Vocab

  • Libraries: collection of precompiled codes that can be used later on in a program for sme specific well-defined operations
  • API: contains specific direction for how the procedures in a library can behave or be used

Libraries and Random Values

  • Libraries
    • collection of precompiled codes that can be used later on in a program for some specific well-defined operations.
    • precompiled codes can be referred to as modules. Each module contains bundles of code that can be used repeatedly in different programs.
    • may also contain documentation, configuration data, message templates, classes, and values, etc.
    • makes Python Programming simpler and convenient for the programmer
    • ex: Pillow, Tensor Flow, Matplotlib
  • APIs
    • application program interface, contains specific direction for how the procedures in a library can behave or be used
    • acts as a gateway for the imported procedures from a library to interact with the rest of your code
import numpy as np
new_matrix = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])
 
print (new_matrix)

[[1 2 3]
 [4 5 6]
 [7 8 9]]

Numpy to find derivatives

import numpy as np
 
# defining polynomial function
var = np.poly1d([2, 0, 1])
print("Polynomial function, f(x):\n", var)
 
# calculating the derivative
derivative = var.deriv()
print("Derivative, f(x)'=", derivative)
 
# calculates the derivative of after
# given value of x
print("When x=5  f(x)'=", derivative(5))

Polynomial function, f(x):
    2
2 x + 1
Derivative, f(x)'=  
4 x
When x=5  f(x)'= 20
  • Random Values
    • Random number generation (RNG) produces a random number
      • procedure with RNG can return different values even if the parameters do not change
import random 
def dice(n):
    sum = 0
    while n > 0:
        sum = sum + random.randint(1,6)
        n = n -1
    return sum

dice(5)

18

HW

  • Find two other libraries and explain their function and how it helps programmers code

  1. Requests

    • allows you to send HTTP/1.1 requests extremely easily
    • coders don't have to manually add query strings to URLs, form encode PUT and POST data
    • creates more efficient ways to:
      • Make requests using the most common HTTP methods
      • Customize your requests’ headers and data, using the query string and message body
      • Inspect data from your requests and responses
      • Make authenticated requests
      • Configure your requests to help prevent your application from backing up or slowing down

  2. Theano

    • allows tou to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays
    • helps you perform data intensive computations up to 140x faster
    • can compute derivatives for functions of one or many inputs
    • evaluates expressions faster
  1. Write a procedure that generates n random numbers, then sorts those numbers into lists of even and odd numbers (JS or Python, Python will be easier).
import random
list = []

n = int(input("How many numbers would you like to generate?"))
min = int(input("What is the smallest number you would like to generate?"))
max = int(input("What is the biggest number you would like to generate?"))

num = [random.randint(min, max) for v in range(n)] #generating 100 random numbers in the range of 0-200 (in this case my n random numbers is 100. Im setting a range to prevent ridiculous numbers)
list.extend(num)

def splitevenodd(list): 
   evenlist = [] 
   oddlist = [] 
   for i in list: 
        if (i % 2 == 0): 
            evenlist.append(i) 
        else: 
            oddlist.append(i) 
   print("Even list:", evenlist) 
   print("Odd list:", oddlist) 

splitevenodd(list)

Even list: [34, 50, 80, 24, 50, 16, 98, 34, 88, 72]
Odd list: [27, 83, 87, 43, 63, 9, 35, 85, 5, 79, 85, 7, 21, 81, 59]
  1. Using NumPy and only coding in python cell, find the answer to the following questions: a. What is the derivative of 2x^5 - 6x^2 + 24x? b. What is the derivative of (13x^4 + 4x^2) / 2 when x = 9?
import numpy as np
# defining polynomial function
eq1 = np.poly1d([2, 0, 0, 6, 24, 0])
print("f(x)=\n", eq1)

# calculating the derivative
derivative = eq1.deriv()
print("The derivative of f(x)=\n", derivative)

#-----------------------------------

eq2 = np.poly1d([6.5, 0, 2, 0, 0]) #i simplified your derivative which shud be fine right?
print("g(x)=\n", eq2)

derivative2 = eq2.deriv()
print("The derivative of 13x^4 + 4x^2 / 2 (g(x)) when x = 9 is:\n", derivative2(9))

f(x)=
    5     2
2 x + 6 x + 24 x
The derivative of f(x)=
     4
10 x + 12 x + 24
g(x)=
      4     2
6.5 x + 2 x
The derivative of 13x^4 + 4x^2 / 2 (g(x)) when x = 9 is:
 18990.0
  1. Suppose you have a group of 10 dogs and 10 cats, and you want to create a random order for them. Show how random number generation could be used to create this random order.
import random
dogs = ["bob", "mark", "lucy", "tod", "frank", "lily", "cookie", "oreo", "ollie", "charlie"] 
cats = ["luna", "ginger", "mochi", "pluto", "jeff", "skippy", "lemon", "boba", "chase", "louis"] #list of 10 dog and cat names

def randomOrder(animals):
    list = []
    random.shuffle(animals)
    result = f'{animals}'
    print("New Random Order: " + result)

randomOrder(dogs)
randomOrder(cats)

New Random Order: ['mark', 'frank', 'lucy', 'bob', 'tod', 'lily', 'oreo', 'charlie', 'cookie', 'ollie']
New Random Order: ['skippy', 'pluto', 'chase', 'mochi', 'ginger', 'louis', 'luna', 'lemon', 'boba', 'jeff']