


How to Efficiently Merge Pandas DataFrames with a Conditioned Join on Date Range?
Merge Pandas Dataframes with Conditioned Join on Date Range
You need to merge two dataframes, A and B, based on an identifier ("cusip") and a condition where the date in dataframe A ("fdate") falls between two dates in dataframe B ("namedt" and "nameenddt").
Despite recognizing the SQL ease of this task, you're stuck with a pandas approach involving unconditional merging followed by filtering, which can be inefficient. Here's why this approach is suboptimal:
df = pd.merge(A, B, how='inner', left_on='cusip', right_on='ncusip') df = df[(df['fdate']>=df['namedt']) & (df['fdate']<=df['nameenddt'])]
Instead of potentially creating a large interim dataframe before filtering, you seek a solution that incorporates filtering within the merge operation itself.
Exploring an Alternative Approach
For scenarios like this, consider utilizing a database like SQLite. Pandas' to_sql method can conveniently write dataframes to a database. Subsequently, SQL queries enable efficient filtering and merging operations.
Here's an example using imaginary dataframes and a database connection:
import pandas as pd import sqlite3 # Sample dataframes presidents = pd.DataFrame({"name": ["Bush", "Obama", "Trump"], "president_id": [43, 44, 45]}) terms = pd.DataFrame({"start_date": pd.date_range('2001-01-20', periods=5, freq='48M'), "end_date": pd.date_range('2005-01-21', periods=5, freq='48M'), "president_id": [43, 43, 44, 44, 45]}) war_declarations = pd.DataFrame({"date": [datetime(2001, 9, 14), datetime(2003, 3, 3)], "name": ["War in Afghanistan", "Iraq War"]}) # Database connection conn = sqlite3.connect(':memory:') # Write dataframes to database terms.to_sql('terms', conn, index=False) presidents.to_sql('presidents', conn, index=False) war_declarations.to_sql('wars', conn, index=False) # SQL query qry = ''' SELECT start_date AS PresTermStart, end_date AS PresTermEnd, wars.date AS WarStart, presidents.name AS Pres FROM terms JOIN wars ON date BETWEEN start_date AND end_date JOIN presidents ON terms.president_id = presidents.president_id ''' # Read query results into pandas dataframe df = pd.read_sql_query(qry, conn)
This approach allows you to join and filter without creating an unnecessarily large intermediate dataframe.
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