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Found 784 Articles for Data Visualization
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To create a surface plot from a grayscale image with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using Numpy.Get the xx and yy data points from a 2d image data raster.Create a new figure or activate an existing figure.Get the current axis of the plot and make it 3d projection axes.Create a surface plot with cmap='gray'.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 data = np.random.rand(5, 5) xx, ... Read More
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To draw a filled arc 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.Initialize two variables, r, yoff.Create x and y data points using Numpy.Fill the area between x and y plots.Set the axis aspect and draw the figure canvas.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 fg, ax = plt.subplots(1, 1) r = 2. yoff = -1 x = np.arange(-1., 1.05, 0.05) y ... Read More
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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
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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
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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
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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
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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
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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
246 Views
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
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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