On Tue, Sep 23, 2014 at 4:40 AM, Eric Moore e...@redtetrahedron.org wrote:
Improving the dtype system requires working on c code.
yes -- it sure does. But I think that is a bit of a Red Herring. I'm barely
competent in C, and don't like it much, but the real barrier to entry for
me is not
This could actually be done by using the structured dtype pretty easily.
The hard work would be improving the ufunc and generalized ufunc mechanism
to handle structured data-types. Numba actually provides some of this
already, so if you have NumPy + Numba you can do this sort of thing now.
On Sun, Sep 21, 2014 at 8:31 PM, Nathaniel Smith n...@pobox.com wrote:
For cases where people genuinely want to implement a new array-like
types (e.g. DataFrame or scipy.sparse) then numpy provides a fair
amount of support for this already (e.g., the various hooks that allow
things like
On Mon, Sep 22, 2014 at 4:31 AM, Nathaniel Smith n...@pobox.com wrote:
On Sun, Sep 21, 2014 at 7:50 PM, Stephan Hoyer sho...@gmail.com wrote:
pandas has some hacks to support custom types of data for which numpy
can't
handle well enough or at all. Examples include datetime and Categorical
On Mon, Sep 22, 2014 at 5:31 AM, Nathaniel Smith n...@pobox.com wrote:
On Sun, Sep 21, 2014 at 7:50 PM, Stephan Hoyer sho...@gmail.com wrote:
My feeling though is that in most of the cases you mention,
implementing a new array-like type is huge overkill. ndarray's
interface is vast and
On Tuesday, September 23, 2014, Todd toddr...@gmail.com wrote:
On Mon, Sep 22, 2014 at 5:31 AM, Nathaniel Smith n...@pobox.com
javascript:_e(%7B%7D,'cvml','n...@pobox.com'); wrote:
On Sun, Sep 21, 2014 at 7:50 PM, Stephan Hoyer sho...@gmail.com
On Sun, Sep 21, 2014 at 6:50 PM, Stephan Hoyer sho...@gmail.com wrote:
pandas has some hacks to support custom types of data for which numpy
can't handle well enough or at all. Examples include datetime and
Categorical [1], and others like GeoArray [2] that haven't make it into
pandas yet.
Travis,
Thank you for your perspective on this issue. Such input is always valuable
in helping us see where we came from and where we might go.
My perspective on NumPy is fairly different, having come into Python right
after the whole Numeric/NumArray transition to NumPy. One of the things
that
Hopefully this is not TL;DR!
Their are 3 'dtype' likes that exist in pandas that could in theory mostly
be migrated back to numpy. These currently exist as the .values in-other-words
the object to which pandas defers data storage and computation for
some/most of operations.
1) SparseArray: This
pandas has some hacks to support custom types of data for which numpy can't
handle well enough or at all. Examples include datetime and Categorical
[1], and others like GeoArray [2] that haven't make it into pandas yet.
Most of these look like numpy arrays but with custom dtypes and type
specific
On Sun, Sep 21, 2014 at 5:50 PM, Stephan Hoyer sho...@gmail.com wrote:
pandas has some hacks to support custom types of data for which numpy
can't handle well enough or at all. Examples include datetime and
Categorical [1], and others like GeoArray [2] that haven't make it into
pandas yet.
On Sun, Sep 21, 2014 at 7:50 PM, Stephan Hoyer sho...@gmail.com wrote:
pandas has some hacks to support custom types of data for which numpy can't
handle well enough or at all. Examples include datetime and Categorical [1],
and others like GeoArray [2] that haven't make it into pandas yet.
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