python – 如何子类化大pandasDataFrame？

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有一些SO线程的主题,但我希望有人在这里可以提供一个更系统的帐户,目前最好的方式,子类化pandas.DataFrame满足两个,我认为一般要求：

import numpy as np
import pandas as pd

class MyDF(pd.DataFrame):
    # how to subclass pandas DataFrame?
    pass

mydf = MyDF(np.random.randn(3,4), columns=['A','B','C','D'])
print type(mydf)  # <class '__main__.MyDF'>

# Requirement 1: Instances of MyDF, when calling standard methods of DataFrame,
# should produce instances of MyDF.
mydf_sub = mydf[['A','C']]
print type(mydf_sub)  # <class 'pandas.core.frame.DataFrame'>

# Requirement 2: Attributes attached to instances of MyDF, when calling standard 
# methods of DataFrame, should still attach to the output.
mydf.myattr = 1
mydf_cp1 = MyDF(mydf)
mydf_cp2 = mydf.copy()
print hasattr(mydf_cp1, 'myattr')  # False
print hasattr(mydf_cp2, 'myattr')  # False

对于分类大pandasSeries有什么显着的差异？谢谢.

对于要求1,只需定义_constructor：

import pandas as pd
import numpy as np

class MyDF(pd.DataFrame):
    @property
    def _constructor(self):
        return MyDF


mydf = MyDF(np.random.randn(3,4), columns=['A','B','C','D'])
print type(mydf)

mydf_sub = mydf[['A','C']]
print type(mydf_sub)

我认为需求2没有简单的解决方案,我想你需要定义__init__,复制或者在_constructor中做一些事情,例如：

import pandas as pd
import numpy as np

class MyDF(pd.DataFrame):
    _attributes_ = "myattr1,myattr2"

    def __init__(self, *args, **kw):
        super(MyDF, self).__init__(*args, **kw)
        if len(args) == 1 and isinstance(args[0], MyDF):
            args[0]._copy_attrs(self)

    def _copy_attrs(self, df):
        for attr in self._attributes_.split(","):
            df.__dict__[attr] = getattr(self, attr, None)

    @property
    def _constructor(self):
        def f(*args, **kw):
            df = MyDF(*args, **kw)
            self._copy_attrs(df)
            return df
        return f

mydf = MyDF(np.random.randn(3,4), columns=['A','B','C','D'])
print type(mydf)

mydf_sub = mydf[['A','C']]
print type(mydf_sub)

mydf.myattr1 = 1
mydf_cp1 = MyDF(mydf)
mydf_cp2 = mydf.copy()
print mydf_cp1.myattr1, mydf_cp2.myattr1