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Data Visualization Articles
Page 37 of 68
How can I get the color of the last figure in Matplotlib?
To get the color of the last figure, we can use get_color() method for every plot.Set the figure size and adjust the padding between and around the subplots.Create x and y data point using numpy.Plot (x, x), (x, x2) and (x, x3) using plot() method.Place a legend for every plot line.Get the color of each plot using get_color() method.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 x = np.arange(10) y = np.arange(10) p = plt.plot(x, y, x, y ** 2, x, y ** 3) ...
Read MoreHow do I customize the display of edge labels using networkx in Matplotlib?
To set the networkx edge labels offset, 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 multiple nodes.Position the nodes using Fruchterman-Reingold force-directed algorithm.Draw the graph G with Matplotlib.Draw edge labels.To display the figure, use show() method.Exampleimport 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), (3, 4), (4, 1), (1, 3)]) pos = nx.spring_layout(G) for u, v, d in G.edges(data=True): d['weight'] ...
Read MoreHow to plot contourf and log color scale in Matplotlib?
To plot contourf and log scale in 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 sample data.Create x, y, X, Y, Z1, Z2 and z data points using numpy.Create a figure and a set of subplots.Plot contours using contourf() method.Create a colorbar for a scalar mappable instance.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np from numpy import ma from matplotlib import ticker, cm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 100 x ...
Read MoreHow to independently set horizontal and vertical, major and minor gridlines of a plot?
To set horizontal and vertical, major and minor grid lines of a plot, we can use grid() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Make horizontal grid lines for major ticks.Locate minor locator on the axes.Use grid() method to make minor grid lines.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.ticker import MultipleLocator plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() ax.yaxis.grid(which="major", color='r', linestyle='-', linewidth=2) ml = MultipleLocator(0.10) ax.xaxis.set_minor_locator(ml) ax.xaxis.grid(which="minor", color='k', linestyle='-.', linewidth=0.7) plt.show()Output
Read MoreContour hatching in Matplotlib plot
To plot contour with hatching, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z data points using numpy.Flat the x and y data points.Create a figure and a set of subplots.Plot a contour with different hatches.Create a colorbar for a scalar mappable instance.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 x = np.linspace(-3, 5, 150).reshape(1, -1) y = np.linspace(-3, 5, 120).reshape(-1, 1) z = np.cos(x) + np.sin(y) x, y = ...
Read MoreHow can I move a tick label without moving corresponding tick in Matplotlib?
To move a tick label without moving corresponding tick in Matplotlib, we can use axvline() method and can annotate it accordingly.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, delta.Create x and y data points using numpy.Plot delta using axvline() methodAnnotate that line using annotate() method.Plot x and y data points using plot() method.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 = 2.0 x = np.linspace(-10, 10, 100) y = np.sinc(x - delta) plt.axvline(delta, ls="--", ...
Read MoreHow to access axis label object in Matplotlib?
To axes axis label object in Matplotlib, we can use ax.xaxis.get_label().get_text() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize a variable, N, for number samples.Create random data points using numpy.Plot x data points using plot() method.Set X-axis label using set_xlabel() method.To get the xlabel, use get_label() method and get_text() method.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() N = 100 x = np.random.rand(N) ax.plot(x) ax.set_xlabel("X-axis") x_lab = ax.xaxis.get_label() print("Label is: ...
Read MoreAdjust one subplot's height in absolute way (not relative) in Matplotlib
To adjust one subplot's height in absolute way in Matplotlib, 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.For absolute height of subplot, use Axes() classAdd an axes to the figure.Plot the data points on the axes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as pl pl.rcParams["figure.figsize"] = [7.50, 4.50] pl.rcParams["figure.autolayout"] = True figure = pl.figure() axes = pl.Axes(figure, [.4, .6, .25, .25]) figure.add_axes(axes) pl.plot([1, 2, 3, 4], [1, 2, 3, 4]) axes = pl.Axes(figure, [.4, ...
Read MoreCalculate the curl of a vector field in Python and plot it with Matplotlib
To calculate the curl of a vector field in Python and plot in with Matplotlib, we can use quiver() method and calculate the corresponding data.StepsSet 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 a 3D axes to the figure as part of a subplot arrangement.Create x, y and z data points using numpy meshgrid.Create u, v and w data curl vector positions.Use quiver() method to get vectors.Turn off the axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] ...
Read MoreHow can you clear a Matplotlib textbox that was previously drawn?
To clear a Matplotlib textbox that was previously drawn, 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.Plot x and y using plot() method.Place characters token on the plot.To clear the text, use text.remove(), where text is a returned artist.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 fig, ax = plt.subplots() x = np.linspace(-10, 10, 100) y = np.sin(x) ax.plot(x, y) text = fig.text(0.5, 0.96, ...
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