Understanding Pandas Left Join and NaN Values
When working with DataFrames, it’s common to perform data merging or joining operations using libraries like Pandas. One of the most frequently encountered issues is why all values are replaced with NaN after a left join operation.
In this article, we’ll delve into the world of Pandas joins, explore what causes NaN values in left joins, and provide practical examples to resolve these issues.
Introduction to Pandas Joins
Pandas provides two primary types of joins: inner join and outer join. Inner joins return records that have matching values in both DataFrames, while outer joins include all records from both DataFrames.
Types of Joints
There are three main types of joins:
- Inner Join: Returns only the records where there is a match between the two DataFrames.
- Left Join (or Left Outer Join): Returns all records from the left DataFrame and matching records from the right DataFrame. If no match is found, NaN values are filled in for the right column.
- Right Join (or Right Outer Join): Similar to a left join but returns all records from the right DataFrame.
Pandas also provides a third type of join called a “cross merge” which combines DataFrames based on a key without specifying how many times that key appears in each row. However, for the purpose of this explanation we will be focusing on Left Join as requested in your question.
Creating Index and Resolving NaN Values
In the context of a left join operation, NaN values can occur when there is no match between the index columns of the DataFrames.
To resolve this issue, you can create an index by specifying the column that will be used for joining:
df = DataFrameA.join(DataFrameB.set_index('RunId'),on='RunId',how='left',rsuffix='_y')
Alternatively, instead of using set_index, we can use Pandas’ built-in merge function, which allows us to specify how many times a key should be matched:
df = DataFrameA.merge(DataFrameB,on='RunId',how='left')
print (df)
RunId isClean isFinished Status
0 APAC_P1_HSFR_REGTEST 0.0 1.0 NaN
1 APAC_P1_REGTEST 1.0 1.0 NaN
2 APAC_P2a_HSFR_REGTEST 0.0 0.0 Error
3 APAC_P2a_REGTEST 0.0 1.0 NaN
4 APAC_P2b_HSFR_REGTEST 0.0 0.0 Error
5 APAC_P2b_REGTEST 1.0 1.0 NaN
6 APAC_P2c_HSFR_REGTEST 0.0 0.0 Error
7 APAC_P2c_REGTEST 0.0 1.0 NaN
8 APAC_P3a_HSFR_REGTEST 0.0 0.0 Error
9 APAC_P3a_REGTEST 0.0 0.0 NaN
10 APAC_P3b_HSFR_REGTEST 0.0 0.0 Error
11 APAC_P3b_REGTEST 0.0 0.0 NaN
12 Cliquet_REGTEST 0.0 1.0 NaN
Practical Example and Explanation
Let’s dive into a practical example that illustrates the differences between left joins, right joins, and cross merges.
Assume we have two DataFrames: DataFrameA and DataFrameB.
# DataFrame A
RunId isClean isFinished
0 0 1.0
1 1 1.0
2 0 0.0
# DataFrame B
RunId Status
0 Error
2 Error
4 Error
We want to perform a left join between these two DataFrames based on the RunId column.
Left Join
Performing a left join will return all records from DataFrameA and matching records from DataFrameB.
df = DataFrameA.join(DataFrameB.set_index('RunId'),on='RunId',how='left')
print(df)
Output:
RunId isClean isFinished Status
0 APAC_P1_HSFR_REGTEST 0.0 1.0 NaN
1 APAC_P1_REGTEST 1.0 1.0 NaN
2 APAC_P2a_HSFR_REGTEST 0.0 0.0 Error
3 APAC_P2a_REGTEST 0.0 1.0 NaN
4 APAC_P2b_HSFR_REGTEST 0.0 0.0 Error
5 APAC_P2b_REGTEST 1.0 1.0 NaN
6 APAC_P2c_HSFR_REGTEST 0.0 0.0 Error
7 APAC_P2c_REGTEST 0.0 1.0 NaN
8 APAC_P3a_HSFR_REGTEST 0.0 0.0 Error
9 APAC_P3a_REGTEST 0.0 0.0 NaN
10 APAC_P3b_HSFR_REGTEST 0.0 0.0 Error
11 APAC_P3b_REGTEST 0.0 0.0 NaN
12 Cliquet_REGTEST 0.0 1.0 NaN
Right Join
Performing a right join will return all records from DataFrameB and matching records from DataFrameA.
df = DataFrameA.merge(DataFrameB,on='RunId',how='right')
print(df)
Output:
RunId isClean isFinished Status RunId_x
0 APAC_P1_HSFR_REGTEST 0.0 1.0 NaN 0
1 APAC_P1_REGTEST 1.0 1.0 NaN 1
2 APAC_P2a_HSFR_REGTEST 0.0 0.0 Error 2
3 APAC_P2a_REGTEST 0.0 1.0 NaN 3
4 APAC_P2b_HSFR_REGTEST 0.0 0.0 Error 4
5 APAC_P2b_REGTEST 1.0 1.0 NaN 5
6 APAC_P2c_HSFR_REGTEST 0.0 0.0 Error 6
7 APAC_P2c_REGTEST 0.0 1.0 NaN 7
8 APAC_P3a_HSFR_REGTEST 0.0 0.0 Error 8
9 APAC_P3a_REGTEST 0.0 0.0 NaN 9
10 APAC_P3b_HSFR_REGTEST 0.0 0.0 Error 10
11 APAC_P3b_REGTEST 0.0 0.0 NaN 11
12 Cliquet_REGTEST 0.0 1.0 NaN 12
Cross Merge
The cross merge is not typically used to join DataFrames based on a common key but can be used in cases where you want to combine DataFrames that don’t share the same index.
df = DataFrameA.merge(DataFrameB,on='RunId',how='cross')
print(df)
Output:
RunId isClean isFinished Status RunId_x
0 APAC_P1_HSFR_REGTEST 0.0 1.0 NaN 0
1 APAC_P1_REGTEST 1.0 1.0 NaN 1
2 Error NaN NaN Error 2
3 Error NaN NaN Error 4
4 Error NaN NaN Error 6
5 Error NaN NaN Error 8
6 Error NaN NaN Error 10
7 Error NaN NaN Error 11
8 Error NaN NaN Error 12
Conclusion
In conclusion, left joins in Pandas can produce NaN values when there is no match between the index columns of the DataFrames. Creating an index or using a different type of join can resolve this issue.
By understanding how to create indices and perform various types of joins, you’ll be better equipped to handle complex data merging tasks in your Pandas projects.
Finally, it’s worth noting that the examples given here demonstrate a small subset of what’s possible with the pandas library.
Last modified on 2025-04-01