I have a dataframe df like this:
symbol P Q V ...
0 00001 10.9 100 100 ...
1 00001 11.0 100 200 ...
2 00001 11.0 100 200 ...
3 00002 12.1 -100 -100 ...
... ... ... ...
46 00004 6.9 300 300 ...
47 00004 7.0 -200 100 ...
48 00004 8.8 100 200 ...
49 00004 8.8 100 200 ...
I also have an index list idx=[2, ... 49]. I would like to change the value of rows which indexings are in this list. For instance, I want to set
df.loc[i,'Q'] = df.loc[i-1,'V']
However, this kind of command threw some errors because it requires a specific index instead of an index list. I tried to use a for loop like this:
for i in idx:
df.at[i, "Q"] = df.at[i-1, "V"]
Although it works, the speed would be slow down due to the for loop if the dataset is large. Is there any pandas command that could assign new values to a column of multiple rows based on an index list, like above?
>Solution :
If you convert idx to a numpy array, it could work:
idx = [2, 3, 48, 49]
idx = np.array(idx)
# Don't forget .values to ignore index
df.loc[idx, 'Q'] = df.loc[idx-1, 'V'].values
Output:
>>> df
symbol P Q V
0 1 10.9 100 100
1 1 11.0 100 200
2 1 11.0 200 200
3 2 12.1 200 -100
46 4 6.9 300 300
47 4 7.0 -200 100
48 4 8.8 100 200
49 4 8.8 200 200