python Pandas : Creating seasonal DateOffset object?

Question

我有一个每小时频率的日期时间索引数据框。我想制作一个 groupby 对象 - 按季节分组。按季节，我的意思是 Spring 是第 3、4、5 个月，夏季是第 6、7、8 个月，依此类推。我想为每个年份-季节组合都有一个独特的组。有没有办法使用自定义 DateOffset 来做到这一点？它需要一个子类来做吗？或者我最好只制作一个季节专栏然后做:grouper = df.groupby([df['season'], df.index.year])。

当前代码很丑陋:

def group_season(df):
    """
    This uses the meteorological seasons
    """
    df['month'] = df.index.month
    spring = df['month'].isin([3,4,5])
    spring[spring] = 'spring'
    summer = df['month'].isin([6,7,8])
    summer[summer] = 'summer'
    fall = df['month'].isin([9,10,11])
    fall[fall] = 'fall'
    winter = df['month'].isin([12,1,2])
    winter[winter] = 'winter'
    df['season'] = pd.concat([winter[winter != False], spring[spring != False],\
    fall[fall != False], summer[summer != False]], axis=0)

    return df.groupby([df['season'], df.index.year])

Answer 1

对于您想进行的分组类型，请使用 anchored quarterly offsets .

import numpy as np
import pandas as pd

dates = pd.date_range('2016-01', freq='MS', periods=12)
df = pd.DataFrame({'num': np.arange(12)}, index=dates)
print(df)

#             num
# 2016-01-01    0
# 2016-02-01    1
# 2016-03-01    2
# 2016-04-01    3
# 2016-05-01    4
# 2016-06-01    5
# 2016-07-01    6
# 2016-08-01    7
# 2016-09-01    8
# 2016-10-01    9
# 2016-11-01   10
# 2016-12-01   11

by_season = df.resample('QS-MAR').sum()
print(by_season)

#             num
# 2015-12-01    1
# 2016-03-01    9
# 2016-06-01   18
# 2016-09-01   27
# 2016-12-01   11

您还可以在索引中制作更好、更具描述性的标签:

SEASONS = {
    'winter': [12, 1, 2],
    'spring': [3, 4, 5],
    'summer': [6, 7, 8],
    'fall': [9, 10, 11]
}
MONTHS = {month: season for season in SEASONS.keys()
                        for month in SEASONS[season]}

by_season.index = (pd.Series(by_season.index.month).map(MONTHS) +
                   ' ' + by_season.index.year.astype(str))
print(by_season)

#              num
# winter 2015    1
# spring 2016    9
# summer 2016   18
# fall 2016     27
# winter 2016   11