How can I overwrite a mapping of a column based on its current value and value of two other columns?

I have the following pandas dataframe

is_and_mp    market_state       reason     
  '100'          None             NaN  
  '400'          None             NaN 
  '100'          ALGO             NaN
  '400'          OPENING          NaN

I want to write two mappings where if is_and_mp is either '100' or '400', and market_state == None and reason == NaN, then map market_state =CONTINUOUS_TRADING.

So the output would be:

is_and_mp         market_state              reason     
  '100'        CONTINUOUS_TRADING             NaN  
  '400'        CONTINUOUS_TRADING             NaN
  '100'             ALGO                      NaN
  '400'           OPENING                     NaN

It is important for the existing mappings not to change! Thanks

>Solution :

Use DataFrame.loc with chained mask by & for bitwise AND:

df.loc[df.is_and_mp.isin([ '100', '400']) & df.market_state.isna() & df. reason.isna(),  'market_stat'] = 'CONTINUOUS_TRADING'

or if values are numeric:

df.loc[df.is_and_mp.isin([ 100, 400]) & df.market_state.isna() & df. reason.isna(),  'market_stat'] = 'CONTINUOUS_TRADING'