To visualize scalar 2D data with 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 data samples.Create x and y data points using numpy.Get coordinate matrices from coordinate vectors.Get z data points using numpy.Create a pseudocolor plot with a non-regular rectangular grid.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 = 256 x = np.linspace(-3., 3., n) y = np.linspace(-3., 3., n) X, Y = np.meshgrid(x, ... Read More

To use pyplot.arrow or patches.Arrow() in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize four variables, x_tail, y_tail, x_head and y_head.Create a figure and a set of subplots.Get a fancy arrow instance.Add an artist (step 4) using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches as mpatches plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x_tail = 0.1 y_tail = 0.1 x_head = 0.9 y_head = 0.9 fig, ax = plt.subplots() arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head), mutation_scale=100, color='green') ... Read More

To add black border to matplotlib 2.0 'ax' object in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set axes edgecolor to black.Set axes linewidth to 2.50.Initialize a variable, N, to get the number of sample data.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 matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.rcParams["axes.edgecolor"] = "black" plt.rcParams["axes.linewidth"] = 2.50 N = 10 x = np.random.randint(low=0, high=N, size=N) y ... Read More

To adjust tick frequency for for Y-axis, 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 points.Create x and y data points using numpy.Plot x and y data points using plot() method.Initialize a variable freq_y to adjust the frequency of the yticks.Use yticks() method to set the yticks.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 N = 10 x = np.random.randint(low=0, high=N, size=N) y = np.random.randint(low=0, high=N, ... Read More

To plot a 3D patch collection 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.Get the current axes and set projection as 3d.Iterate ["x", "y", "z"] list, and set the circle patch using pathpatch_2d_to_3d() method to convert a PathPatch to a PathPatch3D object.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.patches import Circle import mpl_toolkits.mplot3d.art3d as art3d plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.gca(projection='3d') for i in ["x", ... Read More

To fill the area under a curve in a Seaborn distribution plot, we can use distplot() and fill_between() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create a list of data points.Plot a univariate distribution of observations.To fill the area under the curve, use fill_between() method.Set or retrieve autoscaling margins, x=0 and y=0.To display the figure, use show() method.Exampleimport seaborn as sns import scipy.stats as stats import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = [2.0, 7.5, 9.0, 8.5] ax = sns.distplot(x, fit_kws={"color": "red"}, kde=False, fit=stats.gamma, hist=None, label="label 1") l1 = ... Read More

To adjust tick frequency for X-axis, 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 points.Create x and y data points using numpy.Plot x and y data points using plot() method.Initialize a variable freq_x to adjust the frequency of the xticks.Use xticks() method to set the xticks.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 N = 10 x = np.random.randint(low=0, high=N, size=N) y = np.random.randint(low=0, high=N, ... Read More

To save scatterplot animations with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize four variables, steps, nodes, positions and solutions.Append positions and solutions values in the list.Create a figure and a set of subplots.Initialize a variable for marker size.Configure the grid lines.Make an animation by repeatedly calling a function *animate*, to clear the axis, add new axis sublot, and plot scatter points on the axis.Save the animated scatter plot as a .gif file.Exampleimport matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np plt.rcParams["figure.figsize"] = [7.50, ... Read More

To plot a line (polygonal chain) with matplotlib with minimal smoothing, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initalize a variable, N, to get the number of data points.Create x and y data points using numpy.Get 1-D monotonic cubic interpolation, using pchip() method.Plot (x, interp(x)) and (x, y) data points using numpy.To display the figure, use show() method.Exampleimport numpy as np from scipy.interpolate import pchip import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 50 x = np.linspace(-10, 10, N) y = np.sin(x) ... Read More

To check if points are inside ellipse faster than contains_point method, 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.Set the aspect ratios, equal.Create x and y data points using numpy.Initialize center, height, width and angle of the ellipse.Get a scale free ellipse.Add a '~.Patch' to the axes' patches; return the patch.If the point lies inside an ellipse, change its color to "red" else "green".Plot x and y data points using scatter() method, with colors.To display the figure, use show() method.Exampleimport matplotlib.pyplot as ... Read More