How to Calculate Values Based on Previous Row Data Using pandas\' apply Function?

Utilizing Pandas' apply Function to Calculate Values Based on Previous Row Values

In Pandas, the apply function allows for the application of custom functions to each row of a DataFrame. This can prove particularly useful when calculations require referencing values from previous rows within the DataFrame.

Consider the following scenario: we have a DataFrame with columns A, B, C, and D. We need to calculate column C for the first row as the value of column D. Subsequently, C for subsequent rows is calculated by multiplying the previous row's C value by the current row's A and adding the current row's B.

Despite attempts using apply and shift, we encounter a key error due to the calculation of C also occurring within the apply function. To resolve this issue, we can adopt the following approach:

1. Explicitly Calculate the First Row:

   • Assign D's value to the first row of C using df.loc[0, 'C'] = df.loc[0, 'D'].

2. Iterate and Calculate Subsequent Rows:

   • Utilize a for loop to iterate through the remaining rows, calculating C for each row as follows:

     • df.loc[i, 'C'] = df.loc[i-1, 'C'] * df.loc[i, 'A'] df.loc[i, 'B']

By following this approach, we ensure that C for each row is available before calculating the subsequent rows' values. The resulting DataFrame will align with the desired output:

  Index_Date   A   B    C    D
0 2015-01-31  10  10   10   10
1 2015-02-01   2   3   23   22
2 2015-02-02  10  60  290  280

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