To get the shape of the Masked Array, use the ma.MaskedArray.shape attribute in Numpy. The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it.As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.StepsAt first, import the required library −import numpy as np import numpy.ma as maCreate an ... Read More

To get the dimensions of the Masked Array, use the ma.MaskedArray.ndim attribute in Python 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.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.StepsAt first, import the required library −import numpy as np import numpy.ma as maCreate an array ... Read More

To return a copy of an array with only the first character of each element capitalized, use the numpy.char.capitalize() method in Python Numpy. The arr is the input array of strings to capitalize. The function returns the output array of str or unicode, depending on input types.The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of string −arr = np.array(['bella', 'toM', 'john', 'katE', 'amy', 'brad']) Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the ... Read More

To return element-wise string multiple concatenation, use the numpy.char.multiply() method in Python Numpy. The function multiply() returns the output array of string_ or unicode_, depending on input types.The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of string −arr = np.array(['Bella', 'Tom', 'John', 'Kate', 'Amy', 'Brad']) Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the Array −print("Array Dimensions...", arr.ndim)Get the shape of the Array −print("Our Array Shape...", arr.shape) Get the number of elements of ... Read More

To return element-wise string concatenation for two arrays of string, use the numpy.char.add() method in Python Numpy.The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.The function add() returns the output array of string_ or unicode_, depending on input types of the same shape as x1 and x2. The x1 and x1 are input arrays.StepsAt first, import the required library −import numpy as npCreate two One-Dimensional arrays of stringarr1 = np.array(['Bella', 'Tom', 'John', 'Kate', 'Amy', 'Brad']) arr2 = np.array(['Cio', 'Hanks', 'Ceo', 'Hudson', 'Adams', 'Pitt'])Display the arrays −print("Array 1...", arr1) print("Array 2...", arr2)Get the ... Read More

To unpack elements of a uint8 array into a binary-valued output array, use the numpy.unpackbits() method in Python Numpy. The result is binary-valued (0 or 1). The axis is the dimension over which bit-unpacking is done. The axis is set using the "axis" parameter. To set the number of elements to unpack, use the "count" parameter.Each element of the input array represents a bit-field that should be unpacked into a binary-valued output array. The shape of the output array is either 1-D (if axis is None) or the same shape as the input array with unpacking done along the axis ... Read More

To unpack elements of a uint8 array into a binary-valued output array, use the numpy.unpackbits() method in Python Numpy. The result is binary-valued (0 or 1). The axis is the dimension over which bit-unpacking is done. The axis is set using the "axis" parameter. To set the number of elements to unpack, use the "count" parameter.Each element of the input array represents a bit-field that should be unpacked into a binary-valued output array. The shape of the output array is either 1-D (if axis is None) or the same shape as the input array with unpacking done along the axis ... Read More

To shift the bits of integer array elements to the right, use the numpy.right_shift() method in Python Numpy. Bits are shifted to the right x2. Because the internal representation of numbers is in binary format, this operation is equivalent to dividing x1 by 2**x2.The x1 is the Input values. The x2 is the number of bits to remove at the right of x1. If x1.shape != x2.shape, they must be broadcastable to a common shape.The function right_shift() returns x1 with bits shifted x2 times to the right. This is a scalar if both x1 and x2 are scalars.StepsAt first, import ... Read More

To expand the shape of an array, use the numpy.expand_dims() method. Insert a new axis that will appear at the axis position in the expanded array shape. We will set axis 0 here. The function returns the View of the input array with the number of dimensions increased.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.StepsAt first, import the required library −import numpy as npCreating an array using the array() method −arr = np.array([5, ... Read More

To shift the bits of array elements of a 2D array to the left, use the numpy.left_shift() method in Python Numpy. Bits are shifted to the left by appending x2 0s at the right of x1. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying x1 by 2**x2. The x1 is the Input values. The x2 is the number of zeros to append to x1. Has to be non-negative. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).The function left_shift() returns x1 with ... Read More