To cycle through both colors and linestyles on a matplotlib figure, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Set the current rcParams, withcolors and linestyle.Plot the data points using plot() method.To display the figure, use Show() method.Exampleimport matplotlib.pyplot as plt from cycler import cycler # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Set the rcParams with color or linestyle plt.rc('axes', prop_cycle=(cycler('color', ['r', 'g', 'b', 'y']) + cycler('linestyle', [':', '-.', '-', '--']))) # Plot the data points plt.plot([0, 5, 2, ... Read More

To rasterize a plot in a bettery way without blurring the labels in matplotlib, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Axis 0 – Fill the area between the curve with alpha and rasterized=False.Add text to the axes.Axis 1 – Fill the area between the curve with alpha and rasterized=True.Add text to the axes.Axes 2 and 3 – Fill the area between the curve without alpha and rasterized=True and False, respectively.Add text to the axes.To display the figure, use Show() method.Exampleimport matplotlib.pyplot as ... Read More

To get the properties of picked objects in matplotlib 3d, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Make a scatter plot of random data points.Bind the function *pick_event_method* to the event *pick_event*.Print x, y and z coordinates of the event.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(111, projection='3d') ... Read More

To plot a plane using some mathematical equation in matplotlib, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Using x and y, find the equation of the plane (eq).Create a new figure or activate an existing figure.Get the current axis with projection='3d'.Create a surface plot with x, y and eq data points.To display the figure, use Show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-10, 10, 100) y = np.linspace(-10, 10, 100) ... Read More

To plot a 3D surface from a list of tuples in matplotlib, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Make a list of tuples.Get the x, y and z data points from the list of tuples.Return the coordinate matrices from the coordinate vectors.Get the h data points for the surface plot.Create a new figure or activate an existing figure.Get the current axis, 3d, of the figure.Create a surface plot.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"] ... Read More

To plot a bar chart for a list in python matplotlib we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Make a list of data points.Make a bar plot with data.To display the figure, use Show() method.Examplefrom matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # List of data points data = [0, 1, 3, 2, 1, 5, 2, 1, 4, 2, 4, 0] # Plot bar chart with data points plt.bar(data, data) # Display the plot plt.show() OutputIt will produce the following output −

To preserve padding while setting axis limit, we can avoid using the tight layout, i.e., plt.rcParams["figure.autolayout"] = False.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 plot() method.Set x and y axes limit.To display the figure, use Show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-10, 10, 100) y = np.sin(x) ** 2 plt.plot(x, y) plt.xlim([0, max(x)+0.125]) plt.ylim([0, max(y)+0.125]) plt.show() OutputIt will produce the following output −Read More

To show (0, 0) on matplotlib graph at the bottom left corner, we can use xlim() and ylim() methods.StepsSet the figure size and adjust the padding between and around the subplots.Make lists of data points for x and y.Plotx and y data points.Setx and y axes scale.To display the figure, use Show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([0, 1, 3, 2, 1, 5]) y = np.array([0, 2, 4, 4, 3, 3]) plt.plot(x, y) plt.xlim([0, max(x)+0.5]) plt.ylim([0, max(y)+0.5]) plt.show() OutputIt will produce the ... Read More

To make graph k-NN decision boundaries in matplotlib, we can take the following Steps.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable n_neighbors for number of neighbors.Load and return the iris dataset (classification).Create x and y data points.Make lists of dark and light colors.Classifier implementing the k-nearest neighbors vote.Create xmin, xmax, ymin and ymax  data points.Create a new figure or activate an existing figure.Create a contourf plot.Create a scatter plot with X dataset.Set x and y axes labels, titles and scale of the axes.To display the figure, use Show() method.Exampleimport numpy as np import ... Read More

To make a mosaic plot in matplotlib, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Install statsmodel package (pip install statsmodels). It is required to create mosaic plots. statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.Make a dictionary for mosaic plot.Create a mosaic plot from a contingency table.To display the figure, use Show() method.Exampleimport matplotlib.pyplot as plt from statsmodels.graphics.mosaicplot import mosaic plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Dictionary for mosaic plot ... Read More