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:
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.
In the new package there is something like the following.
import old_p
