How to Identify Rows and Columns Unique to One DataFrame When Comparing Two DataFrames?

Determining Differences Between Data Frames: A Comprehensive Approach

Given two data frames, df1 and df2, with df2 being a subset of df1, the objective is to create a new data frame, df3, that captures the elements present in df1 but not in df2. This essentially helps identify the unique rows and columns in df1 that are missing in df2.

Using drop_duplicates: A Simple Solution

One common method to achieve this is by employing the drop_duplicates function. By concatenating df1 and df2 and subsequently calling drop_duplicates with keep=False, a new data frame is obtained that retains only the non-duplicate rows. This approach works effectively for data frames that do not contain duplicate entries within themselves.

Addressing Data Frames with Duplicates

However, in scenarios where the initial data frames may contain duplicates internally, the drop_duplicates method may not yield accurate results. To handle such cases, alternative techniques are necessary.

Method 1: Employing isin with Tuple

In this approach, a tuple is created from each row of df1 and df2, and the isin function is used to compare these tuples. The resulting data frame will include rows from df1 that do not have corresponding tuples in df2, effectively highlighting the unique elements.

Method 2: Utilizing Merge with Indicator

Another method involves merging df1 and df2 using the merge function with an indicator set to "True". This operation adds a column named "_merge" that indicates the provenance of each row. By filtering the resulting data frame based on rows where "_merge" is not "both", it is possible to isolate the rows that are present in df1 but not in df2.

By leveraging these techniques, developers can effectively determine the differences between two data frames and create a new data frame that contains only the unique elements present in df1 but not in df2.

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