In the Object-Oriented Programming Paradigm, Inheritance is used for acquiring the properties of the base class and using them in a derived class. Considering the case for a Tkinter application, we can inherit all the properties of a frame defined in a base class such as background color, foreground color, font properties, etc., into a derived class or a frame.In order to support Inheritance, we have to define a class that contains some basic properties of a frame such as height, width, bg, fg, font, etc.Example# Import Tkinter Library from tkinter import * # Create an instance of Tkinter ... Read More

To add a title on Seaborn Implot, we can take the following steps−Set the figure size and adjust the padding between and around the subplots.Make a Pandas dataframe with two columns, X-Axis and Y-AxisUse implot() method.Get the current axis using gca() method.To display the figure, use show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame({"X-Axis": [np.random.randint(10) for i in range(10)], "Y-Axis": [i for i in range(10)]}) bar_plot = sns.lmplot(x='X-Axis', y='Y-Axis', data=df, height=3.5) ax = plt.gca() ax.set_title("Random Data Implot") ... Read More

To plot a transparent Poly3DCollection plot in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplotsCreate a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement with projection=3d.Create x, y and z data points.Make a list of vertices.Convert x, y and z data points into a zipped list of tuples.Get a list of instance of Poly3d.Add a 3D collection object to the plot using add_collection3d() method.Turn off the axes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt ... Read More

To install Matplotlib in virtualenv, we can take the following steps in terminal −vritualenv source env/bin/activatepip install matplotlibpip freeze > requirements.txtcat requirements.txt (To see the Matplotlib details)

To plot two horizontal bar charts sharing the same Y-axis, we can use sharey=ax1 in subplot() method and for horizontal bar, we can use barh() method.StepsCreate lists for data points.Create a new figure or activate an existing figure using figure() methodAdd a subplot to the current figure using subplot() method, at index=1.Plot horizontal bar on axis 1 using barh() method.Add a subplot to the current figure using subplot() method, at index=2. Share the Yaxis of axis 1.Plot the horizontal bar on axis 2.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, ... Read More

To remove a specific line or curve in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Plot line1 and line2 using plot() method.Pop the second line and remove it.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, image as mimg plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True line_1 = plt.plot([1, 2, 3]) line_2 = plt.plot([2, 4, 6]) line = line_2.pop(0) line.remove() plt.show()Output

To set a colormap of an image, 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.Pick one channel of your data.Display the data as an image, i.e., on a 2D regular raster with "hot" colormapTurn off the axes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, image as mimg plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True img = mimg.imread('bird.jpg') lum_img = img[:, :, 0] plt.imshow(lum_img, cmap="hot") plt.axis('off') plt.show()OutputRead More

To plot at full resolution with matplotlib.pyplot, imshow() and savefig(), we can keep the dpi value from 600 to 1200.StepsSet the figure size and adjust the padding between and around the subplots.Set random values in a given shape.Display the data as an image, i.e., on a 2D regular rasterSave the figure with 1200 dpi.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(5, 5) plt.imshow(data, cmap="plasma") plt.savefig("myimage.eps", dpi=1200) plt.show()Output

Pseudocolor can be a useful tool for enhancing the contrast and visualizing your data more easily. This is especially useful when making presentations of your data using projectors(because their contrast is typically quite poor).Pseudocolor is only relevant to single-channel, grayscale, luminosity images. We currently have an RGB image. Since R, G, and B are all similar, we can just pick one channel of our data−StepsSet the figure size and adjust the padding between and around the subplots.Read an image from a file into an array.Pick one channel of our data.Display data as an image, i.e., on a 2D regular raster.Turn ... Read More

To plot 3D scatter plots in Python with hue colormap and legend, we can take the following steps−Set the figure size and adjust the padding between and around the subplotsCreate x, y and z data points using numpy.Create a new figure or activate an existing figure using figure() method.Get the current axes, creating one if necessary.Get the hue colormap, defining a palette.Plot x, y and z data points using scatter() method.Place a legend on the plot.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap ... Read More