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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.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 may not be the fastest language, but what it lacks in speed, it makes up for in versatility. Even though scripting and automation cover a large chunk of Python's use cases (more on that below), Python 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.Most modern object types - Unicode strings, for instance - are built directly into the language.

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Python syntax is meant to be readable and clean, with little pretense.

It is no longer a back-room utility language, but a major force in web application development and systems management and a key driver behind the explosion in big data analytics and machine intelligence.

Python's success revolves around several advantages it provides for beginners and experts alike: Python is easy to learn.

Python isn't just a replacement for shell scripts or batch files, but is also used to automate interactions with web browsers or application GUIs or system provisioning and configuration in tools such as Ansible and Salt.

But scripting and automation represent only the tip of the iceberg with Python.

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