Hello all,

My problem is particularly with the left out join. I already know that the resulting take should always have fewer rows than the corresponding left table, right?

Here's a scenario:

My left table is 200000 rows and 8 columns.

My right table is 50000 rows and 5 columns.

Note that the left table has the field id which basically matches the data with a corresponding column which is in the right table and I call it key.

For merging these two, I make use of the following code:

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

And the combined shape becomes 250000.

Is there anything that I am doing wrong here? Jan 24, 2019 in Python 108 views

## 1 answer to this question.

Hi, there is one scenario where the number increases. This is if the keys match more than one single time of the same row in the different dataframe.

Check out this below:

```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```

So, what we usually do to avoid this is we sure we drop the duplicates in the df2 by using the following piece of code:

```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```

Hope this helped! answered Jan 24, 2019 by
• 3,520 points

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