pyPandas functionality request: reverse/negative df.drop

Question

I think a reverse/negative dataframe.drop functionality would be a very useful tool. Has anybody have a overcome to this?

Answer 1

Generally, I find myself using boolean indexing and the tilde operator when obtaining the inverse of a selection, rather than df.drop(), though the same concept applies to df.drop when boolean indexing is used to form the array of labels to drop. Hope that helps.

In [44]: df
Out[44]: 
          A         B
0  0.642010  0.116227
1  0.848426  0.710739
2  0.563803  0.416422

In [45]: cond = (df.A > .6) & (df.B > .3)

In [46]: df[cond]
Out[46]: 
          A         B
1  0.848426  0.710739

In [47]: df[~cond]
Out[47]: 
          A         B
0  0.642010  0.116227
2  0.563803  0.416422

Answer 2

If I understand you right, you can get this effect just by indexing with an "isin" on the index:

>>> df
         A         B         C
0  0.754956 -0.597896  0.245254
1 -0.987808  0.162506 -0.131674
2 -1.064639 -2.193629  1.814078
3 -0.483950 -1.290789  1.776827
4 -0.191055 -0.461204  0.412220
>>> df[df.index.isin([0, 2, 3])]   # Drop rows whose label is not in the set [0, 2, 3]
         A         B         C
0  0.754956 -0.597896  0.245254
2 -1.064639 -2.193629  1.814078
3 -0.483950 -1.290789  1.776827