在不获取NaN的情况下添加具有不同索引的 pandas 系列 [英] Adding pandas Series with different indices without getting NaNs

问题描述

我正在尝试做我认为对熊猫来说是直截了当的操作，但是我似乎无法使其正常工作.

I'm trying to do what I think is a straight froward operation in pandas but I can't seem to make it work.

我有两个具有不同索引数量的pandas系列，如果它们共享一个索引，我想将它们加在一起，否则，我只想传递没有相应索引的值.

I have two pandas Series with different numbers of indices, I would like to add values together if they share an index, otherwise I would just like to pass the values that don't have corresponding indices along.

例如

Sr1 = pd.Series([1,2,3,4], index = ['A', 'B', 'C', 'D'])
Sr2 = pd.Series([5,6], index = ['A', 'C'])
Sr1        Sr2
A     1    A     5
B     2    C     6
C     3
D     4

Sr1 + Sr2或Sr1.add(Sr2)给予

A     6
B   NaN
C     9
D   NaN

但是我想要的是

A     6
B     2
C     9
D     4

其中Sr1的B和D值刚刚传递.

有什么建议吗?

推荐答案

您可以使用fill_value:

>>> import pandas as pd
>>> Sr1 = pd.Series([1,2,3,4], index = ['A', 'B', 'C', 'D'])
>>> Sr2 = pd.Series([5,6], index = ['A', 'C'])
>>> Sr1+Sr2
A     6
B   NaN
C     9
D   NaN
>>> Sr1.add(Sr2, fill_value=0)
A    6
B    2
C    9
D    4

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