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To make multipartite graph in networkx, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a list of subset sizes and colors.Define a method for multilayered graph that could return a multilayered graph object.Set the color of the nodes.Position the nodes in layers of straight lines.Draw the graph G with Matplotlib.Set equal axis properties.To display the figure, use show() method.Exampleimport itertools import matplotlib.pyplot as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True subset_sizes = [5, 5, 4, 3, 2, 4, 4, 3] subset_color = ... Read More
To plot the difference of two distributions in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a and b datasets using Numpy.Get kdea and kdeb, i.e., representation of a kernel-density estimate using Gaussian kernels.Create a grid using Numpy.Plot the gird with kdea(grid), kdeb(grid) and kdea(grid)-kdeb(grid), using plot() method.Place the legend at the upper-left corner.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import scipy.stats plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True a = np.random.gumbel(50, 28, 100) b = np.random.gumbel(60, 37, 100) ... Read More
The Axes class contains most of the figure elements − Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system.stepsSet the figure size and adjust the padding between and around the subplots.Set the axes linewidth using rcParams.Add an axes to the current figure and make it the current axes.Set the axes spines color.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.rcParams['axes.linewidth'] = 5 ax = plt.axes() ax.spines['bottom'].set_color('yellow') ax.spines['top'].set_color('red') ax.spines['right'].set_color('black') ax.spines['left'].set_color('blue') plt.show()OutputRead More
To visualize 95% confidence interval in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data sets.Get the confidence interval dataset.Plot the x and y data points using plot() method.Fill the area within the confidence interval range.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(0, 10, 0.05) y = np.sin(x) # Define the confidence interval ci = 0.1 * np.std(y) / np.mean(y) plt.plot(x, y, color='black', ... Read More
To plot an imshow() image in 3D in Matplotlib, we can take the following steps −Create xx and yy data points using numpy.Get the data (2D) using X, Y and Z.Create a new figure or activate an existing figure using figure() method.Add an 'ax1' to the figure as part of a subplot arrangement.Display the data as an image, i.e., on a 2D regular raster with data.Add an 'ax2' to the figure as part of a subplot arrangement.Create and store a set of contour lines or filled regions.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np ... Read More
To add extra contour lines using Matplotlib 2D contour plotting, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create e a function f(x, y) to get the z data points from x and y.Create x and y data points using numpy.Make a list of levels using Numpy.Make a contour plot using contour() method.Label the contour plot and set the title of the plot.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True def f(x, y): return ... Read More
To remove the digits after the decimal point in axis ticks in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots.To set the xtick labels only in digits, we can use x.astype(int) method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([1.110, 2.110, 4.110, 5.901, 6.00, 7.90, 8.90]) y = np.array([2.110, 1.110, 3.110, 9.00, 4.001, 2.095, 5.890]) fig, ... Read More
To set the unit length of an axis in Matplotlib, we can use xlim or ylim with scale factor of the axes, i.e., of unit length times.stepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot the x and y data points using plot() method.Get the x and y axes, limit range.Use xlim and ylim methods to set the unit length scale.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 100) y ... Read More
To hide major tick labels while showing minor ticklabels in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot the x and y data points.Set a property on an artist object, using setp() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 100) y = np.log(x) plt.plot(x, y) plt.setp(plt.gca().get_xmajorticklabels(), visible=False) plt.show()OutputRead More
To mark a specific level in a contour map on Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z data points using Numpy.Use contour() method to make contour plot.Label the contour plot.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True def f(x, y): return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x) x = np.linspace(0, 5, 50) y = np.linspace(0, 5, 40) X, Y = ... Read More