python - 堆栈后删除多个值

Question

我有这样的方阵。

            ACSM3     ACSX12    ADXM28  ...   UGT2B15      VCAN        XK
ACSM3    1.000000  0.929347  0.999914  ...  0.986433  0.999947 -0.999680
ACSX12    0.929347  1.000000  0.924428  ...  0.977350  0.925496 -0.919704
ADXM28   0.999914  0.924428  1.000000  ...  0.984196  0.999996 -0.999925
ADAM28   0.999976  0.926774  0.999981  ...  0.985275  0.999994 -0.999831
ADH1B   -0.999509 -0.917317 -0.999834  ... -0.980802 -0.999778  0.999982
ADTRP   -0.999039 -0.912273 -0.999528  ... -0.978290 -0.999438  0.999828
AEBP1    0.983312  0.846668  0.985611  ...  0.940104  0.985133 -0.987601
AKR1B10 -0.999658 -0.919371 -0.999915  ... -0.981800 -0.999874  1.000000
UBL3     0.997347  0.900002  0.998215  ...  0.971864  0.998043 -0.998870
UGT2B15  0.986433  0.977350  0.984196  ...  1.000000  0.984690 -0.981961
VCAN     0.999947  0.925496  0.999996  ...  0.984690  1.000000 -0.999887
XK      -0.999680 -0.919704 -0.999925  ... -0.981961 -0.999887  1.000000

使用堆栈函数后，我将数据调整为我想要的形状，但正如您所见，由于相互比较，所有数据都有多个值。

dfHealty = df_healtyWithGenes.stack().reset_index()
dfHealty.columns = ['gene1', 'gene2', 'score']
dfHealty = dfHealty[dfHealty.gene1 != dfHealty.gene2]

我可以按分数过滤，但这不是个好主意，数据可能会损坏。

如何按基因列过滤？

gene1   gene2   score
EPB41L4B PGC    0.496713249 
PGC EPB41L4B    0.496713249 
CHGA    MT1G    0.496751983
MT1G    CHGA    0.496751983
AEBP1   FCER1G  0.497061368 
FCER1G  AEBP1   0.497061368 
ADTRP   CAPN9   0.497122603
CAPN9   ADTRP   0.497122603
FAM189A2 GLUL   0.49721763
GLUL FAM189A2 0.49721763
CA9 DUOX1   0.497233294
DUOX1   CA9 0.497233294
EDNRA   MSLN    0.497267565
MSLN    EDNRA   0.497267565
HRASLS2 LIPF    0.497581499
LIPF    HRASLS2 0.497581499
EPB41L4B    NEDD4L  0.497613643
NEDD4L  EPB41L4B    0.497613643

我需要像这样转换数据。

gene1   gene2   score
EPB41L4B PGC    0.496713249 
CHGA    MT1G    0.496751983
AEBP1   FCER1G  0.497061368 
ADTRP   CAPN9   0.497122603
FAM189A2 GLUL   0.49721763
CA9 DUOX1   0.497233294
EDNRA   MSLN    0.497267565

Answer 1

使用给定的数据，您可以像这样删除数据中的重复对

import pandas as pd

cols = ['gene1','gene2','score']
data = [['EPB41L4B', 'PGC',0.496713249], 
        ['PGC','EPB41L4B',0.496713249], 
        ['CHGA','MT1G',0.496751983],
        ['MT1G','CHGA',0.496751983],
        ['AEBP1','FCER1G',0.497061368 ],
        ['FCER1G','AEBP1',0.497061368], 
        ['ADTRP','CAPN9',0.497122603],
        ['CAPN9','ADTRP',0.497122603],
        ['FAM189A2','GLUL',0.49721763],
        ['GLUL','FAM189A2',0.49721763],
        ['CA9','DUOX1',0.497233294],
        ['DUOX1','CA9',0.497233294],
        ['EDNRA','MSLN',0.497267565],
        ['MSLN','EDNRA',0.497267565],
        ['HRASLS2','LIPF',0.497581499],
        ['LIPF','HRASLS2',0.497581499],
        ['EPB41L4B','NEDD4L',0.497613643],
        ['NEDD4L','EPB41L4B',0.497613643]]

df = pd.DataFrame(data,columns=cols)
df = df[df['gene1'] < df['gene2']]
print(df)

产生这样的输出

       gene1   gene2     score
0   EPB41L4B     PGC  0.496713
2       CHGA    MT1G  0.496752
4      AEBP1  FCER1G  0.497061
6      ADTRP   CAPN9  0.497123
8   FAM189A2    GLUL  0.497218
10       CA9   DUOX1  0.497233
12     EDNRA    MSLN  0.497268
14   HRASLS2    LIPF  0.497581
16  EPB41L4B  NEDD4L  0.497614