How to create Pandas groupby plot with subplots?

Question

I have a data frame like this:

     value     identifier
2007-01-01  0.781611      55
2007-01-01  0.766152      56
2007-01-01  0.766152      57
2007-02-01  0.705615      55
2007-02-01  0.032134      56 
2007-02-01  0.032134      57
2008-01-01  0.026512      55
2008-01-01  0.993124      56
2008-01-01  0.993124      57
2008-02-01  0.226420      55
2008-02-01  0.033860      56
2008-02-01  0.033860      57

So I do a groupby per identifier:

df.groupby('identifier')

And now I want to generate subplots in a grid, one plot per group. I tried both

df.groupby('identifier').plot(subplots=True)

or

df.groupby('identifier').plot(subplots=False)

and

plt.subplots(3,3)
df.groupby('identifier').plot(subplots=True)

to no avail. How can I create the graphs?

Answer 1

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 pd
from numpy.random import randint
import matplotlib.pyplot as plt


df = pd.DataFrame(randint(0,10,(200,6)),columns=list('abcdef'))
grouped = df.groupby('a')
rowlength = grouped.ngroups/2                         # fix up if odd number of groups
fig, axs = plt.subplots(figsize=(9,4), 
                        nrows=2, ncols=rowlength,     # fix as above
                        gridspec_kw=dict(hspace=0.4)) # Much control of gridspec

targets = 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()

Answer 2

You do use pivot to get the identifiers in columns and then 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        57
index
2007-01-01  0.781611  0.766152  0.766152
2007-02-01  0.705615  0.032134  0.032134
2008-01-01  0.026512  0.993124  0.993124
2008-02-01  0.226420  0.033860  0.033860

Answer 3

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

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

Answer 4

Here is a solution to those, who need to plot graphs for exploring different levels of aggregation by multiple columns grouping.

from numpy.random import randint
from numpy.random import randint
import matplotlib.pyplot as plt
import numpy as np

levels_bool = np.tile(np.arange(0,2), 100)
levels_groups = np.repeat(np.arange(0,4), 50)
x_axis = np.tile(np.arange(0,10), 20)
values = randint(0,10,200)

stacked = np.stack((levels_bool, levels_groups, x_axis, values), axis=0)
df = pd.DataFrame(stacked.T, columns=['bool', 'groups', 'x_axis', 'values'])

columns = len(df['bool'].unique())
rows = len(df['groups'].unique())
fig, axs = plt.subplots(rows, columns, figsize = (20,20))

y_index_counter = count(0)
groupped_df = df.groupby([ 'groups', 'bool','x_axis']).agg({
    'values': ['min', 'mean', 'median', 'max']
})
for group_name, grp in groupped_df.groupby(['groups']):
    y_index = next(y_index_counter)
    x_index_counter = count(0)
    for boolean, grp2 in grp.groupby(['bool']):
        x_index = next(x_index_counter)
        axs[y_index, x_index].plot(grp2.reset_index()['x_axis'], grp2.reset_index()['values'], 
                                   label=str(key)+str(key2))
        axs[y_index, x_index].set_title("Group:{} Bool:{}".format(group_name, boolean))

ax.legend()
plt.subplots_adjust(hspace=0.5)
plt.show()