How Can I Efficiently Find the Unique Rows in DataFrame1 That Are Not in DataFrame2?

Finding the Difference Between Two DataFrames

In data analysis, identifying the discrepancies between datasets is crucial. Suppose you have two dataframes, df1 and df2, where df2 is a subset of df1. To efficiently retrieve the unique rows and columns that are present in df1 but not in df2, you can leverage the concept of set difference.

Approach: Using pd.concat and drop_duplicates**

The primary approach involves combining both dataframes using pd.concat and subsequently eliminating duplicate rows or columns using drop_duplicates. By setting keep=False, it ensures that only the rows or columns that exist solely in df1 are retained.

df3 = pd.concat([df1, df2]).drop_duplicates(keep=False)

Caveat: Handling Duplicates

However, this method assumes that both dataframes themselves do not contain duplicate values. If they do, the outcome can be inaccurate. To address this, we can employ the following alternative approaches:

Method 1: Using isin with Tuple

This method involves converting each row into a tuple using df.apply(tuple, 1) and then checking if the tuples are present in df2 using df.apply(tuple, 1).isin(df2.apply(tuple, 1)). The resulting dataframe will contain the unique rows from df1 that are not in df2.

df1[~df1.apply(tuple, 1).isin(df2.apply(tuple, 1))]

Method 2: Merging with Indicator

Another approach is to merge df1 with df2 using pd.merge with an indicator to identify rows that exist only in df1. By employing the lambda function, we can filter out rows where the '_merge' column is not equal to 'both'.

df1.merge(df2, indicator=True, how='left').loc[lambda x: x['_merge']!='both']

Conclusion

By utilizing these techniques, you can effectively find the difference between two dataframes and gain insights into the unique data points present in each dataframe.

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