5 Best Ways to Find Common Rows Between Two DataFrames Using Pandas Merge

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💡 Problem Formulation: Data scientists and analysts often need to find common rows shared between two separate pandas DataFrames. This task is crucial for data comparison, merging datasets, or performing joins for further analysis. For example, given two DataFrames containing customer details, we might want to identify customers appearing in both datasets. The desired output is a new DataFrame containing only the rows that are present in both original DataFrames.

Method 1: Inner Merge

The merge() function in pandas can perform an inner join, which yields rows that have matching values in both DataFrames. This method is akin to a SQL ‘INNER JOIN’ and is commonly used for finding commonalities between datasets.

Here’s an example:

import pandas as pd

# Sample DataFrames
df1 = pd.DataFrame({'CustomerID': [1, 2, 3],
                    'Name': ['Alice', 'Bob', 'Charlie']})

df2 = pd.DataFrame({'CustomerID': [2, 3, 4],
                    'Name': ['Bob', 'Charlie', 'Dan']})

# Perform inner merge to find common rows
common_rows = pd.merge(df1, df2, on='CustomerID')

print(common_rows)

Output:

   CustomerID     Name_x   Name_y
0         2        Bob       Bob
1         3      Charlie   Charlie

This code snippet demonstrates how to utilize the merge() function to perform an inner join on two DataFrames using a common ‘CustomerID’ column. The result is a new DataFrame with rows that have matching ‘CustomerID’ in both original DataFrames.

Method 2: Merge with Indicator Flag

Using the merge() method with an indicator flag allows us to find common data while also keeping track of each row’s origin. This can be helpful for more complex analyses that require understanding of row provenance.

Here’s an example:

merge_with_flag = pd.merge(df1, df2, on='CustomerID', how='outer', indicator=True)
common_rows_with_flag = merge_with_flag[merge_with_flag['_merge'] == 'both']

print(common_rows_with_flag)

Output:

   CustomerID   Name_x   Name_y     _merge
1         2       Bob      Bob      both
2         3     Charlie  Charlie  both

This code first performs an ‘outer’ merge to include all rows from both DataFrames, while the indicator flag denotes the source of each row. The resulting DataFrame is then filtered to include only the rows marked as ‘both’, indicating they are common to both original DataFrames.

Method 3: Merge with Specific Columns

When dealing with DataFrames with non-unique indexes or multiple columns to match on, merging on specific columns can accurately return common rows. This method adds precision and control to the merge operation.

Here’s an example:

common_rows_specific_columns = pd.merge(df1, df2, on=['CustomerID', 'Name'])

print(common_rows_specific_columns)

Output:

   CustomerID     Name
0         2        Bob
1         3      Charlie

By specifying a list of column names in the on parameter, the merge() function performs an inner join on these columns. In doing so, it extracts rows that have matching values in all listed columns across both DataFrames.

Method 4: Concatenation and Duplicate Removal

Concatenating the two DataFrames and then removing duplicates can also unearth common rows. This method might be less efficient than others but offers an alternative approach.

Here’s an example:

concatenated = pd.concat([df1, df2])
common_rows_concat = concatenated[concatenated.duplicated(subset='CustomerID')]

print(common_rows_concat)

Output:

   CustomerID   Name
1         2       Bob
2         3     Charlie

The concatenation of DataFrames stacks them vertically. After which, the duplicated() method helps in identifying rows which appear more than once based on the ‘CustomerID’ column, effectively finding common rows.

Bonus One-Liner Method 5: Using Query and Merge

A one-liner approach using query() after an inner merge can achieve the same result in a compact form.

Here’s an example:

common_rows_query = pd.merge(df1, df2).query('CustomerID in @df1.CustomerID and CustomerID in @df2.CustomerID')

print(common_rows_query)

Output:

   CustomerID   Name
0         2       Bob
1         3     Charlie

This one-liner first performs an inner merge and then filters the result using query() to include only those ‘CustomerID’ values that are common to both df1 and df2.

Summary/Discussion

  • Method 1: Inner Merge. This method provides a straightforward and common approach to finding common rows. It might require additional parameters when dealing with non-unique indexes or multiple keys.
  • Method 2: Merge with Indicator Flag. Offers more insight into the merge process and is especially useful when row provenance is important. It does require an additional step to filter the result.
  • Method 3: Merge with Specific Columns. Gives more control over the merge operation, allowing for precision when dealing with multiple keys. It is a go-to approach when dealing with composite keys.
  • Method 4: Concatenation and Duplicate Removal. This is a less conventional method but can be simpler in certain cases. However, it may be less efficient for larger datasets.
  • Method 5: Using Query and Merge. Compact and neat for quick operations, though it might compromise readability for those unfamiliar with chaining methods in pandas.