5 Best Ways to Merge Python Pandas DataFrames Using a Common Column and Set NaN for Unmatched Values

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💡 Problem Formulation: When working with data in pandas, a common challenge is merging two DataFrames based on a shared column, while ensuring that any unmatched entries are filled with NaN to maintain data integrity. A frequent scenario involves combining customer order data from two separate months, where the combined DataFrame should reflect all customers, filling in those without orders in either month with NaN.

Method 1: Using Merge with How=’Outer’

The merge() function in pandas allows for different types of joins. An ‘outer’ join can be used to merge two DataFrames based on a common column, ensuring that unmatched values are set to NaN. This method is useful for comprehensive data consolidation while preserving all unique entries.

Here’s an example:

import pandas as pd

df1 = pd.DataFrame({
    'CustomerID': [1, 2, 3],
    'Product': ['Apples', 'Bananas', 'Cherries']
})
df2 = pd.DataFrame({
    'CustomerID': [2, 3, 4],
    'Quantity': [3, 5, 2]
})

merged_df = df1.merge(df2, on='CustomerID', how='outer')
print(merged_df)

Output:

   CustomerID   Product  Quantity
0           1    Apples       NaN
1           2   Bananas       3.0
2           3  Cherries       5.0
3           4       NaN       2.0

This example code first creates two pandas DataFrames and then merges them with an outer join on ‘CustomerID’. The resulting DataFrame includes all customer IDs from both original DataFrames, with NaNs for missing information in either ‘Product’ or ‘Quantity’ columns.

Method 2: Using Concat with Outer Join

For a mere stacking of two DataFrames along either axis, pandas offers the concat() function. This function is useful especially when DataFrames have a common column but different rows, and you want to stack them vertically while filling unmatched entries with NaN.

Here’s an example:

pd.concat([df1.set_index('CustomerID'), df2.set_index('CustomerID')], axis=1, join='outer')

Output:

            Product  Quantity
CustomerID                   
1            Apples       NaN
2           Bananas       3.0
3          Cherries       5.0
4               NaN       2.0

This code snippet uses concat() to stack DataFrames vertically based on ‘CustomerID’ as the index. By setting join='outer', it ensures that all CustomerIDs are present, and NaN is set for unmatched rows.

Method 3: Using DataFrame.join with How=’Outer’

The DataFrame.join() method is a convenient way to combine DataFrames on their indices. By using an ‘outer’ join, unmatched entries are returned as NaN, making it an ideal approach when one DataFrame has a subset of indices from the other.

Here’s an example:

df1.set_index('CustomerID').join(df2.set_index('CustomerID'), how='outer')

Output:

            Product  Quantity
CustomerID                   
1            Apples       NaN
2           Bananas       3.0
3          Cherries       5.0
4               NaN       2.0

This code snippet demonstrates the use of DataFrame.join() with ‘outer’ join specified. The DataFrames are first indexed on ‘CustomerID’, and then joined together, filling any unmatched entries with NaN.

Method 4: Using DataFrame.combine_first

Another method to merge DataFrames and fill unmatched values with NaN is the use of the combine_first() method. This is useful in scenarios where you want to update a DataFrame with another, wherever the original DataFrame has NaN values.

Here’s an example:

df1.set_index('CustomerID').combine_first(df2.set_index('CustomerID'))

Output:

            Product  Quantity
CustomerID                   
1            Apples       NaN
2           Bananas       3.0
3          Cherries       5.0
4               NaN       2.0

This snippet uses combine_first() to merge the DataFrames with ‘CustomerID’ as the index. This method takes all values from the calling DataFrame and fills NaN values where they exist with values from the passed DataFrame, resulting in a combined DataFrame with NaN for any remaining unmatched entries.

Bonus One-Liner Method 5: Using DataFrame.update with Overwrite=’False’

For a quick and in-place DataFrame merging while keeping NaN for unmatched cells, update() method with overwrite=False can be used.

Here’s an example:

df1.set_index('CustomerID').update(df2.set_index('CustomerID'), overwrite=False)
print(df1)

Output:

            Product  Quantity
CustomerID                   
1            Apples       NaN
2           Bananas       3.0
3          Cherries       5.0

This one-liner demonstrates an in-place update of df1 with df2. The update() method with overwrite=False ensures that only NaN values in df1 are overwritten with values from df2, without changing existing non-NaN values.

Summary/Discussion

  • Method 1: Merge with How=’Outer’. Strengths: Easy to use and understand. Ideal for merging on columns. Weaknesses: Not the most performant for large data sets.
  • Method 2: Concat with Outer Join. Strengths: Flexible for both vertical and horizontal stacking. Weaknesses: Can require additional steps like setting the index.
  • Method 3: DataFrame.join with How=’Outer’. Strengths: Simplified syntax for joining on indexes. Weaknesses: Requires the join key to be index on both DataFrames.
  • Method 4: DataFrame.combine_first. Strengths: Great for patching up DataFrames with NaN values. Weaknesses: Not as intuitive as other methods for general merging tasks.
  • Bonus Method 5: DataFrame.update with Overwrite=’False’. Strengths: In-place update is memory efficient. Weaknesses: Limited control over the merging logic and possible data loss if not used carefully.