This is a common use-case for lambda functions, small anonymous functions that maintain no external state. This makes the sorting case-insensitive by changing all the strings to lowercase before the sorting takes place. The key parameter to sorted is called for each item in the iterable. > x = > print ( sorted ( x )) > print ( sorted ( x, key = lambda arg : arg. Now that you know some of the terms and concepts, you can explore how those ideas manifest in the Python ecosystem. Python exposes anonymous functions using the lambda keyword, not to be confused with AWS Lambda functions.
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This means that your code avoids global variables and always returns new data instead of manipulating the data in-place.Īnother common idea in functional programming is anonymous functions. The core idea of functional programming is that data should be manipulated by functions without maintaining any external state. You can learn many of the concepts needed for Big Data processing without ever leaving the comfort of Python. Luckily for Python programmers, many of the core ideas of functional programming are available in Python’s standard library and built-ins.
This is the power of the PySpark ecosystem, allowing you to take functional code and automatically distribute it across an entire cluster of computers. You can work around the physical memory and CPU restrictions of a single workstation by running on multiple systems at once. This means it’s easier to take your code and have it run on several CPUs or even entirely different machines.
Writing in a functional manner makes for embarrassingly parallel code. One paradigm that is of particular interest for aspiring Big Data professionals is functional programming.įunctional programming is a common paradigm when you are dealing with Big Data. Big Data Concepts in Pythonĭespite its popularity as just a scripting language, Python exposes several programming paradigms like array-oriented programming, object-oriented programming, asynchronous programming, and many others.
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