To adjust the space between legend markers and labels, we can use labelspacing in legend method.StepsPlot lines with label1, label2 and label3.Initialize a space variable to increase or decrease the space between legend markers and label.Use legend method with labelspacing in the arguments.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([0, 1], [0, 1.0], label='Label 1') plt.plot([0, 1], [0, 1.1], label='Label 2') plt.plot([0, 1], [0, 1.2], label='Label 3') space = 2 plt.legend(labelspacing=space) plt.show()Output

To redefine a color for a specific value in matplotlib colormap, we can take the following steps −Get a colormap instance, defaulting to rc values if *name* is None using get_cmap() method, with gray colormap.Set the color for low out-of-range values when "norm.clip = False" using set_under() method.Using imshow() method, display data an image, i.e., on a 2D regular raster.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, cm plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True cmap = cm.get_cmap('gray') cmap.set_under('red') plt.imshow(np.arange(25).reshape(5, 5),    interpolation='none',    cmap=cmap,    vmin=.001) plt.show()OutputRead More

To set X-axis values in matplotlib in Python, we can take the following steps −Create two lists for x and y data points.Get the xticks range value.Plot a line using plot() method with xtick range value and y data points.Replace xticks with X-axis value using xticks() 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 x = [45, 1, 34, 78, 100] y = [8, 10, 23, 78, 2] default_x_ticks = range(len(x)) plt.plot(default_x_ticks, y) plt.xticks(default_x_ticks, x) plt.show()Output

To make a rotating 3D graph in matplotlib, we can use Animation class for calling a function repeatedly.StepsInitialize variables for number of mesh grids, frequency per second to call a function, frame numbers.Create x, y, and z array for a curve.Make a function to make z array using lambda function.To pass a function into the animation class, make a user-defined function to remove the previous plot and plot a surface using x, y, and zarray.Create a new figure or activate an existing figure.Add a subplot arrangement using subplots() method.Set the Z-axis limit using set_zlim() method.Call the animation class to animate the surface plot.To display ... Read More

To plot two graphs side-by-side in Seaborn, we can take the following steps −To create two graphs, we can use nrows=1, ncols=2 with figure size (7, 7).Create a data frame with keys, col1 and col2, using Pandas.Use countplot() to show the counts of observations in each categorical bin using bars.Adjust the padding between and around the subplots.To display the figure, use show() method.Exampleimport pandas as pd import numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True f, axes = plt.subplots(1, 2) df = pd.DataFrame(dict(col1=np.linspace(1, 10, 5), col2=np.linspace(1, 10, 5))) sns.countplot(df.col1, x='col1', ... Read More

To show matplotlib graphs as full screen, we can use full_screen_toggle() method.StepsCreate a figure or activate an existing figure using figure() method.Plot a line using two lists.Return the figure manager of the current figure.To toggle full screen image, use full_screen_toggle() 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 plt.figure() plt.plot([1, 2], [1, 2]) manager = plt.get_current_fig_manager() manager.full_screen_toggle() plt.show()Output

To make a 4D plot, we can create x, y, z and c standard data points. Create a new figure or activate an existing figure.StepsUse figure() method to create a figure or activate an existing figure.Add a figure as part of a subplot arrangement.Create x, y, z and c data points using numpy.Create a scatter plot using scatter method.To display the figure, use show() method.Examplefrom matplotlib import 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') x = np.random.standard_normal(100) y = np.random.standard_normal(100) z = np.random.standard_normal(100) c = np.random.standard_normal(100) img = ax.scatter(x, ... Read More

To plot a very simple bar chart from an input text file, we can take the following steps −Make an empty list for bar names and heights.Read a text file and iterate each line.Append names and heights into lists.Plot the bar using lists (Step 1).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 bar_names = [] bar_heights = [] for line in open("test_data.txt", "r"):    bar_name, bar_height = line.split()    bar_names.append(bar_name)    bar_heights.append(bar_height) plt.bar(bar_names, bar_heights) plt.show()"test_data.txt" contains the following data −Javed 75 Raju 65 Kiran 55 Rishi 95OutputRead More

To make two histograms having same bin width, we can compute the histogram of a set of data.StepsCreate random data, a, and normal distribution, b.Initialize a variable, bins, for the same bin width.Plot a and bins using hist() method.Plot b and bins using hist() 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 a = np.random.random(100) * 0.5 b = 1 - np.random.normal(size=100) * 0.1 bins = 10 bins = np.histogram(np.hstack((a, b)), bins=bins)[1] plt.hist(a, bins, edgecolor='black') plt.hist(b, bins, edgecolor='black') plt.show()OutputRead More

To increase/reduce the fontsize of x and y tick labels in matplotlib, we can initialize the fontsize variable to reduce or increase font size.StepsCreate a list of numbers (x) that can be used to tick the axes.Get the axis using subplot() that helps to add a subplot to the current figure.Set ticks on x and y axes using set_xticks and set_yticks methods respectively and list x (from step 1).Set tick labels with label lists (["one", "two", "three", "four"]) using set_xticklabels() and set_yticklabels() with fontsize variable.To add space between axes and tick labels, we can use tick_params() method with pad argument that helps to ... Read More