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

To plot a cumulative graph of python datetimes, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a Pandas dataframe with some college data, where one key for time difference and another key for number students have admissioned in the subsequent year.Plot the dataframe using plot() method where kind='bar', i.e., class by name.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True college_student_data = {'durations': [1, 2, 2.5, 3, 4.5, 5, 5.5, 6, 6.5, 7], ... Read More

To select a specific subplot in matplotlib, 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.Iterate in a range, i.e., number subplots to be placed.In the loop itself, add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.Now, select an axes plot line with red color.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 fig = plt.figure() for index ... Read More

To get smooth interpolation when using pcolormesh, we can use shading="gouraud" class by name.StepsSet the figure size and adjust the padding between and around the subplots.Create data, x and y using numpy meshgrid.Create a pseudocolor plot with a non-regular rectangular grid using pcolormesh() method.To display the figure, use show() method.Exampleimport matplotlib.pylab as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.random((3, 3)) x = np.arange(0, 3, 1) y = np.arange(0, 3, 1) x, y = np.meshgrid(x, y) plt.pcolormesh(x, y, data, cmap='RdBu', shading='gouraud') plt.show()OutputRead More

To change the axes background color, we can use set_facecolor() method.StepsSet the figure size and adjust the padding between and around the subplots.Get the current axes using gca() method.Set the facecolor of the axes.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 ax = plt.gca() ax.set_facecolor("orange") x = np.linspace(-2, 2, 10) y = np.exp(-x) plt.plot(x, y, color='red') plt.show()OutputRead More

To populate matplotlib subplots through a loop and a function, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots with number of rows = 3 and number of columns = 2.Make a function to iterate the columns of each row and plot the x data points using plot() method at each column index.Iterate rows (Step 2) and create random x data points and call iterate_columns() function (Step 3).To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] ... Read More

To hide the colorbar of a Seaborn heatmap, we can use cbar=False in heatmap() method.StepsSet the figure size and adjust the padding between and around the subplots.Make a dataframe using 4 columns.Use heatmap() method to plot rectangular data as a color-encoded matrix.To display the figure, use show() method.Exampleimport seaborn as sns import pandas as pd import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(np.random.random((4, 4)), columns=["a", "b", "c", "d"]) sns.heatmap(df, cbar=False) plt.show()Output

To set active subplot axes object in matplotlib, we can use subplots() method to add the axes as a subplot arrangement.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y lists for data points.Create a figure and a set of subplots using subplots() method with one row and two columns.Add an axes to the current figure and make it the current axes, with axis object at the 0th index.Plot x and y data points using plot() method.Add an axes to the current figure and make it the current axes, with axis object at the ... Read More

To insert a scale bar in a map in matplotlib, we can use AnchoredBar() class to instantiate the scalebar object.StepsSet the figure size and adjust the padding between and around the subplots.Create random data using numpy.Use imshow() method to display data as an image, i.e., on a 2D regular raster.Get the current axis using gca() method.Draw a horizontal scale bar with a center-aligned label underneath.Add a scalebar artist to the current axis.Turn off the axes.To display the figure, use show() mthod.Examplefrom matplotlib import pyplot as plt from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] ... Read More

To plot gamma distribution with alpha and beta parameters in Python, we can use gamma.pdf() function.StepsSet the figure size and adjust the padding between and around the subplots.Create x using numpy and y using gamma.pdf() function at x of the given RV.Plot x and y data points using plot() method.Use legend() method to place the legend elements for the plot.To display the figure, use show() method.Exampleimport numpy as np import scipy.stats as stats from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(0, 10, 10) y = stats.gamma.pdf(x, a=5, scale=0.333) plt.plot(x, ... Read More