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Python has become one of the most widely used programming languages to date, and it’s quickly becoming the language of choice for data science professionals in particular. With its simplicity, versatility, and transparency, it’s easy to see why Python makes such an excellent tool for data analysis and interpretation of large volumes of data. Whether you’re building your own open-source software or using Python to conduct cutting-edge research, there are many compelling reasons why Python is used as a mainstay in this field. Python has grown significantly in popularity in recent years, especially in the field of data science and machine learning, where it’s now used by more than 60% of companies across industries including finance, technology, and retail. This makes Python the most popular programming language in the world today, especially as it relates to data science applications and solutions. The last decade has seen the emergence of Python as one of the leading programming languages in the data science and big data arena. Here are 10 reasons why Python has become so ubiquitous in the realm of data science and machine learning.
1) It’s Easy To Get Started
Learning Python does not require years of math training. The basics are simple, but don’t let that fool you — it is a powerful language that can be used for many types of data analysis and machine learning. If you have experiences in another programming language, such as Java or C++, you’ll probably find working with Python to be very easy. It’s also great for beginners; It has clear syntax and an emphasis on code readability, which helps anyone familiarize themselves with programming concepts quickly. The Jupyter Notebook: This open-source tool was developed by IPython (used in Matlab) and is a superb environment for interactive computing in data science projects. Using the notebook, you can combine text, equations, graphics, and other elements into one document that supports real-time computation. You can then run your program cells at will without having to retype commands or restart your session from scratch. You just need to type Run All at any time when using Jupyter Notebooks! What makes it even better is that there are hundreds of add-ons available for use within notebooks.
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2) It’s An Elegant Language
With its roots in ABC, a programming language developed by Guido van Rossum, Python is a clear and concise language. Its primary competitors are R and SAS. The big difference between R and SAS is that R was made for statistics and SAS is an older software that has been around for decades. But what about Python? Well, much like R, it can be used for data analytics of course! In fact, most companies that do data science use both languages at once to handle different kinds of data. It’s just easier to code with one language when you have more than one kind of data set. It’s intuitive: Python is pretty intuitive when it comes to coding as well. All you need to know is how your variables work together, and that’s all there really is to it. You don’t need complex formulas or functions because Python takes care of all those things for you! It’s easy on your eyes: One thing I love about Python is how easy it makes the coding look. There aren’t any long strings of text or weird symbols cluttering up your screen – just simple code that does exactly what you want it to do without any fuss or confusion on your part.
3) It Runs Everywhere
Python, as a language and a platform, runs on Windows (in fact, it’s probably more popular on Windows than any other OS), Linux, and macOS. And, if you don’t have access to a full-on OS X or Linux machine but need to do some coding and data munging at home, there are virtualization products like VirtualBox that allow you to run all three operating systems from within Windows or OS X. Another option is something like Parallels or VMWare Fusion. VirtualBox is an open-source application for x86 hardware platforms which can be downloaded for free from Oracle (formerly Sun). There are versions available for download for every major operating system including those from Microsoft, Apple, and Ubuntu. This means that regardless of what computer you use, whether it’s a PC running Windows or a Mac running OS X, your code will work exactly as expected. There’s no shortage of libraries: In addition to having great tools built into its core, Python has thousands of additional libraries written by developers around the world who’ve created them specifically for use with Python. These libraries cover everything from image processing to machine learning and they’re all available through one central repository called PyPI (Python Package Index). To find out what packages are available just type pip search into your terminal and let pip do its thing.
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4) All Tools Are Open Source
You don’t have to pay a license fee or submit to a code review if you want to use another company’s software. You can run it on any computer: All platforms, including Windows, Mac OS X, and Linux, have free open source tools for working with data. You can download them and install them yourself—no IT department required. If you already use open source tools for running your business (like Apache or MySQL), there’s no learning curve when moving from a scripting language to something more powerful like Python. It has great interfaces: No matter what operating system you prefer, chances are there’s an open-source tool for displaying data in interactive graphs that will look native on your screen. There are also several popular user-friendly libraries for manipulating large datasets in memory using simple commands. It’s cross-platform: Python is available on all major operating systems, so you can easily move between different computers without having to worry about compatibility issues. There are also several versions of Python (including Jython and IronPython) that allow you to write programs for specific hardware architectures like .NET or Java virtual machines. It has big community support: From tutorials to forums, Stack Overflow answers to mailing lists, there is no shortage of ways to get help with your programming questions. There is even a group called NumFOCUS that funds the development of new features in open source data science tools through donations from its members.
5) There Are Lots Of Libraries
Over 50,000 libraries and frameworks exist for Python. There is a wide range of packages to choose from in every field. And there are plenty of free ones too! This is one advantage that makes Python a very user-friendly language among developers. They can use it to develop numerous apps and support many areas such as web development, big data, machine learning, artificial intelligence, and scientific computing. That’s why so many programmers prefer using Python over other languages. It’s easy to learn: The syntax of Python is quite simple and straightforward. It follows a certain style known as Pythonic Style which makes code look more natural than other programming languages like C++ or Java. So if you have any experience with these languages, you will find it easier to learn Python than others. You don’t need to spend much time on tutorials before you start writing your own codes in Python because its syntax is easy enough for anyone to understand. If you have no prior experience with coding, then you may take some time getting used to its structure but once you do, things will get easier when coding becomes second nature.
6) Collaboration Is Great
One of Python’s major strengths is its sheer versatility. It has a virtually limitless range of uses, and can be used to create anything from games to web services. With such an extensive range of applications, there are ample opportunities for collaboration between developers with different sets of skills. Not only does that make things easier in some respects (such as having multiple people work on a project), but it also ensures that everyone is sharing their expertise instead of creating silos. It’s like Voltron – instead of being five robots made up into one super robot, they become one super robot with five arms and legs, each with different roles to play. That’s how you get efficiency. The same goes for data science; if you have a team of experts who all know what they’re doing, then your final product will be better than if one person had done everything themselves. This may seem obvious, but when we’re looking at an area as vast and intricate as data science, every little helps. Collaboration leads to innovation. This is especially true when looking at new technology; by working together, we learn about new possibilities that hadn’t even occurred to us before. Of course, it isn’t always easy or possible to collaborate with other developers: after all, if someone else has already built something similar to what you want to build, then it doesn’t really make sense for both of you to do so separately… unless one of you happens upon a way of improving upon or adding something extra while making use of someone else’s hard work!
7) Everyone Can Contribute Code
Not only does open source make life easier for users, but it makes it easier for developers too. Since any developer can contribute a fix or new feature, you don’t have to go through a lengthy proposal process just to add something simple to your software. It also means that contributions are more likely to be of high quality, because they’re coming from other developers in similar roles who understand what you want out of your product and how important it is. If contributors are giving free-of-charge support on GitHub anyway, why not use that as an unofficial community forum? That way, if someone needs help with your project (or has questions about how to do something), they can simply ask for help instead of having to set up an entire separate platform for discussions. Asking questions is one of the best ways to learn, after all! Open source encourages collaboration: A huge benefit of open source projects is that they encourage collaboration. When people see others contributing code and getting involved in projects, they’re much more likely to get involved themselves. And since anyone can contribute code, there’s no reason not to try—it doesn’t cost anything! Working together means sharing knowledge: One of the best things about working together on a project is learning from each other along the way. In addition to being able to talk directly with people who might know something you don’t (and vice versa), many developers love getting into debates over different programming languages or methods—the perfect place for everyone involved to learn something new!
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8) It Has a Very Active Community
The open source nature of Python ensures that there is a very active community around it. This helps with finding solutions to issues quickly. There are many conferences and meetups related to it which can get you started in using it for your own projects right away. With such a large community, one is never short of help or support with any problem they face while working on it. This ensures that even if something is missing, you will find someone who knows how to solve it and assist you accordingly. Also, unlike other languages, there are enough tutorials and resources available online that one can easily find anything they need as well as gain knowledge through these easily accessible materials. It’s easy to learn: One of the biggest reasons why people choose Python over other programming languages is because it’s easy to learn. Even though it has its own set of rules and regulations, learning them doesn’t take much time at all. It also makes use of white spaces instead of curly brackets like most other programming languages do which makes it easier for beginners to read and understand code written by others as well as their own code. It has great libraries: Another reason why people love Python so much is because of its great libraries. These libraries make coding easier and faster thus saving time overall. One example is NumPy which provides vectorization functionality making calculations faster than ever before!
9) The Community Is Friendly And Welcoming
While you can use Python on its own, there are several helpful third-party libraries that make it a lot easier to do data science tasks. These free and open-source packages are developed in collaboration with other members of the data science community and include everything from visualization tools (such as Matplotlib) to AI helpers (like Scikit-Learn). As we’ll see later, you also get access to all of these when using Jupyter. Most of these tools have user interfaces that help make working with datasets easier and handle some of the code for you so all you need to do is write specific commands or functions into your code editor. This makes Python particularly easy to learn and allows you to focus on what matters: solving problems. The syntax is simple: Python has an easy-to-understand syntax that makes it very readable even if you don’t know much about programming languages. It’s designed for readability rather than speed, which means programs tend to run slower than those written in C++ or Java but still fast enough for most uses cases. It’s very powerful: You can solve complex mathematical problems using just a few lines of code in Python, making it perfect for scientific computing and machine learning tasks where big data sets need processing quickly but without sacrificing accuracy.
10) Libraries Make Everything Simple
Python’s big library of standard modules and libraries enables data scientists to perform a variety of tasks, including manipulating text, images, and sound. And thanks to projects like SciPy, developers can also make use of third-party packages for even more advanced capabilities. This is especially important as data science becomes an increasingly popular field: By relying on open source software and code from other developers, data scientists are able to get things done faster while still building new features into their programs. You can learn more about some of Python’s most useful libraries here. # pip installs -U scipy # pip install -U numpy # pip install -U pandas # pip install -U matplotlib # Learn how to use these in depth! 🙂 Coding makes it easier to get started: It’s true that learning a programming language takes time—but using one doesn’t have to be difficult. Using languages like Python for your data science work means you’ll spend less time getting used to tools and more time working with them, which means you’ll be able to experiment with different models without wasting too much time or money. This will help you create valuable insights faster than if you were trying to do everything manually or without any programming knowledge at all. Check out our guide on getting started with machine learning using Python if you’re looking for more information on getting started with programming (or see our list of resources). If you are interested to learn new coding skills, the Entri app will help you to acquire them very easily. Entri app is following a structural study plan so that the students can learn very easily. If you don’t have a coding background, it won’t be any problem. You can download the Entri app from the google play store and enroll in your favorite course.
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