Numpy MaskedArray.flatten() function | Python (original) (raw)

Last Updated : 03 Oct, 2019

numpy.MaskedArray.flatten() function is used to return a copy of the input masked array collapsed into one dimension.

Syntax : numpy.ma.flatten(order='C')

Parameters:
order : [‘C’, ‘F’, ‘A’, ‘K’, optional] Whether to flatten in C (row-major), Fortran (column-major) order, or preserve the C/Fortran ordering from a. The default is ‘C’.

Return : [ ndarray] A copy of the input array, flattened to one dimension.

Code #1 :

import numpy as geek

import numpy.ma as ma

in_arr = geek.array([[ 10 , 20 ], [ - 10 , 40 ]])

print ( "Input array : " , in_arr)

mask_arr = ma.masked_array(in_arr, mask = [[ 1 , 0 ], [ 0 , 0 ]])

print ( "Masked array : " , mask_arr)

out_arr = mask_arr.flatten()

print ( "Output flattened masked array : " , out_arr)

Output:

Input array : [[ 10 20] [-10 40]] Masked array : [[-- 20] [-10 40]] Output flattened masked array : [-- 20 -10 40]

Code #2 :

import numpy as geek

import numpy.ma as ma

in_arr = geek.array([[[ 2e8 , 3e - 5 ]], [[ - 4e - 6 , 2e5 ]]])

print ( "Input array : " , in_arr)

mask_arr = ma.masked_array(in_arr, mask = [[[ 1 , 0 ]], [[ 0 , 0 ]]])

print ( "Masked array : " , mask_arr)

out_arr = mask_arr.flatten(order = 'F' )

print ( "Output flattened masked array : " , out_arr)

Output:

Input array : [[[ 2.e+08 3.e-05]]

[[-4.e-06 2.e+05]]] Masked array : [[[-- 3e-05]]

[[-4e-06 200000.0]]] Output flattened masked array : [-- -4e-06 3e-05 200000.0]

Similar Reads

• Numpy MaskedArray.any() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 3 min read
• Numpy MaskedArray.astype() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 3 min read
• Numpy MaskedArray.anom() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 2 min read
• Numpy MaskedArray.all() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays. Masked arrays are arr 3 min read
• Numpy MaskedArray.dot() function | Python numpy.MaskedArray.dot() function is used to calculate the dot product of two mask arrays. Syntax : numpy.ma.dot(arr1, arr2, strict=False) Parameters: arr1, arr2:[ ndarray] Inputs arrays. strict : [bool, optional] Whether masked data are propagated (True) or set to 0 (False) for the computation. Defa 3 min read
• Numpy MaskedArray.argmin() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 3 min read
• Numpy ndarray.flatten() function in Python The flatten() function is used to convert a multi-dimensional NumPy array into a one-dimensional array. It creates a new copy of the data so that original array stays unchanged. If your array has rows and columns or even more dimensions, then flatten() line up every single value into a straight list 3 min read
• Numpy MaskedArray.argsort() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 3 min read
• Numpy MaskedArray.average() function | Python numpy.MaskedArray.average() function is used to return the weighted average of array over the given axis. Syntax : numpy.ma.average(arr, axis=None, weights=None, returned=False) Parameters: arr :[ array_like] Input masked array whose data to be averaged. Masked entries are not taken into account in 3 min read
• Numpy MaskedArray.conjugate() function | Python numpy.MaskedArray.conjugate() function is used to return the complex conjugate, element-wise.The conjugate of a complex number is obtained by changing the sign of its imaginary part. Syntax : numpy.ma.conjugate(arr, out=None, where=True, casting='same_kind', order='K', dtype=None, subok=True)Paramet 2 min read