Adding Two Numeric Pandas Columns with Different Lengths Based on Condition

In this article, we will explore a common problem in data manipulation using pandas. We are given two pandas DataFrames dfA and dfB with numeric columns A and B respectively. Both DataFrames have a different number of rows denoted by n and m. Here, we assume that n > m.

We also have a binary column C in dfA, which has m times 1 and the rest 0. Our goal is to add the values in B to the values in column A if column C == 0.

Problem Statement

Given:

• Two pandas DataFrames dfA and dfB with numeric columns A and B respectively.
• Both DataFrames have a different number of rows denoted by n and m.
• Binary column C in dfA, which has m times 1 and the rest 0.

We need to add the values in B to the values in column A if column C == 0.

Approach Using pandas Data Manipulation

One common approach is to use pandas data manipulation techniques, specifically the where() function. This function allows us to specify a condition to apply when selecting values from one or more Series.

Solution 1: Using where() Function

m = dfA['C'].eq(1)
dfA['C'] = dfA['A'].where(m, dfA['A']+dfB['B'].set_axis(dfA.index[~m]))

In this solution, we first create a boolean mask m to identify the rows where column C equals 1. We then use the where() function to apply two different conditions:

• If the condition is True (i.e., column C equals 1), we assign the original value of column A.
• If the condition is False (i.e., column C does not equal 1), we add the values in column B to column A.

Finally, we use set_axis() to reset the index of dfB['B'] for rows where column C equals 0.

Solution 2: Using Loc[] with Boolean Mask

dfA.loc[m, 'C'] = dfA.loc[m, 'A']
dfA.loc[~m, 'C'] = dfB['B'].values

In this solution, we use the loc[] function to select rows and columns based on a boolean mask. We first assign the original value of column A to the rows where column C equals 1. Then, we assign the values in column B to the rows where column C does not equal 1.

Expected Output

The expected output is:

   A   C
0  7   7
1  7  10
2  7   7
3  7  12
4  7  11
5  7   7

Conclusion

In this article, we have explored a common problem in data manipulation using pandas. We discussed two solutions that use the where() function and the loc[] function to add values from one DataFrame to another based on a condition.

By understanding these techniques, you can efficiently manipulate data in your Python projects using pandas.

Last modified on 2024-04-16