具有MultiIndex的Pandas DataFrame:按年份的DateTime级别值分组 [英] Pandas DataFrame with MultiIndex: Group by year of DateTime level values
问题描述
我的熊猫数据框具有如下所示的多索引:
I have and pandas dataframe with a multiindex that looks like this:
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
# multi-indexed dataframe
df = pd.DataFrame(np.random.randn(8760 * 3, 3))
df['concept'] = "some_value"
df['datetime'] = pd.date_range(start='2016', periods=len(df), freq='60Min')
df.set_index(['concept', 'datetime'], inplace=True)
df.sort_index(inplace=True)
控制台输出:
df.head()
Out[23]:
0 1 2
datetime
2016 0.458802 0.413004 0.091056
2016 -0.051840 -1.780310 -0.304122
2016 -1.119973 0.954591 0.279049
2016 -0.691850 -0.489335 0.554272
2016 -1.278834 -1.292012 -0.637931
df.head()
...: df.tail()
Out[24]:
0 1 2
datetime
2018 -1.872155 0.434520 -0.526520
2018 0.345213 0.989475 -0.892028
2018 -0.162491 0.908121 -0.993499
2018 -1.094727 0.307312 0.515041
2018 -0.880608 -1.065203 -1.438645
现在,我想沿日期时间"级别创建年度总和.
Now I want to create annual sums along the level 'datetime'.
我的第一次尝试是以下操作,但这不起作用:
My first try was the following but this doesn't work:
# sum along years
years = df.index.get_level_values('datetime').year.tolist()
df.index.set_levels([years], level=['datetime'], inplace=True)
df = df.groupby(level=['datetime']).sum()
这对我来说似乎也很繁重,因为该任务很容易实现.
And it also seems quite heavy handed to me as this task is probably pretty easy to realize.
这是我的问题:如何获得日期时间"级别的年度总和?有没有简单的方法可以通过对DateTime级别值应用函数来实现这一点?
So here's my question: How can I get annual sums for the level 'datetime'? Is there a simple way to realize this by applying a function to the DateTime level values?
推荐答案
您可以 groupby
加上第二级multiindex
和
You can groupby
by second level of multiindex
and year
:
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
# multi-indexed dataframe
df = pd.DataFrame(np.random.randn(8760 * 3, 3))
df['concept'] = "some_value"
df['datetime'] = pd.date_range(start='2016', periods=len(df), freq='60Min')
df.set_index(['concept', 'datetime'], inplace=True)
df.sort_index(inplace=True)
print df.head()
0 1 2
concept datetime
some_value 2016-01-01 00:00:00 1.973437 0.101535 -0.693360
2016-01-01 01:00:00 1.221657 -1.983806 -0.075609
2016-01-01 02:00:00 -0.208122 -2.203801 1.254084
2016-01-01 03:00:00 0.694332 -0.235864 0.538468
2016-01-01 04:00:00 -0.928815 -1.417445 1.534218
# sum along years
#years = df.index.get_level_values('datetime').year.tolist()
#df.index.set_levels([years], level=['datetime'], inplace=True)
print df.index.levels[1].year
[2016 2016 2016 ..., 2018 2018 2018]
df = df.groupby(df.index.levels[1].year).sum()
print df.head()
0 1 2
2016 -93.901914 -32.205514 -22.460965
2017 205.681817 67.701669 -33.960801
2018 67.438355 150.954614 -21.381809
或者您可以使用 get_level_values
和 year
:
Or you can use get_level_values
and year
:
df = df.groupby(df.index.get_level_values('datetime').year).sum()
print df.head()
0 1 2
2016 -93.901914 -32.205514 -22.460965
2017 205.681817 67.701669 -33.960801
2018 67.438355 150.954614 -21.381809
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