pandas 与熊猫总结数月
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Summing over months with pandas
提问by Joop
I know there is a simple implementation to do this but I cannot remember the syntax. Have a simple pandas time series and I want to summarize the data by month. Specifically I want to add data over months and years to get some summary of it. Can write it with slicing, but I remember seeing syntax that does it automatically.
我知道有一个简单的实现来做到这一点，但我不记得语法。有一个简单的Pandas时间序列，我想按月总结数据。具体来说，我想添加数月和数年的数据以获取一些摘要。可以用切片来编写它，但我记得看到自动执行它的语法。
import pandas as pd
df = Series(randn(100), index=pd.date_range('2012-01-01', periods=100))
a Multi-indexed Series with Years and sub endexed to months would be first prize.
带有年份和分到月份的多索引系列将是一等奖。
Partial Answer:
部分答案：
ds.resample('M', how=sum) # for calendar monthly
ds.resample('A', how=sum) # for calendar yearly
Any idea how to elegantly get to multindexed by year sums?
知道如何优雅地按年总和进行多重索引吗？
回答by Jeff
In [1]: import pandas as pd
from numpy.random import randn
In [2]: df = Series(randn(500), index=pd.date_range('2012-01-01', periods=500))
In [3]: s2 = df.groupby([lambda x: x.year, lambda x: x.month]).sum()
In [4]: s2
Out[4]:
2012 1 3.853775
2 4.259941
3 4.629546
4 -10.812505
5 -16.383818
6 -5.255475
7 5.901344
8 13.375258
9 1.758670
10 6.570200
11 6.299812
12 7.237049
2013 1 -1.331835
2 3.399223
3 2.011031
4 7.905396
5 1.127362
dtype: float64
