Startups planning for product development need to consider many things when it comes to picking the right programming language. Often working on a tight budget, startups must carefully consider many factors: the speed of development, the popularity of language, the cost of developers, libraries, integrations, security, scalability, and stability. It is for these reasons that your best bet will be to go for a robust and established technology such as Python.
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Python is a programming language that has been around for quite a while now and has become very popular. However, it seemed like it wasn’t as much in the spotlight as some of the other languages, despite being used by Google. In reality, Python developers weren’t a hot commodity because the language itself wasn’t used widely as some of the others, such as JavaScript, Java, C, or C++.
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Two programming languages probably couldn’t be more different from each other than Python and PHP. The former is hailed for its simplicity and straightforwardness, while the latter is often criticized for its quirks and tendency to generate spaghetti-like codebases. Python brings to mind modern applications such as machine learning and data science, while PHP is mostly associated with old-timey web development and old-school discussion forums. Still, both remain popular and have their benefits and disadvantages. In this blogpost, we’ll try to present a fair comparison of Python and PHP, taking into account a number of factors which you should consider when choosing the language for your new software project.
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Let’s say that you’re at the very beginning of developing your app. Equipped with a solid business plan, you decide to go with Python, and you’re betting on the Django framework, ready to make the first decisions that will take this project from concept to reality. One of the most fundamental things to do at this stage is to find a Django-friendly hosting service.
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For many years, R has been the obvious choice for those going into data science. In recent years, something’s changed, however, and R got dethroned. How is Python challenging R’s well-established position and why is Python the king of data science now?
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Are you launching a development project and having a hard time choosing between Python and C#? Don't worry, we've got you covered.
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The First version of Django was released in 2005. It is implemented in Python, still on the market, and still gaining more and more users every year. The framework follows the Zen of Python and describes itself as “the web framework for perfectionists with deadlines”. It’s true: in each aspect it tries to help developers and encourages them not to invent the wheel all over again. It’s open source and has a huge, ready-to-help community.
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Clear and simple, fast and reliable, flexible and scalable. The Django programming framework is great when it comes to building Python-based applications. As a fully-equipped, open-source toolkit with highly customisable architecture, it encourages quick development and simple code structure.
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The app development process involves countless decisions, though few are as important as the language you choose to bring your project to life. Two of the most popular and powerful programming languages in use today are Python and Java.
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Are you planning a machine learning project and deciding between Python and R? You’re in the right place. Python and R share many characteristics. They're the two most popular tools used by data scientists. They're both open-source and free. But while Python was designed as a general-purpose programming language, R was developed for statistical analysis. Read on to find out what are the pros and cons of Python or R for projects that take advantage of machine learning.
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