Colormap lines showing up as the same color.

Question

I am extending this question to figure out how to make each of the lines a different shade of red or black. This might require making a custom color map or somehow tweaking the colormap "RdGy".

Using the data and packages from the last question, this is what I have so far:

df0.groupby([df0.ROI.str.split('_').str[0],'Band']).Mean.plot.kde(colormap='RdGy')
plt.legend()
plt.show()

And the figure looks like this:

But I want the 'bcs' lines to be shades of black and the 'red' lines to be shades of red. How is this possible?

It would also be great to customize the names of the lines in the legend, such as "BCS Band 1", etc.. but not sure how to do this either.

Answer 1

In principle @Robbies answer to the linked question gives you all the tools needed to create lines of any color and label you want.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame()
nam = ["red", "bcs"]
for i in range(8):
    df['{}_{}'.format(nam[i//4],i%4)] = np.random.normal(i, i%4+1, 100)

nam = {"red":plt.cm.Reds, "bcs": plt.cm.gray_r}
fig, ax = plt.subplots()
for i, s in enumerate(df.columns):
    df[s].plot(kind='density', color=nam[s.split("_")[0]]((i%4+1)/4.), 
                label=" Band ".join(s.split("_")))

plt.legend()
plt.show()

Of course you can also just use a list of strings as legend entries. Either by supplying them to the label argument of the plotting function,

labels = "This is a legend with some custom entries".split()
for i, s in enumerate(df.columns):
    df[s].plot(kind='density', color=nam[s.split("_")[0]]((i%4+1)/4.), 
                label=labels[i])

or

by using the labels argument of the legend,

labels = "This is a legend with some custom entries".split()
for i, s in enumerate(df.columns):
    df[s].plot(kind='density', color=nam[s.split("_")[0]]((i%4+1)/4.) )

plt.legend(labels=labels)