Numpy recarray.ravel () function | python



numpy.recarray.ravel() returns the returned continuous smoothed array of records, i.e. one-dimensional array with all elements of the input array and the same type as it.

Syntax: numpy.recarray.ravel ([order])

Parameters:

order: [`C`, `F`, `A`, `K `, optional]

  • ` C `means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest.
  • `F` means to index the elements in column-major, Fortran-style order, with the first index changing fastest, and the last index changing slowest.
  • `A` means to read the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise.
  • `K` means to read the elements in the order they occur in memory, except for reversing the data when strides are negative.

By default, `C` index order is used.

Return: [array_like] Flattened array having same type as the Input array and a nd order as per choice.

Code # 1:

# Python program, explaining
# numpy.recarray.ravel () method

 
# importing numy as a geek

import numpy as geek

 
# create an input array with two different fields

in_arr = geek.array ([[( 5.0 , 2 ) , ( 3.0 , - 4 ) , ( 6.0 , 9 )],

  [( 9.0 , 1 ), ( 5.0 , 4 ), ( - 12.0 , - 7 )]],

dtype = [( `a` , float ), ( `b` , int )] )

 

print ( " Input array: " , in_arr)

 
# convert it to an array of posts,
# using arr.view (np.recarray)

rec_arr = in_arr.view (geek.recarray)

print ( " Record array of float: " , rec_arr.a)

print ( "Record array of int:" , rec_arr.b)

 
# using recarray.ravel methods
# float array of records

out_arr = rec_arr.a.ravel ()

print ( "Output flattenrd float array:" , out_arr) 

 
# applying recarray.ravel methods
# into an array of int records

out_arr = rec_arr.b.ravel ()

print ( "Output flattenrd int array:" , out_arr) 

Output:

 Input array: [[(5., 2) (3., -4) (6., 9)] [(9., 1) (5., 4) (-12., -7 )]] Record array of float: [[5. 3. 6.] [9. 5. -12.]] Record array of int: [[2 -4 9] [1 4 -7]] Output flattenrd float array: [5. 3. 6. 9. 5. -12.] Output flattenrd int array: [2 -4 9 1 4 -7]  

Code # 2:

We apply numpy.recarray.ravel () to the entire array of records.

# Python program explaining
# numpy.recarray.ravel () method

 
# import numy as a geek

import numpy as geek

 
# create an input array with two different fields

in_arr = geek.array ([[( 5.0 , 2 < / code> ), ( 3.0 , 4 ), ( 6.0 , - 7 )],

[( 9.0 , 1 ), ( 6.0 , 4 ), ( - 2.0 , - 7 )]],

dtype = [( ` a` , float ), ( `b` , int )])

 

print ( "Input array:" , in_arr)

 
# convert it to an array of posts,
# using arr.view (np.recarray)

rec_arr = in_arr.view (geek.recarray)

 
# using recarray.ravel methods to write an array

out_arr = rec_arr.ravel ()

 

print ( "Output array:" , out_arr)

Output:

 Input array: [[(5., 2) (3., 4) (6., -7)] [(9., 1) (6., 4) (-2. , -7)]] Output array: [(5., 2) (3., 4) (6., -7) (9., 1) (6., 4) (-2., -7)]