To find the area between two curves plot in matplotlib, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create x, c1 and c2 data points using numpy.Plot (x, c1) and (x, c2) using plot() methods.Fill the area between the two curves, c1 and c2, with grey color and hatch "|", using fill_between() 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(0, 1, 100) c1 = x ** 2 c2 = x plt.plot(x, c1) plt.plot(x, c2) plt.fill_between(x, ... Read More

To make the Parula colormap in matplotlib, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create colormap data using numpy.Create a 'LinearSegmentedColormap' from a list of colors.Viscum is a little tool for analyzing colormaps and creating new colormaps.Use imshow() method to display data as an image, i.e., on a 2D regular raster.To display the figure, use show() method.Examplefrom matplotlib.colors import LinearSegmentedColormap import matplotlib.pyplot as plt import numpy as np from viscm import viscm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True cm_data = np.random.rand(4, 4) parula_map = LinearSegmentedColormap.from_list('parula', cm_data) viscm(parula_map) plt.imshow(np.linspace(0, 100, ... Read More

To set the size of the plotting canvas in matplotlib, we can take the following steps:Set the figure size and adjust the padding between and around the subplots. Use figsize 7.50 and 3.50 to set the figure size.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 from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-2, 2, 100) y = np.sin(x) plt.plot(x, y) plt.show()Output

To get the default Seaborn color palette, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Return a list of colors or continuous colormap defining a palette.Plot the values in a color palette as a horizontal array.To display the figure, use show() method.Exampleimport seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True current_palette = sns.color_palette() sns.palplot(current_palette) plt.show()Output

To add different font sizes in the same annotation method, we can take the following stepsMake lists of x and y data points where text could be placed.Initialize a variable 'labels', i.e., a string.Make a list of sizes of the fonts.Use subplots() method to create a figure and a set of subplots.Iterate above lists and annotate each label's text and set its fontsize.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 X = [0.1, .2, .3, .4, .5, .6, 0.8] Y = [0.1, 0.12, 0.13, 0.20, 0.23, 0.25, 0.27] labels = 'Welcome' ... Read More

To load an image and show the image using Keras, we will use load_image() method to load an image and set the target size of the image to be shown.StepsUse load_img() method to load the figure.Set the target size of the image.To display the figure, use show() method.Examplefrom keras.preprocessing import image img = image.load_img('bird.jpg', target_size=(350, 750)) img.show()Output

To adjust the spacing between the edge of the plot and the X-axis, we can use tight_layout() method or set the bottom padding of the current figure.Set 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.linspace(-2, 2, 100) y = np.exp(x) plt.plot(x, y, c='red', lw=1) plt.show()OutputRead More

To add footnote under the X-axis using matplotlib, we can use figtext() and text() method.StepsSet 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 numpy.To place the footnote, use figtext() method with x, y position and box properties.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.linspace(-2, 2, 100) y = np.exp(x) plt.plot(x, y) plt.figtext(0.5, 0.01, "footnote: $y=e^{x}$", ha="center", fontsize=18, bbox={"facecolor": "green", "alpha": 0.75, "pad": 5}) plt.show()OutputRead More

To plot yscale with class by name, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create y data points using numpy.Create x data points using numpy.Add a subplot to the current figure at index 1.Plot x and y data points using plot() method.For linear class by name, use yscale("linear") method.Set the title of the current subplot.Repeat the steps from 4 to 5 with different indices, yscale() class by name, and title of the plot.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] ... Read More

To locate the median in a seaborn KDE plot, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create random data using numpy.Find the median of data (Step 2).Use kdeplot() to plot the shaded region.Use axvline() method to plot the vertical line.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randn(30) xmedian = np.median(data) k = sns.kdeplot(x=data, shade=True) plt.axvline(xmedian, c='red') plt.show()OutputRead More