[Python] Pandas add column with value based on condition based on other columns

I have the following pandas dataframe:



import pandas as pd
import numpy as np

d = {'age' : [21, 45, 45, 5],
'salary' : [20, 40, 10, 100]}

df = pd.DataFrame(d)


and would like to add an extra column called "is_rich" which captures if a person is rich depending on his/her salary. I found multiple ways to accomplish this:

# method 1
df['is_rich_method1'] = np.where(df['salary']>=50, 'yes', 'no')

# method 2
df['is_rich_method2'] = ['yes' if x >= 50 else 'no' for x in df['salary']]

# method 3
df['is_rich_method3'] = 'no'
df.loc[df['salary'] > 50,'is_rich_method3'] = 'yes'


resulting in:



However I don't understand what the preferred way is. Are all methods equally good depending on your application?

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