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To convert the row values in a matrix to row percentage, we can find the row sums and divide each row value by this sum. For example, if we have a matrix called M then we can convert the row values in M to row percentage by using the commandround((M/rowSums(M))*100,2)ExampleConsider the below matrix − Live DemoM1
Whenever Y value list will be made, then we will convert those datasets into a new list, with ceil and floor value of the given list accordingly. Then, we can plot the graph for the new list data.StepsTake an input list.Find the minimum and maximum values in the input list (Step 1).Create a range between min and max value (Step 2).Get or set the current tick locations and labels of the Y-axis, with a new list.Set the X-axis label using plt.xlabel() method.Set the Y-axis label using plt.ylabel() method.Set a title for the axes.To show the figure we can use the ... Read More
In this problem, we will find the intersection between two numpy arrays. Intersection of two arrays is an array with elements common in both the original arraysAlgorithmStep 1: Import numpy. Step 2: Define two numpy arrays. Step 3: Find intersection between the arrays using the numpy.intersect1d() function. Step 4: Print the array of intersecting elements.Example Codeimport numpy as np array_1 = np.array([1,2,3,4,5]) print("Array 1:", array_1) array_2 = np.array([2,4,6,8,10]) print("Array 2:", array_2) intersection = np.intersect1d(array_1, array_2) print("The intersection between the two arrays is:", intersection)OutputArray 1: [1 2 3 4 5] Array 2: [2 4 6 8 10] The intersection between the two arrays is: [2 4]
pylab is a module that imports matplotlib.pyplot (for plotting) and numpy (for mathematics and working with arrays) in a single namespace.Although many examples use pylab, it is no longer recommended. For non-interactive plotting, it is suggested to use pyplot to create the figures and then the OO interface for plotting.Exampleimport matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 2, 100) plt.plot(x, x, label='linear') plt.plot(x, x**2, label='quadratic') plt.plot(x, x**3, label='cubic') plt.xlabel('x label') plt.ylabel('y label') plt.title("Simple Plot") plt.legend() plt.show()Output
Using plt.figure() method, we can create a figure and thereafter, we can create an axis. Using set_xticks and set_yticks, we can change the ticks format and ax.grid could help to specify the grid interval.StepsCreate a new figure, or activate an existing figure, using fig = plt.figure() method.Add an `~.axes.Axes` to the figure as part of a subplot arrangement, where nrow = 1, ncols = 1 and index = 1.Get or set the current tick locations and labels of the X-axis.Get or set the current tick locations and labels of the X-axis. With minor = True, Grid.Get or set the current ... Read More
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 More
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 More
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 More
In this program, we will add all the terms of a numpy matrix using the sum() function in the numpy library. We will first create a random numpy matrix and then, we will obtain the sum of all the elements.AlgorithmStep 1: Import numpy. Step 2: Create a random m×n matrix using the random() function. Step 3: Obtain the sum of all the elements in the matrix using the sum() function.Example Codeimport numpy as np matrix = np.random.rand(3, 3) print("The numpy matrix is: ", matrix) print("The sum of the matrix is: ", np.sum(matrix))OutputThe numpy matrix is: [[0.66411969 0.43672579 0.48448593] [0.76110384 ... Read More
First, we will create a polygon using the mplPath.Path method and to check whether a given point is in the polygon or not, we will use the method, poly_path.contains_point.StepsCreate a list of points to make the polygon.Create a new path with the given vertices and codes, using mplPath.Path().Check if point (200, 100) exists in the polygon or not, using contains_point() method. Return whether the (closed) path contains the given point. => TrueCheck if point (1200, 1000) exists in the polygon or not, using contains_point() method. Return whether the (closed) path contains the given point. => FalseExampleimport matplotlib.path as mplPath import ... Read More