Python Tutorial: Review of functional programming in Python

Want to learn more? Take the full course at https://learn.datacamp.com/courses/big-data-fundamentals-with-pyspark at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Understanding PySpark becomes a lot easier if we understand functional programming principles in Python. In this video, let's review some of the Python functions such as lambda, map and filter. Python supports the creation of anonymous functions. That is functions that are not bound to a name at runtime, using a construct called the lambda. lambda functions are very powerful, well integrated into Python, and are often used in conjunction with typical functional concepts like map and filter functions. Like def, the lambda creates a function to be called later in the program. However, it returns the function instead of assigning it to a name. This is why lambdas are known as anonymous functions. In practice, they are used as a way to inline a function definition, or to defer execution of a code. Lambda functions can be used whenever function objects are required. They can have any number of arguments but only one expression and the expression is evaluated and returned. The general syntax of lambda function is shown here. Here is an example of a lambda function. In this example, lambda x: x * 2, x is the argument and x * 2 is the expression that gets evaluated and returned. This function has no name. It returns a function object which is assigned to the identifier "double" here. Applying the lambda function to a number such as 3 returns 6 which is the double of the original number. Let's take a look at the differences between def and lambda. Here is the Python code to illustrate cube of a number showing the difference between normal python function using def and anonymous function using lambda. As you can see, both def and lambda do exactly the same. The main difference is that the lambda definition does not include a return