Pandas Dataframe Mutli index sorting by level and column value

Question

I have a pandas dataframe which looks like this:

                         value
           Id              
2014-03-13 1          -3
           2          -6
           3          -3.2
           4          -3.1
           5          -5
2014-03-14 1          -3.4
           2          -6.2
           3          -3.2
           4          -3.2
           5          -5.9

which is basically a groupby object with two levels of multi-index.

I want to sort it in ascending order according to the value column, but keeping the level 0 (dates) untouched so that the result should look like this:

                         value
           Id              
2014-03-13 2          -6
           5          -5
           3          -3.2
           4          -3.1
           1          -3
2014-03-14 2          -6.2
           5          -5.9
           1          -3.4
           3          -3.2
           4          -3.2

Here is the code to generate the initial data:

import pandas as pd

dates = [pd.to_datetime('2014-03-13', format='%Y-%m-%d'), pd.to_datetime('2014-03-13', format='%Y-%m-%d'), pd.to_datetime('2014-03-13', format='%Y-%m-%d'), pd.to_datetime('2014-03-13', format='%Y-%m-%d'),
         pd.to_datetime('2014-03-13', format='%Y-%m-%d'),pd.to_datetime('2014-03-14', format='%Y-%m-%d'), pd.to_datetime('2014-03-14', format='%Y-%m-%d'), pd.to_datetime('2014-03-14', format='%Y-%m-%d'), 
         pd.to_datetime('2014-03-14', format='%Y-%m-%d'), pd.to_datetime('2014-03-14', format='%Y-%m-%d')]

values = [-3,-6,-3.2,-3.1,-5,-3.4,-6.2,-3.2,-3.2,-5.9]
Ids = [1,2,3,4,5,1,2,3,4,5]
df = pd.DataFrame({'Id': pd.Series(Ids, index=dates),
                   'value': pd.Series(values, index=dates)})

df = df.groupby([df.index,'Id']).sum()

Answer 1

For me works reset_index + sort_values + set_index + rename_axis:

df = df.reset_index() \
       .sort_values(['level_0','value']) \
       .set_index(['level_0','Id']) \
       .rename_axis([None, 'Id'])
print (df)
               value
           Id       
2014-03-13 2    -6.0
           5    -5.0
           3    -3.2
           4    -3.1
           1    -3.0
2014-03-14 2    -6.2
           5    -5.9
           1    -3.4
           3    -3.2
           4    -3.2

Another solution with sort_values + swaplevel + sort_index:

df = df.sort_values('value')
       .swaplevel(0,1)
       .sort_index(level=1, sort_remaining=False)
       .swaplevel(0,1)
print (df)
               value
           Id       
2014-03-13 2    -6.0
           5    -5.0
           3    -3.2
           4    -3.1
           1    -3.0
2014-03-14 2    -6.2
           5    -5.9
           1    -3.4
           3    -3.2
           4    -3.2

---

Swap levels is necessary because:

print (df.sort_values('value').sort_index(level=0, sort_remaining=False))
               value
           Id       
2014-03-13 1    -3.0
           2    -6.0
           3    -3.2
           4    -3.1
           5    -5.0
2014-03-14 1    -3.4
           2    -6.2
           3    -3.2
           4    -3.2
           5    -5.9

---

For pandas 0.23.0 is possible sort columns and index levels together:

df.index.names = ['level1','level2']
print (df.sort_values(['level1','value']))
                   value
level1     level2       
2014-03-13 2        -6.0
           5        -5.0
           3        -3.2
           4        -3.1
           1        -3.0
2014-03-14 2        -6.2
           5        -5.9
           1        -3.4
           3        -3.2
           4        -3.2