python - Divide part of a dataframe by another while keeping columns that are not being divided -


i have 2 data frames below:

   sample_name   c14-cer   c16-cer    c18-cer  c18:1-cer   c20-cer   0          1 1  0.161456  0.033139   0.991840   2.111023  0.846197   1         1 10  0.636140  1.024235  36.333741  16.074662  3.142135   2         1 13  0.605840  0.034337   2.085061   2.125908  0.069698    3         1 14  0.038481  0.152382   4.608259   4.960007  0.162162    4          1 5  0.035628  0.087637   1.397457   0.768467  0.052605    5          1 6  0.114375  0.020196   0.220193   7.662065  0.077727         sample_name   c14-cer   c16-cer    c18-cer  c18:1-cer    c20-cer   0          1 1  0.305224  0.542488  66.428382  73.615079  10.342252    1         1 10  0.814696  1.246165  73.802644  58.064363  11.179206    2         1 13  0.556437  0.517383  50.555948  51.913547   9.412299    3         1 14  0.314058  1.148754  56.165767  61.261950   9.142128    4          1 5  0.499129  0.460813  40.182454  41.770906   8.263437    5          1 6  0.300203  0.784065  47.359506  52.841821   9.833513

i want divide numerical values in selected cells of first second , using following code:

df1_int.loc[:,'c14-cer':].div(df2.loc[:,'c14-cer':])

however, way lose information column "sample_name". 

    c14-cer   c16-cer   c18-cer  c18:1-cer   c20-cer   0   0.528977  0.061088  0.014931   0.028677  0.081819   1   0.780831  0.821909  0.492309   0.276842  0.281070 2   1.088785  0.066367  0.041243   0.040951  0.007405 3   0.122529  0.132650  0.082047   0.080964  0.017738 4   0.071381  0.190178  0.034778   0.018397  0.006366 5   0.380993  0.025759  0.004649   0.145000  0.007904

how can perform division while keeping column "sample_name" in resulting dataframe?

you can selectively overwrite using loc, same way you're performing division:

df1_int.loc[:,'c14-cer':] = df1_int.loc[:,'c14-cer':].div(df2.loc[:,'c14-cer':])

this preserves sample_name col:

in [12]: df.loc[:,'c14-cer':] = df.loc[:,'c14-cer':].div(df1.loc[:,'c14-cer':]) df  out[12]:       sample_name   c14-cer   c16-cer   c18-cer  c18:1-cer   c20-cer index                                                                0             1 1  0.528975  0.061087  0.014931   0.028677  0.081819 1            1 10  0.780831  0.821910  0.492309   0.276842  0.281070 2            1 13  1.088785  0.066367  0.041243   0.040951  0.007405 3            1 14  0.122528  0.132650  0.082047   0.080964  0.017738 4             1 5  0.071380  0.190179  0.034778   0.018397  0.006366 5             1 6  0.380992  0.025758  0.004649   0.145000  0.007904

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