Programming Articles - Page 1210 of 3363

To adjust gridlines and ticks in matplotlib imshow(), we can take the following steps−Create data, a 2D array, using numpy.Using imshow() method, display data as an image.Set xticks and yticks using set_xticks and set_yticks method.To set the xticklabels and yticklabels, use set_xticklabels and set_yticklabels method.Lay out a grid in current line style. Supply the list of x an y positions using grid() 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 data = np.random.rand(9, 9) plt.imshow(data, interpolation="nearest") ax = plt.gca() ax.set_xticks(np.arange(-.5, 9, 1)) ax.set_yticks(np.arange(-.5, 9, 1)) ... Read More

To plot lines with colors through colormap, we can take the following steps−Create x and y data points using numpyPlot x and y data points using plot() method.Count n finds, number of color lines has to be plotted.Iterate in a range (n) and plot the lines.Limit the x ticks range.Use show() method to display the figure.Exampleimport numpy as np import matplotlib.pylab as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(0, 2 * np.pi, 64) y = np.exp(x) plt.plot(x, y) n = 20 colors = plt.cm.rainbow(np.linspace(0, 1, n)) for i in range(n): plt.plot(x, i * y, color=colors[i]) plt.xlim(4, ... Read More

To plot a confusion matrix with string axis rather than integer in Python, we can take the following steps−Make a list for labels.Create a confusion matrix. Use confusion_matrix() to calculate accuracy of classification.3. Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Plot the values of a 2D matrix or array as a color-coded image.Using colorbar() method, create a colorbar for a ScalarMappable instance, *mappable*6. Set x and y ticklabels using set_xticklabels and set_yticklabels methods.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt from sklearn.metrics import confusion_matrix plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True labels ... Read More

To hide the axes in matplotlib 3D, we can take the following steps−Create a 2D array, where x, y, z, u, v and w are the coordinates of the arrow locations and direction components of the arrow vectors.Using figure() method, create a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement, using add_subplot() methodPlot a 3D field of arrows, using quiver() method.Using ylim, xlim, zlim, limit the range of the axesSet the title of the plot.Create two axes (ax1 and ax2). Set the titles "With Axes" and "Without Axes". Using set_axis_off() ... Read More

To add caption below X-axis for a scatter plot, we can use text() method for the current figure.StepsCreate x and y data points using numpy.Create a new figure or activate an existing figure using figure() method.Plot the scatter points with x and y data points.To add caption to the figure, use text() 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 x = np.random.rand(10) y = np.random.rand(10) fig = plt.figure() plt.scatter(x, y, c=y) fig.text(.5, .0001, "Scatter Plot", ha='center') plt.tight_layout() plt.show()OutputRead More

To draw a rectangle over a specific region in a matplotlib graph, we can take the following steps −Using subplots() method, create a figure and a set of subplots, where nrows=1.Using rectangle, we can create a rectangle, defined via an anchor point and its width and height. Where, edgecolor=orange, linewidth=7, and facecolor=green.To plot a diagram over the axis, we can create a line using plot() method, where line color is red.Add a rectangle patch on the diagram, using add_patch() 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 figure, ax = plt.subplots(1) ... Read More

To create a draggable legend in matplotlib, we can take the following steps −Create two lines, line1 and line2, using plot() method.Place the legend for plot line1 and line2 with ordered lables at location 1, using legend() method.To create a draggable legend, use set_draggable() method, where state=True. If state=False, then we can't drag the legend.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, 2, 3]) line2, = plt.plot([3, 2, 1]) leg = plt.legend([line2, line1], ["line 2", "line 1"], loc=1) leg.set_draggable(state=True) plt.show()OutputOn the output window, you can drag the legend around with ... Read More

To rescale ylim and xlim automatically, we can take the following steps −To plot a line, use plot() method and data range from 0 to 10.To scale the xlim and ylim automatically, we can make the variable scale_factore=6.Use scale_factor (from Step 2) to rescale the xlim and ylim, using xlim() and ylim() methods, respectively.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 plt.plot(range(0, 10)) scale_factor = 6 xmin, xmax = plt.xlim() ymin, ymax = plt.ylim() plt.xlim(xmin * scale_factor, xmax * scale_factor) plt.ylim(ymin * scale_factor, ymax * scale_factor) plt.show()OutputRead More

To plot shapely polygons and objects using matplotlib, the steps are as follows −Create a polygon object using (x, y) data points.Get x and y, the exterior data, and the array using polygon.exterior.xy.Plot x and y data points using plot() method with red color.Examplefrom shapely.geometry import Polygon import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True polygon1 = Polygon([(0, 5),    (1, 1),    (3, 0),    (4, 6), ]) x, y = polygon1.exterior.xy plt.plot(x, y, c="red") plt.show()Output

To set the variable point size in matplotlib, we can take the following steps−Initialize the coordinates of the point.Make a variable to store the point size.Plot the point using scatter method, with marker=o, color=red, s=point_size.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 xy = (3, 4) point_size = 100 plt.scatter(x=xy[0], y=xy[1], marker='o', c='red', s=point_size) plt.show()Output