I have the following data,
name marks team1 team2
John 30 kk vv.
Sera 56 gg ww.
Saara 66 dd gg.
Dirg 33 rr dd.
maget 34 ff rr.
fared 56 vv ff.
juile 32 ww kk.
I need to have general formula to get records row wise, which have 'kk' in team 1 and team 2 This is just a sample data, I have actual data which is more than 100k rows.
Use boolean indexing with mask created by filter for get all columns with team, compare by eq (==) and get at least one True per rows by any:
df = df[df.filter(like='team').eq('kk').any(axis=1)]
#if want select columns by names
#df = df[df[['team1','team2']].eq('kk').any(axis=1)]
For better performance use numpy.any:
df = df[np.any(df.filter(like='team').values == 'kk', axis=1)]
print (df)
name marks team1 team2
0 John 30 kk vv
6 juile 32 ww kk
Details:
print (df.filter(like='team').eq('kk'))
team1 team2
0 True False
1 False False
2 False False
3 False False
4 False False
5 False False
6 False True
print (df.filter(like='team').eq('kk').any(axis=1))
0 True
1 False
2 False
3 False
4 False
5 False
6 True
dtype: bool
df[(df['team1']=='kk')&(df['team2']=='kk')]