How to Calculate Sequential Row Values in a Pandas DataFrame Using Apply and Shifting?

Pandas Apply for Sequential Row Value Calculations in Dataframes

When working with Pandas dataframes, you may encounter situations where you need to use the value of a previous row in a calculation. However, this can present challenges, especially when the previous value is also calculated within the same apply function.

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

We want to create a new column, C, where:

• For 2015-01-31, C is equal to D.
• For all other rows, C is calculated as the previous row's C multiplied by the current row's A, plus the current row's B.

Using an apply function and a shift with an if-else condition may not work due to key errors. Instead, we can follow these steps:

Step 1: Initialize Derived Value

First, we set the C value for the first row to be equal to D:

df.loc[0, 'C'] = df.loc[0, 'D']

Step 2: Iterate and Calculate

Next, we iterate through the remaining rows and calculate C using the previous row's C value:

for i in range(1, len(df)):
    df.loc[i, 'C'] = df.loc[i-1, 'C'] * df.loc[i, 'A'] + df.loc[i, 'B']

Result:

  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
3 2015-02-03  10  100  3000  250

The above is the detailed content of How to Calculate Sequential Row Values in a Pandas DataFrame Using Apply and Shifting?. For more information, please follow other related articles on the PHP Chinese website!