Python

Math API module:

• Math module provide all mathematical operation.
• help(math) after importing math module you can use to see all math module functions.
• numpi is module which has all advance math operation which most commonly use in AI ML projects.

Example, -

num_var = 4.35

Method	Description	Output
math.floor(num_var)	returns the largest integer value that is less than or equal to a number.	4
math.ceil(num_var)	returns the smallest integer value that is bigger than or equal to a number.	5
round(num_var)	rounding the number with the help of expotential number value	4
round(4.5)	round function always goes closer to event number in case of mid number of expotential value.	4
round(4.7)	rounding the number with the help of expotential number value.	5
round(5.5)	round function always goes closer to event number in case of mid number of expotential value.	6

Example of few inbuilt keywords in math module, -

math.pi   # ==> PI value
math.e    # ==> Base of natural logarithms value
math.inf  # ==> A floating-point positive infinity.
math.nan  # ==> A floating-point “not a number” (NaN) value

Random API module:

• random module available in random package

Example:

import random
random.randint(0, 100) # ==> generate random number which is in range of 0 to 100

seed example:

import random
random.seed(100) # ==> seed used to generate same series of random integer.
random.randint(0, 100) # ==> generate random number which is in range of 0 to 100

Note:

• Above example when we run into Jupyter Notebook it should be in same row otherwise seed will not work on randint function
• All time when we run above code it will always return same series of rundom integer

random item from given list example:

my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ,11, 12, 13, 14, 15]
random.choice(my_list) # ==> 5 (Random item will pick up from my_list here list is not affected)

select multiple number with and without replacement from random series example:

my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ,11, 12, 13, 14, 15]
# Sample with replacement. that means it allow duplicate item to be choosen
random.choices(population = my_list, k = 5) # ==> [2, 10, 14, 3, 2] (Random 5 items will pick up from my_list here list is not affected)
# Sample without replacement. that means duplicate item not allowed to be choosen
random.sample(population = my_list, k = 5) # ==> [2, 10, 14, 5, 15] (Random 5 items will pick up from my_list here list is not affected)

random shuffle function example:

my_list = [1, 2, 3, 4, 5, 6, 7]
random.shuffle(my_list) # shuffle the given list. shuffle function does not return anything.
print(my_list) # ==> [5, 3, 7, 1, 2, 6, 4]

random uniform function example:

import random
my_var = random.uniform(1, 10) #Random number is choose along with expotential value also
print(my_var) # ==> 5.609960188291158

**random gauss function ** Gaussian distribution. mu is the mean, and sigma is the standard deviation

import random
my_var = random.gauss(mu = 2, sigma = 1) #Random gauss function
print(my_var) # ==> 2.632637726905687

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