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import numpy as np
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arr1 = np.random.randint(10,30, size=8)
arr1
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arr2 = np.random.randint(20,200,size=50).reshape(5,10) #method chaining - numbers from 0 to 50
arr2
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arr1[0]
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arr1[3]
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arr1[:3] #get the first 3 elements. Gets lower bounds inclusive, upper bound exclusive
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arr1[2:] #lower bound inclusive
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arr1[2:5] #get elements at index 2,3,4
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arr2
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arr2[0,0] #style 1 - you pass in a list of indices
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arr2[0][0] #style 2 - parse it as list of lists - not so popular
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arr2[1] # get a full row
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#get the second column
arr2[:,1]
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Thus, you specify : for all columns, followed by 1 for column. And you get a 1D array of the result
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#get the 3rd row
arr2[2,:] #which is same as arr2[2]
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#get the center 3,3 elements - columns 4,5,6 and rows 1,2,3
arr2[1:4, 4:7]
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arr2
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arr2_subset = arr2[1:4, 4:7]
arr2_subset
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arr2_subset[:,:] = 999 #assign this entire numpy the same values
arr2_subset
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arr2 #notice the 999 in the middle
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arr2_subset_a = arr2_subset
arr2_subset_a is arr2_subset
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Notice they are same obj in memory
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arr3_subset = arr2_subset.copy()
arr3_subset
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arr3_subset is arr2_subset
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Notice they are different objects in memory. Thus changing arr3_subset will not affect its source
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arr3_subset[:,:] = 0.1
arr2_subset
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arr1
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Get all numbers greater than 15
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arr1[arr1 > 15]
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arr1[arr1 > 12]
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just the condition returns a boolean matrix of same dimension as the one being queried
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arr1 > 12
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arr2[arr2 > 50] #looses the original shape as its impossible to keep the 2D shape
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arr2[arr2 < 30]
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arr_sum = arr1 + arr1
arr_sum
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arr_cubed = arr1 ** 2
arr_cubed
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Similarly, you can add a scalar to an array and NumPy will broadcast that operation on all the elements.
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arr_cubed - 100
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arr_cubed[0] = 0
arr_cubed
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arr_cubed / 0
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Thus 0/0 = nan and num/0 = inf
Numpy has a bunch of universal functions that work on the array elements one at a time and allow arrays to be used or treated as scalars.
Before writing a loop, look up the function list here