根据确切日期按季节对数据进行分组

Question

我有一个包含 4 年数据的 csv 文件，我试图将 4 年中每个季节的数据分组，换言之，我只需要将我的整个数据汇总并绘制成 4 个季节。看看我的数据文件：

timestamp,heure,lat,lon,impact,type
2006-01-01 00:00:00,13:58:43,33.837,-9.205,10.3,1
2006-01-02 00:00:00,00:07:28,34.5293,-10.2384,17.7,1
2007-02-01 00:00:00,23:01:03,35.0617,-1.435,-17.1,2
2007-02-02 00:00:00,01:14:29,36.5685,0.9043,36.8,1
2008-01-01 00:00:00,05:03:51,34.1919,-12.5061,-48.9,1
2008-01-02 00:00:00,05:03:51,34.1919,-12.5061,-48.9,1
....
2011-12-31 00:00:00,05:03:51,34.1919,-12.5061,-48.9,1

这是我想要的输出：

winter     (the mean value of impacts)
summer     (the mean value of impacts)
autumn      ....
spring      .....

其实我试过这个代码：

names =["timestamp","heure","lat","lon","impact","type"]
data = pd.read_csv('flash.txt',names=names, parse_dates=['timestamp'],index_col=['timestamp'], dayfirst=True)

spring = range(80, 172)
summer = range(172, 264)
fall = range(264, 355)

def season(x):
    if x in spring:
       return 'Spring'
    if x in summer:
       return 'Summer'
    if x in fall:
       return 'Fall'
   else :
       return 'Winter'

 data['SEASON'] = data.index.to_series().dt.month.map(lambda x : season(x))
 data['impact'] = data['impact'].abs()
 seasonly = data.groupby('SEASON')['impact'].mean()

我得到了这个可怕的结果：

我错在哪里？

Answer 1

你需要DatetimeIndex.dayofyear：

data['SEASON'] = data.index.dayofyear.map(season)

另一个解决方案pandas.cut：

bins = [0, 91, 183, 275, 366]
labels=['Winter', 'Spring', 'Summer', 'Fall']
doy = data.index.dayofyear
data['SEASON1'] = pd.cut(doy + 11 - 366*(doy > 355), bins=bins, labels=labels)