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import numpy as np


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# Numpy: The commonly used functions - memorize these


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# Initialize an array from a list
my_list = [1,2,3,3]
x = np.array(my_list)


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# If you need to check the type
type(x)


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# Create a matrix from an array
my_matrix = [
    [1,2,3],
    [4,5,6],
    [7,8,9]]


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print(my_matrix)


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# Use the same initializer
np.array(my_matrix)


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# Create a range from 0 -> 5
list(range(0,5))


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# Create a range with space in between
list(range(0,10,2))


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# Easier way: use a range
print(np.arange(0,100,2))


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# An array of zeroes rows by columns
np.zeros((2,3))


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# An array of ones
np.ones((10,10))


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# This gives us numbers from 0 to 10, with 100 elements evenly spaced between them
np.linspace(0,10,100)


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# Identity matrix 
np.eye(10,10)


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# Random Library! Want a random set of numbers? Gives us random numbers between 0-1 using a uniform distribution
# Meaning from 0-1 each number is picked randomly given probability. 
np.random.rand(5,5)


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# The closer you are to the mean, the higher likelihood you are picked as a number
np.random.randn(5,5)


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# How about a random integer between two values, not including 100? Give me 10
np.random.randint(0,100,10)


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# Reshape into 5 by 5 matrix
arr = np.arange(25)
arr.reshape(5,5) # Quick trick for reshaping: 5*5 must equal 25


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# Max number gives you the max number
arr.max() 
# ArgMax gives you the argument (index) of the max, same with min
arr.argmax()
# Gives you the type of the array
arr.dtype


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