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To plot a 2D matrix in Python with colorbar, we can use numpy to create a 2D array matrix and use that matrix in the imshow() method.StepsCreate data2D using numpy.Use imshow() method to display data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance *mappable* using colorbar() method and imshow() scalar mappable image.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data2D = np.random.random((50, 50)) im = plt.imshow(data2D, cmap="copper_r") plt.colorbar(im) plt.show()OutputRead More
To retrieve colorbar instance from figure in matplotlib, we can use imshow scalar mappable object in colorbar to retrieve colorbar instance.StepsGet random data with 10×10 dimension of array, data points between -1 to 1.Use imshow() method to display data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, *mappable*, with imshow() object.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randint(-1, 1, (10, 10)) im = plt.imshow(data, interpolation="nearest") cbar = plt.colorbar(im) plt.show()OutputRead More
To rotate axis text for each subplot, we can use text with rotation in the argument.StepsCreate a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.Adjust the subplot layout parameters using subplots_adjust() method.Add a centered title to the figure using suptitle() method.Set the title of the axis.Set the x and y label of the plot.Create the axis with some co-ordinate points.Add text to the figure with some arguments like fontsize, fontweight and add rotation.Plot a single point and annotate that point with some text and arrowhead.To display the ... Read More
To layer a contourf plot and surface_plot in matplotlib, we can take the following Steps −Initialize the variables, delta, xrange, yrange, x and y using numpy.Create a new figure or activate an existing figure using figure() method.Get the current axis where projection='3d'.Create a 3d countour plot with x and y data points.Plot the surface with x and y data points.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True delta = 0.025 xrange = np.arange(-5.0, 20.0, delta) yrange = np.arange(-5.0, 20.0, delta) x, y = np.meshgrid(xrange, yrange) ... Read More
To make a heatmap square in Seaborn facetgrid, we cn use heatmap() method with 10×10 random data set.StepsCreate a random data of size 10×10, with minimum -1 and maximum 10.Plot rectangular data as a color-encoded matrix using heatmap() method with data and color map "twilight_r".To display the figure, use show() method.Exampleimport numpy as np import seaborn as sn import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randint(low=-1, high=10, size=(10, 10)) hm = sn.heatmap(data=data, cmap="twilight_r") plt.show()Output
To plot points on the surface of a sphere in Python, we can use plot_surface() method.StepsCreate a new figure or activate an existing figure using figure() method.Add a set of subplots using add_subplot() method with 3d projection.Initialize a variable, r.Get the theta value for spherical points and x, y, and z data points using numpy.Plot the surface using plot_surface() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(projection='3d') r = 0.05 u, v = np.mgrid[0:2 * np.pi:30j, 0:np.pi:20j] x = np.cos(u) * ... Read More
To create a 3D plot from a 3D numpy array, we can create a 3D array using numpy and extract the x, y, and z points.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 using add_subplot() method.Create a random data of size=(3, 3, 3).Extract x, y, and z data from the 3D array.Plot 3D scattered points on the created axisTo display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax ... Read More
To animate a contour plot in matplotlib in Python, we can take the following steps−Create a random data of shape 10☓10 dimension.Create a figure and a set of subplots using subplots() method.Makes an animation by repeatedly calling a function *func* using FuncAnimation() class.To update the contour value in a function, we can define a method animate that can be used in FuncAnimation() class.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randn(800).reshape(10, 10, 8) fig, ax = plt.subplots() def animate(i): ax.clear() ax.contourf(data[:, ... Read More
To position and align a matplotlib figure legend, we can take the following steps−Plot line1 and line2 using plot() method.Place a legend on the figure. Use bbox_to_anchor to set the position and make horizontal alignment of the legend elements.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True line1, = plt.plot([1, 5, 1, 7], linewidth=0.7) line2, = plt.plot([5, 1, 7, 1], linewidth=2.0) plt.legend([line1, line2], ["line1", "line2"], bbox_to_anchor=(0.45, 1.0), ncol=2) plt.show()Output
To convert numbers to a color scale in matplotlib, we can take the following steps.StepsCreate x, y and c data points using numpy.Convert the data points to Pandas dataframe.Create a new figure or activate an existing figure using subplots() method.Get the hot colormap.To linearly normalize the data, we can use Normalize() class.Plot the scatter points with x and y data points and linearly normalized colormap.Set the xticks for x data points.To make the colorbar, create a scalar mappable object.Use colorbar() method to make the colorbar.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, colors import numpy as ... Read More