Pandas Left Outer Join results in table larger than left table

Question

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...

My left table is 192572 rows and 8 columns.

My right table is 42160 rows and 5 columns.

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

Therefore I merge them as such:

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

But then the combined shape is 236569.

What am I misunderstanding?

Answer 1

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 

To avoid this behaviour drop the duplicates in df2:

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