熊猫如何根据最后一个元素的条件保持值或更改列的值

Question

I have a dataset that looks like exactly the dataset below, what i need to do is transform the column 'status_final' based on what i have in the column 'status'. I need to keep the column status_final with the value of the last row if the column 'status' isn't equal to Realized. Just Realized can change the column status_final value, if not, i need to keep the value from the last one. One problem is that if the value is one, after a realized 0, i can't change it to 0 as the logic should suggest. The other problem is i'm trying with a loop, and as i have more than 10k rows, it takes too much time.

     status       status_final
0    Nothing           1
1    Nothing           0
2    Realized          0
3     Doing            0
4    Realized          1
5    Doing             0
6    Nothing           0
7    Realized          0
8    Nothing           1

And i need to transform it to:

     status       status_final
0    Nothing           1
1    Nothing           1
2    Realized          0
3     Doing            0
4    Realized          1
5    Doing             1
6    Nothing           1
7    Realized          0
8    Nothing           1

Answer 1

Let's try:

# mask `status_final` where `status` is not Realized
s = df['status_final'].where(df.status.eq('Realized'))

# override the first `nan` row
s.iloc[0] = df['status_final'].iloc[0]

# then ffill
df['status_final'] = np.maximum(df['status_final'], s.ffill())

Output:

     status  status_final
0   Nothing           1.0
1   Nothing           1.0
2  Realized           0.0
3     Doing           0.0
4  Realized           1.0
5     Doing           1.0
6   Nothing           1.0
7  Realized           0.0
8   Nothing           1.0

Answer 2

This operates off of the assumption that after the first realized every value will be equal to the value of the preceding realized . Everything before that can be grouped by status and have the first status_final passed forward.

r_row = df[df['status']=='Realized'].index.min()
df.loc[(df.index >= r_row) & (df['status']!='Realized'), 'status_final'] = np.nan
df.loc[df.index < r_row, 'status_final'] = df.loc[df.index < r_row].groupby('status')['status_final'].transform('first')
df.ffill()

Output

    status  status_final
0   Nothing          1.0
1   Nothing          1.0
2   Realized         0.0
3   Doing            0.0
4   Realized         1.0
5   Doing            1.0
6   Nothing          1.0

Answer 3

If time is an issue, maybe the fastest is to work with numpy arrays:

import pandas as pd
import numpy as np

df = pd.DataFrame([ ['Nothing', 1],
                    ['Nothing', 0],
                    ['Realized',0],
                    ['Doing',   0],
                    ['Realized',1],
                    ['Doing',   0],
                    ['Nothing', 0]],\
                  columns=['status', 'status_final'])

arr = np.array(df.values)
for i in np.arange(1,arr.shape[0]):
    if not(arr[i][0] == 'Realized'):
        arr[i][1] = arr[i-1][1] 

df = pd.DataFrame(data=arr,\
                  columns=['status', 'status_final'])
print(df)