pandas 左外联接的结果表大于左表

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根据我对左外部联接的了解，结果表的行数永远不应超过左表的行数……如果这是错误的话，请告诉我...

From what I understand about a left outer join, the resulting table should never have more rows than the left table...Please let me know if this is wrong...

我的左表是192572行和8列.

My left table is 192572 rows and 8 columns.

我的右边表格是42160行和5列.

My right table is 42160 rows and 5 columns.

我的左"表中有一个名为"id"的字段，该字段与我的右表中的一列键"相匹配.

My Left table has a field called 'id' which matches with a column in my right table called 'key'.

因此，我将它们合并为这样:

Therefore I merge them as such:

combined = pd.merge(a,b,how='left',left_on='id',right_on='key')

但是合并后的形状是236569.

But then the combined shape is 236569.

我误会什么?

推荐答案

如果键与另一个DataFrame中的多个行匹配，则可以预期这种情况会增加:

You can expect this to increase if keys match more than one row in the other DataFrame:

In [11]: df = pd.DataFrame([[1, 3], [2, 4]], columns=['A', 'B']) In [12]: df2 = pd.DataFrame([[1, 5], [1, 6]], columns=['A', 'C']) In [13]: df.merge(df2, how='left') # merges on columns A Out[13]: A B C 0 1 3 5 1 1 3 6 2 2 4 NaN

为避免此行为，请将重复项放在df2中:

In [21]: df2.drop_duplicates(subset=['A']) # you can use take_last=True Out[21]: A C 0 1 5 In [22]: df.merge(df2.drop_duplicates(subset=['A']), how='left') Out[22]: A B C 0 1 3 5 1 2 4 NaN