change specific values in dataframe if one cell in a row is null

Question

I have the following dataframe in pandas:

>>>name   food  beverage  age
0  Ruth   Burger Cola      23
1 Dina    Pasta  water     19
2 Joel    Tuna   water     28
3 Daniel  null   soda      30
4 Tomas   null   cola     10

I want to put condistion that if value in food column is null, the age and beverage will change into ' ' (blank as well),

I have wrote this code for that:

if df[(df['food'].isna())]:
  df['beverage']=' '
  df['age']=' '

but I keep getting error:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I have playes with the location of the ([ but didn't help, what do I do wrong?

Answer 1

You can use boolean indexing to assign the values based on the condition:

df.loc[df['food'].isna(), ['age', 'beverage']] = ''

---

     name    food beverage age
0    Ruth  Burger     Cola  23
1    Dina   Pasta    water  19
2    Joel    Tuna    water  28
3  Daniel     NaN             
4   Tomas     NaN             

Answer 2

Try with mask

df[['beverage','age']] = df[['beverage','age']].mask(df['food'].isna(),'')

df
Out[86]: 
     name    food beverage age
0    Ruth  Burger     Cola  23
1    Dina   Pasta    water  19
2    Joel    Tuna    water  28
3  Daniel     NaN             
4   Tomas     NaN             

Answer 3

You can use np.where:

cols = ['beverage','age']
arr = np.where(df['food'].isna()[:,None],'',df[cols])
#for NaN : arr = np.where(df['food'].isna()[:,None],np.nan,df[cols])
df[cols] = arr

---

     name    food beverage age
0    Ruth  Burger     Cola  23
1    Dina   Pasta    water  19
2    Joel    Tuna    water  28
3  Daniel     NaN             
4   Tomas     NaN