I realize this question is similar to join or merge with overwrite in pandas, but the accepted answer does not work for me since I want to use the on='keys' from df.join().
I have a DataFrame df which looks like 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 I have a Series s (which is a result from some df.groupy.apply()) with the same keys:
keys 0 0.183328 1 0.239322 2 0.574962 Name: new_values, dtype: float64 Basically I want to replace the 'values' in the df with the values in the Series, by keys so every keys block gets the same new value. Currently, I do it as follows:
df = df.join(s, on='keys') df['values'] = df['new_values'] df = df.drop('new_values', axis=1) The obtained (and desired) result is 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, I add it as a new column and by using on='keys' it gets the corrects shape. Then I assign values to be new_values and remove the new_values column. This of course works perfectly, the only problem being that I find it extremely ugly.
Is there a better way to do this?
