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Standard Normal Distribution with examples using Python

Iterables and Iterators-- Part 1

How would you generally traverse any Python data structure? Or in plain english , how do you visit each element in a Python data structure? Don't know? ... Guess what .. Use a "for loop". Now, if you can use a "for loop" to traverse through a Python data structure, that data structure is called an Iterable .. Simple!! Let's create a Python list


lst = [1,2,3,4]
for element in lst:
    print(element)
---> 1
     2
     3
     4

Now let's create a Python set

set_new = set([4,5,6,7])
for element in set_new:
    print(element)

---> 4
     5
     6
     7

Let's create a Python string now

strng = 'Python'
for element in strng:
    print(element) 

---> 'P'
     'y'
     't' 
     'h'
     'o'
     'n'

If you try a similar approach with dictionaries and tuples, the effect will be the same. We call Python data structures like lists, tuples, set, string, dictionary as Iterables. More on this in the next part

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Standard Normal Distribution with examples using Python

Standard Normal Distribution with examples In our previous post, we talked about Normal Distribution and its properties . In this post, we extend those ideas and discuss about Standard Normal Distribution in detail. What is a Standard Normal Distribution? A Normal Distribution with mean 0 and standard deviation 1 is called a Standard Normal Distribution . Mathematicallty, it is given as below. Fig 1:Standard Normal Probability Distribution Function For comparison, have a look at the Normal Probability Distribution Function. If you substitute mean as 0 ,standard deviation as 1, you derive the standard normal probability distribution function Fig 2: Normal Probability Distribution Function Need for a standard normal probability distribution function We need to extract probability information about events that we are interested in. For this, first we need to convert any normal random variable