As compiled languages that offer type annotations, Swift and Julia are a lot faster and robust for development than Pyth

Julia allows you to avoid some of these problems. Despite being dynamically typed, it has a just-in-time compiler. The JIT compiler either generates the machine code right before it’s executed or uses previously stored, cached compilations, which makes it as performant as statically typed languages. More importantly, it has a key feature known as multiple dispatch that is like function overloading of OOPs, albeit at runtime. The power of multiple dispatch lies in its ability to handle different argument types without the need to create separate function names or nested if statements. This helps in writing compact code, which is a big win in numeric computations since unlike Python, you can easily scale solutions to deal with all types of arguments.

Rust is still trying to catch up with the machine learning community, and so I believe Swift and Julia are the languages that will dethrone Python and eventually rule data science. Let’s see why odds are against Python.

This realization led to a larger number of developers incorporating Python into their applications. The emergence of Django eventually led to the doom of Perl, and Python started gaining more momentum. Still, it wasn’t even close in popularity to Java and JavaScript, both of which were newer than Python.

For Python, that time first arrived during the early 2000s when people started realizing it has an easier learning curve than Perl and offers interoperability with other languages.

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doesn’t mean giving up, he says. It means not resisting or fighting reality so that you can apply your energy elsewhere. “It allows you to step into a more spacious mental space that allows you to do things that are constructive instead of being mired in a state of psychological self torment.”

You don’t have to be full-on passionate about everything, that’s exhausting. But to be excited about things — life in general — is irresistible. Except when you’re excited about everything but your relationship. That won’t be at all helpful. Just saying.

It was also the fastest-growing programming language of the previous decade. Python’s rise in popularity has a lot to do with the emergence of big data in the 2010s as well as developments in machine learning and artificial intelligence. Businesses urgently required a language for quick development with low barriers of entry that could help manage large-scale data and scientific computing tasks. Python was well-suited to all these challenges.

Despite its clean syntax, the exposure Python got in its first decade wasn’t encouraging, and it didn’t really find inroads into the developer’s workspace. Perl was the first choice scripting language and Java had established itself as the go-to in the object-oriented programming arena. Of course, any language takes time to mature and only gets adopted when it’s better suited to a task than the existing tools.

Make joint plans — from what you’re doing at the weekend, for the holidays, or with your shared lives. It’s so affirming for your partner to know you’re “two feet in” the relationship— fully invested in building a life with them.

Even better, Swift is a statically typed language and is highly optimized due to its LLVM (Low-Level Virtual Machine) compiler. The LLVM makes it possible to quickly compile into assembly code, making Swift super-efficient and almost as fast as C. Also, Swift boasts better memory safety and management tools known as Automatic Reference Counting. Unlike garbage collectors, ARC is a lot more deterministic as it reclaims memory whenever the reference count hits zero.

Worse, unlike compiled code, Python’s interpreter analyzes every line of code at execution time. This leads to an overhead that causes a significantly slower performance when compared to other languages.

Yet, despite the growing demand for machine learning and AI at the turn of this decade, Python won’t stay around for long. Like every programming language, it has its own set of weaknesses. Those weaknesses make it vulnerable to replacement by languages more suited to the common tasks businesses ask of them. Despite the presence of R, the emergence of newer languages such as Swift, Julia, and Rust actually poses a bigger threat to the current king of data science.

All good things come at a cost, and Python’s dynamically typed nature is no exception. It hampers developers, especially when running the code in production. Dynamic typing that makes it easy to write code quickly without defining types increases the risk of running into runtime issues, especially when the codebase size increases. Bugs that a compiler would easily figure out could go unidentified in Python, causing hindrances in production and ultimately slowing the development process in large-scale applications.

Make joint plans — from what you’re doing at the weekend, for the holidays, or with your shared lives. It’s so affirming for your partner to know you’re “two feet in” the relationship— fully invested in building a life with them.

Besides having those factors in its favor, Python was an interpreted language with dynamic typing support. More importantly, it had the backing of Google, who’d invested in Python for Tensorflow, which led to its emergence as the preferred language for data analysis, visualization, and machine learning.