Reputation: 31040
I have a pandas
dataframe, the result of a groupby()
operation, c
:
>>> c.index.names
FrozenList([u'Thing1', u'Thing2', u'Month'])
>>> c.columns
Index([u'Tot'], dtype='object')
>>> c
Tot
Thing1 Thing2 Month
G P 2012-12-01 0.017640
2013-01-01 0.012062
2013-02-01 0.029022
2013-03-01 0.007593
2013-04-01 0.004862
2013-05-01 0.002671
2013-06-01 0.014895
2013-07-01 0.029641
2013-08-01 0.051129
2013-09-01 0.023913
2013-10-01 0.061406
2013-11-01 0.054781
2014-01-01 0.017115
2014-02-01 0.011919
H K 2013-06-01 2.390632
2013-07-01 7.066034
2013-08-01 5.426312
2013-09-01 8.276066
2013-10-01 5.745811
2013-11-01 2.250162
2013-12-01 0.976822
2014-01-01 1.438316
2014-02-01 3.507220
M 2012-06-01 3.050136
2012-07-01 5.911788
2012-08-01 2.794381
2012-09-01 4.418268
2012-10-01 5.312635
2012-11-01 1.810977
2012-12-01 3.097878
2013-01-01 0.811326
2013-02-01 3.105154
2013-03-01 2.384704
I can plot a graph for a particular pair of Thing1
and Thing2
e.g. G
and P
like so:
c.loc[('G', 'P'), :].plot(kind='bar')
However I'd like to iterate through the DataFrame and plot separate graphs for all combinations of Thing1
and Thing2
. I have tried using index.get_level_values
however this results in combinations that do not exist e.g. G
and M
and therefore produces an error:
for x in c.index.get_level_values(0).unique():
for y in c.index.get_level_values(1).unique():
c.loc[(x, y), :].plot(kind='bar')
Does anyone know how to best do this?
Upvotes: 1
Views: 349
Reputation: 25662
Do this
c.groupby(level=['Thing1', 'Thing2']).plot(kind='bar')
This will give you len(df.index.levels[0]) * len(df.index.levels[1])
plots.
Upvotes: 3