Numpy eye()

Numpy

numpy.eye(N, M=None, k=0, dtype=, order='C')
Return ndarray ( N, M ) shape.
NInt, number of rows
MInt (optional ), number of columns ( default is equal to N )
kInt (optional ), default is 0,Position of diagonal,
Positive value for upper and negative for lower diagonal
dtypedata-type( Optional ), Data Type of returned array.
order{'C','F'} Optional, how the output is to be stored. C- style or Fortan style
Eye array of shape(3,4)Eye array of  shape(4,3)
Shape: (3, 4)
Dimension 2
Shape: (4, 3)
Dimension 2

Using N and M

import numpy as np
my_data=np.eye(3)
print(my_data)
Output, we used N=3 here, so default value of M is also 3.
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]
Let us use different value for M
my_data=np.eye(4,M=3)
Output
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]
 [0. 0. 0.]]

Using k

We can use k to change the postion of diagonal. Try with positive value of key
my_data=np.eye(4,k=2)
Output
[[0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]
Let us try with negative value of k
my_data=np.eye(4,k=-1)
Output
[[0. 0. 0. 0.]
 [1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]]

dtype

We will try to return string dtype
my_data=np.eye(4,dtype=str)
Output
[['1' '' '' '']
 ['' '1' '' '']
 ['' '' '1' '']
 ['' '' '' '1']]
dtype=float
my_data=np.eye(4,dtype=float)
Output
[[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]]
Numpy ones() bincount() arange() linspace()


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