Define Recursive Function In Pandas Dataframe

I can't seem to find the answer to my question so I'm trying my luck on here. Would very much appreciate your help. I've got a Pandas dataframe with values in Col1 and Col2. Instea

Solution 1:

You could try something like this.

import pandas as pd
import numpy as np
df = pd.DataFrame({'date': [1,2,3,4,5,6],
               'col_1': [951, 909, 867, 844, 824, 826],
               'col_2': [179, 170, 164, 159, 153, 149]})

col_2_update_list = []

for i, row in df.iterrows():

    if i != 0:

        today_col_1 = df.at[i,'col_1']
        prev_day_col_2 = df.at[i-1,'col_2'] 

        new_col_2_val = prev_day_col_2 * today_col_1

        col_2_update_list.append(new_col_2_val)

    else:
        col_2_update_list.append(np.nan)


df['updated_col_2'] = col_2_update_list

Solution 2:

This avoids the use of loops but you need to create 2 new columns:

import pandas as pd
import numpy as np
import sys

df = pd.DataFrame({'date': [1,2,3,4,5,6],
               'col_1': [951, 909, 867, 844, 824, 826],
               'col_2': [179, np.nan, 164, 159, np.nan, 149]})
print(df)

# Compare 2 columns
df['col_4'] = df['col_2'].fillna(method='ffill')*df['col_1']
df['col_3'] = df['col_2'].fillna(sys.maxsize)
df['col_2'] = df[['col_4','col_3']].min(axis=1).astype(int)

df = df.drop(['col_4', 'col_3'], axis = 1)
print(df)