Resolved: Python Pandas: GROUPBY AND COUNT OF VALUES OF DIFFERENT COLUMNS in minimal steps and in a very fast way

UID,CONTINENT,AGE_GROUP,APPROVAL_STATUS
user1,ASIA,26-30,YES
user10,ASIA,26-30,NO
user11,ASIA,36-40,YES
user12,EUROPE,21-25,NO
user13,AMERICA,31-35,not_confirmed
user14,ASIA,26-30,YES
user15,EUROPE,41-45,not_confirmed
user16,AMERICA,21-25,NO
user17,ASIA,26-30,YES
user18,EUROPE,41-45,NO
user19,AMERICA,31-35,YES
user2,AMERICA,31-35,NO
user20,ASIA,46-50,NO
user21,EUROPE,18-20,not_confirmed
user22,ASIA,26-30,not_confirmed
user23,ASIA,36-40,YES
user24,AMERICA,26-30,YES
user25,EUROPE,36-40,NO
user26,EUROPE,Above 50,NO
user27,ASIA,46-50,YES
user28,AMERICA,31-35,NO
user29,AMERICA,Above 50,not_confirmed
user3,ASIA,36-40,YES
user30,EUROPE,41-45,YES
user4,EUROPE,41-45,NO
user5,ASIA,26-30,not_confirmed
user6,ASIA,46-50,not_confirmed
user7,ASIA,26-30,YES
user8,AMERICA,18-20,YES
user9,EUROPE,31-35,NO

CONTINENT,18-20,21-25,26-30,31-35,36-40,41-45,46-50,Above 50,NO,YES,not_confirmed,USER_COUNT
AMERICA,1,1,1,4,0,0,0,1,3,3,2,8
ASIA,0,0,7,0,3,0,3,0,2,8,3,13
EUROPE,1,1,0,1,1,4,0,1,6,1,2,9

in_file = "/Users/user1/groupby.csv"
out_file = "/Users/user1/groupby1.csv"

df= pd.read_csv(in_file)
print(df)
df1 = df.groupby(['CONTINENT', 'AGE_GROUP']).size().unstack(fill_value=0).reset_index()
df1 = df1.sort_values(["CONTINENT"], axis=0, ascending=True)
print(df1)
df2 = df.groupby(['CONTINENT', 'APPROVAL_STATUS']).size().unstack(fill_value=0).reset_index()
df2 = df2.sort_values(["CONTINENT"], axis=0, ascending=True)
print(df2)
df3 = df.groupby("CONTINENT").count().reset_index()
df3 = df3[df3.columns[0:2]]
df3.columns = ["CONTINENT", "USER_COUNT"]
df3 = df3.sort_values(["CONTINENT"], axis=0, ascending=True)
df3.reset_index(drop=True, inplace=True)
# df3.to_csv(out_file, index=False)
print(df3)
df2.drop('CONTINENT', axis=1, inplace=True)
df3.drop('CONTINENT', axis=1, inplace=True)

df_final = pd.concat([df1, df2, df3], axis=1)
print(df_final)
df_final.to_csv(out_file, index=False)

s1 = pd.crosstab(df['CONTINENT'], df['AGE_GROUP'])
s2 = pd.crosstab(df['CONTINENT'], df['APPROVAL_STATUS'])

pd.concat([s1, s2, s2.sum(1).rename('USER_COUNT')], axis=1)

           18-20  21-25  26-30  31-35  36-40  41-45  46-50  Above 50  NO  YES  not_confirmed  USER_COUNT
CONTINENT                                                                                               
AMERICA        1      1      1      4      0      0      0         1   3    3              2           8
ASIA           0      0      7      0      3      0      3         0   2    8              3          13
EUROPE         1      1      0      1      1      4      0         1   6    1              2           9