How to change particular column value when defined mask is true?

Question

I have a dataframe in which I have these column names

• 'team1',
• 'team2',
• 'city',
• 'date'.

What I want to do is to assign value of 'city' as 'dubai' when certain condition meets(which I am defining using mask).

This is what I am doing exactly:

 matches[((matches['team1']=='mi') & (matches['team2']=='rcb') & (matches['date']=='2014-04-19')),'city']='Dubai'

When all the above condition meets I want to change value in 'city'(which is null now) to 'Dubai'

The problem which arises:

'Series' objects are mutable, thus they cannot be hashed

How can I do this?

Answer 1

Bracket ([]) notation accesses the __getitem__ method of a python object (if it has a method defined). For a pd.DataFrame object, you can pass an array like object via the brackets df[array_like_object] and it will do one of a few things

possibility 1

# returns a copy of df with columns ['col1', 'col2']
df[['col1', 'col2']]

possibility 2

# returns a slice of which rows have corresponding trues in the mask
df[boolean_mask]

skipping other possibilities

---

You've got a boolean_mask

((matches['team1']=='mi') & 
 (matches['team2']=='rcb') & 
 (matches['date']=='2014-04-19'))

And a column

'city'

In this case, it's perfect for loc which can process exactly that
Per @JohnGalt

matches.loc[
    ((matches['team1']=='mi') &
     (matches['team2']=='rcb') &
     (matches['date']=='2014-04-19')),
    'city'
] = 'Dubai'