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We can plot statsmodels linear regression (OLS) with a non-linear curve but with linear data.StepsSet the figure size and adjust the padding between and around the subplots.To create a new one, we can use seed() method.Initialize the number of sample and sigma variables.Create linear data points x, X, beta, t_true, y and res using numpy.Res is an ordinary Least Square class instance.Calculate the standard deviation. Confidence interval for prediction applies to WLS and OLS, not to general GLS, that is, independently but not identically distributed observations.Create a figure and a set of subplots using subplot() method.Plot all the curves using ... Read More
To control the alpha value on a 3D scatter plot using Python and Matplotlib, we can set the facecolor and edgecolors value.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.Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Create x, y and z data points using numpy.Plot x, y and z points using scatter() method.Set the facecolors and edgecolors.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 = plt.figure() ax ... Read More
To show all X coordinates (or Y coordinates), we can use xticks() method (or yticks()).StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Set x=0 and y=0 margins on the axes.Plot x and y data points using plot() method.Use xticks() method to show all the X-coordinates in the plot.Use yticks() method to show all the Y-coordinates in the plot.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(0, 10, 1) y =np.arange(0, 10, 1) plt.margins(x=0, y=0) ... Read More
To customize the axis label in a Seaborn jointplot, 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.Use jointplot() method to plot a joint plot in Seaborn.To set the customized axis label, we can use LaTex representation or set_xlabel() method properties.To display the figure, use show() method.Exampleimport seaborn as sns import numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.randn(1000, ) y = 0.2 * np.random.randn(1000) + 0.5 h = sns.jointplot(x, y, height=3.50) h.ax_joint.set_xlabel('$\bf{X-Axis\ ... Read More
To decrease the density of x-ticks in Seaborn, we can use set_visible=False for odd positions.StepsSet the figure size and adjust the padding between and around the subplots.Create a dataframe with X-axis and Y-axis keys.Show the point estimates and confidence intervals with bars, using barplot() method.Iterate bar_plot.get_xticklabels() method. If index is even, then make them visible; else, not visible.To display the figure, use show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame({"X-Axis": [i for i in range(10)], "Y-Axis": [i for i in range(10)]}) bar_plot = sns.barplot(x='X-Axis', y='Y-Axis', data=df) for ... Read More
To remove the space between subplots in matplotlib, we can use GridSpec(3, 3) class and add axes as a subplot arrangement.StepsSet the figure size and adjust the padding between and around the subplots.Add a grid layout to place subplots within a figure.Update the subplot parameters of the gridIterate in the range of dimension of grid specs.Add a subplot to the current figure.Set the aspect ratios.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import matplotlib.gridspec as gridspec plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True gs1 = gridspec.GridSpec(3, 3) gs1.update(wspace=0.5, hspace=0.1) for i in range(9): ax1 = plt.subplot(gs1[i]) ax1.set_aspect('equal') plt.show()OutputRead More
A simple call to the imread method loads our image as a multi-dimensional NumPy array (one for each Red, Green, and Blue component, respectively) and imshow displays our image on the screen. Whereas, cv2 represents RGB images as multi-dimensional NumPy arrays, but in reverse order.StepsSet the figure size and adjust the padding between and around the subplots.Initialize the filename.Add a subplot to the current figure using nrows=1, ncols=2, and index=1.Read the image using cv2.Off the axes and show the figure in the next statement.Add a subplot to the current figure using nrows=1, ncols=2, and index=2.Read the image using plt.Off the ... Read More
To make matplotlib scatterplots transparent as a group, we can change the alpha value in the scatter() method argument with a different group value.StepsSet the figure size and adjust the padding between and around the subplots.Make a method to return a grouped x and y points.Get group 1 and group 2 data points.Plot group1, x and y points using scatter() method with color=green and alpha=0.5.Plot group2, x and y points using scatter() method with color=red and alpha=0.5.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 def ... Read More
To align rows in a matplotlib legend with 2 columns, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Using plot() method, plot lines with the labels line1, line2 and line3.Place a legend on the figure with two columns. Use ncol=2.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 plt.plot([1, 2, 3], label="line1") plt.plot([3, 2, 1], label="line2") plt.plot([2, 3, 1], label="line3") plt.legend(ncol=2, loc="upper right") plt.show()Output
To map values to a colors tuple(red, green and blue) in matplotlib, we can take the following steps −Create a list of values from 1.00 to 2.00, count=10.Get linearly normalized data into the vmin and vmax interval.Get an object to map the scalar data to rgba.Iterate the values to map the color values.Print the values against the mapped red, green, and blue values.Exampleimport numpy as np from matplotlib import cm, colors values = np.linspace(1.0, 2.0, 10) norm = colors.Normalize(vmin=1.0, vmax=2.0, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap=cm.Greys_r) for value in values: print("%.2f" % value, "=", "red:%.2f" % mapper.to_rgba(value)[0], ... Read More