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Rukhshan Haroon

Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

The ** binom.pmf** function is a part of Python’s SciPy library and is used to model probabilistic experiments with the help of binomial distribution.

To use the `binom.pmf`

function, you must import `scipy`

at the very start of the program:

```
from scipy.stats import binom
```

The `binom.pmf`

method has the following syntax:

```
scipy.stats.binom.pmf(r,n, p)
```

The `binom.pmf`

function takes in three parameters:

`n`

: the total number of trials or times the experiment will be carried out.`r`

: a list of integers from 0 to`n`

, inclusive.`p`

: the probability that the outcome of a single experiment will be a success. The value of`p`

must be between 0 and 1, inclusive.

Binomial distributionis used to model experiments that have only one of two outcomes, success or failure.

The `binom.pmf`

method returns a list with the same number of values and sequence as `r`

. The return value is the probability mass function for the values in `r`

.

The code below shows how to use the `binom.pmf`

function in Python.

First, we set the values of `n`

and `p`

to 10 and 0.35, respectively. This is to denote that the experiment will be carried out 10 times, and the probability that the outcome of a single experiment is a success is 0.35.

Next, we call the `binom.pmf`

function and print its return value with the `print`

function. The return value is a list of 10 values, which corresponds to the probability for 0-10 trials of the experiment being a success. For example, the first value corresponds to the probability for none of the 10 experiments being a success, and the last value corresponds to the probability for all 10 experiments being a success.

Since all of the values in the returned list are probabilities, any value in this list and its numerical difference from 1 must sum to 1. We affirm our observation with the

`for`

loop at the end of the snippet.

from scipy.stats import binomimport matplotlib.pyplot as plt# setting the values# of n and pn = 10p = 0.35#defining list of r valuesr= list(range(n + 1))#calling the binom.pmf function and printing its return valuereturn_val=binom.pmf(r, n, p)print('Return Value:')print(return_val)#print the sum of 1-return_value[i] and return_value[i]print("Loop to attest our observation:")for i in range(0,n+1):print(return_val[i]+1-return_val[i])

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Rukhshan Haroon

Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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