根据datetimeindex的值有条件地为新列赋值

Question

我正在尝试使用pandas数据帧的datetimeindex来分配一个名为“season”的新列。你知道吗

winter =[12,1,2]
spring =[3,4,5]
summer =[6,7,8]
autumn =[9,10,11]

DTX_index = [datetime(2017, 2, 1).date(), datetime(2017, 3, 1).date(), datetime(2017, 6, 1).date(), datetime(2017, 9, 1).date()]
DTX_index = pd.to_datetime(DTX_index, utc=True)
df = pd.DataFrame(index=DTX_index)

我希望这样：

                           season
2017-02-01 00:00:00+00:00   winter
2017-03-01 00:00:00+00:00   spring
2017-06-01 00:00:00+00:00   summer
2017-09-01 00:00:00+00:00   autumn

指定一个月

df['month'] = df.index.month

为单个季节指定布尔值

df['season'] = df.index.month.isin([12,1,2])

我不知道如何在整个df中根据月份分配季节？我尝试了应用函数：

def add_season(x):

    if x.index.month.isin([12,1,2]):
        return 'winter'
    elif x.index.month.isin([3,4,5]):
        return 'spring'
    elif x.index.month.isin([6,7,8]):
        return 'summer'
    elif x.index.month.isin([9,10,11]):
        return 'autumn'

df['season'] = df.apply(add_season)

但这会返回一个值错误：

ValueError: ('The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()', 'occurred at index season')

可能是因为函数是对整个序列而不是元素进行操作。你知道吗

我相信比我更有应用函数经验的人可以很快解决这个问题？你知道吗

非常感谢

Answer 1

IIUC公司

d={**dict.fromkeys(winter,'winter'),**dict.fromkeys(spring,'spring'),**dict.fromkeys(summer,'summer'),**dict.fromkeys(autumn,'autumn')}
df['Value']=list(map(d.get,df.index.month))
df
Out[697]: 
                            Value
2017-02-01 00:00:00+00:00  winter
2017-03-01 00:00:00+00:00  spring
2017-06-01 00:00:00+00:00  summer
2017-09-01 00:00:00+00:00  autumn