Slicing NumPy Arrays for Data Selection

1-D Array

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‍Accessing 1-D Array (Indexing and Slicing), we can have both positive and negative indexing

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import numpy as np
colors = np.array(["red","blue","yellow","orange"])
colors

OUTPUT:

array(['red', 'blue', 'yellow', 'orange'], dtype='<U6')

colors[-3]

OUTPUT:

'blue'

-5 -4 -3 -2 -1

0 1 2 3 4

a, b, c, d, e

arr = np.arange(10,20)
arr

OUTPUT:

array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])

print("FIRST Element is ",arr[0])

OUTPUT:

FIRST Element is 10

arr[0:4]

OUTPUT:

array([10, 11, 12, 13])

print("5th to 8th elements are",arr[5:8])

OUTPUT:

5th to 8th elements are [15 16 17]

arr = np.arange(10,20)
arr

OUTPUT:

array([10,11,12,13,14,15,16,17,18,19])

print("with step 2 :",arr[0:9:2])

OUTPUT:

with step 2 : [10 12 14 16 18]

array[start : end : jump ]  

print("Last element:",arr[-1])

OUTPUT:

Last element: 19

print("All the elements except the last one ",arr[0:-2])

OUTPUT:

All the elements except the last one  [10 11 12 13 14 15 16 17]

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Changing the values of an array

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arr=np.array([1,2,3,4,5,6,7,8,9,10])

arr[0]= 99
arr

OUTPUT:

array([99,  2,  3,  4,  5,  6,  7,  8,  9, 10])

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Copying one array to another using simple assignment

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arr1 = np.array([1, 2, 3, 4])
print("First array:",arr1)

arr2 = arr1
print("After copying ,second array:",arr2)


arr1[0] =500
print("After changing a value in arr1")


print("arr1:",arr1)
print("arr2:",arr2)

OUTPUT:

First array: [1 2 3 4]
After copying ,second array: [1 2 3 4]
After changing a value in arr1
arr1: [500   2   3   4]
arr2: [500   2   3   4]

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Copy one array to another using copy function

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import numpy as np
arr1 = np.array([1, 2, 3, 4])


print("First array:",arr1)
arr2 = arr1.copy()


print("After copying ,second array:",arr2)
arr2[0] = 100


print("After changing a value in arr1")
print("arr1:",arr1)
print("arr2:",arr2)

OUTPUT:

First array: [1 2 3 4]After copying ,second array: [1 2 3 4]After changing a value in arr1arr1: [1 2 3 4]arr2: [100   2   3   4]

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Filtering data

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score= np.array([34,56,78,94,29,56,79])

passing_score = score > 80

score[passing_score]

OUTPUT:

array([94])‍

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Functions in Numpy

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import numpy as np
A=np.array([4,16,36,49])
A

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OUTPUT:

array([ 4, 16, 36, 49])

np.std(A)

OUTPUT:

17.41228014936585

arr = np.array([3.4, 5.6, 2.1, 6.9])

print(np.ceil(arr))
print(np.floor(arr))

OUTPUT:

[4. 6. 3. 7.]
[3. 5. 2. 6.]

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import numpy as np
A=np.array([4,7,3,4,2,8])


print("Maximum Value:",np.max(A))
print("Mainimum Value:",np.min(A))
print("Average:",np.mean(A))  
print("Square root:",np.sqrt(A))
print("Median:",np.median(A))
print("Standard Deviation:",np.std(A))

OUTPUT:

Maximum Value: 8
Mainimum Value: 2
Average: 4.666666666666667
Square root: [2.        2.64575131 1.73205081 2.        1.41421356 2.82842712]
Median: 4.0
Standard Deviation: 2.134374745810949

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arr = np.array([3.4,5.6,2.1,6.9])
print("floor function")
print (np.floor(arr))

OUTPUT:

floor function[3. 5. 2. 6.]