To make two markers share the same label in the legend using Matplotlib, we can take the following stepsStepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot x and y(as a sin(x) and cos(x)), using plot() method.Place legend with location=1.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 x = np.linspace(-5, 5, 100) plt.plot(x, np.sin(x), ls="dotted", label='y=f(x)') plt.plot(x, np.cos(x), ls="-", label='y=f(x)') plt.legend(loc=1) plt.show()OutputIt is not recommended to make two markers share the same label ... Read More

To manipulate on horizontal space in Matplotlib subplots, we can use wspace=1 in subplots_adjust() method without tight plot layout.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots with 4 indices.To adjust the vertical space, we can use wspace=1.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 x = np.linspace(0, 2 * np.pi, 400) y = np.sin(x ** 2) fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2) fig.subplots_adjust(wspace=1) ... Read More

o get the list of font family in Matplotlib, we can take the following steps −Iterate fonts manager ttflist and print the names.Iterate fonts manager afmlist and print the names.Exampleimport matplotlib.font_manager as fm for f in fm.fontManager.ttflist:    print(f.name) for f in fm.fontManager.afmlist:    print(f.name)OutputSTIXNonUnicode STIXGeneral STIXSizeFiveSym cmr10 ... ... ... Nimbus Sans L Bitstream Charter Nimbus Sans L Nimbus Sans L

To pass parameters to on_key in fig.canvas.mpl_connect('key_press_event', on_key), we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Set x and y scale of the axes.Bind the function to the event.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 fig, ax = plt.subplots() ax.set_xlim(0, 10) ax.set_ylim(0, 10) def onkey(event):    if event.key.isalpha():       if event.xdata is not None and event.ydata is not None:          ax.plot(event.xdata, event.ydata, 'bo-')       ... Read More

To customizing annotation with seaborn's face grid, we can take following steps −Set the figure size and adjust the padding between and around the subplots.Create a data frame with col1 and col2 columns.Multi-plot grid for plotting conditional relationships.Apply a plotting function to each facet's subset of the data.Set the title of each grids.To display the figure, use show() method.Exampleimport pandas as pd import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'col1': [3, 7, 8, 1], 'col2': ["three", "seven", "one", "zero"]}) g = sns.FacetGrid(data=df, col='col2', height=3.5) g.map(plt.hist, 'col1', ... Read More

To manipulate on vertical space in Matplotlib subplots, we can use hspace=1 in subplots_adjust() method without tight plot layout.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots with 4 indices.To adjust the vertical space, we can use hspace=1.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 x = np.linspace(0, 2 * np.pi, 400) y = np.sin(x ** 2) fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2) fig.subplots_adjust(hspace=1) ... Read More

To convert data values into color information for Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Get a colormap instance, defaulting to rc values if *name* is None.Create random values that could be converted into color information.Create random data points, x and y.Use scatter() method to plot x and y.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 plasma = plt.get_cmap('GnBu_r') values = np.random.rand(100) x = np.random.rand(len(values)) y = np.random.rand(len(values)) sc = plt.scatter(x, ... Read More

To get interactive plots, we need to activate the figure. Using plt.ioff() and plt.ion(), we can perform interactive actions with a plot.Open Ipython shell and enter the following commands on the shell.ExampleIn [1]: %matplotlib auto Using matplotlib backend: GTK3Agg In [2]: import matplotlib.pyplot as In [3]: fig, ax = plt.subplots() # Diagram will pop up. Let’s interact. In [4]: ln, = ax.plot(range(5)) # Drawing a line In [5]: ln.set_color("orange") # Changing drawn line to orange In [6]: plt.ioff() # Stopped interaction In [7]: ln.set_color("red") # Since we have stopped the interaction ... Read More

To display different images with actual size in a Matplotlib subplot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Read two images using imread() method (im1 and im2)Create a figure and a set of subplots.Turn off axes for both the subplots.Use imshow() method to display im1 and im2 data.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 im1 = plt.imread("bird.jpg") im2 = plt.imread("opencv-logo.png") fig, ax = plt.subplots(nrows=1, ncols=2) ax[1].axis('off') ax[1].imshow(im1, cmap='gray') ax[0].axis('off') ax[0].imshow(im2, cmap='gray') plt.show()OutputRead More

To male bar plots automatically cycle across different colors, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set automatic cycler for different colors.Make a Pandas dataframe to plot the bars.Use plot() method with kind="bar" to plot the bars.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.rc('axes', prop_cycle=(plt.cycler('color', ['r', 'g', 'b', 'y']))) df = pd.DataFrame(dict(name=["John", "Jacks", "James"], age=[23, 20, 26], marks=[88, 90, 76], salary=[90, 89, 98])) df.set_index('name').plot(kind='bar') plt.show()OutputRead More