如何使用 pandas 季节性地分组数据

本文介绍了如何使用 pandas 季节性地分组数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有一个包含4年数据的csv文件，我需要在4年内对每个季节的数据进行分组：这里是我的数据：

i have a csv file containing 4 years of data, i need to group my data per season over the 4 years : here's a look of my data :

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

以下是我所需的输出：

and here's my desired output :

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

所以我期待4个季节总结所有月份的4行。
i开始如下：

so i am expecting 4 rows summarizing all month in 4 seasons .i started as below :

data['impact'] = data['impact'].abs()
yearly = data.groupby(data.index.month)['impact'].mean()

任何想法

推荐答案

确切日期

With exact dates

import pandas as pd
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'

df = pd.DataFrame({'_date' :pd.date_range(start=pd.datetime(2016,1,1), end=pd.datetime(2016,12,31), freq='D'),'impact' : range(0,366)})

df['SEASON'] = df['_date'].dt.dayofyear.apply(lambda x : season(x))
df.groupby('SEASON')['impact'].mean()

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