Closures in Python:

For understanding closures, first we need to understand nested functions and non-local variables in python that are there for data science.

Nested Functions:

A function defined inside another function is known as a nested function. These functions can access variables of the enclosing scope.

In case of Python non-local variables can be accessed only within their scope and not outside it. It will be clear with an example:

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From the above example, we can say that innerFunction() is treated as a nested function and it uses text as a non-local variable.

Closures:

Closures are function objects which remember the values in enclosing scopes even if they are not present in memory.

  • It acts as a record for storing a function along with an environment. It is a mapping associated with each free variable of the function with the value of reference to which name was bound while the creation of closure took place.
  • A closure allows a function to gain access to captured variables by the closure’s copies of values or reference. It will take place even when the function invoked is outside the scope.

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It is clear from the above example that the function innerFunction had its scope only inside the outerFunction. But when closures came into the picture we were able to invoke a function outside its scope.

When and Why Closures should be used?

  • Closures being a call back function provide data hiding of some sort. It, in turn, helps in reducing the use of global variables.
  • Closures are efficient when we are dealing in codes with few functions.

To learn more about functions in python for data science, you can check this and this as well.

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