Filtering efficiently and transforming a dataframe based on a column containing names

Question

I am looking for an efficient way to transform a dataframe... I have a dataframe of the form where: pandas dataframe

I would like to transpose the columns and transform that into a dataframe of the form (I dont care about column names): enter image description here

Thank you!

I dont know if this is efficient but I tried getting the names I was interested in, in this way:

def players_names(df,col,names):
    new_df=df.loc[df[col].isin(names)].reset_index(drop=True)
    e_names=new_df[col].unique().tolist()
    return e_names

And then transform the columns into arrays and transpose them like this (a loop is missing):

def players_points(df,col,name):
    filt=(df[col]==name)
    new_df=df[filt].reset_index(drop=True).drop(col,axis=1).T
    pts=new_df.iloc[:,0:].values.tolist()
    return pts