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} | 5 | 2013-07-29T16:41:35Z | 2014-05-29T18:30:27Z | 2014-05-29T18:30:27Z | NONE | null | In [287]: %timeit zip(*df.index)[0]
100000 loops, best of 3: 6.48 µs per loop
In [288]: %timeit df.index.get_level_values(0)
10000 loops, best of 3: 39.4 µs per loop
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} | 3 | 2013-07-29T18:29:47Z | 2013-07-31T15:33:34Z | 2013-07-31T15:33:34Z | NONE | null | The following command returns an error message for a Series
``` python
s.plot(style='k--', color='k')
```
It says that the color '[k]' is not recognized.
I am using pandas 0.11 so it may have been fixed in 0.12.
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} | 31 | 2013-07-30T03:03:33Z | 2013-08-16T02:10:14Z | 2013-07-31T01:06:11Z | NONE | null | I just upgrage from 0.11 to 0.12 version. And meet dataframe rename error caused by upgrading. (It worked well in 0.11) .
```
>>> df4
TClose RT TExg
STK_ID RPT_Date
600809 20130331 22.02 0.0454 0.0422
>>> df5
STK_ID RPT_Date STK_Name TClose
STK_ID RPT_Date
600809 20120930 600809 20120930 山西汾酒 38.05
20121231 600809 20121231 山西汾酒 41.66
20130331 600809 20130331 山西汾酒 30.01
>>> k=pd.merge(df4, df5, how='inner', left_index=True, right_index=True)
>>> k
TClose_x RT TExg STK_ID RPT_Date STK_Name TClose_y
STK_ID RPT_Date
600809 20130331 22.02 0.0454 0.0422 600809 20130331 山西汾酒 30.01
>>> k.rename(columns={'TClose_x':'TClose', 'TClose_y':'QT_Close'})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "d:\Python27\lib\site-packages\pandas\core\base.py", line 40, in __repr__
return str(self)
File "d:\Python27\lib\site-packages\pandas\core\base.py", line 20, in __str__
return self.__bytes__()
File "d:\Python27\lib\site-packages\pandas\core\base.py", line 32, in __bytes__
return self.__unicode__().encode(encoding, 'replace')
File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 668, in __unicode__
self.to_string(buf=buf)
File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 1556, in to_string
formatter.to_string()
File "d:\Python27\lib\site-packages\pandas\core\format.py", line 294, in to_string
strcols = self._to_str_columns()
File "d:\Python27\lib\site-packages\pandas\core\format.py", line 239, in _to_str_columns
str_columns = self._get_formatted_column_labels()
File "d:\Python27\lib\site-packages\pandas\core\format.py", line 435, in _get_formatted_column_labels
dtypes = self.frame.dtypes
File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 1696, in dtypes
return self.apply(lambda x: x.dtype)
File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 4416, in apply
return self._apply_standard(f, axis)
File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 4491, in _apply_standard
raise e
TypeError: ("'NoneType' object is not iterable", u'occurred at index TExg')
>>> df4.dtypes
TClose float64
RT float64
TExg float64
dtype: object
>>> df5.dtypes
STK_ID object
RPT_Date object
STK_Name object
TClose float64
dtype: object
>>>
```
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} | 73 | 2013-07-30T13:57:58Z | 2014-06-17T12:47:53Z | 2013-08-21T23:15:09Z | CONTRIBUTOR | null | in the excel.py there is a fix enabling reading xlsx files with both
datemodes: (see #4332)
in the parsers.py there is the fix for readinh the header even if there
are additional rows (to be skipped) between a header and the data (see: #4340)
I hope this PR is adequate. If not, please let me know.
I can supply the sample file for testing proving that it works. See also:
https://github.com/timmie/example_code_data
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} | 6 | 2013-07-30T16:49:30Z | 2016-10-12T23:04:03Z | 2013-08-03T03:29:56Z | NONE | null | When converting a pandas Series object to type string using `astype(str)`, long strings are truncated to 64 characters silently.
Pandas version: 0.12
Numpy version: 1.7.1
``` python
In [1]: s = '0123456789' * 10
In [2]: tmp = np.array([s]).astype(str)
tmp
Out[2]: array([ '0123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789'],
dtype='|S100')
In [3]: tmp = pd.Series([s]).astype(str)
tmp[0]
Out[3]: '0123456789012345678901234567890123456789012345678901234567890123'
In [4]: len(tmp[0])
Out[4]: 64
```
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} | 6 | 2013-07-30T18:13:27Z | 2014-06-24T15:22:39Z | 2013-07-30T23:10:06Z | CONTRIBUTOR | null | closes #3192
This pull request fixes a problem arising from using the csv parser with the usecols option. In that case, all the dtypes will be converted to object. This code fixes that issue, resulting in the correct dtypes.
Thanks,
Tiago
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} | 3 | 2013-07-30T18:24:04Z | 2016-11-11T10:25:39Z | 2016-11-11T10:25:29Z | CONTRIBUTOR | null | XBRL (http://en.wikipedia.org/wiki/XBRL) is the standard format for financial filings.
As of 2009, the SEC requires companies to file in XBRL format (http://xbrl.sec.gov/)
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} | 5 | 2013-07-30T21:54:35Z | 2016-10-12T23:04:03Z | 2013-07-31T15:33:33Z | NONE | null | I run Python 2.7.5 on Windows 7 64-bit and IPython 0.13.2.
I have a data frame with some portfolio weights and I do the following:
```
def weight_histograms(field, name, bins=100):
if flag_inline:
plt.figure(figsize=(16, 8))
else:
plt.figure()
plt.subplot(121)
portfolio_data[field].hist(normed=True, bins=bins)
plt.title('{}: All'.format(name))
plt.subplot(122)
portfolio_data.ix[portfolio_data[field] != 0, field].hist(normed=True, bins=bins)
plt.title('{}: Non-Zero'.format(name))
```
With pandas 0.11.0, running
```
weight_histograms(field_benchmark_weight, 'Benchmark Weights')
```
in IPython with
```
%pylab inline
```
and
```
flag_inline=True
```
I get

Doing the same thing with pandas 0.12.0 results in

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} | 14 | 2013-07-30T23:08:27Z | 2013-08-02T00:33:27Z | 2013-08-02T00:33:27Z | CONTRIBUTOR | null | Testing and playing with HDFStores a lot, I have found several times that a store.close() leaves things hanging, like this:
``` python
store.close()
# no output
store
ClosedFileError: the file object is closed
df.to_hdf('test.h5','df',data_columns=['A','B'],mode='w',table=True)
ValueError: The file 'test.h5' is already opened. Please close it before reopening in write mode.
# The store was opened with 'test.h5' as filename.
```
pandas version 0.12.0
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} | 0 | 2013-07-31T00:17:47Z | 2014-06-19T20:25:43Z | 2013-07-31T01:06:11Z | CONTRIBUTOR | null | closes #4403
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} | 5 | 2013-07-31T00:59:51Z | 2013-09-20T22:58:55Z | 2013-09-20T22:58:55Z | NONE | null | Using Pandas 0.11 (this does not happen on 0.09):
``` python
In [114]: print panel
<class 'pandas.core.panel.Panel'>
Dimensions: 7 (items) x 6 (major_axis) x 14 (minor_axis)
Items axis: ticker1 to ticker7
Major_axis axis: 2013-07-23 00:00:00 to 2013-07-30 00:00:00
Minor_axis axis: fut_trading_hrs to exp_date
In [115]: panel['ticker1']['exp_date']
Out[115]:
2013-07-23 2013-09-17 00:00:00
2013-07-24 2013-09-17 00:00:00
2013-07-25 2013-09-17 00:00:00
2013-07-26 2013-09-17 00:00:00
2013-07-29 2013-09-17 00:00:00
2013-07-30 2013-09-17 00:00:00
Name: exp_date, dtype: datetime64[ns]
In [116]: panel.minor_xs('exp_date') __# where did the data go!?__
Out[116]:
ticker1 ticker2 ticker3
2013-07-23 NaN NaN NaN
2013-07-24 NaN NaN NaN
2013-07-25 NaN NaN NaN
2013-07-26 NaN NaN NaN
2013-07-29 NaN NaN NaN
2013-07-30 NaN NaN NaN
ticker4 ticker5 ticker6
2013-07-23 NaN NaN NaN
2013-07-24 NaN NaN NaN
2013-07-25 NaN NaN NaN
2013-07-26 NaN NaN NaN
2013-07-29 NaN NaN NaN
2013-07-30 NaN NaN NaN
ticker7
2013-07-23 NaN
2013-07-24 NaN
2013-07-25 NaN
2013-07-26 NaN
2013-07-29 NaN
2013-07-30 NaN
In [117]: panel['ticker1']['exp_date'] __# even this is gone now!__
Out[117]:
2013-07-23 NaN
2013-07-24 NaN
2013-07-25 NaN
2013-07-26 NaN
2013-07-29 NaN
2013-07-30 NaN
Name: exp_date, dtype: object
```
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} | 3 | 2013-07-31T01:24:12Z | 2013-08-10T12:07:18Z | 2013-08-10T12:07:18Z | CONTRIBUTOR | null | I don't think this is true anymore (from install.html on pandas site)
> Officially Python 2.6 to 2.7 and Python 3.1+, although Python 3 support is less well tested. Python 2.4 support is being phased out since the userbase has shrunk significantly. Continuing Python 2.4 support will require either monetary development support or someone contributing to the project to maintain compatibility.
I'm pretty sure that 2.4 (and 2.5) will fail, if only because the `from __future__ import with_statement` have been removed from many files. I also think we're at the point where we can say that Python 3.1+ support is actually there (this could be a change in docs for v0.13)
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} | 1 | 2013-07-31T02:14:13Z | 2013-08-02T09:37:02Z | 2013-08-02T09:37:02Z | CONTRIBUTOR | null | I just upgraded to the latest stable version of Pandas:
```
import pandas as pd
import numpy as np
pd.DataFrame(np.random.random((10,5)))
```
returns the following warning:
```
/opt/python/virtualenvs/work/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.
```
Why? And how can I silence it?
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} | 5 | 2013-07-31T03:56:52Z | 2014-06-25T07:26:53Z | 2013-07-31T15:33:34Z | MEMBER | null | closes #4408.
closes #4402.
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> C:\Python27\lib\site-packages\pandas\io\pytables.py:1795: PerformanceWarning:
> your performance may suffer as PyTables will pickle object types that it cannot map
> directly to c-types [inferred_type->mixed,key->block2_values]
>
> warnings.warn(ws, PerformanceWarning)
for the following DataFrame
> MSCI.ACWI float64
> MarketCap float64
> alpha float64
> gics_code float64
> investable bool
> issuer_country object
> universe bool
> weight_benchmark float64
> weight_portfolio float64
> weight_active float64
> dtype: object
The culprit is issuer_country column, which is a string column. I find it surprising. Is there a way to get around this?
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} | 18 | 2013-07-31T14:50:47Z | 2013-08-15T01:18:00Z | 2013-08-07T17:50:22Z | MEMBER | null | I think it could be time to start running all tests (slow + not slow) since travis has been sped up quite a bit by wheels and now py3 builds don't use `2to3`
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closes #4367
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} | 37 | 2013-07-31T15:38:31Z | 2014-07-16T08:21:17Z | 2013-08-01T11:41:40Z | MEMBER | null | Bunch of doc build cleanups
closes #4418.
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```
portfolio_data[FIELD_NAME_COUNTRY] = portfolio_data[FIELD_NAME_ISSUER_COUNTRY]
countries = portfolio_data[FIELD_NAME_COUNTRY]
flag_no_country = countries.isnull()
countries[flag_no_country] = portfolio_data.ix[flag_no_country, FIELD_NAME_MSCI_COUNTRY]
portfolio_data[FIELD_NAME_COUNTRY] = countries
```
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} | 14 | 2013-07-31T20:43:46Z | 2014-05-01T13:52:50Z | 2013-10-17T20:49:58Z | CONTRIBUTOR | null | From [this](http://stackoverflow.com/questions/17979327/drop-rows-with-multiple-keys-in-pandas/17979614?noredirect=1#comment26286280_17979614) SO question.
The user had two dataframes and wanted to use the second as the values argument to `isin`.
We'd need to think about how to handle the other index. In this case the user only cared about the columns, not the index labels.
Previous issues/PRs: #4258 and #4211
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} | 1 | 2013-07-31T21:03:32Z | 2013-08-10T01:52:13Z | 2013-08-10T01:52:13Z | CONTRIBUTOR | null | I'm going to look into this at some point and see if it can be made better. You end up with cryptic error messages on importing pandas if there are any kind of top level errors because (for some reason), issues with nearly _any_ `*.py` file cause the exception below. My theory is that it's because the C-extensions import from the pure python parts of pandas, but silently fail instead of giving the better error.
``` bash
======================================================================
ERROR: Failure: ImportError (cannot import name hashtable)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python27\lib\site-packages\nose\loader.py", line 413, in loadTestsFromName
addr.filename, addr.module)
File "C:\Python27\lib\site-packages\nose\importer.py", line 47, in importFromPath
return self.importFromDir(dir_path, fqname)
File "C:\Python27\lib\site-packages\nose\importer.py", line 94, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "D:\development\GitHub\pandas\pandas\__init__.py", line 6, in <module>
from . import hashtable, tslib, lib
ImportError: cannot import name hashtable
----------------------------------------------------------------------
Ran 1 test in 0.000s
FAILED (errors=1)
```
I'm not sure whether this is really possible to improve, but I definitely hit this error when it turned out there was actually a circular import on `Series` or a syntax error or something else. Mostly this would require moving C-extension imports out of the top level of files, but if it means better and more debuggable errors, would be an improvement.
It's also possible that I'm wrong on this, but we can see.
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} | 3 | 2013-08-01T00:25:40Z | 2013-10-18T00:21:35Z | 2013-09-28T17:03:06Z | CONTRIBUTOR | null | Currently `merge` is a function in the pandas namespace, and it is also available as a DataFrame instance method, with the calling DataFrame being implicitly considered the left object in the join.
However, the related DataFrame.join method, which uses merge internally for the index-on-index and index-on-column(s) joins, but joins on indexes by default rather than trying to join on common columns is NOT in the pandas namespace.
Adding join to the pandas name space will avoid having to write:
```
if len(values) == 0:
joined = None
elif len(values) == 1:
joined = values[0]
else:
joined = values[0].join(values[1:])
```
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} | 6 | 2013-08-01T01:46:48Z | 2013-08-01T18:52:50Z | 2013-08-01T18:52:50Z | CONTRIBUTOR | null | The following last lines from `_write_to_group()` from io/pytables.py show that kwargs are available but are not provided to create_index(). This prevents the user from creating a `kind='full'` index at `append` time:
``` python
s.write(obj = value, append=append, complib=complib, **kwargs)
if s.is_table and index:
s.create_index(columns = index)
```
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} | 4 | 2013-08-01T01:52:06Z | 2013-08-04T06:53:01Z | 2013-08-01T18:52:39Z | CONTRIBUTOR | null | This should enable the creation of a 'full' index at append time.
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} | 6 | 2013-08-01T09:35:42Z | 2017-07-09T02:30:15Z | 2017-07-09T02:30:02Z | CONTRIBUTOR | null | Has the json module always been here (I thought it was only np and datetime)?
```
pd.json
```
Should it be? confusingly I can't actually see where it's coming from...
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} | 22 | 2013-08-01T09:59:04Z | 2013-09-20T23:15:40Z | 2013-09-20T17:25:43Z | NONE | null | Python 2.7.5, Windows 7 64-bit
> .........................F.........SSS.....SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
> SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.SSS.................S............
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> .......................SSSS......................S..............................
> ..........................SSSSSSSSSS...................SS.......................
> ................................................................................
> ...........................................................................S....
> ...................SSSSSSS.........................................S............
> ..................................S.............................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ......................can't invoke "event" command: application has been destro
> yed
> while executing
> "event generate $w <<ThemeChanged>>"
> (procedure "ttk::ThemeChanged" line 6)
> invoked from within
> "ttk::ThemeChanged"
> ................................................................................
> ................................................................................
> ................................................................................
> ............S...................................................................
> ................................................................................
> .S..............................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> .............................................S..................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> ..................................S.............................................
> ............................................S...................................
> ................................................................................
> ...........................................S..................S...S............S
> SS.........S............SS............SS.....SSSS...............................
> ................................................................................
> ................................................................................
> ................................................................................
> ................................................................................
> # ..........................................
> ## FAIL: Throws an exception when DataReader can't get a 200 response from
>
> Traceback (most recent call last):
> File "C:\Python27\lib\site-packages\pandas\util\testing.py", line 765, in netw
> ork_wrapper
> return t(_args, *_kwargs)
> File "C:\Python27\lib\site-packages\pandas\io\tests\test_data.py", line 370, i
> n test_fred
> 15984.1)
> AssertionError: DATE
> 2013-01-01 16535.3
> Name: GDP, dtype: float64 != 15984.1
> ---
>
> Ran 3661 tests in 179.605s
>
> FAILED (SKIP=137, failures=1)
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} | 4 | 2013-08-01T10:05:20Z | 2020-05-03T19:53:58Z | 2020-05-03T19:53:58Z | CONTRIBUTOR | null | Some of the string methods return a Series of lists (e.g. findall) which isn't that convenient for doing more analysis:
```
df = pd.DataFrame([['@a @b'], ['@a'], ['@c']], columns=['tweets'])
at_mentions = df.tweets.str.findall('@[a-zA-Z0-9_]+')
In [3]: at_mentions
Out[3]:
0 [@a, @b]
1 [@a]
2 [@c]
Name: tweets, dtype: object
```
I think it would be nice to be able to stack the results:
```
In [4]: at_mentions.apply(pd.Series).stack()
Out[4]:
tweets
1 0 @a
1 @b
2 0 @a
3 0 @c
dtype: object
```
_see this [SO answer](http://stackoverflow.com/a/17989510/1240268)_
Could just do it directly like that, or perhaps make more efficient. Thoughts?
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} | 21 | 2013-08-01T10:14:54Z | 2013-08-19T20:29:23Z | 2013-08-02T15:26:32Z | NONE | null | ```
ols = pd.ols(y=portfolio_weights, x=alphas, intercept=intercept)
```
> ---
>
> ImportError Traceback (most recent call last)
> <ipython-input-53-2a147656afb3> in <module>()
> 1 # Regress weights on alphas
> ----> 2 ols = pd.ols(y=portfolio_weights, x=alphas, intercept=intercept)
> 3 #ols = pd.ols(y=portfolio_weights, x=alphas, intercept=intercept, weights=weights)
> 4 print(ols.summary)
>
> C:\Python27\lib\site-packages\pandas\stats\interface.pyc in ols(**kwargs)
> 133 klass = MovingOLS
> 134
> --> 135 return klass(**kwargs)
>
> C:\Python27\lib\site-packages\pandas\stats\ols.pyc in **init**(self, y, x, intercept, weights, nw_lags, nw_overlap)
> 51 import statsmodels.api as sm
> 52 except ImportError:
> ---> 53 import scikits.statsmodels.api as sm
> 54
> 55 self._x_orig = x
>
> ImportError: No module named scikits.statsmodels.api
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} | 9 | 2013-08-01T13:56:39Z | 2014-07-16T08:21:21Z | 2013-08-01T14:31:05Z | CONTRIBUTOR | null | closes #4397
a start at least
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https://api.github.com/repos/pandas-dev/pandas/issues/4431 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4431/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4431/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4431/events | https://github.com/pandas-dev/pandas/issues/4431 | 17,516,077 | MDU6SXNzdWUxNzUxNjA3Nw== | 4,431 | date_range not defined error | {
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} | [] | closed | false | null | [] | null | 5 | 2013-08-01T16:14:05Z | 2013-08-01T16:48:41Z | 2013-08-01T16:48:41Z | NONE | null | http://pandas.pydata.org/pandas-docs/stable/10min.html
Setting
```
In [63]: s1 = pd.Series([1,2,3,4,5,6],index=date_range('20130102',periods=6))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-63-bfe1d6561bb0> in <module>()
----> 1 s1 = pd.Series([1,2,3,4,5,6],index=date_range('20130102',periods=6))
NameError: name 'date_range' is not defined
```
Python - pandas newbie, any help kindly appreciated.
I assume i can't move on in the 10 min tutorial if i can't get pass this error.
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https://api.github.com/repos/pandas-dev/pandas/issues/4432 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4432/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4432/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4432/events | https://github.com/pandas-dev/pandas/pull/4432 | 17,523,601 | MDExOlB1bGxSZXF1ZXN0NzMzMTQyNQ== | 4,432 | BUG: Fixes issue #3334: brittle margin in pivot_table. | {
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} | [] | closed | false | null | [] | null | 0 | 2013-08-01T18:41:53Z | 2014-06-23T03:12:36Z | 2013-08-01T18:47:28Z | CONTRIBUTOR | null | closes #3334 Brittle pivot margins
Issue was that pivot tables that use all columns of the original DataFrame in rows and cols failed on the margin computation. This is a special case for margins: one should use the index itself as the value.
```
>> df = DataFrame({'Response' : ['Y', 'N' ,'N', 'Y', 'Y', 'N'],
'Type' : ['A', 'A', 'B', 'B', 'B', 'C']})
>> pivot_table(df, rows='Response',cols='Type',aggfunc=len,margins=True)
>> Type A B C All
>> Response
>> N 1 1 1 3
>> Y 1 2 NaN 3
>> All 2 3 1 6
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4433 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4433/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4433/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4433/events | https://github.com/pandas-dev/pandas/pull/4433 | 17,525,140 | MDExOlB1bGxSZXF1ZXN0NzMzMjI5OQ== | 4,433 | BUG: Fixes issue #3334: brittle margin computation in pivot_table | {
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} | [] | closed | false | null | [] | null | 0 | 2013-08-01T19:08:57Z | 2013-08-01T19:14:11Z | 2013-08-01T19:14:11Z | CONTRIBUTOR | null | Adds support for margin computation when all columns are used in rows and cols.
Issue was that pivot tables that use all columns of the original DataFrame in rows and cols failed on the margin computation. This is a special case for margins: one should use the index itself as the value.
```
>> df = DataFrame({'Response' : ['Y', 'N' ,'N', 'Y', 'Y', 'N'],
'Type' : ['A', 'A', 'B', 'B', 'B', 'C']})
>> pivot_table(df, rows='Response',cols='Type',aggfunc=len,margins=True)
>> Type A B C All
>> Response
>> N 1 1 1 3
>> Y 1 2 NaN 3
>> All 2 3 1 6
```
This is my first PR to Pandas, so apologies for anything out of the ordinary.
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} | 27 | 2013-08-01T20:21:41Z | 2013-10-09T20:30:19Z | 2013-10-09T20:30:19Z | CONTRIBUTOR | null | related #1892, #1479
Is there any interest in giving interpolate and resample (to higher frequency) some additional methods?
For example:
``` python
from scipy import interpolate
df = pd.DataFrame({'A': np.arange(10), 'B': np.exp(np.arange(10) + np.random.randn())})
xnew = np.arange(10) + .5
In [46]: df.interpolate(xnew, method='spline')
```
Could return something like
``` python
In [47]: pd.DataFrame(interpolate.spline(df.A, df.B, xnew, order=4), index=xnew)
Out[47]:
0
0.5 1.044413
1.5 0.798392
2.5 3.341909
3.5 8.000314
4.5 22.822819
5.5 60.957659
6.5 166.844351
7.5 451.760621
8.5 1235.969910
9.5 0.000000 # falls outside the original range so interpolate.spline sets it to 0.
```
I have never used the DataFrame's interpolate, but a quick glance says that something like the above wouldn't be backwards compatible with the current calling convention. Maybe a different name? This may be confusing two issues: interpolating over missing values and interpolating / predicting non-existent values. Or are they similar enought that they can be treated the same. I would think so.
These are just some quick thoughts before I forget. I haven't spent much time thinking a design through yet. I'd be happy to work on this in a month or so.
Also does this fall in the realm of statsmodels?
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} | 0 | 2013-08-02T00:23:37Z | 2014-05-08T23:10:20Z | 2013-09-29T19:20:49Z | CONTRIBUTOR | null | This is and API change in numpy >= 1.7, but need to work around. Numpy now
looks at the structure for a p`rod` method (if the function is not operating on a numpy array), if it has one, then it passes the arguments
`axis,dtype,out`, currently these types of methods only aceept `axis`, need to accept `dtype,out` as dummy arguments for compat
affects `mean,sum,prod,std`...prob others
```
In [1]: df = DataFrame(randn(10,2))
In [2]: np.prod(df)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-99feb18ea783> in <module>()
----> 1 np.prod(df)
/usr/local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc in prod(a, axis, dtype, out, keepdims)
2111 return _methods._prod(a, axis=axis, dtype=dtype,
2112 out=out, keepdims=keepdims)
-> 2113 return prod(axis=axis, dtype=dtype, out=out)
2114 else:
2115 return _methods._prod(a, axis=axis, dtype=dtype,
TypeError: prod() got an unexpected keyword argument 'dtype'
In [3]: np.prod(df.values)
Out[3]: -2.2566177751865827e-07
In [4]: np.prod(df.values,axis=1)
Out[4]:
array([-0.00912739, 0.00569453, 0.58508784, 0.87462665, -0.32556754,
0.12015118, 1.01860104, -0.15380337, -0.54518287, -2.5393832 ])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4436 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4436/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4436/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4436/events | https://github.com/pandas-dev/pandas/pull/4436 | 17,542,550 | MDExOlB1bGxSZXF1ZXN0NzM0MjIwNw== | 4,436 | DOC: Fix typo | {
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} | 2 | 2013-08-02T03:03:21Z | 2014-07-16T08:21:25Z | 2013-08-02T03:43:08Z | CONTRIBUTOR | null | Fixes minor typo in groupby documentation
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} | 2 | 2013-08-02T04:07:23Z | 2014-06-13T02:10:37Z | 2013-08-03T03:29:56Z | MEMBER | null | closes #4405.
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closes #4438
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``` python
In [106]: res
Out[106]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 93705 entries, 13 to 93713
Columns: 3561 entries, 2) to 3561)
dtypes: float64(3561)
In [107]: res.loc[12]
Out[107]:
0
2) 3.362026
3) 1.574658
4) 1.905283
6) 2.069734
8) 4.777519
9) 4.316618
11) 2.231455
......
```
my res's index starts with 13, but the numeric index loc[12] has values.
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```
In [11]: s = pd.Series({(0, 0): 1, (0, 1): 2, (0, 3): 3, (1, 0): 1, (1, 2): 4, (3, 0): 5})
In [12]: s.loc[(1, 0):]
Out[12]:
(1, 0) 1
(1, 2) 4
(3, 0) 5
dtype: int64
In [13]: s.loc[(1, 1):]
KeyError: 'start bound [(1, 1)] is not the [index]'
```
Should this work? cc @jreback
_From [SO question](http://stackoverflow.com/questions/18015094/slice-pandas-series-with-elements-not-in-the-index)._
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} | 1 | 2013-08-02T13:06:41Z | 2013-08-26T23:43:59Z | 2013-08-26T23:43:59Z | CONTRIBUTOR | null | get_dummies seems to get caught out by NaNs
```
In [11]: s1 = pd.Series(['a', 'a', np.nan, 'c', 'c', 'c'])
In [12]: s1
Out[12]:
0 a
1 a
2 NaN
3 c
4 c
5 c
dtype: object
In [13]: pd.get_dummies(s1)
Out[13]:
a c
0 1 0
1 1 0
2 0 1
3 0 1
4 0 1
5 0 1
```
A rogue c has been used as the NaN value, I think expected is:
```
In [14]: pd.get_dummies(s1[s1.notnull()])
Out[14]:
a c
0 1 0
1 1 0
3 0 1
4 0 1
5 0 1
```
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} | 15 | 2013-08-02T14:26:57Z | 2013-10-13T23:37:53Z | 2013-10-13T23:37:53Z | NONE | null | see also #4527
When trying to filter a grouped dataframe with more than 2 columns raises an ValueError:
``` python
In [90]: dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc'), 'C': np.arange(8)})
In [91]: dff.groupby('B').filter(lambda x: len(x) > 2)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-91-89d79df28299> in <module>()
----> 1 dff.groupby('B').filter(lambda x: len(x) > 2)
C:\Anaconda\lib\site-packages\pandas\core\groupby.pyc in filter(self, func, dropna, *args, **kwargs)
2092 res = path(group)
2093
-> 2094 if res:
2095 indexers.append(self.obj.index.get_indexer(group.index))
2096
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [93]: pd.__version__
Out[93]: '0.12.0'
```
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} | 11 | 2013-08-02T15:17:00Z | 2016-10-12T23:04:04Z | 2013-08-02T18:43:57Z | NONE | null | I am up to reshaping and got the below error.
## In [127]: tuples = lzip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])
NameError Traceback (most recent call last)
<ipython-input-127-e86111d4f47b> in <module>()
----> 1 tuples = lzip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])
NameError: name 'lzip' is not defined
Any help kindly appreciated.
TIA
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} | [] | closed | false | null | [] | null | 11 | 2013-08-02T18:58:56Z | 2013-08-02T23:21:23Z | 2013-08-02T23:21:23Z | NONE | null | 10 min to pandas, p to Plotting and got the below
In [187]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D'])
In [188]: df = df.cumsum()
In [189]: plt.figure(); df.plot(); plt.legend(loc='best')---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-189-7376f1bcd0b9> in <module>()
----> 1 plt.figure(); df.plot(); plt.legend(loc='best')
NameError: name 'plt' is not defined
In [190]:
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} | [] | closed | false | null | [] | null | 4 | 2013-08-02T21:04:09Z | 2014-06-29T23:18:48Z | 2013-08-02T23:17:27Z | CONTRIBUTOR | null | Internally, definitely want to use `lzip` and `lrange`, but for clarity I think it's better to just use the written out versions of them, especially since they aren't really key components of pandas. (and generally would not expect someone working through an example to care about compatibility issues).
This only changes docs.
cc @cpcloud
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} | [] | closed | false | null | [] | null | 16 | 2013-08-02T21:25:27Z | 2014-07-16T08:21:34Z | 2013-08-02T23:21:57Z | CONTRIBUTOR | null | A few things
1. Make with-statement-capable `assertRaises` and `assertRaisesRegexp` (which I want to use later in a cleanup of some of the parser tests).
2. Earlier introduction of `assert_attr_equal` changed it so that, if one object had an attribute that was `None` and the other did not have that attribute at all, it would pass. This is now fixed.
3. combines `assert_panel_equal` and `assert_panel4d_equal`, because they are both doing the same thing. Now it's just `assert_panelnd_equal` with specific assert functions for each.
4. Switched unnecessary `check_index_freq` check in `pandas.io.tests.test_pytables:TestHDFStore.test_index_types` to be `check_series_type`, (because none of the tests ever produce objects with `freqstr`)
5. Removed `check_index_freq` argument and corresponding code from `assert_series_equal` since it's not being actively used anywhere in the test suite
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} | 9 | 2013-08-02T23:12:25Z | 2018-06-28T02:28:14Z | 2018-06-28T02:28:14Z | NONE | null | I believe the problem here is that setup.py is trying to install numpy without using the proxy server passed to pip.
```
pip-2.7 install --proxy=http://sampleproxy.com:3128 pandas
Downloading/unpacking pandas
Downloading pandas-0.12.0.zip (3.4MB): 3.4MB downloaded
Running setup.py egg_info for package pandas
Download error on https://pypi.python.org/simple/numpy/: [Errno 110] Connection timed out -- Some packages may not be found!
Couldn't find index page for 'numpy' (maybe misspelled?)
```
Installing numpy using `pip --proxy=proxy.com:3128 install numpy` and then install pandas as above worked fine.
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} | 1 | 2013-08-02T23:14:13Z | 2014-01-29T04:05:13Z | 2014-01-29T04:05:13Z | MEMBER | null | had this thought while looking at #3606
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} | 22 | 2013-08-02T23:16:39Z | 2021-04-11T02:56:19Z | null | CONTRIBUTOR | null | renamed `carray` package: https://github.com/Blosc/bcolz
Soliciting any comments on this proposal to create a columnar access table in `HDFStore`.
This is actually very straightforward to do.
need a new kw argument to describe the type of format for
storage: ``format='s|t|c'` (also allows expansion in the future to other formats)
- `s` is the `Storer` format (e.g. `store['df'] = value`), implied currently
with `put``
- `t` is the `Table` format (e.g. `store.append('df',value)`, created
with `table=True` when using `put` or using `append`
- `c` is a `CTable` format (new), which is a column oriented table
so will essentially deprecate `append=,table=` keywords (or just translate them)
to a `format=` kw.
```
df.to_hdf('test.h5','df',format='c')
```
Will have a master node which holds the structure.
Will store a format with a single column from a `DataFrame` in a sub-node of the
master.
advantages:
- index(s) are kept in their own columns (this is true with `Table` now)
- allows easy delete/add of columns (somewhat tricky in the `Table` format)
- allows appends (interesting twist is that have to keep the indices in sync)
- selection is straightforward as everything is indexed the same
- selecting a small number of columns relative to the total should be faster than an equivalent `Table`
- API will be the same as current. This is essentially an extension of the `append_as_multiple / select_as_multiple` multi-table accssors.
- can be included/coexist alongside existing `Table/Storer`s
disadvantages:
- selecting lots of columns will be somewhat slower that an equivalent `Table`
- requires syncing of all the indices (the coordinates of all rows)
- delete operations will be somewhat slower than an equivalent `Table`
There are actually 2 different formats that could be used here, I propose just the single-file for now. However, The sub-nodes could be spread out in a directory and stored as separate files. This allows concurrent access with some concurrent reads allowed (this is pretty tricky, so hold off on this for now).
This `CTable` format will use the existing `PyTables` infrastructure under the hood; it is possible to use the `ctable` module however http://carray.pytables.org/docs/manual/ (this is basically what BLAZE uses under the hood for its storage backend)
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} | 0 | 2013-08-03T01:02:40Z | 2013-08-03T20:01:22Z | 2013-08-03T20:01:22Z | MEMBER | null | might be an edge case as i'm not sure how useful storing byte strings in `Series` objects is, nonetheless they are repring like `tuple` objects in Python 3
```
In [37]: import string
In [38]: d = string.digits * 10
In [39]: s = Series([d, tm.rands(10), tm.rands(10000)])
In [40]: s
Out[40]:
0 0123456789012345678901234567890123456789012345...
1 c0HYcb0BlY
2 IghbSU964QRgNimc0jZn2etcTFhRgHnHQepH1dwAqVZLar...
dtype: object
In [41]: b = lambda x, encoding='utf8': bytes(x, encoding=encoding)
In [42]: s.map(b)
Out[42]:
0 (48, 49, 50, 51, 52, 53, 54, 55, 56, 57, ...)
1 (99, 48, 72, 89, 99, 98, 48, 66, 108, 89)
2 (73, 103, 104, 98, 83, 85, 57, 54, 52, 81, ...)
dtype: object
```
PR coming soon with a couple of other output formatting fixes as well regarding py3 compat
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} | 3 | 2013-08-03T01:21:45Z | 2014-06-18T13:51:07Z | 2013-08-03T20:01:22Z | MEMBER | null | closes #4455.
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https://api.github.com/repos/pandas-dev/pandas/issues/4457 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4457/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4457/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4457/events | https://github.com/pandas-dev/pandas/pull/4457 | 17,597,762 | MDExOlB1bGxSZXF1ZXN0NzM3MDc4OQ== | 4,457 | PERF: enhance HDFStore Table writing performance | {
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} | 2 | 2013-08-03T17:38:27Z | 2014-07-16T08:21:39Z | 2013-08-05T12:06:32Z | CONTRIBUTOR | null | Took out all of the cython code, turns out you can assign directly to numpy recarrays per
column if you put in the correct shape yourself
```
Invoked with :
--ncalls: 5
--repeats: 5
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
write_store_table_mixed | 45.6208 | 121.5352 | 0.3754 |
write_store_table_panel | 59.4140 | 99.5456 | 0.5969 |
write_store_table | 40.2264 | 65.8978 | 0.6104 |
write_store_table_dc | 143.3910 | 174.9424 | 0.8196 |
read_store_table_wide | 16.8930 | 17.5310 | 0.9636 |
query_store_table_wide | 9.6692 | 9.7564 | 0.9911 |
write_store_table_wide | 117.8906 | 118.8232 | 0.9922 |
read_store_table_mixed | 4.7604 | 4.7478 | 1.0026 |
query_store_table | 4.2646 | 4.2320 | 1.0077 |
read_store_table | 2.3678 | 2.3178 | 1.0216 |
read_store_table_panel | 20.9388 | 20.3526 | 1.0288 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [d11041d] : PERF: enhance HDFStore Table writing performance
Base [aca1a42] : Merge pull request #4437 from cpcloud/fix-astype-calls
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4458 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4458/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4458/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4458/events | https://github.com/pandas-dev/pandas/pull/4458 | 17,610,488 | MDExOlB1bGxSZXF1ZXN0NzM3NTI1Ng== | 4,458 | Get dummies | {
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} | 14 | 2013-08-04T17:32:01Z | 2014-06-17T22:55:05Z | 2013-08-26T23:43:59Z | CONTRIBUTOR | null | fixes #4446, #4444
Added new functionality dummy_na (thoughts?). it's slightly different to a possible dropna argument, which I haven't included (which can be achieved using `pd.get_dummies(s.dropna())`.
Example:
```
In [3]: s = ['a', 'b', np.nan]
In [4]: pd.get_dummies(s)
Out[4]:
a b
0 1 0
1 0 1
2 0 0
In [5]: pd.get_dummies(s, dummy_na=True)
Out[5]:
a b NaN
0 1 0 0
1 0 1 0
2 0 0 1
In [6]: pd.get_dummies(pd.Series(s).dropna()) # different
Out[6]:
a b
0 1 0
1 0 1
```
Note: atm there is a (strange) test Failure with above example, not quite sure what's going on:
```
res_na = get_dummies(s, dummy_na=True)
exp_na = DataFrame({nan: {0: 0.0, 1: 0.0, 2: 1.0},
'a': {0: 1.0, 1: 0.0, 2: 0.0},
'b': {0: 0.0, 1: 1.0, 2: 0.0}}).iloc[:, [1, 2, 0]] # need to reorder cols
assert_frame_equal(res_na, exp_na)
-> assert(left.columns.equals(right.columns))
(Pdb) left
a b NaN
0 1 0 0
1 0 1 0
2 0 0 1
(Pdb) right
a b NaN
0 1 0 0
1 0 1 0
2 0 0 1
```
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} | 6 | 2013-08-04T19:10:44Z | 2014-06-26T11:48:10Z | 2013-08-05T01:44:08Z | CONTRIBUTOR | null | The change is minor but so easy I thought I'd submit it: If you switch the list comprehensions to generators, it sidesteps leaking the temporary variables into the class variable scope.
This will be microscopically slower, but they are just class variables so it really doesn't matter.
fixes #3440
https://travis-ci.org/danbirken/pandas/builds/9836459
```
import numpy as np
from pandas import DataFrame, Panel
df0 = DataFrame(np.zeros([3,4]))
df1 = DataFrame(np.ones([3,4]))
p = Panel({'a':df0, 'b':df1})
In [1]: p.a
Out[1]:
0 1 2 3
0 0 0 0 0
1 0 0 0 0
2 0 0 0 0
In [2]: p.b
Out[2]:
0 1 2 3
0 1 1 1 1
1 1 1 1 1
2 1 1 1 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4460 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4460/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4460/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4460/events | https://github.com/pandas-dev/pandas/pull/4460 | 17,616,495 | MDExOlB1bGxSZXF1ZXN0NzM3NzUzOQ== | 4,460 | BUG: values array for non-unique, interleaved cols incorrect | {
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} | 5 | 2013-08-05T02:06:35Z | 2014-06-20T16:03:18Z | 2013-08-05T11:41:19Z | CONTRIBUTOR | null | I came across this problem when I was doing some work for #4362. When a frame with mixed dtypes including Timestamps has dupe column labels the values array returned is different, as illustrated below (master branch):
``` python
In [3]: df = pd.DataFrame([[pd.Timestamp('20130101'),3.5],[pd.Timestamp('20130102'),4.5]], columns=['x', 'x'], index=[1,2])
In [4]: df
Out[4]:
x x
1 2013-01-01 00:00:00 3.5
2 2013-01-02 00:00:00 4.5
In [5]: df.values
Out[5]:
array([[1356998400000000000L, 3.5],
[1357084800000000000L, 4.5]], dtype=object)
In [6]: df = pd.DataFrame([[pd.Timestamp('20130101'),3.5],[pd.Timestamp('20130102'),4.5]], columns=['x', 'y'], index=[1,2])
In [7]: df
Out[7]:
x y
1 2013-01-01 00:00:00 3.5
2 2013-01-02 00:00:00 4.5
In [8]: df.values
Out[8]:
array([[datetime.datetime(2013, 1, 1, 0, 0), 3.5],
[datetime.datetime(2013, 1, 2, 0, 0), 4.5]], dtype=object)
```
The included changes should fix the problem. This is my first time messing about in pandas internals so any feedback is appreciated!
Possibly related to #4377
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} | 32 | 2013-08-05T09:23:07Z | 2017-08-24T09:53:17Z | 2015-01-18T21:39:22Z | CONTRIBUTOR | null | ## Fix Issue #4160
While renamed a `MultiIndex`, the `mapper` walk through every tuple in the MultiIndex and every element in a tuple. So the mapper { (index1, index2): (new_index1, new_index2) } didn't work for the MultiIndex case.
This pull request is to modify it to walk through every tuple in a MultiIndex and use the mapper to map each tuple.
Also, the test case has been added.
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} | 11 | 2013-08-05T13:48:34Z | 2013-08-16T19:28:19Z | 2013-08-16T19:28:19Z | CONTRIBUTOR | null | All of the following current fail because of in-place dtype conversions
```
In [2]: s = Series([1,2,3])
In [3]: s.iloc[0] = np.nan
ValueError: cannot convert float NaN to integer
In [4]: s.loc[0] = np.nan
ValueError: cannot convert float NaN to integer
In [5]: s[0] = np.nan
TypeError: 'int' object is not iterable
```
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} | 37 | 2013-08-05T15:04:16Z | 2021-06-06T00:01:38Z | null | NONE | null | xref #9133, maybe allow a dict of dtypes to be passed as well
I trying to use the dtype argument in the DataFrame constructor to set the types of several columns, and I'm getting incorrect types. Everything works well, however, when the dtypes come from the recarray itself.
```
In [61]:
data = [(1,1.2), (2,2.3)]
dtype = [('a','i4'),('b','f4')]
a = np.array(data, dtype=dtype)
pd.DataFrame(a).dtypes
Out [61]:
a int32
b float32
dtype: object
```
But if I use the dtype constructor argument, I get incorrect types:
```
In [65]:
pd.DataFrame(data, dtype=dtype).dtypes
Out [65]:
0 object
1 object
dtype: object
```
The astype() member function doesn't work either:
```
In [75]:
pd.DataFrame(data).astype(dtype)
Truncated Traceback (Use C-c C-x to view full TB):
c:\Anaconda\lib\site-packages\pandas\core\common.pyc in take_nd(arr, indexer, axis, out, fill_value, mask_info, allow_fill)
491 indexer = _ensure_int64(indexer)
492 if not allow_fill:
--> 493 dtype, fill_value = arr.dtype, arr.dtype.type()
494 mask_info = None, False
495 else:
TypeError: function takes exactly 1 argument (0 given)
```
I'm using Pandas 0.11.0 from Anaconda.
Thanks in advance.
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} | 1 | 2013-08-05T17:59:02Z | 2014-07-25T22:43:48Z | 2014-07-25T22:43:48Z | CONTRIBUTOR | null | {
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} | 6 | 2013-08-05T19:02:01Z | 2014-09-19T17:56:24Z | 2014-09-19T17:56:24Z | NONE | null | Currently pandas preserves empty lines when reading CSV files. I think the default should be to ignore empty lines, with a possible optional parameter to retain them.
Implementing this in the C and Python parsers should be an easy extension of my work on #2685 , but I wanted some feedback before I do so.
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} | 3 | 2013-08-05T19:04:57Z | 2013-08-05T23:05:05Z | 2013-08-05T23:05:05Z | CONTRIBUTOR | null | From the PyTables ML
Select where month=5 from the index
(this could be done internally maybe)
big issues is that Coordinates is sort of 'private' here,
make where take a boolean array / coordinates
```
# create a frame
In [45]: df = DataFrame(randn(1000,2),index=date_range('20000101',periods=1000))
In [53]: df
Out[53]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1000 entries, 2000-01-01 00:00:00 to 2002-09-26 00:00:00
Freq: D
Data columns (total 2 columns):
0 1000 non-null values
1 1000 non-null values
dtypes: float64(2)
# store it as a table
In [46]: store = pd.HDFStore('test.h5',mode='w')
In [47]: store.append('df',df)
# select out the index (a datetimeindex in this case)
In [48]: c = store.select_column('df','index')
# get the coordinates of matching index
In [49]: coords = c[pd.DatetimeIndex(c).month==5]
# select those rows
In [51]: from pandas.io.pytables import Coordinates
In [50]: store.select('df',where=Coordinates(coords.index,None,None))
Out[50]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 93 entries, 2000-05-01 00:00:00 to 2002-05-31 00:00:00
Data columns (total 2 columns):
0 93 non-null values
1 93 non-null values
dtypes: float64(2)
```
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} | 4 | 2013-08-05T19:48:10Z | 2013-10-17T22:33:19Z | 2013-10-02T21:03:35Z | CONTRIBUTOR | null | related #4438 #4679
For example see the "INCOME_STATEMENT" sheet on http://shareholder.api.edgar-online.com/efx_dll/edgarpro.dll?FetchFilingXLS1?sessionid=F7D_689jk-BEFvN&ID=9277772
There are four columns in the desired DataFrame:
```
1 2 3 4
Thirteen weeks ended Twenty-six weeks ended
March 30, March 24, March 30, March 24,
```
Currently the `ExcelFile` parser only takes an int for the header parameter, whereas the html parser takes "The row (or rows for a MultiIndex) to use to make the columns headers."
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} | 42 | 2013-08-05T20:12:21Z | 2014-01-24T20:14:06Z | 2014-01-24T20:14:06Z | CONTRIBUTOR | null | Currently `pandas.io.html.read_html` returns a list of DataFrames. This offers no context as to where in the source HTML the table was found. For example, a user might be interested in the title or caption of the table.
For example in an SEC 10-Q filing (see for example: http://apps.shareholder.com/sec/viewerContent.aspx?companyid=GMCR&docid=9277772#A13-6685_110Q_HTM_UNAUDITEDCONSOLIDATEDSTATEMENTSOF_223103), there are many tables. The user might be interested in one or more of them. They are not returned in a "standard" order such that without the title provided in the HTML document, finding the desired table in the return list is difficult.
My suggestion is to offer some way of linking the returned table to the context in which it was found.
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} | 0 | 2013-08-05T21:53:53Z | 2014-06-28T10:41:55Z | 2013-08-05T23:05:05Z | CONTRIBUTOR | null | closes #4467
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} | 18 | 2013-08-05T22:58:49Z | 2016-12-20T13:49:05Z | 2016-12-20T13:49:05Z | MEMBER | null | xref #11440 for addtl tests
Observed in the wild. cc @blais
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} | 24 | 2013-08-06T03:21:47Z | 2014-01-21T22:11:39Z | 2014-01-21T22:11:39Z | CONTRIBUTOR | null | gory details
```
Starting program: /usr/bin/python2.7-dbg /usr/bin/nosetests -s -v -a \!network pandas/tests/test_frame.py:TestDataFrame.test_from_records_sequencelike
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/arm-linux-gnueabi/libthread_db.so.1".
Program received signal SIGILL, Illegal instruction.
Program received signal SIGILL, Illegal instruction.
test_from_records_sequencelike (pandas.tests.test_frame.TestDataFrame) ...
Program received signal SIGSEGV, Segmentation fault.
0xb59b5478 in __pyx_pf_6pandas_3lib_122is_string_array (__pyx_self=0x0, __pyx_v_values=0x1727668) at pandas/lib.c:32348
32348 __Pyx_INCREF((PyObject*)__pyx_t_3);
(gdb) bt
#0 0xb59b5478 in __pyx_pf_6pandas_3lib_122is_string_array (__pyx_self=0x0, __pyx_v_values=0x1727668) at pandas/lib.c:32348
#1 0xb59b4248 in __pyx_pw_6pandas_3lib_123is_string_array (__pyx_self=0x0, __pyx_v_values=<numpy.ndarray at remote 0x1727668>) at pandas/lib.c:32136
#2 0x0009e498 in PyCFunction_Call (func=<built-in function is_string_array>, arg=(<numpy.ndarray at remote 0x1727668>,), kw=0x0) at ../Objects/methodobject.c:101
#3 0x0002e178 in PyObject_Call (func=<built-in function is_string_array>, arg=(<numpy.ndarray at remote 0x1727668>,), kw=0x0) at ../Objects/abstract.c:2529
#4 0xb59abe48 in __pyx_pf_6pandas_3lib_108infer_dtype (__pyx_self=0x0, __pyx_v__values=<numpy.ndarray at remote 0x1727668>) at pandas/lib.c:30465
#5 0xb59a81d8 in __pyx_pw_6pandas_3lib_109infer_dtype (__pyx_self=0x0, __pyx_v__values=<numpy.ndarray at remote 0x1727668>) at pandas/lib.c:29761
#6 0x00152aa4 in call_function (pp_stack=0xbeffaa74, oparg=1) at ../Python/ceval.c:4009
#7 0x0014d524 in PyEval_EvalFrameEx (
f=Frame 0x12bf850, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/common.py, line 1220, in _possibly_cast_to_datetime (value=<numpy.ndarray at remote 0x1727668>, dtype=None, coerce=False, v=<numpy.ndarray at remote 0x1727668>), throwflag=0) at ../Python/ceval.c:2666
```
python backtrace
```
(gdb) py-bt
#7 Frame 0x12bf850, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/common.py, line 1220, in _possibly_cast_to_datetime (value=<numpy.ndarray at remote 0x1727668>, dtype=None, coerce=False, v=<numpy.ndarray at remote 0x1727668>)
inferred_type = lib.infer_dtype(v)
#11 Frame 0x12a37b0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/series.py, line 3318, in _try_cast (arr=<numpy.ndarray at remote 0x1727668>, take_fast_path=True)
arr = com._possibly_cast_to_datetime(arr, dtype)
#15 Frame 0x115d810, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/series.py, line 3349, in _sanitize_array (data=<numpy.ndarray at remote 0x1727668>, index=<Int64Index(name=None) at remote 0x17289d8>, dtype=None, copy=False, raise_cast_failure=False, _try_cast=<function at remote 0x1728a30>, subarr=<numpy.ndarray at remote 0x1727668>)
subarr = _try_cast(data, True)
#19 Frame 0x17579c0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/frame.py, line 5936, in _homogenize (data=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>, <numpy.ndarray at remote 0x1727668>, <numpy.ndarray at remote 0x17271a8>, <numpy.ndarray at remote 0x17274e8>, <numpy.ndarray at remote 0x1727228>, <numpy.ndarray at remote 0x17270e8>], index=<Int64Index(name=None) at remote 0x17289d8>, dtype=None, _sanitize_array=<function at remote 0x10f1b90>, oindex=None, homogenized=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>], v=<numpy.ndarray at remote 0x1727668>)
raise_cast_failure=False)
#23 Frame 0x17571e0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/frame.py, line 5675, in _arrays_to_mgr (arrays=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>, <numpy.ndarray at remote 0x1727668>, <numpy.ndarray at remote 0x17271a8>, <numpy.ndarray at remote 0x17274e8>, <numpy.ndarray at remote 0x1727228>, <numpy.ndarray at remote 0x17270e8>], arr_names=<Index(name=None) at remote 0x17286c0>, index=<Int64Index(name=None) at remote 0x17289d8>, columns=<...>, dtype=None)
arrays = _homogenize(arrays, index, dtype)
#27 Frame 0x17ce8a0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/frame.py, line 1124, in from_records (cls=<type at remote 0x129f680>, data=<recarray at remote 0x165dc40>, index=None, exclude=set([]), columns=<Index(name=None) at remote 0x17286c0>, coerce_float=False, nrows=None, arr_columns=<...>, arrays=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>, <numpy.ndarray at remote 0x1727668>, <numpy.ndarray at remote 0x17271a8>, <numpy.ndarray at remote 0x17274e8>, <numpy.ndarray at remote 0x1727228>, <numpy.ndarray at remote 0x17270e8>], result_index=None)
columns)
```
and here what I think was original/generated code
```
(gdb) bt 1
#0 0xb59b5478 in __pyx_pf_6pandas_3lib_122is_string_array (__pyx_self=0x0, __pyx_v_values=0x1727668) at pandas/lib.c:32348
(More stack frames follow...)
(gdb) l 32335
32330 /* "pandas/src/inference.pyx":230
32331 *
32332 * for i in range(n):
32333 * if not PyString_Check(objbuf[i]): # <<<<<<<<<<<<<<
32334 * return False
32335 * return True
32336 */
32337 __pyx_t_13 = __pyx_v_i;
32338 __pyx_t_8 = -1;
32339 if (__pyx_t_13 < 0) {
(gdb)
32340 __pyx_t_13 += __pyx_pybuffernd_objbuf.diminfo[0].shape;
32341 if (unlikely(__pyx_t_13 < 0)) __pyx_t_8 = 0;
32342 } else if (unlikely(__pyx_t_13 >= __pyx_pybuffernd_objbuf.diminfo[0].shape)) __pyx_t_8 = 0;
32343 if (unlikely(__pyx_t_8 != -1)) {
32344 __Pyx_RaiseBufferIndexError(__pyx_t_8);
32345 {__pyx_filename = __pyx_f[1]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
32346 }
32347 __pyx_t_3 = (PyObject *) *__Pyx_BufPtrStrided1d(PyObject **, __pyx_pybuffernd_objbuf.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_objbuf.diminfo[0].strides);
32348 __Pyx_INCREF((PyObject*)__pyx_t_3);
32349 __pyx_t_7 = ((!(PyString_Check(__pyx_t_3) != 0)) != 0);
```
any obvious clues? ;)
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} | 3 | 2013-08-06T03:28:52Z | 2013-08-07T00:27:58Z | 2013-08-07T00:27:58Z | NONE | null | https://github.com/pydata/pandas/pull/4384
when run in HEAD mode, checking out the test_perf.py revision in git master
should run without issues no matter what version of pandas you have checked out
(at least as far back as 9.1, probably further).
Now that the script imports `pandas.compat`, it's broken for anything older
then a couple of weeks.
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} | 16 | 2013-08-06T03:40:33Z | 2014-06-18T16:01:24Z | 2014-01-19T05:05:35Z | CONTRIBUTOR | null | WIP for #4163. Not ready to merge yet
New test structure for sql, and working through sql/test_sql with abstract syntax (aim is to not be sql-platform specific anywhere).
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} | 1 | 2013-08-06T08:31:22Z | 2018-06-28T02:30:00Z | 2018-06-28T02:30:00Z | CONTRIBUTOR | null | There is an asymmetry in Pandas wrt. indexes vs. columns that is not present in the relational model. For example, for the purposes of merging one has to decide whether the column on which to merge is an index or not, despite the fact that indexing is incidental complexity for optimization. This leads to many of us having duplicate indexes and columns with the same name, which can cause errors and wastes memory for large data frames.
A possible fix would be to address the column lookup in all the current operations that require columns or indexes so that on NameError for columns the named indexes would be fetched instead. Or one may as well go all the way relational and make _any_ `df[my_col_or_named_index]` lookup fetch a named index if the column name is not found.
The latter behaviour is being implemented in the eval/query functionality by @jreback and @cpcloud in #4164, so it would also be 'least surprise' to use this policy across the board.
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} | 2 | 2013-08-06T11:03:33Z | 2013-08-07T00:25:47Z | 2013-08-07T00:25:47Z | NONE | null | looks like lmap is now called by some vbenches, which breaks
the tests on all older versions.
```
.....................................................groupby_transform died with:
name 'lmap' is not defined
Skipping...
groupby_transform died with:
name 'lmap' is not defined
Skipping...
```
#4384, #4474
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} | 20 | 2013-08-06T11:12:28Z | 2014-07-16T08:21:54Z | 2013-08-07T00:25:47Z | CONTRIBUTOR | null | Fixes #4477, #4474
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} | 6 | 2013-08-06T13:53:49Z | 2013-09-27T20:15:34Z | 2013-09-27T20:15:34Z | NONE | null | When parsing this CSV file (http://genes.mit.edu/burgelab/yarden/test.res), pandas gives the error `CParserError: Error tokenizing data`. Test case:
```
In [1]: import pandas
In [2]: data = pandas.read_table("./test.res", sep="\t")
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-2-6b1a2e105fb1> in <module>()
----> 1 data = pandas.read_table("./test.res", sep="\t")
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
397 buffer_lines=buffer_lines)
398
--> 399 return _read(filepath_or_buffer, kwds)
...
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in read(self, nrows)
952
953 try:
--> 954 data = self._reader.read(nrows)
955 except StopIteration:
956 if nrows is None:
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader.read (pandas/src/parser.c:5925)()
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader._read_low_memory (pandas/src/parser.c:6145)()
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader._read_rows (pandas/src/parser.c:6750)()
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader._tokenize_rows (pandas/src/parser.c:6634)()
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.raise_parser_error (pandas/src/parser.c:17055)()
CParserError: Error tokenizing data. C error: Expected 2073 fields in line 4, saw 2074
```
The pandas version was:
```
In [3]: pandas.__version__
Out[3]: '0.10.1'
```
Is this a known issue? Could I reformat the file somehow to make it work? Thanks. Best, --Yarden
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} | 7 | 2013-08-06T16:48:03Z | 2014-06-14T19:56:36Z | 2013-08-22T17:12:13Z | CONTRIBUTOR | null | closes #4484
Reversing an index and forward filling succeeds but produces misleading data:
``` python
>>> dr = pd.date_range('2013-08-01', periods=6, freq='B')
>>> df = pd.DataFrame(np.random.randn(6,1), index=dr, columns=list('A'))
>>> df['A'][3] = np.nan
>>> df.reindex(df.index[::-1], method='ffill')
A
2013-08-08 2.127302
2013-08-07 NaN
2013-08-06 NaN
2013-08-05 NaN
2013-08-02 NaN
2013-08-01 NaN
```
This change enforces "monotocity" (well actually weakly increasing) and causes a ValueError to be raised.
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} | 1 | 2013-08-06T17:55:25Z | 2013-09-15T12:00:28Z | 2013-09-15T12:00:28Z | CONTRIBUTOR | null | created by @prossahl
Reversing an index and forward filling succeeds but produces misleading data:
```
>>> dr = pd.date_range('2013-08-01', periods=6, freq='B')
>>> df = pd.DataFrame(np.random.randn(6,1), index=dr, columns=list('A'))
>>> df['A'][3] = np.nan
>>> df.reindex(df.index[::-1], method='ffill')
A
2013-08-08 2.127302
2013-08-07 NaN
2013-08-06 NaN
2013-08-05 NaN
2013-08-02 NaN
2013-08-01 NaN
```
This change enforces "monotocity" (well actually weakly increasing) and causes a ValueError to be raised.
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} | 24 | 2013-08-06T19:32:15Z | 2014-07-16T08:22:01Z | 2013-09-20T02:57:56Z | CONTRIBUTOR | null | Imagine num_runs is two, then runs will take on the values [0, 1] and runs <
num_uns will always be False. Hence any genuine Exception the code might
raise will be silently ignored and just printed.
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} | 16 | 2013-08-06T21:13:06Z | 2014-04-15T20:21:17Z | 2013-08-07T17:22:27Z | NONE | null | I have what appears to be a valid `Series` with a time series index as follows:
```
In [41]: inactive
Out[41]:
2010-06-10 20:43:07 37.2
2010-06-21 06:37:28 0.0
2009-07-20 06:53:38 0.0
2012-05-17 01:50:13 27.4
2009-07-27 21:09:15 0.0
2010-05-04 09:06:54 0.0
2010-05-06 03:38:54 32.5
2010-05-09 08:33:16 56.9
2010-05-26 18:58:42 0.0
2010-05-31 18:40:41 0.0
2010-06-13 08:46:17 0.0
2010-06-16 18:27:51 0.0
2010-06-19 11:59:48 0.0
2010-06-24 15:24:26 33.4
2010-06-28 10:54:27 0.0
...
2008-11-02 07:35:22 71.2
2008-11-05 17:33:39 97.6
2009-10-22 23:15:39 75.7
2009-10-27 20:55:07 67.5
2009-06-02 13:57:15 35.8
2009-06-06 14:15:41 25.9
2008-11-07 01:22:46 0.0
2009-10-12 17:08:20 100.0
2010-09-19 10:21:39 0.0
2010-09-24 15:46:50 0.0
2010-09-30 16:48:09 0.0
2010-10-08 21:43:10 0.0
2010-10-15 19:27:11 0.0
2010-10-28 19:10:42 0.0
2010-06-13 10:32:45 100.0
Name: pdgt10, Length: 13536, dtype: float64
```
Note also:
```
In [43]: inactive.isnull().sum()
Out[43]: 0
In [45]: inactive.index.size
Out[45]: 13536
In [46]: inactive.values.size
Out[46]: 13536
```
However, when I try calling `inactive.plot()` in order to get a time series plot, I receive a puzzling `AssertionError`:
```
In [40]: inactive.plot()
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-40-cdcfa4d871e5> in <module>()
----> 1 inactive.plot()
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
1730 secondary_y=secondary_y, **kwds)
1731
-> 1732 plot_obj.generate()
1733 plot_obj.draw()
1734
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.pyc in generate(self)
856 self._compute_plot_data()
857 self._setup_subplots()
--> 858 self._make_plot()
859 self._post_plot_logic()
860 self._adorn_subplots()
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.pyc in _make_plot(self)
1244 lines = []
1245 labels = []
-> 1246 x = self._get_xticks(convert_period=True)
1247
1248 plotf = self._get_plot_function()
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.pyc in _get_xticks(self, convert_period)
1034 x = index._mpl_repr()
1035 elif is_datetype:
-> 1036 self.data = self.data.reindex(index=index.order())
1037 x = self.data.index._mpl_repr()
1038 else:
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in reindex(self, index, method, level, fill_value, limit, copy, takeable)
2649
2650 # GH4246 (dispatch to a common method with frame to handle possibly duplicate index)
-> 2651 return self._reindex_with_indexers(new_index, indexer, copy=copy, fill_value=fill_value)
2652
2653 def _reindex_with_indexers(self, index, indexer, copy, fill_value):
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in _reindex_with_indexers(self, index, indexer, copy, fill_value)
2653 def _reindex_with_indexers(self, index, indexer, copy, fill_value):
2654 new_values = com.take_1d(self.values, indexer, fill_value=fill_value)
-> 2655 return Series(new_values, index=index, name=self.name)
2656
2657 def reindex_axis(self, labels, axis=0, **kwargs):
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in __new__(cls, data, index, dtype, name, copy)
491 else:
492 subarr = subarr.view(Series)
--> 493 subarr.index = index
494 subarr.name = name
495
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/lib.so in pandas.lib.SeriesIndex.__set__ (pandas/lib.c:29092)()
AssertionError: Index length did not match values
```
As I have shown above, my index length and my values are the same length.
Running a recent (<2 weeks) build from master on OS X 10.8.4 and Python 2.7.2.
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} | 6 | 2013-08-06T23:27:43Z | 2016-10-12T23:04:04Z | 2013-09-30T13:23:04Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/dev/io.html#clipboard
there are error msgs
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} | 14 | 2013-08-06T23:49:27Z | 2013-09-30T11:23:28Z | 2013-09-30T10:18:12Z | NONE | null | Here's a quick hack for generating the `widths` arg.
It's basic, but it works for me.
```
def detect_fwf_widths(rows,wsratio=0.5,nlines=100):
from collections import Counter # 2.6, is this in pd.compat?
# could do a p-value thing instead, but this works fine in practice
nlines = min(len(rows),nlines)
# columwise, get occurence counts, use threshold to prune candidates
# for "blank".
counts = [{k:v for k,v in d.iteritems() if v > nlines*wsratio}
for d in map(Counter,zip(*rows))]
# among candidates, find the most common and choose it as "blank"
# then extract only the cols where "blank" appears "frequently".
c=Counter()
for d in counts:
c.update(d)
delim=c.most_common(1)[0][0]
cols = [i for i,d in enumerate(counts) if delim in d]
# filter the col indexes to keep only the rightmost index of
# each contiguous sequence
width_list =[]
lidx=0
prev = cols[0]
for v in cols[1:]:
if v-prev != 1:
width_list.append(prev+1-lidx)
lidx=prev+1
prev = v
width_list.append(len(rows[0])-lidx)
return width_list
```
any takers to flesh this out into a PR with tests?
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} | 4 | 2013-08-07T01:05:29Z | 2016-09-29T22:44:20Z | 2016-09-29T22:35:08Z | MEMBER | null | Many operations in pandas trigger the population of a hash table from the underlying index values. This can be very costly in memory usage, especially for very long Series / DataFrames.
Example:
```
s = pd.Series(np.random.randn(10000000))
s[100]
```
Note that after indexing into `s`, the first time this happens a hash table `s.index._engine` is populated. So something larger than 80MB of space probably gets used up by this.
We can have a whole discussion about the hash table infra and how it could be better than it is now (I may spend some time on this myself soon). For the time being, one solution would be to have a weakref dictionary someplace (or something of that nature) that enables us to globally destroy all hash tables.
Separately, the hash table scheme can be modified to have much smaller memory footprint than it does now -- keys can be integers stored as `uint32_t` resulting in roughly 4 bytes per index value. Right now there are two arrays: one for hash keys, another for hash values.
motivated by http://stackoverflow.com/questions/18070520/pandas-memory-usage-when-reindexing
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} | 2 | 2013-08-07T02:12:03Z | 2014-06-23T05:29:07Z | 2013-08-07T17:22:27Z | MEMBER | null | closes #4486.
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} | 34 | 2013-08-07T02:32:17Z | 2016-10-12T23:04:04Z | 2014-02-27T17:48:47Z | NONE | null | I have two columns of a `DataFrame` that I am trying to plot using `scatter` in Matplotlib. Here are the two series:
```
mmsi
210234000 52.600000
211109000 62.250000
211200350 48.333333
211201390 52.887500
211203370 41.962500
211204500 32.233333
211205780 87.050000
211205790 42.500000
211207740 38.233333
211220290 53.233333
211262460 40.100000
211262630 31.716667
211327410 54.300000
211335760 43.175000
211378120 58.361290
...
636090986 62.812500
636091049 70.175000
636091052 45.600000
636091078 82.725000
636091137 59.075000
636091138 61.083333
636091195 75.030000
636091278 68.241667
636091394 57.700000
636091452 44.275000
636091501 63.480000
636091506 67.578571
636091545 44.500000
636091595 46.475000
636091800 61.850000
Name: pdgt10_early, Length: 235, dtype: float64
mmsi
210234000 True
211109000 True
211200350 True
211201390 True
211203370 True
211204500 True
211205780 True
211205790 True
211207740 True
211220290 True
211262460 True
211262630 True
211327410 True
211335760 True
211378120 True
...
636090986 True
636091049 True
636091052 True
636091078 True
636091137 True
636091138 True
636091195 True
636091278 True
636091394 True
636091452 True
636091501 True
636091506 True
636091545 True
636091595 True
636091800 True
Length: 235, dtype: bool
```
(Actually the latter is the result of a comparison operation on the column to see if values are greater or less than some threshold value)
Unfortunately, calling `scatter` on these two axes raises a `KeyError: 0` exception for some reason.
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-52-4b02ddd54f50> in <module>()
1 for sma in sma_list:
----> 2 plot_logistic('Cargo', sma, nova_traces[np.where(sma_list==sma)[0][0]].traces[1], season_a=-3)
<ipython-input-51-94c541930a55> in plot_logistic(ship, sma, trace, dataset, season_a, season_b, nlines)
14 print x
15 print y
---> 16 scatter(x, y)
17 title('{0} in {1}'.format(ship, sma))
18
/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/pyplot.pyc in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, hold, **kwargs)
3088 ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,
3089 vmin=vmin, vmax=vmax, alpha=alpha,
-> 3090 linewidths=linewidths, verts=verts, **kwargs)
3091 draw_if_interactive()
3092 finally:
/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/axes/_axes.pyc in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs)
3210 self.cla()
3211
-> 3212 self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs)
3213 x = self.convert_xunits(x)
3214 y = self.convert_yunits(y)
/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/axes/_base.pyc in _process_unit_info(self, xdata, ydata, kwargs)
1652 # we only need to update if there is nothing set yet.
1653 if not self.xaxis.have_units():
-> 1654 self.xaxis.update_units(xdata)
1655 #print '\tset from xdata', self.xaxis.units
1656
/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/axis.pyc in update_units(self, data)
1330 """
1331
-> 1332 converter = munits.registry.get_converter(data)
1333 if converter is None:
1334 return False
/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/units.pyc in get_converter(self, x)
135
136 if isinstance(x, np.ndarray) and x.size:
--> 137 converter = self.get_converter(x.ravel()[0])
138 return converter
139
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in __getitem__(self, key)
617 def __getitem__(self, key):
618 try:
--> 619 return self.index.get_value(self, key)
620 except InvalidIndexError:
621 pass
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc in get_value(self, series, key)
722 """
723 try:
--> 724 return self._engine.get_value(series, key)
725 except KeyError as e1:
726 if len(self) > 0 and self.inferred_type == 'integer':
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2646)()
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2461)()
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3198)()
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6422)()
/Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6366)()
KeyError: 0
```
This worked like a charm prior to updating pandas and matplotlib a week or so ago.
Running Python 2.7.2 on OS X 10.8.4.
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https://api.github.com/repos/pandas-dev/pandas/issues/4494 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4494/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4494/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4494/events | https://github.com/pandas-dev/pandas/pull/4494 | 17,727,474 | MDExOlB1bGxSZXF1ZXN0NzQzNTE3NA== | 4,494 | FIX: resample to single group with custom function | {
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} | 4 | 2013-08-07T02:46:53Z | 2014-06-13T23:41:52Z | 2013-09-25T12:31:36Z | CONTRIBUTOR | null | Using master code, when re-sampling to a frequency that would result in a single group the use of custom aggregation functions do not work as expected. The included changes fix this, and should fix #3849 too.
``` python
In [17]: import pandas as pd
In [18]: import numpy as np
In [19]: mysum = lambda x: x.sum()
In [20]: rng = pd.date_range('2000-1-1', '2000-1-10', freq='D')
In [21]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [22]: ts.resample('M', how='sum')
Out[22]:
2000-01-31 0.985236
Freq: M, dtype: float64
In [23]: ts.resample('M', how=mysum)
Out[23]:
2000-01-31 0
Freq: M, dtype: float64
```
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} | 8 | 2013-08-07T03:11:53Z | 2014-07-16T08:22:10Z | 2013-08-07T14:51:16Z | CONTRIBUTOR | null | Tox fails to build pandas without a specific version of pytz.
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} | 1 | 2013-08-07T11:25:00Z | 2013-08-23T13:26:54Z | 2013-08-23T13:26:54Z | CONTRIBUTOR | null | The following code produces "Out of bounds on buffer access (axis 0)" exception when accessing idx.hour:
---
import pandas as pd
idx = pd.date_range('26/3/2011', '27/3/2011', freq='1min')
idx = idx.tz_localize('UTC')
idx = idx.tz_convert('Europe/Moscow')
hour = idx.hour
---
Traceback (most recent call last):
File "test.py", line 7, in <module>
hour = idx.hour
File "C:\Python27\lib\site-packages\pandas\tseries\index.py", line 41, in f
values = self._local_timestamps()
File "C:\Python27\lib\site-packages\pandas\tseries\index.py", line 397, in _local_timestamps
return tslib.tz_convert(self.asi8, utc, self.tz)
File "tslib.pyx", line 1356, in pandas.tslib.tz_convert (pandas\tslib.c:21388)
IndexError: Out of bounds on buffer access (axis 0)
My research indicated there was a bug in the following piece of code in tslib.tz_convert(vals, tz1, tz2) in Pandas 0.12. There is no check that "(pos + 1) < len(trans)" when referencing trans[pos + 1] inside the for loop:
```
offset = deltas[pos]
for i in range(n):
v = utc_dates[i]
if v >= trans[pos + 1]:
pos += 1
offset = deltas[pos]
result[i] = v + offset
```
As a result in some circumstances this code raises "Out of bounds on buffer access (axis 0)" exception. For example, in Russia last day-light saving transition took place in somewhere in 26/3/2011-27/3/2011, and there were no transitions since then.
If ts_lib.tz_convert() is asked to convert time zones for interval that includes 26/3/2011-27/3/2011, it raises an exception while trying to access trans[pos + 1].
I have a working patch that solves this. Going to contribute it soon.
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} | 7 | 2013-08-07T11:32:59Z | 2014-07-16T08:22:12Z | 2013-08-23T13:26:18Z | CONTRIBUTOR | null | Bug-fix for Issue #4496 Bug: tslib.tz_convert(vals, tz1, tz2) may raise an IndexError exception.
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} | 21 | 2013-08-07T12:43:08Z | 2014-06-14T10:38:30Z | 2013-08-15T01:14:12Z | CONTRIBUTOR | null | This adds support at the C level for encoding datetime values in JSON. I added a new parameter `date_unit` which controls the precision of the encoding, options are seconds, milliseconds, microseconds and nanoseconds. It defaults to milliseconds which should fix #4362.
I also added support for NaTs and tried to detect when numpy arrays of datetime64 longs are passed.
Note that datetime decoding is still handled at the Python level but I added a date_unit param here too so timestamps can be converted correctly.
valgrind looks happy with the changes and all tests pass ok for me on Python 2.7 & 3.3
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} | 9 | 2013-08-07T13:10:33Z | 2014-07-02T21:24:28Z | 2013-08-22T10:16:05Z | CONTRIBUTOR | null | The information in the docs, in the parameter description and in the body of the docstring is conflicting. This should hopefully make them consistent amongst themselves (and with the code).
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https://api.github.com/repos/pandas-dev/pandas/issues/4500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4500/events | https://github.com/pandas-dev/pandas/issues/4500 | 17,750,715 | MDU6SXNzdWUxNzc1MDcxNQ== | 4,500 | Docs for lurking groupby methods | {
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} | 13 | 2013-08-07T14:23:42Z | 2014-04-23T22:02:39Z | 2014-04-23T21:36:55Z | MEMBER | null | see [this SO question](http://stackoverflow.com/questions/18104704/counting-unique-values-in-a-pandas-grouped-object)
related: http://stackoverflow.com/questions/21763660/api-ref-missing-dataframegroupby-objects
methods like `nunique` should be documented; i bet there are more like this that you can see in tab completion but that are valid method calls.
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