Asked By – Terence Chow
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?
Now we will see solution for issue: Pandas Left Outer Join results in table larger than left table
You can expect this to increase if keys match more than one row in the other DataFrame:
In : df = pd.DataFrame([[1, 3], [2, 4]], columns=['A', 'B']) In : df2 = pd.DataFrame([[1, 5], [1, 6]], columns=['A', 'C']) In : df.merge(df2, how='left') # merges on columns A Out: 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 : df2.drop_duplicates(subset=['A']) # you can use take_last=True Out: A C 0 1 5 In : df.merge(df2.drop_duplicates(subset=['A']), how='left') Out: A B C 0 1 3 5 1 2 4 NaN
This question is answered By – Andy Hayden
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