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Numpy Articles
Page 52 of 81
Compute the bit-wise XOR of two Numpy arrays element-wise
To compute the bit-wise XOR of two arrays element-wise, use the numpy.bitwise_xor() method in Python Numpy. Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ^.The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. ...
Read MoreTrue Divide each element of a masked Array by a scalar value in-place in Numpy
To true divide each element of a masked Array by a scalar value in-place, use the ma.MaskedArray.__itruediv__() method in Python 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 ...
Read MoreAdd a scalar value with each element of a masked Array in-place in Numpy
To add a scalar value with each element of a masked Array in-place, use the ma.MaskedArray.__iadd__() method in Python 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 ...
Read MoreReplace tab characters by a fixed tabsize in a string array in Numpy
To replace tab characters by a fixed tabsize in a string array, use the numpy.char.expandtabs() method in Python Numpy. The "tabsize" parameter is used to replace tabs with tabsize number of spaces. If not given defaults to 8 spaces.The function expandtabs() returns a copy of each string element where all tab characters are replaced by one or more spaces, depending on the current column and the given tabsize. The column number is reset to zero after each newline occurring in the string. This doesn’t understand other non-printing characters or escape sequences.The numpy.char module provides a set of vectorized string operations ...
Read MoreReturn a copy of each string element where all tab characters are replaced by spaces in Numpy
To return a copy of each string element where all tab characters are replaced by spaces, use the numpy.char.expandtabs() method in Python Numpy. We can also set the "tabsize" parameter i.e. replace tabs with tabsize number of spaces. If not given defaults to 8 spaces.The function expandtabs() returns a copy of each string element where all tab characters are replaced by one or more spaces, depending on the current column and the given tabsize. The column number is reset to zero after each newline occurring in the string. This doesn’t understand other non-printing characters or escape sequences.The numpy.char module provides ...
Read MoreReturn a copy of an array with its elements centered in a string of length width in Numpy
To return a copy of an array with its elements centered in a string of length width, use the numpy.char.center() method in Python Numpy. The width is the length of the resulting strings. 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 MoreGet the number of elements of the Masked Array in Numpy
To get the number of elements of the Masked Array, use the ma.MaskedArray.size attribute in Numpy. The array.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value, which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.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.StepsAt ...
Read MoreGet the current shape of the Masked Array in Numpy
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 MoreGet the Masked Array Dimensions in Numpy
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 MoreReturn element-wise string multiple concatenation in Numpy
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 ...
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