熊猫重复数据删除并返回重复的索引列表

Question

I have a pandas dataframe with 500k rows. Structured like this, where the document column are strings:

   document_id                                           document
0            0                               Here is our forecast
1            1  Traveling to have a business meeting takes the...
2            2                      test successful. way to go!!!
3            3  Randy, Can you send me a schedule of the salar...
4            4                  Let's shoot for Tuesday at 11:45.

When I de-dupe the dataframe based on the contents of the document column using df.drop_duplicates(subset='document') , I end up with half the number of documents.

Now that I have my original dataframe and a second dataframe with the unique set of document values, I would like to compare the two to get a list of document_id 's that are duplicates.

For example, if the associated document for document_id 4, 93, and 275 are all 'Let's shoot for Tuesday at 11:45.', then how do I get a dataframe with document in one column, and list of associated duplicate document_id 's in another column?

     document_ids                                           document    
        ...
4    [4, 93, 275]                  Let's shoot for Tuesday at 11:45.

I know that I could use a for loop, and compare each document every other document in the dataframe, and save all matches, but I am trying to avoid iterating over 500k lines multiple times. What instead is the most pythonic way of going about this?

Answer 1

I would like to compare the two to get a list of document_id's that are duplicates.

You should be able to do this using your "initial" DataFrame with .duplicated(keep=False) . Here's an example:

In [1]: import pandas as pd                                                                                                                                   

In [2]: df = pd.DataFrame({ 
   ...:     'document_id': range(10), 
   ...:     'document': list('abcabcdedb') # msg 'e' is not duplicated
   ...: })

In [3]: dupes = df.document.duplicated(keep=False)                                                                                                            
In [4]: df.loc[dupes].groupby('document')['document_id'].apply(list).reset_index()                                                                           
Out[4]: 
  document document_id
0        a      [0, 3]
1        b   [1, 4, 9]
2        c      [2, 5]
3        d      [6, 8]