问题描述
我有一个datetime对象序列和一系列跨越几年的数据. A可以创建一个Series对象并对其重新采样以按月分组:
I have a sequence of datetime objects and a series of data which spans through several years. A can create a Series object and resample it to group it by months:
df=pd.Series(varv,index=dates)
multiMmean=df.resample("M", how='mean')
print multiMmean
这会输出
2005-10-31 172.4
2005-11-30 69.3
2005-12-31 187.6
2006-01-31 126.4
2006-02-28 187.0
2006-03-31 108.3
...
2014-01-31 94.6
2014-02-28 82.3
2014-03-31 130.1
2014-04-30 59.2
2014-05-31 55.6
2014-06-30 1.2
是该系列每个月平均值的列表.这不是我想要的.我想要12个值，一年中的每个月一个，多年来的每个月均值.如何获得multiMmean的内容?
which is a list of the mean value for each month of the series. This is not what I want. I want 12 values, one for every month of the year with a mean for each month through the years. How do I get that for multiMmean?
我尝试在multiMmean上使用resample("M",how='mean')并列出理解，但是我无法使其正常工作.我想念什么?
I have tried using resample("M",how='mean') on multiMmean and list comprehensions but I cannot get it to work. What am I missing?
谢谢.
推荐答案
以下内容对我有用:
# create some random data with datetime index spanning 17 months
s = pd.Series(index=pd.date_range(start=dt.datetime(2014,1,1), end = dt.datetime(2015,6,1)), data = np.random.randn(517))
In [25]:
# now calc the mean for each month
s.groupby(s.index.month).mean()
Out[25]:
1 0.021974
2 -0.192685
3 0.095229
4 -0.353050
5 0.239336
6 -0.079959
7 0.022612
8 -0.254383
9 0.212334
10 0.063525
11 -0.043072
12 -0.172243
dtype: float64
因此我们可以groupby datetimeindex的month属性并调用mean这将计算所有月份的平均值
So we can groupby the month attribute of the datetimeindex and call mean this will calculate the mean for all months
这篇关于 pandas 系列的每月平均数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！