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如何使用pandas进行左连接

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我有2个数据框,它看起来像这样:DF1:

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

DF2:

Region, RegionScore
R1,1
R2,2

我如何让这2个加入1个数据帧,结果如下:

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

非常感谢!

EDIT1:

我使用了df.merge(df_new)得到了这个错误消息:

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:我意识到我的df_new是一个数据系列(使用groupby创建)而不是数据帧 . 现在我已将其转换为数据框,这里是info:print(df.info())Int64Index:1111条目,0到1110数据列(共8列):product 1111非null对象reviewuserId 1111非null object reviewprofileName 1111非null对象reviewelpfulness 881非null float64 reviewcore 1111非null float64 reviewtime 1111非null int64 reviewummary 1111非null对象reviewtext 1111非null对象dtypes:float64(2),int64(1),object (5)内存使用量:56.4 KB无

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 "&#34;MoviesMusicTheatre&#34;"   

   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)
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

现在错误消息msg看起来像这样:

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

打印完这些信息后,我只需添加以下内容即可解决问题: df_new_2 = df_new.to_frame().reset_index()

1 回答

  • 2

    当你跳过 R3 的行时,你're asking for isn'左边的合并,你只想执行内部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
    

    左合并会导致:

    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
    

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