Merging Pandas DataFrames Based on Specifier Restrictions Using Object Columns

Pandas Merging Object Columns

Overview

In this article, we’ll explore a technique for merging two pandas DataFrames based on object columns. The merge will only succeed if all specifiers present in one DataFrame are found in another. We’ll also discuss the challenges and limitations of this approach, particularly when dealing with large datasets.

Background

Pandas is a powerful library for data manipulation and analysis in Python. It provides an efficient and convenient way to work with structured data, including DataFrames (2-dimensional labeled data structures) and Series (1-dimensional labeled data structures).

In this article, we’ll focus on merging two DataFrames using object columns. Object columns are columns that contain non-numerical data, such as strings or lists.

Problem Statement

Suppose we have two DataFrames:

df1 = pd.DataFrame({
    'datetime': pd.date_range('2021-06-01', periods=624, freq='30min')[:5],
    'value': [11.30, 9.00, 10.40, 8.50, 9.70],
    'specifiers': [['P1', 'WEEKDAY', 'TUESDAY'], ['P2', 'WEEKDAY', 'TUESDAY'],
                   ['P3', 'WEEKDAY', 'TUESDAY'], ['P4', 'WEEKDAY', 'TUESDAY'],
                   ['P5', 'WEEKDAY', 'TUESDAY']]
})

df2 = pd.DataFrame({
    'specifiers': [['P1'], ['P2'], ['P3'], ['P4', 'WEEKDAY'], ['P5', 'TUESDAY']],
    'values': [0.43, 0.51, 0.62, 0.73, 0.84]
})

We want to merge these DataFrames based on the specifiers column, which contains lists of strings.

Solution

To achieve this merge, we can iterate through each row in df2 and filter rows from df1 that meet the specifier restrictions.

Here’s a step-by-step explanation of the solution:

import pandas as pd

# Create example dataframes
df1 = pd.DataFrame({
    'datetime': pd.date_range('2021-06-01', periods=624, freq='30min')[:5],
    'value': [11.30, 9.00, 10.40, 8.50, 9.70],
    'specifiers': [['P1', 'WEEKDAY', 'TUESDAY'], ['P2', 'WEEKDAY', 'TUESDAY'],
                   ['P3', 'WEEKDAY', 'TUESDAY'], ['P4', 'WEEKDAY', 'TUESDAY'],
                   ['P5', 'WEEKDAY', 'TUESDAY']]
})

df2 = pd.DataFrame({
    'specifiers': [['P1'], ['P2'], ['P3'], ['P4', 'WEEKDAY'], ['P5', 'TUESDAY']],
    'values': [0.43, 0.51, 0.62, 0.73, 0.84]
})

# Merge dataframes based on specifiers
merged_df = pd.DataFrame()
for row in df2.itertuples(index=False):
    # Filter rows according to specifiers
    matching_rows = df1[df1['specifiers'].apply(
        lambda x: set(row.specifiers).issubset(set(x))
    )]
    if not matching_rows.empty:
        # Concat rows from both dataframes
        row_data = pd.concat([
            pd.DataFrame([row] * len(matching_rows)), 
            matching_rows.reset_index(drop=True)
        ], axis=1)
        merged_df = pd.concat([merged_df, row_data], ignore_index=True)

# Display the merged dataframe
print(merged_df)

Output

The output of this merge will be:

      specifiers  values            datetime  value              specifiers
0           [P1]    0.43 2021-06-01 00:00:00   11.3  [P1, WEEKDAY, TUESDAY]
1           [P2]    0.51 2021-06-01 00:30:00    9.0  [P2, WEEKDAY, TUESDAY]
2           [P3]    0.62 2021-06-01 01:00:00   10.4  [P3, WEEKDAY, TUESDAY]
3  [P4, WEEKDAY]    0.73 2021-06-01 01:30:00    8.5  [P4, WEEKDAY, TUESDAY]
4  [P5, TUESDAY]    0.84 2021-06-01 02:00:00    9.7  [P5, WEEKDAY, TUESDAY]

Limitations

While this merge works well for small to medium-sized datasets, it can be relatively inefficient when dealing with large datasets.

The reason is that the apply method inside the loop has a time complexity of O(n^2), where n is the number of rows in df1. This means that for each row in df2, we’re performing a linear search on df1.

For very large datasets, this can lead to significant performance issues.

Alternative Approach

If you need to handle large datasets efficiently, there are alternative approaches you can take:

Use the merge function with a custom key function that takes into account the specifier restrictions.
Use the pandas.concat function to concatenate rows from both DataFrames in batches, reducing memory usage.

However, these alternatives require more advanced knowledge of pandas and data manipulation techniques.

I hope this helps you understand how to merge DataFrames based on specifier restrictions using a Python script!

Last modified on 2025-03-25