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Matplotlib does not support the functionality to plot implicit equations, however, you can try a code like the one we have shown here.StepsCreate xrange and yrange data points using numpy.Return coordinate matrices from coordinate vectors using meshgrid() method.Create an equation from x and y.Create a 3D contour using contour() method with x, y and the equation.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 delta = 0.025 xrange = np.arange(-5.0, 20.0, delta) yrange = np.arange(-5.0, 20.0, delta) x, y = np.meshgrid(xrange, yrange) equation = np.sin(x) - ... Read More
To set the aspect ratio of a 3D plot in matplotlib, we can take the following steps−Using figure() method, create a new figure or activate an existing figure.Get the current axes, creating one if necessary, with projection='3d'.Create data points, R, Y and z, using numpy.Create a surface plot using R, Y and z.Set the aspect ratio using set_aspect('auto').Save the figure using savefig() method.Examplefrom matplotlib import pyplot as plt from matplotlib import cm import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.gca(projection='3d') R, Y = np.meshgrid(np.arange(0, 100, 1), np.arange(0, 60, 1)) z = ... Read More
To plot a Cartesian coordinate system in matplotlib, we can take the following Steps −Initialize a variable (N) with a value.Create random data points for x and y.Plot the points using scatter method with x and y data points.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True N = 50 x = np.random.rand(N) y = np.random.rand(N) plt.scatter(x, y) plt.show()Output
To plot multiple X or Y axis, we can use twinx() or twiny() methods, we can take the following Steps −Using subplots() method, create a figure and a set of subplots.Plot [1, 2, 3, 4, 5] data points on the left Y-axis scales.Using twinx() method, create a twin of Axes with a shared X-axis but independent Y-axis, ax2.Plot [11, 12, 31, 41, 15] data points on the right Y-axis scale, with blue color.Using tight_layout() method, adjust the padding between and around the subplots.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ... Read More
To get empty tick labels before showing a plot in matplotlib, we can take the following Steps −Create a list of data points.Add a subplot to the current figure using subplot() method.Set ticks and ticklabels using set_xticks() method and set_xticklabels() method.To get the empty tick labels, use get_xticklabels(which='minor').To display the method, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 2, 3, 4] ax1 = plt.subplot() ax1.set_xticks(x) ax1.set_xticklabels(["one", "two", "three", "four"]) print("Empty tick labels: ", ax1.get_xticklabels(which='minor')) plt.show()OutputRead More
Gaussian filtering an image with NaN values makes all the values of a matrix NaN, which produces an NaN valued matrix.StepsCreate a figure and a set of subplots.Create a matrix with NaN value in that matrix.Display the data as an image, i.e., on a 2D regular raster, data.Apply Gaussian filter on the data.Display the data as an image, i.e., on a 2D regular raster, gaussian_filter_data.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt from scipy.ndimage import gaussian_filter plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, axes = plt.subplots(2) data = np.array([[1., 1.2, 0.89, ... Read More
To change the figsize for mathshow, we can use figsize in figure method argument and use fignum in matshow() method.StepsCreate a new figure or activate an existing figure using figure() method.Create a dataframe using Pandas.Use matshow() method to display an array as a matrix in a new figure window.The argument fignum can take the values None, int, or FalseIf *None*, create a new figure window with automatic numbering.If a nonzero integer, draw into the figure with the given number. Create one, if it does not exist.If 0, use the current axes (or create one if it does not exist).To display ... Read More
To change the font size of ticks of a colorbar, we can take the following steps−Create a random data set of 5☓5 dimension.Display the data as an image, i.e., on a 2D regular raster.Create a colorbar with a scalar mappable object image.Initialize a variable for fontsize to change the tick size of the colorbar.Use axis tick_params() method to set the tick size of the colorbar.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(5, 5) im = plt.imshow(data, interpolation="nearest", cmap="copper") cbar = plt.colorbar(im) tick_font_size ... Read More
To modify the outline color of a node in networkx, we can use set_edgecolor() method.StepsCreate a Pandas dataframe with from and to keys.Return a graph from Pandas DataFrame containing an edge list.Get the position of the nodes.Draw the nodes of the graph using draw_networkx_nodes().Set the outline color of the nodes using set_edgecolor().To display the figure, use show() method.Examplefrom networkx import * import matplotlib.pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'from': ['A', 'B', 'C', 'A'], 'to': ['D', 'A', 'E', 'C']}) G = nx.from_pandas_edgelist(df, 'from', 'to') pos = spring_layout(G) nodes = draw_networkx_nodes(G, pos) ... Read More
To set the number of ticks in a colorbar, we can take the following steps−Create random data using numpyDisplay the data as an image, i.e., on a 2D regular raster.Make a colorbar using colorbar() method with an image scalar mappable object.Set the ticks and tick labels of the colorbar using set_ticks() and set_ticklabels() methods.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) im = plt.imshow(data, cmap="copper") cbar = plt.colorbar(im) cbar.set_ticks([0.2, 0.4, 0.6, 0.8]) cbar.set_ticklabels(["A", "B", "C", "D"]) plt.show()OutputRead More