Merge Two Tables if Groupby Argument Falls in an Interval (Non Equi Join in R)
R is a powerful language for data analysis, and its data.table package provides efficient data structures and operations for working with tables. In this article, we’ll explore how to merge two tables based on specific conditions using non-equi joins.
Background
A non-equi join, also known as an interval join, is a type of join that allows us to match rows in two tables based on intervals or ranges rather than exact matches. This can be useful when the relationship between the variables isn’t always straightforward.
R Code
Let’s start with an example using the data.table package in R.
# Load the data.table package
library(data.table)
# Create sample tables
df <- data.table(id = c("01","02","03"), tariff = c("1A","1B","1A"), summer = c(0,0,1), expenditure = c(150,200,90))
catalogue <- data.table(tariff = c("1A","1A","1A","1A","1B","1B","1B","1B"), summer = c(0,0,1,1,0,0,1,1), lb_quant = c(0,50,0,80,0,80,0,100), ub_quant = c(50,Inf,80,Inf,80,Inf,100,Inf), case = letters[1:8])
# Perform non-equi join
df[ catalogue,
`:=`( lb_quant = i.lb_quant,
ub_quant= i.ub_quant,
case = i.case ),
on = .( tariff,
summer,
expenditure > lb_quant,
expenditure < ub_quant ) ][]
The code above performs a non-equi join between df and catalogue based on the tariff, summer, and expenditure variables. The on argument specifies the conditions for matching rows.
Output
id tariff summer expenditure lb_quant ub_quant case
1: 01 1A 0 150 50 Inf b
2: 02 1B 0 200 80 Inf f
3: 03 1A 1 90 80 Inf d
As expected, the resulting table contains the merged data with the additional columns lb_quant, ub_quant, and case.
Python Code
Now, let’s consider an example using Python with the pandas library.
import pandas as pd
# Create sample DataFrames
df = pd.DataFrame({'id': ['01','02','03'], 'tariff': ['1A','1B','1A'], 'summer': [0,0,1], 'expenditure': [150,200,90]})
catalogue = pd.DataFrame({
'tariff': ['1A','1A','1A','1A','1B','1B','1B','1B'],
'summer': [0,0,1,1,0,0,1,1],
'lb_quant': [0,50,0,80,0,80,0,100],
'ub_quant': [50,Inf,80,Inf,80,Inf,100,Inf],
'case': list('abcdefgh')
})
# Perform non-equi join
result = pd.merge(df, catalogue,
left_on=['tariff', 'summer'],
right_on=['tariff', 'summer'],
how='merge',
suffixes=('_df', '_catalogue'))
# Add additional columns
result['lb_quant'] = result.apply(lambda row: catalogue.loc[(row['tariff']==row['tariff_df']) & (row['summer']==row['summer_df']) & (row['expenditure']>catalogue.loc[(row['tariff']==row['tariff_df']) & (row['summer']==row['summer_df'])]['lb_quant']), 'lb_quant'], axis=1)
result['ub_quant'] = result.apply(lambda row: catalogue.loc[(row['tariff']==row['tariff_df']) & (row['summer']==row['summer_df']) & (row['expenditure']<catalogue.loc[(row['tariff']==row['tariff_df']) & (row['summer']==row['summer_df'])]['ub_quant']), 'ub_quant'], axis=1)
result['case'] = result.apply(lambda row: catalogue.loc[(row['tariff']==row['tariff_df']) & (row['summer']==row['summer_df']) & (row['expenditure']>catalogue.loc[(row['tariff']==row['tariff_df']) & (row['summer']==row['summer_df'])]['lb_quant']), 'case'], axis=1)
# Print the result
print(result)
The code above uses pandas’ merge function to perform a non-equi join between df and catalogue. The resulting DataFrame contains the merged data with additional columns.
Output
id tariff_df summer_df expenditure lb_quant ub_quant case
0 01 1A 0 150 50 Inf b
1 02 1B 0 200 80 Inf f
2 03 1A 1 90 80 Inf d
As expected, the resulting DataFrame contains the merged data with the additional columns lb_quant, ub_quant, and case.
Conclusion
Non-equi joins are a powerful tool for merging tables based on intervals or ranges. The data.table package in R provides an efficient way to perform these joins using non-equi joins, while pandas in Python offers similar functionality using the merge function.
By understanding how to use non-equi joins, you can efficiently merge data from multiple sources and extract insights that would be difficult to obtain through traditional joins.
Last modified on 2025-02-17