Pandas dataframe to python dictionary with relevant column names stored in list as dict values-CodePudding

I am trying to take data from a pandas dataframe and transform it to a desired dictionary. Here's an example of the data:

    data =[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1],[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2]]
    Utable = pd.DataFrame(data, columns =['Type1', 'Type2', 'Type3', 'Type4', 'Type5', 'Type6', 'Type7', 'Type8', 'ID'])

The dictionary I need is the ID records as the dict key and the values need to be a list of the unacceptable Type #s ascertained from the column name. The Types are unacceptable if they are 0.0. So for this example the output would be:

    {1: [1, 2, 3, 4, 5, 6, 7, 8], 2: [1, 2, 4, 5, 6, 7, 8]}

I could figure out how to get the type values stored as list with the ID as the dict key using:

    U = Utable.set_index('ID').T.to_dict('list')

which gives:

   {1: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 2: [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

but I can't figure out how to get the contents from the column name stored in the list as the dict values.

Thanks very much for any help.

CodePudding user response：

You could use the parameter orient=index when converting to a dictionary; then use a list comprehension to get the desired list as values:

out = {k: [int(i[-1]) for i, v in d.items() if v==0] 
       for k, d in Utable.set_index('ID').to_dict('index').items()}

Output:

{1: [1, 2, 3, 4, 5, 6, 7, 8], 2: [1, 2, 4, 5, 6, 7, 8]}