# Set storage-indexed locations to corresponding values and wrap out-of-bounds indices in Numpy

NumpyServer Side ProgrammingProgramming

To set storage-indexed locations to corresponding values, use the ma.MaskedArray.put() method in Python Numpy. Sets self._data.flat[n] = values[n] for each n in indices. If values is shorter than indices then it will repeat. If values has some masked values, the initial mask is updated in consequence, else the corresponding values are unmasked.

The indices are the target indices, interpreted as integers. The mode specifies how out-of-bounds indices will behave. ‘raise’ : raise an error. ‘wrap’ : wrap around. ‘clip’ : clip to the range.

## Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create an array with int elements using the numpy.array() method −

arr = np.array([[55, 85, 59, 77], [67, 33, 39, 57], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...\n",arr.ndim)


Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])
print("\nOur Masked Array type...\n", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...\n",maskArr.ndim)


Get the shape of the Masked Array −

print("\nOur Masked Array Shape...\n",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...\n",maskArr.size)


To set storage-indexed locations to corresponding values, use the ma.MaskedArray.put() method in Numpy. The "mode" parameter is specify how out-of-bounds indices will behave. The value ‘raise’ : raise an error. ‘wrap’ : wrap around. ‘clip’ : clip to the range. We have set an out-of-bounds indice here i.e. 32. The "wrap" parameter will wrap around −

maskArr.put([1, 5, 6, 9, 32],[99, 88, 33, 55, 66], mode = 'wrap')
print("\nResult...\n",maskArr)

## Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[55, 85, 59, 77], [67, 33, 39, 57], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",arr.ndim)

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# To set storage-indexed locations to corresponding values, use the ma.MaskedArray.put() method in Numpy
# The "mode" parameter is specify how out-of-bounds indices will behave.
# The value ‘raise’ : raise an error. ‘wrap’ : wrap around. ‘clip’ : clip to the range.
# We have set an out-of-bounds indice here i.e. 32
# The "wrap" parameter will wrap around
maskArr.put([1, 5, 6, 9, 32],[99, 88, 33, 55, 66], mode = 'wrap')
print("\nResult...\n",maskArr)

## Output

Array...
[[55 85 59 77]
[67 33 39 57]
[29 88 51 37]
[56 45 99 85]]

Array type...
int64

Array Dimensions...
2

[[-- -- 59 77]
[67 33 -- 57]
[29 88 51 --]
[56 -- 99 85]]

int64

2

[56 -- 99 85]]