7 Tips To Increase Your Productivity in Python | by Bharath K | Towards Data Science

Python is a fantastic programming language for almost all programmers of any skill level. Python is one of the most popular language choices in the modern generation, and it will probably remain so for the upcoming decade.
In this article, we will focus on 7 essential tips that will help us to improve our overall productivity with Python as well as achieve better results while using this fabulous programming language. The seven tips are a combination of both technical and practical points of implementation.
The main topic of the article assumes at least a brief introductory understanding of the Python language. If you have a decent grasp of the programming language, you can feel free to skip ahead to the tips section of the article.
However, if you are still new to Python and want to briefly learn more about it, then refer to the next couple of sections to understand what is Python and why exactly we make use of this programming language.
What is Python?
Python is an object-oriented, high-level programming language that was released way back in 1991. Python is highly interpretable and efficient. Python is versatile, and thanks to its resourcefulness, it is a suitable fit for Data Science. I initially started with languages like C, C++, and Java. When I finally encountered Python, I found it to be quite elegant, simple to learn, and easy to use.
Python is the best way for anyone, even people with no prior experience with programming or coding languages, to get started with machine learning. Despite having some flaws, like being considered a “slow” language, Python is still one of the best languages for AI and machine learning.
Although there are a variety of other languages such as Julia, Golang, etc., which might be quite competitive against Python in the future years, the latter remains the better choice at this point.
Why Python?
The main reasons for the popularity of Python for Data Science and Artificial Intelligence, alongside various other applications, despite other languages like Java, JavaScript, R, etc., is as follows —
As mentioned previously, Python is a simple language and is overall consistent.The rapid increase in popularity in comparison to other programming languages makes it a suitable pick for beginner-level programmers.Has extensive resources concerning a wide range of libraries and frameworks for supporting Data Science.Versatility and platform independence, which means Python can import essential modules built in other programming languages as well.It has a great community with continuous updates. The Python community, in general, is filled with amazing people, with constant updates made to improve Python.
As mentioned previously, Python is a simple language and is overall consistent.
The rapid increase in popularity in comparison to other programming languages makes it a suitable pick for beginner-level programmers.
Has extensive resources concerning a wide range of libraries and frameworks for supporting Data Science.
Versatility and platform independence, which means Python can import essential modules built in other programming languages as well.
It has a great community with continuous updates. The Python community, in general, is filled with amazing people, with constant updates made to improve Python.
To get started with Python, you can download it from here.
With that simple introduction out of the way, let us understand the seven essential tips to improve your overall coding and achieve better results in the Python programming language.
An Integrated Development Environment (IDE) is a software application that provides comprehensive facilities to computer programmers for software development. An IDE normally consists of at least a source code editor, build automation tools and a debugger.
I strongly believe that beginners to Python should use either the Python IDLE or an editor like the Sublime Text 3 without any additional installations. This method helps to achieve a better understanding of the python programming language as you tend to make more mistakes, you have to work hard to figure out on your own what these blunders exactly are.
However, when you become more of an expert in Python, want to start achieving faster typing and better results, using an IDE for this purpose poses no harm. In the hindsight, it can be extremely useful to use an IDE for performing and solving complex problem statements in Python. It is also beneficial for building more advanced Python projects as well.
Since python is a popular language of the modern era, it has a wide array of the development software available such as Pycharm, visual studio code, Jupyter notebooks, etc. These are three main editors that I use on a regular basis.
The main reason for this is because Pycharm is specifically developed for Python it has every extensive feature and additional support that you desire. These include code completion, code inspections, error-highlighting, and fixes, debugging, version control system and code refactoring.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. The Jupyter Notebook is an absolutely fantastic option to get started with data science and machine learning.
These Notebooks can be shared with anyone and helps to collaborate code more efficiently and effectively. I would highly recommend using the Jupyter Notebook as well because you can use each code block separately and you also have the option to use markdowns. It is widely used in lots of profitable companies.
Visual Studio Code is a free source-code editor made by Microsoft for Windows, Linux, and macOS. Features include support for debugging, syntax highlighting, intelligent code completion, snippets, code refactoring, and embedded Git.
It supports various programming languages including python. You might need a few additional installations to get started with Python but it is quite simple. It has continuous updates and is one of the best platforms for Python and other programming languages. I use this a lot and would highly recommend it as well.
To have a more in-depth understanding of the various Integrated Development Environments that you can use for programming your Python projects, I would highly recommend all of you to check out my article on a concise list of more than Ten such editor options available from the following link provided below.
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Lists are a type of data structures and can be used effectively to solve a variety of complex structures. Before moving further ahead, let us understand what data structures and lists are in more detail.
Data Structures are a collection of data elements that are structured in some way. Data Structures are the core of any programming language, and this holds true for python as well. There are many built-in data structures in python.
A list is a mutable ordered sequence of elements. Mutable means that the list can be modified or changed. Lists are enclosed within Square Brackets ‘[ ]’. Lists 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. Each item or element in a list is separated by a comma. (,)
Solving a variety of tasks with the help of lists and its wide range of function choices available is a suitable choice for most problems. However, you should highly considering using the procedure of list comprehensions to improve productivity and achieve better results.
List comprehensions provide a concise way to create lists. 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.
To understand the concept of list comprehensions with the help of a simple example, let us consider the below code block.
In the above code block, the list with the name “squares” was created. Using an iterative “for” loop and the append function, we were able to calculate the squares of the numbers ranging from one to ten. However, the same problem can be solved in a single line with the help of list comprehensions as shown below.
The above code block shows the representation of how a list comprehension can simplify a code of a few lines to just a single line while getting the exact same output faster. The only issue with list comprehensions is sometimes it could be hard for the reader to understand the code.
By using the approach of nested list comprehensions, you can also solve more complex tasks. Overall, list comprehensions prove to be extremely useful for solving tasks and computations while consuming lesser space and time complexities for general problems.
Refer to my article on Mastering Lists with Python from the following link provided below for a more concise and detailed understanding of this topic discussed.
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Let us briefly understand what classes and functions exactly are in a bit more detail, and then we can dive into why making better use of these can yield better productive results.
Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.
Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3.
Python classes provide all the standard features of Object Oriented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name.
Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at runtime, and can be modified further after creation.
Functions are a block of code that is written in a program so that they can be recalled multiple times. The main utility of a function is so that it can be repeatedly called numerous times in the same program, and you don’t need to write the same codes over and over again. However, you can also use it to provide more structure and an overall better look to your programs.
Functions are defined using the keyword ‘def,’ which can be called with defined or undefined parameters. When you call the particular the particular function, then whatever the value is to be returned is interpreted by the python compiler.
Scope of a variable is the portion of a program where the variable is recognized. Parameters and variables defined inside a function are not visible from outside the function. Hence, they have a local scope.
The lifetime of a variable is the period throughout which the variable exits in the memory. The lifetime of variables inside a function is as long as the function executes. They are destroyed once we return from the function. Hence, a function does not remember the value of a variable from its previous calls.
Classes are a mechanism to organize your code into generic, reusable pieces of code. At their best they are reusable code snippets that will be used over and over again with little or no modification. The class concept was inspired by biological collections of features (attributes) and abilities (methods).
Functions are great to use when data is critical to the work being done. Classes are great when you need to represent a collection of attributes and methods that will be used repeatedly in other places.
Generally, if you end up writing functions inside of functions you should consider writing a class instead. If you only have only one function in a class then better stick with just writing a function. A good reason to move from functions to classes in your programming is to write classes using composition over inheritance.
To learn more information on each of these topics, I would highly recommend checking out the official documentation of Python, which explains all of these concepts in further detail.
The best part about working with any programming language is the eventual errors that you will encounter. When you run into these bugs, you could easily decipher if it is a silly blunder, or you can end up having a hard time finding out your mistake.
However, to solve an error that you have no clue why it occurred and researching in detail on how to fix the following bug that you just encountered, can be a complicated task.
The best approach to dealing with these types of bugs is to make use of the browser and search engine of your preferred choice. By searching for the solutions to the error that you need to fix, there is a high chance you will be able to solve it.
The internet is a wonderful place, and websites like Stack overflow, Data stack exchange, and GitHub, are some of the most popular sites to receive in-depth solutions and answers to the problems or errors that you are encountering with the running or installation of your program or the respective code blocks.
To be able to decode the error on your own by constant effort is one of the greatest feelings ever. And for helping you to achieve this task successfully your web browser and search engine are some of the best tools at your disposal. Hence, it is essential to utilize these tools effectively to improve your overall productivity in Python.
In Python, an anonymous function is a function that is defined without a name. While normal functions are defined using the def keyword in Python, anonymous functions are defined using the lambda keyword.
Hence, anonymous functions are sometimes also referred to as lambda functions, and they are often used interchangeably. The syntax for the function is as follows:
lambda arguments: expression
The main advantage of using the lambda function is executing a lambda function that evaluates its expression and then automatically returns its result. So there’s always an implicit return statement.
That’s why some people refer to lambdas as single expression functions. It is very useful in most scenarios to simplify code and is an integral part of the programming language.
Most times, lambda functions are good, but the times you should consider not using them is if utilizing these functions make the single line code longer than anticipated, and it becomes hard for the user to read. Basically, you should consider not to use lambda functions when the readability of your code decreases.
For a more detailed explanation on this topic with more concise codes and details, I would highly recommend the viewers to check out the following article on advanced functions in Python from the following link provided below.
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It is not uncommon in programming to get stuck on a problem that you are working on for a long time. The best part is python as mentioned earlier is that is has a brilliant community with very helpful people and lots of resources at your disposal for your benefit.
Effective interaction is a key concept for most things in life and also in most jobs as well. Especially in the field of programming, communication skills play a key role. To perform a complex project efficiently while coordinating and communicating effectively is a must requirement for every python programmer.
Stack Overflow, discord channels, YouTube videos, free online code camps, GitHub, towards data science, etc. are all helpful resources that are available for all of us to utilize and improve our skills.
I thought it was best to find all the solutions to these problems on my own. I considered this was the best practice for a long time, and that is only partially right. Sometimes you may have misunderstood a concept or aren’t doing something perfectly alright. After trying by yourself, if you still have confusion, it is a good practice to ask your friends or experts who can help you out!
Communication with other people and experts while sharing ideas is a great way to learn more. Not effectively communicating can lead to quite a few issues like misleading understandings in queries you might have about a particular topic.
Also, talking to people is extremely helpful to share your views, as well as gain knowledge. By talking to more people, you develop better ideas and most importantly interactivity, which will be very useful while working in a company with a team on data science projects.
The interesting part about python is the wide array of options it offers you. Also, the best part is, with each mistake you make, you learn something new and what you did wrong, provided you find a solution by looking it up on the internet or cracking it by yourself. This feeling makes the overall experience even more satisfactory.
There are tons of practical projects and ideas available to implement. Just pick one project of your choice and start working on it. Doing more projects is the best way to keep learning! Find more projects and continuously upgrade your skills!
Practice becomes significantly to keep yourself updated with all the latest trends and process the on-going techniques in this tremendous subject. There is a lot of scope in every aspect with continuous developments. So, keep coding and keep working on practical implementations!
Try to actively participate in competitions on websites. Kaggle is one such site that hosts some of the best data science, related competitions. Don’t worry about which place you finish. It does not matter much as long as you learn something new.
As discussed earlier, there are a lot of websites to improve your coding as well as participate in competitions like HackerRank, which you should consider. Involving in the community is helpful to consistently learn more from fellow data science enthusiasts.
Every model you construct and every project you complete in data science has a lot of room for improvement. It is always a good practice to consider alternatives and various other methods or improvements that you can make to achieve better results.
So, Keep Practicing!
In this article, we have discussed about the seven tips, which are in my opinion, some of the best ways to achieve better results with the Python programming language. I hope the tips discussed in this article will help you in improving your code.
From my experience, the tips suggested in this article are extremely useful in achieving better overall results and also help in improving your productivity in performing tasks at a high-level to obtain the necessary solutions.
The most significant aspect of any programming language is to keep continuously creating new projects, building your profile or resume, and practice on a daily basis to stay in touch with the various concepts of the language.
If you have any queries about the topic addressed in this article, then please feel free to let me in the comments below, and I will try to reply back as soon as possible.
Check out some of my other articles that you might enjoy reading!
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Thank you all for sticking on till the end. I hope you guys enjoyed reading this article. I wish you all have a wonderful day ahead!