Merging Two Similar DataFrames Using Conditions

In this article, we will explore how to merge two similar dataframes using conditions. The goal is to update the first dataframe with changes from the second dataframe while maintaining a history of previous updates.

We’ll discuss the context of the problem, the current solution approach, and then provide a simplified solution using pandas merging.

Context

The problem arises when dealing with updating databases that have a history of changes. We need to keep track of these changes while still allowing for new data to be added. In this scenario, we’re working with two similar dataframes: one is the current state (Database dataframe, DD) and the other is the updated version from our client (Database client, DC).

Current Solution Approach

The original solution approach involves using nested if statements to compare rows between the two dataframes. This approach can become cumbersome and resource-intensive.

Here’s an example of the original code:

Select last_in_date rows in DD (for each IDs) in new dataframe DD_pending 
For rowC in DC
new = true
 For rowD in DD_pending
  If matching IDs then
   new = false
   If same "important_data" then overwrite_everything
   Else create_new_row in DD
if new then create_new_row in DD
Merge DD_pending with DD, drop duplicates

Simplified Solution Using Pandas Merging

Pandas provides a powerful and efficient way to merge dataframes using various methods. In this section, we’ll explore how to use pandas merging to achieve our goal.

One of the most suitable methods for this task is the concat function combined with the drop_duplicates method. Here’s an example code snippet that demonstrates how to merge the two dataframes:

# imports
import pandas as pd
import datetime

# ignore (just for setting up the problem)
db_df = pd.DataFrame({
    "ID1": [1, 4],
    "ID2": [2, 5],
    "ID3": [3, 6],
    "important_data": [10, 10],
    "some_date1": ["X", "M"],
    "some_date2": ["Y", "N"],
    "date": [datetime.datetime.strptime("2019-09-19 14:53:16.107278", '%Y-%m-%d %H:%M:%S.%f'),
             datetime.datetime.strptime("2019-09-19 14:53:16.107278", '%Y-%m-%d %H:%M:%S.%f')]
})

# ignore (just for setting up the problem)
db_df_client = pd.DataFrame({
    "ID1": [1, 4],
    "ID2": [2, 5],
    "ID3": [3, 6],
    "important_data": [15, 10],
    "some_date1": ["A", "O"],
    "some_date2": ["B", "P"],
    "date": [datetime.datetime.strptime("2019-09-19 14:53:16.107278", '%Y-%m-%d %H:%M:%S.%f'),
             datetime.datetime.strptime("2019-09-19 14:53:16.107278", '%Y-%m-%d %H:%M:%S.%f')]
})

# the line you care about
pd.concat([db_df, db_df_client]).drop_duplicates(subset=['ID1','ID2','ID3','important_data'], keep='first')

In this example, we first concatenate the two dataframes using concat. Then, we use the drop_duplicates method to remove duplicate rows based on the specified columns. The keep='first' argument ensures that the first occurrence of each row is kept in the resulting dataframe.

This approach provides a concise and efficient way to merge the two dataframes while maintaining the history of previous updates.

Conclusion

In this article, we explored how to merge two similar dataframes using conditions. We discussed the context of the problem, the current solution approach, and then provided a simplified solution using pandas merging. The example code demonstrates how to use concat combined with drop_duplicates to achieve our goal. This approach provides a concise and efficient way to merge dataframes while maintaining a history of previous updates.

Last modified on 2023-09-11