How to convert df['month'] and df['year'] into the quarter and display into anot-CodePudding

This is my df, I want to convert column month and year into quarter. please help]

CodePudding user response：

You can create a very simple function to get quarter value based on month value and use df.apply() to calculate the quarter for each row. Here is quick sample:

def get_quarter(mon):
    if mon == 'jan' or mon == 'feb' or mon == 'mar':
        return 1
    elif mon == 'apr' or mon == 'may' or mon == 'jun':
        return 2
    elif mon == 'jul' or mon == 'aug' or mon == 'sep':
        return 3
    else: 
        return 4
    
df['quarter'] = df['month'].apply(lambda x: get_quarter(x))

df.head()

  month year    quarter
0   jan 2012    1
1   apr 2016    2
2   dec 1998    4

CodePudding user response：

Since you mentioned month and year. I'm assuming you may want yyQx format. I used @Devesh Shukla code as a starting point for the code below.

import pandas as pd

cols = ["something","month","year"]
data=[ [1,'jan',2020],
    [1,'feb',2020],
    [2,'jun',2021],
    [2,'aug',2021],
    [3,'sep',2022],
    [3,'nov',2022],
    [3,'dec',2022] ]
df = pd.DataFrame(data, columns = cols )

def get_quarter(m):
    if m == 'jan' or m == 'feb' or m == 'mar':
        return 1
    elif m == 'apr' or m == 'may' or m == 'jun':
        return 2
    elif m == 'jul' or m == 'aug' or m == 'sep':
        return 3
    else:
        return 4

def get_year_quarter(m, y):

    y = str(y)[-2:]
    m = m.lower()
    if m == 'jan' or m == 'feb' or m == 'mar':
        q = 'Q1'
    elif m == 'apr' or m == 'may' or m == 'jun':
        q = 'Q2'
    elif m == 'jul' or m == 'aug' or m == 'sep':
        q = 'Q3'
    elif m == 'oct' or m == 'nov' or m == 'dec':
        q = 'Q3'
    else:
        q = 'XX'

    return str(y) q

df['quarter'] = df['month'].apply(lambda x: get_quarter(x))
df['YrQtr'] = df.apply(lambda x: get_year_quarter(x.month, x.year), axis=1)

Output:

   something month  year  quarter YrQtr
0          1   jan  2020        1  20Q1
1          1   feb  2020        1  20Q1
2          2   jun  2021        2  21Q2
3          2   aug  2021        3  21Q3
4          3   sep  2022        3  22Q3
5          3   nov  2022        4  22Q3
6          3   dec  2022        4  22Q3