Since 1991, Python has been ruling the programming roost. If you are looking to cash in on the Python boom, knowing the advantages and disadvantages of Python is important.
\What is expected in Google, Instagram, Facebook, Netflix, Spotify, Pinterest, NASA, and IBM? Besides being the world’s most recognizable brands, each one relies on Python to stay at the cutting edge.
Python continues to be among the most searched programming languages on Google. Plus, over 1.4% of websites in the virtual world are programmed in Python, and 1.3% of them are positioned among the one million most visited websites.
Proficiency in Python continues to be one of the coveted programming skills with 90 K and 70 K Python-related job vacancies on Glassdoor and Indeed respectively. Even the payouts are decent for Python specialists across roles and profiles.
Guess what? Python even has a poem dedicated to it, “The Zen of Python.”
The stats and frenzy around Python make it a force to reckon with. But is it worth your time and money? Something that works for most programmers might not work for you. The last thing on your mind would be to waste thousands of dollars and countless hours learning something that doesn’t live up to your learning requirements and career objectives. That calls for knowing the advantages and disadvantages of Python.
But before diving into that, let’s walk you through what Python is?. A basic understanding of the language will help shape informed decisions.
Python is a programming language with the following defining traits.
Unlike a Domain Specific Language, Python lends itself well to multiple application domains. Feel free to use Python for different programs for different domains, even though it is best suited to scripting.
Python is tailor-made for the operator, not the system. That makes it user-friendly, like any other high-level language. When working in Python, you spend more time on programming logic without bothering about the system’s hardware constraints.
Contrary to popular perception, it is both compiled as well as interpreted. However, the user has nothing much to do with the compilation part. So, the hassle of converting the code into machine language is eliminated outright. As the code execution gets underway, an automatically generated byte code is transformed via a PVM to deliver the result.
A source code in Python is freely accessible to all users. Access a code, tweak it as you like, pass it around in Python, and seek peer review and community support - it’s all possible with an OSI-approved open-source license. As the code can be reused, time savings come by default for a Python developer.
Python applications can run on any platform without the need for a major rework. That means a code primarily designed for Windows can be run on Mac or vice-versa without rewriting.
Python codes covey the instructions to the readers, rendering Python a high degree of readability. You can attribute it to the nearly all-inclusive set of Code Style rules and idioms inherent in this programming language.
At the heart of Python is the “object,” making it an object-oriented language. The object here represents data with well-defined attributes, states, and behavior. As the data takes center stage, the logic, and functions are pushed out of the equation.
Syntax is a set of rules in a programming language that should be followed for the accuracy of statements. In Python, the syntax is simple and greatly resembles that of C, Java, Perl, and other popular programming languages.
By now, you know what Python is all about. So, it’s time to focus on the advantages and disadvantages of Python, one at a time. Let’s start with the advantages. Good things first - it’s the tradition, after all.
Python is exploding in popularity with healthy adoption rates. Here’s what makes Python so special.
Python is everywhere, for every need. From web and software development to data analysis and task automation, Python supports it all and more. Here’re a few key use cases.
Simple, flexible, and well-equipped, Python is tailor-made for Machine Learning. With a couple of lines of code, you can create an ML model to make sense of complex data and preempt trends. The Python ML libraries like TensorFlow, OpenNN, Theano, Keras, and PyTorch are there to make things easy and quick for you.
Businesses spend time and money gathering and making sense of data for business intelligence. That’s where Python kicks in with an extensive library and additional modules tailored for analytics. From refining and switching between formats to identifying patterns, Python allows you to do virtually anything with data. Your go-to data analytics Python libraries include Pandas (Python data analysis) and NumPy in matplotlib.
Python walks into any stage of the software development lifecycle. Coding, bug tracking, testing, deployment, and post-production maintenance, you name it. Whether it's simple programs or multi-protocol network ones, everything is possible with Python.
Python’s popularity for back-end web development is unrivaled. Feel free to use Cherrypy, Django, Grok, Flask, and other frameworks and built-in modules to execute a variety of web development tasks. These include database access, data processing, data exchange with servers, URL routing, and virtually everything the back-end web development entails.
Python finds applications in other domains, including Data Visualization, Task Automation, and more. Even day-to-day tasks can be handled using Python. From staying on top of the share market, and extracting data from websites to keeping shopping lists up-to-minute and managing social media posts, the possibilities are simply endless.
STATISTA rates Python third in the list of most recruiter-friendly languages. Glassdoor and Indeed respectively have 90 K and 70 K Python-related job vacancies. With ML & AI sectors expected to rise exponentially, the scope for Python will broaden. It follows that Python can open up rewarding career opportunities across fields, whether you are a newbie keen to adopt Python as the primary language or a working professional with programming experience in any other high-level language. The top Python-related profiles include:
Though the salary depends on your proficiency, experience, country, and other factors, you can expect handsome earnings as a Python developer. Per Glassdoor, an entry-level Python professional can walk away with USD 72,020 as yearly remuneration plus other perks. The same for a seasoned pro could be USD 115,944 a year. Even working knowledge of Python can get you hired in roles that don’t need Python directly, for example, business analyst.
Python has a lot in common with C, Java, and other popular languages in terms of syntax. So, working knowledge of any of these languages will fast-track your progress in Python. Even if you are new to coding, Python could be a godsend. Its syntax resembles that of the English language, making it easy to understand and follow for complete beginners. Being an interpreted language, it allows you to execute codes immediately after writing. So, you are in a position to check codes and make necessary adjustments. Like any object-oriented language, Python eliminates intricacies via encapsulation and data-binding techniques.
Python has a thriving 15.2 million-strong community, second only to the Java community with 17.4 million users. Such a vast, active community means complete support at every step of the way. Whether you are having issues learning Python, need assistance in solving an issue, hunting for jobs, or wish to interact and collaborate with fellow programmers, the community can come to your rescue. Feel free to be a part of Python community forums to engage, share, learn, and even showcase your Python skills. Then, there’s Python Weekly, a free weekly email newsletter to keep you abreast of the developments in Python.
As developers around the world continue to realize, Python’s most significant advantage has to be its productivity-friendliness. You are dealing with an easy-to-use language that lets you accomplish more with less effort. Python code can deliver the same outcome as those written in C, C++, and Java, but with fewer lines of code. You don’t have to toil with the syntax, since it is in simple English. Even the statements convey the instruction with clarity. Since Python is portable, Python applications can run on any platform without the need for a major rework. All that helps save time and increase the output.
As an interpreted language, Python implements code, line-wise. So, each time an error crops up, the implementation stops automatically. As a user, you’ll be alerted to the error, making it easy to identify the error and debug it there and then. On the contrary, you’ll have a hard time debugging C++, Scala, Vala, and Rust.
Given the advantages of Python, we can understand your eagerness to embrace it - but not so fast. You need to be aware of the trade-offs before jumping into the Python Bandwagon. Remember, the advantages and disadvantages of Python go hand-in-hand.
The major disadvantage of python is its lackluster speed, which can be pinned down to its very nature. Like in all interpreted programming languages, coding in Python requires thorough deliberation. Each code has to be accurately written before you can run it. Any inaccuracies can cause agonizing delays. Its dynamically-typed nature doesn’t help either. You confront additional delays during the code execution due to the additional work it has to perform. As such, Python can be a liability in projects where rapid acceleration is a priority.
Agreed, Python offers a vast library and several modules tailor-made for analytics. But it’s a different scenario altogether when it has to speak to the database and data access layers. Python lacks in-built resources to establish a connection with the database. Instead, it has to rely on a database driver (special program). That puts it at a disadvantage, as opposed to the likes of SQL, JDBC, and ODBC, which are optimized for database access. Corporations requiring prompt, unhindered database access treat Python as a secondary language.
Python’s ability to support multiple data types is a mixed blessing. While the feature makes it versatile, it can lead to excessive memory consumption. So, you might find it hard to pull off memory-intensive applications on Python unless you switch to more memory-efficient data structures or try eliminating bottlenecks in the code. However, if that’s not your cup of tea, you are better off using C, Rust, C++, Java, and Ada for RAM-heavy applications.
Python dominates desktop platforms, but doesn’t fare too well on the mobile computing front. You can attribute it to Python’s hunger for memory and lackluster speed. That’s not the case with HTML5, Swift, C++, Java, and other options used for mobile app development.
The language is vulnerable to those irritating and time-consuming run-time errors. They are a result of Python’s dynamically-typed nature. The script is executed only if its syntax is free of errors. But when the error goes unnoticed during script parsing, errors show up during runtime. That calls for frequent script testing, which is way more tedious than you think.
In the programming world, things change continuously and rapidly. However, Python has managed to stay alive and kicking even after three decades. It comes across as a high-level, general-purpose, object-oriented,