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Numpy Articles
Page 80 of 81
Return a view of the MaskedArray data in Numpy
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 MoreCopy an element of a masked array to a standard Python scalar and return it
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 MoreHow to save the plot to a numpy array in RGB format?
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 MoreFinding the multiples of a number in a given list using NumPy
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 MoreHow to print array elements within a given range using Numpy?
In this program, we have to print elements of a numpy array in a given range. The different numpy functions used are numpy.where() and numpy.logical_and().AlgorithmStep 1: Define a numpy array. Step 2: Use np.where() and np.logical_and() to find the numbers within the given range. Step 3: Print the result.Example Codeimport numpy as np arr = np.array([1,3,5,7,10,2,4,6,8,10,36]) print("Original Array:",arr) result = np.where(np.logical_and(arr>=4, arr
Read MoreHow to add a vector to a given Numpy array?
In this problem, we have to add a vector/array to a numpy array. We will define the numpy array as well as the vector and add them to get the result arrayAlgorithmStep 1: Define a numpy array. Step 2: Define a vector. Step 3: Create a result array same as the original array. Step 4: Add vector to each row of the original array. Step 5: Print the result array.Example Codeimport numpy as np original_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) print("Original Array: ", original_array) vector = np.array([1, 1, 0]) print("Vector: ...
Read MoreLinear regression with Matplotlib/Numpy
To get a linear regression plot, we can use sklearn’s Linear Regression class, and further, we can draw the scatter points.StepsGet x data using np.random.random((20, 1)). Return random floats in the half-open interval[20, 1).Get the y data using np.random.normal() method. Draw random samples from a normal (Gaussian) distribution.Get ordinary least squares Linear Regression, i.e., model.Fit the linear model.Return evenly spaced numbers over a specified interval, using linspace() method.Predict using the linear model, using predict() method.Create a new figure, or activate an existing figure, with a given figsize tuple (4, 3).Add an axis to the current figure and make it the ...
Read MoreHow to find the sum of rows and columns of a given matrix using Numpy?
In this problem, we will find the sum of all the rows and all the columns separately. We will use the sum() function for obtaining the sum.AlgorithmStep 1: Import numpy. Step 2: Create a numpy matrix of mxn dimension. Step 3: Obtain the sum of all the rows. Step 4: Obtain the sum of all the columns.Example Codeimport numpy as np a = np.matrix('10 20; 30 40') print("Our matrix: ", a) sum_of_rows = np.sum(a, axis = 0) print("Sum of all the rows: ", sum_of_rows) sum_of_cols = np.sum(a, axis = 1) print("Sum of all the columns: ", sum_of_cols)OutputOur ...
Read MoreHow to create an identity matrix using Numpy?
In this program, we will print an identity matrix of size nxn where n will be taken as an input from the user. We shall use the identity() function in the numpy library which takes in the dimension and the data type of the elements as parametersAlgorithmStep 1: Import numpy. Step 2: Take dimensions as input from the user. Step 3: Print the identity matrix using numpy.identity() function.Example Codeimport numpy as np dimension = int(input("Enter the dimension of identitiy matrix: ")) identity_matrix = np.identity(dimension, dtype="int") print(identity_matrix)OutputEnter the dimension of identitiy matrix: 5 [[1 0 0 0 0] [0 1 0 0 0] [0 0 1 0 0] [0 0 0 1 0] [0 0 0 0 1]]
Read MoreFinding the number of rows and columns in a given matrix using Numpy
We will first create a numpy matrix and then find out the number of rows and columns in that matrixAlgorithmStep 1: Create a numpy matrix of random numbers. Step 2: Find the rows and columns of the matrix using numpy.shape function. Step 3: Print the number of rows and columns.Example Codeimport numpy as np matrix = np.random.rand(2,3) print(matrix) print("Total number of rows and columns in the given matrix are: ", matrix.shape)Output[[0.23226052 0.89690884 0.19813164] [0.85170808 0.97725669 0.72454096]] Total number of rows and columns in the given matrix are: (2, 3)
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