Merging DataFrames without Duplicate Columns in Pandas

When working with large datasets, it’s not uncommon to encounter situations where we need to merge multiple DataFrames together. However, in some cases, the resulting DataFrame may contain duplicate columns due to shared keys between DataFrames. In this article, we’ll explore a solution that merges DataFrames while avoiding duplicate columns and maintaining the original order.

Understanding the Problem

The provided Stack Overflow question highlights a common challenge when merging multiple DataFrames using pd.merge. The issue arises when there are overlapping columns in the DataFrames, resulting in duplicate column names. In this case, the problem is further complicated by the presence of over 40,000 columns in each DataFrame and exactly 50,000 common rows identified by the “id” column.

Background: Pandas Merging Mechanism

To understand why pd.merge fails to meet our requirements, let’s delve into its underlying mechanism. The merging process involves comparing the index values of the two DataFrames being merged and creating a new DataFrame with the specified columns. When duplicate columns are present, pandas will keep the last occurrence in the resulting DataFrame.

Exploring the Solution

The proposed solution involves using Python’s built-in functools.reduce function to union all column names from the input DataFrames. This approach ensures that we don’t have duplicate columns in our final output.

Step 1: Uniting Column Names

from functools import reduce

# Define the list of DataFrames
dfs = [df1, df2, df3]

# Get the union of all column names from the input DataFrames
cols = reduce(lambda a, b: a.union(b, sort=False),
              (x.columns for x in dfs))

In this step, we define our list of DataFrames dfs and use reduce to compute the union of all column names. The sort=False parameter ensures that the resulting set contains duplicate column names.

Step 2: Setting Up the Output

# Create an empty DataFrame with the combined column names
out = pd.DataFrame(index=dfs[0].index,
                   columns=cols)

We create a new, empty DataFrame out with the combined column names and set its index to match that of our first DataFrame.

Step 3: Updating the Output

# Iterate over each input DataFrame
for x in dfs:
    # Update the output with the current DataFrame's columns
    out.update(x)

Here, we iterate through each input DataFrame x and update our out DataFrame by adding its columns.

Alternative Implementation

Alternatively, you can also create your output DataFrame before updating it:

# Create an empty DataFrame with the combined column names
out = pd.DataFrame(dfs[0],
                   columns=cols)

# Iterate over each input DataFrame (starting from the second one)
for x in dfs[1:]:
    # Update the output with the current DataFrame's columns
    out.update(x)

In this implementation, we create an initial out DataFrame using the first input DataFrame and then update it iteratively for the remaining DataFrames.

Example Use Case

To illustrate the solution, let’s revisit the provided minimal example:

# Import necessary libraries
import pandas as pd

# Define the DataFrames
df1 = pd.DataFrame({
    'id': ['a', 'b', 'c'],
    'col1': [123, 121, 111],
    'col2': [456, 454, 444],
    'col3': [786, 787, 777],
})

df2 = pd.DataFrame({
    'id': ['a', 'b', 'c'],
    'col1': [123, 121, 111],
    'col2': [456, 454, 444],
    'col4': [11, 44, 77],
})

df3 = pd.DataFrame({
    'id': ['a', 'b', 'c'],
    'col1': [123, 121, 111],
    'col2': [456, 454, 444],
    'col5': [1786, 1787, 1777],
})

# Define the list of DataFrames
dfs = [df1, df2, df3]

from functools import reduce

# Get the union of all column names from the input DataFrames
cols = reduce(lambda a, b: a.union(b, sort=False),
              (x.columns for x in dfs))

# Create an empty DataFrame with the combined column names
out = pd.DataFrame(index=df1.index,
                   columns=cols)

# Iterate over each input DataFrame
for x in dfs:
    # Update the output with the current DataFrame's columns
    out.update(x)

The resulting out DataFrame will contain the specified columns, without duplicates:

  id col1 col2 col3 col4  col5
0  a  123  456  786   11  1786
1  b  121  454  787   44  1787
2  c  111  444  777   77  1777

In conclusion, this solution provides a robust approach to merging multiple DataFrames while avoiding duplicate columns and maintaining the original order. By leveraging Python’s built-in functools.reduce function, we can efficiently union all column names from our input DataFrames.

Last modified on 2024-07-22