Merging DataFrames by Date Values Using pandas Merge Asof Functionality

Merging DataFrames by Date Values Using Merge Asof Functionality

In this article, we will explore how to update values in a DataFrame based on the values in another DataFrame using the merge_asof function from pandas library.

Introduction

When working with data manipulation tasks, it is often necessary to merge two or more DataFrames together. In such cases, when one DataFrame has an index column and the other DataFrame has a column with dates, we can use the merge_asof function to perform the join operation based on the date values.

The original code from Stack Overflow provided in this article attempts to achieve the same result using a nested loop structure. However, it is not efficient and takes around 1-2 minutes for 200 items, which indicates a need for an optimized approach.

Understanding `merge_asof` Function

The merge_asof function merges two DataFrames based on the keys that are specified as the merge_on parameters. It performs a forward search by default, meaning it finds the first row in the second DataFrame where the key is less than or equal to the corresponding value in the other DataFrame.

# Create sample DataFrames
Item = pd.DataFrame({"ID":["A1","A1","A2","A2","A3","B1"],"DATE":["2021-07-05","2021-08-01","2021-02-02","2021-02-03","2021-01-01","2021-10-12"]})
Ver = pd.DataFrame({"ver_date" : ["2021-01-01","2021-07-07","2021-09-09"],"version":["1.1","1.2","1.3"]})

# Convert date columns to datetime objects
Item['DATE'] = pd.to_datetime(Item['DATE'])
Ver['ver_date'] = pd.to_datetime(Ver['ver_date'])

# Merge DataFrames using merge_asof function
out = (pd.merge_asof(Item.sort_values(by='DATE'), 
                     Ver.sort_values(by='ver_date'), 
                     left_on='DATE', right_on='ver_date')
       .drop(columns='ver_date')
       .sort_values(by='ID')
       .rename(columns={'version':'VER'}))

Benefits of Using `merge_asof` Function

Efficiency: The merge_asof function is faster and more efficient than the original nested loop structure.
Simplified Code: It simplifies the code by reducing the need for explicit loops and conditional checks.

How to Use `merge_asof` Function with Sample Data

To demonstrate how to use the merge_asof function, we will create sample DataFrames and then perform the join operation using this function.

# Create a DataFrame with item IDs, dates, and version numbers for 'A1' items
Item_A1 = pd.DataFrame({"ID":["A1","A1","A2","A2","A3"],
                       "DATE":["2021-07-05","2021-08-01","2021-02-02","2021-02-03","2021-01-01"]})

# Create a DataFrame with 'ver_date' and version numbers for 'A1' items
Ver_A1 = pd.DataFrame({"ver_date" : ["2021-07-05","2021-08-01"],
                      "version":["1.1","1.2"]})

# Perform merge_asof operation on Item_A1 and Ver_A1
out = (pd.merge_asof(Item_A1.sort_values(by='DATE'), 
                     Ver_A1.sort_values(by='ver_date'),
                     left_on='DATE', right_on='ver_date')
       .drop(columns='ver_date')
       .sort_values(by='ID')
       .rename(columns={'version':'VER'}))

Customizing Merge Asof Function

The merge_asof function allows for customization using the direction parameter, which can be either ‘backward’ or ‘forward’. The default direction is ‘backward’.

# Perform merge_asof operation with forward direction on Item_A1 and Ver_A1
out_forward = (pd.merge_asof(Item_A1.sort_values(by='DATE'), 
                             Ver_A1.sort_values(by='ver_date'),
                             left_on='DATE', right_on='ver_date',
                             direction='forward')
               .drop(columns='ver_date')
               .sort_values(by='ID')
               .rename(columns={'version':'VER'}))

# Perform merge_asof operation with forward direction on Item_A1 and Ver_A1
out_forward = (pd.merge_asof(Item_A1.sort_values(by='DATE'), 
                             Ver_A1.sort_values(by='ver_date'),
                             left_on='DATE', right_on='ver_date',
                             direction='forward')
               .drop(columns='ver_date')
               .sort_values(by='ID')
               .rename(columns={'version':'VER'}))

Conclusion

The merge_asof function is a powerful tool for joining DataFrames based on date values. It provides an efficient and simplified way to perform this type of join operation, making it easier to work with data in pandas.

In this article, we have explored how to use the merge_asof function to update values in one DataFrame based on the values in another DataFrame using sample DataFrames. We have also demonstrated how to customize the merge operation by specifying the direction parameter.

Last modified on 2023-07-26