How to fill in the blanks for one (or more) columns by calculating based on multiple conditions?

Question

I collect and process hundreds of jumbled tables every day, and some of the data requires attention.

(Sorry for my English and typography skills)

The item with [LEVEL]"S" rarely appears, and when it happens(row 3,6), if the ["Check"] column is "Go"(row 6), the blank of the target column needs to be filled in. (If not empty, the original value is retained)

1. The blank in the ["FillNA"] column needs to be filled with "DANGER".

(2. If possible, the blank in the ["Detail"] column needs to be filled with the row number of the dataframe && the action indicated in the ["Check"] column.)

N = pd.NA  
df= pd.DataFrame({  "LEVEL":["E","D","C","S","A","B","S","A","B","C"],
                    "CHECK":["HELLO",N ,N,"Keep",N,N,"Go",N,N,N],
                    "FillNA":["WORLD",N ,N,"Data",N,N,N,N,N,N],
                    "Detail":[N,N,N,N,N,N,N,N,N,N] })
print(df)

Answer 1

IUUC,

m = df['LEVEL'].eq('S') & df['CHECK'].eq('Go')
df['FillNA'] = df['FillNA'].mask(m & df['FillNA'].isna(), 'DANGER')
df['Detail'] = df['Detail'].mask(m & df['Detail'].isna(), df.index.astype(str) + ' ' + df['CHECK'])

# or

cols = ['FillNA', 'Detail']
df.loc[m, cols] = df.loc[m, cols].fillna({'FillNA': 'DANGER', 'Detail': df.index.astype(str) + ' ' + df['CHECK']})

print(df)

  LEVEL  CHECK  FillNA Detail
0     E  HELLO   WORLD   <NA>
1     D   <NA>    <NA>   <NA>
2     C   <NA>    <NA>   <NA>
3     S   Keep    Data   <NA>
4     A   <NA>    <NA>   <NA>
5     B   <NA>    <NA>   <NA>
6     S     Go  DANGER   6 Go
7     A   <NA>    <NA>   <NA>
8     B   <NA>    <NA>   <NA>
9     C   <NA>    <NA>   <NA>