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De Nederlander Guido van Rossum bedacht Python inen daarmee is het een relatief nieuwe programmeertaal. Vanaf het begin werd bleek Python een gat op te vullen, een manier om scripts te schrijven die 'het saaie werk te automatiseren', zoals een populair boek over Python het beschreef. Or to rapidly prototype applications that will be implemented in one or more other languages. However, over the past few years, Python has emerged as a first-class citizen in modern software development, infrastructure management, and data analysis.

Python is easy to learn. The number of features in the language itself is modest, requiring relatively little investment of time or effort to produce one's first programs. Python syntax is designed to be readable and straightforward.

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This simplicity makes Python an ideal teaching language, and allows newcomers to pick it up quickly. Developers spend more time thinking about the problem they're trying to solve, and less time thinking about language complexities or deciphering code left by others. Python runs on every major operating system and platform, and most minor ones too.

Many major libraries and API-powered services have Python bindings or wrappers, allowing Python to interface freely with those services or make direct use of those libraries. Python is not a "toy" language. Even though scripting and automation cover a large chunk of Python's use cases more on that belowPython is also used to build robust, professional-quality software, both as standalone applications and as web services. The most basic use case for Python is as a scripting and automation language.

But scripting and automation represent only the tip of the iceberg with Python. Python source used for data science and machine learning. Sophisticated data analysis has become one of fastest moving areas of IT and one of Python's star use cases. The vast majority of the libraries used for data science or machine learning have Python interfaces, making the language the most popular high-level command interface to for machine learning libraries and other numerical algorithms.

Python's native libraries and third-party web frameworks provide fast and convenient ways to create everything from simple REST APIs in a few lines of code, to full-blown, data-driven sites. Python is used for metaprogramming. In Python, everything in the language is an object, including Python modules and libraries themselves.

This allows Python to work as a highly efficient code generator, making it possible to write applications that manipulate their own functions and have the kind of extensibility that would be difficult or impossible to pull off in other languages. Python is used for glue code. Python is often described Atlanta Bodybuilder Hookup Meme Trash Cosplay House a "glue language," meaning it can allow disparate code typically libraries with C language interfaces to interoperate.

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Its use in data science and machine learning is in this vein, but that's just one incarnation of the general idea. Python is a high-level language, so it's not suitable for system-level programming - How To Je T drivers or OS kernels are straight out. You could build a standalone Python app for Windows, Mac, and Linux, but not Atlanta Bodybuilder Hookup Meme Trash Cosplay House or simply.

Finally, Python is not the best choice when speed is an absolute priority in every aspect of the application. Python syntax is meant to be readable and clean, with little pretense.

A standard "hello world" in Python 3. Python provides many syntactical elements that make it possible to concisely express many common program flows. Consider a sample program for reading lines from a text file into a list object, stripping each line of its terminating newline character along the way:.

This takes the place of several lines of boilerplate to open the file, read individual lines from it, then close it up. The point is that Python has a way to economically express things like loops that iterate over multiple objects and perform some simple operation on each element in the loop, or work with things that require explicit instantiation and disposal.

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Constructions like this allow Python developers to balance terseness and readability. Python's other language features are meant to complement common use cases. Most modern object types - Unicode strings, for instance - are built directly into the language.

Data structures - like lists, dictionaries i. Like CJava, and Go, Python has garbage-collected memory management, meaning the programmer doesn't have to implement code to track and release objects. Everything in the language, including functions and modules themselves, are handled as objects. This comes at the expense of speed more on that belowbut makes it far easier to write high-level code.

Developers can perform complex object manipulations with only a few instructions, and even treat parts of an application as abstractions that can be altered if needed. The indentation on the second line shown above isn't just for readability; it is part of Python's syntax. Python interpreters will reject programs that don't use proper indentation to indicate control flow.

Syntactical white space might cause noses to wrinkle, and some people do reject Python out of hand for this reason. But strict indentation rules are far less obtrusive in practice than they might seem in theory, even with the most minimal of code editors, and the end result is code that is cleaner and more readable. Another potential turnoff, especially for those coming from languages like C or Java, is the way Python handles variable typing.

By default, Python uses dynamic or "duck" typing - great for quick coding, but potentially problematic in large code bases.

Python is available in two versions, which are different enough to trip up many new users. Python 3 adoption was slowed for the longest time by the relative lack of third-party library support. Many Python libraries supported only Python 2, making it difficult to switch. Today, there are few reasons against using Python 3. The success of Python rests on a rich ecosystem of first- and third-party software.

Python benefits Atlanta Bodybuilder Hookup Meme Trash Cosplay House both a robust standard library and a generous assortment of easily obtained and readily used libraries from third-party developers. Python has been enriched by decades of expansion and contribution. Python's standard library provides modules for common programming tasks - math, string handling, file and directory access, networking, asynchronous operations, threading, multiprocess management, and so on.

But it also includes modules that manage common, high-level programming tasks needed by modern applications: Most any external code that exposes a C-compatible foreign function interface can be accessed with Python's ctypes module. The default Python distribution also provides a rudimentary, but useful, cross-platform GUI library by way of Tkinter, and an embedded copy of the SQLite 3 database.

The thousands of third-party libraries, available through the Python Package Index PyPIconstitute the strongest showcase for Python's popularity and versatility. CPython, as this edition is called, is used as the stock Python runtime in every major Linux distribution as well as MacOS.

That said, a wealth of other Python distributions exist to serve specific audiences. Python for enterprise developers. Installing those libraries by hand can be check this out, especially on Windows. Anaconda saves you that trouble, and provides mechanisms for keeping them up to date and installing other libraries in the same vein.

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The biggest limitation with PyPy is that it works best with Python apps that don't use external C libraries, but its development team has been addressing that problem. Net and Java developers. Editions of Python exist that run the. Both of them allow Python to interoperate with other languages on their respective runtimes - such as an IronPython app can interoperate with. Jython development hasn't budged much in the last couple of years, but work on IronPython has been rejuvenated with a new development team.

One common caveat about Python is that it's slow. Some Python programs will be slower by an order of magnitude or more. It isn't just because most Python runtimes are interpreters rather than compilers. That said, Python's speed may click to see more be as much of an issue as it might seem, and there are ways to alleviate it.

Python has many routes for speed optimization. It isn't always the fate of a slow Python program to be forever slow. Many Python programs are slow because they don't properly leverage the functionality present in Python or its standard library. Math and statistics operations can be boosted tremendously by way of libraries such as NumPy and Pandas, and the PyPy runtime can provide orders-of-magnitude speedups for many Python apps. A common adage of software development is that 90 percent of the activity for a program tends to be in 10 percent of the code, so optimizing that 10 percent can yield major improvements.

The end result is often a program that runs within striking distance of a counterpart written entirely in C, but without being cluttered with C's memory micromanagement details. In Python, developer time is usually far more valuable than machine time. Or to put it another way: A given Python program might take six seconds to execute versus a fraction of a second in another language.

But it might take only ten minutes for Atlanta Bodybuilder Hookup Meme Trash Cosplay House developer to put that Python program together, versus an hour Atlanta Bodybuilder Hookup Meme Trash Cosplay House more of development time in another language. The amount of time lost in the execution of the Python program is continue reading than gained back by the time saved in the development process.

Obviously, this is less true when you're writing software that has high-throughput, low-concurrency demands, such as a source application.

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But for many real-world applications, in domains ranging from systems management to machine learning, Python will prove to be rich enough and fast enough for the job. And the flexibility and pace of development that Python enables may allow for innovation that would be more difficult and time-consuming to achieve in other languages.

When speed of development and programmer comfort are more important than shaving a few seconds off the machine clock, Python may well be the best tool for the job.

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Wist u dat u met een gratis Insider-account altijd op de hoogte blijft over al het nieuws, achtergrondartikelen, opinies, interviews en events rondom IT? Alles wat je moet weten Waarom de Python programmeertaal zo geschikt is voor data science, machine learning, systeem automatisering, web- en API-ontwikkeling en meer. Gerelateerde vragen Hoe snel gaat de implementatie van hyper converged? Moet ik voor AWS betalen als ik mijn creditcardnummer opgeef?

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