python 如何根据条件同时扩展和收缩 Dataframe ？

pkwftd7m 于 2022-12-02 发布在 Python

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我有一个如下的df：

df = pd.DataFrame.from_dict({'Type': {0: 'A1', 1: 'A2',   2: 'A2',   3: 'A2',   4: 'A2',   5: 'A3',   6: 'A3',   7: 'A3',   8: 'A3',   9: 'A3',   10: 'A3',   11: 'A3',   12: 'A3',   13: 'A3',   14: 'A3',   15: 'A3',   16: 'A3',   17: 'A3',   18: 'A3',   19: 'A3',   20: 'A3',   21: 'A3',   22: 'A3',   23: 'A3',   24: 'A3',   25: 'A3',   26: 'A3',   27: 'A3',   28: 'A3',   29: 'A3',   30: 'A3',   31: 'A3',   32: 'A3',   33: 'A3',   34: 'A3',   35: 'A3',   36: 'A3',   37: 'A3',   38: 'A3',   39: 'A3',   40: 'A3',   41: 'A3',   42: 'A3',   43: 'A3',   44: 'A3',   45: 'A3',   46: 'A3',   47: 'A3',   48: 'A3',   49: 'A3',   50: 'A3',   51: 'A3',   52: 'A3',   53: 'A3',   54: 'A3',   55: 'A3',   56: 'A3',   57: 'A3',   58: 'A3',   59: 'A3',   60: 'A3',   61: 'A3',   62: 'A3',   63: 'A3',   64: 'A3',   65: 'A3',   66: 'A3',   67: 'A3',   68: 'A3',   69: 'A3',   70: 'A3',   71: 'A3',   72: 'A3',   73: 'A3',   74: 'A3',   75: 'A3'},  'FN': {0: 'F1',   1: 'F2',   2: 'F3',   3: 'F3',   4: 'F4',   5: 'F5',   6: 'F5',   7: 'F5',   8: 'F6',   9: 'F6',   10: 'F6',   11: 'F6',   12: 'F7',   13: 'F7',   14: 'F1',   15: 'F1',   16: 'F8',   17: 'F8',   18: 'F8',   19: 'F8',   20: 'F8',   21: 'F9',   22: 'F9',   23: 'F9',   24: 'F10',   25: 'F10',   26: 'F11',   27: 'F12',   28: 'F12',   29: 'F13',   30: 'F13',   31: 'F14',   32: 'F14',   33: 'F15',   34: 'F15',   35: 'F16',   36: 'F16',   37: 'F16',   38: 'F17',   39: 'F17',   40: 'F18',   41: 'F3',   42: 'F3',   43: 'F3',   44: 'F3',   45: 'F19',   46: 'F20',   47: 'F21',   48: 'F22',   49: 'F23',   50: 'F23',   51: 'F24',   52: 'F25',   53: 'F26',   54: 'F26',   55: 'F27',   56: 'F27',   57: 'F27',   58: 'F28',   59: 'F29',   60: 'F30',   61: 'F30',   62: 'F31',   63: 'F31',   64: 'F32',   65: 'F32',   66: 'F33',   67: 'F34',   68: 'F34',   69: 'F35',   70: 'F35',   71: 'F36',   72: 'F37',   73: 'F37',   74: 'F38',   75: 'F39'},  'ID': {0: 'S1',   1: 'S2',   2: 'S3',   3: 'S4',   4: 'S5',   5: 'S6',   6: 'S6',   7: 'S7',   8: 'S8',   9: 'S9',   10: 'S10',   11: 'S11',   12: 'S12',   13: 'S13',   14: 'S1',   15: 'S1',   16: 'S14',   17: 'S15',   18: 'S16',   19: 'S17',   20: 'S17',   21: 'S18',   22: 'S18',   23: 'S19',   24: 'S20',   25: 'S21',   26: 'S22',   27: 'S23',   28: 'S23',   29: 'S24',   30: 'S25',   31: 'S26',   32: 'S27',   33: 'S28',   34: 'S28',   35: 'S29',   36: 'S29',   37: 'S29',   38: 'S30',   39: 'S30',   40: 'S31',   41: 'S32',   42: 'S32',   43: 'S3',   44: 'S3',   45: 'S33',   46: 'S34',   47: 'S35',   48: 'S36',   49: 'S37',   50: 'S38',   51: 'S39',   52: 'S40',   53: 'S41',   54: 'S41',   55: 'S42',   56: 'S43',   57: 'S44',   58: 'S45',   59: 'S46',   60: 'S47',   61: 'S48',   62: 'S49',   63: 'S49',   64: 'S50',   65: 'S50',   66: 'S51',   67: 'S52',   68: 'S52',   69: 'S53',   70: 'S53',   71: 'S54',   72: 'S55',   73: 'S55',   74: 'S56',   75: 'S57'},  'DN': {0: 'D1',   1: 'D2',   2: 'D3',   3: 'D4',   4: 'D5',   5: 'D6',   6: 'D6',   7: 'D7',   8: 'D8',   9: 'D9',   10: 'D10',   11: 'D11',   12: 'D12',   13: 'D13',   14: 'D1',   15: 'D1',   16: 'D14',   17: 'D15',   18: 'D16',   19: 'D17',   20: 'D17',   21: 'D18',   22: 'D18',   23: 'D19',   24: 'D20',   25: 'D21',   26: 'D22',   27: 'D23',   28: 'D23',   29: 'D24',   30: 'D25',   31: 'D26',   32: 'D27',   33: 'D28',   34: 'D28',   35: 'D29',   36: 'D29',   37: 'D29',   38: 'D30',   39: 'D30',   40: 'D31',   41: 'D32',   42: 'D32',   43: 'D3',   44: 'D3',   45: 'D33',   46: 'D34',   47: 'D35',   48: 'D36',   49: 'D37',   50: 'D38',   51: 'D39',   52: 'D40',   53: 'D41',   54: 'D41',   55: 'D42',   56: 'D43',   57: 'D44',   58: 'D45',   59: 'D46',   60: 'D47',   61: 'D48',   62: 'D49',   63: 'D49',   64: 'D50',   65: 'D50',   66: 'D51',   67: 'D52',   68: 'D52',   69: 'D53',   70: 'D53',   71: 'D54',   72: 'D55',   73: 'D55',   74: 'D56',   75: 'D57'},  'Group': {0: 'FC',   1: 'SCZ',   2: 'FC',   3: 'SCZ',   4: 'SCZ',   5: 'FC',   6: 'FC',   7: 'FC',   8: 'FC',   9: 'FC',   10: 'FC',   11: 'FC',   12: 'FC',   13: 'FC',   14: 'FC',   15: 'FC',   16: 'BPAD',   17: 'BPAD',   18: 'FC',   19: 'FC',   20: 'FC',   21: 'FC',   22: 'FC',   23: 'FC',   24: 'BPAD',   25: 'SCZ',   26: 'FC',   27: 'PC',   28: 'PC',   29: 'FC',   30: 'FC',   31: 'FC',   32: 'FC',   33: 'FC',   34: 'FC',   35: 'FC',   36: 'FC',   37: 'FC',   38: 'FC',   39: 'FC',   40: 'FC',   41: 'FC',   42: 'FC',   43: 'FC',   44: 'FC',   45: 'FC',   46: 'FC',   47: 'FC',   48: 'FC',   49: 'FC',   50: 'FC',   51: 'FC',   52: 'FC',   53: 'FC',   54: 'FC',   55: 'FC',   56: 'FC',   57: 'SCZ',   58: 'FC',   59: 'FC',   60: 'FC',   61: 'SCZ',   62: 'PC',   63: 'PC',   64: 'PC',   65: 'PC',   66: 'PC',   67: 'PC',   68: 'PC',   69: 'PC',   70: 'PC',   71: 'PC',   72: 'PC',   73: 'PC',   74: 'PC',   75: 'PC'},  'POS': {0: 'C1',   1: 'C2',   2: 'C3',   3: 'C3',   4: 'C4',   5: 'C5',   6: 'C6',   7: 'C7',   8: 'C5',   9: 'C5',   10: 'C5',   11: 'C5',   12: 'C5',   13: 'C5',   14: 'C8',   15: 'C7',   16: 'C9',   17: 'C7',   18: 'C5',   19: 'C5',   20: 'C6',   21: 'C5',   22: 'C7',   23: 'C5',   24: 'C7',   25: 'C7',   26: 'C5',   27: 'C5',   28: 'C10',   29: 'C11',   30: 'C5',   31: 'C5',   32: 'C5',   33: 'C5',   34: 'C7',   35: 'C12',   36: 'C5',   37: 'C7',   38: 'C5',   39: 'C7',   40: 'C5',   41: 'C13',   42: 'C5',   43: 'C13',   44: 'C5',   45: 'C5',   46: 'C5',   47: 'C5',   48: 'C5',   49: 'C5',   50: 'C5',   51: 'C5',   52: 'C5',   53: 'C5',   54: 'C14',   55: 'C5',   56: 'C5',   57: 'C5',   58: 'C5',   59: 'C5',   60: 'C5',   61: 'C5',   62: 'C5',   63: 'C7',   64: 'C5',   65: 'C7',   66: 'C5',   67: 'C5',   68: 'C7',   69: 'C5',   70: 'C7',   71: 'C5',   72: 'C5',   73: 'C7',   74: 'C5',   75: 'C15'},  'VC': {0: 'MI',   1: 'MI',   2: 'IN',   3: 'IN',   4: 'MI',   5: 'MI',   6: 'LOF',   7: 'MI',   8: 'MI',   9: 'MI',   10: 'MI',   11: 'MI',   12: 'MI',   13: 'MI',   14: 'MI',   15: 'MI',   16: 'MI',   17: 'MI',   18: 'MI',   19: 'MI',   20: 'LOF',   21: 'MI',   22: 'MI',   23: 'MI',   24: 'MI',   25: 'MI',   26: 'MI',   27: 'MI',   28: 'MI',   29: 'MI',   30: 'MI',   31: 'MI',   32: 'MI',   33: 'MI',   34: 'MI',   35: 'MI',   36: 'MI',   37: 'MI',   38: 'MI',   39: 'MI',   40: 'MI',   41: 'MI',   42: 'MI',   43: 'MI',   44: 'MI',   45: 'MI',   46: 'MI',   47: 'MI',   48: 'MI',   49: 'MI',   50: 'MI',   51: 'MI',   52: 'MI',   53: 'MI',   54: 'MI',   55: 'MI',   56: 'MI',   57: 'MI',   58: 'MI',   59: 'MI',   60: 'MI',   61: 'MI',   62: 'MI',   63: 'MI',   64: 'MI',   65: 'MI',   66: 'MI',   67: 'MI',   68: 'MI',   69: 'MI',   70: 'MI',   71: 'MI',   72: 'MI',   73: 'MI',   74: 'MI',   75: 'MI'}})

我想同时展开和收缩列，以便输出如下所示：

Type    POS FN  VC  ID              DN              FC  SCZ BPAD PC
A1      C1  F1  MI  S1              D1              1   0   0    0
A2      C2  F2  MI  S2              D2              0   1   0    0
        C3  F3  IN  S3|S4           D3|D4           1   1   0    0
        C4  F4  MI  S5              D5              0   1   0    0
A3      C5  F5  MI  S6              D6              1   0   0    0
            F6  MI  S8|S9|S10|S11   D8|D9|D10|D11   3   0   0    1
            F7  MI  S12|S13         D11|D12         2   0   0    0
        C6  F5  LOF S6              D6              1   0   0    0
        C7  F1  MI  S1              D1              1   0   0    0
            F5  MI  S7              D7              1   0   0    0
            F8  MI  S15             D15             0   0   1    0
        C8  F1  MI  S1              D1              1   0   0    0
            F8  MI  S14             D14             0   0   1    0

我尝试了以下代码来收缩和展开数据

df1 = df.groupby(['Type', 'FN']).agg(lambda x: '|'.join(x.unique()))[['POS', 'VC', 'ID', 'DN']]

df2 = pd.get_dummies(df.set_index(['Type', 'FN'])['Group']).sum(level=[0, 1])
pd.concat([df1, df2], axis=1)

但在输出中，POS也被拆分了，但我想对此进行扩展

python

来源：https://stackoverflow.com/questions/74651970/how-to-simultaneoulsy-expand-and-shrink-the-dataframe-as-per-some-conditions

1条答案

按热度按时间

oknrviil1#

我认为您需要按3列进行汇总：

df1 = df.groupby(['Type','POS', 'FN'])[['VC','ID','DN']].agg(lambda x: '|'.join(x.unique()))

df2 = pd.get_dummies(df.set_index(['Type','POS', 'FN'])['Group']).sum(level=[0, 1, 2])
df = pd.concat([df1, df2], axis=1)
print (df.head(20))
              VC       ID       DN  BPAD  FC  PC  SCZ
Type POS FN                                          
A1   C1  F1   MI       S1       D1     0   1   0    0
A2   C2  F2   MI       S2       D2     0   0   0    1
     C3  F3   IN    S3|S4    D3|D4     0   1   0    1
     C4  F4   MI       S5       D5     0   0   0    1
A3   C10 F12  MI      S23      D23     0   0   1    0
     C11 F13  MI      S24      D24     0   1   0    0
     C12 F16  MI      S29      D29     0   1   0    0
     C13 F3   MI   S32|S3   D32|D3     0   2   0    0
     C14 F26  MI      S41      D41     0   1   0    0
     C15 F39  MI      S57      D57     0   0   1    0
     C5  F11  MI      S22      D22     0   1   0    0
         F12  MI      S23      D23     0   0   1    0
         F13  MI      S25      D25     0   1   0    0
         F14  MI  S26|S27  D26|D27     0   2   0    0
         F15  MI      S28      D28     0   1   0    0
         F16  MI      S29      D29     0   1   0    0
         F17  MI      S30      D30     0   1   0    0
         F18  MI      S31      D31     0   1   0    0
         F19  MI      S33      D33     0   1   0    0
         F20  MI      S34      D34     0   1   0    0

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python 如何根据条件同时扩展和收缩 Dataframe ？

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