Line color as a function of column values in pandas dataframe

Question

I am trying to plot two columns of a pandas dataframe against each other, grouped by a values in a third column. The color of each line should be determined by that third column, i.e. one color per group.

For example:

import pandas as pd
from matplotlib import pyplot as plt

fig, ax = plt.subplots()

df = pd.DataFrame({'x': [0.1,0.2,0.3,0.1,0.2,0.3,0.1,0.2,0.3],'y':[1,2,3,2,3,4,4,3,2], 'colors':[0.3,0.3,0.3,0.7,0.7,0.7,1.3,1.3,1.3]}) 

df.groupby('colors').plot('x','y',ax=ax)

If I do it this way, I end up with three different lines plotting x against y, with each line a different color. I now want to determine the color by the values in 'colors'. How do I do this using a gradient colormap?

RGBA values should be within 0-1 range, what should we do about the 1.3? — Celius Stingher, Oct 21 '20 at 14:13
Good point. Normalizing so that 0 becomes min(df['colors']) and 1.0 max(df['colors']) would be optimal. — nishuba, Oct 21 '20 at 14:40

Prateek · Accepted Answer · 2020-10-21T16:03:17.870

3

Looks like seaborn is applying the color intensity automatically based on the value in hue..

import pandas as pd 
from matplotlib import pyplot as plt

df = pd.DataFrame({'x': [0.1,0.2,0.3,0.1,0.2,0.3,0.1,0.2,0.3,0.1,0.2,0.3],'y':[1,2,3,2,3,4,4,3,2,3,4,2], 'colors':[0.3,0.3,0.3,0.7,0.7,0.7,1.3,1.3,1.3,1.5,1.5,1.5]})

import seaborn as sns

sns.lineplot(data = df, x = 'x', y = 'y', hue = 'colors')

Gives:

you can change the colors by adding palette argument as below:

import seaborn as sns

sns.lineplot(data = df, x = 'x', y = 'y', hue = 'colors', palette = 'mako')
#more combinations : viridis, mako, flare, etc.

gives:

Edit (for colormap):

based on answers at Make seaborn show a colorbar instead of a legend when using hue in a bar plot?

import seaborn as sns

fig = sns.lineplot(data = df, x = 'x', y = 'y', hue = 'colors', palette = 'mako')

norm = plt.Normalize(vmin = df['colors'].min(), vmax = df['colors'].max())
sm = plt.cm.ScalarMappable(cmap="mako", norm = norm)
fig.figure.colorbar(sm)
fig.get_legend().remove()
plt.show()

gives..

Hope that helps..

edited Oct 21 '20 at 16:03

answered Oct 21 '20 at 14:20

Prateek

359
1
10

This is exactly what I was looking for. Thank you! Follow up: When I do this with more lines the legend still only shows four entries. Is there a way to have a colorbar instead? – nishuba Oct 21 '20 at 14:51
1

look at this answer.. https://stackoverflow.com/questions/49761221/make-seaborn-show-a-colorbar-instead-of-a-legend-when-using-hue-in-a-bar-plot – Prateek Oct 21 '20 at 15:21
@nishuba I had a go at it.. edited my original answer to include colormap – Prateek Oct 21 '20 at 15:59
That answers my question. Thanks so much! Works great now. – nishuba Oct 21 '20 at 16:00

Celius Stingher · Answer 2 · 2020-10-21T15:05:44.147

Complementing to Prateek's very good answer, once you have assigned the colors based on the intensity of the palette you choose (for example Mako):

plots = sns.lineplot(data = df, x = 'x', y = 'y', hue = 'colors',palette='mako')

You can add a colorbar with matplotlib's function plt.colorbar() and assign the palette you used:

sm = plt.cm.ScalarMappable(cmap='mako')
plt.colorbar(sm)

After plt.show(), we get the combined output:

Line color as a function of column values in pandas dataframe

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