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To adjust (offset) the colorbar title in matplotlib, we can take the following steps −Create a random data of 4×4 dimension.Use imshow() method to display the data as an imgage.Create a colorbar for a scalar mappable instance using colorbar() method, with im mappable instance.Now, adjust (offset) the colorbar title in matplotlib, with labelpad=-1. You can assign different values to labelpad to see how it affects the colorbar title.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, cm plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) im = plt.imshow(data, cmap=cm.jet) cb = plt.colorbar(im) cb.set_label('Image Colorbar', labelpad=-1) plt.show()OutputRead More
To increase plt.title font size, we can initialize a variable fontsize and can use it in the title() method's argument.StepsCreate x and y data points using numpy.Use subtitle() method to place the title at the center.Plot the data points, x and y.Set the title with a specified fontsize.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 x = np.linspace(-1, 1, 10) y = x ** 2 fontsize = 12 plt.suptitle("Quadratic Equation", fontsize=fontsize) plt.plot(x, y) plt.title("y=x$^{2}$", fontdict={'fontsize': fontsize}) plt.show()OutputRead More
To configure the behaviour of the backend, we can use matplotlib.rcParams['backend'] with a new backend name.StepsUse get_backend() method to get the backend name.Override the existing backend name using matplotlib.rcParams.Use get_backend() method to get the configured backend name.Exampleimport matplotlib backend = matplotlib.get_backend() print("The current backend name is: ", backend) matplotlib.rcParams['backend'] = 'TkAgg' backend = matplotlib.get_backend() print("Configured backend name is: ", backend)OutputThe current backend name is: GTK3Agg Configured backend name is: TkAgg
We can change the strength of antialiasing by using True or False flag in the argument of plot() method.StepsCreate x data points and colors list with different colors.Defining a method that accepts antialiased flag and axis.We can iterate in the range of 5, to print 5 different colors of curves from x data points (Step 1).Create a new figure or activate an existing figure.Add an axis to the figure as part of a subplot arrangement, at index 1.Plot a line with antialiased flag set as False and ax1 (axis 1) and set the title of the figure.Add an axis to the figure ... Read More
Meshgrid − Coordinate matrices from coordinate vectors.Let's take an example to see how we can apply a function to a Python meshgrid. We can consider two lists, x and y, using numpy vectorized decorator.Exampleimport numpy as np @np.vectorize def foo(a, b): return a + b x = [0.0, 0.5, 1.0] y = [0.0, 1.0, 8.0] print("Function Output: ", foo(x, y))OutputFunction Output: [0. 1.5 9. ]
Using plt.figure(), we can create multiple figures and to close them all explicitly, call plt.close(). If you are creating many figures, make sure you explicitly call pyplot.close on the figures you are not using, because this will enable pyplot to properly clean up the memory.Using subplots(), we can create a figure and set of subplots.Here we creating two figures, fig1 and fig2. fig1 is 8×8 in size, whereas fig2 has the default figsize. There are 4×4=16 subplots added in fig2.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig2, ax_lst = plt.subplots(4, 4) plt.show()OutputRead More
To adjust transparency, i.e., aplha in Seaborn pairplot, we can change the value of alpha.StepsCreate a dataframe using Pandas with two keys, col1 and col2.Initialize the variable, alpha, for transparency.Use pairplot() method to plot pairwise relationships in a dataset. Use df (from step 1), kind="scatter", and set the plot size, edgecolor, facecolor, linewidth and alpha vaues in the arguments.To display the figure, use show() method.Exampleimport pandas as pd import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({"col1": [1, 3, 5, 7, 1], "col2": [1, 5, 7, 9, 1]}) alpha = 0.75 ... Read More
To retrieve XY data from a matplotlib figure, we can use get_xdata() and get_ydata() methods.StepsCreate x and y data points using numpy.Limit X and Y axes range, using xlim() and ylim() methods.Plot xs and ys data points using plot() method with marker=diamond, color=red, and markersize=10, store the returned tuple in a line.Use get_xdata() and get_ydata() methods on the line to get xy data.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 xs = np.random.rand(10) ys = np.random.rand(10) plt.xlim(0, 1) plt.ylim(0, 1) line, = plt.plot(xs, ys, marker='d', c='red', markersize=10) xdata = line.get_xdata() ydata = ... Read More
To let my matplotlib plot go beyond the axes, we can turn off the flag clip_on in the argument of plot() method.StepsCreate xs and ys data points using numpy.Limit the X and Y axis range in the plot to let the line go beyond this limit, using xlim() and ylim() method.Plot the xs and ys data points using plot() method, where marker is a diamond shape, color is orange and clip_on=False (to go beyond the plot).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 xs = np.arange(10) ys ... Read More
To avoid overlapping of labels and autopct in a matplotlib pie chart, we can follow label as a legend, using legend() method.StepsInitialize a variable n=20 to get a number of sections in a pie chart.Create slices and activities using numpy.Create random colors using hexadecimal alphabets, in the range of 20.Use pie() method to plot a pie chart with slices, colors, and slices data points as a label.Make a list of labels (those are overlapped using autopct).Use legend() method to avoid overlapping of labels and autopct.To display the figure, use show() method.Exampleimport random import numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = ... Read More