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To plot categorical variables in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a dictionary with some details.Extract the keys and values from the dictionary (Step 2).Create a figure and a set of subplots.Plot bar, scatter and plot with names and values data.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 data = {'apple': 10, 'orange': 15, 'lemon': 5} names = list(data.keys()) values = list(data.values()) fig, axs = plt.subplots(1, 3) axs[0].bar(names, values) axs[1].scatter(names, values) axs[2].plot(names, values) ... Read More
Tests are generally classified on where they are implemented in the SDLC or the degree of information they include. There are four types of testing overall: unit testing, integration testing, system testing, and acceptance testing. The goal of Levels of testing is to make software testing more methodical and to make it easier to discover all feasible test scenarios at a given level.There are several testing tiers available to aid in the evaluation of software behavior and performance. These testing stages are intended to identify gaps and reconcile the development lifecycle phases. SDLC models define stages such as requirement gathering, ... Read More
You will learn the following in this article −What is Design Verification?Difference between Design Verification and ValidationDesign Verification ProcessDesign Validation ProcessAdvantages of Design Validation and VerificationDesign ValidationDesign Validation is the process of testing a software product to ensure that it meets the specific needs of the customer or partners. The goal of design validation is to check the software product after it has been developed to confirm that it fits the criteria for implementations in the user's environment.Validation is focused on establishing the design's accuracy and reliability in relation to the user's demands. This is the step in which you ... Read More
To use fivethirtyeight stylesheet, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.To use fivethirtyeight, we can use plt.style.use() method.Create x data points using numpy.Create a figure and a set of subplots using subplots() method.Plot three curves using plot() method.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 plt.style.use('fivethirtyeight') x = np.linspace(0, 10) fig, ax = plt.subplots() ax.plot(x, np.sin(x) + x + np.random.randn(50)) ax.plot(x, np.sin(x) + 0.5 * x + ... Read More
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