Grouping index produced in accordance with the foregoing conditions plurality of composite index
First, the index
# A, obtaining index df.index # B, designated index df.index = [] # C, reset the index df.reindex ([ ' A ' , ' B ' , ' C ' ]) # Note: generally do # D designating a column as the index DF2 = df1.set_index ( ' O ' , drop = False) # drop default is True, discarding the row designated # E, designated as a multi-column index DF2 = df1.set_index ([ ' M ' , ' O ' ], drop = False) #f, to re-index operation de df1.set_index ( ' O ' , drop = False) .index.unique ()
Second, the composite index
1, Basics
# A, a composite index df.set_index ([ ' C ' , ' D ' ]) # B, in order to exchange composite index df.swaplevel ()
2、Series
# A, taken Series df.set_index ([ ' C ' , ' D ' ]) [ ' A ' ] # Series # B, taking particular value df.set_index ([ ' C ' , ' D ' ]) [ ' A ' ] [ ' index column value c ' ] [ ' index column value d ' ] # or df.set_index ([ ' c ' , ' d ' ]) [ 'a'] [ ' Index column c ' , ' index column value d ' ]
3、DataFrame
# A, taken DataFrame df.set_index ([ ' C ' , ' D ' ]) [[ ' A ' ]] # B, taking particular value df.set_index ([ ' C ' , ' D ' ]) [[ ' A ' ]]. LOC [ ' index column c ' ] .loc [ ' index column value d ' ]