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} | 5 | "2013-03-10T08:47:39Z" | "2013-08-21T20:52:12Z" | "2013-08-21T20:52:12Z" | CONTRIBUTOR | null | ``` python
In [8]: import pandas as pd
In [9]: from StringIO import StringIO
In [10]: s = """# date, utc, jdate, orbit, sundist, sunlat, sunlon, sclk, sclat, sclon, scrad, scalt, el_cmd, az_cmd, af, orientlat, orientlon, c, det, vlookx, vlooky, vlookz, radiance, tb, clat, clon, cemis, csunzen, csunazi, cloctime, qca, qge, qmi"16-Apr-2011", "00:00:00.025", 2455667.500000290, 8352, 1.00538, 1.33678, 27.55755, 0324604799.65011, -35.60774, 186.36107, 1798.15230, 60.70145, 180.000, 240.000, 110, 0.42378, 275.92764, 1, 1, 0.790839, 0.052956, 0.609729, 0.0077, 0.022, -35.53812, 186.44733, 2.91887, 140.57684, 10.78604, 22.59250, 000, 012, 000"""
In [11]: s = StringIO(s)
In [12]: columns = ['date', 'utc', 'jdate', 'orbit', 'sundist', 'sunlat', 'sunlon', 'sclk', 'sclat', 'sclon', 'scrad', 'scalt', 'el_cmd', 'az_cmd', 'af', 'orientlat', 'orientlon', 'c', 'det', 'vlookx', 'vlooky', 'vlookz', 'radiance', 'tb', 'clat', 'clon', 'cemis', 'csunzen', 'csunazi', 'cloctime', 'qca', 'qge', 'qmi']
In [13]: pd.io.parsers.read_csv(s,sep='\s+',names=columns,comment='#')
Out[13]: <class 'pandas.core.frame.DataFrame'>Int64Index: 2 entries, 0 to 1
Data columns:date 1 non-null valuesutc 1 non-null valuesjdate 1 non-null values
orbit 1 non-null values
sundist 1 non-null values
sunlat 1 non-null values
sunlon 1 non-null values
sclk 1 non-null values
sclat 1 non-null values
sclon 1 non-null values
scrad 1 non-null values
scalt 1 non-null values
el_cmd 1 non-null values
az_cmd 1 non-null values
af 1 non-null values
orientlat 1 non-null values
orientlon 1 non-null values
c 1 non-null values
det 1 non-null values
vlookx 1 non-null values
vlooky 1 non-null values
vlookz 1 non-null values
radiance 1 non-null values
tb 1 non-null values
clat 1 non-null values
clon 1 non-null values
cemis 1 non-null values
csunzen 1 non-null values
csunazi 1 non-null values
cloctime 1 non-null values
qca 1 non-null values
qge 1 non-null values
qmi 1 non-null values
dtypes: float64(1), object(32)
In [14]: pd.__version__
Out[14]: '0.11.0.dev-b3e696c'
```
expected: 1 entry (comment line should have been ignored)
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} | 6 | "2013-03-10T17:41:32Z" | "2013-03-14T14:03:09Z" | "2013-03-14T14:03:09Z" | NONE | null | I am seeing odd behavior of dates being converted to `datetime64[ns]` times when that is inappropriate.
```
In [272]: file = StringIO("xxyyzz20100101PIE\nxxyyzz20100101GUM\nxxyyww20090101EGG\nfoofoo20080909PIE")
In [273]: df = pd.read_fwf(file, widths=[6,8,3], names=["person_id", "dt", "food"], parse_dates=["dt"])
In [274]: df
Out[274]:
person_id dt food
0 xxyyzz 2010-01-01 00:00:00 PIE
1 xxyyzz 2010-01-01 00:00:00 GUM
2 xxyyww 2009-01-01 00:00:00 EGG
3 foofoo 2008-09-09 00:00:00 PIE
```
Everything looks good. However:
```
In [275]: df.dt.value_counts()
Out[275]:
1970-01-17 00:00:00 2
1970-01-18 00:00:00 1
1970-01-25 08:00:00 1
In [276]: df.dt.dtype
Out[276]: dtype('datetime64[ns]')
```
The type is being stored as epoch nanoseconds internally, but usually displaying usually as the dates I want. But then everything is getting messed up when we drop down to numpy land for things like value_counts. What is going on? Is this a bug or am I making mistakes?
```
In [280]: pd.__version__
Out[280]: '0.10.1'
In [281]: np.__version__
Out[281]: '1.6.1'
```
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} | 0 | "2013-03-10T18:30:29Z" | "2014-06-16T11:59:45Z" | "2013-03-10T23:48:18Z" | CONTRIBUTOR | null | Fixes issue #3002
- Fixed for datetime64[ns]
- Fixed for timedelta64[ns](though the returned index
is actually int64, which is wrong, should make a TimeDeltaIndex class....someday)
- Fixed NaT display in a DateTimeIndex (was displaying funny)
```
0 2010-01-01 00:00:00
1 2010-01-01 00:00:00
2 2009-01-01 00:00:00
3 2008-09-09 00:00:00
Name: dt, dtype: datetime64[ns]
2010-01-01 2
2008-09-09 1
2009-01-01 1
dtype: int64
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-01-01 00:00:00, ..., 2009-01-01 00:00:00]
Length: 3, Freq: None, Timezone: None
```
WIth NaTs
```
0 2010-01-01 00:00:00
1 2010-01-01 00:00:00
2 2009-01-01 00:00:00
3 2008-09-09 00:00:00
4 NaT
dtype: datetime64[ns]
2010-01-01 2
2009-01-01 1
NaT 1
2008-09-09 1
dtype: int64
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-01-01 00:00:00, ..., 2008-09-09 00:00:00]
Length: 4, Freq: None, Timezone: None
```
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} | 4 | "2013-03-10T19:08:24Z" | "2014-11-18T11:18:23Z" | "2014-11-18T11:18:23Z" | CONTRIBUTOR | null | What do people think of adding a `closest_time` method to the DatetimeIndex, which will behave a bit like `asof` but will return the row of the DataFrame (or element of the Series) which is closest to the given time.
I have written code to do this manually (and discussed some Pandas bugs I found while doing this in #2948), but thought it would be a good idea to include it as a built-in method, as it is something which is often needed when dealing with time-series data.
I have a number of questions:
1. Do you think this is a good idea - and would it be likely to be accepted into Pandas?
2. Is `closest_time` a good name? If not, what should it be called?
3. I was planning to allow `closest_time` to take an argument of almost any type of date-time (pandas Timestamp, datetime instance, string which can then be processed through `dateutil.parser.parse`) - does that sound sensible, and within the Pandas design philosophy?
If people think this is a good idea then I'll get started on adapting my code to a proper Pandas method - and writing the appropriate tests etc.
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https://api.github.com/repos/pandas-dev/pandas/issues/3005 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3005/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3005/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3005/events | https://github.com/pandas-dev/pandas/pull/3005 | 11,856,356 | MDExOlB1bGxSZXF1ZXN0NDUzNjU5MA== | 3,005 | df.from_records should accept values deriving from ABC collections.Mapping #3000 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3006 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3006/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3006/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3006/events | https://github.com/pandas-dev/pandas/issues/3006 | 11,859,727 | MDU6SXNzdWUxMTg1OTcyNw== | 3,006 | 0.11.0.dev-2a6ee46: MultiIndex column slicing doesn't work with tuples | {
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import numpy as np
from pandas import MultiIndex, DataFrame
factors = [('a', 'b'), 'c']
names = ['n1', 'n2']
n_factors = len(factors)
n_names = len(names)
columns = MultiIndex(levels=[factors, names],
labels=[[i for i in xrange(n_factors) for _ in xrange(n_names)], range(n_names) * n_factors])
x = DataFrame(np.random.randn(3, n_factors * n_names), columns=columns)
```
Slicing by tuple doesn't work:
```
x[factors[0]]
```
> Traceback (most recent call last):
> File "C:\Python27\lib\site-packages\IPython\core\interactiveshell.py", line 2721, in run_code
> exec code_obj in self.user_global_ns, self.user_ns
> File "<ipython-input-4-fdab96104a77>", line 1, in <module>
> x[factors[0]]
> File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1955, in **getitem**
> return self._getitem_multilevel(key)
> File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1994, in _getitem_multilevel
> loc = self.columns.get_loc(key)
> File "C:\Python27\lib\site-packages\pandas\core\index.py", line 2200, in get_loc
> return self._engine.get_loc(key)
> File "index.pyx", line 130, in pandas.index.IndexEngine.get_loc (pandas\index.c:3236)
> File "index.pyx", line 150, in pandas.index.IndexEngine.get_loc (pandas\index.c:3116)
> File "hashtable.pyx", line 670, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:10498)
> File "hashtable.pyx", line 676, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:10452)
> KeyError: ('a', 'b')
Slicing by string works just fine:
```
x[factors[-1]]
```
> n1 n2
> 0 1.601271 0.786259
> 1 -0.232438 1.026608
> 2 -0.569793 0.170947
Specifying the index value works, even with a tuple:
```
x[factors[0], names[0]]
```
> 0 1.089592
> 1 1.621236
> 2 -1.030415
> Name: ((a, b), n1), dtype: float64
The culprit seems to be the tuple, because the following will lead to the same problem:
```
factors = [('a', 'b')]
names = ['n1', 'n2']
n_factors = len(factors)
n_names = len(names)
columns = MultiIndex(levels=[factors, names],
labels=[[i for i in xrange(n_factors) for _ in xrange(n_names)], range(n_names) * n_factors])
x = DataFrame(np.random.randn(3, n_factors * n_names), columns=columns)
```
This will lead to the same error message as above:
```
x[factors[0]]
```
This is surprising. Tuples are hashable. Why does pandas treat a tuple of strings different from a string?
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} | 3 | "2013-03-11T00:19:27Z" | "2019-01-01T17:29:08Z" | "2019-01-01T17:29:07Z" | CONTRIBUTOR | null | rank 1d/2d
hash table (for value counts)
lots of rolling algos
use fused types!
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} | 3 | "2013-03-11T00:24:25Z" | "2013-07-22T20:00:48Z" | "2013-07-22T20:00:48Z" | NONE | null | 1. Cython supports parallelization using openMP.
2. ...
3. World Domination!
Perhaps a good candidate for a future GSoC.
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} | 16 | "2013-03-11T02:00:02Z" | "2014-09-13T23:00:04Z" | "2014-09-13T22:59:30Z" | CONTRIBUTOR | null | - [x] disallow operations in series that are invalid that now create timedelta64
(e.g. dt + dt, only dt - dt?) #4534
- [x] create the TimeDeltaIndex object, inherit from Int64, #8184
- [x] create associated scalar (np.timedelta64 wrapper), like
Timestamp (Timedelta?), direct wrapper might work, #8184
that forwards methods, internally repr as timedelta64 (or view?) as array
- [x] move pretty printing to the new class (from function in lib), done???
see #3002 and #2990, #4378
- [x] need a Timedelta64 Block, with to_native_types (for printing), PR #4684
- [x] ~~read_csv to have a `parse_timedelta` to automatically apply the dtype, see #4650, #4684, moved to #8185~~
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} | 5 | "2013-03-11T07:58:44Z" | "2013-03-13T03:23:33Z" | "2013-03-13T03:23:33Z" | NONE | null | I want to add a column to DataFrame using a list of arrays, but I forgot to change the 2d array into list first, so I got a "Wrong number of items passed" error.
After that, the df seemed to be corrupted, whenever I want to print it, it kept raising a "NoneType object is not iterable error".
While it could still be added new columns, the improperly assigned columns couldn't be deleted yet (also prompting the NoneType error).
This the code that could reproduce my problem:
``` python
df = DataFrame(np.ones((4,4)))
df['foo'] = np.ones((4,2)).tolist() # OK, and that's what I should type
df['test'] = np.ones((4,2)) # the improper column creation
AsssertionError: Wrong number of items passed （1 vs 2)
df
TypeError: 'NoneType' object is not iterable, u'occured at index foo'
del df['test']
TypeError: 'NoneType' object is not iterable
```
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} | 3 | "2013-03-11T11:44:35Z" | "2013-03-16T23:53:25Z" | "2013-03-16T23:53:25Z" | NONE | null | ```
import numpy as np
import pandas as pd
series = pd.Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4])
bins = pd.cut(series.dropna(), 4)
# this works
series.dropna().groupby(bins).mean()
# but this results in:
# [1] 42448 segmentation fault python
series.groupby(bins).mean()
```
So, I can see why trying to group a series with bins based on a different (trimmed) series can't really work and don't consider that to be a bug, but it might be nice if `pandas` threw a proper error message instead of segfaulting.
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} | [] | closed | false | null | [] | null | 3 | "2013-03-11T13:43:30Z" | "2013-03-14T03:24:02Z" | "2013-03-14T03:24:02Z" | NONE | null | - I load a dataframe from mysql:
``` python
df_bugs_activity_4w = psql.read_frame('select * from bugs_activity limit 0, 40000', conn)
```
- and the structure of df_bugs_activity_4w:
``` python
In[19]: df_bugs_activity_4w
Out[19]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 40000 entries, 0 to 39999
Data columns:
bug_id 40000 non-null values
attach_id 13 non-null values
who 40000 non-null values
bug_when 40000 non-null values
fieldid 40000 non-null values
added 40000 non-null values
removed 40000 non-null values
id 40000 non-null values
dtypes: float64(1), int64(4), object(3)
```
- then, convert the object columns
``` python
In [60]: df_bugs_activity_4w = df_bugs_activity_4w.convert_objects()
In [61]: df_bugs_activity_4w
Out[61]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 40000 entries, 0 to 39999
Data columns:
bug_id 40000 non-null values
attach_id 13 non-null values
who 40000 non-null values
bug_when 40000 non-null values
fieldid 40000 non-null values
added 40000 non-null values
removed 40000 non-null values
id 40000 non-null values
dtypes: datetime64[ns](1), float64(1), int64(4), object(2)
```
- I put it into a hdfstore, and then get it out, found the number of dataframe entries changed from 40,000 to 13 ! That's weird. It seems that the number of 'attach_id' columns limits the total number of dataframe when putting it into a hdfstore.
```
In [63]: %prun store.put('df_bugs_activity_4w1', df_bugs_activity_4w, table=True)
In [64]: %time store.get('df_bugs_activity_4w1')
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
Wall time: 0.01 s
Out[64]:
bug_id attach_id who bug_when fieldid added removed id
2012 301879 0 35 1999-04-06 11:22:11 16 dev bug 2013
2014 301879 0 35 1999-04-06 11:22:12 5 para us 2015
2835 301879 0 56 1999-05-14 15:56:12 10 clo op 2836
31244 301879 0 207 2001-07-18 14:11:38 10 op clo 31245
31252 301879 0 207 2001-07-18 15:40:52 10 ana op 31253
31283 301879 0 35 2001-07-18 21:21:33 16 lui dev 31284
31285 301879 0 35 2001-07-18 21:21:34 15 296 10 31286
31287 301879 0 35 2001-07-18 21:21:35 5 unk para 31288
31393 301879 0 159 2001-07-19 12:41:07 16 prat lui 31394
31472 301879 0 207 2001-07-19 17:27:31 10 ope ana 31473
32675 301879 0 207 2001-08-02 10:09:08 10 clos op 32676
38609 235837 0 201 2001-09-26 20:28:11 15 310- 300-3 38610
38610 235838 0 201 2001-09-26 20:28:11 15 310- 300 38611
```
```
In [66]: store
Out[66]:
<class 'pandas.io.pytables.HDFStore'>
File path: sample_no_fill.h5
/df_bugs_4w frame_table (typ->appendable,nrows->40000,ncols->52,indexers->[index])
/df_bugs_4w1 frame_table (typ->legacy,nrows->None,ncols->0,indexers->[])
/df_bugs_activity_4w frame_table (typ->appendable,nrows->13,ncols->8,indexers->[index])
/df_bugs_activity_4w1 frame_table (typ->appendable,nrows->13,ncols->8,indexers->[index])
```
- ps: I have posted this question on [stackoverflow](http://stackoverflow.com/questions/15271389/better-solution-when-dealing-with-the-missing-data-for-those-dataframe-objs-expo)
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if the first column of a block was all-nan was not writing that row
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} | 4 | "2013-03-11T18:25:42Z" | "2014-07-01T11:00:16Z" | "2013-03-21T07:38:36Z" | CONTRIBUTOR | null | `Timestamp` cannot be constructed correctly by given an `datetime.date` instance and `tz`.
The issued has been solved in this patch and the test case is add in `pandas/tseries/test/test_timezones.py`
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} | 3 | "2013-03-12T02:49:36Z" | "2013-08-26T20:18:07Z" | "2013-03-16T00:39:22Z" | CONTRIBUTOR | null | modified "Selective Plotting on Secondary Y-axis" example to show how to set ylabels for both left and right y axis
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} | 0 | "2013-03-12T12:47:40Z" | "2014-06-14T07:24:11Z" | "2013-03-12T16:14:07Z" | CONTRIBUTOR | null | Bug in DataFrame column insertion when the column creation fails
existing frame is left in an irrecoverable state, fixes #3010
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} | 1 | "2013-03-12T13:12:42Z" | "2013-03-14T10:43:08Z" | "2013-03-14T10:43:08Z" | NONE | null | So far so good...
pd.Series(pd.date_range("2000-1-1","2000-1-10"))
0 2000-01-01 00:00:00
1 2000-01-02 00:00:00
2 2000-01-03 00:00:00
3 2000-01-04 00:00:00
4 2000-01-05 00:00:00
5 2000-01-06 00:00:00
6 2000-01-07 00:00:00
7 2000-01-08 00:00:00
8 2000-01-09 00:00:00
9 2000-01-10 00:00:00
pd.Series(pd.date_range("2000-1-1","2000-1-10")).shift(1)
0 NaN
1 9.466848e+17
2 9.467712e+17
3 9.468576e+17
4 9.469440e+17
5 9.470304e+17
6 9.471168e+17
7 9.472032e+17
8 9.472896e+17
9 9.473760e+17
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} | 1 | "2013-03-12T13:46:53Z" | "2014-03-29T00:53:55Z" | "2013-03-18T17:25:38Z" | NONE | null | When resampling from a TimeSeries with freq='MS' to freq='D' the TimeSeries is shifted forward by one day, skipping the first element:
``` python
In [64]: dates = pd.date_range('01-Apr-13','01-May-13',freq='MS')
In [65]: ts = pd.TimeSeries([1,2],dates)
In [66]: ts
Out[66]:
2013-04-01 1
2013-05-01 2
Freq: MS
In [67]: ts.resample('d',closed='left',label='left')
Out[67]:
2013-04-02 NaN
2013-04-03 NaN
2013-04-04 NaN
2013-04-05 NaN
2013-04-06 NaN
2013-04-07 NaN
2013-04-08 NaN
2013-04-09 NaN
2013-04-10 NaN
2013-04-11 NaN
2013-04-12 NaN
2013-04-13 NaN
2013-04-14 NaN
2013-04-15 NaN
2013-04-16 NaN
2013-04-17 NaN
2013-04-18 NaN
2013-04-19 NaN
2013-04-20 NaN
2013-04-21 NaN
2013-04-22 NaN
2013-04-23 NaN
2013-04-24 NaN
2013-04-25 NaN
2013-04-26 NaN
2013-04-27 NaN
2013-04-28 NaN
2013-04-29 NaN
2013-04-30 NaN
2013-05-01 2
2013-05-02 NaN
Freq: D
```
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} | 0 | "2013-03-12T14:28:16Z" | "2014-06-12T23:49:54Z" | "2013-03-12T22:38:01Z" | CONTRIBUTOR | null | fixes #3016
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/21"
} | 1 | "2013-03-12T15:02:43Z" | "2013-03-12T15:20:22Z" | "2013-03-12T15:20:18Z" | CONTRIBUTOR | null | As shown in the example below, when passing in two series with the same name to `concat` you end up with duplicates of the last series passed in.
``` python
In [42]: pd.__version__
Out[42]: '0.10.1'
In [43]: dates = pd.date_range('01-Jan-2013','01-Dec-2013', freq='MS')
...: L = pd.TimeSeries('L', dates, name='LeftRight')
...: R = pd.TimeSeries('R', dates, name='LeftRight')
...:
In [44]: L
Out[44]:
2013-01-01 L
2013-02-01 L
2013-03-01 L
2013-04-01 L
2013-05-01 L
2013-06-01 L
2013-07-01 L
2013-08-01 L
2013-09-01 L
2013-10-01 L
2013-11-01 L
2013-12-01 L
Freq: MS, Name: LeftRight
In [45]: R
Out[45]:
2013-01-01 R
2013-02-01 R
2013-03-01 R
2013-04-01 R
2013-05-01 R
2013-06-01 R
2013-07-01 R
2013-08-01 R
2013-09-01 R
2013-10-01 R
2013-11-01 R
2013-12-01 R
Freq: MS, Name: LeftRight
In [46]: pd.concat([L,R], axis=1)
Out[46]:
LeftRight LeftRight
2013-01-01 R R
2013-02-01 R R
2013-03-01 R R
2013-04-01 R R
2013-05-01 R R
2013-06-01 R R
2013-07-01 R R
2013-08-01 R R
2013-09-01 R R
2013-10-01 R R
2013-11-01 R R
2013-12-01 R R
In [47]: pd.concat([L,R], axis=1, keys=['Left','Right'])
Out[47]:
Left Right
2013-01-01 L R
2013-02-01 L R
2013-03-01 L R
2013-04-01 L R
2013-05-01 L R
2013-06-01 L R
2013-07-01 L R
2013-08-01 L R
2013-09-01 L R
2013-10-01 L R
2013-11-01 L R
2013-12-01 L R
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3023 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3023/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3023/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3023/events | https://github.com/pandas-dev/pandas/pull/3023 | 11,929,208 | MDExOlB1bGxSZXF1ZXN0NDU3MTEwMA== | 3,023 | Continuing #2057, s/assert/raise AssertionError/g | {
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} | 3 | "2013-03-12T15:25:14Z" | "2014-06-17T07:37:07Z" | "2013-04-23T02:10:48Z" | NONE | null | #2057
Though I've never encountered usage of python -O in the wild.
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https://api.github.com/repos/pandas-dev/pandas/issues/3024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3024/events | https://github.com/pandas-dev/pandas/issues/3024 | 11,929,385 | MDU6SXNzdWUxMTkyOTM4NQ== | 3,024 | TODO: Decorate asserts with helpful messages | {
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} | 7 | "2013-03-12T15:28:41Z" | "2014-01-10T12:36:44Z" | "2014-01-10T12:36:44Z" | NONE | null | See changes made in #3023, #2057.
Enhancing an assert with a meaningful message
is an easy way to make your first contribution to the
pandas project.
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} | 4 | "2013-03-12T15:32:24Z" | "2019-02-22T19:02:58Z" | null | CONTRIBUTOR | null | promotes consistency, see #3021, #3016, #9065
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https://api.github.com/repos/pandas-dev/pandas/issues/3026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3026/events | https://github.com/pandas-dev/pandas/pull/3026 | 11,932,591 | MDExOlB1bGxSZXF1ZXN0NDU3Mjk3MQ== | 3,026 | Add viewcode extension to allow for links to source in API docs | {
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} | [] | closed | false | null | [] | null | 5 | "2013-03-12T16:31:00Z" | "2014-01-18T03:44:29Z" | "2014-01-18T03:44:29Z" | CONTRIBUTOR | null | This creates the [source] links in the API documentation sections. You can read more about this Sphinx extension [here](http://sphinx-doc.org/ext/viewcode.html). There could be a reason this is NOT already added so let me know. I find it useful to have this because often times I want to easily jump to the source in the HTML docs. Of course, I typically just use this to quickly find the file and open it in a real editor.
It might be worth noting this functionality was added in Sphinx 1.0, which was released in July 2010. I'm not sure what the rules are on backwards compatibility for the documentation build.
I tested this with Sphinx 1.1.3 and it worked without any issues.
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https://api.github.com/repos/pandas-dev/pandas/issues/3027 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3027/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3027/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3027/events | https://github.com/pandas-dev/pandas/pull/3027 | 11,937,890 | MDExOlB1bGxSZXF1ZXN0NDU3NTgzNQ== | 3,027 | BUG: Bug in user-facing take with negative indicies was incorrect | {
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} | 0 | "2013-03-12T18:20:24Z" | "2014-06-26T05:58:14Z" | "2013-03-12T23:17:20Z" | CONTRIBUTOR | null | issue mentioned in #2892
note: does not solve the actual segmentation faults issue in 2892, only user facing take with neg indicies
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https://api.github.com/repos/pandas-dev/pandas/issues/3028 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3028/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3028/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3028/events | https://github.com/pandas-dev/pandas/issues/3028 | 11,939,096 | MDU6SXNzdWUxMTkzOTA5Ng== | 3,028 | Fortify cython functions against segfaults (without killing perf) | {
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} | 15 | "2013-03-12T18:45:28Z" | "2013-04-27T20:27:44Z" | "2013-04-27T20:23:34Z" | NONE | null | Following discussion in #2892, and segfaults in #3011, #2775, #2778 and others.
manually insert needed checks before inner loop, leaving @cython.boundscheck(False)
inplace.
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https://api.github.com/repos/pandas-dev/pandas/issues/3029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3029/events | https://github.com/pandas-dev/pandas/pull/3029 | 11,940,401 | MDExOlB1bGxSZXF1ZXN0NDU3NzE0MA== | 3,029 | ENH: safety bounds checks for take funcs | {
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} | 13 | "2013-03-12T19:14:26Z" | "2014-06-12T13:27:50Z" | "2013-07-29T05:14:12Z" | CONTRIBUTOR | null | as per #3028; haven't done vb_suite yet but presumably we're ok paying the price for safety; will run to see what the hit is though
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https://api.github.com/repos/pandas-dev/pandas/issues/3030 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3030/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3030/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3030/events | https://github.com/pandas-dev/pandas/issues/3030 | 11,940,782 | MDU6SXNzdWUxMTk0MDc4Mg== | 3,030 | BUG: inconsistent behavior of set_value | {
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For instance:
The following will not update the original DataFrame
```
In [99]: a = pd.DataFrame(rand(5,5), columns=['a','b','c','d','e'])
In [100]: a
Out[100]:
a b c d e
0 0.195670 0.357315 0.185997 0.555534 0.511327
1 0.638141 0.091384 0.970876 0.500370 0.522014
2 0.818335 0.779157 0.925812 0.443544 0.630370
3 0.188840 0.419840 0.088478 0.782161 0.059741
4 0.938865 0.884169 0.514422 0.279719 0.282072
In [101]: a.set_value(8,col='a',value=3)
Out[101]:
a b c d e
0 0.195670 0.357315 0.185997 0.555534 0.511327
1 0.638141 0.091384 0.970876 0.500370 0.522014
2 0.818335 0.779157 0.925812 0.443544 0.630370
3 0.188840 0.419840 0.088478 0.782161 0.059741
4 0.938865 0.884169 0.514422 0.279719 0.282072
8 3.000000 NaN NaN NaN NaN
In [102]: a
Out[102]:
a b c d e
0 0.195670 0.357315 0.185997 0.555534 0.511327
1 0.638141 0.091384 0.970876 0.500370 0.522014
2 0.818335 0.779157 0.925812 0.443544 0.630370
3 0.188840 0.419840 0.088478 0.782161 0.059741
4 0.938865 0.884169 0.514422 0.279719 0.282072
```
However when using an existing label the update is done inplace:
```
In [103]: a.set_value(3,col='a',value=3)
Out[103]:
a b c d e
0 0.195670 0.357315 0.185997 0.555534 0.511327
1 0.638141 0.091384 0.970876 0.500370 0.522014
2 0.818335 0.779157 0.925812 0.443544 0.630370
3 3.000000 0.419840 0.088478 0.782161 0.059741
4 0.938865 0.884169 0.514422 0.279719 0.282072
In [104]: a
Out[104]:
a b c d e
0 0.195670 0.357315 0.185997 0.555534 0.511327
1 0.638141 0.091384 0.970876 0.500370 0.522014
2 0.818335 0.779157 0.925812 0.443544 0.630370
3 3.000000 0.419840 0.088478 0.782161 0.059741
4 0.938865 0.884169 0.514422 0.279719 0.282072
```
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} | 4 | "2013-03-12T20:09:24Z" | "2014-06-26T01:13:26Z" | "2013-03-17T15:32:36Z" | NONE | null | continues #3017, does an easier bit under #3028
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} | 20 | "2013-03-12T21:38:22Z" | "2013-03-20T15:21:32Z" | "2013-03-20T15:21:32Z" | NONE | null | A pandas array I have contains some image data, recorded from a camera during a behavioral experiment. A simplified version looks like this:
```
num_frames = 100
mouse = [{"velocity":np.random.random((1,))[0], \
"image":np.random.random((80,80)).astype('float32'), \
"spine":np.r_[0:80].astype('float32'),
#"time":millisec(i*33),
"mouse_id":"mouse1",
"special":i} for i in range(num_frames)]
df = DataFrame(mouse)
```
I understand I can't query over the `image` or `spine` entries. Of course, I can easily query for low velocity frames, like this:
```
low_velocity = df[df['velocity'] < 0.5]
```
However, there is a lot of this data (several hundred gigabytes), so I'd like to keep it in an HDF5 file, and pull up frames only as needed from disk.
In v0.10, I understand that "mixed-type" frames now can be appended into the HDFStore. However, I get an error when trying to append this dataframe into the HDFStore.
```
store = HDFStore("mouse.h5", "w")
store.append("mouse", df)
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-30-8f0da271e75f> in <module>()
1 store = HDFStore("mouse.h5", "w")
----> 2 store.append("mouse", df)
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.11.0.dev_95a5326-py2.7-macosx-10.5-x86_64.egg/pandas/io/pytables.pyc in append(self, key, value, columns, **kwargs)
543 raise Exception("columns is not a supported keyword in append, try data_columns")
544
--> 545 self._write_to_group(key, value, table=True, append=True, **kwargs)
546
547 def append_to_multiple(self, d, value, selector, data_columns=None, axes=None, **kwargs):
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.11.0.dev_95a5326-py2.7-macosx-10.5-x86_64.egg/pandas/io/pytables.pyc in _write_to_group(self, key, value, index, table, append, complib, **kwargs)
799 raise ValueError('Compression not supported on non-table')
800
--> 801 s.write(obj = value, append=append, complib=complib, **kwargs)
802 if s.is_table and index:
803 s.create_index(columns = index)
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.11.0.dev_95a5326-py2.7-macosx-10.5-x86_64.egg/pandas/io/pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, **kwargs)
2537 # create the axes
2538 self.create_axes(axes=axes, obj=obj, validate=append,
-> 2539 min_itemsize=min_itemsize, **kwargs)
2540
2541 if not self.is_exists:
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.11.0.dev_95a5326-py2.7-macosx-10.5-x86_64.egg/pandas/io/pytables.pyc in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
2279 raise
2280 except (Exception), detail:
-> 2281 raise Exception("cannot find the correct atom type -> [dtype->%s,items->%s] %s" % (b.dtype.name, b.items, str(detail)))
2282 j += 1
2283
Exception: cannot find the correct atom type -> [dtype->object,items->Index([image, mouse_id, spine], dtype=object)] cannot set an array element with a sequence
```
I'm working with a relatively new release of pandas:
```
pandas.__version__
'0.11.0.dev-95a5326'
import tables
tables.__version__
'2.4.0+1.dev'
```
It would be immensely convenient to have a single repository for all of this data, instead of fragmenting just the queryable parts off to separate nodes.
Is this possible currently with some work-around (maybe with record arrays), and will this be supported officially in the future?
As a side-note, this kind of heterogeneous data ("ragged" arrays) is incredibly wide-spread in neurobiology and the biological sciences in general. Any extra support along these lines would be incredibly well-received.
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} | 2 | "2013-03-13T00:13:41Z" | "2016-08-17T10:21:25Z" | "2016-08-17T10:21:25Z" | CONTRIBUTOR | null | just throwing this out there, spliting off from discussion at #2892... right now index arrays are constantly being converted back and forth between platform int and int64 on 32-bit platforms, since `ndarray.take` requires the former but the cython routines use the latter
this can probably be removed if all `ndarray.take` usages can be converted to use cython routines, simplifying things considerably and probably improving performance (due to avoiding the conversions, and also since `ndarray.take` is generic and isn't specialized to handle 1-d and 2-d arrays or particular types efficiently)
the only caveats are that `take_nd` doesn't allow normal negative indices and uses promotion/fill behavior by default, the latter can be turned off with `allow_fill=False` but that might get pretty redundant if it's used everywhere; probably best to write a helper which does it. as for the former, there might not be any more cases of places where normal negative indicies are required anymore after #3027...not really sure
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} | 12 | "2013-03-13T01:17:49Z" | "2014-08-13T08:36:17Z" | "2013-03-13T16:37:44Z" | CONTRIBUTOR | null | Formatting of an index that has `nan` was inconsistent or wrong,
(would fill from last value), closes GH #2850
Also changed in test_format.py/test_index
1) printing of 'nan' rather than the na_rep (NaN) is inconcsistent
with everywhere else
2) a 'None' in the index is defacto treated as NaN, is this wrong?
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} | 2 | "2013-03-13T10:43:07Z" | "2013-03-17T15:33:15Z" | "2013-03-17T15:33:15Z" | CONTRIBUTOR | null | It _is_ a dumb thing to do, but pandas probably shouldn't segfault regardless:
``` python
In [1]: dates = pd.date_range('01-Jan-2013', periods=12, freq='MS')
...: ts = pd.TimeSeries(randn(12), index=dates)
In [2]: ts.groupby(lambda key: key[0:4]).first()
It seems the kernel died unexpectedly. Use 'Restart kernel' to continue using this console.
```
Tested with pandas 10.1 on win32/win64, Python 2.7 and pandas 0.11.0.dev-2f7b0e4 on win32, Python 2.7.
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https://api.github.com/repos/pandas-dev/pandas/issues/3037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3037/events | https://github.com/pandas-dev/pandas/issues/3037 | 11,987,146 | MDU6SXNzdWUxMTk4NzE0Ng== | 3,037 | ENH: should we relax frame/setitem for non-numerics | {
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} | [] | closed | false | null | [] | null | 0 | "2013-03-13T18:29:49Z" | "2013-03-14T11:38:44Z" | "2013-03-14T11:38:44Z" | CONTRIBUTOR | null | I came across the case where if you try setitem with a boolean mask on a frame
that is mixed dtype an exception is raised
this is easily relaxed in the int/float case (and will leave/upcast the int columns as needed)
```
In [68]: df
Out[68]:
0 1 2 3 4 y
35 NaN NaN NaN NaN 0.342153 0
40 NaN 0.326323 NaN NaN NaN 0
43 NaN NaN 0.290126 NaN NaN 0
49 NaN 0.326323 NaN NaN NaN 0
50 NaN 0.391147 NaN NaN NaN 1
In [75]: df.dtypes
Out[75]:
0 float64
1 float64
2 float64
3 float64
4 float64
y int64
```
This will currently raise because its mixed_type (this is easily fixed and I think should be,
as the IntBlock will upcast if needed)
```
In [72]: df[df>0.3] = 1
In [73]: df
Out[73]:
0 1 2 3 4 y
35 NaN NaN NaN NaN 1 0
40 NaN 1 NaN NaN NaN 0
43 NaN NaN 0.290126 NaN NaN 0
49 NaN 1 NaN NaN NaN 0
50 NaN 1 NaN NaN NaN 1
```
What about a mixed type that invovles non-numerics though,
```
In [77]: df
Out[77]:
0 1 2 3 4 y foo
35 NaN NaN NaN NaN 0.342153 0 test
40 NaN 0.326323 NaN NaN NaN 0 test
43 NaN NaN 0.290126 NaN NaN 0 test
49 NaN 0.326323 NaN NaN NaN 0 test
50 NaN 0.391147 NaN NaN NaN 1 test
In [78]: df.get_dtype_counts()
Out[78]:
float64 5
int64 1
object 1
```
Should raise here? or allow just the non-numerics to 'work'?
am leaning toward allowing a purely numeric frame to work (e.g. mixed int/float),
but raising on this last case? (then its explicity that you did something 'wrong')
any opinons?
Note that the getitem case works on mixed....
```
n [80]: df[df>0.3]
Out[80]:
0 1 2 3 4 y foo
35 NaN NaN NaN NaN 0.3421533 NaN test
40 NaN 0.3263232 NaN NaN NaN NaN test
43 NaN NaN NaN NaN NaN NaN test
49 NaN 0.3263232 NaN NaN NaN NaN test
50 NaN 0.3911472 NaN NaN NaN 1 test
```
and this would preclude a pathological case where (and maybe this is another bug),
you can fillna this and it doesn't convert to float64, so it 'looks' like a numeric but actually isn't
Note: I am letting this go thru, (e.g. only try the numeric case if the mixed type fails, more
for backward compatibilty that anything else)
```
In [94]: df = DataFrame({"col1": [2, 5.0, 123, None],
....: "col2": [1, 2, 3, 4]}, dtype=object)
In [95]: df
Out[95]:
col1 col2
0 2 1
1 5 2
2 123 3
3 None 4
In [96]: df.dtypes
Out[96]:
col1 object
col2 object
```
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} | 9 | "2013-03-13T19:26:34Z" | "2014-06-19T09:13:11Z" | "2013-04-23T02:11:03Z" | NONE | null | **Part 1**
currently:
``` python
In [4]: from pandas.core.common import pprint_thing as pp_t
...: pp_t([1,None])
Out[4]: u'[1, ]'
```
after PR:
``` python
In [13]: from pandas.core.common import pprint_thing as pp_t
...: pp_t([1,None])
Out[13]: u'[1, None]'
```
and jeff fixed index NaN printing in #3034, at a higher level.
**Part 2**
currently:
``` python
In [1]: pd.Index(['a','b'])
...:
Out[1]: Index([a, b], dtype=object)
```
after PR:
``` python
pd.Index(['a','b'])
Out[3]: Index([u'a', u'b'], dtype=object)
```
so:
``` python
In [7]: from pandas import Index
In [10]: eval(repr(pd.Index([u'a', u'b'], dtype=object)))
Out[10]: Index([u'a', u'b'], dtype=object)
```
Which is what `repr()` should enable, in principle.
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} | 6 | "2013-03-13T20:14:34Z" | "2013-04-17T19:26:55Z" | "2013-04-17T19:25:56Z" | CONTRIBUTOR | null | Wanted to get opinions on this. It would be nice to formally expose a GroupBy object that uses BinGrouper. This would be useful for those of us who monkey patch the GroupBy object for convenience.
Do people use the default behavior outside of quick demo-ing purposes?
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} | 0 | "2013-03-13T22:04:45Z" | "2014-07-26T15:38:19Z" | "2013-03-14T00:37:44Z" | CONTRIBUTOR | null | fixes #2929
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groupby and first/last too
www.stackoverflow.com/questions/15349795/pandas-dataframe-combine-first-and-update-methods-have-strange-behavior
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} | 4 | "2013-03-13T23:39:00Z" | "2017-07-25T16:19:20Z" | "2013-03-22T17:37:10Z" | CONTRIBUTOR | null | I have a datetime.datetime, convert it to ordinal and try to read it back with pd.Timestamp.fromordinal(). This produces the following error:
``` python
In [112]: dt
Out[112]: datetime.datetime(2011, 4, 16, 0, 0)
In [113]: pd.Timestamp.fromordinal(dt.toordinal())
Exception AttributeError: "'int' object has no attribute 'utcoffset'" in 'pandas.tslib._localize_tso' ignored
Out[113]: <Timestamp: 1970-01-01 00:00:00.000002011>
```
Am I expecting something wrong by thinking this should work or is it a bug?
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https://api.github.com/repos/pandas-dev/pandas/issues/3043 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3043/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3043/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3043/events | https://github.com/pandas-dev/pandas/pull/3043 | 12,000,450 | MDExOlB1bGxSZXF1ZXN0NDYwNjYxOA== | 3,043 | BUG: frame combine_first where non-specified values could cause dtype changes (#3041) | {
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DOC: docstring updates in core/common.py for _possibily_cast_to_timedelta
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} | [] | closed | false | null | [] | null | 6 | "2013-03-14T02:08:35Z" | "2014-06-24T00:19:54Z" | "2013-03-15T09:36:48Z" | NONE | null | - Supercedes #1904
- Namespaces rows/cols as `df.r.<row label>`, `df.c.<col label>`
- get/set, multindex in both axes
- Designates df.<colname> (75310383) for future removal.
- No perf hit on attribute lookup, if you don't use it (raised in #1904).
Open issues:
- .r and .c (which i like), don't generalize to dim>2. Use a numbered scheme instead? (a0, a1,etc')
- no panel support. No reason, I just never use them.
- Setting columns has a caveat, need your numpy-fu help here (see bottom).
Demo:
``` python
In [1]: import pandas as pd
...: from pandas.util.testing import makeCustomDataframe as mkdf
...:
...: pd.options.display.notebook_repr_html=False
In [2]: df=mkdf(4,2,r_idx_nlevels=1)
In [3]: df
Out[3]:
C0 C_l0_g0 C_l0_g1
R0
R_l0_g0 R0C0 R0C1
R_l0_g1 R1C0 R1C1
R_l0_g2 R2C0 R2C1
R_l0_g3 R3C0 R3C1
In [4]: df.r.R_l0_g1 # tab-complete
Out[4]:
C0
C_l0_g0 R1C0
C_l0_g1 R1C1
Name: R_l0_g1, dtype: object
In [5]: df.r.R_l0_g1 = df.r.R_l0_g0
In [6]: df
Out[6]:
C0 C_l0_g0 C_l0_g1
R0
R_l0_g0 R0C0 R0C1
R_l0_g1 R0C0 R0C1
R_l0_g2 R2C0 R2C1
R_l0_g3 R3C0 R3C1
In [7]: df.c.C_l0_g1 # # tab-complete
Out[7]:
R0
R_l0_g0 R0C1
R_l0_g1 R0C1
R_l0_g2 R2C1
R_l0_g3 R3C1
Name: C_l0_g1, dtype: object
In [8]: df.c.C_l0_g1 = df.c.C_l0_g0
In [9]: df
Out[9]:
C0 C_l0_g0 C_l0_g1
R0
R_l0_g0 R0C0 R0C0
R_l0_g1 R0C0 R0C0
R_l0_g2 R2C0 R2C0
R_l0_g3 R3C0 R3C0
```
**Multindex example (note recursive syntax):**
``` python
In [1]: import pandas as pd
...: from pandas.util.testing import makeCustomDataframe as mkdf
...:
...: pd.options.display.notebook_repr_html=False
In [2]: df=mkdf(4,2,r_idx_nlevels=2,c_idx_nlevels=2)
In [3]: df
Out[3]:
C0 C_l0_g0 C_l0_g1
C1 C_l1_g0 C_l1_g1
R0 R1
R_l0_g0 R_l1_g0 R0C0 R0C1
R_l0_g1 R_l1_g1 R1C0 R1C1
R_l0_g2 R_l1_g2 R2C0 R2C1
R_l0_g3 R_l1_g3 R3C0 R3C1
In [4]: df.r.R_l0_g1.r.R_l1_g1 # tab-complete. twice
Out[4]:
C0 C1
C_l0_g0 C_l1_g0 R1C0
C_l0_g1 C_l1_g1 R1C1
Name: R_l1_g1, dtype: object
In [5]: df.c.C_l0_g0.c.C_l1_g0 # tab-complete. twice
Out[5]:
R0 R1
R_l0_g0 R_l1_g0 R0C0
R_l0_g1 R_l1_g1 R1C0
R_l0_g2 R_l1_g2 R2C0
R_l0_g3 R_l1_g3 R3C0
Name: C_l1_g0, dtype: object
In [6]: df=mkdf(4,2,r_idx_nlevels=2,c_idx_nlevels=2)
In [7]: df.c.C_l0_g0.c.C_l1_g0=df.c.C_l0_g1.c.C_l1_g1
In [8]: df
Out[8]:
C0 C_l0_g0 C_l0_g1
C1 C_l1_g0 C_l1_g1
R0 R1
R_l0_g0 R_l1_g0 R0C1 R0C1
R_l0_g1 R_l1_g1 R1C1 R1C1
R_l0_g2 R_l1_g2 R2C1 R2C1
R_l0_g3 R_l1_g3 R3C1 R3C1
```
Suggest a fix for this? I'm sure I'm just lacking in numpy-fu here.
``` python
In [7]: df.r.R_l0_g0.r.R_l1_g0 = df.r.R_l0_g1.r.R_l1_g1 # this works
In [8]: df.r.R_l0_g0.r.R_l1_g0 = df.r.R_l0_g1 # this works
In [3]: df.c.C_l0_g0.c.C_l1_g0=df.c.C_l0_g1.c.C_l1_g1 # this works
In [5]: df.c.C_l0_g0.c.C_l1_g0=df.c.C_l0_g1 # this doesn't
```
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} | 10 | "2013-03-14T02:26:37Z" | "2013-09-27T00:36:48Z" | "2013-09-27T00:36:48Z" | NONE | null | So, 0.11 was released a couple of months ago, 0.12 is fast approaching and `set_printoptions`/`reset_printoptions` have had a deprecation warning attached
to them since 0.10.0.
Isn't it time to remove them?
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} | 10 | "2013-03-14T03:13:54Z" | "2013-03-20T12:40:35Z" | "2013-03-15T12:59:04Z" | NONE | null | - pd.version: 0.11.0.dev-156e03c
- if dataframe had a column which had been recognized as 'datetime64[ns]' type, calling the dataframe's fillna function would cause an expection.
``` python
In [149]: df_t1 = DataFrame({'int': [1, 2, None], 'date_obj': [datetime.datetime(2012, 1, 1), datetime.datetime(2012, 1, 1), None], 'date_date':[datetime.datetime(2012, 1, 1), datetime.datetime(2012, 1, 1), datetime.datetime(2012, 1, 1)]})
In [150]: df_t1.dtypes
Out[150]:
date_date datetime64[ns]
date_obj object
int float64
dtype: object
In [151]: df_t1.fillna(np.nan)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-151-782ffd0df53e> in <module>()
----> 1 df_t1.fillna(np.nan)
/usr/local/lib/python2.6/dist-packages/pandas-0.11.0.dev_156e03c-py2.6-linux-x86_64.egg/pandas/core/frame.pyc in fillna(self, value, method, axis, inplace, limit)
3385 return result
3386 else:
-> 3387 new_data = self._data.fillna(value, inplace=inplace)
3388
3389 if inplace:
/usr/local/lib/python2.6/dist-packages/pandas-0.11.0.dev_156e03c-py2.6-linux-x86_64.egg/pandas/core/internals.pyc in fillna(self, *args, **kwargs)
944
945 def fillna(self, *args, **kwargs):
--> 946 return self.apply('fillna', *args, **kwargs)
947
948 def astype(self, *args, **kwargs):
/usr/local/lib/python2.6/dist-packages/pandas-0.11.0.dev_156e03c-py2.6-linux-x86_64.egg/pandas/core/internals.pyc in apply(self, f, *args, **kwargs)
915 applied = f(blk, *args, **kwargs)
916 else:
--> 917 applied = getattr(blk,f)(*args, **kwargs)
918
919 if isinstance(applied,list):
/usr/local/lib/python2.6/dist-packages/pandas-0.11.0.dev_156e03c-py2.6-linux-x86_64.egg/pandas/core/internals.pyc in fillna(self, value, inplace)
212 new_values = self.values if inplace else self.values.copy()
213 mask = com.isnull(new_values)
--> 214 np.putmask(new_values, mask, value)
215
216 if inplace:
ValueError: Must be a datetime.date or datetime.datetime object
In [153]: del df_t1['date_date']
In [155]: df_t1.fillna(np.nan)
Out[155]:
date_obj int
0 2012-01-01 00:00:00 1
1 2012-01-01 00:00:00 2
2 NaN NaN
```
- and i tried to convert the 'datetime64[ns]' column's type to object, but i failed
``` python
In [162]: df_t1.date_date = df_t1.date_date.astype(df_t1.date_obj.dtype)
In [164]: df_t1.dtypes
Out[164]:
date_date datetime64[ns]
date_obj object
int float64
dtype: object
```
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} | 0 | "2013-03-14T03:14:26Z" | "2014-07-01T21:30:07Z" | "2013-03-14T03:40:17Z" | NONE | null | closes #2806
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} | 2 | "2013-03-14T11:39:34Z" | "2014-06-27T07:44:21Z" | "2013-03-15T12:22:45Z" | CONTRIBUTOR | null | this implements #3037
previously would failed if mixed_types was true,
so mixed int/float would fail
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https://api.github.com/repos/pandas-dev/pandas/issues/3053 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3053/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3053/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3053/events | https://github.com/pandas-dev/pandas/issues/3053 | 12,041,593 | MDU6SXNzdWUxMjA0MTU5Mw== | 3,053 | BUG: loc is not expanding an integer slice as labels | {
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} | 0 | "2013-03-14T22:11:46Z" | "2013-03-14T23:58:14Z" | "2013-03-14T23:58:14Z" | CONTRIBUTOR | null | see #2904 at the end
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} | 4 | "2013-03-14T23:00:05Z" | "2013-03-20T01:00:09Z" | "2013-03-20T01:00:09Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15417574/what-is-the-fastest-way-to-output-large-dataframe-into-a-csv-file
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https://api.github.com/repos/pandas-dev/pandas/issues/3055 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3055/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3055/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3055/events | https://github.com/pandas-dev/pandas/pull/3055 | 12,044,049 | MDExOlB1bGxSZXF1ZXN0NDYyODUzNA== | 3,055 | BUG: loc was failing on integer slices (incorrect implementation), GH 3053 | {
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see also #2904
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https://api.github.com/repos/pandas-dev/pandas/issues/3056 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3056/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3056/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3056/events | https://github.com/pandas-dev/pandas/issues/3056 | 12,045,510 | MDU6SXNzdWUxMjA0NTUxMA== | 3,056 | BUG: Add DataFrame column with dot notation fails | {
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} | [] | closed | false | null | [] | null | 2 | "2013-03-15T00:14:48Z" | "2013-03-15T20:47:46Z" | "2013-03-15T20:47:46Z" | CONTRIBUTOR | null | It seems like something weird is going on with the example below.
I often create a new column by mapping an old column. If I use dot notation with the creation of the new column the column is created and is accessible using dot notation, but it isn't listed as a column and can't be indexed the normal way (i.e. df['idx'])
Is this a bug or should I not be doing this?
Thanks, and btw pandas is terrific. This is my first bug report so apologies if its not helpful.
``` python
import pandas as pd
df=pd.DataFrame({'x':[1.,3.,5.],'y':[4,1.,7]})
df.columns
Out[3]: Index([x, y], dtype=object)
df.log_x=df.x.map(log)
df.columns
Out[5]: Index([x, y], dtype=object)
df.log_x
Out[6]:
0 0.000000
1 1.098612
2 1.609438
Name: x
df['log_x']=df.x.map(log)
df.columns
Out[8]: Index([x, y, log_x], dtype=object)
df.log_x
Out[9]:
0 0.000000
1 1.098612
2 1.609438
Name: x
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3057 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3057/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3057/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3057/events | https://github.com/pandas-dev/pandas/issues/3057 | 12,045,687 | MDU6SXNzdWUxMjA0NTY4Nw== | 3,057 | Partial Selection on MultiIndex: Control what index depth is returned. | {
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} | 0 | "2013-03-15T00:22:57Z" | "2014-02-13T23:12:31Z" | "2014-02-13T23:12:31Z" | NONE | null | Partial selection using .xs() & .ix[] on a subset of index levels returns a df with the fixed/selected levels dropped (a very nice feature). However, when you partially select using a tuple on all levels you get a data frame with all indices (levels) returned. It would be nice to have an option to return any/all indices when sub-selecting using subset of levels so there is consistency when you reach the lowest index level.
Perhaps there could be a "drop_fixed_index" parameter option when sub-selecting.
``` python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: print pd.__version__
0.11.0.dev-80945b6
In [4]: # Generate Test DataFrame
...: NUM_ROWS = 100000
...:
In [5]: NUM_COLS = 10
In [6]: col_names = ['A'+num for num in map(str,np.arange(NUM_COLS).tolist())]
In [7]: index_cols = col_names[:5]
In [8]: # Set DataFrame to have 5 level Hierarchical Index & Sort Index!
...: # The dtype does not matter try str or np.int64 same results.
...: df = pd.DataFrame(np.random.randint(5, size=(NUM_ROWS,NUM_COLS)), dtype=np.int64, columns=col_names)
...:
In [9]: df = df.set_index(index_cols).sort_index()
...
In [79]: df
Out[79]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 100000 entries, (0, 0, 0, 0, 0) to (4, 4, 4, 4, 4)
Data columns:
A5 100000 non-null values
A6 100000 non-null values
A7 100000 non-null values
A8 100000 non-null values
A9 100000 non-null values
dtypes: int64(5)
In [80]: df.ix[(0)]
Out[80]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 20011 entries, (0, 0, 0, 0) to (4, 4, 4, 4)
Data columns:
A5 20011 non-null values
A6 20011 non-null values
A7 20011 non-null values
A8 20011 non-null values
A9 20011 non-null values
dtypes: int64(5)
In [81]: df.ix[(0,1)]
Out[81]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 4007 entries, (0, 0, 0) to (4, 4, 4)
Data columns:
A5 4007 non-null values
A6 4007 non-null values
A7 4007 non-null values
A8 4007 non-null values
A9 4007 non-null values
dtypes: int64(5)
In [82]: df.ix[(0,1,2)]
Out[82]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 817 entries, (0, 0) to (4, 4)
Data columns:
A5 817 non-null values
A6 817 non-null values
A7 817 non-null values
A8 817 non-null values
A9 817 non-null values
dtypes: int64(5)
In [83]: df.ix[(0,1,2,3)]
Out[83]: <class 'pandas.core.frame.DataFrame'>
Int64Index: 162 entries, 0 to 4
Data columns:
A5 162 non-null values
A6 162 non-null values
A7 162 non-null values
A8 162 non-null values
A9 162 non-null values
dtypes: int64(5)
In [84]: df.ix[(0,1,2,3,4)]
Out[84]: A5 A6 A7 A8 A9
A0 A1 A2 A3 A4
0 1 2 3 4 1 2 2 4 2
4 1 4 4 1 0
4 2 1 4 1 3
4 2 4 2 1 1
4 1 1 2 1 4
4 0 0 2 1 1
4 2 0 0 3 1
4 2 2 3 3 1
4 3 0 3 4 1
4 1 1 0 0 1
4 2 1 0 2 4
4 3 4 1 2 3
4 0 4 3 1 0
4 4 1 4 1 2
4 1 3 4 3 3
4 0 1 1 3 1
4 2 2 2 0 3
4 0 0 1 4 0
4 1 0 1 4 2
4 1 4 2 2 0
4 4 2 0 3 1
4 2 1 2 3 2
4 4 2 0 1 4
4 1 4 1 1 4
4 1 0 1 2 4
4 2 3 0 1 3
4 2 1 3 3 3
4 1 2 0 4 2
4 3 0 4 4 0
4 4 4 2 3 0
4 0 0 1 3 2
4 4 0 0 0 3
4 2 0 3 4 2
4 3 3 3 0 2
4 4 2 2 0 1
4 2 1 3 4 0
In [86]: df.index.lexsort_depth
Out[86]: 5
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3058 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3058/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3058/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3058/events | https://github.com/pandas-dev/pandas/pull/3058 | 12,048,962 | MDExOlB1bGxSZXF1ZXN0NDYzMTEwMQ== | 3,058 | BUG: Handle concat of series with same name, GH2972 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3059 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3059/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3059/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3059/events | https://github.com/pandas-dev/pandas/pull/3059 | 12,049,172 | MDExOlB1bGxSZXF1ZXN0NDYzMTE5OQ== | 3,059 | ENH: improve performance of df.to_csv GH3054 | {
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} | 102 | "2013-03-15T03:26:12Z" | "2014-06-12T04:44:09Z" | "2013-03-19T11:37:53Z" | NONE | null | Needs more testing before merging.
Following [SO question](http://stackoverflow.com/questions/15417574) mentioned in #3054:
``` python
In [7]: def df_to_csv(df,fname):
...: fh=open(fname,'w')
...: fh.write(','.join(df.columns) + '\n')
...: for row in df.itertuples(index=False):
...: slist = [str(x) for x in row]
...: ss = ','.join(slist) + "\n"
...: fh.write(ss)
...: fh.close()
...:
...: aa=pd.DataFrame({'A':range(100000)})
...: aa['B'] = aa.A + 1.0
...: aa['C'] = aa.A + 2.0
...: aa['D'] = aa.A + 3.0
...:
...: %timeit -r10 aa.to_csv('/tmp/junk1',index=False)
...: %timeit -r10 df_to_csv(aa,'/tmp/junk2')
...: from hashlib import sha1
...: print sha1(open("/tmp/junk1").read()).hexdigest()
...: print sha1(open("/tmp/junk2").read()).hexdigest()
```
<table>
<tr>
<th>current pandas</th><th>with PR</th><th>example code</th>
</tr>
<tr>
<td>2.3 s</td><td>1.29 s</td><td>1.28 s</td>
</tr>
</table>
wins:
- convert numpy numerics to native types to eliminate expensive numpy specific
stringify calls.
- if number of columns is < 10000, precompute the `cols` loop range rather
then creating and walking a generator at each iteration of the inner loop.
- some cargo cult stuff that's probably in the noise.
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} | 67 | "2013-03-15T07:24:05Z" | "2014-06-18T11:32:43Z" | "2013-09-30T19:43:55Z" | CONTRIBUTOR | null | closes #1812
This pull request fixes handling of nanosecond times in periods and offsets. In addition it simplifies the internal processing of intraday periods.
``` python
In []: pd.date_range('2013-01-01', periods=5, freq=pd.offsets.Nano(5))
Out[]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-01 00:00:00.000000020]
Length: 5, Freq: 5N, Timezone: None
```
It requires Numpy 1.7 to work, using Numpy 1.6 yields the original behaviour before this PR.
This is the continuation of #2555 after it was merged without effect fixing issue #1812.
The biggest part is a refactoring of pandas/src/period.c to use a conversion factor matrix for intraday conversion. This minimizes the impact of adding three more intraday time units (ms, us and ns). Thus the amount of frequency conversion methods is reduced to one per intraday frequency source or target. The intraday conversion factor is looked up in the conversion factor matrix inside the method.
Nanosecond support is only available when using numpy >= 1.7. When using with numpy < 1.7 everything except nanosecond support should work as expected.
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] | closed | false | null | [] | null | 2 | "2013-03-15T17:06:29Z" | "2014-05-06T12:01:53Z" | "2013-03-19T23:08:05Z" | NONE | null | Hi all,
Below is a link to some tutorials I put together for new pandas users, feel free to add them where you see fit.
https://bitbucket.org/hrojas/learn-pandas
David
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} | 1 | "2013-03-15T20:30:26Z" | "2013-05-16T13:38:33Z" | "2013-05-16T13:38:33Z" | CONTRIBUTOR | null | This is known, but putting up for reference
easy (after reading) workaround, but could be done inilne
```
In [28]: df = pd.DataFrame(dict({
'A' : np.asarray(range(10),dtype='float64'),
'B' : pd.Timestamp('20010101') }))
In [29]: df.ix[3:6,:] = np.nan
In [30]: df
Out[30]:
A B
0 0 2001-01-01 00:00:00
1 1 2001-01-01 00:00:00
2 2 2001-01-01 00:00:00
3 NaN NaT
4 NaN NaT
5 NaN NaT
6 NaN NaT
7 7 2001-01-01 00:00:00
8 8 2001-01-01 00:00:00
9 9 2001-01-01 00:00:00
In [31]: df.dtypes
Out[31]:
A float64
B datetime64[ns]
dtype: object
In [32]: df.to_csv('test.h5')
In [33]: df2 = pd.read_csv('test.h5',index_col=0)
In [34]: df2.dtypes
Out[34]:
A float64
B object
dtype: object
```
To fix, force a conversion to datetimes
```
In [35]: df2['B'] = pd.to_datetime(df2['B'])
In [36]: df2
Out[36]:
A B
0 0 2001-01-01 00:00:00
1 1 2001-01-01 00:00:00
2 2 2001-01-01 00:00:00
3 NaN NaT
4 NaN NaT
5 NaN NaT
6 NaN NaT
7 7 2001-01-01 00:00:00
8 8 2001-01-01 00:00:00
9 9 2001-01-01 00:00:00
In [37]: df2.dtypes
Out[37]:
A float64
B datetime64[ns]
dtype: object
```
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} | 5 | "2013-03-16T00:54:27Z" | "2014-06-12T12:30:37Z" | "2013-03-18T19:07:09Z" | CONTRIBUTOR | null | fix scatter matrix axis labels for odd number of columns
https://github.com/pydata/pandas/issues/2756
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} | 0 | "2013-03-16T14:05:29Z" | "2014-07-16T08:05:32Z" | "2013-03-16T15:00:05Z" | CONTRIBUTOR | null | - replace on an IntBlock with a float will upcast, it may yield multiple blocks if the original IntBlock didn't fully replace
- small fix in com.mask_missing to guarantee that a bool ndarray is returned even in the case of a string comparison with a numeric array, (which for some reason numpy returns a single, non-ndarray value)
allows #3064 to proceed (e.g. df.replace(0,0.5) will work on an integer dtype)
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} | 7 | "2013-03-16T15:09:42Z" | "2014-06-07T16:57:58Z" | "2013-11-22T10:01:18Z" | NONE | null | xref: http://stackoverflow.com/questions/13353233/best-way-to-split-a-dataframe-given-an-edge/15449992#15449992
Here's one for you, jeff.
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} | 0 | "2013-03-16T19:45:30Z" | "2014-07-01T17:38:18Z" | "2013-03-16T20:18:25Z" | CONTRIBUTOR | null | ENH:
- _maybe_upcast_putmask now has the keyword, change to provide inline putmask changes to an object (series)
- apply in BlockManager now has a keyword, filter to allow acting on only those items contained in the filter (if supplied)
CLN:
- consolidated all replace subs to main replace in DataFrame (which calls replace in BlockManager)
now passed test in GH #3064
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} | 8 | "2013-03-17T01:21:36Z" | "2013-12-04T00:46:54Z" | "2013-03-31T23:44:44Z" | NONE | null | Same commit passed/failed:
https://travis-ci.org/y-p/pandas/builds/5559816
https://travis-ci.org/pydata/pandas/builds/5558843
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} | 0 | "2013-03-17T01:55:05Z" | "2013-03-22T16:20:40Z" | "2013-03-22T16:20:40Z" | NONE | null | ``` python
In [17]: s=pd.date_range("2001-1-1", periods=12)
In [20]: ts['2001']
Out[20]:
2001-01-01 0
2001-01-02 1
2001-01-03 2
2001-01-04 3
2001-01-05 4
2001-01-06 5
2001-01-07 6
2001-01-08 7
2001-01-09 8
2001-01-10 9
2001-01-11 10
2001-01-12 11
Freq: D, dtype: int64
In [24]: df=pd.DataFrame(range(len(s)),index=s)
In [25]: df['2001']
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
KeyError: u'no item named 2001'
In [27]: df.index is ts.index
Out[27]: True
```
related #2091
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} | 2 | "2013-03-17T02:11:47Z" | "2013-03-18T16:32:44Z" | "2013-03-17T03:11:33Z" | CONTRIBUTOR | null | ``` python
from cStringIO import StringIO
log_file = StringIO(
'posix_timestamp,elapsed,sys,user,queries,query_time,rows,'
'accountid,userid,contactid,level,silo,method\n'
'1343103150,0.062353,0,4,6,0.01690,3,'
'12345,1,-1,3,invoice_InvoiceResource,search\n'
)
import pandas as pd
from datetime import datetime
import gc
def posix_string_to_datetime(posix_string):
return datetime.utcfromtimestamp(int(posix_string))
# This works on pandas 0.9.0, but not on 0.10.1 or github master
df = pd.io.parsers.read_csv(
log_file,
# index_col is the first column, our posix_timestamp
index_col=0,
# Interpret the index column as a date
parse_dates=0,
date_parser=posix_string_to_datetime)
```
The crash looks like this (on 0.11.0.dev-6e7b37b, OSX 10.6 if it matters):
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-c3fa4840399b> in <module>()
17 # Interpret the index column as a date
18 parse_dates=0,
---> 19 date_parser=posix_string_to_datetime)
20 gc.enable()
/Users/taavi/src/pandas/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)
398 buffer_lines=buffer_lines)
399
--> 400 return _read(filepath_or_buffer, kwds)
401
402 parser_f.__name__ = name
/Users/taavi/src/pandas/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
213 return parser
214
--> 215 return parser.read()
216
217 _parser_defaults = {
/Users/taavi/src/pandas/pandas/io/parsers.pyc in read(self, nrows)
630 # self._engine.set_error_bad_lines(False)
631
--> 632 ret = self._engine.read(nrows)
633
634 if self.options.get('as_recarray'):
/Users/taavi/src/pandas/pandas/io/parsers.pyc in read(self, nrows)
1006
1007 names, data = self._do_date_conversions(names, data)
-> 1008 index = self._make_index(data, alldata, names)
1009
1010 return index, names, data
/Users/taavi/src/pandas/pandas/io/parsers.pyc in _make_index(self, data, alldata, columns)
706 elif not self._has_complex_date_col:
707 index = self._get_simple_index(alldata, columns)
--> 708 index = self._agg_index(index)
709
710 elif self._has_complex_date_col:
/Users/taavi/src/pandas/pandas/io/parsers.pyc in _agg_index(self, index, try_parse_dates)
789 self.na_values)
790
--> 791 arr, _ = self._convert_types(arr, col_na_values)
792 arrays.append(arr)
793
/Users/taavi/src/pandas/pandas/io/parsers.pyc in _convert_types(self, values, na_values, try_num_bool)
815 def _convert_types(self, values, na_values, try_num_bool=True):
816 na_count = 0
--> 817 if issubclass(values.dtype.type, (np.number, np.bool_)):
818 mask = lib.ismember(values, na_values)
819 na_count = mask.sum()
AttributeError: 'datetime.datetime' object has no attribute 'dtype'
```
Thanks!
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https://api.github.com/repos/pandas-dev/pandas/issues/3072 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3072/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3072/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3072/events | https://github.com/pandas-dev/pandas/pull/3072 | 12,104,115 | MDExOlB1bGxSZXF1ZXN0NDY1NjA5NA== | 3,072 | BUG: replace with a dict misbehaving (GH 3064), due to incorrect filtering | {
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} | 0 | "2013-03-17T03:14:09Z" | "2013-03-17T18:38:37Z" | "2013-03-17T03:54:56Z" | CONTRIBUTOR | null | rolled #3064 changes in here
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https://api.github.com/repos/pandas-dev/pandas/issues/3073 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3073/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3073/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3073/events | https://github.com/pandas-dev/pandas/pull/3073 | 12,105,245 | MDExOlB1bGxSZXF1ZXN0NDY1NjQ4Mw== | 3,073 | Fix figsize issue when using matplotlib locally | {
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} | 1 | "2013-03-17T05:51:24Z" | "2013-04-08T16:24:58Z" | "2013-04-08T16:24:46Z" | CONTRIBUTOR | null | This is a pretty minor issue, but I'm seeing the following when passing `figsize=(20,10)` when plotting a Series in the `ipython --pylab` environment:
Everything's there - the window is just initialized to the wrong size. Without the `--pylab` flag and manually calling `plt.show()`, the window shows up as the default size (which is incorrect).
The issue appears to be that `plot_series()` will call `plt.gca()` before `figsize=` has been passed to `plt.figure()`. This initializes the window to the default size and it apparently isn't updated when redrawn.
What this patch does is use `plt.get_fignums()` to see if we have any existing figures. If so, we can call `plt.gca()` without automatically creating a figure and window at the default sizes.
The addition of `new_ax.right_axis = new_ax` is to accommodate Series plotted with `secondary_y=True` and thus getting `new_ax` as their `Axes`. It renders fine without it but a few tests expect `right_axis` to exist. This could be handled differently if this is inappropriate.
Finally, there's a test to check that `figsize=` is at least implemented on the figure. I don't know of a way to test this bug in a backend-independent way, so this only checks that we're not regressing to the pre-v0.10.0 state where `figsize=` was silently ignored by `plot_series()`.
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} | 34 | "2013-03-17T15:34:20Z" | "2016-10-13T09:13:53Z" | "2013-03-20T08:16:47Z" | NONE | null | the error log is
Searching for pandas
Reading http://pypi.python.org/simple/pandas/
Reading http://pandas.pydata.org
Reading http://pandas.sourceforge.net
Download error: [Errno 60] Operation timed out -- Some packages may not be found!
Best match: pandas 0.10.1
Downloading http://pypi.python.org/packages/source/p/pandas/pandas-0.10.1.zip#md5=b9127d9695aa501e3696a608427622ca
Processing pandas-0.10.1.zip
Running pandas-0.10.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-Oiy1Kb/pandas-0.10.1/egg-dist-tmp-Vl3z2X
error: Setup script exited with pandas requires NumPy >= 1.6.1 due to datetime64 dependency
haibos-MacBook-Air:tyrannosaurus timger$ sudo pip install pandas
Password:
Downloading/unpacking pandas
Downloading pandas-0.10.1.zip (2.8Mb): 2.8Mb downloaded
Running setup.py egg_info for package pandas
pandas requires NumPy >= 1.6.1 due to datetime64 dependency
Complete output from command python setup.py egg_info:
pandas requires NumPy >= 1.6.1 due to datetime64 dependency
---
Command python setup.py egg_info failed with error code 1 in /Users/timger/bitbucket/tyrannosaurus/build/pandas
Storing complete log in /Users/timger/Library/Logs/pip.log
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} | 6 | "2013-03-17T17:44:34Z" | "2013-03-23T00:42:45Z" | "2013-03-23T00:42:45Z" | NONE | null | https://gist.github.com/huyng/816622
``` python
s={'axes.axisbelow': True,
'axes.color_cycle': ['#348ABD',
'#7A68A6',
'#A60628',
'#467821',
'#CF4457',
'#188487',
'#E24A33'],
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'axes.titlesize': 'x-large',
'figure.edgecolor': 'white',
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'figure.figsize': (6.0, 4.0),
'figure.subplot.hspace': 0.5,
'font.family': 'monospace',
'font.monospace': ['Andale Mono',
'Nimbus Mono L',
'Courier New',
'Courier',
'Fixed',
'Terminal',
'monospace'],
'font.size': 10,
'interactive': True,
'keymap.all_axes': ['a'],
'keymap.back': ['left', 'c', 'backspace'],
'keymap.forward': ['right', 'v'],
'keymap.fullscreen': ['f'],
'keymap.grid': ['g'],
'keymap.home': ['h', 'r', 'home'],
'keymap.pan': ['p'],
'keymap.save': ['s'],
'keymap.xscale': ['L', 'k'],
'keymap.yscale': ['l'],
'keymap.zoom': ['o'],
'legend.fancybox': True,
'lines.antialiased': True,
'lines.linewidth': 1.0,
'patch.antialiased': True,
'patch.edgecolor': '#EEEEEE',
'patch.facecolor': '#348ABD',
'patch.linewidth': 0.5,
'toolbar': 'toolbar2',
'xtick.color': '#555555',
'xtick.direction': 'in',
'xtick.major.pad': 6.0,
'xtick.major.size': 0.0,
'xtick.minor.pad': 6.0,
'xtick.minor.size': 0.0,
'ytick.color': '#555555',
'ytick.direction': 'in',
'ytick.major.pad': 6.0,
'ytick.major.size': 0.0,
'ytick.minor.pad': 6.0,
'ytick.minor.size': 0.0}
matplotlib.RcParams.update(s)
```
Just need to figure out a way not to clobber user's custom matplotlibrc
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} | 2 | "2013-03-17T19:31:23Z" | "2016-07-31T18:35:14Z" | "2016-07-31T18:35:14Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15462344/writing-the-result-of-multiple-calls-on-a-manipulated-dataframe
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https://api.github.com/repos/pandas-dev/pandas/issues/3078 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3078/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3078/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3078/events | https://github.com/pandas-dev/pandas/pull/3078 | 12,117,832 | MDExOlB1bGxSZXF1ZXN0NDY2MDg4MQ== | 3,078 | ENH: support iteration on returned results in select and select_as_multiple in HDFStore | {
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`chunksize=number_in_a_chunk`, (default is 50,000) rows are
provided to support iteration on `select` and `select_as_multiple` results
DOC and TST included
closes #3076
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} | 4 | "2013-03-18T06:11:14Z" | "2014-06-29T10:22:15Z" | "2013-04-08T17:04:06Z" | CONTRIBUTOR | null | slightly different fix for #3071
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Eg:
``` python
In [3]: t = np.arange(start='2012-01-01 00:00:01', stop='2012-01-01 00:00:10', d
type='datetime64')
In [4]: df = pd.DataFrame(np.arange(len(t)))
In [5]: df['time'] = t
```
Using the `diff` method gives an error:
``` python
In [6]: df['time'].diff()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-83fe0aa0c1ed> in <module>()
----> 1 df['time'].diff()
C:\Anaconda\lib\site-packages\pandas\core\series.pyc in diff(self, periods)
1736 diffed : Series
1737 """
-> 1738 result = com.diff(self.values, periods)
1739 return Series(result, self.index, name=self.name)
1740
C:\Anaconda\lib\site-packages\pandas\core\common.pyc in diff(arr, n, axis)
475 na_indexer = [slice(None)] * arr.ndim
476 na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
--> 477 out_arr[tuple(na_indexer)] = np.nan
478
479 if arr.ndim == 2 and arr.dtype.name in _diff_special:
ValueError: Could not convert object to NumPy datetime
```
while the numpy diff works as expected:
``` python
In [7]: np.diff(df['time'].values)
Out[7]:
array([1000000000, 1000000000, 1000000000, 1000000000, 1000000000,
1000000000, 1000000000, 1000000000], dtype='timedelta64[ns]')
```
The problems seems to be that `np.nan` cannot be converted to a numpy datetime64.
My code also did work with a previously version of numpy and/or pandas (but am not certain wich version. Before I updated my scientific stack with anaconda).
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] | closed | false | null | [] | null | 1 | "2013-03-18T16:26:32Z" | "2013-03-18T16:33:53Z" | "2013-03-18T16:33:53Z" | MEMBER | null | from @dhirschfeld
I just ran across the bug described in issue #3020 (https://github.com/pydata/pandas/issues/3020) as our unit-tests started failing after upgrading pandas.
I did a git bisection and found the first bad commit to be 9deda9e6:
```
9deda9e687915017cc9db7698504ead1f89ead9e is the first bad commit
commit 9deda9e687915017cc9db7698504ead1f89ead9e
Author: Chang She
Date: Sun Dec 2 02:21:10 2012 -0500
API: change resample to infer correct closed and label value by default #2363
:040000 040000 3abca1d3eb70bcd6ade31581acc4684a3035b81e 4cdeae975685b996ef2f41c738ca70bfd41ca583 M pandas
```
I think this is a fairly serious regression as (apart from causing our unit-tests to fail) it will silently give incorrect answers for an aggregation if the data has been resampled beforehand.
The following unit-test demonstrates the problem:
def test_resample_doesnt_truncate():
"""Test for issue #3020"""
import pandas as pd
dates = pd.date_range('01-Jan-2014','05-Jan-2014', freq='D')
series = pd.TimeSeries(1, index=dates)
series = series.resample('D')
assert series.index[0] == dates[0]
#
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} | 7 | "2013-03-18T18:59:46Z" | "2015-01-26T00:45:21Z" | "2015-01-26T00:45:21Z" | CONTRIBUTOR | null | similar to one encountered in @wesm pydata 2013 tutorial
```
In [9]: import pandas as pd
In [10]: idx = pd.MultiIndex.from_arrays([[2011,2011,2012,2012,2013], [1,2,1,2,1]])
In [11]: ser = pd.Series(randn(len(idx)), idx)
In [12]: ser
Out[12]:
2011 1 -0.485310
2 -0.679528
2012 1 1.089995
2 -0.174895
2013 1 -1.219255
dtype: float64
In [13]: ser.ix[2011:2012]
Out[13]:
2011 1 -0.485310
2 -0.679528
2012 1 1.089995
2 -0.174895
dtype: float64
In [14]: ser[2011:2012]
Out[14]: Series([], dtype: float64)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3085 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3085/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3085/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3085/events | https://github.com/pandas-dev/pandas/pull/3085 | 12,152,861 | MDExOlB1bGxSZXF1ZXN0NDY3NzgzNw== | 3,085 | Resample docstring default values changed | {
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} | 4 | "2013-03-18T20:30:09Z" | "2014-07-09T07:31:43Z" | "2013-03-25T15:34:30Z" | CONTRIBUTOR | null | This docstring seems better, since TimeGrouper specifies it [that way](https://github.com/pydata/pandas/blob/master/pandas/tseries/resample.py#L27) too:
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} | 4 | "2013-03-18T21:26:15Z" | "2019-01-01T17:29:36Z" | "2019-01-01T17:29:35Z" | CONTRIBUTOR | null | maybe do it like the nltk tokenizer and handle stuff like punctuations, various brackets, and quotes. And have optional delimiters.
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https://api.github.com/repos/pandas-dev/pandas/issues/3087 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3087/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3087/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3087/events | https://github.com/pandas-dev/pandas/issues/3087 | 12,174,559 | MDU6SXNzdWUxMjE3NDU1OQ== | 3,087 | BUG: resample doesn't work for non-numeric types | {
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} | 4 | "2013-03-19T10:43:47Z" | "2018-07-06T23:03:46Z" | "2018-07-06T23:03:36Z" | CONTRIBUTOR | null | After discussion on the ML it was decided the below function _should_ work in pandas.
It fails on the current master (0.11.0.dev-308beb1) with 32bit Python 2.7 on windows
``` python
def test_resample_nonnumeric():
import numpy as np
import pandas as pd
dates = pd.date_range('01-Jan-2014','05-Jan-2014', freq='D')
series = pd.TimeSeries(['a','b','c','d','e'], index=dates)
resampled_series = series[[0,1,3,4]].resample('D', fill_method='ffill')
assert (resampled_series.index == dates).all()
assert (resampled_series.values == np.asarray(['a','b','b','d','e'], dtype=object)).all()
```
https://groups.google.com/forum/?fromgroups=#!topic/pydata/NFA10wTVNu8
addtl example (related), how doing weird things:
```
In [30]: df = pd.DataFrame({
'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
DT.datetime(2013,9,1,13,0),
DT.datetime(2013,9,1,13,5),
DT.datetime(2013,10,1,20,0),
DT.datetime(2013,10,3,10,0),
DT.datetime(2013,12,2,12,0),
DT.datetime(2013,12,2,14,0),
]}).set_index(['Date'])
In [31]: df
Out[31]:
Buyer Quantity
Date
2013-09-01 13:00:00 Carl 1
2013-09-01 13:05:00 Mark 3
2013-10-01 20:00:00 Carl 5
2013-10-03 10:00:00 Joe 8
2013-12-02 12:00:00 Joe 9
2013-12-02 14:00:00 Carl 3
In [33]: df['Quantity'].resample('10D',how=sum)
Out[33]:
Date
2013-09-01 13:00:00 4.000000e+00
2013-09-11 13:00:00 NaN
2013-09-21 13:00:00 NaN
2013-10-01 13:00:00 1.300000e+01
2013-10-11 13:00:00 NaN
2013-10-21 13:00:00 NaN
2013-10-31 13:00:00 NaN
2013-11-10 13:00:00 NaN
2013-11-20 13:00:00 NaN
2013-11-30 13:00:00 1.200000e+01
2013-12-10 13:00:00 7.637868e-317
Freq: 10D, dtype: float64
```
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} | 32 | "2013-03-19T15:42:34Z" | "2013-03-21T21:32:55Z" | "2013-03-21T11:37:11Z" | CONTRIBUTOR | null | f6ac8c091dfb2bbbbb02a34c13022c5427b290eb to 05a737dbb9f857e25a211739d90c42f3d9428b8e
Bad news:
```
Results:
t_head t_baseline ratio
name
reindex_frame_level_reindex 1.1343 0.7571 1.4982
reindex_frame_level_align 1.1690 0.7548 1.5488
```
Good news:
```
Results:
t_head t_baseline ratio
name
frame_get_dtype_counts 0.0968 208.6380 0.0005
frame_wide_repr 0.5321 207.0730 0.0026
groupby_first_float32 3.0386 336.2439 0.0090
groupby_last_float32 3.2206 335.2571 0.0096
frame_to_csv2 181.1550 2188.9260 0.0828
indexing_dataframe_boolean 12.9179 125.7350 0.1027
write_csv_standard 36.3702 230.9210 0.1575
frame_reindex_axis0 0.3122 1.0984 0.2842
frame_to_csv_mixed 340.1070 1091.9340 0.3115
frame_to_csv 106.5671 219.1830 0.4862
frame_add 22.0693 34.1867 0.6456
frame_mult 22.0064 33.9859 0.6475
frame_reindex_upcast 11.4179 17.3291 0.6589
frame_fancy_lookup_all 14.3931 20.5621 0.7000
series_string_vector_slice 148.3371 195.5922 0.7584
```
???
```
frame_reindex_axis1 2.3612 2.3386 1.0097
```
Fixed:
```
indexing_dataframe_boolean_rows 0.2180 0.1953 1.1164
```
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} | 5 | "2013-03-19T16:06:42Z" | "2013-04-30T04:15:15Z" | "2013-04-24T22:40:47Z" | NONE | null | a reminder, new `df.to_csv()` code was introduced in 0.11.
the old code is still available via the `engine=python` kwd as a failsafe
against bugs discovered after release.
If all is well come 0.12 time, 7776128 can be revert and dead
code eliminated.
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https://api.github.com/repos/pandas-dev/pandas/issues/3091 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3091/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3091/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3091/events | https://github.com/pandas-dev/pandas/issues/3091 | 12,193,075 | MDU6SXNzdWUxMjE5MzA3NQ== | 3,091 | Pandas HDFStore conflicts with osgeo library, but works fine if osgeo is imported after HDFStore was used | {
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} | [] | closed | false | null | [] | null | 4 | "2013-03-19T17:53:44Z" | "2013-03-20T10:15:37Z" | "2013-03-20T10:15:37Z" | NONE | null | If I import some osgeo modules (e.g., osgeo.ogr or osgeo.gdal), I get some serious error message (**_HDF5 library version mismatched error**_) when I try to store a pandas DataFrame in a just created HDFStore. Following, it requests the Runtime to terminate it in an unusual way. See below:
> > > store = pd.HDFStore(fnh5)
Warning! **_HDF5 library version mismatched error**_
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 will suppress the warning messages totally.
Headers are 1.8.8, library is 1.8.2
Bye...
This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.
---
I am not sure where the bug is, in the osgeo library, or the pandas library, but I do know that if I import the osgeo library after I have used and closed a HDF5 file, the problem does not anymore occur, as I tried with the following script (while commenting/uncommenting the "import osgeo.ogr as ogr" line:
# import osgeo.ogr as ogr
import pandas as pd
import os.path
### Test whether osgeo cause problems with pandas HDFStore:
fnh5 = 'test5.h5'
df = pd.DataFrame([1,2,3,4], index=pd.date_range('1/1/2000', periods=4), columns=['A'])
store = pd.HDFStore(fnh5)
store['df'] = df
store.close()
os.remove(fnh5)
### I get the warning and the application is terminated
### Now try to use HDFStore with a later import of osgeo:
import osgeo.ogr as ogr
store = pd.HDFStore(fnh5)
store['df'] = df
store.close()
os.remove(fnh5)
### I receive no error/warning
I am using Python 2.7.2 and tried the script in Spyder IDE and running it from the Windows command line (Windows 7, 64bit). Pandas version is 0.10.1', numpy 1.6.2 and I use osgeo 1.9.2.
Using this trick, I managed to run my scripts, but it is annoying. Therefore it would be good to find out what causes this behaviour, but I have no clue on where to start looking for it.
Thanks for your support (and the great work on pandas!!!!)
Many thanks for
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} | 4 | "2013-03-19T18:25:16Z" | "2013-05-02T14:52:43Z" | "2013-05-02T14:52:43Z" | CONTRIBUTOR | null | see discussion in #3059, #3095, also see #1943, #3102
This only applies with a non-unique column index
Currently if duplicate columns across dtypes there are issues in getting the correct block given a column name.
I think it is possible, though non-trivial, to instead have a positional map from the frame columns to the BlockManager blocks, will simplify BlockManager.iget.
Primary motivation is to_csv currently cannot handle these types of lookups.
Also should eliminate need for _find_block
```
In [6]: df = pd.DataFrame(np.random.randn(8,4))
In [12]: df = pd.DataFrame(np.random.randn(8,4))
In [13]: df._data.blocks[0].ref_locs
Out[13]: array([0, 1, 2, 3])
In [14]: df = pd.DataFrame(np.random.randn(8,4),columns=['a']*4)
In [15]: df._data.blocks[0].ref_locs
---------------------------------------------------------------------------
/mnt/home/jreback/pandas/pandas/core/internals.py in ref_locs(self)
52 def ref_locs(self):
53 if self._ref_locs is None:
---> 54 indexer = self.ref_items.get_indexer(self.items)
55 indexer = com._ensure_platform_int(indexer)
56 if (indexer == -1).any():
/mnt/home/jreback/pandas/pandas/core/index.pyc in get_indexer(self, target, method, limit)
835
836 if not self.is_unique:
--> 837 raise Exception('Reindexing only valid with uniquely valued Index '
838 'objects')
839
Exception: Reindexing only valid with uniquely valued Index objects
```
This is the root of all evil, this should raise the same as above (but doesn't even if
I consolidate)......
```
In [16]: df = pd.DataFrame(np.random.randn(8,4))
In [17]: df.columns = ['a']*4
In [18]: df._data.blocks[0].ref_locs
Out[18]: array([0, 1, 2, 3])
```
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} | 0 | "2013-03-19T18:51:23Z" | "2013-03-19T23:54:32Z" | "2013-03-19T18:51:33Z" | CONTRIBUTOR | null | mainly for user facing routines (defaults to True)
was causing a perf regression (see #3089)
#3033 will 'fix' this as will create a new internal routine to do takes
(and make DataFrame/take) user facing only
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} | 14 | "2013-03-19T19:00:26Z" | "2013-03-23T17:38:05Z" | "2013-03-23T17:38:05Z" | NONE | null | ## ERROR: test_operators_timedelta64 (pandas.tests.test_series.TestSeries)
Traceback (most recent call last):
File "C:\Python27\lib\site-packages\pandas\tests\test_series.py", line 1752, i
n test_operators_timedelta64
result = resultb + df['A']
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 144, in wrapp
er
lvalues = lvalues.values
AttributeError: 'numpy.ndarray' object has no attribute 'values'
---
Ran 3113 tests in 241.670s
FAILED (SKIP=94, errors=1)
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} | 13 | "2013-03-19T19:11:25Z" | "2013-04-30T18:20:28Z" | "2013-04-30T18:18:36Z" | NONE | null | Continuing #3059.
See also #3092
- allow dupe columns when they are in the same block/dtype
- Perhaps figure out a way to handle that case as well.
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} | 3 | "2013-03-19T19:12:20Z" | "2014-05-29T06:26:58Z" | "2014-05-29T06:26:58Z" | CONTRIBUTOR | null | This was found while attempting to add thousand separators (commas) on a Series.to_string() call. It looks like you can specify the format for floats, but not for integers.
It's impossible right now. I could transform the Series instance into a FP representation, but that is the wrong thing to do.
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} | 4 | "2013-03-19T20:04:10Z" | "2013-03-20T06:53:45Z" | "2013-03-20T06:53:45Z" | MEMBER | null | There's a build issue with the latest version. The issue is that an `import time` statement in `tslib.pyx` triggers an error from Cython stating: "Assignment to non-lvalue 'time'"
`git bisect` between `HEAD` and the SHA1 of the 10.1 release (builds correctly) shows that 015447a introduces this bug. What's strange is that I've successfully compiled pandas many times since this commit.
I thought it might be a Cython issue so I tried a simple `import time` statement using IPython's cython cell magic and that works. Also, my fork with the latest changes also builds correctly. It builds correctly when `import time` is moved to the top of the file. I'm guessing that `PyDateTime_IMPORT` imports something called `time` and another import tries to write over it although I'm not sure why that would be an issue since in regular Python the old name just gets overwritten.
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} | 5 | "2013-03-19T21:21:38Z" | "2013-03-20T00:31:03Z" | "2013-03-20T00:30:53Z" | MEMBER | null | See #3097.
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} | 1 | "2013-03-19T22:15:02Z" | "2013-03-20T18:55:45Z" | "2013-03-20T18:55:28Z" | NONE | null | This looks like it broke during the cython refactor a couple of months ago,
which is a shame, because it allowed to test python-only (no pyx changes)
changes on py26/27/32/33 in about a minute, where travis takes about 15.
There's also the detox project for doing what tox_prll does, but it seems
to have diverged from the latest tox version. perhaps worth it to fix it upstream,
tox_prll is a (useful) hack.
this was 0947ff0 and c8e8779
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] | {
"closed_at": "2013-07-24T23:25:44Z",
"closed_issues": 410,
"created_at": "2013-04-30T04:12:30Z",
"creator": null,
"description": "Major release (formerly 0.11.1 bugfix but has outgrown its britches)",
"due_on": "2013-07-05T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/22",
"id": 323643,
"labels_url": "https://api.github.com/repos/pandas-dev/pandas/milestones/22/labels",
"node_id": "MDk6TWlsZXN0b25lMzIzNjQz",
"number": 22,
"open_issues": 0,
"state": "closed",
"title": "0.12",
"updated_at": "2013-07-24T23:25:44Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/22"
} | 1 | "2013-03-19T23:56:04Z" | "2013-06-14T16:14:45Z" | "2013-05-09T18:53:09Z" | CONTRIBUTOR | null | see #2984
workaround right now is
```
In [4]: s = pd.Series(pd.date_range('20130102',periods=5))
In [5]: s
Out[5]:
0 2013-01-02 00:00:00
1 2013-01-03 00:00:00
2 2013-01-04 00:00:00
3 2013-01-05 00:00:00
4 2013-01-06 00:00:00
dtype: datetime64[ns]
This is the workaround right now
In [6]: s-s.shift(1)
Out[6]:
0 NaT
1 1 days, 00:00:00
2 1 days, 00:00:00
3 1 days, 00:00:00
4 1 days, 00:00:00
dtype: timedelta64[ns]
```
Bad.....
```
In [7]: s.diff()
Out[7]:
0 NaN
1 8.640000e+13
2 8.640000e+13
3 8.640000e+13
4 8.640000e+13
dtype: float64
```
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