Multi Index Seaborn Line Plot

Question

I have a multi index dataframe, with the two indices being Sample and Lithology

 Sample        20EC-P     20EC-8  20EC-10-1  ...     20EC-43    20EC-45    20EC-54
Lithology         Pd     Di-Grd         Gb  ... Hbl Plag Pd     Di-Grd         Gb
Rb          7.401575  39.055118   6.456693  ...    0.629921  56.535433  11.653543
Ba         24.610102  43.067678  10.716841  ...    1.073115  58.520532  56.946630
Th          3.176471  19.647059   3.647059  ...    0.823529  29.647059   5.294118

I am trying to put it into a seaborn lineplot as such.

spider = sns.lineplot(data = data, hue = data.columns.get_level_values("Lithology"),
                      style = data.columns.get_level_values("Sample"),
                      dashes = False, palette = "deep")

The lineplot comes out as

1

I have two issues. First, I want to format hues by lithology and style by sample. Outside of the lineplot function, I can successfully access sample and lithology using data.columns.get_level_values , but in the lineplot they don't seem to do anything and I haven't figured out another way to access these values. Also, the lineplot reorganizes the x-axis by alphabetical order. I want to force it to keep the same order as the dataframe, but I don't see any way to do this in the documentation.

Answer 1

To use hue= and style= , seaborn prefers it's dataframes in long form . pd.melt() will combine all columns and create new columns with the old column names, and a column for the values. The index too needs to be converted to a regular column (with .reset_index() ).

Most seaborn functions use order= to set an order on the x-values, but with lineplot the only way is to make the column categorical applying a fixed order.

from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

column_tuples = [('20EC-P', 'Pd '), ('20EC-8', 'Di-Grd'), ('20EC-10-1 ', 'Gb'),
                 ('20EC-43', 'Hbl Plag Pd'), ('20EC-45', 'Di-Grd'), ('20EC-54', 'Gb')]
col_index = pd.MultiIndex.from_tuples(column_tuples, names=["Sample", "Lithology"])
data = pd.DataFrame(np.random.uniform(0, 50, size=(3, len(col_index))), columns=col_index, index=['Rb', 'Ba', 'Th'])

data_long = data.melt(ignore_index=False).reset_index()
data_long['index'] = pd.Categorical(data_long['index'], data.index) # make categorical, use order of the original dataframe
ax = sns.lineplot(data=data_long, x='index', y='value',
                  hue="Lithology", style="Sample", dashes=False, markers=True, palette="deep")
ax.set_xlabel('')

ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.02))
plt.tight_layout()  # fit legend and labels into the figure
plt.show()

The long dataframe looks like:

   index      Sample    Lithology      value
0     Rb      20EC-P          Pd    6.135005
1     Ba      20EC-P          Pd    6.924961
2     Th      20EC-P          Pd   44.270570
...