Python has some functions from the functional world of programming that can help (be faster and more concise) when certain looping patterns are needed.

They rely on functions being first class objects.

The Python map function allows a function to be mapped onto a list, returning an object that can be converted to a list. Here is an example.

Here is the Python code [#1]
def double1(x1): return 2 * x1 dataList1 = [2, 3, 5, 7, 11, 13] dataList2 = list(map(double1,dataList1)) dataList3 = list(map(lambda x1: 2*x1,dataList1)) s1 = str(dataList1) s2 = str(dataList2) s3 = str(dataList3) print("Map:") print("") print("\t{0:s} (original list)".format(s1)) print("\t{0:s} (mapped list using explicit function)".format(s2)) print("\t{0:s} (mapped list using lambda function)".format(s3))
Note the use of list to covert the map object into a list. Here is the output of the Python code.
Map: [2, 3, 5, 7, 11, 13] (original list) [4, 6, 10, 14, 22, 26] (mapped list using explicit function) [4, 6, 10, 14, 22, 26] (mapped list using lambda function)

The Python function filter can make it easy to filter a list. Here is an example.

Here is the Python code [#2]
def even1(x1): return (x1 % 2) == 0 dataList1 = [0, 1, 2, 3, 4, 5, 6, 7] dataList2 = list(filter(even1,dataList1)) dataList3 = list(filter(lambda x1: (x1 % 2) == 0,dataList1)) s1 = str(dataList1) s2 = str(dataList2) s3 = str(dataList3) print("Filter:") print("") print("\t{0:s} (original list)".format(s1)) print("\t{0:s} (filtered list using explicit function)".format(s2)) print("\t{0:s} (filtered list using lambda function)".format(s3))
Here is the output of the Python code.
Filter: [0, 1, 2, 3, 4, 5, 6, 7] (original list) [0, 2, 4, 6] (filtered list using explicit function) [0, 2, 4, 6] (filtered list using lambda function)

The Python reduce function, requiring the functools package, allows a list to be summarized as a single value. Here is an example. Here is the Python code [#3]
from functools import reduce def add2(x1, x2): return x1 + x2 dataList1 = [0, 1, 2, 3, 4] x3 = reduce(add2,dataList1) x4 = reduce(lambda x1,x2: x1+x2,dataList1) s1 = str(dataList1) print("Reduce:") print("") print("\t{0:s} (original list)".format(s1)) print("\t{0:d} (value reduced from list using explicit function)".format(x3)) print("\t{0:d} (value reduced from list using lambda function)".format(x4))
Here is the output of the Python code.
Reduce: [0, 1, 2, 3, 4] (original list) 10 (value reduced from list using explicit function) 10 (value reduced from list using lambda function)

Here is a Python program to compute and output the powers of two from 0 to 8. You should understand each and every part of what is happening.

Here is the Python code [#4]
from functools import reduce print(reduce(lambda i1,s1 : str(i1)+"2^"+str(s1[0])+"="+str(s1[1])+"\n", [""]+[(i1,2**i1) for i1 in range(0,9)]))
Here is the output of the Python code.
2^0=1 2^1=2 2^2=4 2^3=8 2^4=16 2^5=32 2^6=64 2^7=128 2^8=256

Note: One would not usually write such a construct as there are better ways to do this, but it shows how the reduce function works.

The reduce function works by using the first element of the list as the base case and all other list elements as the step cases. In the powers of two example, the base case needs to be the empty string. Then all other cases are the tuples representing the integer and the power of two of that integer. These are joined together using concatenation.

One way to do the above in a better way is as follows.

Here is the Python code [#5]
print("\n".join(["2^"+str(i1)+"="+str(i1**2) for i1 in range(0,9)]))
Here is the output of the Python code.
2^0=0 2^1=1 2^2=4 2^3=9 2^4=16 2^5=25 2^6=36 2^7=49 2^8=64