To create a line chart using matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make lists of years and population growth.Plot years and population on the line using plot() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True years = [1901, 1911, 1921, 1931, 1941, 1951,         1961, 1971, 1981, 1991, 2001, 2011] population = [237.4, 238.4, 252.09, 251.31, 278.98, 318.66, 361.09, 439.23, 548.16, 683.33, 846.42, 1028.74] plt.plot(years, population, color='red', marker='o') plt.show()Output

To use different markers for different points in a Pylab (Pyplot) scatter plot, we can use the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data.Create x and y random data points.Make a list of markers.Zip the x, y and markers.Iterate the zipper objects and plot the data points with different markers.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 10 x = np.random.rand(N) y = np.random.rand(N) ... Read More

To slice an image into Red, Green and Blue channels with misc.imread, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Read an image from a file into an array.Make lists of colormaps and titles.Create a figure and a set of subplots.Zip the axes, images, titles and colormaps.Iterate zipped objs and set the title of each channel image.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 image = plt.imread('bird.png') titles = ['With red channel', 'With green channel', 'With blue channel'] cmaps ... Read More

To add a second X-axis at the bottom of the first one in Matplotlib, we can take the followingStepsSet the figure size and adjust the padding between and around the subplots.Get the current axis (ax1) using gca() method.Create a twin axis (ax2) sharing the Y-axis.Set X-axis ticks at AxisSet X-axis labels at Axis 1 andTo display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ax1 = plt.gca() ax2 = ax1.twiny() ax2.set_xticks([1, 2, 3, 4, 5]) ax1.set_xlabel("X-axis 1") ax2.set_xlabel("X-axis 2") plt.show()Output

To pixelate a square image to 256 big pixels with Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Open and identify the given image file.Resize the image samples.Make resultant images and resize them.Save the resultant figure.Examplefrom PIL import Image from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True img = Image.open("bird.png") imgSmall = img.resize((16, 16), resample=Image.BILINEAR) result = imgSmall.resize(img.size, Image.NEAREST) result.save('result.png')Input ImageOutput Image

To maximize plt.show() using Python on Mac, we can use full_screen_toggle().StepsSet the figure size and adjust the padding between and around the subplots.Add a subplot to the current figure.Make a piechart with input list.Get the Figure Manager of the current figure.Use full_screen_toggle() to create a full-screen pop-up window.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 plt.subplot(1, 1, 1) plt.pie([1, 2, 3]) mng = plt.get_current_fig_manager() mng.full_screen_toggle() plt.show()Output

To animate a Seaborn heatmap or correlation matrix, 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.Make a dimension tuple.Make a Seaborn heatmap.Create an init() method for the first heatmap.Use FuncAnimation() class to make an animation by repeatedly calling a function animate that will create a random dataset and create a heatmap.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns import matplotlib.pyplot as plt from matplotlib import animation plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ... Read More

To turn off transparency in Matplotlib's 3D scatter plot, we can use depthshade to shade the scatter markers to give the appearance of depth.StepsSet the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Add an ax to the figure as part of a subplot arrangement.Create random data points x, y and z using numpy.Use scatter method to plot x, y and z data points on 3D axes with depthshade=False.To display the figure, use show() methpod.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] ... Read More

To plot the outline of the outer edges on a Matplotlib in Python, 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 data points with linewidth set to 10 and 5, to get the visible outline edges.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-10, 10, 100) y = np.sin(x) plt.plot(x, y, lw=10, color='red') plt.plot(x, y, lw=5, color='yellow') plt.show()OutputRead More

To merge two existing matplotlib plots into one plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y1 and y2 data points using numpy.Plot (x, y1) and (x, y2) points using plot() method.Get the xy data points of the current axes.Use argsort() to return the indices that would sort an array.Append x and y data points of each plot.Plot X and Y data points at the 2nd index subplot.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"] ... Read More