How to Efficiently Merge Pandas DataFrames with a Conditioned Join on Date Range?-Python Tutorial-php.cn

How to Efficiently Merge Pandas DataFrames with a Conditioned Join on Date Range?

Mary-Kate Olsen

Release： 2024-10-31 07:33:30

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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'])]

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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)

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This approach allows you to join and filter without creating an unnecessarily large intermediate dataframe.

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