Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
To add a line to a scatter plot using Python's Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for number of data points.Plot x and y data points using scatter() method.Plot a line using plot() method.Limt the X-axis using xlim() method.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 = 100 x = np.random.rand(n) y = np.random.rand(n) plt.scatter(x, y, c=x) plt.plot([0.1, 0.4, 0.3, 0.2]) plt.xlim(0, 1) ... Read More
To disable the keyboard shortcuts in Matplotlib, we can use remove('s') method.StepsSet the figure size and adjust the padding between and around the subplots.To disable the shortcut "s" to save the figure, use remove("s") method.Initialize a variable n for number of data points.Create x and y data points using numpyPlot x and y data points using plot() method.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 plt.rcParams['keymap.save'].remove('s') n = 10 x = np.random.rand(n) y = np.random.rand(n) plt.plot(x, y) plt.show()OutputRead More
To label and change the scale of a Seaborn kdeplot's axes, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using numpy.Plot Kernel Density Estimate (KDE) using kdeplot() method.Set Y-axis tscale and label.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randn(10) k = sns.kdeplot(x=data, shade=True) plt.yticks(k.get_yticks(), k.get_yticks()) plt.ylabel('Y', fontsize=7) plt.show()OutputRead More
To update the plot title with Matplotlib using animation, 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 using figure() method.Create x and y data points using numpy.Get the current axis.Add text to the axes using text() method.Add an animate method that can be used to make an animation by repeatedly calling a function.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, animation plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ... Read More
To color the edges by weight in networkx, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a graph with edges, name, or graph attributes.Add nodes to the current graph.Add edges to the current graph's nodes.Iterate the given graph's edges and set some weight to them.Draw current graphs with weights for edge color.To display the figure, use show() method.Exampleimport random as rd import matplotlib.pylab as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True G = nx.DiGraph() G.add_nodes_from([1, 2, 3, 4]) G.add_edges_from([(1, 2), (2, 3), ... Read More
To animate quivers 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.Create u and v data points using numpy.Create a figure and a set of subplots.Plot a 2D field of arrows using quiver() method.To animate the quiver, we can change the u and v values, in animate() method. Update the u and v values and the color of the vectors.To display the figure, use show() method.Exampleimport numpy as np import random as rd from matplotlib import pyplot as plt, animation ... Read More
To make markers on lines smaller in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points, x.Plot x data points using plot() method, with linewidth =0.5 and color="black".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 x = np.random.rand(20) plt.plot(x, '*-', color='black', markersize=10, lw=0.5) plt.show()Output
To make matplotlib.pyplot stop forcing the style of markers, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random x and y data points using numpy.Plot x and y data points using plot() method, with "r*" marker with markersize=10.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 x = np.random.rand(20) y = np.random.rand(20) plt.plot(x, y, 'r*', markersize=10) plt.show()Output
To set the limits on a colorbar of a countour plot in Matplotlib, 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.Get the data using x and y.Get the coordinate matrices from the coordinate vectors.Initialize vmin and vmax to set the limits on a colorbar of a contour plot in matplotlib.Plot contours using contourf() method.Make the colorbar using scalar mappable within the range of vmin and vmax.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np from ... Read More
To make xtick labels of a plot be simple drawings using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize the y position of simple drawings.Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot()method.Plot a line using plot() method.Set the X-axis ticks using set_ticks() method.Set empty tick labels.Add circles and rectangles patches using add_patch() method. Instantiate Circle() and Rectangle() class.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import matplotlib.patches as patches ... Read More