pandas 添加具有不同索引的熊猫系列而不会获得 NaN
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Adding pandas Series with different indices without getting NaNs
提问by A Alstone
I'm trying to do what I think is a straight froward operation in pandas but I can't seem to make it work.
我正在尝试在 Pandas 中做我认为是直接向前的操作，但我似乎无法让它发挥作用。
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.
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，我想将它们相加，否则我只想传递没有相应索引的值。
For example
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 + Sr2or Sr1.add(Sr2)give
Sr1 + Sr2或Sr1.add(Sr2)给予
A 6
B NaN
C 9
D NaN
But what I want is
但我想要的是
A 6
B 2
C 9
D 4
where the Band Dvalues for Sr1are just passed along.
其中B和 的D值Sr1刚刚传递。
Any suggestions?
有什么建议？
回答by DSM
You could use fill_value:
你可以使用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
回答by kartik Garg
Sr1 = pd.Series([1,2,3,4], index = ['A', 'B', 'C', 'D'])
Sr2 = pd.Series([5,6,7], index = ['A', 'C','E'])
(Sr1+Sr2).fillna(Sr2).fillna(Sr1)
An alternative approach using fillna. It will work on all cases when indeces do not match too
使用 fillna 的另一种方法。当 indeces 不匹配时，它将适用于所有情况
回答by Nafeez Quraishi
A solution using fillna():
使用的解决方案fillna()：
>>> 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).fillna(Sr1 + 0)
A 6.0
B 2.0
C 9.0
D 4.0
