I have a large dataframe, here's an extract:
| Index | Protocol ID | Activity ID | Detail 1 | Detail 2 |
|---|---|---|---|---|
| 0 | 1509 | 15 | a | s |
| 1 | 636 | 159 | b | t |
| 2 | 787 | 159 | c | u |
| 3 | 796 | 159 | d | v |
| 4 | 1174 | 159 | e | w |
| 5 | 787 | 252 | f | x |
| 6 | 1029 | 252 | g | y |
| 7 | 1188 | NaN | h | z |
| 8 | 1848 | NaN | i | a |
| 9 | TBD | NaN | j | b |
| 10 | TBD | NaN | k | c |
| 11 | 1029 | 253 | l | d |
| 12 | 1170 | 253 | m | e |
| 13 | 1468 | NaN | n | f |
| 14 | 957 | NaN | o | g |
| 15 | 1029 | 254 | p | h |
| 16 | 957 | 254 | q | i |
| 17 | 841 | 166 | r | j |
I need to create a dictionary of dictionaries named by Activity ID. In each "activity dictionary", I need all the dataframes named by the protocol ID and in each of the protocol dataframe, I need the complete informations from all the initial columns.
So, dictionary of activities → In each one, dictionary of their protocols → In each one, dataframe of all the information linked to the protocol
When I code
activity_dict = initial_dataframe.set_index('Activity ID').T.to_dict('dict')
It does create me a dictionary named by activity ID but when I click on an activity, it only shows the last of the protocols and the information linked to it. Since this is basically doing the last step I need, I'm trying to add the intermediate step which is the protocols dataframes which should appear when I click on an activity dictionary or run for example:
activity_dict["159"]
Which right now is showing me
{'Protocol ID': '1174',
'Protocol Name': 'My analyses',
'I/O': 'O',
'Prot Owner': 'lorem.ipsum',
'Notes': 'Done',
'Activity Name': 'Test',
'Comments': nan,
'Activity Owner': nan,
'Protocol description': nan,
'Fonte': nan}
When I would like for it to show me a link to the dataframes linked to not only protocol 1174, but also 636 and 787.
Does anybody know how to do this?
Thank you in advance
CodePudding user response：
Because dictionary has unique keys one possible solution is create lists for all values:
activity_dict = initial_dataframe.groupby('Activity ID').agg(list).to_dict('index')
print (activity_dict)
{'15': {'Protocol ID': ['1509'], 'Detail 1': ['a'], 'Detail 2': ['s']},
'159': {'Protocol ID': ['636', '787', '796', '1174'], 'Detail 1': ['b', 'c', 'd', 'e'], 'Detail 2': ['t', 'u', 'v', 'w']},
'166': {'Protocol ID': ['841'], 'Detail 1': ['r'], 'Detail 2': ['j']},
'252': {'Protocol ID': ['787', '1029'], 'Detail 1': ['f', 'g'], 'Detail 2': ['x', 'y']},
'253': {'Protocol ID': ['1029', '1170'], 'Detail 1': ['l', 'm'], 'Detail 2': ['d', 'e']},
'254': {'Protocol ID': ['1029', '957'], 'Detail 1': ['p', 'q'], 'Detail 2': ['h', 'i']}}
Or use join:
activity_dict = initial_dataframe.groupby('Activity ID').agg(','.join).to_dict('index')
print (activity_dict)
{'15': {'Protocol ID': '1509', 'Detail 1': 'a', 'Detail 2': 's'},
'159': {'Protocol ID': '636,787,796,1174', 'Detail 1': 'b,c,d,e', 'Detail 2': 't,u,v,w'},
'166': {'Protocol ID': '841', 'Detail 1': 'r', 'Detail 2': 'j'},
'252': {'Protocol ID': '787,1029', 'Detail 1': 'f,g', 'Detail 2': 'x,y'},
'253': {'Protocol ID': '1029,1170', 'Detail 1': 'l,m', 'Detail 2': 'd,e'},
'254': {'Protocol ID': '1029,957', 'Detail 1': 'p,q', 'Detail 2': 'h,i'}}