# Return all the non-masked data as a 1-D 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 all the non-masked data as a 1-D array, use the ma.MaskedArray.compressed() method in Numpy. 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.

The numpy.ma.MaskedArray is a subclass of ndarray designed to manipulate numerical arrays with missing data. An instance of MaskedArray can be thought as the combination of several elements:

## 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([[35, 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 all the non-masked data as a 1-D array, use the ma.MaskedArray.compressed() method in Numpy −

print("\nReturn Value...\n",maskArr.compressed())

## Example

# Python ma.MaskedArray - Return all the non-masked data as a 1-D array

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, 45], [67, 33, 59]])
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, 1], [ 0, 1, 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 return all the non-masked data as a 1-D array, use the ma.MaskedArray.compressed() method in Numpy
print("\nReturn Value...\n",maskArr.compressed())

## Output

Array...
[[35 85 45]
[67 33 59]]

Array type...
int64

Array Dimensions...
2

[[35 85 --]
[67 -- 59]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(2, 3)

Elements in the Masked Array...
6

Return Value...
[35 85 67 59]