Found 784 Articles for Data Visualization

To sharex when using subplot2grid, we can take the following steps −Create random data, t, x, y1 and y2 using numpy.Create a new figure or activate an existing figure using figure() method.Create a subplot at a specific location inside a regular grid with colspan=3 and rowspan=2.Create a subplot at a specific location inside a regular grid with colspan=3 and sharex=ax1 (step 3).Plot curve using t and y1 and y2 using plot() method.Adjust the padding between and around the subplots.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"] = True t = np.arange(0.0, ... Read More

To plot 3D graphs using Python, we can take the following steps −Create a new figure or activate an existing figure using figure() method.Get the 3D axes object.Make x, y, and z lists for data points.Add 3D scatter points using scatter3D() method, with x, y, and z data points with markersize=150 and marker=diamond.To display the figure, use show() method.Examplefrom mpl_toolkits.mplot3d import Axes3D from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = Axes3D(fig) x = [2, 4, 6, 3, 1] y = [1, 6, 8, 1, 3] z = [3, 4, 10, 3, 1] ax.scatter3D(x, y, ... Read More

To add a text into a rectangle in matplotlib, we can add a label in annotate method at the center point of the rectangle.StepsCreate a figure or activate an existing figure using figure() method.Add a subplot arrangement in the current axis.To add a rectangle in the plot, use Rectangle() class to get the rectangle object.Add a rectangle patch on the plot.To add text label in the rectangle, we can get the center value of the rectangle, i.e., cx and cy.Use annotate() method to place text on the rectangle.Limit x and y axes to get a visible rectangle.To display the figure, use show() method.Examplefrom matplotlib ... Read More

To get the center of a set of points, we can add all the elements of the list and divide that sum with the length of the list so that result could be the center of the corresponding axes.StepsMake two lists of data points.Plot x and y data points using plot() method.Get the center tuple of the x and y data points.Place the center point on the plot.Annotate the center as label for center of the x and y data points.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [5, ... Read More

To set color to a rectangle in matplotlib, we can take the following steps −Create a figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.A rectangle is defined via an anchor point with width and heights.Add a rectangle patch to the plot.Set the x and y limit using xlim() and ylim() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(111) rectangle = patches.Rectangle((0, 0), 3, 3, edgecolor='orange', facecolor="green", linewidth=7) ax.add_patch(rectangle) plt.xlim([-5, 5]) plt.ylim([-5, 5]) ... Read More

To scale axes in mplot3d, we can take the following steps −Create a figure or activate an existing figure using figure() method.Instantaite 3D axes instance using Axes3D() class.To scale X-axis, use set_xlim3d() method.To scale Y-axis, use set_ylim3d() method.To scale Z-axis, use set_zlim3d() method.To display the plot, use show() method.Examplefrom mpl_toolkits.mplot3d import Axes3D from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = Axes3D(fig) ax.set_xlim3d(-100, 100) ax.set_ylim3d(-100, 100) ax.set_zlim3d(-100, 100) plt.show()OutputRead More

To increase the font size of the legend in a Seaborn plot, we can use the fontsize variable and can use it in legend() method argument.StepsCreate a data frame using Pandas. The keys are number, count, and select.Plot a bar in Seaborn using barplot() method.Initialize a variable fontsize to increase the fontsize of the legend.Use legend() method to place legend on the figure with fontsize in the argument.To display the figure, use show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame(dict(    number=[2, 5, 1, 6, 3],    count=[56, 21, 34, 36, ... Read More

To adjust the size of a matplotlib legend box, we can use borderpad arguments in the legend method.StepsCreate line1 and line2 using two lists with different line widths.To place a legend on the figure and to adjust the size of legend box, use borderpad=2 in legend() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True line1, = plt.plot([1, 5, 1, 7], linewidth=0.7) line2, = plt.plot([5, 1, 7, 1], linewidth=2.0) plt.legend([line1, line2], ["line1", "line2"], bbox_to_anchor=(0.35, 1.0), borderpad=2) plt.show()Output

To decrease the density of tick labels in subplots in matplotlib, we can assign the minimum value to density.StepsInitialize a variable, density.Create x and y data points using numpy.Plot x and y data points using plot() method.Get or set the current tick locations and labels of the X-axis using xticks() method.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"] = True density = 10 x = np.linspace(-2, 2, density) y = np.sin(x) plt.plot(x, y) plt.xticks(x) plt.show()OutputRead More

To switch axes in matplotlib, we can create a figure and add two subplots using subplots() method. Plot curves, extract x and y data, and set these data in a second plotted curve.StepsCreate x and y data points using numpy.Create a figure and add a set of two subplots.Set the title of the plot on both the axes.Plot x and y data points using plot() method.Extract the x and y data points using get_xdata and get_ydata.To switch the axes of the plot, set x_data and y_data of the axis 1 curve to axis 2 curve.Adjust the padding between and around the subplots.To display the ... Read More