How to Compare Two Dataframes and Extract Differences Based on Specific Columns?

Comparing Two Dataframes and Identifying Differences

In your scenario, you have two dataframes, df1 and df2, with identical structures and row indices. Your goal is to determine which rows exist in df2 but not in df1 by comparing their date and fruit values.

Direct Comparison

The approach of using df1 != df2 is not suitable because it requires identically labeled dataframes. Removing the Date index also fails to resolve the issue.

Concatenation and Grouping

To find the differences, you can concatenate the dataframes into a single dataframe df:

<code class="python">import pandas as pd

df = pd.concat([df1, df2])
df = df.reset_index(drop=True)</code>

Group df by all its columns to identify unique records:

<code class="python">df_gpby = df.groupby(list(df.columns))</code>

Filtering Unique Records

Next, retrieve the indices of unique records, which are those with a group size of 1:

<code class="python">idx = [x[0] for x in df_gpby.groups.values() if len(x) == 1]</code>

Finally, you can use these indices to filter the concatenated dataframe to obtain only the rows that are exclusive to df2:

<code class="python">df.reindex(idx)</code>

This will return a dataframe containing the desired differences:

         Date   Fruit   Num   Color
9  2013-11-25  Orange   8.6  Orange
8  2013-11-25   Apple  22.1     Red

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