pandas 使用 NaN 添加两个系列
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Adding two Series with NaNs
提问by BubbleGuppies
I'm working through the "Python For Data Analysis" and I don't understand a particular functionality. Adding two pandas series objects will automatically align the indexed data but if one object does not contain that index it is returned as NaN. For example from book:
我正在研究“用于数据分析的 Python”，但我不了解特定功能。添加两个 Pandas 系列对象将自动对齐索引数据，但如果一个对象不包含该索引，它将作为 NaN 返回。例如从书中：
a = Series([35000,71000,16000,5000],index=['Ohio','Texas','Oregon','Utah'])
b = Series([NaN,71000,16000,35000],index=['California', 'Texas', 'Oregon', 'Ohio'])
Result:
结果：
In [63]: a
Out[63]: Ohio 35000
Texas 71000
Oregon 16000
Utah 5000
In [64]: b
Out[64]: California NaN
Texas 71000
Oregon 16000
Ohio 35000
When I add them together I get this...
当我将它们加在一起时，我得到了这个......
In [65]: a+b
Out[65]: California NaN
Ohio 70000
Oregon 32000
Texas 142000
Utah NaN
So why is the Utah value NaN and not 500? It seems that 500+NaN=500. What gives? I'm missing something, please explain.
那么为什么犹他州的值为 NaN 而不是 500？好像是500+NaN=500。是什么赋予了？我错过了一些东西，请解释一下。
Update:
更新：
In [92]: # fill NaN with zero
b = b.fillna(0)
b
Out[92]: California 0
Texas 71000
Oregon 16000
Ohio 35000
In [93]: a
Out[93]: Ohio 35000
Texas 71000
Oregon 16000
Utah 5000
In [94]: # a is still good
a+b
Out[94]: California NaN
Ohio 70000
Oregon 32000
Texas 142000
Utah NaN
回答by Dan Allan
Pandas does not assume that 500+NaN=500, but it is easy to ask it to do that: a.add(b, fill_value=0)
Pandas 不假设 500+NaN=500，但很容易让它这样做： a.add(b, fill_value=0)
回答by BrenBarn
The default approach is to assume that any computation involving NaN gives NaN as the result. Anything plus NaN is NaN, anything divided by NaN is NaN, etc. If you want to fill the NaN with some value, you have to do that explicitly (as Dan Allan showed in his answer).
默认方法是假设任何涉及 NaN 的计算都会给出 NaN 作为结果。任何加 NaN 的东西都是 NaN，任何除以 NaN 的东西都是 NaN，等等。如果你想用某个值填充 NaN，你必须明确地这样做（正如 Dan Allan 在他的回答中所示）。
回答by Anton vBR
It makes more sense to use pd.concat()as it can accept more columns.
使用更有意义，pd.concat()因为它可以接受更多列。
import pandas as pd
import numpy as np
a = pd.Series([35000,71000,16000,5000],index=['Ohio','Texas','Oregon','Utah'])
b = pd.Series([np.nan,71000,16000,35000],index=['California', 'Texas', 'Oregon', 'Ohio'])
pd.concat((a,b), axis=1).sum(1, min_count=1)
Output:
输出：
California NaN
Ohio 70000.0
Oregon 32000.0
Texas 142000.0
Utah 5000.0
dtype: float64
Or with 3 series:
或与 3 系列：
import pandas as pd
import numpy as np
a = pd.Series([1, np.NaN, 4, 5])
b = pd.Series([3, np.NaN, 5, np.NaN])
c = pd.Series([np.NaN,np.NaN,np.NaN,np.NaN])
print(pd.concat((a,b,c), axis=1).sum(1, min_count=1))
#0 4.0
#1 NaN
#2 9.0
#3 5.0
#dtype: float64
