To set a colormap of an image, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Read an image from a file into an array.Pick one channel of your data.Display the data as an image, i.e., on a 2D regular raster with "hot" colormapTurn off the axes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, image as mimg plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True img = mimg.imread('bird.jpg') lum_img = img[:, :, 0] plt.imshow(lum_img, cmap="hot") plt.axis('off') plt.show()OutputRead More

To plot at full resolution with matplotlib.pyplot, imshow() and savefig(), we can keep the dpi value from 600 to 1200.StepsSet the figure size and adjust the padding between and around the subplots.Set random values in a given shape.Display the data as an image, i.e., on a 2D regular rasterSave the figure with 1200 dpi.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(5, 5) plt.imshow(data, cmap="plasma") plt.savefig("myimage.eps", dpi=1200) plt.show()Output

Pseudocolor can be a useful tool for enhancing the contrast and visualizing your data more easily. This is especially useful when making presentations of your data using projectors(because their contrast is typically quite poor).Pseudocolor is only relevant to single-channel, grayscale, luminosity images. We currently have an RGB image. Since R, G, and B are all similar, we can just pick one channel of our data−StepsSet the figure size and adjust the padding between and around the subplots.Read an image from a file into an array.Pick one channel of our data.Display data as an image, i.e., on a 2D regular raster.Turn ... Read More

To plot 3D scatter plots in Python with hue colormap and legend, we can take the following steps−Set the figure size and adjust the padding between and around the subplotsCreate x, y and z data points using numpy.Create a new figure or activate an existing figure using figure() method.Get the current axes, creating one if necessary.Get the hue colormap, defining a palette.Plot x, y and z data points using scatter() method.Place a legend on the plot.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap ... Read More

To fix the deprecation warning that comes while using a deprecated method, we can use warnings.filterwarnings("ignore") in the code.−Examplefrom matplotlib import pyplot as plt, pylab as pl import warnings plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True warnings.filterwarnings("ignore") pl.pause(0) plt.show()OutputProcess finished with exit code 0

To plot customized legend symbols on a plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Inherit HandlerPatch class, override create artists method, add an elliptical patch to the plot, and return the patch handler.Plot a circle on the plot using Circle class.Add a circle patch on the current axis.Use legend() method to place the legend on the plot.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt, matplotlib.patches as mpatches from matplotlib.legend_handler import HandlerPatch plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True class HandlerEllipse(HandlerPatch):    def create_artists(self, legend, ... Read More

To plot a single line that continuously changes color, we can take the following steps−Set the figure size and adjust the padding between and around the subplots.Create random x and y data points using numpy.Create a figure and a set of subplots.Iterate the index in the range of 1 to 100.Plot x and y data points with random color in a loop.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np import random plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 100) y = np.sin(x) fig, ax = plt.subplots() for ... Read More

To set the Matplotlib title in bold while using "Times New Roman", we can use fontweight="bold".StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Create x and y data points using numpy.Plot x and y data points using scatter() method.Set the title of the plot using fontname="Times New Roman" and fontweight="bold"To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, font_manager as fm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.random.rand(100) y = np.random.rand(100) ax.scatter(x, y, ... Read More

To plot a 3D surface from x, y and z scatter data in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure() method.Add an axes to the figure as part of a subplot arrangement.Create x, y, X, Y and Z data points using numpy.Plot x, y and z data points using plot_surface() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ... Read More

To get the (x, y) values of a line that is plotted by a contour plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a 3D contour plot using contour() method.Get the contour plot collections and get the paths.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 m = [[3, 2, 1, 0], [2, 4, 1, 0], [2, 4, 1, 3], [4, 3, 1, 3]] cs = plt.contour([3, 4, 2, 1], [5, 1, 2, 3], m) p1 = cs.collections[0].get_paths() for item in p1:    print(item.vertices) plt.show()Output