Python 使用Panda每月平均一分钟时间序列数据集

我有一个非常大的分钟时间序列数据集（3个月），其格式如下

datetime,val1,val2,val3,val4,val5,val6,val7,val8,val9,val10,val11,val12
1/06/2017 0:00,0,0,0,0,0,0,0,0,0,0.011,0,0.036
1/06/2017 0:01,0,0,0,0,0,0,0,0,0,0.011,0,0.036
...
1/06/2017 23:59,0,0,0,0,0,0,0,0,0,0.011,0,0.035
2/06/2017 0:00,0,0,0,0,0,0,0,0,0,0.014,0,0.036
2/06/2017 0:01,0,0,0,0,0,0,0,0,0,0.011,0,0.036
...
2/06/2017 23:59,0,0,0,0,0,0,0,0,0,0.011,0,0.035
....
31/08/2017 0:00,0,0.2,0,0,0,0.56,0,0,0,0.014,0,0.036
31/08/2017 0:01,0,0.23,0,0,0,0,0,0,0,0.011,0,0.032
...
31/08/2017 23:59,0,0,0,0,0,0,.55,0,0,0.011,0,0.034

使用panda获得每个列每月平均值的最有效方法是什么？ 预期产出为

month,val1,val2,val3,val4,val5,val6,val7,val8,val9,val10,val11,val12
06/2017,0,0,0,0,0,0,0,0,0,0.011,0,0.036
07/2017,0,0,0,0,0,0,0,0,0,0.014,0,0.036
08/2017,0,0,0.21,0,0,0,0,0.52,0,0.011,0,0.036

目前，我正在做的是逐日读取数据集，然后得到一个累计天数的数据集，然后除以每月天数。但这非常低效，而且需要花费大量时间。

首先将列转换为，然后在月份开始时将列转换为

MS

，最后将DatetimeIndex的格式更改为

MM/yyyy

：

或者将转换后的日期时间列按传递给

groupby

并聚合

mean

：

df = df.groupby(df['datetime'].dt.strftime('%m/%Y')).mean()
print (df)
          val1      val2  val3  val4  val5      val6      val7  val8  val9  \
datetime                                                                     
06/2017      0  0.000000     0     0     0  0.000000  0.000000     0     0   
08/2017      0  0.143333     0     0     0  0.186667  0.183333     0     0   

           val10  val11     val12  
datetime                           
06/2017   0.0115      0  0.035667  
08/2017   0.0120      0  0.034000  

---

熊猫

read_csv

和

to_csv

是您需要的：

df = pd.read_csv('input.csv', parse_dates=['datetime'])
df.groupby(df.datetime.dt.strftime('%m/%Y')).mean().rename_axis('month').to_csv(out, float_format='%.06f')

通过您的输入数据（从…）它给出：

month，val1，val2，val3，val4，val5，val6，val7，val8，val9，val10，val11，val12
01/2017,0,0.000000,0,0,0,0.000000,0.000000,0,0,0.011000,0,0.035667
02/2017,0,0.000000,0,0,0,0.000000,0.000000,0,0,0.012000,0,0.035667
08/2017,0,0.143333,0,0,0,0.186667,0.183333,0,0,0.012000,0,0.034000

df = pd.read_csv('input.csv', parse_dates=['datetime'])
df.groupby(df.datetime.dt.strftime('%m/%Y')).mean().rename_axis('month').to_csv(out, float_format='%.06f')