Grouping by Multi-Indices of both Row and Column
I have created a table using Pandas following material from here.
The table created makes use of Multi-Indices for both columns and rows.
I am trying to compute the descriptive statistics for each year and subject, meaning, displaying for instance the mean of 2013 for Bob, the mean for 2013 for Guido, and the mean for 2013 for Sue, for all subjects, and for all years. The means for Bob would consider the means for HR and Temp. Note: The types are the same as a coincidence, as this is not the case for the table implemented. Other subjects not included in the screenshot have varying types.
The closest I have managed to come to the solution is through the following code
df.groupby(level = 0, axis = 0).describe()
This grouped the data by the year, however, did not group by subject also.
Providing links to external websites is also discouraged as they may change/disappear at any time without SO control
Having said that, the link provides most of the tools you need to answer your questions. More specifically, a combination of stack and mean should give you what you specifically asked about:
health_data.stack().mean(level = 'year')
produces
subject Bob Guido Sue
year
2013 28.4 40.400 34.15
2014 43.2 38.025 41.10
or more generally
health_data.stack().groupby('year').describe()
produces a long dataframe with the statistics grouped by year, for each subject:
subject Bob Guido Sue
count mean std min 25% 50% 75% max count mean ... 75% max count mean std min 25% 50% 75% max
year
2013 4.0 28.4 11.580443 13.0 22.75 31.3 36.95 38.0 4.0 40.400 ... 42.500 50.0 4.0 34.15 4.297674 30.0 30.75 33.95 37.35 38.7
2014 4.0 43.2 7.566593 36.4 37.15 42.2 48.25 52.0 4.0 38.025 ... 39.875 44.0 4.0 41.10 12.961996 28.0 35.65 38.70 44.15 59.0