根据确切日期按季节分组数据 [英] group data by season according to the exact dates

问题描述

我有一个包含4年数据的csv文件，并且我试图将这4年中每个季节的数据进行分组，换句话说，我只需要将我的整个数据汇总并绘制为4个季节. 这是我的数据文件:

i have a csv file containing 4 years of data and i am trying to group data per season over the 4 years , differently saying, i need to summarize and plot my whole data into 4 season only . here's a look on my data file :

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      .....

实际上我已经尝试过此代码:

Actually i've tried this code :

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:

and i got this horrible result :

我在哪里弄错了?

推荐答案

您需要 DatetimeIndex.dayofyear :

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

使用 pandas.cut 的另一种解决方案:

Another solution with 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)

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