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

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

In this program, we will find the set difference of two numpy arrays. We will use the setdiff1d() function in the numpy library. This function takes two parameters: array1 and array2 and returns the unique values in array1 that are not in array2.AlgorithmStep 1: Import numpy. Step 2: Define two numpy arrays. Step 3: Find the set difference between these arrays using the setdiff1d() function. Step 4: Print the output.Example Codeimport numpy as np array_1 = np.array([2, 4, 6, 8, 10, 12]) print("Array 1: ", array_1) array_2 = np.array([4, 8, 12]) print("Array 2: ", array_2) set_diff = ... 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]

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

 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

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 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

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

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