How to change Pandas dataframe index value?

With this setup:

import pandas as pd
import io

text = '''\
STK_ID RPT_Date sales cash
000568 20120930 80.093 57.488
000596 20120930 32.585 26.177
000799 20120930 14.784 8.157
'''

df = pd.read_csv(io.BytesIO(text), delimiter = ' ', 
                 converters = {0:str})
df.set_index(['STK_ID','RPT_Date'], inplace = True)

The index, df.index can be reassigned to a new MultiIndex like this:

index = df.index
names = index.names
index = [('000999','20121231')] + df.index.tolist()[1:]
df.index = pd.MultiIndex.from_tuples(index, names = names)
print(df)
#                   sales    cash
# STK_ID RPT_Date                
# 000999 20121231  80.093  57.488
# 000596 20120930  32.585  26.177
# 000799 20120930  14.784   8.157

Or, the index could be made into columns, the values in the columns could be then reassigned, and then the columns returned to indices:

df.reset_index(inplace = True)
df.ix[0, ['STK_ID', 'RPT_Date']] = ('000999','20121231')
df = df.set_index(['STK_ID','RPT_Date'])
print(df)

#                   sales    cash
# STK_ID RPT_Date                
# 000999 20121231  80.093  57.488
# 000596 20120930  32.585  26.177
# 000799 20120930  14.784   8.157

Benchmarking with IPython %timeit suggests reassigning the index (the first method, above) is significantly faster than resetting the index, modifying column values, and then setting the index again (the second method, above):

In [2]: %timeit reassign_index(df)
10000 loops, best of 3: 158 us per loop

In [3]: %timeit reassign_columns(df)
1000 loops, best of 3: 843 us per loop