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To draw a network graph with networkx and matplotlib, plt.show() −Set the figure size and adjust the padding between and around the subplots.Make an object for a dataframe with the keys, from and to.Get a graph containing an edgelist.Draw a graph (Step 3) using draw() method with some node properties.To display the figure, use show() method.Exampleimport pandas as pd import networkx as nx from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 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') nx.draw(G, with_labels=True, node_size=100, alpha=1, linewidths=10) plt.show()OutputRead More
To draw R-style (default is regular style) axis ticks that point outward from the axes in matplotlib, we can use rcParams["xticks.direction"]="out" for X-axis.StepsSet the figure size and adjust the padding between and around the subplots.Set outwaord tick points using plt.rcParams.Initialize a variable for the number of data points.Create x and y data points using numpy.Plot 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['ytick.direction'] = 'out' # in plt.rcParams['xtick.direction'] = 'out' # in n = 10 x = ... Read More
To make width of title box span the entire plot in matplotlib, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot x and y data points using plot() method, with color=black and linewidth=7.Get the current axes using gca() method.Set the title of of the plot.Return the bbox patch using get_bbox_patch() methodTo 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 x = np.linspace(-2, 2, 100) y = np.sin(x) plt.plot(x, y, c='black', ... Read More
To convert or scale the axis values and redefine the tick frequency in matplotlib, we can make a list of xticks and xtick_labels using xticks() method. Place the axis scale and redefine the tick frequency.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for the number of data points.Create x and y data points using numpy.Plot x and y data points using plot() method.Make lists of ticks and tick labels.Use xticks() method to place axis scale and redefine tick frequency.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot ... Read More
To make a scatter plot with multiple Y values for each X, we can create x and y data points using numpy, zip and iterate them together to create the scatter plot.StepsSet the figure size and adjust the padding between and around the subplots.Create random xs and ys data points using numpy.Zip xs and ys. Iterate them together.Make a scatter plot with each x and y values.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 xs = np.random.rand(100) ys = np.random.rand(100) for x, y in zip(xs, ... Read More
Axes.flat means a 1D iterator over the array. Let's take an example to see how to use axes.flat.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots using subplots() method.Create x and y data points using numpy.Use axes.flat and iterate all the axes (step 2).Plot 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 fig, axes = plt.subplots(nrows=2, ncols=3) x = np.random.rand(10) y = np.random.rand(10) for _, ax ... Read More
To write text above the bars on a bar plot, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create lists of year, population and x. Initialize a width variable.Create a figure and a set of subplots using subplots() method.Set ylabels, title, xtickas and xticklabels.Plot the bars using bar() method with x, population and width data.Iterate the bar patches and place text at the top of the bars using text() 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"] = ... Read More
To plot horizontal and vertical lines passing through a point, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create two lines using slopes (m1, m2) and intercepts (c1 and c2). Initialize the slopes and intercepts values.Create x data points using numpy.Plot x, m1, m2, c2 and c1 data points using plot() method.Using intercepts and slopes values, find the point of intersection.Plot horizontal and vertical lines with dotted linestyle.Plot xi and yi points on the plotTo display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, ... Read More
To plot 3D bars without axes, we can take the following 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 an axes to the cureent figure as a subplot arrangement.Create x3, y3 and z3 data points using numpy.Create dx, dy and dz data points using numpy.Use bar3d() method to plot 3D bars.To hide the axes, use axis('off') class by name.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 = plt.figure() ... Read More
To plot overlapping lines in matplotlib, we can use variable overlapping that basically sets the opacity or alpha value in the plot.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable overlapping to set the alpha value of the line.Plot line1 and line2 with red and green colors, respectively, with the same alpha value.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True overlapping = 0.150 line1 = plt.plot([1, 3, 5, 2, 5, 3, 1], c='red', alpha=overlapping, lw=5) line2 = plt.plot([7, 2, 5, 7, 5, 2, ... Read More