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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
To change the tick size using ggplot2, we can use theme function with argument axis.ticks.length. For example, if we have a data frame called df that contains two columns say x and y then the scatterplot between x and y with larger size of tick marks can be created by using the below command −ggplot(df,aes(x,y))+geom_point()+theme(axis.ticks.length=unit(0.8,"inch"))ExampleConsider the below data frame − Live Demox
If we have a data frame that contains a character column and a named vector which has the same names as in the character column of the data frame then we can combine this data frame and the vector by using match function be appropriately defining the names and the character column. Check out the below example to understand how it can be done.ExampleConsider the below data frame df1 and the vector v1 − Live Demodf1
First, we can initialize an array matrix and pass it into the imshow method that can help to get the image for the given matrix.StepsCreate a 2D Array i.e., img.Using imshow() method, display the data as an image, i.e., on a 2D regular raster.Use plt.show() method to show the figure.Exampleimport matplotlib.pyplot as plt img = [[1, 2, 4, 5, 6, 7], [11, 12, 14, 15, 16, 17], [101, 12, 41, 51, 61, 71], [111, 121, 141, 151, 161, 171]] plt.imshow(img, extent=[0, 5, 0, 5]) plt.show()Output
To make a custom color, we can create a hexadecimal string. From it, we can make different sets of color representation and can pass into the scatter method to get the desired output.StepsTake an input from the user for the number of colors, i.e., number_of_colors = 20.Use Hexadecimal alphabets to get a color.Create a color from (step 2) by choosing a random character from step 2 data.Plot scatter points for step 1 input data, with step 3 colors.To show the figure, use plt.show() method.Exampleimport matplotlib.pyplot as plt import random number_of_colors = int(input("Please enter number of colors: ")) hexadecimal_alphabets ... Read More
We have to create a numpy array in the range provided by the user. We will use the arange() function in the numpy library to get our output.AlgorithmStep1: Import numpy. Step 2: Take start_value, end_value and Step from the user. Step 3: Print the array using arange() function in numpy.Example Codeimport numpy as np start_val = int(input("Enter starting value: ")) end_val = int(input("Enter ending value: ")) Step_val = int(input("Enter Step value: ")) print(np.arange(start_val, end_val, Step_val))OutputEnter starting value: 5 Enter ending value: 50 Enter Step value: 5 [ 5 10 15 20 25 30 35 40 45]
To convert number to words in an R data frame column, we can use english function from english package. For example, if we have a data frame called df that contains a number column x then we can convert the numbers into words by using the command as.character(english(df$x)).ExampleConsider the below data frame − Live Demox
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]]