How to Calculate a Column in a DataFrame Using Previous Row Values with `apply()`?

Iterating Through DataFrames with Previous Value Considerations Using apply()

In pandas, the apply() function is commonly employed to apply a function to each row of a DataFrame. However, challenges arise when the previous row value is also calculated using the same apply() method.

Consider the following DataFrame:

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

The objective is to derive Column C:

• For 2015-01-31, set it to the value of D.
• For subsequent rows, multiply the previous row value of C by the current row value of A and add it to the current row value of B.

Solution:

To achieve this, we first set the initial value of C for 2015-01-31:

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

Then, we iterate through the remaining rows and update the C values with the desired calculations:

<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>

The final DataFrame after these operations:

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

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