python 3.x - Filling in nans for numbers in a column-specific way -


given dataframe , list of indexes, there efficient pandas function put nan value values vertically preceeding each of entries of list?

for example, suppose have list [4,8] , following dataframe:

index     0      1 5         1      2 2         9      3  4         3.2    3 8         9      8.7

the desired output simply:

index     0        1 5         nan      nan 2         nan      nan  4         3.2      nan 8         9        8.7

any suggestions such function fast?

here's 1 numpy approach based on np.searchsorted -

s = [4,8]  = df.values idx = df.index.values sidx = np.argsort(idx) matching_row_indx = sidx[np.searchsorted(idx, s, sorter = sidx)] mask = np.arange(a.shape[0])[:,none] < matching_row_indx a[mask] = np.nan

sample run -

in [107]: df out[107]:           0    1 index           5      1.0  2.0 2      9.0  3.0 4      3.2  3.0 8      9.0  8.7  in [108]: s = [4,8]  in [109]: = df.values      ...: idx = df.index.values      ...: sidx = np.argsort(idx)      ...: matching_row_indx = sidx[np.searchsorted(idx, s, sorter = sidx)]      ...: mask = np.arange(a.shape[0])[:,none] < matching_row_indx      ...: a[mask] = np.nan      ...:   in [110]: df out[110]:           0    1 index           5      nan  nan 2      nan  nan 4      3.2  nan 8      9.0  8.7

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