On Tue, Apr 16, 2019 at 8:32 AM Chris Barker - NOAA Federal <
chris.bar...@noaa.gov> wrote:
> Another consideration is that some organizations, the US federal
> government, for instance, cannot make donations. I wouldn’t be surprised if
> some Universities might have similar restrictions on using
I'm not a numpy dev, but I think you should start here:
https://docs.scipy.org/doc/numpy/dev/
And then look at the Github issues, and search the tags for something along
the lines of "documentation" & "good first issue" / "beginner friendly"
-paul
On Thu, Mar 28, 2019 at 10:07 AM Sargam Monga
I'm not a numpy dev, but I think you should start here:
https://docs.scipy.org/doc/numpy/dev/
And then look at the Github issues, and search the tags for something along
the lines of "documentation" & "good first issue" / "beginner friendly"
-paul
On Thu, Mar 28, 2019 at 10:37 AM Aishwarya Dash
On Mon, Mar 18, 2019 at 1:19 PM Paul Hobson wrote:
>
>> I'm a civil engineer who adopted Python early in his career and became
>> the "data guy" in the office pretty early on. Our company's IT department
>> manages lots of Windows Servers running SQL Serv
Just reply to the discussion, I may have made a boo boo in replying to your
> first post.
>
> I'm curious. Implementing NumPy in another language seems like quite a bit
> of work. Did you have any tools to make it easier? I assume the C api is
> gone, so that the translation is NumPy program specif
As a user, I think this is a good change.
On Wed, Mar 13, 2019 at 2:05 PM Warren Weckesser
wrote:
> On 3/13/19, Stefan van der Walt wrote:
> > In PR 4808, I propose changing the default padding mode (for `np.pad`)
> > to constant (0).
>
> +1
>
> Warren
>
> >
> > It was suggested that I mention
On Fri, Feb 15, 2019 at 5:12 AM Mike C wrote:
> The original data was in CSV format. I read it in using pd.read_csv(). It
> does have column names, but no row names. I don’t think numpy reads csv
> files.
>
If you read a file into a pandas structure, it will have row labels. The
default labels a
This is more a question for the pandas list, but since i'm here i'll take a
crack.
- numpy aligns arrays by position.
- pandas aligns by label.
So what you did in pandas is roughly equivalent to the following:
a = pandas.Series([85, 86, 87, 86], name='a').iloc[1:4].to_frame()
b = pandas.S
As a user, I found np.pad relatively complex to get up and running (with
all of the string parameters and different formats of the parameters). Some
defaults would be good to just prime the pump, so to speak, when users are
tinkering in e.g., and interactive session.
-Paul
On Tue, Sep 25, 2018 at
In addition to this, at least half the Windows-using Python people in my
social circle of switched to Windows Subsystem for Linux, which is quite
good now. In include myself in this, and only use python from "Windows"
when I have to deal with Access or MS-SQL Server databases (probably 10% of
my wo
I'm not sure I understand the question, but in python, you can get the
first and elements of most types of sequences with e.g., X[0] and Y[-1],
respectively.
Is the second part of your question just a dot product?
On Sat, Jan 27, 2018 at 10:07 PM, Vincent Douce Mathoscope <
mathosc...@netcourrie
I think this is a floating point precision issue.
https://docs.python.org/3.6/tutorial/floatingpoint.html
On Fri, Dec 15, 2017 at 1:40 PM, Jesper Larsen
wrote:
> Hi numpy people,
>
> I was just wondering whether this behaviour is intended:
>
> >>> import numpy as np
> >>> np.ma.masked_values(np
Can your provide representative examples for tmp_p[x|y|z]?
-paul
On Sat, Oct 21, 2017 at 10:45 AM, Andrei Berceanu
wrote:
> Hi,
>
> I am new to Numpy, and would like to start by translating a (badly
> written?) piece of MATLAB code.
> What I have come up with so far is this:
>
> px = np.zeros_li
Just a thought that popped into my head:
It'd be cool with the sci/py/data stack had a convention of
.citation so I could look it up w/o leaving my jupyter notebook :)
-paul
On Tue, Sep 5, 2017 at 1:29 PM, Stefan van der Walt
wrote:
> On Tue, Sep 5, 2017, at 13:25, Charles R Harris wrote:
>
>
>
Maybe I'm missing something.
This seems fine to me:
>>> bool(np.array([]))
False
But I would have expected these to raise ValueErrors recommending any() and
all():
>>> bool(np.array([1]))
True
>>> bool(np.array([0]))
False
On Fri, Aug 18, 2017 at 3:00 PM, Stephan Hoyer wrote:
> I agree, this b
I would do something like:
diff_is_large = (array1 - array2) > threshold
index_at_large_diff = numpy.nonzero(diff_is_large)
array1[index_at_large_diff].tolist()
On Wed, May 17, 2017 at 9:50 AM, Nissim Derdiger
wrote:
> Hi,
> In my script, I need to compare big NumPy arrays (2D or 3D), and retu
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