pandas Python 将类方法应用于数据框的行
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Python apply class method to row of data frame
提问by Conan
My class takes a row of a dataframe to construct an object and I would like to create an array of objects by applying init to every row of a dataframe. Is there a way to vectorize this? My class definition looks like
我的类需要一行数据帧来构造一个对象，我想通过将 init 应用于数据帧的每一行来创建一个对象数组。有没有办法将其矢量化？我的类定义看起来像
class A(object):
def __init__(self,row):
self.a = row['a']
self.b = row['b']
Any suggestion will be highly appreciated!
任何建议将不胜感激！
I have one way which I am not that satisfied with to solve this problem. Define another function outside of class and then use apply.
我有一种我不太满意的方法来解决这个问题。在类之外定义另一个函数，然后使用 apply。
def InitA(row):
return A(row)
Assume df is the data frame I want to use as argument.
假设 df 是我想用作参数的数据框。
xxx = df.apply(InitA,axis=1)
gives what I want. However, I don't think InitA is necessary.
给我想要的。但是，我不认为 InitA 是必要的。
My original problem is a bit more complicated. The class definition is
我原来的问题有点复杂。类定义是
class A(object):
def __init__(self):
return
def add_parameter(self,row):
self.a = row['a']
I intend to apply add_parameter to every row of a data frame. But I think defining another (lambda) function is necessary to solve this problem.
我打算将 add_parameter 应用于数据框的每一行。但我认为定义另一个 (lambda) 函数对于解决这个问题是必要的。
采纳答案by McRip
Just use a lambda function?
只使用 lambda 函数？
xxx = df.apply(lambda x: A(x),axis=1)
xxx = df.apply(lambda x: A(x),axis=1)
edit: Another solution is to directly pass the class, the apply-function then calls the constructor:
编辑：另一种解决方案是直接传递类，然后应用函数调用构造函数：
xxx = df.apply(A,axis=1)
xxx = df.apply(A,axis=1)
this works:
这有效：
import pandas as pd
class C(object):
def __init__(self,dat):
return
A = pd.DataFrame({'a':pd.Series([1,2,3])})
A.apply(lambda x: C(x),axis=1)
