numpy.ravel_multi_index() function | Python

Syntax : numpy.ravel_multi_index(multi_index, dims, mode = 'raise', order = 'C) Parameters : multi_index : [tuple of array_like] A tuple of integer arrays, one array for each dimension. dims : [tuple of ints] The shape of array into which the indices from multi_index apply. mode : [{‘raise’, ‘wrap’, ‘clip’}, optional] Specifies how out-of-bounds indices are handled. Can specify either one mode or a tuple of modes, one mode per index. ‘raise’ – raise an error (default) ‘wrap’ – wrap around ‘clip’ – clip to the range In ‘clip’ mode, a negative index that would normally wrap will clip to 0 instead. order : [{‘C’, ‘F’}, optional] Determines whether the multi-index should be viewed as indexing in row-major (C-style) or column-major (Fortran-style) order. Return : [ndarray] An array of indices into the flattened version of an array of dimensions dims.

# Python program explaining
# numpy.ravel_multi_index() function

# importing numpy as geek 
import numpy as geek

arr = geek.array([[3, 6, 6], [4, 5, 1]])

gfg = geek.ravel_multi_index(arr, (7, 6))

print(gfg) 

[22 41 37]

# Python program explaining
# numpy.ravel_multi_index() function

# importing numpy as geek 
import numpy as geek

arr = geek.array([[3, 6, 6], [4, 5, 1]])

gfg = geek.ravel_multi_index(arr, (7, 6), order = 'F')

print(gfg) 

[31 41 13]

# Python program explaining
# numpy.ravel_multi_index() function

# importing numpy as geek 
import numpy as geek

arr = geek.array([[3, 6, 6], [4, 5, 1]])

gfg = geek.ravel_multi_index(arr, (7, 6), mode = 'clip')

print(gfg) 

[22 41 37]

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Article Tags :

• Machine Learning
• Python-numpy
• Python numpy-arrayManipulation
• python

Practice Tags :

• Machine Learning
• python