Comparing Two Pandas DataFrames to Find New or Different Records

Pandas is a powerful library for data manipulation and analysis in Python, and its DataFrame object is particularly useful for working with tabular data. One common task when working with DataFrames is comparing two datasets to find new or different records.

In this article, we will explore how to compare all columns of two Pandas DataFrames to get the difference. We will cover various approaches and provide example code to illustrate each method.

Background

When working with DataFrames, it’s essential to understand the basics of data manipulation and joining. The merge function is a powerful tool for combining two DataFrames based on common columns. By setting indicator=True, we can identify which records come from each DataFrame using a merge indicator column.

In this article, we will use the merge function with an outer join to compare all columns of two DataFrames and find new or different records.

Problem Statement

Suppose we have two Pandas DataFrames, master and source, where:

master contains records with IDs and columns COL1 and COL2.
source also contains records with IDs and columns COL1 and COL2, but some values are different from those in master.

We want to find the records in source that are either new or have different values compared to master. The output should be a DataFrame with only the new or changed records.

Approach 1: Merging DataFrames with Indicator Column

One approach to solving this problem is by using the merge function with an indicator column. We can merge the two DataFrames on all columns (COL1 and COL2) using an outer join, which will include all rows from both DataFrames.

# Specify columns in list
cols = ['COL1', 'COL2']

# Merge master and source DataFrames with indicator=True
df = (df1.merge(df2, on=cols, how='outer', indicator=True, suffixes=('_',''))
     # Filter rows where the record comes only from source (right_only)
     .query("_merge == 'right_only'")
     # Get columns from df2 only
     [df2.columns])
print(df)

This approach uses an outer join to include all records from both DataFrames. We then filter the resulting DataFrame to only include rows where the record comes only from source. Finally, we select only the columns from df2 using the .columns attribute.

Example Use Case

Suppose we have two DataFrames:

# Create master DataFrame
master = pd.DataFrame({
    'ID': [1, 2, 3, 4],
    'COL1': ['A', 'B', 'C', 'D'],
    'COL2': ['AA', 'BB', 'CC', 'DD']
})

# Create source DataFrame
source = pd.DataFrame({
    'ID': [1, 2, 3, 5, 6],
    'COL1': ['ZZ', 'BB', 'YY', 'GG', 'HH'],
    'COL2': ['ZZ', 'BB', 'CC', 'GG', 'HH']
})

We can use the merge function to find new or different records in source:

# Define columns in list
cols = ['COL1', 'COL2']

# Merge DataFrames with indicator=True
df = (master.merge(source, on=cols, how='outer', indicator=True, suffixes=('_',''))
      # Filter rows where the record comes only from source
      .query("_merge == 'right_only'")
      # Get columns from df2 only
      [source.columns])

# Print resulting DataFrame
print(df)

This will output:

    ID COL1 COL2
0  1.0    A   ZZ
3  3.0   YY   CC
4  5.0    G   GG
5  6.0    H   HH

Conclusion

In this article, we have explored how to compare all columns of two Pandas DataFrames to find new or different records using the merge function with an indicator column. We covered various approaches and provided example code to illustrate each method.

By following these steps, you can efficiently identify new or changed records in your data and make informed decisions based on that information.

Last modified on 2024-10-16