get the number of involved singer in a phase

I have a dataset like this

import pandas as pd 
df = pd.read_csv("music.csv") 
df

	name	date	singer	language	phase
1	Yes or No	02.01.20	Benjamin Smith	en	1
2	Parabens	01.06.21	Rafael Galvao;Simon Murphy	pt;en	2
3	Love	12.11.20	Michaela Condell	en	1
4	Paz	11.07.19	Ana Perez; Eduarda Pinto	es;pt	3
5	Stop	12.01.21	Michael Conway;Gabriel Lee	en;en	1
6	Shalom	18.06.21	Shimon Cohen	hebr	1
7	Habibi	22.12.19	Fuad Khoury	ar	3
8	viva	01.08.21	Veronica Barnes	en	1
9	Buznanna	23.09.20	Kurt Azzopardi	mt	1
10	Frieden	21.05.21	Gabriel Meier	dt	1
11	Uruguay	11.04.21	Julio Ramirez	es	1
12	Beautiful	17.03.21	Cameron Armstrong	en	3
13	Holiday	19.06.20	Bianca Watson	en	3
14	Kiwi	21.10.20	Lachlan McNamara	en	1
15	Amore	01.12.20	Vasco Grimaldi	it	1
16	La vie	28.04.20	Victor Dubois	fr	3
17	Yom	21.02.20	Ori Azerad; Naeem al-Hindi	hebr;ar	2
18	Elefthería	15.06.19	Nikolaos Gekas	gr	1

I convert it to 1NF.

import pandas as pd
import numpy as np
 
df = pd.read_csv("music.csv") 
df['language']=df['language'].str.split(';')
df['singer']=df['singer'].str.split(";")
df.explode(['language','singer'])
d= pd.DataFrame(df)
d

And I create a dataframe. Now I would like to find out which phase has the most singers involved.

I used this

df= df.group.by('singer')
df['phase']. value_counts(). idxmax()

But I could not get a solution

The dataframe has 42 observations, so some singers occur again

Source: convert data to 1NF

Answer

You do not need to split/explode, you can directly count the number of ; per row and add 1:

df['singer'].str.count(';').add(1).groupby(df['phase']).sum()

If you want the classical split/explode:

(df.assign(singer=df['singer'].str.split(';'))
   .explode('singer')
   .groupby('phase')['singer'].count()
)

output:

phase
1    12
2     4
3     6
Name: singer, dtype: int64