Changing column based on multiple conditions and previous rows values pandas

Question

I have this dataframe. I need to replace NaNs in column rank to a value based on multiple conditions. If column min is higher than 3 previous rows of max column then rank equals to min . Otherwise, I need to copy the previous value of rank

      max     min    rank
0  128.20  117.87  117.87
1  132.72  122.29  122.29
2  138.07  124.89  124.89
3  137.02  128.46     NaN
4  130.91  129.86     NaN
5  200.15  190.54     NaN
6  199.18  191.79     NaN
7  210.44  201.94     NaN

The desired result is

      max     min    rank
0  128.20  117.87  117.87
1  132.72  122.29  122.29
2  138.07  124.89  124.89
3  137.02  128.46  124.89
4  130.91  129.86  124.89
5  200.15  190.54  190.54
6  199.18  191.79  190.54
7  210.44  201.94  201.94 

Answer 1

IIUC, here's one way:

df['rank'].mask(pd.concat([df['min'].shift(i) for i in range(3)], 1).apply(
    lambda x: x < df['min']).all(1), df['min']).ffill()

OUTPUT:

      max     min    rank
0  128.20  117.87  117.87
1  132.72  122.29  122.29
2  138.07  124.89  124.89
3  137.02  128.46  124.89
4  130.91  129.86  124.89
5  200.15  190.54  190.54
6  199.18  191.79  190.54
7  210.44  201.94  201.94

Answer 2

You can try:

df["rank"].update(df[df["min"]>df["max"].rolling(3).max().shift(1)]["min"])
df["rank"].ffill(inplace=True)
>>> df
      max     min    rank
0  128.20  117.87  117.87
1  132.72  122.29  122.29
2  138.07  124.89  124.89
3  137.02  128.46  124.89
4  130.91  129.86  124.89
5  200.15  190.54  190.54
6  199.18  191.79  190.54
7  210.44  201.94  201.94

The rolling and shift functions are being used to check if the current min is greater than the max of the three previous max .

The ffill carries forward the previous value.