Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
To generate a Vandermonde matrix, use the np.ma.vander() method in Python Numpy. Set the number of columns in the output using the N parameter. If N is not specified, a square array is returned (N = len(x)).The columns of the output matrix are powers of the input vector. The order of the powers is determined by the increasing boolean argument. Specifically, when increasing is False, the i-th output column is the input vector raised element-wise to the power of N - i - 1. Such a matrix with a geometric progression in each row is named for Alexandre- Theophile Vandermonde.StepsAt ... Read More
In this article, we will understand how to construct a simple calculator using switch-case. The switch statement evaluates an expression, matching the expression's value to a case clause, and executes statements associated with that case.Following are the arithmetic operations we are going to perform.AdditionSubtractionMultiplicationDivisionFloor DivisionModuloBelow is a demonstration of the same −InputSuppose our input is −The two inputs: 40.0 and 12.0 Operator:%OutputThe desired output would be −The result is 40.0 % 12.0 = 4.0AlgorithmStep 1 - START Step 2 - Declare three values namely my_input_1, my_input_2 and my_result and declare a character value namely operator. Step 3 - Read the required ... Read More
To generate a Vandermonde matrix, use the np.ma.vander() method in Python Numpy. A Vandermonde matrix, named after Alexandre-Théophile Vandermonde, is a matrix with the terms of a geometric progression in each row.The columns of the output matrix are powers of the input vector. The order of the powers is determined by the increasing boolean argument. Specifically, when increasing is False, the i-th output column is the input vector raised element-wise to the power of N - i - 1. Such a matrix with a geometric progression in each row is named for Alexandre- Theophile Vandermonde.StepsAt first, import the required library ... Read More
To return the outer product of two masked 1D arrays, use the ma.outer() method in Python Numpy. The first parameter is the input vector. Input is flattened if not already 1-dimensional. The second parameter is the second input vector. Input is flattened if not already 1-dimensional.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.StepsAt first, import the required library −import ... Read More
To return the outer product of two 3D masked arrays, use the ma.outer() method in Python Numpy. The first parameter is the input vector. Input is flattened if not already 1-dimensional. The second parameter is the second input vector. Input is flattened if not already 1-dimensional.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.StepsAt first, import the required library −import ... Read More
To return the outer product of two masked arrays, use the ma.outer() method in Python Numpy. The first parameter is the input vector. Input is flattened if not already 1-dimensional. The second parameter is the second input vector. Input is flattened if not already 1-dimensional.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.StepsAt first, import the required library −import numpy ... Read More
To mask rows and/or columns of a 2D array that contain masked values, use the np.ma.mask_rowcols() method in Numpy. The function returns a modified version of the input array, masked depending on the value of the axis parameter.Mask whole rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the axis parameter −If axis is None, rows and columns are masked.If axis is 0, only rows are masked.If axis is 1 or -1, only columns are masked.StepsAt first, import the required library −import numpy as np import numpy.ma as maCreate an array with ... Read More
To compare and return True if an array is less than equal to another, use the numpy.char.less_equal() method in Python Numpy. The arr1 and arr2 are the two input string arrays of the same shape.Unlike numpy.less_equal, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray.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 two One-Dimensional arrays of string −arr1 = np.array(['Cio', 'Tom', 'Cena', 'Kate', 'Adams', 'brad', 'aa']) arr2 = ... Read More
To compute the bit-wise XOR of two 2D 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 ... Read More
To check which element in a masked array is greater than the given value, use the ma.MaskedArray.__gt__() method. True is returned for every array element greater than the given value val. 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 ... Read More