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To decrease the hatch density in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a customized horizontal hatch class to override the density.Append the horizontal hatch class.Create a new figure or activate an existing figure.Add an 'ax1' to the figure as part of a subplot arrangement.Make lists of data points.Make a bar plot with x and ydata points, with hatch='o', color='green' and edgecolor='red'.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, hatch plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True class MyHorizontalHatch(hatch.HorizontalHatch): def ... Read More
To give matplotlib imshow() plot colorbars a label, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create 5×5 data points using Numpy.Use imshow() method to display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, im.Set colorbar label using set_label() 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 data = np.random.rand(5, 5) im = plt.imshow(data, cmap="copper") cbar = plt.colorbar(im) cbar.set_label("Colorbar") plt.show()OutputRead More
To set a title above each marker which represents the same label in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x data points using Numpy.Create four curves, c1, c2, c3 and c4 using plot() method.Place a legend on the figure, such that the same label marker would come together.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, legend_handler plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-10, 10, 100) c1, = plt.plot(x, np.sin(x), ls='dashed', label='y=sin(x)') c2, ... Read More
To change the color and add grid lines to a Python surface plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and h data points using numpy.Create a new figure or activate an existing figure.Get 3D axes object, with figure (from Step 3).Create a surface plot, with orange color, edgecolors and linewidth.Exampleimport numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(-5, 5, 0.25) y = np.arange(-5, 5, 0.25) x, y = np.meshgrid(x, ... Read More
To find the matplotlib style name, we can take the following steps −import matplotlib.pyplot as pltprint(plt.style.library)Exampleimport matplotlib.pyplot as plt print(plt.style.library)Output{'bmh': RcParams({'axes.edgecolor': '#bcbcbc', 'axes.facecolor': '#eeeeee', 'axes.grid': True, 'axes.labelsize': 'large', 'axes.prop_cycle': cycler('color', ['#348ABD', '#A60628', '#7A68A6', '#467821', '#D55E00', '#CC79A7', '#56B4E9', '#009E73', '#F0E442', '#0072B2']), 'axes.titlesize': 'x-large', 'grid.color': '#b2b2b2', 'grid.linestyle': '--', 'grid.linewidth': 0.5, 'legend.fancybox': True, 'lines.linewidth': 2.0, 'mathtext.fontset': 'cm', 'patch.antialiased': True, 'patch.edgecolor': '#eeeeee', 'patch.facecolor': 'blue', 'patch.linewidth': 0.5, 'text.hinting_factor': 8, 'xtick.direction': 'in', 'ytick.direction': 'in'}), 'classic': RcParams({'_internal.classic_mode': True, 'agg.path.chunksize': 0, ... Read More
To get an interactive plot of a pyplot when using PyCharm, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set the background style.Plot the data on the axes.To display the figure, use show() method.Exampleimport matplotlib as mpl import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True mpl.use('Qt5Agg') plt.plot(range(10)) plt.show()Output
To increase the spacing between subplots with subplot2grid, we can take the following steps −Set 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 grid.Add a subplot to the current figure.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 ax = plt.GridSpec(2, 2) ax.update(wspace=0.5, hspace=0.5) ax1 = plt.subplot(ax[0, :]) ax2 = plt.subplot(ax[1, 0]) ax3 = plt.subplot(ax[1, 1]) plt.show()Output
To get multiple overlapping plots with independent scaling in Matplotlib, 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.Plot a list of data points using plot() method on a seperate Y-axis and overlapping X-axis.Create a twin Axes sharing the X-axis.Plot a list of data points using plot() method on a seperate Y-axis and overlapping X-axis.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax1 = plt.subplots() ax1.plot([1, 2, 3, 4, 5], color='red') ... Read More
RESTREST (Representational State Transfer) is a modern technique of enabling communication between two software systems. One such system is known as REST Client; the other is known as REST Server. It is an architectural technique based on a stateless communications protocol, such as HTTP. It organizes or structures data in XML, YAML, and other machine-readable formats. However, JSON is mostly used. REST is based on objectoriented programming model.REST is data-driven, unlike SOAP which is function-driven. REST is also referred to as RESTful APIs or RESTful web services. The description format of REST services does not follow a standard. REST service ... Read More
To display a sequence of images using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of images that have to be drawn.Turn off the axes.Iterate the images and redraw over the axes.Take a pause after each draw.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True images = ['opera.jpg', 'mountain.jpg', '9.jpg'] plt.axis('off') img = None for f in images: im = plt.imread(f) if img is None: img = plt.imshow(im) plt.pause(0.5) else: ... Read More