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对pandas的算术运算和数据对齐实例详解

更新时间:2020-06-17 04:24:01 作者:startmvc
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

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脚本之家。

pandas 算术运算 数据对齐