How to Efficiently Extract Rows from One Pandas DataFrame that are Absent in Another?

Retrieving Rows from One Dataframe that are Excluded from Another

In pandas, it is common to have multiple dataframes with potentially overlapping data. One task that frequently arises is isolating rows from one dataframe that are not present in another. This operation is particularly useful when working with subsets or filtering data.

Problem Formulation:

Given two pandas dataframes, where df1 contains a superset of rows compared to df2, we aim to obtain the rows in df1 that are not found in df2. The example below illustrates this scenario with a simple case:

import pandas as pd

df1 = pd.DataFrame(data={'col1': [1, 2, 3, 4, 5], 'col2': [10, 11, 12, 13, 14]})
df2 = pd.DataFrame(data={'col1': [1, 2, 3], 'col2': [10, 11, 12]})

print(df1)
print(df2)

# Expected result:
#   col1  col2
# 3     4    13
# 4     5    14

Solution:

To effectively address this problem, we employ a technique known as a left join. This operation merges df1 and df2 while ensuring that all rows from df1 are retained. Additionally, we include an indicator column to identify the origin of each row after the merge. By leveraging the unique rows from df2 and excluding duplicates, we achieve the desired result.

The python code below implements this solution:

df_all = df1.merge(df2.drop_duplicates(), on=['col1', 'col2'], how='left', indicator=True)
result = df_all[df_all['_merge'] == 'left_only']

Explanation:

1. Left Join: The merge function performs a left join between df1 and df2.drop_duplicates(). This operation merges rows from df1 with rows from df2 based on the matching values in columns col1 and col2.
2. Merge Indicator: The indicator parameter is set to True to include an extra column named _merge in the resulting dataframe df_all. This column indicates the origin of each row: 'both' for rows that exist in both df1 and df2, 'left_only' for rows exclusive to df1, and 'right_only' for rows exclusive to df2.
3. Filter by 'left_only': To isolate rows from df1 that are not in df2, we filter the df_all dataframe by checking rows with _merge equal to 'left_only'. This gives us the desired result.

Avoiding Common Pitfalls:

It is important to note that some solutions may incorrectly check for individual column values instead of matching rows as a whole. Such approaches may lead to incorrect results, as illustrated in the example below:

~df1.col1.isin(common.col1) & ~df1.col2.isin(common.col2)

This code does not consider the joint occurrence of values in rows and may produce incorrect results when rows in df1 have values that appear individually in df2 but not in the same row.

By adopting the left join approach described above, we ensure that the derived rows are correctly identified as exclusive to df1. This technique provides a reliable and efficient solution to extracting rows that are present in one dataframe but not in another.

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