import pandas as pd

person = {
    "first": ["Kushagra", "Jane", "John","Adam","Eve"],
    "last": ["Gupta", "Doe", "Doe","Doe","Damm"],
    "email": ["Kushagra225@gmail.com", "JaneDoe123@gmail.com", "John@gmail.com","Adam@gmail.com","Eve@gmail.com"],
    "salary":[72000,60000,50000,100000,80000]
}

df = pd.DataFrame(person)

df

#sorting ascending
df.sort_values(by='last')

#sorting descending
df.sort_values(by='last', ascending=False)

#sorting by multiple columns
df.sort_values(by=['last','first'], ascending=False)

#sorting last name with ascending and first name with descending order
df.sort_values(by=['last','first'], ascending=[True,False])

#saving the changes in original df
df.sort_values(by=['last','first'], ascending=[True,False], inplace=True)

df

#sorting based on index using sort_index method
df = df.sort_index()

df

#sorting series objects
df['last'].sort_values()

4     Damm
1      Doe
2      Doe
3      Doe
0    Gupta
Name: last, dtype: object

#top 3 salary
df['salary'].nlargest(3)

3    100000
4     80000
0     72000
Name: salary, dtype: int64

#top 3 salary with details
df.nlargest(3,'salary')

#bottom 3 salary with details
df.nsmallest(3,'salary')

	first	last	email	salary
0	Kushagra	Gupta	Kushagra225@gmail.com	72000
1	Jane	Doe	JaneDoe123@gmail.com	60000
2	John	Doe	John@gmail.com	50000
3	Adam	Doe	Adam@gmail.com	100000
4	Eve	Damm	Eve@gmail.com	80000