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

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

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.

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.

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....

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.

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?

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.