passing np.std to resample does not actually call the numpy function

[jsudheer]

Hi,
Again the std issue
When I call numpy.std outside pandas I get the population std where as if I call inside it gives me sample std. How can I ask pandas to allow np.std to behave as it originally does?
wit best regards,
Sudheer

In [13]: ts[53153:53155]
Out[13]:
2007-09-15 15:30:00 30.1
2007-09-15 15:40:00 31.6
Freq: 10T

In [14]: np.std(ts[53153:53155])
Out[14]: 0.75

In [15]: ts[53153:53155].resample('D',how=np.std)
Out[15]:
2007-09-15 1.06066
Freq: D

[cpcloud]

i believe the method name is looked up and so it calls the pandas version...marking as a bug

[cpcloud]

@jreback i think it's a bit magical to look up np.std.__name__ and then call a cythonized method based on that. i'm assuming this is for perf reasons; what do u think about just using string for the aforementioned purpose and just calling callables...

[jreback]

Do it this way, the std is defined as is, to get another behavior, you need to provide a custom function

In [18]: s.groupby(pd.TimeGrouper('D')).apply(lambda x: np.std(x))
Out[18]: 
2007-09-15    0.75

[jreback]

@cpcloud this is not a bug

[cpcloud]

oh ok my bad...

[jreback]

@cpcloud well there is a bug somewhat related

This should work I think, something screwy here
(but looking up and translating np.std -> pandas std is done everywhere), for good reason,
numpy doesn't handle nans

In [19]: s.resample('D',how=lambda x: np.std(x))
Out[19]: 
2007-09-15   NaN
Freq: D, dtype: float64

[cpcloud]

yep this is the thing i brought up in @jsudheer's last issue. right, i always forget about nans because pandas handles them so gracefully most of the time and so i just don't have to think about it or i have to use the stupid nan* functions (most only in scipy) or used masked arrays :'(

[jreback]

@cpcloud actually this is the only case where it fails (when its a single day), so its a bug, but edge case (e.g. when crossing multiple days it works)

[jsudheer]

So even if we call how=np.std it does the pandas std? ie how=std and
how=np.std are made same by pandas.... I had the impression that I can get
population standard deviation by calling np.std which is now turned out to
be wrong...
with best regards,
Sudheer

On Tue, Jun 11, 2013 at 7:50 PM, jreback [email protected] wrote:

@cpcloud https://github.com/cpcloud actually this is the only case
where it fails (when its a single day), so its a bug, but edge case (e.g.
when crossing multiple days it works)

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with best regards

Sudheer

---

Dr. Sudheer Joseph

Scientist

INDIAN NATIONAL CENTRE FOR OCEAN INFORMATION SERVICES (INCOIS)
MINISTRY OF EARTH SCIENCES, GOVERNMENT OF INDIA
"OCEAN VALLEY" PRAGATHI NAGAR (BO)
OPP.JNTU, NIZAMPET SO
Andhra Pradesh, India. PIN- 500 090.
TEl:+91-40-23044600(R),Tel:+91-9440832534(Mobile)
Tel:+91-40-23886047(O),Fax:+91-40-23892910(O)
E-mail: [email protected]; [email protected].
Web- http://oppamthadathil.tripod.com
--------------* ---------------
"The ultimate measure of a man is
not where he stands in moments of
comfort and convenience, but where
he stands at times of challenge and
controversy."
Martin Luther King, Jr.
"What we have done for ourselves alone dies with us.
What we have done for others and the world remains and is immortal."

• Albert Pines

[jreback]

that is a feature, most numpy routines are NOT nan-safe (and slower) than pandas routines

you can also get tthe results you want by doing this. As I said many times, you should just define a function of your own (which is what this lambda is), then you can control exactly what you get

In [12]: s.groupby(pd.TimeGrouper('D')).apply(lambda x: x.std(ddof=0))
Out[12]: 
2007-09-15    0.75
dtype: float64

[jsudheer]

Thank you,
I missed one of your mails which was send an hour ago..
which now I am seeing. In fact I did not understand the what you mentioned
in past mails regarding function which is clear now.
with best regards,
Sudheer

On Tue, Jun 11, 2013 at 9:04 PM, jreback [email protected] wrote:

that is a feature, most numpy routines are NOT nan-safe (and slower) than
pandas routines

you can also get tthe results you want by doing this. As I said many
times, you should just define a function of your own (which is what this
lambda is), then you can control exactly what you get

In [12]: s.groupby(pd.TimeGrouper('D')).apply(lambda x: x.std(ddof=0))
Out[12]:
2007-09-15 0.75
dtype: float64

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Reply to this email directly or view it on GitHubhttps://github.com//issues/3844#issuecomment-19269613
.

with best regards

Sudheer

---

Dr. Sudheer Joseph

Scientist

INDIAN NATIONAL CENTRE FOR OCEAN INFORMATION SERVICES (INCOIS)
MINISTRY OF EARTH SCIENCES, GOVERNMENT OF INDIA
"OCEAN VALLEY" PRAGATHI NAGAR (BO)
OPP.JNTU, NIZAMPET SO
Andhra Pradesh, India. PIN- 500 090.
TEl:+91-40-23044600(R),Tel:+91-9440832534(Mobile)
Tel:+91-40-23886047(O),Fax:+91-40-23892910(O)
E-mail: [email protected]; [email protected].
Web- http://oppamthadathil.tripod.com
--------------* ---------------
"The ultimate measure of a man is
not where he stands in moments of
comfort and convenience, but where
he stands at times of challenge and
controversy."
Martin Luther King, Jr.
"What we have done for ourselves alone dies with us.
What we have done for others and the world remains and is immortal."

• Albert Pines

[jreback]

np....if you can think of places in the docs where things are not clear let us know!

[jsudheer]

Is this the doc or there are others
http://pandas.pydata.org/pandas-docs/dev/pandas.pdf

Also from below statement, I was expecting I will get std with 15 days and
on either side (1- 15- 31) with result printed against 15th of each month.
Is this the correct behaviour or I am wrong?
Even with below statement I get results as

2006-08-31 28.765038
2006-09-30 16.037042

ts.groupby(pd.TimeGrouper('M',closed='center',label='center')).apply(lambda
x: x.std(ddof=0))

I expected
2006-08-15
2006-09-15
etc....
On Tue, Jun 11, 2013 at 9:13 PM, jreback [email protected] wrote:

np....if you can think of places in the docs where things are not clear
let us know!

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Reply to this email directly or view it on GitHubhttps://github.com//issues/3844#issuecomment-19270313
.

with best regards

Sudheer

---

Dr. Sudheer Joseph

Scientist

INDIAN NATIONAL CENTRE FOR OCEAN INFORMATION SERVICES (INCOIS)
MINISTRY OF EARTH SCIENCES, GOVERNMENT OF INDIA
"OCEAN VALLEY" PRAGATHI NAGAR (BO)
OPP.JNTU, NIZAMPET SO
Andhra Pradesh, India. PIN- 500 090.
TEl:+91-40-23044600(R),Tel:+91-9440832534(Mobile)
Tel:+91-40-23886047(O),Fax:+91-40-23892910(O)
E-mail: [email protected]; [email protected].
Web- http://oppamthadathil.tripod.com
--------------* ---------------
"The ultimate measure of a man is
not where he stands in moments of
comfort and convenience, but where
he stands at times of challenge and
controversy."
Martin Luther King, Jr.
"What we have done for ourselves alone dies with us.
What we have done for others and the world remains and is immortal."

• Albert Pines

[jreback]

http://pandas.pydata.org/pandas-docs/dev/timeseries.html

you are doing something subtle, so you really need to read and understand the docs

[jsudheer]

Thank you,
This is really good one.
With best regards
Sudheer
On Tuesday, June 11, 2013, jreback wrote:

http://pandas.pydata.org/pandas-docs/dev/timeseries.html

you are doing something subtle, so you really need to read and understand
the docs

—
Reply to this email directly or view it on GitHubhttps://github.com//issues/3844#issuecomment-19271880
.

with best regards

Sudheer

---

Dr. Sudheer Joseph

Scientist

INDIAN NATIONAL CENTRE FOR OCEAN INFORMATION SERVICES (INCOIS)
MINISTRY OF EARTH SCIENCES, GOVERNMENT OF INDIA
"OCEAN VALLEY" PRAGATHI NAGAR (BO)
OPP.JNTU, NIZAMPET SO
Andhra Pradesh, India. PIN- 500 090.
TEl:+91-40-23044600(R),Tel:+91-9440832534(Mobile)
Tel:+91-40-23886047(O),Fax:+91-40-23892910(O)
E-mail: [email protected]; [email protected].
Web- http://oppamthadathil.tripod.com
--------------* ---------------
"The ultimate measure of a man is
not where he stands in moments of
comfort and convenience, but where
he stands at times of challenge and
controversy."
Martin Luther King, Jr.
"What we have done for ourselves alone dies with us.
What we have done for others and the world remains and is immortal."

• Albert Pines