Image for "Basic plots - plot" should hint at markers about cheatsheets HOT 9 OPEN

as general case plot is for lines and scatter is for points.

scatter is for when you need a third dimension, either represented as marker size or color. Otherwise, you should just use plot.

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You're right. I think it might be nicer to add markers along the sine with marker and markevery. Can you make a PR?

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import matplotlib.pyplot as plt
import numpy as np
np.random.seed(19680801)
x = np.arange(0,4*np.pi,0.1) # start,stop,step
y = np.sin(x)
X=[1,2,3,4,5,6]
Y=[0,6,2,4,6,9]
plt.scatter(X, Y, c='orange')
plt.plot(x, y, c= "orange")
plt.gca().invert_yaxis()
plt.show()
will this code will work

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I think it might be nicer to add markers along the sine with marker and markevery.

IMHO it's important to show independent lines and markers.

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Somewhere on the cheatsheet it's mentioned that plot is a better option for very specific types of scatter situations and I think it's OK to leave this as general case plot is for lines and scatter is for points.

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Can anyone plzzz tell me my code will work or not ...as I am new to open source contribution so I am understanding things slowly....

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scatter is for when you need a third dimension, either represented as marker size or color. Otherwise, you should just use plot.

for smaller datasets the difference is negligible & so you may as well not overload a function with two semantically distinct (continuous versus discrete) use cases.

@Raghibshams456 can you be more specific? How is your code not working?

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for smaller datasets the difference is negligible & so you may as well not overload a function with two semantically distinct

I argue the other way round: From the data semantics:

• plot() is primarily for functional relationship y = f(x) so that there is only one y per x. Whether you draw this with single markers or lines or both is a stylistic choice.
• scatter() is for drawing points in the (x, y) plane, and optionally adding additional information like color and size.

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