How to create Pandas groupby plot with subplots

Here's an automated layout with lots of groups (of random fake data) and playing around with grouped.get_group(key) will show you how to do more elegant plots.

import pandas as pdfrom numpy.random import randintimport matplotlib.pyplot as pltdf = pd.DataFrame(randint(0,10,(200,6)),columns=list('abcdef'))grouped = df.groupby('a')rowlength = grouped.ngroups/2                         # fix up if odd number of groupsfig, axs = plt.subplots(figsize=(9,4),                         nrows=2, ncols=rowlength,     # fix as above                        gridspec_kw=dict(hspace=0.4)) # Much control of gridspectargets = zip(grouped.groups.keys(), axs.flatten())for i, (key, ax) in enumerate(targets):    ax.plot(grouped.get_group(key))    ax.set_title('a=%d'%key)ax.legend()plt.show()

You could use pd.pivot_table to get the identifiers in columns and then call plot()

pd.pivot_table(df.reset_index(),               index='index', columns='identifier', values='value'              ).plot(subplots=True)

And, the output of

pd.pivot_table(df.reset_index(),               index='index', columns='identifier', values='value'               )

Looks like -

identifier        55        56        57index2007-01-01  0.781611  0.766152  0.7661522007-02-01  0.705615  0.032134  0.0321342008-01-01  0.026512  0.993124  0.9931242008-02-01  0.226420  0.033860  0.033860

If you have a series with multiindex. Here's another solution for the wanted graph.

df.unstack('indentifier').plot.line(subplots=True)