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The frame widget in Tkinter works like a container where we can place widgets and all the other GUI components. To change the frame width dynamically, we can use the configure() method and define the width property in it.ExampleIn this example, we have created a button that is packed inside the main window and whenever we click the button, it will update the width of the frame.# Import the required libraries from tkinter import * from tkinter import ttk # Create an instance of tkinter frame or window win=Tk() # Set the size of the window win.geometry("700x350") ... Read More
We can use the Tkinter Label widget to display text and images. By configuring the label widget, we can dynamically change the text, images, and other properties of the widget.To dynamically update the Label widget, we can use either config(**options) or an inline configuration method such as for updating the text, we can use Label["text"]=text; for removing the label widget, we can use pack_forget() method.Example# Import the required libraries from tkinter import * from tkinter import ttk from PIL import ImageTk, Image # Create an instance of tkinter frame or window win=Tk() # Set the size of the ... Read More
A color gradient defines the range of position-dependent colors. To be more specific, if you want to create a rectangular scale in an application that contains some color ranges in it (gradient), then we can follow these steps −Create a rectangle with a canvas widget and define its width and height.Define a function to fill the color in the range. To fill the color, we can use hex values inside a tuple.Iterate over the range of the color and fill the rectangle with it.Example# Import the required libraries from tkinter import * from tkinter import ttk # Create an ... Read More
The radiobutton widget in Tkinter allows the user to make a selection for only one option from a set of given choices. The radiobutton has only two values, either True or False.If we want to get the output to check which option the user has selected, then we can use the get() method. It returns the object that is defined as the variable. We can display the selection in a label widget by casting the integer value in a string object and pass it in the text attributes.Example# Import the required libraries from tkinter import * from tkinter import ttk # ... 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 create a Swarm Plot with Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe, i.e., a two-dimensional, size-mutable, potentially heterogeneous tabular data.Initialize the plotter, swarmplot.To plot the boxplot, use boxplot() method.To display the figure, use show() method.Exampleimport seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = pd.DataFrame({"Box1": np.arange(10), "Box2": np.arange(10)}) ax = sns.swarmplot(x="Box1", y="Box2", data=data, zorder=0) ... Read More
To display the matrix value and colormap in Matplotlib, 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.Initialize max and min values for matrix.Plot the values of a 2D matrix or array as color-coded image.Iterate each cell of the color-code image and place value at the center.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 fig, ax = plt.subplots() min_val, max_val = 0, 5 matrix = np.random.randint(0, 5, size=(max_val, ... Read More
To annotate a range of the X-axis in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create xx and yy data points using numpy.Create a figure and a set of subplots.Plot xx and yy data points using plot() method.Set ylim of the axis.Use annotate method to place arrow heads and range tag name.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True xx = np.linspace(0, 10) yy = np.sin(xx) fig, ax = plt.subplots(1, 1) ... Read More
To add annotated text in Matplotlib for several points, 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.To set the label for each scattered point, make a list of labels.Plot xpoints, ypoints using scatter() method. For color, use xpoints.Iterate zipped labels, xpoints and ypoints.Use annotate() method with bold LaTeX representation in a for loop.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 xpoints = np.linspace(1, 10, 10) ... Read More
To plot a line in Matplotlib with an interval at each data point, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make an array of means and standard deviations.Plot means using plot() method.Fill the area between means+stds and means-stds, alpha=0.7 and color='yellow'.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 means = np.array([3, 5, 1, 8, 4, 6]) stds = np.array([1.3, 2.6, 0.78, 3.01, 2.32, 2.9]) plt.plot(means, color='red', lw=7) plt.fill_between(range(6), means - stds, means ... Read More