how to do left join using pandas

Question

i have 2 data frame, it looks like this: DF1:

Product, Region, ProductScore
AAA, R1,100
AAA, R2,100
BBB, R2,200
BBB, R3,200

DF2:

Region, RegionScore
R1,1
R2,2

How can I let these 2 join into 1 dataframe, the result shall be like this:

Product, Region, ProductScore, RegionScore
AAA, R1,100,1
AAA, R2,100,2
BBB, R2,200,2

Thanks a lot!

Edit1:

I used the df.merge(df_new) Got this error msg:

  File "C:\Python34\lib\site-packages\pandas\core\frame.py", line 4071, in merge
    suffixes=suffixes, copy=copy)
  File "C:\Python34\lib\site-packages\pandas\tools\merge.py", line 37, in merge
    copy=copy)
  File "C:\Python34\lib\site-packages\pandas\tools\merge.py", line 183, in __init__
    self.join_names) = self._get_merge_keys()
  File "C:\Python34\lib\site-packages\pandas\tools\merge.py", line 318, in _get_merge_keys
    self._validate_specification()
  File "C:\Python34\lib\site-packages\pandas\tools\merge.py", line 409, in _validate_specification
    if not self.right.columns.is_unique:
AttributeError: 'list' object has no attribute 'is_unique'

EDIT2: I realize my df_new is a data series (created by using groupby) instead of data frame. now I have converted it to a data frame, here is the info: print(df.info()) Int64Index: 1111 entries, 0 to 1110 Data columns (total 8 columns): product 1111 non-null object reviewuserId 1111 non-null object reviewprofileName 1111 non-null object reviewelpfulness 881 non-null float64 reviewscore 1111 non-null float64 reviewtime 1111 non-null int64 reviewsummary 1111 non-null object reviewtext 1111 non-null object dtypes: float64(2), int64(1), object(5) memory usage: 56.4+ KB None

print(df_new_2.info())

<class 'pandas.core.frame.DataFrame'>
Index: 1089 entries, A100Y8WSLFJN7Q to AZWBQPQN96SS6
Data columns (total 1 columns):
reviewelpfulnessbyuserid    864 non-null float64
dtypes: float64(1)
memory usage: 12.8+ KB
None

---

print(df.head())

      product    reviewuserId                         reviewprofileName  \
0  B003AI2VGA  A141HP4LYPWMSR          Brian E. Erland "Rainbow Sphinx"   
1  B003AI2VGA  A328S9RN3U5M68                                Grady Harp   
2  B003AI2VGA  A1I7QGUDP043DG                 Chrissy K. McVay "Writer"   
3  B003AI2VGA  A1M5405JH9THP9                              golgotha.gov   
4  B003AI2VGA   ATXL536YX71TR  KerrLines ""MoviesMusicTheatre""   

   reviewelpfulness  reviewscore  reviewtime  \
0               1.0            3  1182729600   
1               1.0            3  1181952000   
2               0.8            5  1164844800   
3               1.0            3  1197158400   
4               1.0            3  1188345600   

                                       reviewsummary  \
0  There Is So Much Darkness Now ~ Come For The M...   
1  Worthwhile and Important Story Hampered by Poo...   
2                      This movie needed to be made.   
3                  distantly based on a real tragedy   
4  What's going on down in Juarez and shining a l...   

                                          reviewtext  
0  Synopsis: On the daily trek from Juarez Mexico...  
1  THE VIRGIN OF JUAREZ is based on true events s...  
2  The scenes in this film can be very disquietin...  
3  THE VIRGIN OF JUAREZ (2006)<br />directed by K...  
4  Informationally this SHOWTIME original is esse...  

---

print(df_new_2.head())

                reviewelpfulnessbyuserid
reviewuserId                            
A100Y8WSLFJN7Q                       NaN
A103VZ3KDF2RT5                  0.555556
A1041HQGJDKFG5                  0.000000
A10FBJXMQPI0LL                  0.333333
A10LIHFA4SSK3F                  0.000000

Now the error msg looks like this:

  File "pandas\hashtable.pyx", line 694, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12245)
KeyError: 'reviewuserId'

After print these information, i fixed the problem by simply add: df_new_2 = df_new.to_frame().reset_index()

Answer 1

What you're asking for isn't a left merge as you skipped the row with R3, you just want to perform an inner merge:

In [120]:
df.merge(df1)

Out[120]:
  Product Region  ProductScore  RegionScore
0     AAA     R1           100            1
1     AAA     R2           100            2
2     BBB     R2           200            2

a left merge would result in this:

In [121]:
df.merge(df1, how='left')

Out[121]:
  Product Region  ProductScore  RegionScore
0     AAA     R1           100            1
1     AAA     R2           100            2
2     BBB     R2           200            2
3     BBB     R3           200          NaN