# Return array of indices of the maximum values along axis 1 from a masked array in NumPy

NumpyServer Side ProgrammingProgramming

#### Python Data Science basics with Numpy, Pandas and Matplotlib

Most Popular

63 Lectures 6 hours

#### Data Analysis using NumPy and Pandas

19 Lectures 8 hours

#### Numpy with Python

Most Popular

12 Lectures 3 hours

To return array of indices of the maximum values, use the ma.MaskedArray.argmax() method in Numpy. The axis parameter is used to set the axis values.

For axis, If None, the index is into the flattened array, otherwise along the specified axis. The out is the array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.

## 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([[49, 85, 45], [67, 33, 59]])
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 =[[0, 0, 1], [ 0, 1, 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)


Return array of indices of the maximum values, use the ma.MaskedArray.argmax() method. Masked values are treated as if they had the value "fill_value". The "fill_value" is a parameter i.e. Value used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead. The axis parameter is used to set the axis values −

print("\nResult...\n",maskArr.argmax(axis = 1))

## Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[35, 85], [67, 33], [29, 88], [56, 45]])
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 =[[0, 0], [ 0, 1], [1, 0], [0,
1]])

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# To return array of indices of the maximum values, use the ma.MaskedArray.argmax() method in Numpy
# Masked values are treated as if they had the value "fill_value".
# The "fill_value" is a parameter i.e. Value used to fill in the masked values.
# If None, the output of maximum_fill_value(self._data) is used instead.
# The axis parameter is used to set the axis values
print("\nResult...\n",maskArr.argmax(axis = 1))

## Output

Array...
[[35 85]
[67 33]
[29 88]
[56 45]]

Array type...
int64

Array Dimensions...
2

[[35 85]
[67 --]
[-- 88]
[56 --]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(4, 2)

Elements in the Masked Array...
8

Result...
[1 0 1 0]