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To define multiple plots to be animated with a for loop in matplotlib, we can take followings steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure method.Add an axes to the current figure and make it the current axes.Initialize two variables, N and x, using numpy.Get the list of lines and bar patches.Animate the lines and rectangles (bar patches) in a for loop.Make an animation by repeatedly calling a function *func*.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt from matplotlib import ... Read More
To return a matplotlib.figure.Figure object from Pandas function, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe, df.Make a horizontal bar plot using barh() method.Get the current figure instance.Place a legend on the axes at the lower-right location.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'a': range(10)}) ax = df.plot.barh(color=(1, 0, 0, 0.25)) fig = ax.get_figure() ax.legend(loc='lower right') plt.show()OutputRead More
To plot a half-black and half-white circle using 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 theta1 and theta2 to draw edges from theta1 to theta2 and vice-versa.Add the wedge instance on the current axes.Set equal scaling by changing axis limits.Set x and y scale.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.patches import Wedge plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() theta1, theta2 = 0, 0 + 180 radius = 2 ... Read More
A Turing machine (TM) can be formally described as seven tuples −(Q, X, ∑, δ, q0, B, F)Where, Q is a finite set of states.X is the tape alphabet.∑ is the input alphabet.δ is a transition function: δ:QxX->QxXx{left shift, right shift}.q0 is the initial state.B is the blank symbol.F is the final state.Binary numbers1 = 12 = 103 = 114 = 1005 = 1016 = 110. . .AlgorithmStep 1 − Move to the right end of the string.Step 2 − Repeat:If the current cell contains 1, write 0 and move left until the current cell contains 0 or blank.Step 3 ... Read More
To change the default background color for Matplotlib plots, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Get the current axis.Add a subplot to the current figure, with nrows=1, ncols=2 and index=1.Plot random x and y data points using plots() method.Set the title of the subplot.Add a subplot to the current figure with nrows=1, ncols=2 and index=2.Get the current axis.Set the customize face color.Plot x and y data points using plot() method.Set the title of the plot.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt ... Read More
To add a shared x-label and shared y-label, we can use plot() method with kind="bar", sharex=True and sharey=True.StepsSet the figure size and adjust the padding between and around the subplots.Create a two-dimensional, size-mutable, potentially heterogeneous tabular data.Plot the dataframe with kind="bar", sharex=True and sharey=True.To display the figure, use show() method.Exampleimport pandas as pd import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame( {'First': [0.3, 0.2, 0.5, 0.2], 'Second': [0.1, 0.0, 0.3, 0.1], 'Third': [0.2, 0.5, 0.0, 0.7], 'Fourth': [0.6, 0.3, 0.4, 0.6]}, index=list('1234')) axes = df.plot(kind="bar", subplots=True, layout=(2, 2), ... Read More
A Turing machine (TM) can be formally described as seven tuples −(Q, X, ∑, δ, q0, B, F)Where, Q is a finite set of states.X is the tape alphabet.∑ is the input alphabet.δ is a transition function:δ:QxX->QxXx{left shift, right shift}.q0 is the initial state.B is the blank symbol.F is the final state.Input − n a natural numberOutput − n + 2Let’s represent natural numbers in unary form (e.g. 3 = 111, 5 = 11111) and 0 will be represented by the empty symbol.AlgorithmMove the tape head to the left of the first 1 (if it exists).Change that empty cell to ... Read More
To plot a phase spectrum, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Get random seed value.Initialize dt for sampling interval and find sampling frequency.Create random data points for t.To generate noise, get nse, r, cnse and s using numpy.Create a figure and a set of subplots using subplots() method.Set the title of the plot.Plot the phase spectrum.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 np.random.seed(0) dt = 0.01 # sampling interval Fs = 1 ... Read More
To generate more colors on a pie chart in Matplotlib, we can generate n number of colors and dataStepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for number of data samples.Create random data points using numpy.Create a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Create a pie chart using pie() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import random import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True n = 40 color = ["#" + ... Read More
To plot vertical histogram in Python and Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a list of data points.Plot a histogram with vertical orientation.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 x = [1, 2, 3, 1, 2, 3, 4, 1, 3, 4, 5] plt.hist(x, orientation="vertical") plt.show()Output