python - How to change line width of a pandas plot if another variable satisfies a condtition -

i want plot series of data:

s = pd.dataframe(np.random.randn(5,2)) ind = pd.dataframe({'ind0':np.random.random_integers(0,1, 5), \                      'ind1':np.random.random_integers(0,1, 5)})    data = pd.concat([s,ind], axis=1) 

where "0" , "1" series plot , line width increases "0" when "ind0" 1, , likewise "1".

          0         1  ind0  ind1 0  2.029756 -1.211402     1     0 1  0.428830  0.508613     1     0 2  1.964346  1.032110     0     1 3  1.424997 -0.363719     1     0 4 -0.581283  0.774375     1     0 

i'm not familiar how pandas dataframes work on small scale, it's enough compatible numpy ndarrays. i'll assume have latter, point should mask values based on variables ind0 , ind1. suggest using plt.plot markers, (or, equivalently, plt.scatter):

import numpy np import matplotlib.pyplot plt  n = 10 s = np.random.randn(n,2) ind0 = np.random.random_integers(0,1, n) ind1 = np.random.random_integers(0,1, n)  srange = np.arange(s.shape[0]) # plotting trueinds0 = ind0.astype(bool)  # readibility trueinds1 = ind1.astype(bool)  # readibility  lw_wide = 3   # larger linewidth lw_narrow = 1 # smaller linewidth  hf,ax = plt.subplots()  # plot first column of s indexing ind0 ax.plot(srange[trueinds0],s[:,0][trueinds0],'bs',markeredgecolor='blue',markeredgewidth=lw_wide) ax.plot(srange[np.logical_not(trueinds0)],s[:,0][np.logical_not(trueinds0)],'bs',markeredgecolor='blue',markeredgewidth=lw_narrow)  # plot second column of s indexing ind1 ax.plot(srange[trueinds1],s[:,1][trueinds1],'ro',markeredgecolor='red',markeredgewidth=lw_wide) ax.plot(srange[np.logical_not(trueinds1)],s[:,1][np.logical_not(trueinds1)],'ro',markeredgecolor='red',markeredgewidth=lw_narrow)  #######  # using scatter , 2 marker sizes:  size_wide = 50 size_narrow = 25  hf,ax = plt.subplots()  # create single array specifying marker sizes: sizes = np.where(trueinds0,size_wide,size_narrow) opts = {'c':'b','marker':'s','s':sizes,'edgecolors':'face'} # plot first column of s indexing ind0 ax.scatter(srange,s[:,0],**opts)  sizes = np.where(trueinds1,size_wide,size_narrow) opts = {'c':'r','marker':'o','s':sizes,'edgecolors':'face'} # plot second column of s indexing ind1 ax.scatter(srange,s[:,1],**opts) 

due more concise form, suggest using latter solution, scatter. result of with

ind0 = np.array([1, 0, 1, 1, 1, 0, 1, 1, 1, 0]) ind1 = np.array([0, 0, 0, 0, 0, 1, 0, 1, 0, 1]) 

is: