Map Column Birthdates In Python Pandas Df To Astrology Signs

I have a dataframe with a column that includes individuals' birthdays. I would like to map that column to the individuals' astrology sign using code I found (below). I am having tr

Solution 1:

Change previous answer by Series.dt.month_name with lowercase strings:

def zodiac_sign(day, month): 
    # checks month and date within the valid range 
    # of a specified zodiac 
    if month == 'december': 
        return 'Sagittarius' if (day < 22) else 'capricorn'

    elif month == 'january': 
        return 'Capricorn' if (day < 20) else 'aquarius'

    elif month == 'february': 
        return 'Aquarius' if (day < 19) else 'pisces'

    elif month == 'march': 
        return 'Pisces' if (day < 21) else 'aries'

    elif month == 'april': 
        return 'Aries' if (day < 20) else 'taurus'

    elif month == 'may': 
        return 'Taurus' if (day < 21) else 'gemini'

    elif month == 'june': 
        return 'Gemini' if (day < 21) else 'cancer'

    elif month == 'july': 
        return 'Cancer' if (day < 23) else 'leo'

    elif month == 'august': 
        return 'Leo' if (day < 23) else 'virgo'

    elif month == 'september': 
        return 'Virgo' if (day < 23) else 'libra'

    elif month == 'october': 
        return 'Libra' if (day < 23) else 'scorpio'

    elif month == 'november': 
        return 'scorpio' if (day < 22) else 'sagittarius'

---

dates =  pd.to_datetime(astrology['birthdate'])
y = dates.dt.year
now = pd.to_datetime('now').year
astrology = astrology.assign(month = dates.dt.month_name().str.lower(),
                             day = dates.dt.day,
                             year = y.mask(y > now, y - 100))
print (astrology)
    birthdate  answer  YEAR MONTH-DAY   month  day  year
0  1970-03-31       5  1970     03-31   march   31  1970
1  1970-05-25       9  1970     05-25     may   25  1970
2  1970-06-05       3  1970     06-05    june    5  1970
3  1970-08-28       2  1970     08-28  august   28  1970

---

astrology['sign'] = astrology.apply(lambda x: zodiac_sign(x['day'], x['month']), axis=1)
print (astrology)
    birthdate  answer  YEAR MONTH-DAY   month  day  year    sign
0  1970-03-31       5  1970     03-31   march   31  1970   aries
1  1970-05-25       9  1970     05-25     may   25  1970  gemini
2  1970-06-05       3  1970     06-05    june    5  1970  Gemini
3  1970-08-28       2  1970     08-28  august   28  1970   virgo

Solution 2:

You can apply the zodiac_sign function to the dataframe as -

import pandas as pd
from io import StringIO

# Sample
x = StringIO("""birthdate,answer,YEAR,MONTH-DAY
1970-03-31,5,1970,03-31
1970-05-25,9,1970,05-25
1970-06-05,3,1970,06-05
1970-08-28,2,1970,08-28
""")


df = pd.read_csv(x, sep=',')

df['birthdate'] = pd.to_datetime(df['birthdate'])
df['zodiac_sign'] = df['birthdate'].apply(lambda x: zodiac_sign(x.day, x.strftime("%B").lower()))
print(df)

Output:

   birthdate  answer  YEAR MONTH-DAY zodiac_sign
0 1970-03-31       5  1970     03-31       aries
1 1970-05-25       9  1970     05-25      gemini
2 1970-06-05       3  1970     06-05      Gemini
3 1970-08-28       2  1970     08-28       virgo