import pandas as pd
import numpy as np
from pprint import pprint




df_dict = {
    'A': [1, 2, 3, 4, 5],
    'B': [5, 2, 3, 1, 5],
    'out': np.nan
}
length = 2
weight = 5


def transform(row):

    row_num = int(row.name)
    out = row['A'] / length

    if (row_num >= length):
        previous_out = df.at[ row_num-1, 'out' ]
        out = (row['B'] - previous_out) * weight + previous_out

    df.at[row_num, 'out'] = out


df = pd.DataFrame(df_dict)
df.apply( lambda x: transform(x), axis=1)

pprint(df)

# Correct output / breakdown / explaantion
   A  B    out
# 0  1  5    0.5
# out = a / b

# 1  2  2    1.0
# out = a / b

# row_num >= length:

# 2  3  3   11.0
# out = (b - previous_out) * weight + previous_out
# out = (3 - 1) * 5 + 1  = 11

# 3  4  1  -39.0
# out = (1 - 11) * 5 + 11 = 39

# 4  5  5  181.0
# out = (5 - (-39)) * 5 + (-39) = 181


df = pd.DataFrame(df_dict)

#  out = row['A'] / length
df['out'] = df['A'] / length

# if (row_num >= length):
#   previous_out = df.at[ row_num-1, 'out' ]
#   out = (row['B'] - previous_out) * weight + previous_out
#  df[length:]['out'] = (df[length:]['B'] - df[length:]['out'].shift() ) * weight + df[length:]['out'].shift()


pprint(df)