Pandas：如何从另一个数据帧中获取出现次数？

Question

I am using Python Pandas. I have 2 data-frames (namely: df1, df2). 'df1' contains header-level data, like card-id, issued-on date etc. 'df2' has granular-level data, like each transaction performed by a specific card-id. 'Card-id' is common between the two dataframes.

df1:
 first_active_month          card_id  feature_1  feature_2  feature_3 
            2017-06  C_ID_92a2005557          5          2          1   
            2017-01  C_ID_3d0044924f          4          1          0   
            2016-08  C_ID_d639edf6cd          2          2          0   
            2017-09  C_ID_186d6a6901          4          3          0   
            2017-11  C_ID_cdbd2c0db2          1          3          0

df2:
   junk_id   authorized_flag          card_id  city_id Authorized 
    13292136               Y  C_ID_92a2005557      101          N   
    20069042               Y  C_ID_7a238b3713       69          N   
     5029656               Y  C_ID_92a2005557       17          N   
    16356907               N  C_ID_3d0044924f       -1          Y   
     8203441               Y  C_ID_fcf33361c2       17          N

I want to add a column "frequency" to df1 which will show me a count of occurrences of each card-id of df1 in df2. So, df1 should look like below:

df1 (after executing the command):
 first_active_month          card_id  feature_1  feature_2  feature_3    frequency
            2017-06  C_ID_92a2005557          5          2          1      2
            2017-01  C_ID_3d0044924f          4          1          0      5
            2016-08  C_ID_d639edf6cd          2          2          0      3
            2017-09  C_ID_186d6a6901          4          3          0      1
            2017-11  C_ID_cdbd2c0db2          1          3          0      7

Please note: I am new to Python / Pandas. I have already gone through multiple threads of this site, but all of them referred to counting in the same data-frame. I am looking for a counting using join/merge functionality. Threads which I have already browsed: this , this , this , this , this , this , this .

Answer 1

I think you need Series.map with Series.value_counts and Series.fillna for replace missing values:

df1['frequency'] = df1['card_id'].map(df2['card_id'].value_counts()).fillna(0).astype(int)
print (df1)
  first_active_month          card_id  feature_1  feature_2  feature_3  \
0            2017-06  C_ID_92a2005557          5          2          1   
1            2017-01  C_ID_3d0044924f          4          1          0   
2            2016-08  C_ID_d639edf6cd          2          2          0   
3            2017-09  C_ID_186d6a6901          4          3          0   
4            2017-11  C_ID_cdbd2c0db2          1          3          0   

   frequency  
0          2  
1          1  
2          0  
3          0  
4          0  

Answer 2

Actually, there is a part of answer in your question. You should count frequency first:

df3 = df2.groupby(["card_id"], as_index=False)[["junk_id"]].count().rename(columns={"junk_id":"frequency"})

The rename part is needed as pandas leaves column names after groupby operation unchanged. Next you can merge your dfs:

df1 = df1.merge(df3, how='left', on='card_id')

And you can surely do that in one line by substituting df3 into the merge statement.