Create a new column for a given range of numbers with same frequency

Question

I haven't been able to find the exact solution to my problem. My data set has a column called 'Priority' which contains string values.

Priority  
low        
low       
low        
low        
low        
medium    
medium     
medium     
medium     
medium    
high       
high       
high       
high       

I want to add column "Num" that will give each 'Priority' number based on a range of numbers (which would be 0 to 3). As a result, each 'Priority' would have a number between 1 to 3 with the same frequency.

For example :

Priority Num 
low        1
low        2
low        3
low        1
low        2
low        3
medium     1
medium     2
medium     3
medium     1
medium     2
high       1
high       2
high       3
high       1

After many other attempts, this is my best solution for now but it's return duplicates in range (1,3)

x = pd.DataFrame(
    [[l, n] for l in data. Priority for n in range(1,6)],
    columns=['Priority', 'Num'])

Do you have another idea?

Answer 1

You can use a lambda function assume df is the name of your data set

df['num'] = df.Priority.apply(lambda x: 1 if x=='low' else(2 if x=='medium' else 3))

Answer 2

to read your data I use StringIO together pandas.read_csv()

data = '''Priority\n  
          low\n        
          low\n      
          low\n        
          low\n        
          low\n        
          medium\n    
          medium\n     
          medium\n     
          medium\n     
          medium\n    
          high\n       
          high\n       
          high\n       
          high\n'''

to call groupby in 'Priority' column to invoke cumcount() method as is explained by @Raymond Kwok

import pandas as pd
from io import StringIO

# read data
df = pandas.read_csv(StringIO(data), header=0)

# use groupby to store value as accumulate count
df['num']=df.groupby('Priority').cumcount()%3+1

output:

>>> df
   Priority  num
0       low    1
1       low    2
2       low    3
3       low    1
4       low    2
5    medium    1
6    medium    2
7    medium    3
8    medium    1
9    medium    2
10     high    1
11     high    2
12     high    3
13     high    1