Tag: ravel

numpy.ravel() in Python

numpy.ravel() is a function present in numpy module which allows us to change a 2 dimensional or multi dimensional array into a contiguous flattened array i.e 1 dimensional array with all input elements and of same type as it. A copy is made only when needed. If input array is masked then returned array is also masked.

Syntax:

numpy.ravel(x, order=’C’)

Parameters:

x : array_like

This reads the input array and all the elements are read in the order specified by the order parameter.

order:{‘C’,’F’,’A’,’K’} (optional)

This order parameter is optional.

  • Order parameter ‘C’ means array flattens in row major order. The last axis change is fastest and the first axis change is slowest.
  • Order parameter ‘F’  (Fortran contiguous memory order) means array flattens in the column major order. Here the first axis change is fastest and last axis change is slowest.
  • Order parameter ‘A’ means read / write elements in Fortran like index order only if array is fortran contiguous memory order otherwise C like order.
  • Order parameter ‘K’ means read / write elements in order present as it is in the system.

Returns:

This function returns a contiguous flattened array with same data type as input array and has equal size (x.size)

Example1:

import numpy as np 

x = np.array([[4, 7, 8, 9], [16, 24, 86,45]]) 

y=np.ravel(x) 

y

Example2:

import numpy as np

x = np.array([[23, 76, 11, 42], [74, 91, 8, 34]])

y = np.ravel(x)

print('Flattened array: \n', y)

y[1] = 121

print('Original array: \n', x)

The above example shows that any changes in flattened array will reflect in original array as you can see that value y[1] = 121 is changed in original array as well.

Example3:

import numpy as np

a = np.arange(12).reshape(3,4)

print('The original array is:\n',a)

print('\n') 

print('After applying ravel function:',a.ravel())

print('\n' )

print('Applying ravel function in F-style ordering:',a.ravel(order = 'F'))

print('Applying ravel function in C-style ordering:',a.ravel(order = 'C'))

print('Applying ravel function in K-style ordering:',a.ravel(order = 'K'))

print('Applying ravel function in A-style ordering:',a.ravel(order = 'A'))

With the above examples we have seen about the ravel function and its ordering styles in detail. Now if you want to know more about numpy click here!

numpy.ndarray.flatten() in Python:

Numpy flatten() converts a multi dimensional array into ‘flattened’ one dimensional array. It returns a copy of the array in one dimension. Now let us see about numpy.ndarray.flatten().

numpy.ndarray.flatten()

Syntax:

ndarray.flatten(order = ‘C’)

Parameters:

  • order: The order in which items from numpy array wil be used.
  • ‘C’ – read items row wise i.e, using row major order
  • ‘F’ – read items column wise i.e, column major order
  • ‘K’ – read items in the order that occur in the memory
  • ‘A’ – read items column wise only when the array is Fortran contiguous in memory.
  • The default is ‘C’

Returns:

A copy of input array, flattened  to 1D array.

Example:

Flatten an array by row:
import numpy as np 

a = np.array([[1,2,4], [3,5,7],[4,6,8]]) 

b=a.flatten('C') 

print('Flattened array by row:\n', b)
Flatten an array by column:
import numpy as np 

a = np.array([[1,2,4], [3,5,7],[4,6,8]]) 

b=a.flatten('F') 

print('Flattened array by column:\n', b)

ndarray.flatten() returns the copy of the original array any changes made in flattened array will not be reflected in original array.

a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

flat_array = a.flatten()

flat_array[2] = 10

print('Flattened 1D Numpy Array:')

print(flat_array)

print('Original Numpy Array')

print(a)

If you look the above code when we change the array value of index 2 as 10 i.e flat_array[2] = 10, it won’t affect the original array only the copy is changed.

To know more about numpy click here!