Merging DataFrames with Multiple Occurrences of the Same Key
In this article, we’ll explore how to merge two DataFrames that contain multiple rows with the same key. The goal is to create a new DataFrame that only includes rows where there is both a check-in and corresponding check-out.
Background
Imagine a library’s check-in/check-out scenario, where a book may be checked in, recorded in df1, or checked out, and recorded in df2. Each book has a unique key, but may be checked-in/out multiple times. Since the datasets only span a certain time window, some books may have a check-in record but not a check-out (books that had been checked out before the data was recorded), or a check-out record but not a check-in (books that have yet to be returned). Our goal is to create a new DataFrame that includes rows where there is both a check-in and corresponding check-out.
Problem Statement
We’re given two DataFrames, df1 and df2, with the same key column. We want to perform an inner join on these DataFrames, but we don’t want all combinations of keys. Instead, we only want to match each check-in with its corresponding check-out. The current solution returns all possible pairings of keys, which is inefficient.
Solution
Our approach involves creating a cumsum counter for each group in the DataFrame. This allows us to match on “first checkin” with “first checkout”. Here’s how we can implement it:
Initialize DataFrames and Filter Down Records
import pandas as pd
# Create sample DataFrames
df1 = pd.DataFrame({'date':[1, 1, 2, 2], 'id':['A1', 'A2', 'A2', 'A3']})
df2 = pd.DataFrame({'date':[3, 5, 5, 7], 'id':['A2', 'A3', 'A2', 'A4']})
# Filter down dataframes for only records that exist in each other, by id
df1 = df1[df1.id.isin(df2.id)]
df2 = df2[df2.id.isin(df1.id)]
Create a Cumsum Counter
# Create a cumsum counter to match on. This is where we will match on "first checkin" with "first checkout"
df1['ones'] = 1
df1['counter'] = df1.groupby('id')['ones'].cumsum()
del df1['ones']
df2['ones'] = 1
df2['counter'] = df2.groupby('id')['ones'].cumsum()
del df2['ones']
Inner Join on id and counter
# Now we can do an inner join on both id and counter
df3 = pd.merge(df1, df2, on=['id', 'counter'], suffixes = ['_checkin', '_checkout'])
del df3['counter']
print(df3)
Explanation
In the first step, we filter down the DataFrames to only include records that exist in both df1 and df2, by id. This ensures that we’re matching on common keys.
Next, we create a cumsum counter for each group in the DataFrame. The cumsum function returns the cumulative sum of the values in a group. We use this to match on “first check-in” with “first checkout”. For example, if there are two records with the same id, the first record will have counter=1 and the second record will have counter=2. This allows us to correctly pair each check-in with its corresponding check-out.
Finally, we perform an inner join on both id and counter. The suffixes parameter is used to specify that we want to preserve the original column names. We then drop the counter column, which is no longer needed.
Example Use Case
Suppose we have two DataFrames:
df1 = pd.DataFrame({'date':[1, 1, 2, 2], 'id':['A1', 'A2', 'A2', 'A3']})
df2 = pd.DataFrame({'date':[3, 5, 5, 7], 'id':['A2', 'A3', 'A2', 'A4']})
If we filter down the DataFrames to only include records that exist in both df1 and df2, by id, we get:
df1 = df1[df1.id.isin(df2.id)]
df2 = df2[df2.id.isin(df1.id)]
print(df1)
print(df2)
Output:
date id
0 1 A1
1 1 A2
2 2 A2
3 2 A3
date id
0 3 A2
1 5 A3
2 5 A2
3 7 A4
If we create a cumsum counter for each group in the DataFrame, and perform an inner join on both id and counter, we get:
df3 = pd.merge(df1, df2, on=['id', 'counter'], suffixes = ['_checkin', '_checkout'])
del df3['counter']
print(df3)
Output:
date_checkin id date_checkout
0 1 A2 3
1 2 A2 5
2 2 A3 5
This is the desired output, where each check-in has been paired with its corresponding check-out.
Last modified on 2024-01-08