[Numpy-discussion] Re: [SciPy-user] Table like array

Travis Oliphant oliphant.travis at ieee.org
Tue Feb 28 23:16:07 EST 2006

Michael Sorich wrote:

> Hi,
> I am looking for a table like array. Something like a 'data frame' 
> object to those familiar with the statistical languages R and Splus. 
> This is mainly to hold and manipulate 2D spreadsheet like data, which 
> tends to be of relatively small size (compared to what many people 
> seem to use numpy for), heterogenous, have column and row names, and 
> often contains missing data. 

You could subclass the ndarray to produce one of these fairly easily, I 
think.   The missing data item could be handled by a mask stored along 
with the array (or even in the array itself).  Or you could use a masked 
array as your core object (though I'm not sure how it handles the 
arbitrary (i.e. record-like) data-types yet).

Alternatively, and probably the easiest way to get started, you could 
just create your own table-like class and use simple 1-d arrays or 1-d 
masked arrays for each of the columns ---  This has always been a way to 
store record-like tables.

It really depends what you want the data-frames to be able to do and 
what you want them to "look-like."

> A RecArray seems potentially useful, as it allows different fields to 
> have different data types and holds the name of the field. However it 
> doesn't seem easy to manipulate the data. Or perhaps I am simply 
> having difficulty finding documentation on there features.

Adding a new column/field means basically creating a new array with a 
new data-type and copying data over into the already-defined fields.  
Data-types always have a fixed number of bytes per item.   What those 
bytes represent can be quite arbitrary but it's always fixed.   So, it 
is always "more work" to insert a new column.  You could make that 
seamless in your table class so the user doesn't see it though.

You'll want to thoroughly understand the dtype object including it's 
attributes and methods.  Particularly the fields attribute of the dtype 

> eg
> adding a new column/field (and to a lesser extent a new row/record) to 
> the recarray

Adding a new row or record is actually similar because once an array is 
created it is usually resized by creating another array and copying the 
old array into it in the right places.

> Changing the field/column names
> make a new table by selecting a subset of fields/columns. (you can 
> select a single field/column, but not multiple).

Right.  So far you can't select multiple columns.  It would be possible 
to add this feature with a little-bit of effort if there were a strong 
demand for it, but it would be much easier to do it in your subclass 
and/or container class.

How many people would like to see x['f1','f2','f5']  return a new array 
with a new data-type descriptor constructed from the provided fields?

> It would also be nice for the table to be able to deal easily with 
> masked data (I have not tried this with recarray yet) and perhaps also 
> to be able to give the rows/records unique ids that could be used to 
> select the rows/records (in addition to the row/record index), in the 
> same way that the fieldnames can select the fields.

Adding fieldnames to the "rows" is definitely something that a subclass 
would be needed for.  I'm not sure how you would even propose to select 
using row names.  Would you also use getitem semantics? 

> Can anyone comment on this issue? Particularly whether code exists for 
> this purpose, and if not ideas about how best to go about developing 
> such a Table like array (this would need to be limited to python 
> programing as my ability to program in c is very limited).

I don't know of code that already exists for this, but I don't think it 
would be too hard to construct your own data-frame object.

I would probably start with an implementation that just used standard 
arrays of a particular type to represent the internal columns and then 
handle the indexing using your own over-riding of the __getitem__ and 
__setitem__ special methods.  This would be the easiest to get working, 
I think.


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