Change values of column in df using conditional in two columns

Question

I'm having the following problem: I'm working with a dataset that can be found at https://www.kaggle.com/datasets/ricardomattos05/jogos-do-campeonato-brasileiro

The column 'home_team' has 2 equal teams ('Atlético') and the difference between them is the column 'home_team_state'. How can I change the name of the teams in the data frame?

I tried to use some conditional like

df['home_team'] = np.where([df.home_team] == 'Atletico' and df['home_team_state'] == 'MG', 'Atlético MG', df.home_team)

but it does not work. So, basically, my question is: Can I change the specif values of a column in a Data Frame using the conditions of 2 columns?

Any help is appreciated!

Answer 1

You can't change but you can create a new one an then replace, like this.

In your case you can create 'home_team_copy':

df.loc[([df.home_team] == 'Atletico') | (df['home_team_state'] == 'MG'),  
'new_home_team]' = "<whatever you want, True or False,  YES OR NO, HOME 
TEAM..>"

Than, if was the case you replace the values or change de name os the column new_home_team to home_team for example.

Example:

data	cts	op
2017-04-24	3	B
2022-04-18	6	S
2017-04-14	10	S
2022-04-13	4	S

Code:

df.loc[(df['cts'] > 5) | (df['op'] == 'S'),  'obs'] = 
'flip'

And the output is:

data	cts	op	obs
2017-04-24	3	B	NaN
2022-04-18	6	S	flip
2017-04-14	10	S	flip
2022-04-13	4	S	NaN

Hope it helps :)