How do I efficiently merge multiple dataframes based on a common date column?

Merging Multiple Dataframes Based on Date

You have multiple dataframes with a common date column but varying numbers of rows and columns. The goal is to merge these dataframes to obtain rows where each date is common to all dataframes.

Inefficient Recursion Approach

Your attempt to use a recursion function to merge dataframes is flawed. The function enters an infinite loop because it continuously calls itself with the same inputs. This approach is inefficient and prone to errors.

Optimized Solution Using reduce

A more efficient method for merging multiple dataframes is to use the reduce function from the functools module. This function reduces a list of dataframes into a single dataframe by repeatedly applying a specified merge operation to adjacent pairs of dataframes.

The following code snippet demonstrates this approach:

import pandas as pd
from functools import reduce

dfs = [df1, df2, df3]  # list of dataframes

df_merged = reduce(lambda left, right: pd.merge(left, right, on='date', how='outer'), dfs)

In this code, the reduce function reduces the dfs list into a single dataframe by iteratively merging adjacent pairs of dataframes. The on='date' parameter specifies that the merge should be performed based on the date column. The how='outer' parameter ensures that all rows from both dataframes are included in the merged result, even if they do not share the same date.

Advantages of reduce Function

Using the reduce function offers several advantages:

• Simplicity: The code is concise and easy to understand.
• No Nesting: Unlike your recursion approach, there is no nesting of merge operations, eliminating the risk of infinite loops.
• Extensibility: You can add or remove dataframes from the dfs list to change the merge operation dynamically.

Example

Using the provided dataframes df1, df2, and df3, you would obtain the following merged dataframe:

       DATE  VALUE1  VALUE2  VALUE3
0  May 15, 2017  1901.00  2902.00  3903.00

This dataframe contains only rows with a date that is common to all three input dataframes.

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