How to Identify Rows Present in df2 But Absent in df1?

Differences Between Two DataFrames

Comparing dataframes to identify differences is essential for data analysis. In this problem, we are given two dataframes, df1 and df2, and need to find rows present in df2 but absent in df1.

Comparing with Boolean Matrix

Direct comparison using operators like != can lead to errors if the dataframes have different structures. A better approach is to concatenate the dataframes, reset their indices, and then compare them. Using df1 == df2 will result in a boolean matrix where True indicates rows present in both dataframes and False indicates differences.

Grouping by Unique Values

Next, we can perform a group-by operation on the concatenated dataframe to identify unique rows. The goal is to find rows that occur only once in the dataframe. We can achieve this by checking the length of the groups; rows with a length of 1 represent unique records.

Filtering the Dataframe

Finally, we can use the identified unique row indices to filter the dataframe. This will provide us with the rows in df2 that are not present in df1.

Example

For instance, considering the example dataframes provided:

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

df1 = ...
df2 = ...

# Concatenate dataframes
df = pd.concat([df1, df2])
df = df.reset_index(drop=True)

# Group by unique values
df_gpby = df.groupby(list(df.columns))

# Get unique row indices
idx = [x[0] for x in df_gpby.groups.values() if len(x) == 1]

# Filter dataframe
result = df.reindex(idx)</code>

The result dataframe will contain the rows in df2 that are not present in df1.

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