Found 1204 Articles for Numpy

To copy an element of an array to a standard Python scalar and return it, use the ma.MaskedArray.item() method in Numpy.The *args parameter, ifnone − in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.int_type − this argument is interpreted as a flat index into the array, specifying which element to copy and return.tuple of int_types − functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.StepsAt first, import the required library −import numpy ... Read More

To return a view of the MaskedArray data in Numpy, use the ma.MaskedArray.view() method.The a.view() is used two different waysa.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array. This does not cause a reinterpretation of the memory.StepsAt first, import the required library −import numpy as np import numpy.ma as maCreate an array with int elements using the numpy.array() method −arr = np.array([[35, 85], [67, 33]]) print("Array...", arr) print("Array type...", arr.dtype)Get the ... Read More

To return specified diagonals, use the ma.MaskedArray.diagonal() method in Numpy. Set the Offset of the diagonal from the main diagonal. Can be positive or negative.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 as np import numpy.ma as maCreate an array with int elements using the numpy.array() method −arr = np.array([[55, 85, 59, ... Read More

The interp1d() function of scipy.interpolate package is used to interpolate a 1-D function. It takes arrays of values such as x and y to approximate some function y = f(x) and then uses interpolation to find the value of new points.Syntaxscipy.interpolate.interp1d(x, y)where x is a 1-D array of real values and y is an N-D array of real values. The length of y along the interpolation axis must be equal to the length of x.Example 1Let us consider the following example −# Import the required libraries import matplotlib.pyplot as plt import numpy as np from scipy import interpolate # ... Read More

The tanm() function of scipy.linalg package is used to compute the tangent of an input matrix. This routine uses expm to compute the matrix exponentials.Syntaxscipy.linalg.tanm(x)where x is the input array or a square matrix. It returns the matrix tangent of x.Example 1Let us consider the following example −# Import the required libraries from scipy import linalg import numpy as np # Define the input array x = np.array([[69 , 12] , [94 , 28]]) print("Input array: ", x) # Calculate the Tangent a = linalg.tanm(x) # Display the Tangent of matrix print("Tangent of X: ", a)OutputIt will ... Read More

The cosm() function of scipy.linalg package is used to compute the cosine of an input matrix. This routine uses expm to compute the matrix exponentials.Syntaxscipy.linalg.cosm(x)where x is the input array.Example 1Let us consider the following example −# Import the required libraries from scipy import linalg import numpy as np # Define the input array q = np.array([[121 , 10] , [77 , 36]]) print("Array Input :", q) # Calculate the Cosine r = linalg.cosm(q) # Display the Cosine of matrix print("Cosine of Q: ", r)OutputThe above program will generate the following output − Array Input : ... Read More

The sinm() function scipy.linalg package is used to compute the sine of an input matrix. This routine uses expm to compute the matrix exponentials.Syntaxscipy.linalg.sinm(x)where x is the inputer array.Example 1Let us consider the following example −# Import the required libraries from scipy from scipy import linalg import numpy as np # Define the input array X = np.array([[110, 12], [79, 23]]) print("Input Matrix, X:", X) # Calculate the Sine of the matrix n = linalg.sinm(X) # Display the Sine print("Sine of X: ", n)OutputIt will generate the following output − Input Matrix, X: [[110 12] ... Read More

The expm() function of scipy.linalg package is used to compute the matrix exponential using Padé approximation. A Padé approximant is the "best" approximation of a function by a rational function of given order. Under this technique, the approximant's power series agrees with the power series of the function it is approximating.Syntaxscipy.linalg.expm(x)where x is the input matrix to be exponentiated.Example 1Let us consider the following example −# Import the required libraries from scipy import linalg import numpy as np # Define the input array e = np.array([[100 , 5] , [78 , 36]]) print("Input Array :", e) # Calculate ... Read More

To save the plot to a numpy array in RGB format, we can take the following steps −Create r, g and b random array using numpy.Zip r, g and b (grom step 1) to make an rgb tuple list.Convert rgb into a numpy array to plot it.Plot the numpy array that is in rgb format.Save the figure at the current location.To display the figure, use the show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True r = np.random.rand(100) g = np.random.rand(100) b = np.random.rand(100) rgb = zip(r, g, b) arr = np.array([item for item in rgb]) plt.plot(arr) plt.savefig("myplot.png") ... Read More

In this program, we will find the index position at which a multiple of a given number exists. We will use both the Numpy and the Pandas library for this task.AlgorithmStep 1: Define a Pandas series. Step 2: Input a number n from the user. Step 3: Find the multiples of that number from the series using argwhere() function in the numpy library.Example Codeimport numpy as np listnum = np.arange(1, 20) multiples = [] print("NumList:", listnum) n = int(input("Enter the number you want to find multiples of: ")) for num in listnum:    if num % n == ... Read More