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To put the line title at the bottom of a figure in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, to get the number of sample data.Plot the x and y data points using scatter() method.Set the title at the bottom of the figure in matplotlib, with y=-0.01.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, ... Read More
To make a simple lollipop plot in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a dataframe, df, of two-dimensional, size-mutable, potentially heterogeneous tabular data.Make an ordered dataframe, using sort_values().Make a list in the range of dataframe index.Create a stem plot, using the ordered dataframe.Set xticks and labels using xticks() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'group': list(map(chr, range(65, 85))), 'values': np.random.uniform(size=20)}) ... Read More
We can use ax.loglog(x, y) and set_major_formatter() methods to replace tick labels with computed values.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Make a plot with log scaling on both the X and Y axis.Set the formatter of the major ticker.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, ticker plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() ax.loglog(np.logspace(0, 5), np.logspace(0, 5)**2) ax.xaxis.set_major_formatter(ticker.LogFormatterExponent()) plt.show()OutputRead More
To have a function return a figure in Python (using 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.Make a function plot(x, y) that creates a new figure or activate an existing figure using figure() method.Plot the x and y data points using plot() method; return fig instance.Call plot(x, y) method and store the figure instance in a variable, f.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"] = ... Read More
To make a quiver plot in polar coordinates using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create radii, thetas, theta and r data points using numpy.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Make poly collections of arrows.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 radii = np.linspace(0, 1, 5) thetas = np.linspace(0, 2 * np.pi, 20) theta, r = ... Read More
Risk-based Testing (RBT)It is a sub-category of software testing based on the probability of risks. In this test, the software is assessed to identify risk. It includes assessing criticality of business, frequency of usage, possible areas with problems, etc. This type of testing emphasizes testing of software’s features and functions which are vulnerable to defects.Risk is the occurrence of nay unwanted event that may have an effect (positive or negative) on the results of the project. Risks can either be events that have occurred previously or current events, or even something that could take place in future. These events affect ... Read More
To decrease the hatch density in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a customized horizontal hatch class to override the density.Append the horizontal hatch class.Create a new figure or activate an existing figure.Add an 'ax1' to the figure as part of a subplot arrangement.Make lists of data points.Make a bar plot with x and ydata points, with hatch='o', color='green' and edgecolor='red'.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, hatch plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True class MyHorizontalHatch(hatch.HorizontalHatch): def ... Read More
To give matplotlib imshow() plot colorbars a label, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create 5×5 data points using Numpy.Use imshow() method to display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, im.Set colorbar label using set_label() 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 data = np.random.rand(5, 5) im = plt.imshow(data, cmap="copper") cbar = plt.colorbar(im) cbar.set_label("Colorbar") plt.show()OutputRead More
To set a title above each marker which represents the same label in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x data points using Numpy.Create four curves, c1, c2, c3 and c4 using plot() method.Place a legend on the figure, such that the same label marker would come together.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, legend_handler plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-10, 10, 100) c1, = plt.plot(x, np.sin(x), ls='dashed', label='y=sin(x)') c2, ... Read More
To change the color and add grid lines to a Python surface plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and h data points using numpy.Create a new figure or activate an existing figure.Get 3D axes object, with figure (from Step 3).Create a surface plot, with orange color, edgecolors and linewidth.Exampleimport numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(-5, 5, 0.25) y = np.arange(-5, 5, 0.25) x, y = np.meshgrid(x, ... Read More