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I have a dataframe containing this structure and I have figured out how to 'unpivot' the df by doing this, but I am pretty sure is not the more pythonic way I would like to have. Can you please suggest a better way to do it?:

v = [[{'BIN_ID_WDM': i, 'DSIMILARITY': df1.D1[i], 'BIN_ID_IHS': df1.ID1[i]},
      {'BIN_ID_WDM': i, 'DSIMILARITY': df1.D2[i], 'BIN_ID_IHS': df1.ID2[i]},
      {'BIN_ID_WDM': i, 'DSIMILARITY': df1.D3[i], 'BIN_ID_IHS': df1.ID3[i]},
      {'BIN_ID_WDM': i, 'DSIMILARITY': df1.D4[i], 'BIN_ID_IHS': df1.ID4[i]},
      {'BIN_ID_WDM': i, 'DSIMILARITY': df1.D5[i], 'BIN_ID_IHS': df1.ID5[i]}]
      for i in df1.index]

dataframe:

    D1  D2  D3  D4  D5  ID1 ID2 ID3 ID4 ID5
WMAC                                        
258403  0.002665    0.003306    0.001396    0.003395    0.003741    100000141725    100000141709    100000141696    100000141676    100000141294
105692  0.000016    0.000257    0.000264    0.000298    0.000349    100000030110    100000030243    100000030109    100000030166    100000323212
70795   0.001588    0.001564    0.000019    0.001828    0.001828    100000040111    100000028683    100000034744    100000324405    100000038952
user_dhrn
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1 Answers1

1

IIUC, use pd.wide_to_long:

pd.wide_to_long(df, ['D', 'ID'], 'WMAC', 'No')

Output:

                  D            ID
WMAC   No                        
258403 1   0.002665  100000141725
105692 1   0.000016  100000030110
70795  1   0.001588  100000040111
258403 2   0.003306  100000141709
105692 2   0.000257  100000030243
70795  2   0.001564  100000028683
258403 3   0.001396  100000141696
105692 3   0.000264  100000030109
70795  3   0.000019  100000034744
258403 4   0.003395  100000141676
105692 4   0.000298  100000030166
70795  4   0.001828  100000324405
258403 5   0.003741  100000141294
105692 5   0.000349  100000323212
70795  5   0.001828  100000038952
Scott Boston
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