How to create a column and fill it with new values based on a separate columns values

Question

I have the dataset that has the no_employees column that is the str object. whats is a best way to create the new column (company_size) in a data frame and fill it with values based on a no_employees column like in example below mental_health_df = pd.read_csv("Mental Health.csv") pd.set_option('display.max_columns', None) mental_health_df.head(100) no_employees company_size | 6-25 |Small More than 1000 |Extremely Large 500-1000 |Very Large 26-100 |Medium 100-500 |Large 1-5 |Very Small Select the correct answer from above options

Answer 1

Please bin using df.cut import numpy as np df['company_size']=pd.cut(df['no_employees']. astype('category').cat.codes*10,[-np.inf,9,19,29,39,49,np.inf], labels=['Very Small','Large','Medium','Very Large','Small','Extremely Large']) print(df) no_employees company_size 0 6-25 Small 1 More than 1000 Extremely Large 2 500-1000 Very Large 3 26-100 Medium 4 100-500 Large 5 1-5 Very Small It works like this: #Converted no of employees to codes but for ease of defining bins multiplied by ten df['no_employees']. astype('category').cat.codes*10 #Decided to bin using df.cut pd.cut(df['no_employees']. astype('category').cat.codes*10,\ [-np.inf,9,19,29,39,49,np.inf], labels=['Very Small','Large','Medium','Very Large','Small','Extremely Large']) Learn Python for Data Science Course to improve your technical knowledge.