us consider an example of a gaming project which you have chosen to build. For these kinds of projects, it is always better to utilize a few or more Python files to segregate the code accordingly. You can have separate Python files for utilities, assets and divide the other necessary functions in a manner you deem suitable. In the main Python file, you can import all the other programming files and create the required code.
Another good example to understand the utility of multiple files while creating a project is when you are designing a website. When working on a website design, and you are trying to run any type of project, such as a machine learning example or any similar venture, it is best to create a Python file that stores some of the essential code related elements, while utilizing other additional HTML, CSS, or JavaScript files for the overall design.
6. Handling Operating System Functionalities:
One of the most essential topics that you will need for most complex projects is the ability to loop through different working files and directories. To manage and operate on numerous folders and acquire the desired files, we will make use of the "os" library module available in Python programming. It allows the developers to manage directory structure by allowing a portable way of using operating system-dependent functionalities.
To understand the basic utility of the os module, let us look at some of the crucial components that are often utilized in Python programming. Most of the elements discussed in the code snippet below are elementary os functionalities that are often used in constructing Python projects. Getting familiar with them is necessary for creating more complicated projects.
import os# get current working directory
cwd = os.getcwd()
print("Current Working Directory", cwd)# Join directories
parent_dir = "D:/Cool Projects/Blogs/"
current_dir = "7 best Python Programming Tips/"
final_path = os.path.join(parent_dir, current_dir)
print(final_path)# Make a new directory
os.mkdir(final_path + "test/")There are several other os commands that are available in Python programming. It is highly recommended for the developers to get familiar with these basic functionalities as they are significant to the construction, creation, and development of more complex projects, where you will need to access the respective operating system of the user to save files, gain access to required files, and so much more.
7. Better use of Debugging Techniques:
When you end up encountering an error while working on Python code, you usually get the specific line that traces to the location of the bug. These mistakes could be logical errors, syntax errors, or run-time warnings. While most of the syntax-related errors are easier to fix with the help of a well-designed integrated development environment and some analysis, it is not uncommon to get stuck at a particular point in your code for a long time.
Now, this brings us the question of what we can do to improve our methods to debug the inevitable errors caused by programming. Make sure to use a lot of print statements to make sure your project or your planned approach is going as expected. You can comment off these statements or remove them later, but while working on the project, ensure that you are using them.
Ideally, once you get a basic understanding of the working of the Python programming language, it is best to switch from a simple text editor such as the Python IDLE to other popular IDEs such as Pycharm or Visual Studio Code (with Kite support), which will help you to identity smaller errors or sometimes bigger bugs by highlighting the instances in which the IDE interprets the error might be occurring.
Whenever you are stuck at a point in programming for longer times, it is always a great idea to Google the error and check out a website like GitHub or Stack Overflow. Almost every common bug or error you might encounter might have already been encountered by someone, and the helpful community provides multiples solutions to such common issues.
There are several other tools that programmers can also utilize to achieve better results while debugging Python code. However, Python itself offers an amazing built-in tool for successfully debugging your Python code. Check out one of of my previous article on the PDB (Python debugger) library that allows you to effectively fix the errors in your Python coding from the link provided below.
Conclusion:
If debugging is the process of removing software bugs, then programming must be the process of putting them in. — Edsger Dijkstra
Programming, once you understand the basics, can seem so easy on the surface. However, no matter how much of a pro coder you become, you will always encounter some issue that will need you to look on the internet for a piece of code or additional functionality. You can also still make the silliest mistakes or not write your code in the most effective manner to achieve the desired results. In hindsight, while all of these mentioned constraints are fine on their own, it is still highly beneficial to adapt to the best coding strategies.
In this article, we discussed seven of the best methods to improve the efficiency of your Python programming. The mentioned points in this blog are some of the best techniques to deploy in your own code and projects to increase the Productivity and overall efficiency of the results obtained. Following these tips will help you to create more effective Python codes for building better and bigger projects!
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If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
Check out some of my other articles in relation to the topic covered in this piece that you might also enjoy reading!
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How To Deploy Your Own Optimized Speech-To-Text Web App with Python
The Ultimate TensorFlow-GPU Installation Guide For 2022 And Beyond
Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!
