Re: [Numpy-discussion] Fortran order in recarray.

[Alex Rogozhnikov]

Hi Stephan, 
thanks for the note. The progress over last two years wasn't impressive IMO, 
but I hope you'll manage.

As you suggest, I'll have a look at xarray too, as I see xarray.Dataset. 
I was sure that it doesn't work with non-homogeneous data at all, clearly I 
need to refresh my opinion.



> 22 февр. 2017 г., в 20:55, Stephan Hoyer  написал(а):
> 
> On Wed, Feb 22, 2017 at 8:57 AM, Alex Rogozhnikov  > wrote:
> Pandas may be nice, if you need a report, and you need get it done tomorrow. 
> Then you'll throw away the code. When we initially used pandas as main data 
> storage in yandex/rep, it looked like an good idea, but a year later it was 
> obvious this was a wrong decision. In case when you build data pipeline / 
> research that should be working several years later (using some other 
> installation by someone else), usage of pandas shall be minimal. 
> 
> The pandas development team (myself included) is well aware of these issues. 
> There are long term plans/hopes to fix this, but there's a lot of work to be 
> done and some hard choices to make:
> https://github.com/pandas-dev/pandas/issues/1 
> 
> https://github.com/pandas-dev/pandas/issues/13862 
>  
> 
>  That's why I am looking for a reliable pandas substitute, which should be: 
> - completely consistent with numpy and should fail when this wasn't 
> implemented / impossible
> - fewer new abstractions, nobody wants to learn 
> one-more-way-to-manipulate-the-data, specifically other researchers
> - it may be less convenient for interactive data mungling
>   - in particular, less methods is ok
> - written code should be interpretable, and hardly can be misinterpreted.
> - not super slow, 1-10 gigabytes datasets are a normal situation
> 
> This has some overlap with our motivations for writing Xarray 
> (http://xarray.pydata.org ), so I encourage you to 
> take a look. It still might be more complex than you're looking for, but we 
> did try to clean up the really ambiguous APIs from pandas like indexing.
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion

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Re: [Numpy-discussion] Fortran order in recarray.

[Alex Rogozhnikov]

> 22 февр. 2017 г., в 20:39, josef.p...@gmail.com написал(а):
> 
> 
> 
> On Wed, Feb 22, 2017 at 11:57 AM, Alex Rogozhnikov 
> > wrote:
> Hi Matthew, 
> maybe it is not the best place to discuss problems of pandas, but to show 
> that I am not missing something, let's consider a simple example.
> 
> # simplest DataFrame
> x = pandas.DataFrame(dict(a=numpy.arange(10), b=numpy.arange(10, 20)))
> 
> # simplest indexing. Can you predict results without looking at comments?
> x[:2] # returns two first rows, as expected
> x[[0, 1]]# returns copy of x, whole dataframe
> x[numpy.array(2)] # fails with IndexError: indices are out-of-bounds (can you 
> guess why?)
> x[[0, 1], :] # unhashable type: list
> 
> just in case - I know about .loc and .iloc, but when you write code with many 
> subroutines, you concentrate on numpy inputs, and at some point you simply 
> forget to convert some of the data you operated with to numpy and it 
> continues to work, but it yields wrong results (while you tested everything, 
> but you tested this for numpy). Checking all the inputs in each small 
> subroutine is strange.
> 
> Ok, a bit more:
> x[x['a'] > 5]# works as expected
> x[x['a'] > 5, :] # 'Series' objects are mutable, thus they cannot be 
> hashed
> lookup = numpy.arange(10)
> x[lookup[x['a']] > 5] # works as expected
> x[lookup[x['a']] > 5, :]  # TypeError: unhashable type: 'numpy.ndarray'
> 
> x[lookup]['a']   # indexError
> x['a'][lookup]   # works as expected
> 
> Now let's go a bit further: train/test splitted the data for machine learning 
> (again, the most frequent operation)
> 
> from sklearn.model_selection import train_test_split
> x1, x2 = train_test_split(x, random_state=42)
> 
> # compare next to operations with pandas.DataFrame
> col = x1['a']
> print col[:2]   # first two elements
> print col[[0, 1]]  # doesn't fail (while there in no row with index 0), fills 
> it with NaN
> print col[numpy.arange(2)] # same as previous
> 
> print col[col > 4] # as expected
> print col[col.values > 4] # as expected
> print col.values[col > 4] # converts boolean to int, uses int indexing, but 
> at least raises warning
> 
> Mistakes done by such silent misoperating are not easy to detect (when your 
> data pipeline consists of several steps), quite hard to locate the source of 
> problem and almost impossible to be sure that you indeed avoided all such 
> caveats. Code review turns into paranoidal process (if you care about the 
> result, of course).
> 
> Things are even worse, because I've demonstrated this for my installation, 
> and probably if you run this with some other pandas installation, you get 
> some other results (that were really basic operations). So things that worked 
> ok in one version, may work different way in the other, this becomes 
> completely intractable. 
> 
> Pandas may be nice, if you need a report, and you need get it done tomorrow. 
> Then you'll throw away the code. When we initially used pandas as main data 
> storage in yandex/rep, it looked like an good idea, but a year later it was 
> obvious this was a wrong decision. In case when you build data pipeline / 
> research that should be working several years later (using some other 
> installation by someone else), usage of pandas shall be minimal. 
> 
> That's why I am looking for a reliable pandas substitute, which should be: 
> - completely consistent with numpy and should fail when this wasn't 
> implemented / impossible
> - fewer new abstractions, nobody wants to learn 
> one-more-way-to-manipulate-the-data, specifically other researchers
> - it may be less convenient for interactive data mungling
>   - in particular, less methods is ok
> - written code should be interpretable, and hardly can be misinterpreted.
> - not super slow, 1-10 gigabytes datasets are a normal situation
> 
> Just to the pandas part
> 
> statsmodels supported pandas almost from the very beginning (or maybe after 
> 1.5 years) when the new pandas was still very young.
> 
> However, what I insisted on is that pandas is in the wrapper/interface code, 
> and internally only numpy arrays are used. Besides the confusing "magic" 
> indexing of early pandas, there were a lot of details that silently produced 
> different results, e.g. default iteration on axis=1, ddof in std and var =1 
> instead of numpy =0.
> 
> Essentially, every interface corresponds to np.asarry, but we store the 
> DataFrame information, mainly the index and column names, wo we can return 
> the appropriate pandas object if a pandas object was used for the input.

Yes, it seems to be the best practice.

But apart from libraries, there is lots of code for my research / research in 
my team, and we don't make such checks all the time, moreover many functions 
are intended to operate with DataFrames (and use particular feature names). So, 
the approach is not completely applicable for research code, which 

Re: [Numpy-discussion] Fortran order in recarray.

[Stephan Hoyer]

On Wed, Feb 22, 2017 at 8:57 AM, Alex Rogozhnikov <
alex.rogozhni...@yandex.ru> wrote:

> Pandas may be nice, if you need a report, and you need get it done
> tomorrow. Then you'll throw away the code. When we initially used pandas as
> main data storage in yandex/rep, it looked like an good idea, but a year
> later it was obvious this was a wrong decision. In case when you build data
> pipeline / research that should be working several years later (using some
> other installation by someone else), usage of pandas shall be *minimal*.
>

The pandas development team (myself included) is well aware of these
issues. There are long term plans/hopes to fix this, but there's a lot of
work to be done and some hard choices to make:
https://github.com/pandas-dev/pandas/issues/1
https://github.com/pandas-dev/pandas/issues/13862

 That's why I am looking for a reliable pandas substitute, which should be:

> - completely consistent with numpy and should fail when this wasn't
> implemented / impossible
> - fewer new abstractions, nobody wants to learn 
> one-more-way-to-manipulate-the-data,
> specifically other researchers
> - it may be less convenient for interactive data mungling
>   - in particular, less methods is ok
> - written code should be interpretable, and hardly can be misinterpreted.
> - not super slow, 1-10 gigabytes datasets are a normal situation
>

This has some overlap with our motivations for writing Xarray (
http://xarray.pydata.org), so I encourage you to take a look. It still
might be more complex than you're looking for, but we did try to clean up
the really ambiguous APIs from pandas like indexing.
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Re: [Numpy-discussion] Fortran order in recarray.

[josef . pktd]

On Wed, Feb 22, 2017 at 11:57 AM, Alex Rogozhnikov <
alex.rogozhni...@yandex.ru> wrote:

> Hi Matthew,
> maybe it is not the best place to discuss problems of pandas, but to show
> that I am not missing something, let's consider a simple example.
>
> # simplest DataFrame
> x = pandas.DataFrame(dict(a=numpy.arange(10), b=numpy.arange(10, 20)))
> # simplest indexing. Can you predict results without looking at comments?
> x[:2] # returns two first rows, as expected
> x[[0, 1]]# returns copy of x, whole dataframe
> x[numpy.array(2)] # fails with IndexError: indices are out-of-bounds (can you 
> guess why?)
> x[[0, 1], :] # unhashable type: list
>
>
> just in case - I know about .loc and .iloc, but when you write code with
> many subroutines, you concentrate on numpy inputs, and at some point you
> simply *forget* to convert some of the data you operated with to numpy
> and it *continues* to work, but it yields wrong results (while you tested
> everything, but you tested this for numpy). Checking all the inputs in each
> small subroutine is strange.
>
> Ok, a bit more:
>
> x[x['a'] > 5]# works as expected
> x[x['a'] > 5, :] # 'Series' objects are mutable, thus they cannot be 
> hashed
> lookup = numpy.arange(10)
> x[lookup[x['a']] > 5] # works as expected
> x[lookup[x['a']] > 5, :]  # TypeError: unhashable type: 'numpy.ndarray'
>
> x[lookup]['a']   # indexError
> x['a'][lookup]   # works as expected
>
>
> Now let's go a bit further: train/test splitted the data for machine
> learning (again, the most frequent operation)
>
> from sklearn.model_selection import train_test_split
> x1, x2 = train_test_split(x, random_state=42)
> # compare next to operations with pandas.DataFrame
> col = x1['a']print col[:2]   # first two elementsprint col[[0, 1]]  # 
> doesn't fail (while there in no row with index 0), fills it with NaNprint 
> col[numpy.arange(2)] # same as previous
> print col[col > 4] # as expectedprint col[col.values > 4] # as expectedprint 
> col.values[col > 4] # converts boolean to int, uses int indexing, but at 
> least raises warning
>
>
> Mistakes done by such silent misoperating are not easy to detect (when
> your data pipeline consists of several steps), quite hard to locate the
> source of problem and almost impossible to be sure that you indeed avoided
> all such caveats. Code review turns into paranoidal process (if you care
> about the result, of course).
>
> Things are even worse, because I've demonstrated this for my installation,
> and probably if you run this with some other pandas installation, you get
> some other results (that were really basic operations). So things that
> worked ok in one version, may work different way in the other, this becomes
> completely intractable.
>
> Pandas may be nice, if you need a report, and you need get it done
> tomorrow. Then you'll throw away the code. When we initially used pandas as
> main data storage in yandex/rep, it looked like an good idea, but a year
> later it was obvious this was a wrong decision. In case when you build data
> pipeline / research that should be working several years later (using some
> other installation by someone else), usage of pandas shall be *minimal*.
>
> That's why I am looking for a reliable pandas substitute, which should be:
> - completely consistent with numpy and should fail when this wasn't
> implemented / impossible
> - fewer new abstractions, nobody wants to learn 
> one-more-way-to-manipulate-the-data,
> specifically other researchers
> - it may be less convenient for interactive data mungling
>   - in particular, less methods is ok
> - written code should be interpretable, and hardly can be misinterpreted.
> - not super slow, 1-10 gigabytes datasets are a normal situation
>

Just to the pandas part

statsmodels supported pandas almost from the very beginning (or maybe after
1.5 years) when the new pandas was still very young.

However, what I insisted on is that pandas is in the wrapper/interface
code, and internally only numpy arrays are used. Besides the confusing
"magic" indexing of early pandas, there were a lot of details that silently
produced different results, e.g. default iteration on axis=1, ddof in std
and var =1 instead of numpy =0.

Essentially, every interface corresponds to np.asarry, but we store the
DataFrame information, mainly the index and column names, wo we can return
the appropriate pandas object if a pandas object was used for the input.

This has worked pretty well. Users can have their dataframes, and we have
pure numpy algorithms.

Recently we have started to use pandas inside a few functions or classes
that are less tightly integrated into the overall setup. We also use pandas
for some things that are not convenient or not available in numpy. Our
internal use of pandas groupby and similar will most likely increase over
time.
(One of the main issues we had was date and time index because that was a
moving target in both numpy and pandas.)


One issue for 

Re: [Numpy-discussion] Fortran order in recarray.

[Alex Rogozhnikov]

Hi Matthew, 
maybe it is not the best place to discuss problems of pandas, but to show that 
I am not missing something, let's consider a simple example.

# simplest DataFrame
x = pandas.DataFrame(dict(a=numpy.arange(10), b=numpy.arange(10, 20)))

# simplest indexing. Can you predict results without looking at comments?
x[:2] # returns two first rows, as expected
x[[0, 1]]# returns copy of x, whole dataframe
x[numpy.array(2)] # fails with IndexError: indices are out-of-bounds (can you 
guess why?)
x[[0, 1], :] # unhashable type: list

just in case - I know about .loc and .iloc, but when you write code with many 
subroutines, you concentrate on numpy inputs, and at some point you simply 
forget to convert some of the data you operated with to numpy and it continues 
to work, but it yields wrong results (while you tested everything, but you 
tested this for numpy). Checking all the inputs in each small subroutine is 
strange.

Ok, a bit more:
x[x['a'] > 5]# works as expected
x[x['a'] > 5, :] # 'Series' objects are mutable, thus they cannot be hashed
lookup = numpy.arange(10)
x[lookup[x['a']] > 5] # works as expected
x[lookup[x['a']] > 5, :]  # TypeError: unhashable type: 'numpy.ndarray'

x[lookup]['a']   # indexError
x['a'][lookup]   # works as expected

Now let's go a bit further: train/test splitted the data for machine learning 
(again, the most frequent operation)

from sklearn.model_selection import train_test_split
x1, x2 = train_test_split(x, random_state=42)

# compare next to operations with pandas.DataFrame
col = x1['a']
print col[:2]   # first two elements
print col[[0, 1]]  # doesn't fail (while there in no row with index 0), fills 
it with NaN
print col[numpy.arange(2)] # same as previous

print col[col > 4] # as expected
print col[col.values > 4] # as expected
print col.values[col > 4] # converts boolean to int, uses int indexing, but at 
least raises warning

Mistakes done by such silent misoperating are not easy to detect (when your 
data pipeline consists of several steps), quite hard to locate the source of 
problem and almost impossible to be sure that you indeed avoided all such 
caveats. Code review turns into paranoidal process (if you care about the 
result, of course).

Things are even worse, because I've demonstrated this for my installation, and 
probably if you run this with some other pandas installation, you get some 
other results (that were really basic operations). So things that worked ok in 
one version, may work different way in the other, this becomes completely 
intractable. 

Pandas may be nice, if you need a report, and you need get it done tomorrow. 
Then you'll throw away the code. When we initially used pandas as main data 
storage in yandex/rep, it looked like an good idea, but a year later it was 
obvious this was a wrong decision. In case when you build data pipeline / 
research that should be working several years later (using some other 
installation by someone else), usage of pandas shall be minimal. 

That's why I am looking for a reliable pandas substitute, which should be: 
- completely consistent with numpy and should fail when this wasn't implemented 
/ impossible
- fewer new abstractions, nobody wants to learn 
one-more-way-to-manipulate-the-data, specifically other researchers
- it may be less convenient for interactive data mungling
  - in particular, less methods is ok
- written code should be interpretable, and hardly can be misinterpreted.
- not super slow, 1-10 gigabytes datasets are a normal situation

Well, that's it. 
Sorry for large letter.

Alex.



> 22 февр. 2017 г., в 18:38, Matthew Harrigan  
> написал(а):
> 
> Alex,
> 
> Can you please post some code showing exactly what you are trying to do and 
> any issues you are having, particularly the "irritating problems with its row 
> indexing and some other problems" you quote above?
> 
> On Wed, Feb 22, 2017 at 10:34 AM, Robert McLeod  > wrote:
> Just as a note, Appveyor supports uploading modules to "public websites":
> 
> https://packaging.python.org/appveyor/ 
> 
> 
> The main issue I would see from this, is the PyPi has my password stored on 
> my machine in a plain text file.   I'm not sure whether there's a way to 
> provide Appveyor with a SSH key instead.
> 
> On Wed, Feb 22, 2017 at 4:23 PM, Alex Rogozhnikov  > wrote:
> Hi Francesc, 
> thanks a lot for you reply and for your impressive job on bcolz! 
> 
> Bcolz seems to make stress on compression, which is not of much interest for 
> me, but the ctable, and chunked operations look very appropriate to me now. 
> (Of course, I'll need to test it much before I can say this for sure, that's 
> current impression).
> 
> The strongest concern with bcolz so far is that it seems to be completely 
> non-trivial to install on 

Re: [Numpy-discussion] Fortran order in recarray.

[Matthew Harrigan]

Alex,

Can you please post some code showing exactly what you are trying to do and
any issues you are having, particularly the "irritating problems with its
row indexing and some other problems" you quote above?

On Wed, Feb 22, 2017 at 10:34 AM, Robert McLeod 
wrote:

> Just as a note, Appveyor supports uploading modules to "public websites":
>
> https://packaging.python.org/appveyor/
>
> The main issue I would see from this, is the PyPi has my password stored
> on my machine in a plain text file.   I'm not sure whether there's a way to
> provide Appveyor with a SSH key instead.
>
> On Wed, Feb 22, 2017 at 4:23 PM, Alex Rogozhnikov <
> alex.rogozhni...@yandex.ru> wrote:
>
>> Hi Francesc,
>> thanks a lot for you reply and for your impressive job on bcolz!
>>
>> Bcolz seems to make stress on compression, which is not of much interest
>> for me, but the *ctable*, and chunked operations look very appropriate
>> to me now. (Of course, I'll need to test it much before I can say this for
>> sure, that's current impression).
>>
>> The strongest concern with bcolz so far is that it seems to be completely
>> non-trivial to install on windows systems, while pip provides binaries for
>> most (or all?) OS for numpy.
>> I didn't build pip binary wheels myself, but is it hard / impossible to
>> cook pip-installabel binaries?
>>
>> ​You can change shapes of numpy arrays, but that usually involves copies
>> of the whole container.
>>
>> sure, but this is ok for me, as I plan to organize column editing in
>> 'batches', so this should require seldom copying.
>> It would be nice to see an example to understand how deep I need to go
>> inside numpy.
>>
>> Cheers,
>> Alex.
>>
>>
>>
>>
>> 22 февр. 2017 г., в 17:03, Francesc Alted  написал(а):
>>
>> Hi Alex,
>>
>> 2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov :
>>
>>> Hi Nathaniel,
>>>
>>>
>>> pandas
>>>
>>>
>>> yup, the idea was to have minimal pandas.DataFrame-like storage (which I
>>> was using for a long time),
>>> but without irritating problems with its row indexing and some other
>>> problems like interaction with matplotlib.
>>>
>>> A dict of arrays?
>>>
>>>
>>> that's what I've started from and implemented, but at some point I
>>> decided that I'm reinventing the wheel and numpy has something already. In
>>> principle, I can ignore this 'column-oriented' storage requirement, but
>>> potentially it may turn out to be quite slow-ish if dtype's size is large.
>>>
>>> Suggestions are welcome.
>>>
>>
>> ​You may want to try bcolz:
>>
>> https://github.com/Blosc/bcolz
>>
>> bcolz is a columnar storage, basically as you require, but data is
>> compressed by default even when stored in-memory (although you can disable
>> compression if you want to).​
>>
>>
>>
>>>
>>> Another strange question:
>>> in general, it is considered that once numpy.array is created, it's
>>> shape not changed.
>>> But if i want to keep the same recarray and change it's dtype and/or
>>> shape, is there a way to do this?
>>>
>>
>> ​You can change shapes of numpy arrays, but that usually involves copies
>> of the whole container.  With bcolz you can change length and add/del
>> columns without copies.​  If your containers are large, it is better to
>> inform bcolz on its final estimated size.  See:
>>
>> http://bcolz.blosc.org/en/latest/opt-tips.html
>>
>> ​Francesc​
>>
>>
>>>
>>> Thanks,
>>> Alex.
>>>
>>>
>>>
>>> 22 февр. 2017 г., в 3:53, Nathaniel Smith  написал(а):
>>>
>>> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" 
>>> wrote:
>>>
>>> Ah, got it. Thanks, Chris!
>>> I thought recarray can be only one-dimensional (like tables with named
>>> columns).
>>>
>>> Maybe it's better to ask directly what I was looking for:
>>> something that works like a table with named columns (but no labelling
>>> for rows), and keeps data (of different dtypes) in a column-by-column way
>>> (and this is numpy, not pandas).
>>>
>>> Is there such a magic thing?
>>>
>>>
>>> Well, that's what pandas is for...
>>>
>>> A dict of arrays?
>>>
>>> -n
>>> ___
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>>
>>> ___
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>
>>
>> --
>> Francesc Alted
>> ___
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>>
>>
>>
>> ___
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>>
>>
>
>
> --
> Robert McLeod, Ph.D.
> Center for Cellular Imaging and Nano Analytics (C-CINA)
> Biozentrum der Universität 

Re: [Numpy-discussion] Fortran order in recarray.

[Francesc Alted]

2017-02-22 16:30 GMT+01:00 Kiko :

>
>
> 2017-02-22 16:23 GMT+01:00 Alex Rogozhnikov :
>
>> Hi Francesc,
>> thanks a lot for you reply and for your impressive job on bcolz!
>>
>> Bcolz seems to make stress on compression, which is not of much interest
>> for me, but the *ctable*, and chunked operations look very appropriate
>> to me now. (Of course, I'll need to test it much before I can say this for
>> sure, that's current impression).
>>
>
​You can disable compression for bcolz by default too:

http://bcolz.blosc.org/en/latest/defaults.html#list-of-default-values​



>
>> The strongest concern with bcolz so far is that it seems to be completely
>> non-trivial to install on windows systems, while pip provides binaries for
>> most (or all?) OS for numpy.
>> I didn't build pip binary wheels myself, but is it hard / impossible to
>> cook pip-installabel binaries?
>>
>
> http://www.lfd.uci.edu/~gohlke/pythonlibs/#bcolz
> Check if the link solves the issue with installing.
>

​Yeah.  Also, there are binaries for conda:

http://bcolz.blosc.org/en/latest/install.html#installing-from-conda-forge​



>
>> ​You can change shapes of numpy arrays, but that usually involves copies
>> of the whole container.
>>
>> sure, but this is ok for me, as I plan to organize column editing in
>> 'batches', so this should require seldom copying.
>> It would be nice to see an example to understand how deep I need to go
>> inside numpy.
>>
>
​Well, if copying is not a problem for you, then you can just create a new
numpy container and do the copy by yourself.​

Francesc


>
>> Cheers,
>> Alex.
>>
>>
>>
>>
>> 22 февр. 2017 г., в 17:03, Francesc Alted  написал(а):
>>
>> Hi Alex,
>>
>> 2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov :
>>
>>> Hi Nathaniel,
>>>
>>>
>>> pandas
>>>
>>>
>>> yup, the idea was to have minimal pandas.DataFrame-like storage (which I
>>> was using for a long time),
>>> but without irritating problems with its row indexing and some other
>>> problems like interaction with matplotlib.
>>>
>>> A dict of arrays?
>>>
>>>
>>> that's what I've started from and implemented, but at some point I
>>> decided that I'm reinventing the wheel and numpy has something already. In
>>> principle, I can ignore this 'column-oriented' storage requirement, but
>>> potentially it may turn out to be quite slow-ish if dtype's size is large.
>>>
>>> Suggestions are welcome.
>>>
>>
>> ​You may want to try bcolz:
>>
>> https://github.com/Blosc/bcolz
>>
>> bcolz is a columnar storage, basically as you require, but data is
>> compressed by default even when stored in-memory (although you can disable
>> compression if you want to).​
>>
>>
>>
>>>
>>> Another strange question:
>>> in general, it is considered that once numpy.array is created, it's
>>> shape not changed.
>>> But if i want to keep the same recarray and change it's dtype and/or
>>> shape, is there a way to do this?
>>>
>>
>> ​You can change shapes of numpy arrays, but that usually involves copies
>> of the whole container.  With bcolz you can change length and add/del
>> columns without copies.​  If your containers are large, it is better to
>> inform bcolz on its final estimated size.  See:
>>
>> http://bcolz.blosc.org/en/latest/opt-tips.html
>>
>> ​Francesc​
>>
>>
>>>
>>> Thanks,
>>> Alex.
>>>
>>>
>>>
>>> 22 февр. 2017 г., в 3:53, Nathaniel Smith  написал(а):
>>>
>>> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" 
>>> wrote:
>>>
>>> Ah, got it. Thanks, Chris!
>>> I thought recarray can be only one-dimensional (like tables with named
>>> columns).
>>>
>>> Maybe it's better to ask directly what I was looking for:
>>> something that works like a table with named columns (but no labelling
>>> for rows), and keeps data (of different dtypes) in a column-by-column way
>>> (and this is numpy, not pandas).
>>>
>>> Is there such a magic thing?
>>>
>>>
>>> Well, that's what pandas is for...
>>>
>>> A dict of arrays?
>>>
>>> -n
>>> ___
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>>>
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>>>
>>
>>
>> --
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Re: [Numpy-discussion] Fortran order in recarray.

[Robert McLeod]

Just as a note, Appveyor supports uploading modules to "public websites":

https://packaging.python.org/appveyor/

The main issue I would see from this, is the PyPi has my password stored on
my machine in a plain text file.   I'm not sure whether there's a way to
provide Appveyor with a SSH key instead.

On Wed, Feb 22, 2017 at 4:23 PM, Alex Rogozhnikov <
alex.rogozhni...@yandex.ru> wrote:

> Hi Francesc,
> thanks a lot for you reply and for your impressive job on bcolz!
>
> Bcolz seems to make stress on compression, which is not of much interest
> for me, but the *ctable*, and chunked operations look very appropriate to
> me now. (Of course, I'll need to test it much before I can say this for
> sure, that's current impression).
>
> The strongest concern with bcolz so far is that it seems to be completely
> non-trivial to install on windows systems, while pip provides binaries for
> most (or all?) OS for numpy.
> I didn't build pip binary wheels myself, but is it hard / impossible to
> cook pip-installabel binaries?
>
> ​You can change shapes of numpy arrays, but that usually involves copies
> of the whole container.
>
> sure, but this is ok for me, as I plan to organize column editing in
> 'batches', so this should require seldom copying.
> It would be nice to see an example to understand how deep I need to go
> inside numpy.
>
> Cheers,
> Alex.
>
>
>
>
> 22 февр. 2017 г., в 17:03, Francesc Alted  написал(а):
>
> Hi Alex,
>
> 2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov :
>
>> Hi Nathaniel,
>>
>>
>> pandas
>>
>>
>> yup, the idea was to have minimal pandas.DataFrame-like storage (which I
>> was using for a long time),
>> but without irritating problems with its row indexing and some other
>> problems like interaction with matplotlib.
>>
>> A dict of arrays?
>>
>>
>> that's what I've started from and implemented, but at some point I
>> decided that I'm reinventing the wheel and numpy has something already. In
>> principle, I can ignore this 'column-oriented' storage requirement, but
>> potentially it may turn out to be quite slow-ish if dtype's size is large.
>>
>> Suggestions are welcome.
>>
>
> ​You may want to try bcolz:
>
> https://github.com/Blosc/bcolz
>
> bcolz is a columnar storage, basically as you require, but data is
> compressed by default even when stored in-memory (although you can disable
> compression if you want to).​
>
>
>
>>
>> Another strange question:
>> in general, it is considered that once numpy.array is created, it's shape
>> not changed.
>> But if i want to keep the same recarray and change it's dtype and/or
>> shape, is there a way to do this?
>>
>
> ​You can change shapes of numpy arrays, but that usually involves copies
> of the whole container.  With bcolz you can change length and add/del
> columns without copies.​  If your containers are large, it is better to
> inform bcolz on its final estimated size.  See:
>
> http://bcolz.blosc.org/en/latest/opt-tips.html
>
> ​Francesc​
>
>
>>
>> Thanks,
>> Alex.
>>
>>
>>
>> 22 февр. 2017 г., в 3:53, Nathaniel Smith  написал(а):
>>
>> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" 
>> wrote:
>>
>> Ah, got it. Thanks, Chris!
>> I thought recarray can be only one-dimensional (like tables with named
>> columns).
>>
>> Maybe it's better to ask directly what I was looking for:
>> something that works like a table with named columns (but no labelling
>> for rows), and keeps data (of different dtypes) in a column-by-column way
>> (and this is numpy, not pandas).
>>
>> Is there such a magic thing?
>>
>>
>> Well, that's what pandas is for...
>>
>> A dict of arrays?
>>
>> -n
>> ___
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>>
>> ___
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>
>
> --
> Francesc Alted
> ___
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>
>
>
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>
>


-- 
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Center for Cellular Imaging and Nano Analytics (C-CINA)
Biozentrum der Universität Basel
Mattenstrasse 26, 4058 Basel
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Re: [Numpy-discussion] Fortran order in recarray.

[Kiko]

2017-02-22 16:23 GMT+01:00 Alex Rogozhnikov :

> Hi Francesc,
> thanks a lot for you reply and for your impressive job on bcolz!
>
> Bcolz seems to make stress on compression, which is not of much interest
> for me, but the *ctable*, and chunked operations look very appropriate to
> me now. (Of course, I'll need to test it much before I can say this for
> sure, that's current impression).
>
> The strongest concern with bcolz so far is that it seems to be completely
> non-trivial to install on windows systems, while pip provides binaries for
> most (or all?) OS for numpy.
> I didn't build pip binary wheels myself, but is it hard / impossible to
> cook pip-installabel binaries?
>

http://www.lfd.uci.edu/~gohlke/pythonlibs/#bcolz
Check if the link solves the issue with installing.

>
> ​You can change shapes of numpy arrays, but that usually involves copies
> of the whole container.
>
> sure, but this is ok for me, as I plan to organize column editing in
> 'batches', so this should require seldom copying.
> It would be nice to see an example to understand how deep I need to go
> inside numpy.
>
> Cheers,
> Alex.
>
>
>
>
> 22 февр. 2017 г., в 17:03, Francesc Alted  написал(а):
>
> Hi Alex,
>
> 2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov :
>
>> Hi Nathaniel,
>>
>>
>> pandas
>>
>>
>> yup, the idea was to have minimal pandas.DataFrame-like storage (which I
>> was using for a long time),
>> but without irritating problems with its row indexing and some other
>> problems like interaction with matplotlib.
>>
>> A dict of arrays?
>>
>>
>> that's what I've started from and implemented, but at some point I
>> decided that I'm reinventing the wheel and numpy has something already. In
>> principle, I can ignore this 'column-oriented' storage requirement, but
>> potentially it may turn out to be quite slow-ish if dtype's size is large.
>>
>> Suggestions are welcome.
>>
>
> ​You may want to try bcolz:
>
> https://github.com/Blosc/bcolz
>
> bcolz is a columnar storage, basically as you require, but data is
> compressed by default even when stored in-memory (although you can disable
> compression if you want to).​
>
>
>
>>
>> Another strange question:
>> in general, it is considered that once numpy.array is created, it's shape
>> not changed.
>> But if i want to keep the same recarray and change it's dtype and/or
>> shape, is there a way to do this?
>>
>
> ​You can change shapes of numpy arrays, but that usually involves copies
> of the whole container.  With bcolz you can change length and add/del
> columns without copies.​  If your containers are large, it is better to
> inform bcolz on its final estimated size.  See:
>
> http://bcolz.blosc.org/en/latest/opt-tips.html
>
> ​Francesc​
>
>
>>
>> Thanks,
>> Alex.
>>
>>
>>
>> 22 февр. 2017 г., в 3:53, Nathaniel Smith  написал(а):
>>
>> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" 
>> wrote:
>>
>> Ah, got it. Thanks, Chris!
>> I thought recarray can be only one-dimensional (like tables with named
>> columns).
>>
>> Maybe it's better to ask directly what I was looking for:
>> something that works like a table with named columns (but no labelling
>> for rows), and keeps data (of different dtypes) in a column-by-column way
>> (and this is numpy, not pandas).
>>
>> Is there such a magic thing?
>>
>>
>> Well, that's what pandas is for...
>>
>> A dict of arrays?
>>
>> -n
>> ___
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>>
>> ___
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>
>
> --
> Francesc Alted
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>
>
>
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>
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Re: [Numpy-discussion] Fortran order in recarray.

[Francesc Alted]

Hi Alex,

2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov :

> Hi Nathaniel,
>
>
> pandas
>
>
> yup, the idea was to have minimal pandas.DataFrame-like storage (which I
> was using for a long time),
> but without irritating problems with its row indexing and some other
> problems like interaction with matplotlib.
>
> A dict of arrays?
>
>
> that's what I've started from and implemented, but at some point I decided
> that I'm reinventing the wheel and numpy has something already. In
> principle, I can ignore this 'column-oriented' storage requirement, but
> potentially it may turn out to be quite slow-ish if dtype's size is large.
>
> Suggestions are welcome.
>

​You may want to try bcolz:

https://github.com/Blosc/bcolz

bcolz is a columnar storage, basically as you require, but data is
compressed by default even when stored in-memory (although you can disable
compression if you want to).​



>
> Another strange question:
> in general, it is considered that once numpy.array is created, it's shape
> not changed.
> But if i want to keep the same recarray and change it's dtype and/or
> shape, is there a way to do this?
>

​You can change shapes of numpy arrays, but that usually involves copies of
the whole container.  With bcolz you can change length and add/del columns
without copies.​  If your containers are large, it is better to inform
bcolz on its final estimated size.  See:

http://bcolz.blosc.org/en/latest/opt-tips.html

​Francesc​


>
> Thanks,
> Alex.
>
>
>
> 22 февр. 2017 г., в 3:53, Nathaniel Smith  написал(а):
>
> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" 
> wrote:
>
> Ah, got it. Thanks, Chris!
> I thought recarray can be only one-dimensional (like tables with named
> columns).
>
> Maybe it's better to ask directly what I was looking for:
> something that works like a table with named columns (but no labelling for
> rows), and keeps data (of different dtypes) in a column-by-column way (and
> this is numpy, not pandas).
>
> Is there such a magic thing?
>
>
> Well, that's what pandas is for...
>
> A dict of arrays?
>
> -n
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
>
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Re: [Numpy-discussion] Fortran order in recarray.

[Alex Rogozhnikov]

Hi Nathaniel, 


> pandas

yup, the idea was to have minimal pandas.DataFrame-like storage (which I was 
using for a long time), 
but without irritating problems with its row indexing and some other problems 
like interaction with matplotlib.

> A dict of arrays?


that's what I've started from and implemented, but at some point I decided that 
I'm reinventing the wheel and numpy has something already. In principle, I can 
ignore this 'column-oriented' storage requirement, but potentially it may turn 
out to be quite slow-ish if dtype's size is large.

Suggestions are welcome.

Another strange question:
in general, it is considered that once numpy.array is created, it's shape not 
changed. 
But if i want to keep the same recarray and change it's dtype and/or shape, is 
there a way to do this?

Thanks, 
Alex.



> 22 февр. 2017 г., в 3:53, Nathaniel Smith  написал(а):
> 
> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov"  > wrote:
> Ah, got it. Thanks, Chris!
> I thought recarray can be only one-dimensional (like tables with named 
> columns).
> 
> Maybe it's better to ask directly what I was looking for: 
> something that works like a table with named columns (but no labelling for 
> rows), and keeps data (of different dtypes) in a column-by-column way (and 
> this is numpy, not pandas). 
> 
> Is there such a magic thing?
> 
> Well, that's what pandas is for...
> 
> A dict of arrays?
> 
> -n
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Re: [Numpy-discussion] Fortran order in recarray.

[Nathaniel Smith]

On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" 
wrote:

Ah, got it. Thanks, Chris!
I thought recarray can be only one-dimensional (like tables with named
columns).

Maybe it's better to ask directly what I was looking for:
something that works like a table with named columns (but no labelling for
rows), and keeps data (of different dtypes) in a column-by-column way (and
this is numpy, not pandas).

Is there such a magic thing?


Well, that's what pandas is for...

A dict of arrays?

-n
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Re: [Numpy-discussion] Fortran order in recarray.

[Alex Rogozhnikov]

Ah, got it. Thanks, Chris!
I thought recarray can be only one-dimensional (like tables with named columns).

Maybe it's better to ask directly what I was looking for: 
something that works like a table with named columns (but no labelling for 
rows), and keeps data (of different dtypes) in a column-by-column way (and this 
is numpy, not pandas). 

Is there such a magic thing?

Alex.


> 22 февр. 2017 г., в 2:10, Chris Barker  написал(а):
> 
> 
> 
> On Tue, Feb 21, 2017 at 3:05 PM, Alex Rogozhnikov  > wrote:
> a question about numpy.recarray:
> There is a parameter order in constructor 
> https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.recarray.html
>  
> ,
>  but it seems to have no effect:
> x = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[1000], order='C')
> 
> you are creating a 1D array here -- there is no difference between Fortran 
> and C order for a 1D array. For 2D:
> 
> In [2]: x = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[10,10], 
> order='C')
> 
> 
> In [3]: x.strides
> Out[3]: (160, 16)
> 
> 
> In [4]: y = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[10,10], 
> order='F')
> 
> 
> In [5]: y.strides
> Out[5]: (16, 160)
> 
> note the easier way to get the strides, too :-)
> 
> -CHB
> 
> 
> 
> -- 
> 
> Christopher Barker, Ph.D.
> Oceanographer
> 
> Emergency Response Division
> NOAA/NOS/OR(206) 526-6959   voice
> 7600 Sand Point Way NE   (206) 526-6329   fax
> Seattle, WA  98115   (206) 526-6317   main reception
> 
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Re: [Numpy-discussion] Fortran order in recarray.

[Chris Barker]

On Tue, Feb 21, 2017 at 3:05 PM, Alex Rogozhnikov <
alex.rogozhni...@yandex.ru> wrote:

> a question about numpy.recarray:
> There is a parameter order in constructor https://docs.scipy.org/doc/
> numpy-1.10.1/reference/generated/numpy.recarray.html, but it seems to
> have no effect:
> x = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[1000],
> order='C')
>

you are creating a 1D array here -- there is no difference between Fortran
and C order for a 1D array. For 2D:

In [2]: x = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[10,10],
order='C')


In [3]: x.strides
Out[3]: (160, 16)


In [4]: y = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[10,10],
order='F')


In [5]: y.strides
Out[5]: (16, 160)

note the easier way to get the strides, too :-)

-CHB



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[Numpy-discussion] Fortran order in recarray.

[Alex Rogozhnikov]

Hi, 

a question about numpy.recarray:
There is a parameter order in constructor 
https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.recarray.html 
,
 but it seems to have no effect:

import numpy
x = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[1000], order='C')
y = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[1000], order='F')
print numpy.array(x.ctypes.get_strides()) # [16]
print numpy.array(y.ctypes.get_strides()) # [16]

is this an intended behavior or bug?

Thanks,
Alex.___
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