How to Calculate a Column Based on Previous Row Values in Pandas Using the `apply()` Function?

Applying Calculations with Previous Row Values in Pandas

In Pandas, encountering the challenge of incorporating previous row values into calculations during data manipulation is not uncommon. One such scenario involves the need to use the previous row value when calculating a new column using the apply() function.

Consider a scenario where we have a DataFrame with the following structure:

Index_Date    A   B     C    D
================================
2015-01-31    10   10   Nan   10
2015-02-01     2    3   Nan   22
2015-02-02    10   60   Nan  280
2015-02-03    10  100   Nan  250

Our goal is to populate the 'C' column with calculated values. For the first row, 'C' is derived from 'D'. For subsequent rows, 'C' is calculated by multiplying the previous row's 'C' value by the 'A' value for the current row and adding the 'B' value.

Approach

To achieve this, we employ a combination of initialization and iteration within the apply() function.

1. Initialize the 'C' value for the first row using the value from 'D'.

<code class="python">df.loc[0, 'C'] = df.loc[0, 'D']</code>

2. Iterate through the remaining rows and calculate the 'C' values:

<code class="python">for i in range(1, len(df)):
    df.loc[i, 'C'] = df.loc[i - 1, 'C'] * df.loc[i, 'A'] + df.loc[i, 'B']</code>

Result

This approach will effectively populate the 'C' column with the desired calculated values:

Index_Date    A   B    C    D
================================
2015-01-31  10  10   10   10
2015-02-01   2   3   23   22
2015-02-02  10  60  290  280
2015-02-03  10  100  3000  250

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