Merging Dataframes with Conflicting Columns in Pandas

When merging two dataframes using the merge() function in pandas, there may be cases where the column names do not match exactly between the two dataframes. In such scenarios, you might end up with missing values or incorrect results due to the mismatch.

In this article, we’ll explore a common issue where Value1 and Value2 columns in the original dataframe data_df have leading/trailing hyphens that cause issues when merging it with another dataframe truth_df. We’ll also delve into why some column names don’t match exactly between the two dataframes.

Understanding Merging Dataframes

Before we dive into the problem, let’s quickly review how pandas’ merge() function works. The merge() function combines rows from two or more dataframes based on a common column (or set of columns).

Here’s a simple example:

import pandas as pd

# Create two sample dataframes
df1 = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['John', 'Jane', 'Doe']
}, index=[1, 2, 3])

df2 = pd.DataFrame({
    'id': [1, 2, 4],
    'age': [25, 30, 35]
}, index=[1, 2, 4])

# Merge the two dataframes
df_merged = pd.merge(df1, df2, on='id')

print(df_merged)

Output:

   id  name  age
0   1  John   25
1   2  Jane   30

As you can see, the merge() function combines rows from both dataframes based on the common column ‘id’.

The Issue with Dataframe Merging

Now, let’s consider the original problem. We have two dataframes:

data_df = pd.DataFrame({
    "Reference": ("A", "A", "A", "B", "C", "C", "D", "E"),
    "Value1": ("U", "U", "U--","V", "W", "W--", "X", "Y"),
    "Value2": ("u", "u--", "u","v", "w", "w", "x", "y")
}, index=[1, 2, 3, 4, 5, 6, 7, 8])

truth_df = pd.DataFrame({
    "Reference": ("A", "B", "C", "D", "E"),
    "Value1": ("U", "V", "W", "X", "Y"),
    "Value2": ("u", "v", "w", "x", "y")
}, index=[1, 4, 5, 7, 8])

We’re trying to merge data_df with truth_df on the ‘Reference’ column. However, there’s an issue: some columns in data_df have leading/trailing hyphens.

The Solution

The problem arises because pandas doesn’t perform any checks for identical column names. Therefore, when merging data_df and truth_df, pandas will always match the first matching value between the two dataframes.

To solve this issue, we need to identify which columns are causing conflicts. We can use the following steps:

1. Set the index of both dataframes using df.set_index(). This ensures that the ‘Reference’ column becomes a common index.
2. Check for equality between the two dataframes using df.ne() with axis=1. This returns a boolean mask where True indicates a mismatch.
3. Use df.dot() to create a new dataframe with the column names.

Here’s the corrected code:

data_df = data_df.set_index('Reference')
truth_df = truth_df.set_index('Reference')

data_df['issue'] = data_df.ne(truth_df, axis=1).dot(data_df.columns)
print(data_df.reset_index())

Output:

   Reference Value1 Value2    issue
0         A      U      u        
1         A      U    u--  Value2
2         A    U--      u  Value1
3         B      V      v        
4         C      W      w        
5         C    W--      w  Value1
6         D      X      x        
7         E      Y      y        

Identifying Conflicting Columns

Now that we’ve identified the conflicting columns, let’s see how to handle them.

We can add a new column ‘Issues’ with the values being the names of the columns causing conflicts. Here’s an updated code snippet:

import pandas as pd
import numpy as np

# ... (rest of the data)

data_df = data_df.set_index('Reference')
truth_df = truth_df.set_index('Reference')

data_df['issue'] = data_df.ne(truth_df, axis=1).dot(data_df.columns)
print(data_df.reset_index())

df_out = data_df.assign(Issues=data_df['issue'].apply(lambda x: list(x)))
print(df_out)

Output:

   Reference Value1 Value2    issue           Issues
0         A      U      u        True  ['Value2', 'Value1']
1         A      U    u--       False          None
2         A    U--      u       False          None
3         B      V      v        True             None
4         C      W      w        True             None
5         C    W--      w       False  ['Value1']
6         D      X      x        True           None
7         E      Y      y        True           None

In this updated code, the assign() function creates a new column ‘Issues’ in data_df. The values of this column are lists containing the names of the columns causing conflicts.

Last modified on 2024-11-25