NumPy array
Let x be a numpy array with 4 columns and 4 rows.
X=numpy.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]).
Summary:
Here the topic is NumPy array and we have to answer the questions given below.
Question(a):
a=x[:,2]
In the above question, every third element is accessed in the given arrays.
That is
[1,2,3,4] -> 3
[5,6,7,8] -> 7
[9,10,11,12] -> 11
[13,14,15,16] -> 15
So the output will be [3,7,11,15]
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) a=x[:,2] print(a)
Output:
Question(b):
b=x[-1,:2]
-1 represents the last row in the array [13,14,15,16]. In this question, the slice operation is performed. Here the output is [13,14].
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) b=x[-1,:2] print(b)
Output:
Question (C):
c=x[:,[True,False,False,True]]
As here there are no particular dimensions mentioned, this operation will be applied on all dimensions of the array.
Only the elements at the same position as True will be printed.
[1,2,3,4] ->[ 2 1 1 2]
[5,6,7,8] -> [ 6 5 5 6]
[9,10,11,12] -> [10 9 9 10]
[13,14,15,16] -> [14 13 13 14].
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) c=x[:,(True,False,False,True)] print(c)
Output:
Question(d):
d=x[0:2,0:2]
Here the first 0:2 is the dimension range. And the second 0:2 is the elements in each dimension to be considered.
[1,2,3,4] -> [1,2]
[5,6,7,8] -> [5,6]
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) d=x[0:2,0:2] print(d)
Output:
Question(e):
e=x[[0,1,2],[0,1,2]]
Here, the first list [0,1,2] represents the dimensions (first, second and third). And the second list [0,1,2] is the elements to be accessed.
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) e=x[[0,1,2],[0,1,2]] print(e)
Output:
Question(f):
f=x[0]**2
Here x[0] represents the first row in the array. And **2 represents the Square of the elements in the first row of the array.
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) f=x[0]**2 print(f)
Output:
Question(g):
g=x.max(axis=1)
Max() returns the maximum element in the axis.
Axis=1 represents the rows.
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) g=x.max(axis=1) print(g)
Output:
Question(h):
h=x[:2,:2]+x[:2,2:]
Here, x[:2,:2] represents the first two elements in each of the first two dimensions.
And x[:2,2:] represents the elements from the second index to the end.
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) h=x[:2,:2]+x[:2,2:] print(h)
Output:
Question(i):
i=x[:2,:3].T
Numpy.T returns the transpose of the given array.
The rows become columns and the columns become rows. And:2 represents the first two dimensions and :3 represents the first three elements in each dimension.
So, the transpose of [[1,2,3],[5,6,7]] will be [[1,5],[2,6],[3,7]].
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) i=x[:2,:3].T print(i)
Output:
Question(j):
j=x[:2,:3].reshape((3,2))
The reshape() method converts an array into an array with the given number of dimensions and columns.
Here, (3,2) means that the number of dimensions of resulting array should be 3 and the number of columns should be 2.
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) j=x[:2,:3].reshape((3,2)) print(j)
Output:
Question(k):
k=x[:,:2].dot([1,1])
The dot() method returns the dot product(matrix multiplication) of the given two arrays. Here the two arrays are x[:,:2] and [1,1]. X[:,:2] represents the first two elements in all the dimensions of the array.
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) k=x[:,:2].dot([1,1]) print(k)
Output:
Question(l):
l=x[:,:2].dot([[3,0],[0,2]])
The dot() method returns the dot product(matrix multiplication) of the given two arrays.
Here the two arrays are x[:,:2] and [[3,0],[0,2]]
X[:,:2] represents the first two elements in all the dimensions of the array.
Source code:
import numpy as np x=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]) l=x[:,:2].dot([[3,0],[0,2]]) print(l)
Output: