python – 如何子類化大pandasDataFrame？

).

有一些SO執行緒的主題,但我希望有人在這裡可以提供一個更系統的帳戶,目前最好的方式,子類化pandas.DataFrame滿足兩個,我認為一般要求：

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

對於分類大pandasSeries有什麼顯著的差異？謝謝.

對於要求1,只需定義_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)

我認為需求2沒有簡單的解決方案,我想你需要定義__init__,複製或者在_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