import numpy
import numpy as np
print(np.__version__)
1.15.4
import numpy as np
arr = np.array([1,2,3,4,5,6,7,8,9,10])
print(type(arr))
print(arr)
for a in arr:
print("a : {}".format(a))
<class 'numpy.ndarray'>
[ 1 2 3 4 5 6 7 8 9 10]
a : 1
a : 2
a : 3
a : 4
a : 5
a : 6
a : 7
a : 8
a : 9
a : 10
import numpy as np
arr = np.array([range(1,10,2)])
print(type(arr))
<class 'numpy.ndarray'>
print(arr)
[[1 3 5 7 9]]
for a in arr:
print(a)
[1 3 5 7 9]
#Tuple to ndarray
myTuple = tuple((1,6,3,2,6,7,2,2))
import numpy as np
arr = np.array(myTuple)
print(arr)
[1 6 3 2 6 7 2 2]
1Dimension Arrray:
import numpy as np
arr1D = np.array(7)
print(arr1D.shape)
print(arr1D)
7
print(arr1D.ndim)
0
2D array:
arr = np.array([1,2,3,4,5,6])
print(arr.shape)
(6,)
print(arr2D.ndim)
2
arr2D = np.array([[1,2,3], [10,11,12]])
print(arr2D)
[[ 1 2 3]
[10 11 12]]
(2, 3)
import numpy as np
arr3D=np.array([[ [1,2,3],[6,7,8]], [[5,3,2],[6,2,1] ] ])
print(arr3D)
print(arr3D.shape)
[[[1 2 3]
[6 7 8]]
[[5 3 2]
[6 2 1]]]
(2, 2, 3)
print(arr3D.ndim)
3
import numpy as np
arr = np.array([5,7,3,2,6,33,2,1,2,3,4,5,6,7,8,9,2,3,5,6,2,12,1,3,5,3])
print(len(arr))
print(arr[0])
print(arr[1])
print(arr[-1])
print(arr[1:2])
print(arr[3:20])
print(arr[3:20:2])
print(arr[3:-1])
print(arr[3:-1:3])
26
5
7
3
[7]
[ 2 6 33 2 1 2 3 4 5 6 7 8 9 2 3 5 6]
[ 2 33 1 3 5 7 9 3 6]
[ 2 6 33 2 1 2 3 4 5 6 7 8 9 2 3 5 6 2 12 1 3 5]
[ 2 2 3 6 9 5 12 5]
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr[0] + arr[-1])
5
import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('3rd element on 2st dim: ', arr[1, 2])
8
import numpy as np
arr = np.array([[1,2,3,4,5,3,2,3,4], [6,7,8,9,10,3,2,5,3]])
print('5th element on 1st dim: ', arr[0, 4])
5th element on 1st dim: 5
import numpy as np
arr = np.array([[[3,2,1], [6,-5,-4]], [[9,8,7], [5,4,3]]])
print(arr[0, 1, 2])
print(arr[1,0,1])
-4
8
import numpy as np
arr = np.array([[1,2,22,453,5], [3,7,8,32,3233]])
print('Last element from 2nd dim: ', arr[1, -1])
print('Last element from 1st dim: ', arr[0, -1])
Last element from 2nd dim: 3233
Last element from 1st dim: 5
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[1:5])
print(arr[:3])
print(arr[:7:2])
print(arr[:-1:2])
print(arr[::2])
[2 3 4 5]
[1 2 3]
[1 3 5 7]
[1 3 5]
[1 3 5 7]
import numpy as np
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[1, 1:4])
print(arr[0,2:-1])
print(arr[0,:4])
print(arr[1,-1])
print(arr[1,1:-1:2])
[7 8 9]
[3 4]
[1 2 3 4]
10
[7 9]
import numpy as np
arr = np.array([6,34,3,2])
print(arr.dtype) #data type
int64
arr = np.array(['silva', 'kalai', 'Raj'])
print(arr.dtype)
<U5
arr = np.array([1, 2, 3, 4], dtype='S') #defined data type
print(arr)
print(arr.dtype)
[b'1' b'2' b'3' b'4']
|S1
arr = np.array([1.3, 2.31, 3.12])
print(arr.dtype) #float
float64
newarr = arr.astype('i') #float into integer
print(newarr)
print(newarr.dtype)
[1 2 3]
int32
arr = np.array([12, 0, 34])
print(arr)
[12 0 34]
newarr = arr.astype(bool) #integer into bool
print(newarr)
print(newarr.dtype)
[ True False True]
bool
Copy Example:
import numpy as np
arr = np.array([6, 2, 7, 4, 5])
x = arr.copy()
arr[0] = 3333 #making changes in the source
print(arr)
[6, 2, 7, 4, 5]
print(x) #source changes not reflected here
[333, 2, 7, 4, 5]
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.view()
arr[0] = 42 #source element got changed
print(arr)
print(x) #target will reflect the source
[42 2 3 4 5]
[42 2 3 4 5]
import numpy as np
arr = np.array(['a','e','i','o','u'])
x = arr.copy()
y = arr.view()
print(x.base)
print(y.base)
#The copy returns None.
#The view returns the original array.
import numpy as np
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
print(arr.shape) # 2 D with 4 elements
(2, 4)
import numpy as np
arr = np.array([['a','b','c','d','e'], ['f','g','h','i','j'], ['k','l','m','n','o'] ])
print(arr.shape) # 3 D with 5 elements
(3, 5)
Reshape exa:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16])
newarr = arr.reshape(8, 2) #8 arrays 2 elements
print(newarr)
[[ 1 2]
[ 3 4]
[ 5 6]
[ 7 8]
[ 9 10]
[11 12]
[13 14]
[15 16]]
newarr = arr.reshape(2, 8) #2 arrays 8 elements
print(newarr)
[[ 1 2 3 4 5 6 7 8]
[ 9 10 11 12 13 14 15 16]]
newarr = arr.reshape(4, 4) #4 arrays 4 elements
print(newarr)
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]
[13 14 15 16]]
1 D to 3 D:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(2, 3, 2) #2 arrays that contains 3 arrays, each with 2 elements:
print(newarr)
[[[ 1 2]
[ 3 4]
[ 5 6]]
[[ 7 8]
[ 9 10]
[11 12]]]
#flattening array
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)
[[1 2 3]
[4 5 6]]
newarr = arr.reshape(-1) #flattening
print(newarr)
[1 2 3 4 5 6]
Iteration - Looping:
--------------------
import numpy as np
arr = np.array(['a','b','c','d','e','f'])
for x in arr:
print(x)
a
b
c
d
e
f
#iteration loop through
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6], [7,8,9]])
for x in arr:
print(x)
[1 2 3]
[4 5 6]
[7 8 9]
#flattening
for x in arr:
for y in x:
print(y)
1
2
3
4
5
6
7
8
9
import numpy as np
arr = np.array([ [[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13,14, 15], [16, 17, 18]] ])
for x in arr:
print("x represents the 2-D array:")
print(x)
x represents the 2-D array:
[[1 2 3]
[4 5 6]]
x represents the 2-D array:
[[ 7 8 9]
[10 11 12]]
x represents the 2-D array:
[[13 14 15]
[16 17 18]]
import numpy as np
arr = np.array([ [[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13,14, 15], [16, 17, 18]] ])
for x in arr:
for y in x:
for z in y:
print(z)
#flattening
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
#nd iteration : nditer
import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in arr:
print(x)
[[1 2]
[3 4]]
[[5 6]
[7 8]]
for x in np.nditer(arr):
print(x)
1
2
3
4
5
6
7
8
#join / Concatenate
import numpy as np
arr1 = np.array([1, 2, 3,7,8,9])
arr2 = np.array([4, 5, 6,10,11,12])
arr = np.concatenate((arr1, arr2))
print(arr)
[ 1 2 3 7 8 9 4 5 6 10 11 12]
import numpy as np
arr1 = np.array([ [1, 2], [3,7],[8,9]])
arr2 = np.array([[4, 5], [6,10],[11,12]])
arr = np.concatenate((arr1, arr2), axis=1)
print(arr)
[[ 1 2 4 5]
[ 3 7 6 10]
[ 8 9 11 12]]
import numpy as np
arr1 = np.array([1, 2, 3,4,5,6,7,8])
arr2 = np.array([9,10,11,12,13,14,15,16])
arr = np.stack((arr1, arr2), axis=1)
print(arr)
[[ 1 9]
[ 2 10]
[ 3 11]
[ 4 12]
[ 5 13]
[ 6 14]
[ 7 15]
[ 8 16]]
import numpy as np
arr1 = np.array([1, 2, 3,4,5,6,7,8])
arr2 = np.array([9,10,11,12,13,14,15,16])
arr3 = np.array([17,18,19,20,21,22,23,24])
arr = np.stack((arr1, arr2,arr3), axis=1)
print(arr)
[[ 1 9 17]
[ 2 10 18]
[ 3 11 19]
[ 4 12 20]
[ 5 13 21]
[ 6 14 22]
[ 7 15 23]
[ 8 16 24]]
split example:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])
newarr = np.array_split(arr, 3)
print(newarr)
[array([1, 2, 3, 4, 5, 6]), array([ 7, 8, 9, 10, 11]), array([12, 13, 14, 15, 16])]
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])
newarr = np.array_split(arr, 4)
print(newarr)
[array([1, 2, 3, 4]), array([5, 6, 7, 8]), array([ 9, 10, 11, 12]), array([13, 14, 15, 16])]
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])
newarr = np.array_split(arr,8)
print(newarr)
[array([1, 2]), array([3, 4]), array([5, 6]), array([7, 8]), array([ 9, 10]), array([11, 12]), array([13, 14]), array([15, 16])]
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])
newarr = np.array_split(arr,2)
print(newarr)
print(newarr[0])
print(newarr[1])
[array([1, 2, 3, 4, 5, 6, 7, 8]), array([ 9, 10, 11, 12, 13, 14, 15, 16])]
#split and slice
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16])
newarr = np.array_split(arr,2)
print(newarr)
print(newarr[0])
print(newarr[1])
print(newarr[0][3:5])
print(newarr[1][3:5])
[array([1, 2, 3, 4, 5, 6, 7, 8]), array([ 9, 10, 11, 12, 13, 14, 15, 16])]
[1 2 3 4 5 6 7 8]
[ 9 10 11 12 13 14 15 16]
[4 5]
[12 13]
import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13,14],[15,16],[17,18]])
newarr = np.array_split(arr, 3)
print(newarr)
[array([[1, 2],
[3, 4],
[5, 6]]),
array([[ 7, 8],
[ 9, 10],
[11, 12]]),
array([[13, 14],
[15, 16],
[17, 18]])]
#find the index position start with 0
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 4, 4]) #0,1,2,3,4,5,6
x = np.where(arr == 4)
print(x)
(array([3, 5, 6]),)
#Even number's position index starts from 0
import numpy as np
arr = np.array([2222,1111,3333,4444,8888,223,224,999,1000])
x = np.where(arr%2 == 0)
print(x)
(array([0, 3, 4, 6, 8]),)
#Odd number's position index starts from 0
import numpy as np
arr = np.array([2222,1111,3333,4444,8888,223,224,999,1000])
x = np.where(arr%2 == 1)
print(x)
(array([1, 2, 5, 7]),)
#Find the indexes where the value 'n' should be inserted:
import numpy as np
arr = np.array([636, 173,285, 492])
x = np.searchsorted(arr, 177)
print(x)
2
x = np.searchsorted(arr, 77)
print(x)
0
x = np.searchsorted(arr, 377)
print(x)
3
x = np.searchsorted(arr, 500)
print(x)
4
x = np.searchsorted(arr, 1000)
print(x)
4
import numpy as np
arr = np.array([66, 17, 38, 69])
x = np.searchsorted(arr, 27, side='left')
print(x)
2
x = np.searchsorted(arr, 27, side='right')
print(x)
2
import numpy as np
arr = np.array([400, 200, 350, 100])
x = np.searchsorted(arr, [22, 444, 266,133,277,342,479])
print(x)
#sort
import numpy as np
arr = np.array([6,3,66,22,33,100,10,-1,-2,-3,0,34,1000,98])
print(np.sort(arr))
[ -3 -2 -1 0 3 6 10 22 33 34 66 98 100 1000]
import numpy as np
arr = np.array(["zeebra","elephant","lion","tiger","deer","cheetah","fox"])
print(np.sort(arr))
['cheetah' 'deer' 'elephant' 'fox' 'lion' 'tiger' 'zeebra']
import numpy as np
arr = np.array([True, False,False,True,True,True,False])
print(np.sort(arr))
[False False False True True True True]
#sort an array
import numpy as np
arr = np.array([[3, 2, 4], [5, 0, 1],[6,3,2], [5,2,1], [6,-1,-2],[0,1,-33] ])
print(np.sort(arr))
[[ 2 3 4]
[ 0 1 5]
[ 2 3 6]
[ 1 2 5]
[ -2 -1 6]
[-33 0 1]]
Filter:
import numpy as np
arr = np.array([41, 42, 43, 44])
x = arr[[True, True, True, True]]
print(x)
[41 42 43 44]
x = arr[[False,False,False,False]]
print(x)
[]
x = arr[[False,False,True,True]]
print(x)
[43 44]
x = arr[[True,True,False,False]]
print(x)
[41 42]
import numpy as np
arr = np.array([100,1000,800,200,700,500,600,300,200,0,900])
filter_arr = arr > 550
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
[False True True False True False True False False False True]
[1000 800 700 600 900]
Even number filtering:
import numpy as np
arr = np.array([100,1000,800,200,700,500,600,300,200,0,900])
filter_arr = arr %2 == 0
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
[ True True True True True True True True True True True]
[ 100 1000 800 200 700 500 600 300 200 0 900]
Odd number filtering:
import numpy as np
arr = np.array([100,1000,800,200,700,500,600,300,200,0,900])
filter_arr = arr %2 == 1
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
[False False False False False False False False False False False]
[]
#odd number filtering
import numpy as np
arr = np.array([100,55,77,200,700,399,600,241,200,0,887])
filter_arr = arr %2 == 1
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
[False True True False False True False True False False True]
[ 55 77 399 241 887]
import numpy as np
arr = np.array([100,55,77,200,700,399,600,241,200,0,887])
filter_arr = arr %2 == 0
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
#even number filtering
[ True False False True True False True False True True False]
[100 200 700 600 200 0]
