Spark: Unraveling the ‘merge_asof’ Function : asof merge between two DataFrames


Pandas API on Spark offers robust capabilities for data manipulations and SQL operations. This article dives deep into leveraging the ‘merge_asof’ function to perform asof merges effortlessly, unlocking new possibilities for data analysis and insights.

Understanding the ‘merge_asof’ Function

The ‘merge_asof’ function in Pandas API on Spark facilitates performing an asof merge between two DataFrames. An asof merge is particularly useful for merging datasets where one DataFrame contains time-series data, and the other DataFrame contains events or observations that need to be matched with the nearest preceding time in the time-series data.


merge_asof(left, right, on=None, left_on=None, right_on=None, ...)
  • left: The left DataFrame.
  • right: The right DataFrame.
  • on: Column name or list of column names to join on.
  • left_on: Column name or list of column names from the left DataFrame to join on.
  • right_on: Column name or list of column names from the right DataFrame to join on.

Example: Performing an Asof Merge

Consider two DataFrames representing stock prices and trade events:

Stock Prices DataFrame:
|Stock_ID|      Date|Price|
|       1|2022-01-01|  100|
|       2|2022-01-02|  110|
|       3|2022-01-03|  105|

Trade Events DataFrame:
|Stock_ID|      Date|Action|
|       1|2022-01-02|   Buy|
|       2|2022-01-03|  Sell|
|       3|2022-01-04|   Buy|

Now, let’s perform an asof merge to match each trade event with the nearest preceding stock price:

from pyspark.sql.functions import expr
# Perform asof merge
asof_merged_df = stock_prices_df.alias("sp").join(trade_events_df.alias("te"), expr("""
    sp.Stock_ID = te.Stock_ID AND 
    te.Date >= sp.Date
"""), 'left').groupBy('sp.Stock_ID', 'sp.Date').agg(expr("last(sp.Price) as Price"), expr("first(te.Action) as Action"))

print("\nAsof Merged DataFrame:")
Asof Merged DataFrame:
|Stock_ID|      Date|Price|Action|
|       1|2022-01-01|  100|   Buy|
|       2|2022-01-02|  110|  Sell|
|       3|2022-01-03|  105|   Buy|
Author: user