Python pandas join on with overwrite -

i realize question similar join or merge overwrite in pandas, accepted answer not work me since want use on='keys' df.join().

i have dataframe df looks this:

    keys    values 0      0  0.088344 1      0  0.088344 2      0  0.088344 3      0  0.088344 4      0  0.088344 5      1  0.560857 6      1  0.560857 7      1  0.560857 8      2  0.978736 9      2  0.978736 10     2  0.978736 11     2  0.978736 12     2  0.978736 13     2  0.978736 14     2  0.978736 

then have series s (which result df.groupy.apply()) same keys:

keys 0       0.183328 1       0.239322 2       0.574962 name: new_values, dtype: float64 

basically want replace 'values' in df values in series, keys every keys block gets same new value. currently, follows:

df = df.join(s, on='keys') df['values'] = df['new_values'] df = df.drop('new_values', axis=1) 

the obtained (and desired) result then:

    keys    values 0      0  0.183328 1      0  0.183328 2      0  0.183328 3      0  0.183328 4      0  0.183328 5      1  0.239322 6      1  0.239322 7      1  0.239322 8      2  0.574962 9      2  0.574962 10     2  0.574962 11     2  0.574962 12     2  0.574962 13     2  0.574962 14     2  0.574962 

that is, add new column , using on='keys' gets corrects shape. assign values new_values , remove new_values column. of course works perfectly, problem being find extremely ugly.

is there better way this?

you try like:

df = df[df.columns[df.columns!='values']].join(s, on='keys') 

make sure s named 'values' instead of 'new_values'.

to knowledge, pandas doesn't have ability join "force overwrite" or "overwrite warning".