You can use scatter: import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 2*np.pi, 10) y = np.sin(x) plt.scatter(x, y) plt.show() Alternatively: plt.plot(x, y, 's') EDIT: If you want error bars you can do: plt.errorbar(x, y, yerr=err, fmt='o') ...

python,pandas,matplotlib,linestyle

It is possible to use consistent colors and line-styles between different subplots with the following approach, import matplotlib.pyplot as plt import numpy as np # load your pandas DataFrames df1 and df2 here ax = [plt.subplot(211), plt.subplot(211)] pars = {'FR': {'color': 'r'}, 'stim_current': {'color': 'k'}} ls_style = ['dashed', 'solid'] for...

r,shape,boxplot,appearance,linestyle

For complete documentation you should look at ?bxp (linked from the ... description in ?boxplot, and in the "See Also" in ?boxplot, and in the pars description in ?boxplot.). It documents that outpch can change the shape of the outliers (though pch works fine too). It also has boxlty, boxlwd,...

There is no need to use two plots commands, just use the pointinterval option: plot 'data' pointinterval 5 with linespoints That plots every line segment, but only every fifth point symbol. The big advantage is, that you can control the behaviour with set style line: set style line 1 lc...