How to implement left outer join in python pandas? [duplicate]

Question

I have been trying to implement left outer join in python.I see that there is slight difference between left join and left outer join.

As in this link : LEFT JOIN vs. LEFT OUTER JOIN in SQL Server

I could get my hands on below with sample examples:

import pandas as pd
import numpy as np

df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
'value1': np.random.randn(4)})

df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'],
 'value2': np.random.randn(4)})

df3 = df1.merge(df2, on=['key'], how='left')

This gives records from df1 in total (including the intersected ones)

But how do I do the left outer join which has only records from df1 which are not in df2?

Not: This is example only.I might have large number of columns (different) in either dataframes.

Please help.

Answer 1

set param indicator=True, this will add a column _merge you then filter just the rows that are left_only:

In [46]:
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
'value1': np.random.randn(4)})
​
df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'],
 'value2': np.random.randn(4)})
​
df3 = df1.merge(df2, on=['key'], how='left', indicator=True)
df3

Out[46]:
  key    value1    value2     _merge
0   A -0.346861       NaN  left_only
1   B  1.120739  0.558272       both
2   C  0.023881       NaN  left_only
3   D -0.598771 -0.823035       both
4   D -0.598771  0.369423       both

In [48]:
df3[df3['_merge'] == 'left_only']

Out[48]:
  key    value1  value2     _merge
0   A -0.346861     NaN  left_only
2   C  0.023881     NaN  left_only

if on older version then use isin with ~ to negate the mask:

In [50]:
df3[~df3['key'].isin(df2['key'])]

Out[50]:
  key    value1  value2
0   A -0.346861     NaN
2   C  0.023881     NaN