How to Incorporate Previous Row Values into Pandas \'apply\' Calculations with Derived Values?

Utilizing Pandas to Incorporate Previous Row Values in 'apply' Calculations with Derived Values

When working with dataframes in Pandas, it is often necessary to perform calculations that involve the use of previous row values. This can be challenging when the previous value is also calculated within the '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

To calculate Column C, we start by copying the value of D to the first row:

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

Now, for the subsequent rows, we can iterate through the dataframe and use a loop to fill in the calculated values for Column C:

<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>

This loop ensures that the value of C for each row is determined by its previous value, as well as the corresponding values of A and B. The resulting dataframe will look like this:

  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

By following this approach, you can effectively incorporate previous row values into 'apply' calculations, even when the previous value is derived from the same 'apply' function.

The above is the detailed content of How to Incorporate Previous Row Values into Pandas 'apply' Calculations with Derived Values?. For more information, please follow other related articles on the PHP Chinese website!