How to change a value in a column based on whether or not a certain string combination is in other columns in the same row? (Pandas) - pandas

Question

I am a very new newbie to Pandas and programming in general. I'm using Anaconda, if that matters.
I have the following on my hands:
The infamous Titanic survival dataset.
So, my idea was to search the dataframe, find the rows where in the "Name" column there would be a string "Mrs." AND at the same time the "age" would be a NaN (in which case the value in the "Age" column needs to be changed to 32). Also, finding "Miss"in the cell, values in two other columns are zeros.
My major problem is that I don't know how to tell Pandas to replace the value in the same row or delete the whole row.
#I decided to collect the indexes of rows with the "Age" value == NaN to further use the
#indices to search through the "Names column."
list_of_NaNs = df[df['Age'].isnull()].index.tolist()
for name in df.Name:
if "Mrs." in name and name (list_of_NaNs):#if the string combination "Mrs."
#can be found within the cell...
df.loc['Age'] = 32.5 #need to change the value in the
#column IN THE SAME ROW
elif "Miss" in name and df.loc[Parch]>0: #how to make a
#reference to a value IN THE SAME ROW???
df.loc["Age"] = 5
elif df.SibSp ==0 and Parch ==0:
df.loc["Age"] = 32.5
else:
#mmm... how do I delete entire row so that it doesn't
#interfere with my future actions?

Answer 1

Here is how you can test if 'Miss' or 'Mrs.'is present in name columns:
df.name.str.contains('Mrs')
So following will give you the rows where 'Mrs' is in name and Age is NaN
df[(df.name.str.contains('Mrs')) & (df.age.isna())]
You can play with different cases and tasks from here on.
Hope this helps :)
And to drop rows with NaN in age column:
df = df.drop(df[df.age.isna()].index)