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Return the pickle of the masked array as a string in NumPy
To pickle the masked array, use the ma.MaskedArray.dumps() method. Load the pickle back to array using the pickle.loads() method in Numpy. 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.
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
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, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...
", arr)
print("
Array type...
", arr.dtype)Get the dimensions of the Array −
print("Array Dimensions...
",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("
Our Masked Array
", maskArr)
print("
Our Masked Array type...
", maskArr.dtype)Get the dimensions of the Masked Array −
print("
Our Masked Array Dimensions...
",maskArr.ndim)
Get the shape of the Masked Array −
print("
Our Masked Array Shape...
",maskArr.shape)Get the number of elements of the Masked Array −
print("
Elements in the Masked Array...
",maskArr.size)
To pickle the masked array, use the ma.MaskedArray.dumps() method −
pickArr = maskArr.dumps()
Display the pickle −
print("
Pickle of the masked array...
",pickArr)
Load the pickle back to array using the pickle.loads() method −
resArr = pickle.loads(pickArr)
Display the resultant array −
print("
Loading the pickle back to the masked array...
",resArr)
Example
import numpy as np
import numpy.ma as ma
import pickle
# Create an array with int elements using the numpy.array() method
arr = np.array([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...
", arr)
print("
Array type...
", arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
",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("
Our Masked Array
", maskArr)
print("
Our Masked Array type...
", maskArr.dtype)
# Get the dimensions of the Masked Array
print("
Our Masked Array Dimensions...
",maskArr.ndim)
# Get the shape of the Masked Array
print("
Our Masked Array Shape...
",maskArr.shape)
# Get the number of elements of the Masked Array
print("
Elements in the Masked Array...
",maskArr.size)
# To pickle the masked array, use the ma.MaskedArray.dumps() method
pickArr = maskArr.dumps()
# Display the pickle
print("
Pickle of the masked array...
",pickArr)
# Load the pickle back to array using the pickle.loads() method
resArr = pickle.loads(pickArr)
# Display the resultant array
print("
Loading the pickle back to the masked array...
",resArr)Output
Array... [[55 85 68 84] [67 33 39 53] [29 88 51 37] [56 45 99 85]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- 68 84] [67 33 -- 53] [29 88 51 --] [56 -- 99 85]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 Pickle of the masked array... b"\x80\x02cnumpy.ma.core
_mareconstruct
q\x00(cnumpy.ma.core
MaskedArray
q\x01cnumpy
ndarray
q\x02K\x00\x85q\x03X\x01\x00\x00\x00bq\x04tq\x05Rq\x06(K\x01K\x04K\x04\x86q\x07cnumpy
dtype
q\x08X\x02\x00\x00\x00i8q\t\x89\x88\x87q
Rq\x0b(K\x03X\x01\x00\x00\x00<q\x0cNNNJ\xff\xff\xff\xffJ\xff\xff\xff\xffK\x00tq\rb\x89c_codecs
encode
q\x0eX\x80\x00\x00\x007\x00\x00\x00\x00\x00\x00\x00U\x00\x00\x00\x00\x00\x00\x00D\x00\x00\x00\x00\x00\x00\x00T\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x00\x00\x00\x00!\x00\x00\x00\x00\x00\x00\x00'\x00\x00\x00\x00\x00\x00\x005\x00\x00\x00\x00\x00\x00\x00\x1d\x00\x00\x00\x00\x00\x00\x00X\x00\x00\x00\x00\x00\x00\x003\x00\x00\x00\x00\x00\x00\x00%\x00\x00\x00\x00\x00\x00\x008\x00\x00\x00\x00\x00\x00\x00-\x00\x00\x00\x00\x00\x00\x00c\x00\x00\x00\x00\x00\x00\x00U\x00\x00\x00\x00\x00\x00\x00q\x0fX\x06\x00\x00\x00latin1q\x10\x86q\x11Rq\x12h\x0eX\x10\x00\x00\x00\x01\x01\x00\x00\x00\x00\x01\x00\x00\x00\x00\x01\x00\x01\x00\x00q\x13h\x10\x86q\x14Rq\x15Ntq\x16b." Loading the pickle back to the masked array... [[-- -- 68 84] [67 33 -- 53] [29 88 51 --] [56 -- 99 85]]
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