对pandas的算术运算和数据对齐实例详解
pandas可以对不同索引的对象进行算术运算，如果存在不同的索引对，结果的索引就是该索引对的并集。
一、算术运算
a、series的加法运算
s1 = Series([1,2,3],index=["a","b","c"]) s2 = Series([4,5,6],index=["a","c","e"]) print(s1+s2) ''' a 5.0 b NaN c 8.0 e NaN '''
sereis相加会自动进行数据对齐操作，在不重叠的索引处会使用NA(NaN)值进行填充，series进行算术运算的时候，不需要保证series的大小一致。
b、DataFrame的加法运算
d1 = np.arange(1,10).reshape(3,3)
dataFrame1 = DataFrame(d1,index=["a","b","c"],columns=["one","two","three"])
d2 = np.arange(1,10).reshape(3,3)
dataFrame2 = DataFrame(d2,index=["a","b","e"],columns=["one","two","four"])
print(dataFrame1+dataFrame2)
'''
four one three two
a NaN 2.0 NaN 4.0
b NaN 8.0 NaN 10.0
c NaN NaN NaN NaN
e NaN NaN NaN NaN
'''
dataFrame相加时，对齐操作需要行和列的索引都重叠的时候才回相加，否则会使用NA值进行填充。
二、指定填充值
s1 = Series([1,2,3],index=["a","b","c"]) s2 = Series([4,5,6],index=["a","c","e"]) print( s1.add(s2,fill_value=0)) ''' a 5.0 b 2.0 c 8.0 e 6.0 '''
需要注意的时候，使用add方法对两个series进行相加的时候，设置fill_value的值是对于不存在索引的series用指定值进行填充后再进行相加。除了加法add，还有sub减法,div除法,mul乘法，使用方式与add相同。DataFrame与series一样。
s1 = Series([1,2,3],index=["a","b","c"]) s2 = Series([4,5,6],index=["a","c","e"]) print(s2.reindex(["a","b","c","d"],fill_value=0)) ''' a 4 b 0 c 5 d 0 ''' s3 = s1 + s2 print(s3.reindex(["a","b","c","e"],fill_value=0)) ''' a 5.0 b NaN c 8.0 e NaN '''
使用reindex进行填充的时候，需要注意的是，不能对已经是值为NaN的进行重新赋值，只能对使用reindex之前不存在的所以使用指定的填充值，DataFrame也是一样的。
三、DataFrame与Series的混合运算
a、DataFrame的行进行广播
a = np.arange(9).reshape(3,3)
d = DataFrame(a,index=["a","b","c"],columns=["one","two","three"])
#取d的第一行为Series
s = d.ix[0]
print(d+s)
'''
one two three
a 0 2 4
b 3 5 7
c 6 8 10
'''
b、DataFrame的列进行广播
a = np.arange(9).reshape(3,3)
d = DataFrame(a,index=["a","b","c"],columns=["one","two","three"])
#取d的第一列为Series
s = d["one"]
print(d.add(s,axis=0))
'''
one two three
a 0 1 2
b 6 7 8
c 12 13 14
'''
对列进行广播的时候，必须要使用add方法，而且还要将axis设置为0，不然就会得到下面的结果
print(d.add(s))
'''
a b c one three two
a NaN NaN NaN NaN NaN NaN
b NaN NaN NaN NaN NaN NaN
c NaN NaN NaN NaN NaN NaN
'''
以上这篇对pandas的算术运算和数据对齐实例详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持【听图阁-专注于Python设计】。