On 2/13/2012 12:58 PM, Miki Tebeka wrote:
import new_pandas as np df =
np.DataFrame({'A':[1,2,3],'B':[4,5,6]}) col_A = df['A']
I'm not familiar with pandas, but my *guess* will be that you'll need
to override __getitem__ in the new DataFrame.
This is essentially the same problem that if you, f
Fabrizio Pollastri wrote:
> Ok. To be more clear, consider the real python package Pandas.
>
> This package defines a Series class and a DataFrame class.
> The DataFrame is a matrix that can have columns of
> different type.
>
> If I write
>
> import pandas as pd
> df = pd.DataFrame({'A':[1,2,3
On Sun, Feb 12, 2012 at 1:46 PM, Fabrizio Pollastri wrote:
> Hello,
>
> I wish to extend the functionality of an existing python package by
> creating
> a new package that redefines the relevant classes of the old package. Each
> new class inherits the equivalent old class and adds new methods.
>
> import new_pandas as np
> df = np.DataFrame({'A':[1,2,3],'B':[4,5,6]})
> col_A = df['A']
I'm not familiar with pandas, but my *guess* will be that you'll need to
override __getitem__ in the new DataFrame.
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Ok. To be more clear, consider the real python package Pandas.
This package defines a Series class and a DataFrame class.
The DataFrame is a matrix that can have columns of
different type.
If I write
import pandas as pd
df = pd.DataFrame({'A':[1,2,3],'B':[4,5,6]})
a data frame with two cols na
> B[some_hash] still returns an instance of the old class A, while I want
an instance of the new class A.
I don't understand this sentence. How does B[some_hash] related to A?
I've tried the below and it seems to work. Can you paste some code to help us
understand more?
-- old.py --
class A:
