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Python is also one of the top 5 programming languages, and it’s the fastest-growing major language in the world right now. While Python has been growing steadily for twenty years or so, it has experienced exponential growth in the past five (5) years because of its adaptation as the primary language for machine learning and data science.
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A brief history of Python is that Guido van Rossum designed it in the 1980s in the Netherlands. The easy-to-use ABC programming language influenced it. The language was not named after the snake. The BBC comedy show called Monty Python’s Flying Circus gave the idea of “Python” to the creator, Guido van Rossum.
The language is used to store and retrieve user credentials from the database that they’re kept in. And, if a website uses Python as its server-side language, the chances are that the Django package is being used. That’s because Django is one of the most popular server-side web frameworks for Python.
It is also important to note that Python separates itself from other server-side programming languages because it’s very easy to use and powerful in analyzing data. This is why it’s been a go-to language for fields like machine learning data science and other math and heavy analytics fields. Popular machine learning packages like Py-Torch and scikit-learn are a lifesaver for Python developers. When topped with popular numeric packages like pandas, NumPy and matplotlib, the use cases of Python are drastically increased.
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Now, let’s take a look at a few examples to see how both of these languages have been used to create some well-known software. One famous project Python was used to create is Spotify. Python is used on the back end of the application. So, when Spotify suggests a similar artist or genre, it’s using Python to analyze your music listening data and then serve a recommendation.
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There are multiple points to ponder upon in this comparison.
Python is extremely easy for beginners to use because with one look at Python you can deduce what the code is going to be doing in the function. Python’s written code is human-readable, and it’s concise as compared to C. Many people even joke that a Python script is executable pseudocode due to its straightforward syntax.
Python, which is designed for handling large data sets, is supported by a large community of data scientists that makes it easier to use by developing beautiful open-source libraries. Topped with efficient file-handling and support for a range of platforms, Python is easily the best language out there for data scientists.
Due to its server-sided nature, Python has easily overshadowed all the other languages used for managing backend processes and flows. Topped with its data handling expertise, which is an essential element of managing a website’s backend, Python provides a complete package for developers.
Also Read: Python Project Ideas & Topics
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1. How to create your own web crawlers?
You can make your own web crawlers in a few different methods. One option is to employ a web crawling-specific library or framework, such as Scrapy, Nutch, or WebSpider. These libraries include everything you'll need to crawl the web, including the ability to explore links, extract data, and save it to a database. You can also crawl the web by writing your own programs. This is a more time-consuming approach, but it allows you to tailor your crawler to your exact requirements. You'll need to know how to explore links, extract data, and store data in a database to get started. Many libraries and frameworks, such as Laravel, Ruby on Rails, and Express, can assist you with this.
2. How useful is OpenAI for automation?
OpenAI is a fantastic tool for automating tasks. It enables programmers to design programs that can self-learn how to perform specific tasks without the need for explicit instructions. As a result, it's a fantastic tool for automating operations that would otherwise be difficult or impossible to complete. By helping computers to learn from example data sets, it can also assist speed up the process of automating processes. Similarly, it can aid in the improvement of automated task accuracy by helping programs to learn from their failures and improve their performance over time. It can also aid in the development of more efficient and dependable automated systems by allowing programs to learn how to collaborate in order to fulfill tasks. Finally, it can aid in the improvement of automated system safety and reliability by allowing programs to learn how to avoid errors and risky circumstances.
How does Google create its search engine?