Basics Of numpy with Examples - MyPythonGuru

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Monday, September 30, 2019

Basics Of numpy with Examples

Here we have collection of examples of basics calculation using numpy functionalities..

Import Numpy as np

1. distances = [10,15,17,26]

In [2]:
cars = ["Dravid","Kohli","Dhoni","Sachin"]

In [3]:
times = [0.3,0.47,0.55,1.20]

In [5]:
speeds = distances / times


2. In [6]:
a = 10

In [7]:
b = 0.3

In [8]:
c = a/b

In [9]:
c

Out[9]:
33.333333333333336

In [12]:
speeds_1 = [] for i in range(4): speeds_1.append(distances[i]/times[i])

In [11]:
speeds_1

Out[11]:
[33.333333333333336, 31.914893617021278, 30.909090909090907, 21.666666666666668]

In [13]:
import numpy as np

In [14]:
d = np.array(distances)

In [15]:
t = np.array(times)

In [16]:
s = d/t

In [17]:
s

Out[17]:
array([33.33333333, 31.91489362, 30.90909091, 21.66666667])

In [59]:
#np.random.seed(0) x1 = np.random.randint(5, size=10) #x2 = np.random.randint(10, size=(3,4)) #x3 = np.random.randint(10, size=(3,4,5)))

In [60]:
x1

Out[60]:
array([0, 4, 1, 1, 0, 3, 2, 3, 4, 0])

In [25]:
x2 = np.random.randint(10, size=(3,8))

In [26]:
x2

Out[26]:
array([[8, 4, 3, 7, 5, 5, 0, 1], [5, 9, 3, 0, 5, 0, 1, 2], [4, 2, 0, 3, 2, 0, 7, 5]])

In [29]:
x3 = np.random.randint(10, size=(4,7,5))

In [30]:
x3

Out[30]:
array([[[8, 4, 3, 0, 4], [3, 6, 9, 8, 0], [8, 5, 9, 0, 9], [6, 5, 3, 1, 8], [0, 4, 9, 6, 5], [7, 8, 8, 9, 2], [8, 6, 6, 9, 1]], [[6, 8, 8, 3, 2], [3, 6, 3, 6, 5], [7, 0, 8, 4, 6], [5, 8, 2, 3, 9], [7, 5, 3, 4, 5], [3, 3, 7, 9, 9], [9, 7, 3, 2, 3]], [[9, 7, 7, 5, 1], [2, 2, 8, 1, 5], [8, 4, 0, 2, 5], [5, 0, 8, 1, 1], [0, 3, 8, 8, 4], [4, 0, 9, 3, 7], [3, 2, 1, 1, 2]], [[1, 4, 2, 5, 5], [5, 2, 5, 7, 7], [6, 1, 6, 7, 2], [3, 1, 9, 5, 9], [9, 2, 0, 9, 1], [9, 0, 6, 0, 4], [8, 4, 3, 3, 8]]])

In [31]:
x4 = np.random.randint(10, size=(4,7,5,12))

In [32]:
x4

Out[32]:
array([[[[8, 7, 0, ..., 4, 7, 0], [4, 9, 0, ..., 3, 7, 8], [5, 0, 8, ..., 3, 9, 2], [5, 2, 3, ..., 5, 0, 0], [3, 1, 9, ..., 7, 0, 8]], [[6, 8, 9, ..., 9, 2, 0], [8, 2, 7, ..., 9, 4, 1], [5, 9, 7, ..., 6, 7, 9], [1, 9, 6, ..., 5, 0, 3], [1, 4, 4, ..., 9, 3, 3]], [[2, 1, 2, ..., 7, 8, 4], [3, 5, 6, ..., 0, 8, 3], [9, 5, 5, ..., 3, 5, 3], [6, 4, 7, ..., 5, 5, 8], [0, 8, 3, ..., 3, 0, 3]], ..., [[5, 8, 2, ..., 6, 3, 6], [2, 6, 5, ..., 3, 3, 8], [9, 5, 5, ..., 5, 6, 6], [8, 7, 5, ..., 5, 4, 1], [5, 8, 3, ..., 1, 2, 1]], [[1, 7, 5, ..., 0, 2, 3], [7, 9, 2, ..., 4, 7, 3], [0, 5, 4, ..., 3, 4, 1], [7, 4, 0, ..., 9, 2, 4], [9, 9, 5, ..., 7, 0, 1]], [[3, 9, 2, ..., 8, 7, 8], [2, 3, 3, ..., 6, 3, 2], [2, 2, 6, ..., 1, 6, 1], [7, 5, 6, ..., 5, 2, 2], [3, 2, 9, ..., 5, 8, 2]]], [[[9, 9, 5, ..., 5, 1, 0], [0, 0, 0, ..., 3, 4, 5], [1, 3, 6, ..., 3, 0, 0], [9, 4, 4, ..., 9, 9, 6], [1, 4, 0, ..., 6, 0, 2]], [[7, 7, 2, ..., 4, 5, 5], [3, 8, 0, ..., 6, 8, 8], [6, 3, 4, ..., 1, 3, 8], [1, 5, 8, ..., 0, 7, 5], [9, 9, 6, ..., 0, 5, 6]], [[3, 6, 1, ..., 4, 2, 7], [5, 2, 8, ..., 4, 5, 5], [6, 3, 8, ..., 1, 8, 2], [3, 3, 4, ..., 1, 5, 9], [4, 5, 7, ..., 0, 1, 9]], ..., [[9, 7, 2, ..., 6, 8, 8], [3, 2, 0, ..., 3, 9, 4], [9, 0, 6, ..., 0, 2, 2], [9, 6, 7, ..., 7, 5, 7], [2, 4, 1, ..., 0, 2, 1]], [[4, 6, 0, ..., 5, 2, 5], [0, 3, 9, ..., 5, 7, 6], [2, 7, 3, ..., 8, 3, 5], [0, 1, 1, ..., 2, 4, 4], [6, 0, 1, ..., 6, 8, 8]], [[2, 1, 0, ..., 0, 4, 5], [7, 2, 4, ..., 4, 0, 2], [5, 5, 3, ..., 1, 0, 1], [6, 3, 4, ..., 3, 3, 8], [1, 3, 3, ..., 5, 7, 1]]], [[[8, 2, 0, ..., 3, 4, 7], [5, 1, 3, ..., 1, 3, 1], [4, 7, 5, ..., 1, 3, 8], [3, 0, 7, ..., 6, 5, 8], [4, 3, 6, ..., 7, 8, 5]], [[7, 2, 7, ..., 4, 0, 4], [8, 0, 0, ..., 2, 1, 7], [0, 7, 5, ..., 1, 4, 5], [8, 2, 1, ..., 1, 3, 7], [1, 1, 7, ..., 4, 0, 0]], [[9, 3, 8, ..., 5, 5, 8], [2, 7, 3, ..., 5, 1, 2], [8, 7, 3, ..., 8, 8, 5], [3, 3, 1, ..., 7, 0, 1], [4, 9, 5, ..., 7, 2, 2]], ..., [[9, 3, 3, ..., 3, 2, 3], [1, 1, 2, ..., 0, 5, 2], [8, 0, 4, ..., 6, 9, 6], [4, 7, 2, ..., 6, 9, 5], [3, 7, 8, ..., 4, 1, 0]], [[7, 6, 2, ..., 1, 5, 2], [0, 2, 6, ..., 2, 6, 9], [1, 5, 1, ..., 9, 6, 4], [4, 3, 5, ..., 5, 0, 6], [3, 8, 9, ..., 7, 5, 9]], [[8, 8, 1, ..., 5, 7, 9], [9, 6, 9, ..., 8, 1, 3], [2, 1, 7, ..., 5, 6, 5], [8, 8, 1, ..., 1, 7, 8], [5, 0, 1, ..., 2, 8, 3]]], [[[3, 7, 4, ..., 5, 7, 7], [3, 2, 1, ..., 2, 2, 8], [4, 1, 1, ..., 9, 3, 4], [9, 3, 0, ..., 0, 7, 8], [6, 2, 8, ..., 5, 4, 3]], [[5, 7, 6, ..., 3, 9, 9], [5, 9, 8, ..., 0, 1, 6], [6, 4, 5, ..., 4, 2, 2], [4, 4, 3, ..., 8, 9, 5], [2, 9, 8, ..., 7, 1, 3]], [[2, 0, 3, ..., 7, 5, 3], [9, 3, 0, ..., 9, 3, 3], [9, 7, 8, ..., 5, 0, 3], [6, 3, 0, ..., 3, 4, 7], [0, 3, 5, ..., 0, 1, 6]], ..., [[5, 7, 9, ..., 0, 2, 6], [5, 1, 5, ..., 0, 1, 2], [2, 3, 4, ..., 9, 6, 1], [5, 6, 2, ..., 4, 1, 6], [6, 8, 8, ..., 5, 4, 1]], [[1, 8, 8, ..., 6, 4, 7], [5, 5, 3, ..., 0, 5, 7], [8, 1, 6, ..., 6, 3, 4], [4, 4, 1, ..., 7, 8, 3], [3, 9, 0, ..., 5, 5, 1]], [[7, 6, 1, ..., 4, 8, 4], [7, 5, 2, ..., 8, 1, 7], [6, 8, 4, ..., 0, 5, 8], [3, 9, 1, ..., 9, 7, 1], [4, 9, 8, ..., 1, 4, 7]]]])

In [33]:
x1

Out[33]:
array([5, 0, 3, 3, 7, 9, 3, 5, 2, 4])

In [34]:
x2

Out[34]:
array([[8, 4, 3, 7, 5, 5, 0, 1], [5, 9, 3, 0, 5, 0, 1, 2], [4, 2, 0, 3, 2, 0, 7, 5]])

In [35]:
x3

Out[35]:
array([[[8, 4, 3, 0, 4], [3, 6, 9, 8, 0], [8, 5, 9, 0, 9], [6, 5, 3, 1, 8], [0, 4, 9, 6, 5], [7, 8, 8, 9, 2], [8, 6, 6, 9, 1]], [[6, 8, 8, 3, 2], [3, 6, 3, 6, 5], [7, 0, 8, 4, 6], [5, 8, 2, 3, 9], [7, 5, 3, 4, 5], [3, 3, 7, 9, 9], [9, 7, 3, 2, 3]], [[9, 7, 7, 5, 1], [2, 2, 8, 1, 5], [8, 4, 0, 2, 5], [5, 0, 8, 1, 1], [0, 3, 8, 8, 4], [4, 0, 9, 3, 7], [3, 2, 1, 1, 2]], [[1, 4, 2, 5, 5], [5, 2, 5, 7, 7], [6, 1, 6, 7, 2], [3, 1, 9, 5, 9], [9, 2, 0, 9, 1], [9, 0, 6, 0, 4], [8, 4, 3, 3, 8]]])

In [36]:
x1.ndim

Out[36]:
1

In [37]:
x2.ndim

Out[37]:
2

In [38]:
x3.ndim

Out[38]:
3

In [39]:
x4.ndim

Out[39]:
4

In [40]:
x1.shape

Out[40]:
(10,)

In [41]:
x2.shape

Out[41]:
(3, 8)

In [42]:
x3.shape

Out[42]:
(4, 7, 5)

In [43]:
x4.shape

Out[43]:
(4, 7, 5, 12)

In [44]:
x1.size

Out[44]:
10

In [45]:
x2.size

Out[45]:
24

In [46]:
x3.size

Out[46]:
140

In [47]:
x4.size

Out[47]:
1680

In [48]:
x1.dtype

Out[48]:
dtype('int64')

In [49]:
x2.dtype

Out[49]:
dtype('int64')

In [50]:
x3.dtype

Out[50]:
dtype('int64')

In [51]:
x1.itemsize

Out[51]:
8

In [53]:
x2.itemsize

Out[53]:
8

In [54]:
x3.itemsize

Out[54]:
8

In [55]:
x1.nbytes

Out[55]:
80

In [56]:
x2.nbytes

Out[56]:
192

In [57]:
x3.nbytes

Out[57]:
1120

In [58]:
x3.size

Out[58]:
140

In [61]:
x1

Out[61]:
array([0, 4, 1, 1, 0, 3, 2, 3, 4, 0])

In [62]:
x2

Out[62]:
array([[8, 4, 3, 7, 5, 5, 0, 1], [5, 9, 3, 0, 5, 0, 1, 2], [4, 2, 0, 3, 2, 0, 7, 5]])

In [63]:
x2[2,0]

Out[63]:
4

In [64]:
x2[0,0] = 56

In [65]:
x2

Out[65]:
array([[56, 4, 3, 7, 5, 5, 0, 1], [ 5, 9, 3, 0, 5, 0, 1, 2], [ 4, 2, 0, 3, 2, 0, 7, 5]])

In [66]:
x1[0] = 3.1234

In [67]:
x1

Out[67]:
array([3, 4, 1, 1, 0, 3, 2, 3, 4, 0])

In [68]:
x1.dtype

Out[68]:
dtype('int64')

In [69]:
arr1 = np.array([1,2,5,3,5])

In [70]:
arr2 = np.array([ [1,2], [2,3], [4,5] ])

In [72]:
print (arr1)


[1 2 5 3 5]

In [73]:
print (arr2)


[[1 2] [2 3] [4 5]]

In [74]:
print (type(arr2))


<class 'numpy.ndarray'>

In [75]:
print (type(arr1))


<class 'numpy.ndarray'>

In [76]:
arr1.shape

Out[76]:
(5,)

In [77]:
arr2.shape

Out[77]:
(3, 2)

In [79]:
np.ones((3,4))

Out[79]:
array([[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]])

In [80]:
np.zeros((3,4))

Out[80]:
array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]])

In [81]:
np.arange(50)

Out[81]:
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

In [85]:
np.arange(1,5,0.5)

Out[85]:
array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])

In [83]:
np.arange(100,1,-2)

Out[83]:
array([100, 98, 96, 94, 92, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72, 70, 68, 66, 64, 62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2])

In [88]:
np.linspace(1,30,100)

Out[88]:
array([ 1. , 1.29292929, 1.58585859, 1.87878788, 2.17171717, 2.46464646, 2.75757576, 3.05050505, 3.34343434, 3.63636364, 3.92929293, 4.22222222, 4.51515152, 4.80808081, 5.1010101 , 5.39393939, 5.68686869, 5.97979798, 6.27272727, 6.56565657, 6.85858586, 7.15151515, 7.44444444, 7.73737374, 8.03030303, 8.32323232, 8.61616162, 8.90909091, 9.2020202 , 9.49494949, 9.78787879, 10.08080808, 10.37373737, 10.66666667, 10.95959596, 11.25252525, 11.54545455, 11.83838384, 12.13131313, 12.42424242, 12.71717172, 13.01010101, 13.3030303 , 13.5959596 , 13.88888889, 14.18181818, 14.47474747, 14.76767677, 15.06060606, 15.35353535, 15.64646465, 15.93939394, 16.23232323, 16.52525253, 16.81818182, 17.11111111, 17.4040404 , 17.6969697 , 17.98989899, 18.28282828, 18.57575758, 18.86868687, 19.16161616, 19.45454545, 19.74747475, 20.04040404, 20.33333333, 20.62626263, 20.91919192, 21.21212121, 21.50505051, 21.7979798 , 22.09090909, 22.38383838, 22.67676768, 22.96969697, 23.26262626, 23.55555556, 23.84848485, 24.14141414, 24.43434343, 24.72727273, 25.02020202, 25.31313131, 25.60606061, 25.8989899 , 26.19191919, 26.48484848, 26.77777778, 27.07070707, 27.36363636, 27.65656566, 27.94949495, 28.24242424, 28.53535354, 28.82828283, 29.12121212, 29.41414141, 29.70707071, 30. ])

In [89]:
x = np.arange(1,5,0.5)

In [90]:
x

Out[90]:
array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])

In [91]:
x[1]

Out[91]:
1.5

In [92]:
x[-2]

Out[92]:
4.0

In [93]:
x = np.arange(10)

In [94]:
x

Out[94]:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [95]:
#extract first five elements from x.....

In [96]:
x[:5]

Out[96]:
array([0, 1, 2, 3, 4])

In [97]:
x[5:]

Out[97]:
array([5, 6, 7, 8, 9])

In [98]:
x[4:]

Out[98]:
array([4, 5, 6, 7, 8, 9])

In [99]:
x[4:7]

Out[99]:
array([4, 5, 6])

In [100]:
x[::2]

Out[100]:
array([0, 2, 4, 6, 8])

In [101]:
x[::3]

Out[101]:
array([0, 3, 6, 9])

In [103]:
x[3::2]

Out[103]:
array([3, 5, 7, 9])

In [104]:
x[::-1]

Out[104]:
array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

In [105]:
x

Out[105]:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [106]:
x2

Out[106]:
array([[56, 4, 3, 7, 5, 5, 0, 1], [ 5, 9, 3, 0, 5, 0, 1, 2], [ 4, 2, 0, 3, 2, 0, 7, 5]])

In [107]:
x2[0,0] =-1

In [108]:
x2

Out[108]:
array([[-1, 4, 3, 7, 5, 5, 0, 1], [ 5, 9, 3, 0, 5, 0, 1, 2], [ 4, 2, 0, 3, 2, 0, 7, 5]])

In [109]:
x2_sub = x2[:2,:2]

In [110]:
x2_sub

Out[110]:
array([[-1, 4], [ 5, 9]])

In [111]:
x2

Out[111]:
array([[-1, 4, 3, 7, 5, 5, 0, 1], [ 5, 9, 3, 0, 5, 0, 1, 2], [ 4, 2, 0, 3, 2, 0, 7, 5]])

In [112]:
x2 = np.arange(12)

In [113]:
x2

Out[113]:
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

In [114]:
x2 = x2.reshape(2,6)

In [115]:
x2

Out[115]:
array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11]])

In [116]:
x2.ndim

Out[116]:
2

In [117]:
x2.flatten()

Out[117]:
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

In [118]:
x2

Out[118]:
array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11]])

In [119]:
x = np.array([1,2,3])

In [120]:
y = np.array([3,2,1])

In [121]:
np.concatenate([x,y])

Out[121]:
array([1, 2, 3, 3, 2, 1])

In [122]:
np.concatenate([y,x])

Out[122]:
array([3, 2, 1, 1, 2, 3])

In [123]:
y

Out[123]:
array([3, 2, 1])

In [124]:
y = np.array([[9,8,7],[6,5,4]])

In [125]:
y

Out[125]:
array([[9, 8, 7], [6, 5, 4]])

In [126]:
x

Out[126]:
array([1, 2, 3])

In [127]:
np.vstack([x,y])

Out[127]:
array([[1, 2, 3], [9, 8, 7], [6, 5, 4]])

In [129]:
z =np.vstack([y,x])

In [130]:
z

Out[130]:
array([[9, 8, 7], [6, 5, 4], [1, 2, 3]])

In [138]:
np.hstack([x,y[0,:]])

Out[138]:
array([1, 2, 3, 9, 8, 7])

In [139]:
np.hstack([y[0,:],x])

Out[139]:
array([9, 8, 7, 1, 2, 3])

In [132]:
x

Out[132]:
array([1, 2, 3])

In [133]:
y

Out[133]:
array([[9, 8, 7], [6, 5, 4]])

In [145]:
X=np.random.randint(0,9,15)

In [146]:
M = np.array(X).reshape(3,5)

In [147]:
M

Out[147]:
array([[6, 4, 2, 3, 2], [0, 5, 8, 1, 0], [5, 2, 1, 3, 0]])

In [148]:
M.sum(axis=0)

Out[148]:
array([11, 11, 11, 7, 2])

In [149]:
M.sum(axis=1)

Out[149]:
array([17, 14, 11])

In [150]:
M.sum()

Out[150]:
42

In [151]:
x = np.linspace(-2,2,100)

In [152]:
F = x**2 + 3*x -6

In [153]:
F

Out[153]:
array([-8. , -8.03877155, -8.07427813, -8.10651974, -8.13549638, -8.16120804, -8.18365473, -8.20283645, -8.21875319, -8.23140496, -8.24079176, -8.24691358, -8.24977043, -8.24936231, -8.24568922, -8.23875115, -8.22854811, -8.21508009, -8.19834711, -8.17834915, -8.15508622, -8.12855831, -8.09876543, -8.06570758, -8.02938476, -7.98979696, -7.94694419, -7.90082645, -7.85144373, -7.79879604, -7.74288338, -7.68370574, -7.62126314, -7.55555556, -7.486583 , -7.41434547, -7.33884298, -7.2600755 , -7.17804306, -7.09274564, -7.00418325, -6.91235588, -6.81726354, -6.71890623, -6.61728395, -6.51239669, -6.40424446, -6.29282726, -6.17814509, -6.06019794, -5.93898582, -5.81450872, -5.68676666, -5.55575962, -5.4214876 , -5.28395062, -5.14314866, -4.99908173, -4.85174982, -4.70115294, -4.54729109, -4.39016427, -4.22977247, -4.0661157 , -3.89919396, -3.72900724, -3.55555556, -3.37883889, -3.19885726, -3.01561065, -2.82909907, -2.63932252, -2.44628099, -2.24997449, -2.05040302, -1.84756657, -1.64146516, -1.43209877, -1.2194674 , -1.00357106, -0.78440975, -0.56198347, -0.33629222, -0.10733599, 0.12488522, 0.36037139, 0.59912254, 0.84113866, 1.08641975, 1.33496582, 1.58677686, 1.84185287, 2.10019386, 2.36179982, 2.62667075, 2.89480665, 3.16620753, 3.44087338, 3.7188042 , 4. ])

In [154]:
x = np.array([-2,-1,0,1,2])

In [155]:
np.abs(x)

Out[155]:
array([2, 1, 0, 1, 2])

In [163]:
theta = np.linspace(0,3.142,6)

In [164]:
theta

Out[164]:
array([0. , 0.6284, 1.2568, 1.8852, 2.5136, 3.142 ])

In [158]:
print (theta)


[0. 0.62831853 1.25663706 1.88495559 2.51327412 3.14159265]

In [159]:
print (np.sin(theta))


[0.00000000e+00 5.87785252e-01 9.51056516e-01 9.51056516e-01 5.87785252e-01 1.22464680e-16]

In [160]:
print (np.cos(theta))


[ 1. 0.80901699 0.30901699 -0.30901699 -0.80901699 -1. ]

In [161]:
print (np.tan(theta))


[ 0.00000000e+00 7.26542528e-01 3.07768354e+00 -3.07768354e+00 -7.26542528e-01 -1.22464680e-16]

In [167]:
x = np.linspace(1,5,5)

In [168]:
x

Out[168]:
array([1., 2., 3., 4., 5.])

In [169]:
np.log(x)

Out[169]:
array([0. , 0.69314718, 1.09861229, 1.38629436, 1.60943791])

In [170]:
np.log10(x)

Out[170]:
array([0. , 0.30103 , 0.47712125, 0.60205999, 0.69897 ])

In [171]:
np.log2(x)

Out[171]:
array([0. , 1. , 1.5849625 , 2. , 2.32192809])

In [174]:
np_sqrt = np.sqrt([2,4,9,16])

In [175]:
np_sqrt

Out[175]:
array([1.41421356, 2. , 3. , 4. ])

In [176]:
from numpy import pi

In [177]:
np.cos(pi)

Out[177]:
-1.0

In [178]:
A = np.array([[1,2,3,4],[5,6,7,8]])

In [179]:
A

Out[179]:
array([[1, 2, 3, 4], [5, 6, 7, 8]])

In [180]:
B = np.transpose(A)

In [181]:
B

Out[181]:
array([[1, 5], [2, 6], [3, 7], [4, 8]])

In [182]:
A1 = np.array([[1,3],[6,9]])

In [183]:
A1

Out[183]:
array([[1, 3], [6, 9]])

In [184]:
np.linalg.inv(A1)

Out[184]:
array([[-1. , 0.33333333], [ 0.66666667, -0.11111111]])

In [185]:
np.trace(A1)

Out[185]:
10

In [186]:
A = np.array(["naman","arun","taranvir","gaurav","raj"])

In [187]:
A

Out[187]:
array(['naman', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [188]:
B = A

In [189]:
B

Out[189]:
array(['naman', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [190]:
A is B

Out[190]:
True

In [191]:
B is A

Out[191]:
True

In [192]:
B[0] = 'NAMAN'

In [193]:
B

Out[193]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [194]:
A

Out[194]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [195]:
A is B

Out[195]:
True

In [196]:
B is A

Out[196]:
True

In [197]:
C = A.copy()

In [198]:
C

Out[198]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [199]:
A

Out[199]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [200]:
A is C

Out[200]:
False

In [201]:
C is A

Out[201]:
False

In [202]:
C[1] = "Mr Arun"

In [203]:
A

Out[203]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [204]:
C

Out[204]:
array(['NAMAN', 'Mr Arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [205]:
D = A.view()

In [206]:
D

Out[206]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [207]:
A

Out[207]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'raj'], dtype='<U8')

In [208]:
D[-1] ="Raj Tripathi"

In [209]:
D

Out[209]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'Raj Trip'], dtype='<U8')

In [210]:
A

Out[210]:
array(['NAMAN', 'arun', 'taranvir', 'gaurav', 'Raj Trip'], dtype='<U8')

In [211]:
D is A

Out[211]:
False

In [212]:
A is D

Out[212]:
False

In [ ]:

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