I have a dataframe df looking as follows:
id cited_ids dummy_paper d
2 [4] NaN NaN
4 [9,18,6] NaN NaN
6 [] 9 0
7 [2] NaN NaN
9 [4] 7 0
14 [18,6] 3 0
18 [7] 1 0
What I would like to do is to substitute into df['cited_ids'] 0 whenever the corresponding id has d=0 (i) and replace d=1 if there is at least one 0 in the list of df['cited_ids'] and the previous d was not 0 (ii). In other words, the first step (i) would result in:
id cited_ids dummy_paper d
2 [4] NaN NaN
4 [0,0,6] NaN NaN
6 [] 9 0
7 [2] NaN NaN
9 [4] 7 0
14 [0,6] 3 0
18 [0] 1 0
The second step (ii) would then result in:
id cited_ids dummy_paper d
2 [4] NaN NaN
4 [0,0,6] NaN 1
6 [] 9 0
7 [2] NaN NaN
9 [4] 7 0
14 [0,6] 3 0
18 [0] 1 0
Please also notice that the dataframe comes with df['cited_ids'] being an object.
df.to_dict() gives:
{'docdb': {0: 2, 1: 4, 2: 6, 3: 7, 4: 9, 5: 14, 6: 18},
'cited_docdb': {0: [4],
1: [9, 18, 6],
2: [],
3: [2],
4: [4],
5: [18, 6],
6: [7]},
'fronteer': {0: nan, 1: nan, 2: 9.0, 3: nan, 4: 7.0, 5: 3.0, 6: 1.0},
'distance': {0: nan, 1: nan, 2: 0.0, 3: nan, 4: 0.0, 5: 0.0, 6: 0.0}}
Thank you
>Solution :
The exact logic is unclear and your output doesn’t seem to match the description, but IIUC:
s = df.set_index('id')['d'].dropna().convert_dtypes()
df['cited_ids'] = [[s.get(i, i) for i in x]
for x in df['cited_ids']]
m = [0 in x for x in df['cited_ids']]
df.loc[m&df['d'].isna(), 'd'] = 1
output:
id cited_ids dummy_paper d
0 2 [4] NaN NaN
1 4 [0, 0, 0] NaN 1.0
2 6 [] 9.0 0.0
3 7 [2] NaN NaN
4 9 [4] 7.0 0.0
5 14 [0, 0] 3.0 0.0
6 18 [7] 1.0 0.0