pandas数组(pandas Series)-(3)向量化运算

这篇介绍下有index索引的pandas Series是如何进行向量化运算的:

1. index索引数组相同:

s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
s2 = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
print s1 + s2

a    11
b    22
c    33
d    44
dtype: int64

直接把各个索引对应的值进行相加

2. index索引数组值相同,顺序不同:

s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
s2 = pd.Series([10, 20, 30, 40], index=['b', 'd', 'a', 'c'])
print s1 + s2

a    31
b    12
c    43
d    24
dtype: int64

把各个索引对应的值相加,顺序以第一个Series的为准

3. index索引数组某些值相同,某些值不相同:

s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
s2 = pd.Series([10, 20, 30, 40], index=['c', 'd', 'e', 'f'])
print s1 + s2

a     NaN
b     NaN
c    13.0
d    24.0
e     NaN
f     NaN

相同索引值对应的值相加,不相同的因为找不到,所以返回NaN

4. index索引数组完全不同:

s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
s2 = pd.Series([10, 20, 30, 40], index=['e', 'f', 'g', 'h'])
print s1 + s2

a   NaN
b   NaN
c   NaN
d   NaN
e   NaN
f   NaN
g   NaN
h   NaN
dtype: float64

因为没有相同的索引,所以无法对Series进行相加,得到的都是NaN