熊猫-通过基于两列的ID链接多行将消息分组为对话

Question

I have some data that tracks tweets and responses based on source_id and response_id. The source_id could be associated with an original post or a response that has its own response. If there are multiple responses, then each response will have a source_id and that source_id will appear in the response_id of the corresponding response.

Take this dataframe for example:

df = pd.DataFrame({
'date': ['2018-10-02', '2018-10-03', '2018-10-03', '2018-10-03', '2018-10-03', '2018-10-03', '2018-10-03', '2018-10-03', '2018-10-03'],
'id': ['334', '335', '336', '337', '338', '340', '341', '343', '358'],
'source_id': ['830', '636', '657', '569', '152', '975', '984', '720', '524'],
'reply_id': [np.nan, '495', '636', '657', '569', '830', '152', np.nan, np.nan]
})

And its output:

         date   id source_id reply_id
0  2018-10-02  334       830      NaN
1  2018-10-03  335       636      495
2  2018-10-03  336       657      636
3  2018-10-03  337       569      657
4  2018-10-03  338       152      569
5  2018-10-03  340       975      830
6  2018-10-03  341       984      152
7  2018-10-03  343       720      NaN
8  2018-10-03  358       524      NaN

Each row contains data for a single message. There is a unique ID for the message whether it's a tweet or a response to a tweet. In this sample, there are two "conversations" with one or more responses to an original post and two standalone tweets with no responses. The tweets with no responses are df.iloc[7] and df.iloc[8] both of which have NaNs in reply_id and their source_ids do not appear in the reply_ids of any other rows. While df.iloc[0] has NaN in reply_id, its source_id appears in the reply_id of df.iloc[5] . So that would be counted as one conversation.

What I'm really struggling with is how to chain together a series of tweets/responses such as df.iloc[1] , df.iloc[2] , df.iloc[3] , df.iloc[4] , and df.iloc[6] and count all of that as one conversation. And for this particular conversation, there is no data available for the original post so there is no row with source_id = 495.

Does anyone have any idea on how to approach this?

Answer 1

From my understanding , this more like a network problem , so we using networkx

import networkx as nx 
G=nx.from_pandas_edgelist(df.dropna(), 'reply_id', 'source_id')
l=list(nx.connected_components(G))
newdf=pd.DataFrame(l)
newdf
Out[334]: 
     0    1     2     3     4     5
0  975  830  None  None  None  None
1  984  495   636   152   569   657 
# here you saw all the value belong to one group, they are in the same line 

More detail , right now same group of index will have same id

d=[dict.fromkeys(y,x)for x , y in enumerate(list(nx.connected_components(G)))]
d={k:v for element in d for k,v in element.items()}
ids=df.reply_id.dropna().map(d)
ids
Out[344]: 
1    1
2    1
3    1
4    1
5    0
6    1
Name: reply_id, dtype: int64