I know this is going to end up being a really messy plot, but I am curious to know what the most efficient way to do this is. I have some data that looks like this in a csv file:

    ROI          Band   Min         Max         Mean        Stdev
1   red_2        Band 1 0.032262    0.124425    0.078073    0.028031
2   red_2        Band 2 0.021072    0.064156    0.037923    0.012178
3   red_2        Band 3 0.013404    0.066043    0.036316    0.014787
4   red_2        Band 4 0.005162    0.055781    0.015526    0.013255
5   red_3        Band 1 0.037488    0.10783     0.057892    0.018964
6   red_3        Band 2 0.02814     0.07237     0.04534     0.014507
7   red_3        Band 3 0.01496     0.112973    0.032751    0.026575
8   red_3        Band 4 0.006566    0.029133    0.018201    0.006897
9   red_4        Band 1 0.022841    0.148666    0.065844    0.0336
10  red_4        Band 2 0.018651    0.175298    0.046383    0.042339
11  red_4        Band 3 0.012256    0.045111    0.024035    0.009711
12  red_4        Band 4 0.001493    0.033822    0.014678    0.007788
13  red_5        Band 1 0.030513    0.18098     0.090056    0.044456
37  bcs_1        Band 1 0.013059    0.076753    0.037674    0.023172
38  bcs_1        Band 2 0.035227    0.08826     0.057672    0.015005
39  bcs_1        Band 3 0.005223    0.028459    0.010836    0.006003
40  bcs_1        Band 4 0.009804    0.031457    0.018094    0.007136
41  bcs_2        Band 1 0.018134    0.083854    0.040654    0.018333
42  bcs_2        Band 2 0.016123    0.088613    0.045742    0.020168
43  bcs_2        Band 3 0.008065    0.030557    0.014596    0.007435
44  bcs_2        Band 4 0.004789    0.016514    0.009815    0.003241
45  bcs_3        Band 1 0.021092    0.077993    0.037246    0.013696
46  bcs_3        Band 2 0.011918    0.068825    0.028775    0.013758
47  bcs_3        Band 3 0.003969    0.021714    0.011336    0.004964
48  bcs_3        Band 4 0.003053    0.015763    0.006283    0.002425
49  bcs_4        Band 1 0.024466    0.079989    0.049291    0.018032
50  bcs_4        Band 2 0.009274    0.093137    0.041979    0.019347
51  bcs_4        Band 3 0.006874    0.027214    0.014386    0.005386
52  bcs_4        Band 4 0.005679    0.026662    0.014529    0.006505

And I want to create one probability density plot with 8 lines: 4 of which the 4 bands for "red" and the other will be the 4 bands for "black".So far I have this for just Band 1 in both red and black ROIs. But my code outputs two different plots. I have tried using subplot but that has not worked for me.

Help? I know my approach is verbose and clunky, so smarter solutions much appreciated!

Load packages

import csv 
import numpy as np
import pandas as pd
import matplotlib.pyplot as pltfiles = ['example.csv']

Organize the data

for f in files:fn = f.split('.')[0]dat = pd.read_csv(f)df0 = dat.loc[:, ['ROI', 'Band', 'Mean']]# parse by soil typered = df0[df0['ROI'].str.contains("red")]black = df0[df0['ROI'].str.contains("bcs")]# parse by band red.b1 = red[red['Band'].str.contains("Band 1")]red.b2 = red[red['Band'].str.contains("Band 2")]red.b3 = red[red['Band'].str.contains("Band 3")]red.b4 = red[red['Band'].str.contains("Band 4")]black.b1 = black[black['Band'].str.contains("Band 1")]black.b2 = black[black['Band'].str.contains("Band 2")]black.b3 = black[black['Band'].str.contains("Band 3")]black.b4 = black[black['Band'].str.contains("Band 4")]

Plot the figure

pd.DataFrame(black.b1).plot(kind="density")
pd.DataFrame(red.b1).plot(kind="density")
plt.show()

I'd like for the figure to have 8 lines on it.

groupby + str.split

df.groupby([df.ROI.str.split('_').str[0], 'Band']).Mean.plot.kde();

If you want a legend

df.groupby([df.ROI.str.split('_').str[0], 'Band']).Mean.plot.kde()
plt.legend();