问题描述
您能否通过基本示例告诉我何时使用这些矢量化方法?
Can you tell me when to use these vectorization methods with basic examples?
我看到map是Series方法，而其余的是DataFrame方法.我对apply和applymap方法感到困惑.为什么我们有两种将函数应用于DataFrame的方法?同样，简单的例子可以很好地说明用法！
I see that map is a Series method whereas the rest are DataFrame methods. I got confused about apply and applymap methods though. Why do we have two methods for applying a function to a DataFrame? Again, simple examples which illustrate the usage would be great!
推荐答案
Wes McKinney的 Python for数据分析书，第pg. 132(我强烈推荐这本书):
Straight from Wes McKinney's Python for Data Analysis book, pg. 132 (I highly recommended this book):
In [116]: frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
In [117]: frame
Out[117]:
b d e
Utah -0.029638 1.081563 1.280300
Ohio 0.647747 0.831136 -1.549481
Texas 0.513416 -0.884417 0.195343
Oregon -0.485454 -0.477388 -0.309548
In [118]: f = lambda x: x.max() - x.min()
In [119]: frame.apply(f)
Out[119]:
b 1.133201
d 1.965980
e 2.829781
dtype: float64
也可以使用基于元素的Python函数.假设您要根据帧中的每个浮点值来计算格式化的字符串.您可以使用applymap做到这一点:
Element-wise Python functions can be used, too. Suppose you wanted to compute a formatted string from each floating point value in frame. You can do this with applymap:
In [120]: format = lambda x: '%.2f' % x
In [121]: frame.applymap(format)
Out[121]:
b d e
Utah -0.03 1.08 1.28
Ohio 0.65 0.83 -1.55
Texas 0.51 -0.88 0.20
Oregon -0.49 -0.48 -0.31
In [122]: frame['e'].map(format)
Out[122]:
Utah 1.28
Ohio -1.55
Texas 0.20
Oregon -0.31
Name: e, dtype: object
总结起来，apply在DataFrame的行/列基础上工作，applymap在DataFrame的元素基础上工作，而map在Series的元素基础上工作.
Summing up, apply works on a row / column basis of a DataFrame, applymap works element-wise on a DataFrame, and map works element-wise on a Series.
这篇关于Pandas中map，applymap和apply方法之间的区别的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！