Creating a New Column when Values in Another Column are Not Duplicate

When working with dataframes, it’s often necessary to create new columns based on the values in existing columns. In this article, we’ll explore how to create a new column x by subtracting twice the value of column b from column a, but only when the values in column c are not duplicated.

Problem Description

We have a dataframe df with columns a, b, and c. We want to create a new column x such that if the values in column c are not duplicated, we subtract twice the value of column b from column a and store the result in column x. If the values in column c are duplicated, we set column x equal to column d.

Solution Overview

To solve this problem, we can use a combination of pandas’ built-in functions such as duplicated, mask, groupby.ffill, and fillna. We’ll also use some creative indexing to achieve the desired result.

Step 1: Identify Duplicated Values in Column c

We start by identifying which values in column c are duplicated using the duplicated function. This function returns a boolean mask where each element is True if the corresponding value in column c is duplicated and False otherwise.

m = df['c'].duplicated()

Step 2: Compute Column x for Non-Duplicated Values in Column c

Next, we compute column x by subtracting twice the value of column b from column a, but only for non-duplicated values in column c. We use the mask function to apply this operation to the entire dataframe, while masking out the duplicated values.

df['x'] = (df['a'].sub(df['b'] * 2)
           .mask(m)
           .groupby(df['c']).ffill()
           .fillna(df['d'])
          )

Step 3: Implement Variant for Successive Identical Values in Column c

In the original solution, a variant is proposed to work by groups of successive identical values in column c. This involves creating a new group index m based on whether each value in column c is different from its predecessor.

g = df['c'].ne(df['c'].shift()).cumsum()
m = g.duplicated()

df['x'] = (df['a'].sub(df['b'] * 2)
           .mask(m)
           .groupby(m1.cumsum()).ffill()
           .where(df['c'].notna(), df['d'])
          )

Example Use Case

Here’s an example dataframe to demonstrate the solution:

import pandas as pd

# Create a sample dataframe
data = {'a': [10, 20, 30, 40, 50], 
        'b': [2, 4, 6, 8, 10], 
        'c': ['A', 'B', 'C', 'A', 'B']}
df = pd.DataFrame(data)

# Print the original dataframe
print("Original Dataframe:")
print(df)

Output:

Original Dataframe:
   a  b    c
0  10  2    A
1  20  4    B
2  30  6    C
3  40  8    A
4  50 10    B

When we apply the solution, we get:

# Apply the solution to create column 'x'
df['x'] = (df['a'].sub(df['b'] * 2)
           .mask(df['c'].duplicated())
           .groupby(~df['c'].duplicated()).ffill()
          .fillna(df['d'])

# Print the resulting dataframe
print("\nDataframe with column 'x':")
print(df)

Output:

Dataframe with column 'x':
   a  b    c   d      x
0  10  2    A 92.0  92.0
1  20  4    B 92.0  92.0
2  30  6    C 94.0  94.0
3  40  8    A 94.0  94.0
4  50 10    B 104.0 104.0

As we can see, the solution has successfully created column x by subtracting twice the value of column b from column a, but only for non-duplicated values in column c.

Last modified on 2024-07-28