[Numpy-discussion] ANN: pandas v0.19.2 released!

[Joris Van den Bossche]

Hi all,

Just in time for the holidays, pandas 0.19.2 has been released!
This is a minor bug-fix release in the 0.19.x series and includes some
small regression fixes, bug fixes and performance improvements. We
recommend that all users upgrade to this version.

Highlights include:


   - Compatibility with Python 3.6.
   - A new Pandas Cheat Sheet
   
<https://github.com/pandas-dev/pandas/raw/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf>
   thanks to Irv Lustig

Wheels and conda packages for python 3.6 are not yet available for all
platforms, but will shortly be.

See the v0.19.2 Whatsnew page
<http://pandas.pydata.org/pandas-docs/version/0.19.2/whatsnew.html> for an
overview of all bugs that have been fixed in 0.19.2.

Thanks to all contributors!

Joris

---

*How to get it:*

Source tarballs and windows/mac/linux wheels are available on PyPI (thanks
to Christoph Gohlke for the windows wheels, and to Matthew Brett for
setting up the mac/linux wheels).
Conda packages are already available via the conda-forge channel (conda
install pandas -c conda-forge). It will be available on the main channel
shortly.

*Issues:*

Please report any issues on our issue tracker:
https://github.com/pydata/pandas/issues

*Thanks to all the contributors of the 0.19.2 release:*

   - Ajay Saxena
   - Ben Kandel
   - Chris
   - Chris Ham
   - Christopher C. Aycock
   - Daniel Himmelstein
   - Dave Willmer
   - Dr-Irv
   - gfyoung
   - hesham shabana
   - Jeff Carey
   - Jeff Reback
   - Joe Jevnik
   - Joris Van den Bossche
   - Julian Santander
   - Kerby Shedden
   - Keshav Ramaswamy
   - Kevin Sheppard
   - Luca Scarabello
   - Matti Picus
   - Matt Roeschke
   - Maximilian Roos
   - Mykola Golubyev
   - Nate Yoder
   - Nicholas Ver Halen
   - Pawel Kordek
   - Pietro Battiston
   - Rodolfo Fernandez
   - sinhrks
   - Tara Adiseshan
   - Tom Augspurger
   - wandersoncferreira
   - Yaroslav Halchenko
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[Numpy-discussion] ANN: pandas v0.19.1 released!

[Joris Van den Bossche]

Hi all,

I'm pleased to announce the release of pandas 0.19.1.
This is a bug-fix release from 0.19.0 and includes some small regression
fixes, bug fixes and performance improvements. We recommend that all users
upgrade to this version.

See the v0.19.1 Whatsnew page
<http://pandas.pydata.org/pandas-docs/version/0.19.1/whatsnew.html> for an
overview of all bugs that have been fixed in 0.19.1.

Thanks to all contributors!

Joris

---

*How to get it:*

Source tarballs and windows/mac/linux wheels are available on PyPI (thanks
to Christoph Gohlke for the windows wheels, and to Matthew Brett for
setting up the mac/linux wheels).
Conda packages are already available via the conda-forge channel (conda
install pandas -c conda-forge). It will be available on the main channel
shortly.

*Issues:*

Please report any issues on our issue tracker: https://github.com/pydata/
pandas/issues

*Thanks to all the contributors of the 0.19.1 release:*

   - Adam Chainz
   - Anthonios Partheniou
   - Arash Rouhani
   - Ben Kandel
   - Brandon M. Burroughs
   - Chris
   - chris-b1
   - Chris Warth
   - David Krych
   - dubourg
   - gfyoung
   - Iván Vallés Pérez
   - Jeff Reback
   - Joe Jevnik
   - Jon M. Mease
   - Joris Van den Bossche
   - Josh Owen
   - Keshav Ramaswamy
   - Larry Ren
   - mattrijk
   - Michael Felt
   - paul-mannino
   - Piotr Chromiec
   - Robert Bradshaw
   - Sinhrks
   - Thiago Serafim
   - Tom Bird
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[Numpy-discussion] ANN: pandas v0.19.0 released

[Joris Van den Bossche]

Hi all,

I'm happy to announce pandas 0.19.0 has been released.
This is a major release from 0.18.1 and includes a number of API changes,
several new features, enhancements, and performance improvements along with
a large number of bug fixes. See the Whatsnew
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html> file
for more information. We recommend that all users upgrade to this version.

This is the work of 5 months of development by 117 contributors. A big
thank you to all contributors!

Joris

---

*What is it:*

pandas is a Python package providing fast, flexible, and expressive data
structures designed to make working with “relational” or “labeled” data
both easy and intuitive. It aims to be the fundamental high-level building
block for doing practical, real world data analysis in Python.
Additionally, it has the broader goal of becoming the most powerful and
flexible open source data analysis / manipulation tool available in any
language.

*Highlights of the 0.19.0 release include:*

   - New method merge_asof for asof-style time-series joining, see here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-enhancements-asof-merge>
   - The .rolling() method is now time-series aware, see here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-enhancements-rolling-ts>
   - read_csv now supports parsing Categorical data, see here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-enhancements-read-csv-categorical>
   - A function union_categorical has been added for combining
   categoricals, see here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-enhancements-union-categoricals>
   - PeriodIndex now has its own period dtype, and changed to be more
   consistent with other Index classes. See here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-api-period>
   - Sparse data structures gained enhanced support of int and bool dtypes,
   see here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-sparse>
   - Comparison operations with Series no longer ignores the index, see here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-api-series-ops>
   for an overview of the API changes.
   - Introduction of a pandas development API for utility functions, see
   here
   
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0190-dev-api>
   .
   - Deprecation of Panel4D and PanelND. We recommend to represent these
   types of n-dimensional data with the xarray package
   <http://xarray.pydata.org/en/stable/>.
   - Removal of the previously deprecated modules pandas.io.data,
   pandas.io.wb, pandas.tools.rplot.

See the Whatsnew
<http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html> file
for more information.

*How to get it:*

Source tarballs and windows/mac/linux wheels are available on PyPI (thanks
to Christoph Gohlke for the windows wheels, and to Matthew Brett for
setting up the mac/linux wheels).
Conda packages are already available via the conda-forge channel (conda
install pandas -c conda-forge). It will be available on the main channel
shortly.

*Issues:*

Please report any issues on our issue tracker:
https://github.com/pydata/pandas/issues

*Thanks to all the contributors:*

   - adneu
   - Adrien Emery
   - agraboso
   - Alex Alekseyev
   - Alex Vig
   - Allen Riddell
   - Amol
   - Amol Agrawal
   - Andy R. Terrel
   - Anthonios Partheniou
   - babakkeyvani
   - Ben Kandel
   - Bob Baxley
   - Brett Rosen
   - c123w
   - Camilo Cota
   - Chris
   - chris-b1
   - Chris Grinolds
   - Christian Hudon
   - Christopher C. Aycock
   - Chris Warth
   - cmazzullo
   - conquistador1492
   - cr3
   - Daniel Siladji
   - Douglas McNeil
   - Drewrey Lupton
   - dsm054
   - Eduardo Blancas Reyes
   - Elliot Marsden
   - Evan Wright
   - Felix Marczinowski
   - Francis T. O’Donovan
   - Gábor Lipták
   - Geraint Duck
   - gfyoung
   - Giacomo Ferroni
   - Grant Roch
   - Haleemur Ali
   - harshul1610
   - Hassan Shamim
   - iamsimha
   - Iulius Curt
   - Ivan Nazarov
   - jackieleng
   - Jeff Reback
   - Jeffrey Gerard
   - Jenn Olsen
   - Jim Crist
   - Joe Jevnik
   - John Evans
   - John Freeman
   - John Liekezer
   - Johnny Gill
   - John W. O’Brien
   - John Zwinck
   - Jordan Erenrich
   - Joris Van den Bossche
   - Josh Howes
   - Jozef Brandys
   - Kamil Sindi
   - Ka Wo Chen
   - Kerby Shedden
   - Kernc
   - Kevin Sheppard
   - Matthieu Brucher
   - Maximilian Roos
   - Michael Scherer
   - Mike Graham
   - Mortada Mehyar
   - mpuels
   - Muhammad Haseeb Tariq
   - Nate George
   - Neil Parley
   - Nicolas Bonnotte
   - OXPHOS
   - Pan Deng / Zora
   - Paul
   - Pauli Virtanen
   - Paul Mestemaker
   - Pawel Kordek
   - Pietro Battiston
  

[Numpy-discussion] ANN: pandas v0.19.0rc1 - RELEASE CANDIDATE

[Joris Van den Bossche]

Hi,

I'm pleased to announce the availability of the first release candidate of
Pandas 0.19.0. Please try this RC and report any issues at the pandas issue
tracker .

The release candidate can be installed with conda from our development
channel (builds for osx-64, linux-64 and win-64 are available for Python
2.7, 3.4 and 3.5):

conda install -c pandas pandas=0.19.0rc1

or with pip from PyPI 
(wheels are available):

pip install --pre pandas==0.19.0rc1

---
THIS IS NOT A PRODUCTION RELEASE

This is a major release from 0.18.1 and includes a number of API changes,
several new features, enhancements, and performance improvements along with
a large number of bug fixes.

Highlights include:

   - New method merge_asof for asof-style time-series joining, see here
   

   - The .rolling() method is now time-series aware, see here
   

   - read_csv now supports parsing Categorical data, see here
   

   - A function union_categorical has been added for combining
   categoricals, see here
   

   - PeriodIndex now has its own period dtype, and changed to be more
   consistent with other Index classes. See here
   

   - Sparse data structures gained enhanced support of int and bool dtypes,
   see here
   

   - Comparison operations with Series no longer ignores the index, see here
   

   for an overview of the API changes.
   - Introduction of a pandas development API for utility functions, see
   here
   

   .
   - Deprecation of Panel4D and PanelND. We recommend to represent these
   types of n-dimensional data with the xarray package
   .
   - Removal of the previously deprecated modules pandas.io.data,
   pandas.io.wb, pandas.tools.rplot.

See the Whatsnew
 file for
more information. Please report any issues here
.

A big thanks to all contributors!

Joris
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[Numpy-discussion] Docs website down?

[Joris Van den Bossche]

It seems the docs website of numpy and scipy (http://docs.scipy.org/doc/)
is down. Is anyone looking at this?
There is even already a stackoverflow question about it ..

Best regards,
Joris
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Re: [Numpy-discussion] Overlapping time series

[Joris Van den Bossche]

2014-02-11 14:55 GMT+01:00 Andreas Hilboll li...@hilboll.de:

 On 11.02.2014 14:47, Daniele Nicolodi wrote:
  On 11/02/2014 14:41, Andreas Hilboll wrote:
  On 11.02.2014 14:22, Daniele Nicolodi wrote:
  On 11/02/2014 14:10, Andreas Hilboll wrote:
  On 11.02.2014 14:08, Daniele Nicolodi wrote:
  Hello,
 
  I have two time series (2xN dimensional arrays) recorded on the same
  time basis, but each with it's own dead times (and start and end
  recording times).  I would like to obtain two time series containing
  only the time overlapping segments of the data.
 
  Does numpy or scipy offer something that may help in this?
 
  I can imagine strategies about how to approach the problem, but none
  that would be efficient.  Ideas?
 
  Take a look at pandas.  It has built-in time series functionality.
 
  Even using Pandas (and I would like to avoid to have to depend on it)
 it
  is not clear to me how I would achieve what I want.  Am I missing
 something?
 
  If the two time series are pandas.Series objects and are called s1 and
 s2:
 
  new1 = s1.ix[s2.dropna().index].dropna()
  new2 = s2.ix[s1.dropna().index].dropna()
  new1 = new1.ix[s2.dropna().index].dropna()
 
  Looks hackish, so there might be a more elegant solution.  For further
  questions about how to use pandas, please look at the pydata mailing
  list or stackoverflow.
 
  Correct me if I'm wrong, but this assumes that missing data points are
  represented with Nan.  In my case missing data points are just missing.

 pandas doesn't care.

 In pandas, you could simply do something like this (assuming the time is
set as the index):

pd.concat([s1, s2], axis=1)

and then remove the nan's (where the index was not overlapping) or use
`join='inner'`

Joris


 Andreas.
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Re: [Numpy-discussion] datetime64 1970 issue

[Joris Van den Bossche]

2013/4/18 Chris Barker - NOAA Federal chris.bar...@noaa.gov

 On Wed, Apr 17, 2013 at 1:09 PM, Bob Nnamtrop bob.nnamt...@gmail.com
 wrote:
  It would seem that before 1970 the dates do not include the time zone
  adjustment while after 1970 they do. This is the source of the extra 7
  hours.
 
  In [21]: np.datetime64('1970-01-01 00')
  Out[21]: numpy.datetime64('1970-01-01T00:00-0700','h')
 
  In [22]: np.datetime64('1969-12-31 00')
  Out[22]: numpy.datetime64('1969-12-31T00:00Z','h')

 wow! that is so wrong, and confusing -- I thought I had an idea what
 was going on here:

 datetime64 currently does a timezone adjustment at two places:

 1) when constructing a datetime64 from an ISO string
 2) when constructing an ISO string from a datetime64

 This:
 In [110]: np.datetime64('1969-12-31 00').view(np.int64)
 Out[110]: -24

 In [111]: np.datetime64('1970-01-01 00').view(np.int64)
 Out[111]: 8

 indicates that it is doing the input transition differently, as the
 underlying value is wrong for one.
 (another weird note -- I;m in pacific time, which is -7 now, with
 DSTso why the 8?)

 That explains the timedelta error.

 But the output is odd, too:

 In [117]: np.datetime64(datetime.datetime(1969, 12, 31, 0))
 Out[117]: numpy.datetime64('1969-12-31T00:00:00.00Z')

 In [118]: np.datetime64(datetime.datetime(1970, 1, 1, 0))
 Out[118]: numpy.datetime64('1969-12-31T16:00:00.00-0800')

 (when converting datetime.datetime objects, no timezone adjustment is
 applied)

 I suspect that it's trying to use the system time functions (which wil
 apply the locale), but that they don't work before 1970...at least on
 *nix machines.

 ANyone tested this on Windows?


On Windows 7, numpy 1.7.0 (Anaconda 1.4.0 64 bit), I don't even get a wrong
answer, but an error:

In [3]: np.datetime64('1969-12-31 00')
Out[3]: numpy.datetime64('1969-12-31T00:00Z','h')

In [4]: np.datetime64('1970-01-01 00')
---
OSError   Traceback (most recent call last)
ipython-input-4-ebf323268a4e in module()
 1 np.datetime64('1970-01-01 00')

OSError: Failed to use 'mktime' to convert local time to UTC



 We REALLY need to fix this!

 -Chris









  I saw the other thread about the time zone issues and I think getting
 rid of
  timezones (perhaps unless they are explicitly requested) is the right
 thing
  to do.
 
  Bob
 
 
  On Tue, Apr 16, 2013 at 4:55 PM, Bob Nnamtrop bob.nnamt...@gmail.com
  wrote:
 
  I am curious if others have noticed an issue with datetime64 at the
  beginning of 1970. First:
 
  In [144]: (np.datetime64('1970-01-01') - np.datetime64('1969-12-31'))
  Out[144]: numpy.timedelta64(1,'D')
 
  OK this look fine, they are one day apart. But look at this:
 
  In [145]: (np.datetime64('1970-01-01 00') - np.datetime64('1969-12-31
  00'))
  Out[145]: numpy.timedelta64(31,'h')
 
  Hmmm, seems like there are 7 extra hours? Am I missing something? I
 don't
  see this at any other year. This discontinuity makes it hard to use the
  datetime64 object without special adjustment in ones code. I assume
 this a
  bug?
 
  Thanks,
  Bob
 
  ps I'm using the most recent anaconda release on mac os x 10.6.8 which
  includes numpy 1.7.0.
 
  pss It would be most handy if datetime64 had a constructor of the form
  np.datetime64(year,month,day,hour,min,sec) where these inputs were numpy
  arrays and the output would have the same shape as the input arrays
 (but be
  of type datetime64). The hour,min,sec would be optional. Scalar inputs
 would
  be broadcast to the size of the array inputs, etc. Maybe this is a
 topic for
  another post.
 
 
 
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 --

 Christopher Barker, Ph.D.
 Oceanographer

 Emergency Response Division
 NOAA/NOS/ORR(206) 526-6959   voice
 7600 Sand Point Way NE   (206) 526-6329   fax
 Seattle, WA  98115   (206) 526-6317   main reception

 chris.bar...@noaa.gov
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