Numpy/Pandas 에서 datetime 다루기

파이썬의 datetime은 iterable 하지 않다.

그래서 넘파이 라이브러리에선 이를 보강해, array도 처리할 수 있는 datetime64 클래스를 만들었다.

넘파이를 기반으로 하는 판다스에서도 사용가능하다.

 

import numpy as np

someday = np.array('2022-05-11', dtype= np.datetime64)
someday
>>> array('2022-05-11', dtype='datetime64[D]')

someday + 10
>>> numpy.datetime64('2022-05-21')

someday - 35
>>> numpy.datetime64('2022-04-06')

someday + np.arange(5)
>>> array(['2022-05-11', '2022-05-12', '2022-05-13', '2022-05-14',
       '2022-05-15'], dtype='datetime64[D]')

 

date_list = ['2022-01-04', '2022-01-07', '2022-05-11', '2022-06-10', '2022-07-03']
# pd.to_datetime : datetime64 타입으로 바꿔준다
pd.to_datetime(date_list)
>>> DatetimeIndex(['2022-01-04', '2022-01-07', '2022-05-11', '2022-06-10',
                   '2022-07-03'],
                  dtype='datetime64[ns]', freq=None)

 

seoul.csv

1.08MB

df = pd.read_csv('../data/seoul.csv', encoding='cp949')

 

df['날짜'] # 문자열
>>> 0        1907-10-01
    1        1907-10-02
    2        1907-10-03
    3        1907-10-04
    4        1907-10-05
                ...    
    39921    2018-03-24
    39922    2018-03-25
    39923    2018-03-26
    39924    2018-03-27
    39925    2018-03-28
    Name: 날짜, Length: 39926, dtype: object
    
df['날짜'] = pd.to_datetime(df['날짜'])
df['날짜']
>>> 0       1907-10-01
    1       1907-10-02
    2       1907-10-03
    3       1907-10-04
    4       1907-10-05
               ...    
    39921   2018-03-24
    39922   2018-03-25
    39923   2018-03-26
    39924   2018-03-27
    39925   2018-03-28
    Name: 날짜, Length: 39926, dtype: datetime64[ns]
    
df['요일'] = df['날짜'].dt.strftime('%a')
df

 

위와 같이 .dt를 써서 기존에 알고 있던 datetime 함수들을 쓸 수 있다.

 

 

 

pandas.date_range()

# 시작날짜와 종료날짜를 셋팅하면, 알아서 날짜를 채워주는 함수
pd.date_range('2022-12-02', '2023-01-05')
>>> DatetimeIndex(['2022-12-02', '2022-12-03', '2022-12-04', '2022-12-05',
                   '2022-12-06', '2022-12-07', '2022-12-08', '2022-12-09',
                   '2022-12-10', '2022-12-11', '2022-12-12', '2022-12-13',
                   '2022-12-14', '2022-12-15', '2022-12-16', '2022-12-17',
                   '2022-12-18', '2022-12-19', '2022-12-20', '2022-12-21',
                   '2022-12-22', '2022-12-23', '2022-12-24', '2022-12-25',
                   '2022-12-26', '2022-12-27', '2022-12-28', '2022-12-29',
                   '2022-12-30', '2022-12-31', '2023-01-01', '2023-01-02',
                   '2023-01-03', '2023-01-04', '2023-01-05'],
                  dtype='datetime64[ns]', freq='D')

# 시간 단위로
pd.date_range('2022-11-30 09:00', '2023-02-05 11:00', freq='H')
>>> DatetimeIndex(['2022-12-02 09:00:00', '2022-12-02 10:00:00',
                   '2022-12-02 11:00:00', '2022-12-02 12:00:00',
                   '2022-12-02 13:00:00', '2022-12-02 14:00:00',
                   '2022-12-02 15:00:00', '2022-12-02 16:00:00',
                   '2022-12-02 17:00:00', '2022-12-02 18:00:00',
                   ...
                   '2022-12-10 02:00:00', '2022-12-10 03:00:00',
                   '2022-12-10 04:00:00', '2022-12-10 05:00:00',
                   '2022-12-10 06:00:00', '2022-12-10 07:00:00',
                   '2022-12-10 08:00:00', '2022-12-10 09:00:00',
                   '2022-12-10 10:00:00', '2022-12-10 11:00:00'],
                  dtype='datetime64[ns]', length=195, freq='H')

# 평일만
pd.date_range('2022-11-30', '2023-02-05', freq='B')
>>> DatetimeIndex(['2022-11-30', '2022-12-01', '2022-12-02', '2022-12-05',
                   '2022-12-06', '2022-12-07', '2022-12-08', '2022-12-09',
                   '2022-12-12', '2022-12-13', '2022-12-14', '2022-12-15',
                   '2022-12-16', '2022-12-19', '2022-12-20', '2022-12-21',
                   '2022-12-22', '2022-12-23', '2022-12-26', '2022-12-27',
                   '2022-12-28', '2022-12-29', '2022-12-30', '2023-01-02',
                   '2023-01-03', '2023-01-04', '2023-01-05', '2023-01-06',
                   '2023-01-09', '2023-01-10', '2023-01-11', '2023-01-12',
                   '2023-01-13', '2023-01-16', '2023-01-17', '2023-01-18',
                   '2023-01-19', '2023-01-20', '2023-01-23', '2023-01-24',
                   '2023-01-25', '2023-01-26', '2023-01-27', '2023-01-30',
                   '2023-01-31', '2023-02-01', '2023-02-02', '2023-02-03'],
                  dtype='datetime64[ns]', freq='B')

 

freq 에 대한 offset은 아래 표를 참고하도록 하자.