To plot an imshow() image in 3D in Matplotlib, we can take the following steps −Create xx and yy data points using numpy.Get the data (2D) using X, Y and Z.Create a new figure or activate an existing figure using figure() method.Add an 'ax1' to the figure as part of a subplot arrangement.Display the data as an image, i.e., on a 2D regular raster with data.Add an 'ax2' to the figure as part of a subplot arrangement.Create and store a set of contour lines or filled regions.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np ... Read More

To add extra contour lines using Matplotlib 2D contour plotting, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create e a function f(x, y) to get the z data points from x and y.Create x and y data points using numpy.Make a list of levels using Numpy.Make a contour plot using contour() method.Label the contour plot and 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 def f(x, y):    return ... Read More

To remove the digits after the decimal point in axis 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.To set the xtick labels only in digits, we can use x.astype(int) method.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 x = np.array([1.110, 2.110, 4.110, 5.901, 6.00, 7.90, 8.90]) y = np.array([2.110, 1.110, 3.110, 9.00, 4.001, 2.095, 5.890]) fig, ... Read More

To set the unit length of an axis in Matplotlib, we can use xlim or ylim with scale factor of the axes, i.e., of unit length times.stepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot the x and y data points using plot() method.Get the x and y axes, limit range.Use xlim and ylim methods to set the unit length scale.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 x = np.linspace(1, 10, 100) y ... Read More

To hide major tick labels while showing minor ticklabels 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.Plot the x and y data points.Set a property on an artist object, using setp() method.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 x = np.linspace(1, 10, 100) y = np.log(x) plt.plot(x, y) plt.setp(plt.gca().get_xmajorticklabels(), visible=False) plt.show()OutputRead More

To mark a specific level in a contour map on Matplotlib, 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 contour() method to make contour plot.Label the contour 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 def f(x, y):    return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x) x = np.linspace(0, 5, 50) y = np.linspace(0, 5, 40) X, Y = ... Read More

To label a patch in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize the center of the rectangle patch.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Add a 'rectangle' to the axes' patches; return the patch.Place a legend on the figure.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import matplotlib.patches as patches plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = y = 0.1 fig = plt.figure() ax = fig.add_subplot(111) patch = ... Read More

To sort bars in a bar plot in ascending order, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of data for bar plots.Create a bar plot using bar() method, with sorted data.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = [3, 5, 9, 15, 12] plt.bar(range(len(data)), sorted(data), color='red', alpha=0.5) plt.show()Output

To add titles to the legend rows in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create y data points using numpy.Make lists of markers and labels.Create a figure and a set of subplots.Plot the lines using plot() method, with different labels and markers.Get the plot handlers for half of the plot.Get the labels for the legends.Place the legends on 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 y = np.exp(-np.arange(5)) markers ... Read More

When we import matplotlib, we are importing all its libraries, whereas importing matplotlib.pyplot only imports pyplot's properties.stepsImport matplotlib.pyplot as pltSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot x and y data points using plot() method.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 = np.random.rand(10) y = np.random.rand(10) plt.plot(x, y) plt.show()Output