Numpy-Programs/Python_nparray.txt at main · janithakrishnan/Numpy-Programs · GitHub

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	1. open a python interpreter
python3
	2. import numpy
import numpy as np
	3. create a 1D array having 5 elements (integers) using array method
a1=np.array([1,2,3,4,5])
a1
Out[5]: array([1, 2, 3, 4, 5])
	4. do 3 using arange
a2=np.arange(5)
a2
Out[6]: array([0, 1, 2, 3, 4])
	5. create a 3 dimensional array containing 1s(integers), there should be 40 members
 a3=np.ones((2,4,5))
In [9]: a3
Out[9]:
array([[[1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.]],

       [[1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.]]])


	6. print the total number of elements (size) for the above array
In [10]: a3.size
Out[10]: 40
	7. Check and explain the difference between array and asarray
In [11]: l1=[7,8,9]

In [12]: a4=np.array(l1)

In [13]: a44=np.array(a4)

In [14]: a4 is a44
Out[14]: False


In [15]: a5=np.asarray(l1)

In [16]: a55=np.asarray(a5)

In [17]: a5 is a55
Out[17]: True

np.array(numpyarray) creates a copy of the given numpyarray
whereas np.asarray(numpyarray) creates just a reference of the given numpyarray
	8. Create an identity matrix of a given specification.
 a6=np.identity(5)

In [19]: a6
Out[19]:
array([[1., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [0., 0., 0., 1., 0.],
       [0., 0., 0., 0., 1.]])
	9. Create an array containing 0s inheriting shape from the arrya created in Q.No.5
 a7=np.zeros_like(a3)

In [21]: a7
Out[21]:
array([[[0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.]],

       [[0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.]]])

	10.Print the size,shape,dimensions and datatype of all the above arrays.
In [22]: a1.size
Out[22]: 5
In [24]: a1.shape
Out[24]: (5,)
In [26]: a1.ndim
Out[26]: 1
In [27]: a2.size
Out[27]: 5

In [28]: a2.shape
Out[28]: (5,)

In [29]: a2.ndim
Out[29]: 1

In [30]: a3.size
Out[30]: 40

In [31]: a3.shape
Out[31]: (2, 4, 5)

In [32]: a3.ndim
Out[32]: 3

In [33]: a4.size
Out[33]: 3

In [34]: a4.shape
Out[34]: (3,)

In [35]: a4.ndim
Out[35]: 1

In [36]: a5.size
Out[36]: 3

In [37]: a5.shape
Out[37]: (3,)

In [38]: a5.ndim
Out[38]: 1
In [39]: a6.size
Out[39]: 25

In [40]: a6.shape
Out[40]: (5, 5)

In [41]: a6.ndim
Out[41]: 2

In [42]: a7.size
Out[42]: 40

In [43]: a7.shape
Out[43]: (2, 4, 5)

In [44]: a7.ndim
Out[44]: 3