To move a tick label without moving corresponding tick in Matplotlib, we can use axvline() method and can annotate it accordingly.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, delta.Create x and y data points using numpy.Plot delta using axvline() methodAnnotate that line using annotate() method.Plot x and y data points using plot() method.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 delta = 2.0 x = np.linspace(-10, 10, 100) y = np.sinc(x - delta) plt.axvline(delta, ls="--", ... Read More

To axes axis label object in Matplotlib, we can use ax.xaxis.get_label().get_text() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize a variable, N, for number samples.Create random data points using numpy.Plot x data points using plot() method.Set X-axis label using set_xlabel() method.To get the xlabel, use get_label() method and get_text() 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 fig, ax = plt.subplots() N = 100 x = np.random.rand(N) ax.plot(x) ax.set_xlabel("X-axis") x_lab = ax.xaxis.get_label() print("Label is: ... Read More

To adjust one subplot's height in absolute way 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.For absolute height of subplot, use Axes() classAdd an axes to the figure.Plot the data points on the axes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as pl pl.rcParams["figure.figsize"] = [7.50, 4.50] pl.rcParams["figure.autolayout"] = True figure = pl.figure() axes = pl.Axes(figure, [.4, .6, .25, .25]) figure.add_axes(axes) pl.plot([1, 2, 3, 4], [1, 2, 3, 4]) axes = pl.Axes(figure, [.4, ... Read More

To calculate the curl of a vector field in Python and plot in with Matplotlib, we can use quiver() method and calculate the corresponding data.StepsSet 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 a 3D axes to the figure as part of a subplot arrangement.Create x, y and z data points using numpy meshgrid.Create u, v and w data curl vector positions.Use quiver() method to get vectors.Turn off the axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] ... Read More

To assign specific colors to specific cells in a Matplotlib table, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a tuple for columns attribute.Make a list of lists, i.e., list of records.Make a list of lists, i.e., color of each cell.Create a figure and a set of subplots.Add a table to an axes ax.Turn off the axes.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 columns = ('name', 'age', 'marks', 'salary') cell_text = [["John", "23", "98", "234"], ["James", ... Read More

To plot with different scales in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create t, data1 and data2 data points using numpyCreate a figure and a set of subplots, ax1.Initialize a color variable.Set x and y labels of axis 1.Plot t and data1 using plot() method.Set label colors using tick_params() method.Create a twin Axes sharing the X-axis, ax2.Perform steps 4, 6, 7 with a different dataset on axis 2.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"] ... Read More

To clear a Matplotlib textbox that was previously drawn, 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 x and y using plot() method.Place characters token on the plot.To clear the text, use text.remove(), where text is a returned artist.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 fig, ax = plt.subplots() x = np.linspace(-10, 10, 100) y = np.sin(x) ax.plot(x, y) text = fig.text(0.5, 0.96, ... Read More

To plot stacked bar chart in Matplotlib, we can use barh() methodsStepsSet the figure size and adjust the padding between and around the subplots.Create a list of years, issues_addressed and issues_pending, in accordance with years.Plot horizontal bars with years and issues_addressed data.To make stacked horizontal bars, use barh() method with years, issues_pending and issues_addressed dataPlace the legend on the plot.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 year = [2014, 2015, 2016, 2017, 2018, 2019] issues_addressed = [10, 14, 0, 10, 15, 15] issues_pending = [5, 10, 50, ... Read More

To have colorbar background and label placement, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data using numpy.Plot the contours.With scalar mappable instance, make the colorbar.Set ticklabels for colorbar with background and label placementTo 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 data = np.linspace(0, 10, num=16).reshape(4, 4) cf = plt.contourf(data, levels=(0, 2.5, 5, 7.5, 10)) cb = plt.colorbar(cf) cb.set_ticklabels([1, 2, 3, 4, 5]) plt.show()Output

To plot true/false or active/deactive data in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create data using numpy with True or False.Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Use imshow() method to display data as an image, i.e., on a 2D regular raster.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 data = np.random.random((20, 20)) > 0.5 fig = ... Read More