Plot multiple lines in subplots

Question

I'd like to plot lines from a 3D data frame, the third dimension being an extra level in the column index. But I can't manage to either wrangle the data in a proper format or call the plot function appropriately. What I'm looking for is a plot where many series are plotted in subplots arranged by the outer column index. Let me illustrate with some random data.

import numpy as np
import pandas as pd

n_points_per_series = 6
n_series_per_feature = 5
n_features = 4

shape = (n_points_per_series, n_features, n_series_per_feature)
data = np.random.randn(*shape).reshape(n_points_per_series, -1)
points = range(n_points_per_series)
features = [chr(ord('a') + i) for i in range(n_features)]
series = [f'S{i}' for i in range(n_series_per_feature)]
index = pd.Index(points, name='point')
columns = pd.MultiIndex.from_product((features, series)).rename(['feature', 'series'])
data = pd.DataFrame(data, index=index, columns=columns)

So for this particular data frame, 4 subplots ( n_features ) should be generated, each containing 5 ( n_series_per_feature ) series with 6 data points. Since the method plots lines in the index direction and subplots can be generated for each column, I tried some variations:

data.plot()
data.plot(subplots=True)
data.stack().plot()
data.stack().plot(subplots=True)

None of them work. Either too many lines are generated with no subplots, a subplot is made for each line separately or after stacking values along the index are joined to one long series. And I think the x and y arguments are not usable here, since converting the index to a column and using it in x just produces a long line jumping all over the place:

data.stack().reset_index().set_index('series').plot(x='point', y=features)

In my experience this sort of stuff should be pretty straight forward in Pandas, but I'm at a loss. How could this subplot arrangement be achieved? If not a single function call, are there any more convenient ways than generating subplots in matplotlib and indexing the series for plotting manually?

Answer 1

If you're okay with using seaborn , it can be used to produce subplots from a data frame column, onto which plots with other columns can then be mapped. With the same setup you had I'd try something along these lines:

import seaborn as sns

# Completely stack the data frame
df = data \
    .stack() \
    .stack() \
    .rename("value") \
    .reset_index()

# Create grid and map line plots
g = sns.FacetGrid(df, col="feature", col_wrap=2, hue="series")
g.map_dataframe(sns.lineplot, x="point", y="value")
g.add_legend()

Output: