Removing rows present in another dataframe

Removing Rows Present in Another DataFrame in Pandas refers to the process of removing rows from one DataFrame that are also present in another DataFrame.

  • The reason why we might want to remove rows present in another DataFrame is to perform data cleaning or data manipulation tasks.

  • Another reason to remove rows present in another DataFrame could be to filter out data that we are not interested in.

Overall, removing rows present in another DataFrame is a common task in data manipulation and can help us to clean and filter our data to better meet our analysis needs.

1. Using "Merge and filter":

  • Use the merge function to join the two DataFrames using an outer join.
  • Set the indicator parameter to True to create a new column _merge indicating which DataFrame each row came from.
  • Filter the merged DataFrame to keep only the rows where _merge is equal to 'left_only', indicating that the row is only present in the left DataFrame.
  • Drop the _merge column to get the final filtered DataFrame.
Example:

2. Using "Set difference":

  • Convert the column values of both DataFrames to tuples using the apply method with the tuple function. This creates a set-like object that can be used with set operations.
  • Compute the set difference between the two sets of tuples using the - operator.
  • Use the resulting set of tuples to filter the original DataFrame.
Example:

3. Using "Boolean indexing":

  • Use the isin method to create a boolean mask indicating which rows of the original DataFrame are also present in the other DataFrame.
  • Invert the boolean mask using the ~ operator to get the mask of rows that are not present in the other DataFrame.
  • Use the boolean mask to filter the original DataFrame.
Example:

Note that these methods have different performance characteristics and may be more or less appropriate depending on the size and structure of your DataFrames.