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import pandas as pd
d = {0: [1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 
         1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0], 
     1: [16, 1, 0, 15, 7, 14, 13, 7, 3, 10, 14, 8, 10, 3,  5, 4, 8, 13, 6, 2, 
         11, 11, 0, 12, 9, 16, 12, 15, 9, 1, 4, 5, 6, 2], 
     2: [32, 32, 32, 32, 32, 33, 46, 64, 97, 97, 99, 99, 100, 101, 101, 102, 103, 
         103, 103, 104, 105, 105, 106, 108, 109, 109, 110, 111, 111, 111, 112, 112, 121, 122]}
print (pd.DataFrame(d).pivot(1,0,2).applymap(chr).agg(''.join))

Most time repeating dupes, not easy find:

pivot dupe
https://stackoverflow.com/q/47152691/
booelan indexing dupe
https://stackoverflow.com/q/17071871
idxmax + groupby dupe
https://stackoverflow.com/q/15705630
idxmin + groupby dupe
https://stackoverflow.com/q/23394476
melt dupe
https://stackoverflow.com/q/28654047
explode dupe
https://stackoverflow.com/q/12680754
cumcount dupe
https://stackoverflow.com/q/23435270
map dupe
https://stackoverflow.com/q/24216425
groupby+size+unstack dupe
https://stackoverflow.com/q/39132742
https://stackoverflow.com/q/38278603
sorting inplace dupe
https://stackoverflow.com/q/42613581
factorize dupe
https://stackoverflow.com/q/39357882
groupby+size dupe
https://stackoverflow.com/q/19384532
groupby+ mean dupe
https://stackoverflow.com/q/30482071
transform sum dupe
https://stackoverflow.com/q/30244952
transform size dupe
https://stackoverflow.com/q/37189878
keyerror dupe
https://stackoverflow.com/q/43736163
merge/map dupe
https://stackoverflow.com/q/53010406
value_count dupe
https://stackoverflow.com/q/15411158
numpy select, where dupe
https://stackoverflow.com/q/19913659
wide_to_long dupe
https://stackoverflow.com/q/55766565
reset_index dupe
https://stackoverflow.com/q/36932759