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Speeding up matplotlib scatter plots


Speeding up matplotlib scatter plots

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Speeding up matplotlib scatter plots
Tag : python , By : WuJanJai
Date : November 25 2020, 07:22 PM


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shared axes in scatter plots in matplotlib


Tag : python , By : arcadian
Date : March 29 2020, 07:55 AM
hop of those help? I've no answer to the why question, but here's how to get rid of it: In your code snippet, giving scatter three points to draw
plt.scatter([1.0,2,3], [1.1,2.2,2.9], s=20, color='tomato')

Python matplotlib superimpose scatter plots


Tag : python , By : OlioEngr
Date : March 29 2020, 07:55 AM
this one helps. You simply call the scatter function twice, matplotlib will superimpose the two plots for you. You might want to specify a color, as the default for all scatter plots is blue. This is perhaps why you were only seeing one plot.
import numpy as np
import pylab as plt

X = np.linspace(0,5,100)
Y1 = X + 2*np.random.random(X.shape)
Y2 = X**2 + np.random.random(X.shape)

plt.scatter(X,Y1,color='k')
plt.scatter(X,Y2,color='g')
plt.show()

Matplotlib alternative for 3D scatter plots


Tag : python , By : ERaubenheimer
Date : March 29 2020, 07:55 AM
hope this fix your issue matplotlib is not quite mature for 3d graphics : http://matplotlib.org/mpl_toolkits/mplot3d/faq.html

Sub-pixel accuracy scatter plots with matplotlib?


Tag : python , By : Chaz
Date : March 29 2020, 07:55 AM
Does that help You may be interested in https://github.com/anntzer/mplcairo, a new cairo-based backend for Matplotlib which I originally wrote exactly because I was unhappy with this issue. It does not display the aforementioned problem, while maintaining reasonable performance.

matplotlib 3: 3D scatter plots with tight_layout


Tag : python , By : James Lupiani
Date : March 29 2020, 07:55 AM
Hope that helps I have some code which produces a 3D scatter plot using matplotlib's scatter in conjunction with tight_layout, see the simplified code below: , to the comment by ImportanceOfBeingErnest, it now works:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import proj3d 

fig = plt.figure()
ax = fig.gca(projection='3d')

N = 100
x = np.random.random(N)
y = np.random.random(N)
z = np.random.random(N)

ax.scatter(x, y, z)

# The fix
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()

plt.savefig('scatter.png')
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