Bubble scatter plot matplotlib1/29/2024 import matplotlib.pyplot as plt import numpy as np create data xval np.random.rand(. Pytorch regression _1. Bubble chart can be created using the () methods.Pytorch regression _2.1_ April 30, 2020.Multioutput Stacking Regressor May 3, 2020.Queueing Theory: Bomber landing analysis May 13, 2020.Measuring the efficiency of banking transaction systems using Queueing Theory May 14, 2020.legend ( handles, labels, loc = "upper left", title = "Sizes" ) # sztuczka żeby mieć podpisy na kólkach for i, txt in enumerate ( df10 ): ax. legend_elements ( prop = "sizes", alpha = 0.1 ) legend2 = ax. colorbar ( CC ) # DRUGI SPOSÓB #im = ax.scatter('horsepower', 'engine_size', data=df2, s='engine_size', c='price', cmap='PuBu', edgecolors='grey', linewidths=0.8) #fig.colorbar(im, ax=ax) # Sztuczka, żeby mieć legende do size - nie działa dla danych ciągłych (musi byc tylko kilka klas) handles, labels = CC. scatter ( 'Happiness Score', 'Freedom', data = df10, s = 'Population2017', c = 'Freedom', cmap = 'RdYlGn', edgecolors = 'grey', linewidths = 0.8 ) plt. set_ylabel ( 'Freedom', fontsize = 18 ) # Sztuczka żeby mieć colorbar CC = ax. set_xlabel ( 'Happiness Score', fontsize = 18 ) ax. set_title ( "AFRICA 2017 Happiness & Freedom n (color: 'Economy (GDP per Capita)' & size: 'Population2017')", fontsize = 16 ) ax. scatter ( 'Happiness Score', 'Freedom', data = df10, s = 'Population2017', c = 'Freedom', cmap = 'RdYlGn', edgecolors = 'grey', linewidths = 0.8 ) ax. subplots ( figsize = ( 14, 7 ), dpi = 280, facecolor = 'white', edgecolor = 'black' ) ax. legend ( handles, labels, loc = "lower right", title = "Sizes" ) # sztuczka żeby mieć podpisy na kólkach for i, txt in enumerate ( df ): ax. legend_elements ( prop = "sizes", alpha = 0.6 ) legend = ax. colorbar ( BB ) # DRUGI SPOSÓB #im = ax.scatter('horsepower', 'engine_size', data=df2, s='engine_size', c='price', cmap='PuBu', edgecolors='grey', linewidths=0.8) #fig.colorbar(im, ax=ax) # legenda do wielkości kółek handles, labels = BB. scatter ( 'area', 'poptotal', data = df, s = 'dot_size', c = 'popdensity', cmap = 'YlGn', edgecolors = 'blue', linewidths = 0.8 ) plt. ![]() set_ylabel ( 'Poptotal', fontsize = 18 ) # Sztuczka żeby mieć colorbar BB = ax. set_xlabel ( 'Area', fontsize = 18 ) ax. set_title ( "Bubble Plot of PopTotal vs Area n color: 'popdensity' & size: 'dot_size'", fontsize = 16 ) ax. scatter ( 'area', 'poptotal', data = df, s = 'dot_size', c = 'popdensity', cmap = 'YlGn', edgecolors = 'blue', linewidths = 0.8 ) ax. ![]() legend ( handles, labels, loc = "upper left", title = "Sizes" ) # sztuczka żeby mieć podpisy na kólkach for i, txt in enumerate ( df2 ): ax. legend_elements ( prop = "sizes", alpha = 0.6 ) legend2 = ax. colorbar ( AA ) # DRUGI SPOSÓB #im = ax.scatter('horsepower', 'engine_size', data=df2, s='engine_size', c='price', cmap='PuBu', edgecolors='grey', linewidths=0.8) #fig.colorbar(im, ax=ax) handles, labels = AA. This section shows many bubble plots made with Python, using both the Matplotlib and Seaborn libraries. scatter ( 'horsepower', 'engine_size', data = df2, s = 'engine_size', c = 'price', cmap = 'PuBu', edgecolors = 'grey', linewidths = 0.8 ) plt. A bubble plot is a scatterplot where the circle size is mapped to the value of a third numeric variable. When creating a bubble plot, field optionally accepts a two-item list, where the first item is the y-axis field and the second item is the proportional symbol. ![]() set_ylabel ( 'engine_size', fontsize = 18 ) # Sztuczka żeby mieć colorbar AA = ax. set_xlabel ( 'horsepower', fontsize = 18 ) ax. set_title ( "Bubble Plot of Autos Area n (color: 'price & size: 'engine_size')", fontsize = 16 ) ax. scatter ( 'horsepower', 'engine_size', data = df2, s = 'engine_size', c = 'price', cmap = 'PuBu', edgecolors = 'grey', linewidths = 0.8 ) ax. I have statistics that correspond to two different variables, X and Y.Fig, ax = plt.
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