A quick introduction to lambda functions in Python.

Below you find some examples of lambda functions in Python.

Documentation is added to the code directly – in the form of comments.

Lambda fucntions in Python are stated in accordance with the following syntax: lamda arguments : expression

# defining functions "cubing" with lambda operator cubing = lambda x: x**3 # let us test function on a numpy array import numpy cubing(numpy.array([1,2,3]))

array([ 1, 8, 27], dtype=int32)

# defining function summing, for summing to arguments summing = lambda x,y: x + y # let us test summing on two numpy arrays of same length summing(numpy.array([1,2,3]), numpy.array([7,9,12]))

array([ 8, 11, 15])

# we can also explicitly mention the parameters when calling the function summing(x = numpy.array([1,2,3]), y = numpy.array([7,9,12]))

array([ 8, 11, 15])

# lambda functions can have a return value, as seen above; # however, they must not return anything; see below printingSomething = lambda : print("something") printingSomething()

something

# lambda functions work with abstract datatypes, i.e. customized classes, too class Person: # constructor method def __init__(self,name,age): self.name = name self.age = age # method for printing personal info def printInfo(self): print(self.name + " has age: " + str(self.age)) # defining a function with lambda operator listingObjects = lambda x,y,z: [x,y,z] # testing lambda function on 3 person objects persons = listingObjects(x = Person(name = "Lisa", age = 22), y = Person(name = "John", age = 65), z = Person(name = "Charles", age = 54)) for i in persons: i.printInfo()

Lisa has age: 22 John has age: 65 Charles has age: 54

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python

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