创建子类表单类返回pandas dataFrame

Question

在我的项目中,我构建了一个以pandas DataFrame为核心的类.数据框中的值取决于某些规范,我用一些代表我想要使用的数据的字母初始化它.我把我的所有函数都放在一个内部创建数据框架,__init__因为我知道这个函数只有一个,并且在初始化之后不需要它们.在我的类在以后的代码中使用后,我也不想访问这个函数.(我不确定这是否是"pythonic"方式).

在使用__str__和plotData()方法构建基本类之后,我想应用一些过滤器并构建一个新类,其中附加列是过滤器.我想这样做,__init__但保留已经完成的一切.换句话说,我不想重写整个__init__只想添加新列到基本数据帧.

以类似的方式,我想在plotData()函数中添加一个额外的图

我的原始代码已经有很多行,但原理与下面列出的代码非常相似.

import pandas as pd
import pylab as pl
class myClass(object):
    def __init__(self, frameType = 'All'):
        def method1():
            myFrame = pd.DataFrame({'c1':[1,2,3],'c2':[4,5,6],'c3':[7,8,9]})
            return myFrame
        def method2():
            myFrame = pd.DataFrame({'c1':[.1,.2,.3],'c2':[.4,.5,.6],'c3':[.7,.8,.9]})
            return myFrame
        def makingChiose(self):
            if self.frameType == 'All':
                variable = method1() + method2() 
            elif self.frameType == 'a':
                variable = method1()
            elif self.frameType == 'b':
                variable = method2()
            else:
                variable =  pd.DataFrame({'c1':[0,0,0],'c2':[0,0,0],'c3':[0,0,0]})
            #print 'FROM __init__ : %s' % variable
            return variable           
        self.frameType = frameType      
        self.cObject = makingChiose(self) # object created by the class
    def __str__(self):
        return str(self.cObject)
    def plotData(self):
        self.fig1 = pl.plot(self.cObject['c1'],self.cObject['c2'])
        self.fig2 = pl.plot(self.cObject['c1'],self.cObject['c3'])
        pl.show()

class myClassAv(myClass):
    def addingCol(self):
        print 'CURRENT cObject \n%s' % self.cObject # the object is visible 
        self.cObject['avarage'] = (self.cObject['c1']+self.cObject['c2']+self.cObject['c3'])/3
        print 'THIS WORKS IN GENERAL\n%s' % str((self.cObject['c1']+self.cObject['c2']+self.cObject['c3'])/3) # creating new column works
    def plotData(self):
        # Function to add new plot to already existing plots
        self.fig3 = pl.plot(self.cObject['c1'],self.cObject['avarage'])
if __name__ == '__main__':
    myObject1 = myClass()
    print 'myObject1 =\n%s' % myObject1
    myObject1.plotData()
    myObject2 = myClass('a')
    print 'myObject2 =\n%s' % myObject2
    myObject3 = myClass('b')
    print 'myObject3 =\n%s' % myObject3
    myObject4 = myClass('c')
    print 'myObject4 =\n%s' % myObject4

    myObject5 = myClassAv('a').addingCol()
    print 'myObject5 =\n%s' % myObject5
    myObject5.plotData()

大多数代码都起作用,至少在初始化时,但是当我尝试使用附加列创建新数据帧时,我遇到了错误.当我把它作为新的__init__我创建一个全新的初始化时,我放弃了所有已经完成的工作.我创建了一个新函数,但是我更喜欢在调用新类之后使用附加列而不是新类中的函数.代码的输出如下所示:

myObject1 =
    c1   c2   c3
0  1.1  4.4  7.7
1  2.2  5.5  8.8
2  3.3  6.6  9.9
myObject2 =
   c1  c2  c3
0   1   4   7
1   2   5   8
2   3   6   9
myObject3 =
    c1   c2   c3
0  0.1  0.4  0.7
1  0.2  0.5  0.8
2  0.3  0.6  0.9
myObject4 =
   c1  c2  c3
0   0   0   0
1   0   0   0
2   0   0   0
CURRENT cObject 
   c1  c2  c3
0   1   4   7
1   2   5   8
2   3   6   9
THIS WORKS IN GENERAL
0    4
1    5
2    6
myObject5 =
None
Traceback (most recent call last):
  File "C:\Users\src\trys.py", line 57, in <module>
    myObject5.plotData()
AttributeError: 'NoneType' object has no attribute 'plotData'

问题是:我可以'部分'覆盖超类的方法,以获得此方法中的一些新功能吗？我想将myClassAv()初始化为带有四列而不是三列的数据帧,如myClass(),我想让myClassAv().plotData()绘制第三行,但保留两个基类.

我不知道如何解释错误以及myObject5为什么是None,但我怀疑它是继承的东西.

此外,如果你有建议我应该以不同的方式做我的所有想法,我将很高兴听到他们.

Answer 1

怎么样只是打电话给myClass.__init__内部myClassAv.__init__:

def __init__(self, frameType='All'):
    myClass.__init__(self, frameType)
    def addingCol(cObject): 
        ...
    addingCol(self.cObject)

具体,

import pandas as pd
import pylab as pl
import numpy as np


class myClass(object):
    def __init__(self, frameType='All'):
        def method1():
            myFrame = pd.DataFrame(
                {'c1': [1, 2, 3], 'c2': [4, 5, 6], 'c3': [7, 8, 9]})
            return myFrame

        def method2():
            myFrame = pd.DataFrame(
                {'c1': [.1, .2, .3], 'c2': [.4, .5, .6], 'c3': [.7, .8, .9]})
            return myFrame

        def makingChoice(self):
            if self.frameType == 'All':
                variable = method1() + method2()
            elif self.frameType == 'a':
                variable = method1()
            elif self.frameType == 'b':
                variable = method2()
            else:
                variable = pd.DataFrame(
                    {'c1': [0, 0, 0], 'c2': [0, 0, 0], 'c3': [0, 0, 0]})
            # print 'FROM __init__ : %s' % variable
            return variable
        self.frameType = frameType
        self.cObject = makingChoice(self)  # object created by the class

    def __str__(self):
        return str(self.cObject)

    def plotData(self):
        self.fig1 = pl.plot(self.cObject['c1'], self.cObject['c2'])
        self.fig2 = pl.plot(self.cObject['c1'], self.cObject['c3'])
        pl.show()


class myClassAv(myClass):
    def __init__(self, frameType='All'):
        myClass.__init__(self, frameType)

        def addingCol(cObject):
            print 'CURRENT cObject \n%s' % cObject  # the object is visible
            cObject['average'] = cObject.mean(axis=1)
            # creating new column works
            print 'THIS WORKS IN GENERAL\n%s' % str(cObject['average'])
            return cObject

        addingCol(self.cObject)

    def plotData(self):
        # Function to add new plot to already existing plots
        self.fig3 = pl.plot(self.cObject['c1'], self.cObject['average'])

if __name__ == '__main__':
    myObject1 = myClass()
    print 'myObject1 =\n%s' % myObject1
    myObject1.plotData()
    myObject2 = myClass('a')
    print 'myObject2 =\n%s' % myObject2
    myObject3 = myClass('b')
    print 'myObject3 =\n%s' % myObject3
    myObject4 = myClass('c')
    print 'myObject4 =\n%s' % myObject4

    myObject5 = myClassAv('a')
    print 'myObject5 =\n%s' % myObject5
    myObject5.plotData()

顺便说一句,而不是

self.cObject['avarage'] = (self.cObject['c1']+self.cObject['c2']+self.cObject['c3'])/3

你可以使用mean(axis = 1):

self.cObject['average'] = self.cObject.mean(axis=1)