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 ... 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 ... 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 ... 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 ... 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 ... Read More

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 ... Read More

To view all colormaps available 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.Add an '~.axes.Axes' to the figure as part of a subplot arrangementMake an axis that is divider ... 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 ... Read More

To plot arbitrary markers on a Pandas data series, we can use pyplot.plot() with markers.StepsSet the figure size and adjust the padding between and around the subplots.Make a Pandas data series with axis labels (including timeseries).Plot the series index using plot() method with linestyle="dotted".Use tick_params() method to rotate overlapping labels.To ... Read More

To plot scattered masked points and add a line to demark the masked regions, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Create N, r0, x, y, area, c, r, area1and area2 data points using numpy.Plot x and y data points ... Read More