Python is a powerful, high-level, object-oriented programming language created by Guido van Rossum. It is one of the easiest languages to learn and a great way to start your programming journey owing to its simplicity and easy-to-use syntax.
It originated as a scripting language to patch together or orchestrate code written in C. Basically, it was used to automate the boring stuff or to quickly prototype applications that were going to be implemented in other languages.
Today, it is one of the most popular languages for software development, infrastructure management and data analysis. It is now a force to be reckoned with in web application development and system management. It’s also a major contributing factor of the recent boom in big data analytics and machine intelligence.
- Easy to learn and use: The features provided by Python are relatively modest compared to other popular languages. It requires little investment of time or effort to produce your first program. This makes it easier for newcomers to pick it up quickly.
- Broadly adopted and supported: Its supported by every major operating system and platform, even most of the minor ones have support for Python. Major libraries and API-powered services have Python bindings or wrappers, letting it interface freely with those services or directly use those libraries.
- Not a toy language: It can be used to build professional-quality software, both as standalone applications and as web services.
- Cannot be used for system-level programming: Device drivers and OS kernels are hence out of the conversation.
- Is relatively slow: Its generally slower than C/C++ or Java because most Python runtimes are interpreters rather than compilers.
What is Python Used for?
Most people use PHP for web development. Many folks do not realize that with the Python web framework named Django, what once took hours in PHP could simply be done in a few minutes using Python. Couple that with Flask, which is smaller and built for customizability, and you won’t ever feel the need for PHP again.
Iterative and Agile Design
Success in startups is achieved by following these guidelines: start with an idea, refine the idea, refine the product, and keep refining until the customer is satisfied with it.
Python is ideal for this process.
The language allows you to code quickly in the least number of lines, helping you go from an idea to implementation extremely swiftly.
AI and Machine Learning
Python is the preferred language for all computer science research. That is largely due to its numerical computation engines such as NumPy and SciPy that allow complex calculations to be done by a single “import” statement and a simple function call.
The future of AI is in Python. Why? Because of its flexibility, speed and libraries. It is dominating and will continue to dominate the machine learning landscape because of said reasons.
How Long Does It Take to Learn Python?
A quick answer would be a couple of hours. Its syntax is very readable and clean, with little to no pretense. A standard “Hello World” in Python 3 is just:
print (“Hello world!”)
To be effective in Python, one requires understanding a couple of core abstract models of the language and the things that the language comes with, like the built-in data types, functions and the standard library.
If you have prior programming experience, you probably need to put in just 4 hours to learn the basics of Python. However, actually knowing how to apply the language in real-life depends on two things: motivation and methodology.
If your motivation is to just add another language to your resume, you might not be as successful with your goal as you might think. You are just trying to generally study the language and are not interested in how it can help you in real life. Python may be easy to learn but it is a vast language with a lot of functionalities.
What your motivation should be is to learn the language for problem-solving. You should pick a small project and just start working on it. This way, you will be asking the right questions and using the language to solve actual problems. Solving actual problems will increase your motivation and eventually your hard work will pay off.
Of course, even starting a small project requires you to know the basics of the language, which leads us to our second condition, methodology.
The methodology of learning Python or any other language should not be to read a book, memorize facts and understand theory. Instead, applying your knowledge to solve a few problems will help you a lot more than just plain studying. Only reading and writing a few lines of code will get dry really quick and result in you losing interest.
Thinking Like a Programmer
Only learning python will not do you much good by itself. It’s not the syntax that needs your attention the most. It’s the way a programmer thinks and acts to solve everyday problems.
How do programmers think, you ask? Well, they follow these simple steps:
- First, break down a problem into multiple problems.
- Second, come up with ways to solve each of these problems efficiently.
- Lastly, be ready to apply those steps across any language, including Python.
Once you learn how to think like a programmer, Python, or any other language, becomes very easy. It’s the simplest and most elegant way for learning a language.
You will need to learn basic syntax like if/elif/else, while and for loops, print statements, functions etc. You can then play around with some of the cooler things in Python like lists, modules, dictionaries etc. After that, we have one word for you:
Build, create, design and optimize. Do this over and over again until every letter of python code is hard wired into your head. Every time you learn something new, try it out on your own using your own style. Make little adventure games using if/elif/else. Make calculators using raw_input and int() statements.
Not only will you learn python, but also the best practices and how to use it in a way that’s most efficient.
Simply learning the language to be fairly comfortable with will only take you 2 to 3 weeks (if you are new to programming). Practicing with it and learning how to use it as a weapon that you can use with accuracy and skill could take much longer.
- Pick a complicated topic. Grab a Python book and look up the definition for it. Repeat this process until you find the book that best describes the topic to you. Read the complete topic from start to finish, code all of the examples, play with the code, and you will soon have a good level of proficiency in that particular topic.
- Take Breaks. It is important to give your brain a breather and let it absorb the concepts. Use the Pomodoro technique: study for 25 minutes, take a short break and repeat.
- It’s a popular saying that the the best way to learn something is to teach it. And it’s true. You can solidify your understanding as well as expose any gaps in it while teaching or preparing to teach someone else.
- Start a project regarding a particular topic that interests you. This will help cement your newly gained knowledge. The idea here is to learn what tools are available and then put something together using those tools.
At the end of the day, Python is evolving and changing all the time. There are probably only a few people who can legitimately claim to completely understand it.
You’ll need to be constantly learning and working on projects. If you do this right, you’ll find yourself looking back on your code from 6 months ago and thinking about how terrible it is. If you get to this point, you’re on the right track.
Python is a really fun and rewarding language to learn, and we think anyone can get to a high level of proficiency in it if they find the right motivation.
To start your Python quest visit:
We make some money when you purchase a product from a link on our website. If you found the content helpful, please use the link to get to the chosen provider of your choice. It doesn’t cost you a thing and it helps us put out great content. The money involved does not effect the ratings of any given product or service, we just link to an affiliate if there is one available after we write the article.