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To add a line to a scatter plot using Python's Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for number of data points.Plot x and y data points using scatter() method.Plot a line using plot() method.Limt the X-axis using xlim() method.To display the figure, use 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 n = 100 x = np.random.rand(n) y = np.random.rand(n) plt.scatter(x, y, c=x) plt.plot([0.1, 0.4, 0.3, 0.2]) plt.xlim(0, 1) ... Read More
To disable the keyboard shortcuts in Matplotlib, we can use remove('s') method.StepsSet the figure size and adjust the padding between and around the subplots.To disable the shortcut "s" to save the figure, use remove("s") method.Initialize a variable n for number of data points.Create x and y data points using numpyPlot x and y data points using plot() method.To display the figure, use 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 plt.rcParams['keymap.save'].remove('s') n = 10 x = np.random.rand(n) y = np.random.rand(n) plt.plot(x, y) plt.show()OutputRead More
Conformance TestingConformance testing is a software testing approach being used to ensure that a software system meets the guidelines and requirements set by IEEE, W3C, or ETSI. Conformance testing determines how well a system undergoing assessment verifies to fulfill the specific needs of a certain regulation.Compliance testing is another name for conformance testing.It may deal with certain technical aspects, but it incorporates the following on purpose −PerformanceFunctionsRobustnessInteroperabilityBehavior of systemYou will learn the following in this tutorial −What is Conformance Testing?Types of Conformance TestingWhy do we need Conformance Testing?What do we need to test?When and how to perform Conformance Testing?Conformance Testing ... Read More
To set the location of the minor 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.Plot x and y data points using plot() method.To locate minor ticks, use set_minor_locator() method.To show the minor ticks, use grid(which='minor').To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import AutoMinorLocator plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 100) y = np.log(x) fig, ax ... Read More
To label and change the scale of a Seaborn kdeplot's axes, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using numpy.Plot Kernel Density Estimate (KDE) using kdeplot() method.Set Y-axis tscale and label.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randn(10) k = sns.kdeplot(x=data, shade=True) plt.yticks(k.get_yticks(), k.get_yticks()) plt.ylabel('Y', fontsize=7) plt.show()OutputRead More
We can take the following steps to make a broken bar plot, Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Plot a horizontal sequence of rectangles.Set x and y axes scale, X-axis label, Y ticks and Y tick labels.Configure the grid lines.Use annotate() method to show text that can refer to a specific position.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() ax.broken_barh([(110, 30), (150, 10)], (10, 9), facecolors='tab:blue') ax.broken_barh([(10, 50), (100, 20), (130, ... Read More
To update the plot title with Matplotlib using animation, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure() method.Create x and y data points using numpy.Get the current axis.Add text to the axes using text() method.Add an animate method that can be used to make an animation by repeatedly calling a function.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, animation plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ... Read More
To color the edges by weight in networkx, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a graph with edges, name, or graph attributes.Add nodes to the current graph.Add edges to the current graph's nodes.Iterate the given graph's edges and set some weight to them.Draw current graphs with weights for edge color.To display the figure, use show() method.Exampleimport random as rd import matplotlib.pylab as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True G = nx.DiGraph() G.add_nodes_from([1, 2, 3, 4]) G.add_edges_from([(1, 2), (2, 3), ... Read More
To animate quivers in Python, 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 u and v data points using numpy.Create a figure and a set of subplots.Plot a 2D field of arrows using quiver() method.To animate the quiver, we can change the u and v values, in animate() method. Update the u and v values and the color of the vectors.To display the figure, use show() method.Exampleimport numpy as np import random as rd from matplotlib import pyplot as plt, animation ... Read More
To make markers on lines smaller in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points, x.Plot x data points using plot() method, with linewidth =0.5 and color="black".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.random.rand(20) plt.plot(x, '*-', color='black', markersize=10, lw=0.5) plt.show()Output