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To plot 3d plot_surface with contour plot projection, we can use plot_surface() and contourf() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create x, y, X, Y and Z data points using numpy.Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement, with 3D projection.Use plot_surface() method to create a surface plot.Create a 3D filled contour plotm using contourf() method.Trurn off the axes.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] ... Read More
To add ticks to the colorbar, 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 imshow() method to display the data as an image, i.e., on a 2D regular raster.Create ticks using numpy in the range of min and max of z.Create a colorbar for a ScalarMappable instance, *mappable*, with ticks=ticks.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x, y = np.mgrid[-1:1:100j, -1:1:100j] z = (x + ... Read More
To change the font properties of a matplotlib colorbar label, 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 imshow() method to display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, *mappable*.Using colorbar axes, set the font properties such that the label is bold.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x, y = np.mgrid[-1:1:100j, -1:1:100j] z = ... Read More
To draw a scatter trend line using matplotlib, we can use polyfit() and poly1d() methods to get the trend line points.StepsSet 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 numpy.Find the trend line data points using polyfit() and poly1d() method.Plot x and p(x) 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 x = np.random.rand(100) y ... Read More
A Dialog is a graphical element, a window that shows information to the window and receives a response. You can create a dialog by instantiating the javafx.scene.control.Dialog class.ExampleThe following Example demonstrates the creation of a Dialog.import javafx.application.Application; import javafx.geometry.Insets; import javafx.scene.Group; import javafx.scene.Scene; import javafx.scene.control.Button; import javafx.scene.control.ButtonBar.ButtonData; import javafx.scene.control.ButtonType; import javafx.scene.control.Dialog; import javafx.scene.layout.HBox; import javafx.stage.Stage; import javafx.scene.paint.Color; import javafx.scene.text.Font; import javafx.scene.text.FontPosture; import javafx.scene.text.FontWeight; import javafx.scene.text.Text; public class DialogExample extends Application { @Override public void start(Stage stage) { //Creating a dialog Dialog dialog = new Dialog(); //Setting the title ... Read More
To plot a hexbin histogram 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 using hexbin() method.Set the title of the plot.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 x = 2 * np.random.randn(5000) y = x + np.random.randn(5000) fig, ax = plt.subplots() _ = ax.hexbin(x[::10], y[::10], gridsize=20, cmap='plasma') ax.set_title('Hexbin Histogram') ... Read More
To plot a 2D histogram 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 using hist2d() method.Set the title of the plot.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 x = 2 * np.random.randn(5000) y = x + np.random.randn(5000) fig, ax = plt.subplots() _ = ax.hist2d(x[::10], y[::10]) ax.set_title('2D Histogram') plt.show()OutputRead More
To make joint bivariate distributions in matplotlib, we can use the scatter method.StepsSet 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 using scatter() 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 x = 2 * np.random.randn(5000) y = x + np.random.randn(5000) fig, ax = plt.subplots() _ = ax.scatter(x, y, alpha=0.08, cmap="copper", c=x) plt.show()OutputRead More
To align the bar and line in matplotlib two Y-axes chart, we can use twinx() method to create a twin of Axes with a shared X-axis but independent Y-axis.StepsSet the figure size and adjust the padding between and around the subplots.Make a Pandas dataframe with columns 1 and 2.Plot the dataframe using plot() method with kind="bar", i.e., class by name.Use twinx() method to create a twin of Axes with a shared X-axis but independent Y-axis.Plot the axis (Step 3) ticks and dataframe columns values to plot the lines.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import ... Read More
To draw a border around subplots in matplotlib, we can use a Rectangle patch on the subplots.StepsSet the figure size and adjust the padding between and around the subplots.Add a subplot to the current figure using subplot(121).Get the subplot axes.Add a rectangle defined via an anchor point *xy* and its *width* and *height*.Add a rectangle patch to the current subplot based on axis (Step 4).Set whether the artist uses clipping.Add a subplot to the current figure using subplot(122).Set the title of the current subplot.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] ... Read More