Differences between Flatten() and Ravel() Numpy Functions

There are two kinds of methods to convert a Ndarray into a 1D array flatten() as well as Ravel()

import numpy as nmp
P = nmp.array( [ (1,8,4,5),(4,3,5,1) ] )
#OUTPUT:
print( P.flatten() )
# [ 1,8,4,5,4,3,5,1 ] 
print ( P.ravel() )
# [ 1,8,4,5,4,3,5,1 ] 

The question here is, what is the reason there are two distinct roles to perform the same job?

Differences between Flatten() and Ravel()

P.ravel():

Returns only the reference/view of the original array
In the event that we alter the array, we will be able to see that the value of the original array changes too.
Ravel is faster than flatten() because it doesn't take up any memory.
Ravel is a library-level function at the library level.

P.flatten():

Return a duplicate of the initial array
When you alter the value of this array, the original array's value is not changed.
Flatten() is considerably faster that ravel() because it takes up memory.
Flatten is a method used by a ndarray.

Let's see the difference between flatter() and ravel() function using this code.

Code:

import numpy as nmp

# Here, we will create a numpy array
P = nmp.array([(3,4,5,6),(5,3,6,7)])

# Now, we will print the array a
print ("Original array:\n ")
print(P)

# For checking the dimension of array (dimension = 2 and type is numpy.ndarray )
print ("Dimension of array: " , (P.ndim))


print("\n The output for RAVEL \n")
# Here, we will convert ndarray to 1D array
Q = P.ravel()

# As the ravel() only passes a view of the original array to array 'Q'
print(Q)
Q[0]=1000
print(Q)

# We can note here that value of the original array 'P' at also P[0][0] becomes 1000
print(P)

# Just for checking the dimension i.e. 1 and type is same numpy.ndarray )
print ("Dimension of array" ,(Q.ndim))

print("\n The output for FLATTEN \n")

# Here, we will convert ndarray to 1D array
R = P.flatten()

# Flatten passes copy of original array to 'R'
print(R)
R[0] = 0
print(R)

# Here, we can note that by changing the value of R 
# there is no affect on value of original array 'P'
print(P)

print ("Dimension of array " , (R.ndim))

Output:

Original array:
 
[[3 4 5 6]
 [5 3 6 7]]
Dimension of array:  2

 The output for RAVEL 

[3 4 5 6 5 3 6 7]
[1000    4    5    6    5    3    6    7]
[[1000    4    5    6]
 [   5    3    6    7]]
Dimension of array 1

 The output for FLATTEN 

[1000    4    5    6    5    3    6    7]
[0 4 5 6 5 3 6 7]
[[1000    4    5    6]
 [   5    3    6    7]]
Dimension of array  1

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