![]() Plot3.fill_between((25, 30), 10, 60, facecolor= "blue", alpha = 0.2) #connecting line between the rightcorner of plot1 and the right #corner of the red hightlightĬonn2 = ConnectionPatch(xyA = (20, 17), coordsA=ansData, t_ylim(25, 35) #highlighting the portion of original plot to zoon in Here is the complete code:įrom matplotlib.patches import ConnectionPatch Please check the comments in the code carefully for more clarity on the code. The portions to zoom will be highlighted with colors and the connecting lines will show clearly. ![]() There will be two small zoom-in windows on top and the original big plot at the bottom. This time I will use subplots to present the zoom_in window. Here is the complete code: from mpl_toolkits.axes_grid1.inset_locatorĪx.set_ylabel("Price") #Setting the limit of x and y direction to define which portion to zoom ![]() Case-insensitive RGB or RGBA string equivalent hex shorthand of duplicated characters. RGB or RGBA (red, green, blue, alpha) tuple of float values in a closed interval 0, 1. Please see the comments in the code below for some clear understanding. Matplotlib recognizes the following formats to specify a color. On the other hand, mark_inset function will draw the line from the original dots to the zoom window. The inset_axes function will define the size of the zoom window and the positioning of the zoom window. We need to import mark_inset and inset_axes functions first. I will use a length vs price plot this time. Please feel free to change those numbers and see what happens. The first two elements 0.2 and 0.7 define the positioning of the zoom window. Here the last two elements 0.2 and 0.2 mean the height and width of the zoom window. The zoom window came from fig.add_axes() function that has one parameter inside. For this pass facecolors argument to scatter () method. I am assuming you know how to do a scatter plot already. Matplotlib scatter plot marker face color We’ll learn to set the face color of the scatter markers. Look, a small zoom-out window inside the plot. Plt.title("Length vs Width and Color Represents the Changes of Price") Here is the complete code for that and I will explain it a bit after the plot: fig = plt.figure(figsize = (8, 6)) 'highway-mpg', 'price', 'city-L/100km', 'horsepower-binned', 'diesel', 'gas'],Īt first, I want to work on the zoom-out technique.įor that, I will make a scatter plot of the length vs width. 'bore', 'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', ![]() 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', Here is the complete code: from matplotlib. Output: Index(['symboling', 'normalized-losses', 'make', 'aspiration', 'num-of-doors', There will be two small zoom-in windows on top and the original big plot at the bottom. I didn't try changing the resolution but I see other comments below that it may not be due to that so I'll hold off for now.This is an open dataset that is mentioned here. It's less noticeable as the markers partially overlap but it's still there. When I try using the same size (s=80) for both, I can still see the offset between "white" and "none" in my case. The issue is that to improve performance, if all of the markers are identical we render it once and then "stamp" it for each marker (which saves us from having to compute the anti-aliasing many times), The down side is that we can then miss the "correct" location of the marker by ~1/2 a pixel which should be "small", but when the figure is rendered at low resolution and then up-sampled on modern monitors the ~1 pixel offset in our rasterization can become a many screen-pixel offset that the user sees. scatter( x, y, marker = "o", s = 80, facecolors = 'none', edgecolors = 'red', alpha =. scatter( x, y, marker = "o", s = 80, facecolors = 'white', edgecolors = 'blue', alpha =.
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