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https://api.github.com/repos/pandas-dev/pandas/issues/5301 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5301/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5301/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5301/events | https://github.com/pandas-dev/pandas/pull/5301 | 21,459,103 | MDExOlB1bGxSZXF1ZXN0OTMzMjI3Mw== | 5,301 | BUG: setting on a frame without an index silently was failing, related (GH5226) | {
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} | 0 | 2013-10-23T14:51:25Z | 2014-06-23T20:59:54Z | 2013-10-23T15:30:29Z | CONTRIBUTOR | null | related to #5226
the following would previously silently fail:
```
In [3]: df = DataFrame(columns=['A','B'])
In [4]: df[0] = Series(1,index=range(4))
In [5]: df
Out[5]:
A B 0
0 NaN NaN 1
1 NaN NaN 1
2 NaN NaN 1
3 NaN NaN 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5302 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5302/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5302/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5302/events | https://github.com/pandas-dev/pandas/issues/5302 | 21,477,686 | MDU6SXNzdWUyMTQ3NzY4Ng== | 5,302 | pd.merge() crashes with core/internals.py error | {
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} | [] | closed | false | null | [] | null | 4 | 2013-10-23T19:12:49Z | 2013-10-23T20:37:43Z | 2013-10-23T20:37:43Z | NONE | null | Hello,
After I did a couple of merges on DataFrames, they crashed with some strange message. After stripping all unnecessary info, here is what I've got:
Pandas version: '0.12.0'
Numpy version: '1.6.1
How to reproduce:
Create three files with the following content:
===input0.csv===
```
,myid,lang,someid
0,0,en,0
1,1,,1
2,2,fr,2
```
===input1.csv===
```
,myid,type,someid
0,0,0.0,0
1,1,,1
2,2,2.0,2
```
===input2.csv===
```
,lang,type
0,en,10.0
1,,1.0
2,fr,
```
Run the following code:
```
x0 = pd.DataFrame.from_csv('input0.csv')
x1 = pd.DataFrame.from_csv('input1.csv')
x2 = pd.DataFrame.from_csv('input2.csv')
x01 = pd.merge(x0, x1, on=['myid'], sort=True)
pd.merge(x01, x2, how='inner', on=['type'])
```
The second merge will fail with the following stacktrace:
TypeError Traceback (most recent call last)
<ipython-input-278-15f969f80378> in <module>()
----> 1 x01 = pd.merge(x0, x1, on=['myid'], sort=True); pd.merge(x01, x2, how='inner', on=['type'])
/google/data/ro/projects/quality/ranklab/tools/iranklab.par/google3/third_party/py/pandas/tools/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
/google/data/ro/projects/quality/ranklab/tools/iranklab.par/google3/third_party/py/pandas/tools/merge.py in get_result(self)
/google/data/ro/projects/quality/ranklab/tools/iranklab.par/google3/third_party/py/pandas/tools/merge.py in _maybe_add_join_keys(self, result, left_indexer, right_indexer)
/google/data/ro/projects/quality/ranklab/tools/iranklab.par/google3/third_party/py/pandas/core/frame.py in **getitem**(self, key)
/google/data/ro/projects/quality/ranklab/tools/iranklab.par/google3/third_party/py/pandas/core/generic.py in _get_item_cache(self, item)
/google/data/ro/projects/quality/ranklab/tools/iranklab.par/google3/third_party/py/pandas/core/internals.py in get(self, item)
TypeError: 'NoneType' object is not iterable
#
Thanks a lot
Olexiy
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} | 0 | 2013-10-23T21:03:57Z | 2014-06-25T23:10:50Z | 2013-10-24T10:21:38Z | CONTRIBUTOR | null | closes #5021
```
In [6]: s = Series(date_range('jan-01-2013','jan-05-2013'))
In [7]: s
Out[7]:
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
3 2013-01-04 00:00:00
4 2013-01-05 00:00:00
dtype: datetime64[ns]
In [8]: s.isin(s[0:2])
Out[8]:
0 True
1 True
2 False
3 False
4 False
dtype: bool
In [9]: s = Series(pd.to_timedelta(list(range(5)),unit='d'))
In [10]: s
Out[10]:
0 00:00:00
1 1 days, 00:00:00
2 2 days, 00:00:00
3 3 days, 00:00:00
4 4 days, 00:00:00
dtype: timedelta64[ns]
In [11]: s.isin(s[0:2])
Out[11]:
0 True
1 True
2 False
3 False
4 False
dtype: bool
```
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} | 6 | 2013-10-23T21:36:02Z | 2013-10-28T01:14:47Z | 2013-10-28T01:14:47Z | CONTRIBUTOR | null | - [x] Add 'rows' as synonym for 'index' for DataFrame and Series - #5309
- [x] Allow Series to accept axis kwarg for flex ops (and only allow axis=0) - #5352
- [x] Fix Panel functions that aren't getting axis correctly - #5354
I think (2) may already be there, but I'm not sure and I want to check that.
This should be really trivial to do but it should make interactive playing around with frame and series a bit more intuitive. Don't think it makes sense for panel.
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} | 0 | 2013-10-23T23:55:58Z | 2014-07-16T08:36:35Z | 2013-10-24T00:13:07Z | CONTRIBUTOR | null | related #4521
```
In [4]: td = pd.to_timedelta([0,1,2],unit='D')
In [5]: td
Out[5]:
0 00:00:00
1 1 days, 00:00:00
2 2 days, 00:00:00
dtype: timedelta64[ns]
In [6]: td / np.timedelta64(1,'s')
Out[6]:
0 0
1 86400
2 172800
dtype: float64
```
This is new (used to raise)
```
In [7]: td.astype('timedelta64[s]')
Out[7]:
0 0
1 86400
2 172800
dtype: float64
```
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} | 2 | 2013-10-24T01:11:47Z | 2014-07-28T14:58:21Z | 2014-07-28T14:58:21Z | CONTRIBUTOR | null | Imports https://github.com/pydata/pandas/blob/master/pandas/tseries/tools.py#L213 and https://github.com/pydata/pandas/blob/master/pandas/tseries/tools.py#L214 are not used:
```
from pandas.tseries.offsets import DateOffset
from pandas.tseries.frequencies import (_get_rule_month, _month_numbers,
_get_freq_str)
```
Is there any reason to leave these?
Is there an open ticket for removing unused imports?
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} | 2 | 2013-10-24T01:34:08Z | 2021-04-11T03:46:38Z | null | CONTRIBUTOR | null | `startingMonth` is the month the quarter ends. This is confusing.
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} | 1 | 2013-10-24T01:42:57Z | 2014-07-16T08:36:37Z | 2013-10-24T11:32:14Z | CONTRIBUTOR | null | goes back to @cpcloud's original plugin that echo'd separately. Just
until @cpcloud has time to implement this for nose-skipped.
Just makes it much easier to read through Travis builds (especially for
new users) if the print_skipped stuff is hidden at first glance.
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} | [] | closed | false | null | [] | null | 2 | 2013-10-24T02:03:08Z | 2014-07-16T08:36:38Z | 2013-10-25T09:59:02Z | CONTRIBUTOR | null | Think this covers it - think I have an appropriate number of cases too.
I don't actually want to put this in the docs, as it's just for
convenience. Thoughts on that?
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https://api.github.com/repos/pandas-dev/pandas/issues/5310 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5310/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5310/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5310/events | https://github.com/pandas-dev/pandas/issues/5310 | 21,508,260 | MDU6SXNzdWUyMTUwODI2MA== | 5,310 | TypeError when plotting TimeSeries with NaN data, maybe a BUG? | {
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} | [] | closed | false | null | [] | null | 7 | 2013-10-24T08:32:06Z | 2013-10-28T12:27:28Z | 2013-10-24T13:53:46Z | NONE | null | I get a TypeError message when I try to plot a pandas TimeSeries object that contains NaN's. The strange thing is, this does not happen when I have 'inf' values, or when a normal series is used with integer index values.
Now, all of a sudden (no recent updates I can think of) all my plotting scripts get the TypeError, but before they worked fine. I can do dropna() before, but then missing data is interpolated in the plot, which I do not want; the data is after all missing... If I do fillna(np.inf), things are plotting as I like.
As discussed below, I am not sure this is a bug in pandas 0.12 / matplotlib 1.3 but something can be wrong with my installation. I just don't know what that can be and a fresh install is not an option now. I hope someone can test the code and provide ideas on how I can solve the problem?
The following code causes problems:
``` python
import numpy as np
import pandas as pd
s = pd.Series(np.arange(10.))
rng = pd.date_range('2012-12-12', periods=10, freq='H')
ts = pd.Series(np.arange(10.), index=rng)
ts.plot() # Works fine
s.plot() # Works fine
# now, if I replace one value with an NaN
ts[2] = np.NaN
ts.plot() # gives the following error:
Traceback (most recent call last):
File "<ipython-input-6-132f3667ee95>", line 1, in <module>
ts.plot()
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1730, in plot_series
plot_obj.generate()
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 856, in generate
self._make_plot()
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1240, in _make_plot
self._make_ts_plot(data, **self.kwds)
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1311, in _make_ts_plot
_plot(data, 0, ax, label, self.style, **kwds)
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1295, in _plot
style=style, **kwds)
File "C:\Python27\lib\site-packages\pandas\tseries\plotting.py", line 77, in tsplot
lines = plotf(ax, *args, **kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 4137, in plot
for line in self._get_lines(*args, **kwargs):
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 317, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 295, in _plot_args
x, y = self._xy_from_xy(x, y)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 214, in _xy_from_xy
by = self.axes.yaxis.update_units(y)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 1336, in update_units
converter = munits.registry.get_converter(data)
File "C:\Python27\lib\site-packages\matplotlib\units.py", line 137, in get_converter
xravel = x.ravel()
File "C:\Python27\lib\site-packages\numpy\ma\core.py", line 3969, in ravel
r._mask = ndarray.ravel(self._mask).reshape(r.shape)
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 981, in reshape
return ndarray.reshape(self, newshape, order)
TypeError: an integer is required
# If I do:
ts[2] = np.inf # Then ts.plot() works...
```
I am using pandas 0.12 with matplotlib 1.3 on python x,y 2.7.5.1 on Windows 7.
Upgrading to matplotlib 1.3.1 or downgrading to pandas 0.11 did not solve the problem.
I have tried the same code in an WinPython-64bit -2.7.4.1.amd64 that I have installed and then the code works fine. Dependencies are then:
pandas 0.11 and matplotlib 1.2.1. Maybe this indicates something is wrong with my python x,y installation?
Happy to provide more details. Looking forward to your help!
Maarten
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} | 2 | 2013-10-24T12:32:48Z | 2014-07-16T08:36:40Z | 2013-10-24T18:18:37Z | CONTRIBUTOR | null | closes #5051
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```
======================================================================
ERROR: statsmodels.iolib.tests.test_foreign.test_genfromdta_datetime
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/usr/lib/python2.7/dist-packages/numpy/testing/decorators.py", line 146, in skipper_func
return f(*args, **kwargs)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/iolib/tests/test_foreign.py", line 139, in test_genfromdta_datetime
pandas=True)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/iolib/foreign.py", line 1049, in genfromdta
args=(fmtlist[i],))
File "/usr/lib/pymodules/python2.7/pandas/core/series.py", line 1978, in apply
return self._constructor(mapped, index=self.index).__finalize__(self)
File "/usr/lib/pymodules/python2.7/pandas/core/series.py", line 217, in __init__
data = SingleBlockManager(data, index, fastpath=True)
File "/usr/lib/pymodules/python2.7/pandas/core/internals.py", line 3295, in __init__
block = make_block(block, axis, axis, ndim=1, fastpath=True)
File "/usr/lib/pymodules/python2.7/pandas/core/internals.py", line 1806, in make_block
return klass(values, items, ref_items, ndim=ndim, fastpath=fastpath, placement=placement)
File "/usr/lib/pymodules/python2.7/pandas/core/internals.py", line 1412, in __init__
values = tslib.cast_to_nanoseconds(values)
File "tslib.pyx", line 1453, in pandas.tslib.cast_to_nanoseconds (pandas/tslib.c:22283)
TypeError: Cannot change data-type for object array.
======================================================================
ERROR: statsmodels.tsa.tests.test_arima.test_arma_predict_indices
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/tsa/tests/test_arima.py", line 975, in test_arma_predict_indices
_check_start(*((model,) + case))
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/tsa/tests/test_arima.py", line 921, in _check_start
start = model._get_predict_start(given, dynamic)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/tsa/arima_model.py", line 562, in _get_predict_start
start = super(ARMA, self)._get_predict_start(start)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/tsa/base/tsa_model.py", line 130, in _get_predict_start
self._set_predict_start_date(start)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/tsa/base/tsa_model.py", line 105, in _set_predict_start_date
start, self.data.freq)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/tsa/base/datetools.py", line 78, in _date_from_idx
return d1 + idx * _freq_to_pandas[freq]
File "/usr/lib/pymodules/python2.7/pandas/tseries/offsets.py", line 193, in __radd__
return self.__add__(other)
File "/usr/lib/pymodules/python2.7/pandas/tseries/offsets.py", line 188, in __add__
return self.apply(other)
File "/usr/lib/pymodules/python2.7/pandas/tseries/offsets.py", line 1537, in apply
return Timestamp(result)
File "tslib.pyx", line 153, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:5375)
File "tslib.pyx", line 773, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:13113)
File "tslib.pyx", line 858, in pandas.tslib._check_dts_bounds (pandas/tslib.c:14219)
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2317-12-31 00:00:00
```
Did something change?
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} | 19 | 2013-10-24T20:27:36Z | 2014-07-14T21:42:11Z | 2014-07-14T21:42:11Z | CONTRIBUTOR | null | tl;dr - add true support for Categoricals in NDFrame.
There was an issue on the mailing list about using cut and sorting the results that brought this to mind. The issue is both that (I believe) a categorical loses its representation when you put it in a DataFrame and so the output of cut has to just be strings. I propose the following:
1. Add a `CategoricalBlock` (or `FactorBlock`) internally that can handle categoricals like those produced from cut that could share most of MI's internals, as a 2D int ndarray with an associated list of indexes for each column (again, nearly the same as MI except most ops would be working on just one 'level' and underlying could/would be 2D rather than list of Int64Index). Probably also would mean abstracting common operations to a separate mixin class.
2. Change `Categorical` to be a Series subclass with a SingleBlockManager that's a CategoricalBlock. This would not change its API, but it would gain Series methods.
3. Add a `to_categorical` method to Series (bonus points if we change convert_objects to detect if there are < Some_Max number of labels and convert object dtypes to categoricals).
4. Add a registration method to make_block so it iterates over a set of functions that either return a klass or None before falling back to ObjectBlock (so abstract existing else clause into a function and make the list of functions semi-public).
I'm going to work on this and I don't think it will be that difficult to implement, but it would make pandas more useful for representing level sets and other normalized data.
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} | 8 | 2013-10-24T20:33:50Z | 2014-07-14T21:42:11Z | 2014-07-14T21:42:11Z | CONTRIBUTOR | null | I recall discussing this before but came up on the mailing list again. Sorting of cut output is always wrong / ugly. I propose creating a str-like that compares with others of the same type as if they were tuples (possibly with nice logic around ranges) - would be facilitated by intro of CategoricalBlock.
Similar to replacing qcut output with this:
``` python
def convert_bins_to_tuple(iterable):
return [(left, right) for elem in iterable for left, right in elem.strip('()[]').split(',')]
```
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} | 0 | 2013-10-25T03:15:51Z | 2013-10-25T13:45:12Z | 2013-10-25T13:45:12Z | CONTRIBUTOR | null | I'm getting three errors in the csv tests:
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
Note: I'm using a 32 bit OS.
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http://nbviewer.ipython.org/7149023
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} | 1 | 2013-10-25T03:30:59Z | 2014-07-16T08:36:42Z | 2013-10-25T13:45:12Z | CONTRIBUTOR | null | Fixes #5316
I think this was just an issue with 32 bit OSes.
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} | 23 | 2013-10-25T06:09:01Z | 2016-10-12T23:04:19Z | 2013-12-01T20:00:10Z | CONTRIBUTOR | null | This code was working in pd 0.12.0, but is failing in current master ('0.12.0-955-ga96cd66'):
``` python
df.last_el_cmd.replace(np.nan, inplace=True)
```
The error:
``` python
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_955_ga96cd66-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in replace(self, to_replace, value, inplace, limit, regex, method, axis)
1903 if not is_dictlike(to_replace):
1904 if not is_dictlike(regex):
-> 1905 raise TypeError('If "to_replace" and "value" are both None'
1906 ' and "to_replace" is not a list, then '
1907 'regex must be a mapping')
TypeError: If "to_replace" and "value" are both None and "to_replace" is not a list, then regex must be a mapping
```
I saw that some filter related stuff was updated to use regex for 0.13dev, but I don't understand why this makes my command failing?
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} | 0 | 2013-10-25T13:27:04Z | 2013-10-25T13:44:35Z | 2013-10-25T13:44:35Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/19578736/timespan-of-groups-in-pandas-timeseries
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closes #5320
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_assign_timeseries_index | 0.7157 | 398.8634 | 0.0018 |
```
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} | 6 | 2013-10-25T16:02:46Z | 2014-06-20T16:31:35Z | 2013-10-25T19:24:09Z | CONTRIBUTOR | null | closes #5324
There was a bug where this wasn't display at all, so this PR fixes
Do we care about formatting _embedded_ pandas objects? (as we don't recommend this anyhow)
```
In [15]: df1 = DataFrame({'a':[1, 2, 3], 'b':[3, 4, 5]})
In [19]: df2.dtypes
Out[19]:
0 object
dtype: object
In [16]: df1
Out[16]:
a b
0 1 3
1 2 4
2 3 5
In [17]: df2 = DataFrame([df1,df1+10])
In [18]: df2
Out[18]:
0
0 a b
0 1 3
1 2 4
2 3 5
1 a b
0 11 13
1 12 14
2 13 15
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5326/events | https://github.com/pandas-dev/pandas/issues/5326 | 21,602,493 | MDU6SXNzdWUyMTYwMjQ5Mw== | 5,326 | objToJSON nonvoid function does not return a value | {
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E: python3-pandas no-return-in-nonvoid-function pandas/src/ujson/python/objToJSON.c:166
I think this may be related to pull request #3874, where the return type was changed to int but the rest of the function remains the same, so no int was actually returned.
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} | 0 | 2013-10-25T19:11:25Z | 2014-07-16T08:37:00Z | 2013-10-25T19:23:46Z | CONTRIBUTOR | null | {
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} | 14 | 2013-10-25T19:38:33Z | 2014-04-09T13:53:48Z | 2014-04-09T13:53:48Z | CONTRIBUTOR | null | I am running this loop over approx 48 large HDFstores, each with approx 17 million rows. After roughly half the list done, it crashes with a memory error on my 96 GB RAM machine running CentOS6 64-bit.
``` python
for fname in fnames:
print fname
df = pd.read_hdf(fname, 'df', columns=[colname])
df['mybool'] = df[colname] == 90
df['label'] = label(df.mybool)[0]
df['time'] = df.index
g = df.groupby('label')['time']
pd.DataFrame({'start':g.first(),
'duration':g.last() - g.first()})[1:].to_hdf('./earth_scans.h5',
'df',
mode='a',
format='table',
append=True)
```
The error is:
``` python
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
<ipython-input-11-6c43f9f5c6be> in <module>()
3 for fname in fnames:
4 print fname
----> 5 df = pd.read_hdf(fname, 'df', columns=[colname])
6 df['mybool'] = df[colname] == 90
7 df['label'] = label(df.mybool)[0]
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/io/pytables.pyc in read_hdf(path_or_buf, key, **kwargs)
308 store = HDFStore(path_or_buf, **kwargs)
309 try:
--> 310 return f(store, True)
311 except:
312
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/io/pytables.pyc in <lambda>(store, auto_close)
300
301 f = lambda store, auto_close: store.select(
--> 302 key, auto_close=auto_close, **kwargs)
303
304 if isinstance(path_or_buf, compat.string_types):
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/io/pytables.pyc in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)
595
596 return TableIterator(self, func, nrows=s.nrows, start=start, stop=stop,
--> 597 auto_close=auto_close).get_values()
598
599 def select_as_coordinates(
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/io/pytables.pyc in get_values(self)
1225
1226 def get_values(self):
-> 1227 results = self.func(self.start, self.stop)
1228 self.close()
1229 return results
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/io/pytables.pyc in func(_start, _stop)
584 def func(_start, _stop):
585 return s.read(where=where, start=_start, stop=_stop,
--> 586 columns=columns, **kwargs)
587
588 if iterator or chunksize is not None:
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/io/pytables.pyc in read(self, where, columns, **kwargs)
3611 df = frames[0]
3612 else:
-> 3613 df = concat(frames, axis=1, verify_integrity=False).consolidate()
3614
3615 # apply the selection filters & axis orderings
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in consolidate(self, inplace)
1505 cons_data = self._protect_consolidate(f)
1506 if cons_data is self._data:
-> 1507 cons_data = cons_data.copy()
1508 return self._constructor(cons_data).__finalize__(self)
1509
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in copy(self, deep)
2458 new_axes = list(self.axes)
2459 return self.apply('copy', axes=new_axes, deep=deep,
-> 2460 ref_items=new_axes[0], do_integrity_check=False)
2461
2462 def as_matrix(self, items=None):
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in apply(self, f, *args, **kwargs)
2142 applied = f(blk, *args, **kwargs)
2143 else:
-> 2144 applied = getattr(blk, f)(*args, **kwargs)
2145
2146 if isinstance(applied, list):
/usr/local/epd/lib/python2.7/site-packages/pandas-0.12.0_963_g383010f-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in copy(self, deep, ref_items)
522 values = self.values
523 if deep:
--> 524 values = values.copy()
525 if ref_items is None:
526 ref_items = self.ref_items
MemoryError:
```
This loop is running inside an iPython notebook via a remote notebook server.
To my understanding, the automatic garbage collection should get rid of the intermediate dataframes I am creating inside the loop, am I doing anything stupid or is this leaking somehow?
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https://api.github.com/repos/pandas-dev/pandas/issues/5330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5330/events | https://github.com/pandas-dev/pandas/pull/5330 | 21,612,480 | MDExOlB1bGxSZXF1ZXN0OTQxNTk4NQ== | 5,330 | ENH: Add option to highlight NaN cells | {
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} | 4 | 2013-10-25T19:53:01Z | 2014-06-14T01:25:10Z | 2013-11-21T13:59:41Z | NONE | null | Useful for quickly inspecting data in an ipynb
- Add display.highlight_nan option (default True)
- Add argument to DataFrame.to_html
and HTMLFormatter.__init__
- Add test
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} | 28 | 2013-10-25T19:58:01Z | 2013-11-06T14:09:25Z | 2013-11-06T12:58:55Z | CONTRIBUTOR | null | https://gist.github.com/changhiskhan/7159783
from @changhiskhan
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https://api.github.com/repos/pandas-dev/pandas/issues/5332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5332/events | https://github.com/pandas-dev/pandas/issues/5332 | 21,621,712 | MDU6SXNzdWUyMTYyMTcxMg== | 5,332 | Period.asfreq broken for freq=MS | {
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} | 10 | 2013-10-25T23:10:41Z | 2014-04-09T03:02:03Z | 2014-04-09T03:01:56Z | CONTRIBUTOR | null | This works:
```
In [9]: pd.Period("2013").asfreq(freq="M", how="S")
Out[9]: Period('2013-01', 'M')
```
but this does not:
```
In [8]: pd.Period("2013").asfreq(freq="MS", how="S")
----> 1 pd.Period("2013").asfreq(freq="MS", how="S")
/usr/local/lib/python2.7/dist-packages/pandas/tseries/period.pyc in asfreq(self, freq, how)
196 how = _validate_end_alias(how)
197 base1, mult1 = _gfc(self.freq)
--> 198 base2, mult2 = _gfc(freq)
199
200 if mult2 != 1:
/usr/local/lib/python2.7/dist-packages/pandas/tseries/frequencies.pyc in get_freq_code(freqstr)
99
100 base, stride = _base_and_stride(freqstr)
--> 101 code = _period_str_to_code(base)
102
103 return code, stride
/usr/local/lib/python2.7/dist-packages/pandas/tseries/frequencies.pyc in _period_str_to_code(freqstr)
744 return _period_code_map[freqstr]
745 except:
--> 746 alias = _period_alias_dict[freqstr]
747 return _period_code_map[alias]
748
KeyError: 'L'
```
it seems to be treating the capital `MS` as lower case `ms`, meaning millisecond. Observer similar results:
```
In [11]: pd.Period("2013").asfreq(freq="ms", how="S")
----> 1 pd.Period("2013").asfreq(freq="ms", how="S")
/usr/local/lib/python2.7/dist-packages/pandas/tseries/period.pyc in asfreq(self, freq, how)
196 how = _validate_end_alias(how)
197 base1, mult1 = _gfc(self.freq)
--> 198 base2, mult2 = _gfc(freq)
199
200 if mult2 != 1:
/usr/local/lib/python2.7/dist-packages/pandas/tseries/frequencies.pyc in get_freq_code(freqstr)
99
100 base, stride = _base_and_stride(freqstr)
--> 101 code = _period_str_to_code(base)
102
103 return code, stride
/usr/local/lib/python2.7/dist-packages/pandas/tseries/frequencies.pyc in _period_str_to_code(freqstr)
744 return _period_code_map[freqstr]
745 except:
--> 746 alias = _period_alias_dict[freqstr]
747 return _period_code_map[alias]
748
KeyError: 'L'
```
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} | 2 | 2013-10-25T23:52:46Z | 2018-10-25T23:56:16Z | 2018-10-25T23:56:16Z | CONTRIBUTOR | null | looking at the definition for the `asfreq_info` struct in `period.h`, I see the following lines:
```
int from_a_year_end; // month the year ends on in the "from" frequency
int from_q_year_end; // month the year ends on in the "from" frequency
int to_a_year_end; // month the year ends on in the "to" frequency
int to_q_year_end; // month the year ends on in the "to" frequency
```
As far as I can tell, the comments are wrong and a union could have been used for these values as they are never simultaneous set.
Thoughts?
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} | 6 | 2013-10-26T00:17:03Z | 2014-06-25T20:51:28Z | 2013-10-26T14:13:31Z | CONTRIBUTOR | null | Make it so that it always returns the same thing as numpy, so that it
matches the right signature whether in or not in the error condition.
Closes #5326.
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} | 11 | 2013-10-26T00:42:38Z | 2014-06-23T09:41:29Z | 2013-11-02T20:58:19Z | CONTRIBUTOR | null | Implemented as suggested in #4651
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} | 2 | 2013-10-26T02:27:57Z | 2014-06-13T13:53:33Z | 2013-10-28T01:18:49Z | CONTRIBUTOR | null | 1. Panel flex docstrings had str representation of functions like
`<builtin-func add..>` (not pretty). Fixed to be name
2. Added some docstrings to miscellaneous properties and clarified
meaning of info_axis/keys.
3. Wrap 0.13's release.rst at 80 characters per line.
I'm happy to revert (3) above...just was bothering me (most of the doc
breaks at 80)
Key change is here - http://jtratner.github.io/example-pandas-docs/html-minor-doc-fixup-10-25/api.html#id17
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} | 1 | 2013-10-26T02:55:02Z | 2014-07-16T08:37:14Z | 2014-01-01T03:14:28Z | CONTRIBUTOR | null | Hi,
Added some docs for error_bad_lines & warn_bad_lines, as currently there aren't any found here: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.parsers.read_csv.html
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} | 24 | 2013-10-26T11:04:26Z | 2016-10-12T23:04:19Z | 2014-02-21T12:00:51Z | CONTRIBUTOR | null | ``` python
import pandas as pd
df = pd.DataFrame({"color":[1,2,3,4]})
print df
color
0 1
1 2
2 3
3 4
print df.replace({"color":{"1":"2","3":"4",}}) # works but shouldn't?
color
0 2
1 2
2 4
3 4
print df.replace({"color":{"1":"2","2":"3","3":"4","4":"5"}}) # strange
color
0 2
1 4
2 3
3 5
print df.replace({"color":{1:"2",2:"3",3:"4",4:"5"}}) # works by replacing each cell once
color
0 2
1 3
2 4
3 5
df = pd.DataFrame({"color":["1","2","3","4"]})
print df
color
0 1
1 2
2 3
3 4
print df.replace({"color":{"1":"2","3":"4",}}) # works
color
0 2
1 2
2 4
3 4
print df.replace({"color":{"1":"2","2":"3","3":"4","4":"5"}}) # works not
color
0 3
1 3
2 5
3 5
print df.replace({"color":{1:"2",2:"3",3:"4",4:"5"}}) # works as expected: shouldn't replace anything!
color
0 1
1 2
2 3
3 4
```
So, my expected behaviour would be:
- don't replace a cell if the type of the cell does not match the key (as it is the case when a string cell is replaced by a int key)
- if a value of a cell is replaced, the cell shouldn't be replaced a second time in the same replace call
I found the problem when I tried to match string values to colors and got blown up color values: like `{"3":"#123456","4":"#000000"}` wouldn't convert `"3"` into `"#123#00000056"`
_Edit_: insert string cell cases and my expected behaviour and deleted the intial comments which had these examples
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} | 9 | 2013-10-26T16:38:59Z | 2013-10-27T21:59:55Z | 2013-10-27T21:59:55Z | CONTRIBUTOR | null | Not sure if that is expected, but if it is expected it would be nice if the error message and the docsring of `Timestamp.now()` could be improved.
```
from pandas.tslib import Timestamp
Timestamp.now()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-634034a47269> in <module>()
----> 1 Timestamp.now()
C:\portabel\Python27\lib\site-packages\pandas\tslib.pyd in pandas.tslib.Timestamp.__new__ (pandas\tslib.c:5167)()
TypeError: __new__() takes at most 5 positional arguments (9 given)
```
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} | 11 | 2013-10-26T18:09:21Z | 2014-07-16T08:37:19Z | 2013-10-26T23:40:01Z | CONTRIBUTOR | null | Also fixes small bug from #5321
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} | 17 | 2013-10-26T22:34:45Z | 2014-06-17T03:29:49Z | 2013-10-27T22:12:25Z | CONTRIBUTOR | null | This was a relatively easy extension of eval to allow in-line creation/assignment.
Allows one to basically use formulas to do things (pandas conquers excel!!!!)
- [x] docs
- [x] tests non-frame
```
In [11]: df = DataFrame(dict(a = range(5), b = range(5,10)))
In [12]: df
Out[12]:
a b
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
In [13]: df.eval('c=a+b')
In [14]: df.eval('d=a+b+c')
In [15]: df.eval('a=1')
In [16]: df
Out[16]:
a b c d
0 1 5 5 10
1 1 6 7 14
2 1 7 9 18
3 1 8 11 22
4 1 9 13 26
```
You can do this (this could maybe have a bit better syntax though)
```
In [31]: df = DataFrame(dict(a = range(5), b = range(5,10)))
In [32]: formulas = Series(['c=a+b','d=a*b'],index=['a','b'])
In [33]: df.apply(lambda x: df.eval(formulas[x.name]))
Out[33]:
a None
b None
dtype: object
In [34]: df
Out[34]:
a b c d
0 0 5 5 0
1 1 6 7 6
2 2 7 9 14
3 3 8 11 24
4 4 9 13 36
```
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} | 0 | 2013-10-27T00:29:56Z | 2013-10-27T00:45:07Z | 2013-10-27T00:45:07Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/19612650/pandas-multiindex-names-not-working
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} | 0 | 2013-10-27T00:36:05Z | 2014-06-17T11:24:24Z | 2013-10-27T00:45:07Z | CONTRIBUTOR | null | closes #5344
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} | 2 | 2013-10-27T13:27:20Z | 2013-11-26T01:04:01Z | 2013-11-26T01:04:01Z | NONE | null | When the frame is too wide or too long, summary/expand_repr kick in
and the contents of the clipboard are unusable.
Could perhaps introduce an option to enforce a limit on row count (10,000? 100,000? )
and/or add a `force` keyword in `to_clipboard()` since copying a huge amount into the
clipboard can hang.
now that it uses to_csv by default (#5070 ), it's only relevent with `excel=false`
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} | 2 | 2013-10-27T15:44:18Z | 2016-10-12T23:04:19Z | 2013-10-28T03:26:02Z | CONTRIBUTOR | null | I found that our /tmp on nibotmi was filled up with tmp*png files. And they are coming from pandas. I did
mkdir tmp; TMPDIR=$PWD/tmp nosetests -s -v pandas
to find 186 .png of 12M total after a single run of tests
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] | closed | false | null | [] | null | 3 | 2013-10-27T17:40:46Z | 2016-12-11T14:06:54Z | 2016-12-11T14:06:54Z | NONE | null | Running into a problem when I attempt to add the first date of a month that is a Sunday.
``` python
import datetime
from pandas.tseries.index import DatetimeIndex
d1 = datetime.date(2002, 9, 1)
d2 = datetime.date(2013, 10, 27)
d3 = datetime.date(2012, 9, 30)
i1 = DatetimeIndex([d1, d2])
i2 = DatetimeIndex([d3])
i1 + i2
ValueError: Could not evaluate WOM-1SUN
```
Returns a ValueError "Could not evaluate WOM-1SUN". However, if I change d1 and then follow the same steps
``` python
d1 = datetime.date(2002, 9, 2)
i1 = DatetimeIndex([d1, d2])
i1 + i2
Out[2]: <class 'pandas.tseries.index.DatetimeIndex'>
[2002-09-02 00:00:00, ..., 2013-10-27 00:00:00]
Length: 3, Freq: None, Timezone: None
```
This create a DatetimeIndex adding the the index i1 and i2.
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} | [] | closed | false | null | [] | null | 1 | 2013-10-27T20:06:22Z | 2013-10-27T21:13:49Z | 2013-10-27T21:13:49Z | NONE | null | Pandas version: '0.12.0'
Numpy version: '1.6.1'
How to reproduce:
ser = pd.Series([2, 4, 6], index=[0, 2, 3])
ids = ser.index
x = np.max(ids)
Now, two things happen:
*) type(x) is Index
*) x is a 0-d array (so getting x[0]) crashes
*) print x crashes as well:
/home/olexiy/clients/olexiy-validators/google3/blaze-bin/quality/ranklab/main/tools/iranklab.runfiles/google3/third_party/py/pandas/core/common.py in pprint_thing(thing, _nest_lvl, escape_chars, default_escapes, quote_strings)
2065 result = fmt % as_escaped_unicode(thing)
2066 else:
-> 2067 result = as_escaped_unicode(thing)
2068
2069 return unicode(result) # always unicode
/home/olexiy/clients/olexiy-validators/google3/blaze-bin/quality/ranklab/main/tools/iranklab.runfiles/google3/third_party/py/pandas/core/common.py in as_escaped_unicode(thing, escape_chars)
2026
2027 try:
-> 2028 result = unicode(thing) # we should try this first
2029 except UnicodeDecodeError:
2030 # either utf-8 or we replace errors
RuntimeError: maximum recursion depth exceeded while calling a Python object
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} | 1 | 2013-10-27T20:17:41Z | 2014-06-24T22:56:53Z | 2013-11-21T14:06:31Z | CONTRIBUTOR | null | Also the copy param in Panel.xs was undocumented. Intentional?
Worth a line in release.rst?
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} | 0 | 2013-10-27T22:01:07Z | 2014-11-17T10:56:44Z | 2014-11-17T10:56:44Z | CONTRIBUTOR | null | See #5342
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} | 2 | 2013-10-27T22:27:17Z | 2014-07-16T08:37:31Z | 2013-10-28T00:48:46Z | CONTRIBUTOR | null | Now flex methods can take axis=0/axis='rows'/axis='index'. Also updated
tests to cover full range of Series flex ops and check that they all
accept an axis argument.
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} | 8 | 2013-10-27T22:58:05Z | 2014-08-19T17:09:14Z | 2014-08-19T17:09:14Z | CONTRIBUTOR | null | Hi,
I posted this on stackoverflow http://stackoverflow.com/questions/19602864/how-to-plot-in-a-specific-axis-with-dataframe-histby-in-pandas?noredirect=1#comment29118427_19602864 and was suggested to file a bug report here.
I am trying plot several histogram groups in the same figure. Each group contains two conditions and I am therefore using the 'by=' argument from pandas histogram options. However, this does not work as I expected and pandas creates a new figure instead of plotting in the axis I am passing. I tried to pass four axes as well, but still no go. Sample code:
``` python
import pandas as pd
df = pd.DataFrame({'color': ['blue','blue','yellow','blue','yellow'], 'area': [2,2,3,4,4]})
fig, (ax1, ax2) = plt.subplots(1,2)
df.area.hist(by=df.color, ax=ax1)
```
I'm using pandas 0.12.0, matplotlib 1.3.0, numpy 1.7.1 and python 2.7.5. Any suggestion that leads to a way of combining/stitching multiple 'hist(by=)-plots' in the same subplot grid is welcome.
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} | 1 | 2013-10-28T00:09:50Z | 2014-06-14T15:19:38Z | 2013-10-28T01:14:38Z | CONTRIBUTOR | null | Still need to add tests for DataFrame and Series, but should work. Very
simple fix to boot :).
Finally closes #5044
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} | 4 | 2013-10-28T00:33:32Z | 2020-03-31T04:12:46Z | null | CONTRIBUTOR | null | Needs to coerce:
e.g.
``` python
In [1]: import pandas.util.testing as tm
In [2]: pan = tm.makePanel()
In [3]: pan
Out[3]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 30 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-02-11 00:00:00
Minor_axis axis: A to D
In [4]: pan.drop('2000-01-03', axis=1)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-11e47d7c99f1> in <module>()
----> 1 pan.drop('2000-01-03', axis=1)
../pandas/core/generic.pyc in drop(self, labels, axis, level)
1112 new_axis = axis.drop(labels, level=level)
1113 else:
-> 1114 new_axis = axis.drop(labels)
1115 dropped = self.reindex(**{ axis_name: new_axis })
1116 try:
../pandas/core/index.pyc in drop(self, labels)
1614 mask = indexer == -1
1615 if mask.any():
-> 1616 raise ValueError('labels %s not contained in axis' % labels[mask])
1617 return self.delete(indexer)
1618
ValueError: labels ['2000-01-03'] not contained in axis
```
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} | 6 | 2013-10-28T00:43:16Z | 2013-11-05T22:53:10Z | 2013-11-05T22:53:10Z | CONTRIBUTOR | null | Now that we have all the arithmetic methods, there's no reason to keep it as before. Not having this means that operating on an int column with missing values switches operation result from floordiv to truediv.
@wesm You had, at one point, said that you would consider this. I believe this change would iron over a long-standing Python wart and make pandas easier to use:
Existing behavior (note how the future import doesn't affect div at all because it's defined in a separate file, but does affect division because Python will call `__truediv__` instead of `__div__`):
``` python
In [22]: from __future__ import division
In [23]: ser1 / ser2
Out[23]:
0 0.0
1 0.5
2 1.0
3 1.5
dtype: float64
In [24]: ser1.div(ser2)
Out[24]:
0 0
1 0
2 1
3 1
dtype: int64
```
And missing values immediately causes this behavior to change (because coerces to float)
``` python
In [11]: from pandas import Series
In [12]: ser1 = Series([0, 1, 2, 3])
In [13]: ser2 = Series([2, 2, 2, 2])
In [14]: ser1.div(ser2)
Out[14]:
0 0
1 0
2 1
3 1
dtype: int64
In [15]: ser1[0] = np.nan
In [16]: ser1.div(ser2)
Out[18]:
0 NaN
1 0.5
2 1.0
3 1.5
dtype: float64
```
And this distinction is totally eliminated in Python 3:
``` python
In [11]: from pandas import Series
In [12]: ser1 = Series([0, 1, 2, 3])
In [13]: ser2 = Series([2, 2, 2, 2])
In [14]: ser1.div(ser2)
Out[14]:
0 0.0
1 0.5
2 1.0
3 1.5
dtype: float64
In [15]: ser1[0] = np.nan
In [16]: ser1.div(ser2)
Out[18]:
0 NaN
1 0.5
2 1.0
3 1.5
dtype: float64
```
Trivial to fix and, while slightly backwards incompatible, would mostly just make things simpler. We should still leave `a / b` to be dependent upon the future import.
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} | 3 | 2013-10-28T00:59:30Z | 2014-04-27T23:39:58Z | 2014-04-27T23:39:58Z | CONTRIBUTOR | null | E.g.:
``` python
from itertools import izip
df = DataFrame(izip(range(3), range(-3, 1), list('abc')), columns=list('abc'))
---------------------------------------------------------------------------
PandasError Traceback (most recent call last)
<ipython-input-3-15234514160e> in <module>()
1 from itertools import izip
2
----> 3 df = DataFrame(izip(range(3), range(-3, 1), list('abc')), columns=list('abc'))
../pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
270 copy=False)
271 else:
--> 272 raise PandasError('DataFrame constructor not properly called!')
273
274 NDFrame.__init__(self, mgr, fastpath=True)
PandasError: DataFrame constructor not properly called!
```
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} | 1 | 2013-10-28T01:59:23Z | 2014-04-04T18:08:48Z | 2014-04-04T18:08:41Z | CONTRIBUTOR | null | Changes it to tuples instead (so it's probably just converting to list or going to values somewhere)
``` python
In [9]: df = DataFrame(list(zip(range(3), range(3), range(3), range(3))))
In [10]: ind = MultiIndex.from_arrays([['x', 'y', 'z'], [4, 4, 5]], names=['ind1', 'ind2'])
In [11]: df.set_index(ind)
Out[11]:
0 1 2 3
(x, 4) 0 0 0 0
(y, 4) 1 1 1 1
(z, 5) 2 2 2 2
In [12]: df.reindex(ind) # works
Out[12]:
0 1 2 3
ind1 ind2
x 4 NaN NaN NaN NaN
y 4 NaN NaN NaN NaN
z 5 NaN NaN NaN NaN
In [13]: df2 = df.copy()
In [14]: df2.index = ind # works too
In [15]: df2
Out[15]:
0 1 2 3
ind1 ind2
x 4 0 0 0 0
y 4 1 1 1 1
z 5 2 2 2 2
```
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} | [] | closed | false | null | [] | null | 1 | 2013-10-28T03:10:35Z | 2014-07-16T08:37:36Z | 2013-10-28T03:26:02Z | CONTRIBUTOR | null | plt.savefig() appends an extension by default, which messes up ensure_clean().
Added option to ensure_clean() to just return file-like.
Closes #5347.
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} | 1 | 2013-10-28T13:27:29Z | 2014-06-07T06:13:38Z | 2014-01-24T22:16:35Z | CONTRIBUTOR | null | Here's a question on SO - http://stackoverflow.com/questions/19635704/keyerror-in-pandas-to-datetime-using-custom-format, also possible to check it like this:
```
pd.to_datetime('09/30/2013 04:14 PM', format="%m/%d/%Y %I:%M %p")
```
It works for python time:
```
time.strptime('09/30/2013 04:14 PM', "%m/%d/%Y %I:%M %p")
```
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} | 4 | 2013-10-28T18:26:15Z | 2014-07-16T08:37:39Z | 2013-10-28T19:52:51Z | CONTRIBUTOR | null | closes #5174
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} | 1 | 2013-10-28T19:53:37Z | 2013-10-28T23:57:12Z | 2013-10-28T23:57:12Z | CONTRIBUTOR | null | See https://github.com/pydata/pandas/issues/5174
May not be just Macs. From that thread, yarikoptic's OS was: `OS: Linux 3.9-1-amd64 #1 SMP Debian 3.9.8-1 x86_64`.
A sample failure is from the generic.interpolate suite (modified to downcast, which is the default):
``` python
def test_interp_quad(self):
sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4])
result = sq.interpolate(method='quadratic')
expected = Series([1., 4, 9, 16], index=[1, 2, 3, 4])
assert_series_equal(result, expected)
```
On (some?) macs and apparently some linux builds this will fail because it the result is not downcast to an `int`. See also stepping through [here](https://github.com/pydata/pandas/issues/5174#issuecomment-26074991) and the PR to patch this failure [here](https://github.com/pydata/pandas/pull/5362).
I'm going to see if I can get my failing scipy/numpy tests to pass cleanly. Hopefully that will fix these.
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} | 6 | 2013-10-28T20:51:13Z | 2013-10-29T20:29:10Z | 2013-10-29T20:29:10Z | CONTRIBUTOR | null | I'm using Pandas HDF5 files on a shared network file system with a distributed lock manager (based on Kazoo) to ensure a single writer at a time.
When there is significant lock contention, a rapid series of flush, close, lock release and lock acquisition in a different process will cause corruption to the HDF5 file. This appears to be due to the operating system cache.
This is resolved by:
```
store = pd.HDFStore(filename, complevel=9, complib='blosc')
f_fd = store._handle.fileno()
store.flush()
os.fsync(f_fd)
store.close()
```
However this requires using `_handle`, which may be removed in the future.
Would you please either expose `_handle` for supported end user use, or the `fileno`, or perhaps modify `flush()` to accept a `fsync=True` parameter to support this use case. Thanks.
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When I run:
```
from pandas.io.ga import GAnalytics
gAnalytics = GAnalytics()
gAnalytics.create_query(‘web_id’, ‘pageviews’, ’2013-08-12′,’2013-08-14′, ‘date’, segment='A1B234').execute().get(‘containsSampledData’)
```
I get the following error:
returned "Invalid value 'ga:A1B234' for segment parameter.">, invalidating data!
I believe that if the segment is not dynamic, Pandas is expecting an integer only.
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} | 20 | 2013-10-28T21:50:57Z | 2013-11-05T06:00:57Z | 2013-11-05T06:00:57Z | NONE | null | The mode function returns the most common value (http://en.wikipedia.org/wiki/Mode_%28statistics%29) and it would be nice to have it is a shortcut for Series.value_counts().idxmax() .
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} | 3 | 2013-10-28T22:58:08Z | 2014-07-16T08:37:41Z | 2013-10-28T23:55:55Z | CONTRIBUTOR | null | related #5174
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} | 2 | 2013-10-28T23:59:11Z | 2014-06-16T19:17:25Z | 2013-10-29T20:28:40Z | CONTRIBUTOR | null | Pull request for #5364.
The `fsync=False` parameter was added to `flush` given this offers utility for non-distribution use cases, such as a user just wanting to ensure their data is regularly flushed to disk.
I could not see any mocking library in use in the Pandas tests, so I skipped adding one just to verify `os.fsync` actually gets called.
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} | [] | closed | false | null | [] | null | 9 | 2013-10-29T03:19:16Z | 2013-10-30T00:27:29Z | 2013-10-29T18:33:10Z | CONTRIBUTOR | null | I am data wrangling some Excel data into a dataframe.
At some point it has an Index like so:
``` python
Index([u'(K)', -440.0, -439.0, -438.0, -437.0, -436.0, -435.0, -434.0, -433.0, -432.0], dtype='object')
```
Naturally, I want to drop the "u'(K)'" index. Interestingly, after doing so the head() and tail() calculation totally goes haywire, and that irrespective of using this Index with dtype object or after converting it to the new Float64Index.:
``` python
<class 'pandas.core.frame.DataFrame'>
Float64Index: 446 entries, -440.0 to 5.0
Data columns (total 7 columns):
Channel 3 (A3) 446 non-null values
Channel 4 (A4) 446 non-null values
Channel 5 (A5) 446 non-null values
Channel 6 (A6) 446 non-null values
Channel 7 (B1) 446 non-null values
Channel 8 (B2) 446 non-null values
Channel 9 (B3) 446 non-null values
dtypes: float64(7)
```
or before, with dtypes still as 'object':
``` python
<class 'pandas.core.frame.DataFrame'>
Float64Index: 446 entries, -440.0 to 5.0
Data columns (total 7 columns):
Channel 3 (A3) 446 non-null values
Channel 4 (A4) 446 non-null values
Channel 5 (A5) 446 non-null values
Channel 6 (A6) 446 non-null values
Channel 7 (B1) 446 non-null values
Channel 8 (B2) 446 non-null values
Channel 9 (B3) 446 non-null values
dtypes: object(7)
```
I think I can see what's happening here: The head calculation somehow picks up the 5.0 value in the Index and choses to display the dataframe until then. Which now makes me question: Did the API of head() change and I didn't notice? My apologies if this actually is a case of PEBKAC.
The process of my data wrangling can be seen here:
http://nbviewer.ipython.org/7208717
pandas version: '0.12.0-1000-gea97682'
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} | 0 | 2013-10-29T12:26:59Z | 2013-10-31T01:24:56Z | 2013-10-31T01:24:56Z | CONTRIBUTOR | null | The `repr` for `PeriodIndex` uses ellipses (i.e. ...) for all number of elements. Ellipses should only be used for more than two elements. `DatetimeIndex` correctly does this:
```
In [9]: pd.period_range('2013Q1', periods=1, freq="Q")
Out[9]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: Q-DEC
[2013Q1, ..., 2013Q1]
length: 1
In [11]: pd.period_range('2013Q1', periods=2, freq="Q")
Out[11]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: Q-DEC
[2013Q1, ..., 2013Q2]
length: 2
In [10]: pd.period_range('2013Q1', periods=3, freq="Q")
Out[10]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: Q-DEC
[2013Q1, ..., 2013Q3]
length: 3
```
compare to:
```
In [16]: pd.date_range('2013-01-01', periods=3, freq="Q")
Out[16]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-03-31 00:00:00, ..., 2013-09-30 00:00:00]
Length: 3, Freq: Q-DEC, Timezone: None
In [13]: pd.date_range('2013-01-01', periods=2, freq="Q")
Out[13]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-03-31 00:00:00, 2013-06-30 00:00:00]
Length: 2, Freq: Q-DEC, Timezone: None
In [14]: pd.date_range('2013-01-01', periods=1, freq="Q")
Out[14]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-03-31 00:00:00]
Length: 1, Freq: Q-DEC, Timezone: None
```
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} | 0 | 2013-10-29T12:43:57Z | 2014-07-01T05:46:01Z | 2013-10-29T18:33:10Z | CONTRIBUTOR | null | closes #5370
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} | 19 | 2013-10-29T14:12:05Z | 2014-06-18T14:06:06Z | 2014-01-21T21:06:58Z | CONTRIBUTOR | null | Some characters have special meaning in latex: & % $ # _ { } ~ ^ . When a dataframe contains a special character (as part of an index label, for example), to_latex() spits out invalid latex.
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} | 27 | 2013-10-29T19:08:50Z | 2014-06-18T17:10:16Z | 2013-11-01T18:34:36Z | CONTRIBUTOR | null | closes #4620
This is a relatively minor change. Previously, `filter` on
`SeriesGroupBy` and `DataFrameGroupby` and `transform` on only
`SeriesGroupBy` referred to the index of each group's object.
This cannot work with repeated indexes, because some repeated
indexes occur between more than one group. Instead, use
`{Series, DataFrame}GroupBy.indices` array, an array of
locations, not labels.
_This was marked for 0.14, but I would really like to start using it._
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} | 9 | 2013-10-29T19:33:00Z | 2014-07-16T08:37:57Z | 2013-10-31T01:24:57Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/5372
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} | 0 | 2013-10-29T20:52:17Z | 2020-02-26T02:20:44Z | 2020-02-26T02:20:44Z | CONTRIBUTOR | null | Enable value_counts on DataFrame by delegating along axis (ie columns/items) to value_counts as convenience, boiling down to same Series value_counts (Panel is more complicated so not going to attempt for now).
Gets weird with heterogeneous dtypes (b/c union of all unique values)
@rockg - can you make up some test cases? I already have the implementation. I can put together panel.
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} | 21 | 2013-10-30T02:46:42Z | 2014-06-22T05:09:45Z | 2013-10-31T03:28:25Z | CONTRIBUTOR | null | Fixed bug introduced in b5265a5b84ae1aeaf5fff88f1f9e5872a9b404e6 which change from picking a single random color for the class to picking a random color for each line.
This returns the color selection to a per-set basis.
See http://stackoverflow.com/questions/19667209/colors-in-andrews-curves
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} | 2 | 2013-10-30T02:53:32Z | 2020-05-22T03:26:25Z | null | CONTRIBUTOR | null | With #5378 - could be a good time to start comparing against images for these plotting tests... matplotlib has a test function and GeoPandas is working on it here - https://github.com/kjordahl/geopandas/pull/43, so we could work off of their example, maybe a decorator that handles ensure_clean and comparisons...
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} | 6 | 2013-10-30T03:32:21Z | 2014-06-15T23:15:45Z | 2013-11-05T06:00:57Z | CONTRIBUTOR | null | Closes #5367 - generally as fast or faster than value_counts (which makes sense because it has to construct Series), so should be relatively good performance-wise. Also, doesn't get stuck on value_counts' pathological case (huge array with # uniques close to/at the size of the array).
Not using result of hashtable.value_count() under the hood and instead iterating over klib table directly gives a _huge_ speedup. I just moved the value_count table creation method to a separate function (zero perf hit). For performance breakouts check out this gist:
https://gist.github.com/jtratner/7225878
DataFrame version delegates to Series' version at each level.
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} | 19 | 2013-10-30T03:43:00Z | 2015-01-18T21:39:35Z | 2015-01-18T21:39:35Z | CONTRIBUTOR | null | - abstract bin generation from cut to use elsewhere. Panel goes to ndarray on
apply so that's a future TODO.
I'm relatively certain that this works, but I don't have explicit tests
for it. I'm hoping others who are interested (cough cough @rockg) will
be willing to create some of them. Key to test are that binning,
normalization and sorting all work correctly.
Will close #5377 when completed.
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https://api.github.com/repos/pandas-dev/pandas/issues/5384 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5384/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5384/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5384/events | https://github.com/pandas-dev/pandas/issues/5384 | 21,822,769 | MDU6SXNzdWUyMTgyMjc2OQ== | 5,384 | io.html.read_html returning table "twice" | {
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``` python
from pandas.io.html import read_html
read_html('http://www.epexspot.com/en/market-data/auction/auction-table/2005-10-25/DE',
attrs={'class': 'list hours responsive'},
skiprows=1,
parse_dates=False)
```
returns a list of two data frames where the first one is equivalent to `read_html()` without the `parse_dates` arg and the second one is the one expected to be returned by calling `read_html()` with `parse_dates` arg.
Am I missing something?
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} | 12 | 2013-10-30T13:16:44Z | 2018-10-25T03:22:23Z | 2018-10-25T03:22:23Z | CONTRIBUTOR | null | Here's the [output](https://gist.github.com/TomAugspurger/7232405) from the current master. I could try to clean some of the warnings up someday _if there's any interest_; a decent excuse to look through the source code.
A lot of the warnings look similar:
- deprecated numpy API
- warning: static variable 'PyArray_API' is used in an inline function with external linkage [-Wstatic-in-inline]
- warning: comparison of constant -1 with expression of type 'PANDAS_DATETIMEUNIT' is always true
- warning: format specifies type 'long' but the argument has type 'long long' [-Wformat]
- various unused functions
What do things look like with gcc (and whatever windows uses)?
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} | 0 | 2013-10-30T13:19:08Z | 2013-10-30T13:43:13Z | 2013-10-30T13:43:13Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/19672508/dtypes-of-dataframe-does-not-fit-dataframe-info
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} | 9 | 2013-10-30T14:13:12Z | 2018-07-06T20:21:02Z | 2017-07-10T21:09:13Z | CONTRIBUTOR | null | Just on panels, works fine for DataFrames.
Gives a `TypeError` when reading:
``` python
In [7]: df = pd.DataFrame({'A': [1, 2], 'B': pd.to_datetime(['2010-01-01', np.nan])})
In [8]: df
Out[8]:
A B
0 1 2010-01-01 00:00:00
1 2 NaT
In [10]: tst = pd.HDFStore('tst.h5')
In [12]: df.to_hdf('tst.h5', 'df')
In [13]: tst.select('df')
Out[13]:
A B
0 1 2010-01-01 00:00:00
1 2 NaT
In [14]: df2 = pd.DataFrame({'A': [1, 2], 'B': pd.to_datetime(['2010-01-01', np.nan])})
In [17]: wp = pd.Panel({'i1': df, 'i2': df2})
In [18]: wp
Out[18]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 2 (major_axis) x 2 (minor_axis)
Items axis: i1 to i2
Major_axis axis: 0 to 1
Minor_axis axis: A to B
In [19]: wp.to_hdf(tst, key='wp')
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.py:2310: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed-integer,key->block0_values] [items->['i1', 'i2']]
warnings.warn(ws, PerformanceWarning)
In [20]: tst.select('wp')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-20-da42baeaf1c6> in <module>()
----> 1 tst.select('wp')
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.pyc in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)
595
596 return TableIterator(self, func, nrows=s.nrows, start=start, stop=stop,
--> 597 auto_close=auto_close).get_values()
598
599 def select_as_coordinates(
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.pyc in get_values(self)
1225
1226 def get_values(self):
-> 1227 results = self.func(self.start, self.stop)
1228 self.close()
1229 return results
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.pyc in func(_start, _stop)
584 def func(_start, _stop):
585 return s.read(where=where, start=_start, stop=_stop,
--> 586 columns=columns, **kwargs)
587
588 if iterator or chunksize is not None:
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.pyc in read(self, **kwargs)
2517 for i in range(self.nblocks):
2518 blk_items = self.read_index('block%d_items' % i)
-> 2519 values = self.read_array('block%d_values' % i)
2520 blk = make_block(values, blk_items, items)
2521 blocks.append(blk)
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.pyc in read_array(self, key)
2075 import tables
2076 node = getattr(self.group, key)
-> 2077 data = node[:]
2078 attrs = node._v_attrs
2079
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/tables-3.0.0-py2.7-macosx-10.8-x86_64.egg/tables/vlarray.pyc in __getitem__(self, key)
659 start, stop, step = self._process_range(
660 key.start, key.stop, key.step)
--> 661 return self.read(start, stop, step)
662 # Try with a boolean or point selection
663 elif type(key) in (list, tuple) or isinstance(key, numpy.ndarray):
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/tables-3.0.0-py2.7-macosx-10.8-x86_64.egg/tables/vlarray.pyc in read(self, start, stop, step)
799 atom = self.atom
800 if not hasattr(atom, 'size'): # it is a pseudo-atom
--> 801 outlistarr = [atom.fromarray(arr) for arr in listarr]
802 else:
803 # Convert the list to the right flavor
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/tables-3.0.0-py2.7-macosx-10.8-x86_64.egg/tables/atom.pyc in fromarray(self, array)
1149 if array.size == 0:
1150 return None
-> 1151 return cPickle.loads(array.tostring())
1152
1153
TypeError: ('__new__() takes exactly one argument (2 given)', <class 'pandas.tslib.NaTType'>, ('\x00\x01\xff\xff\x00\x00\x00\x00\x00\x00',))
```
Haven't had a chance to look at what's going on.
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} | 40 | 2013-10-30T21:50:14Z | 2014-06-12T12:24:22Z | 2013-11-06T22:24:20Z | CONTRIBUTOR | null | ```
In [1]: df = DataFrame({"A": [1, 2, 3, 4, 5], "B": [3.125, 4.12, 3.1, 6.2, 7.]})
In [2]: row = df.loc[0]
```
Default is to warn
```
In [3]: row["A"] = 0
pandas/core/generic.py:1001: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
warnings.warn(t,SettingWithCopyWarning)
In [4]: row
Out[4]:
A 0.000
B 3.125
Name: 0, dtype: float64
In [5]: df
Out[5]:
A B
0 1 3.125
1 2 4.120
2 3 3.100
3 4 6.200
4 5 7.000
```
You can turn it off
```
In [6]: pd.set_option('chained',None)
In [7]: row["A"] = 0
In [8]: row
Out[8]:
A 0.000
B 3.125
Name: 0, dtype: float64
In [9]: df
Out[9]:
A B
0 1 3.125
1 2 4.120
2 3 3.100
3 4 6.200
4 5 7.000
```
Or set to raise
```
In [10]: row["A"] = 0
SettingWithCopyError: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
```
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} | 4 | 2013-10-30T22:01:55Z | 2014-08-21T22:11:28Z | 2014-08-21T22:11:28Z | CONTRIBUTOR | null | It seems to me that join operation fails if the index is not of unique values. The particular circumastance that I observed this was with multi-index:
```
df1.set_index( [ 'col1', 'col2', 'col3' ], inplace=True )
df2.join ( df1, on=['cola', 'colb', 'colc' ], how='left' )
```
I understand that the above join operation is not well-defined for non-unique index values, but pandas gives wrong values even for unique matches. ( no warnings, error messages whatsoever )
In case checking for index integrity has a heavy performance cost, it should be documented that this method fails if the index is not unique. ( or alternatively have the optional argument to enforce integrity check )
I could get correct join by doing below:
```
df1.drop_duplicates( cols=[ 'col1', 'col2', 'col3' ], inplace=True )
df1.set_index( [ 'col1', 'col2', 'col3' ], inplace=True )
df2.join ( df1, on=['cola', 'colb', 'colc' ], how='left' )
```
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} | 5 | 2013-10-30T23:07:29Z | 2013-12-20T17:54:33Z | 2013-11-07T21:53:41Z | CONTRIBUTOR | null | @wesm - can you do this - http://about.travis-ci.org/blog/2012-09-04-pull-requests-just-got-even-more-awesome/ ?
Looks like you just have to tweak a setting and then Travis will build every PR automatically. I don't have admin rights to pydata so I can't change it.
And then we wouldn't have to tell everyone to set up Travis. That would be _great_
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All Excel numeric data is stored as floats, so have to convert it specifically.
Changing default because it's suprising to save something with what looks like
a row/column of integers and get a column of floats instead. (especially
because it can lead to annoying Float64Indexes)
Resolves recent ML issue too...
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} | 43 | 2013-10-31T02:43:58Z | 2016-03-20T15:25:36Z | 2014-02-16T22:04:20Z | CONTRIBUTOR | null | - Can pass `Flavor` to HTML parse (closes #4594 and #5130). Instead of #5131
- Cleanup API for HTML parsers
I added new (public class) `Flavor` which users can subclass. Currently the expectation is to pass in the class (not an instance of the class)
CC @cpcloud @jtratner @jreback
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Here is the full error report :
Process: Python [95313]
Path: /Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier: Python
Version: 2.7.6rc1 (2.7.6rc1)
Code Type: X86-64 (Native)
Parent Process: bash [94753]
Responsible: iTerm [160]
User ID: 501
Date/Time: 2013-10-31 01:16:01.018 -0700
OS Version: Mac OS X 10.9 (13A603)
Report Version: 11
Anonymous UUID: 592E522A-F4DC-667B-6F11-E35383BBC4BE
Sleep/Wake UUID: F91ECED6-C437-4874-B521-5F139FBA707A
Crashed Thread: 0 Dispatch queue: com.apple.main-thread
Exception Type: EXC_BAD_ACCESS (SIGSEGV)
Exception Codes: KERN_INVALID_ADDRESS at 0x0000000000000000
VM Regions Near 0:
-->
__TEXT 0000000100000000-0000000100001000 [ 4K] r-x/rwx SM=COW /Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 umath.so 0x0000000101293567 _extract_pyvals + 403
1 umath.so 0x0000000101295ea4 _get_bufsize_errmask + 49
2 umath.so 0x0000000101293ad4 PyUFunc_GenericFunction + 1181
3 umath.so 0x00000001012977b7 ufunc_generic_call + 307
4 org.python.python 0x000000010000c702 PyObject_Call + 98
5 org.python.python 0x0000000100010e45 _PyObject_CallFunction_SizeT + 197
6 multiarray.so 0x0000000101104500 array_richcompare + 761
7 org.python.python 0x000000010005ae9c try_rich_compare + 92
8 org.python.python 0x000000010005c86f PyObject_RichCompare + 95
9 org.python.python 0x00000001000bf443 PyEval_EvalFrameEx + 8355
10 org.python.python 0x00000001000c4746 PyEval_EvalCodeEx + 2118
11 org.python.python 0x00000001000c2b80 PyEval_EvalFrameEx + 22496
12 org.python.python 0x00000001000c338a PyEval_EvalFrameEx + 24554
13 org.python.python 0x00000001000c4746 PyEval_EvalCodeEx + 2118
14 org.python.python 0x00000001000c2b80 PyEval_EvalFrameEx + 22496
15 org.python.python 0x00000001000c4746 PyEval_EvalCodeEx + 2118
16 org.python.python 0x00000001000c2b80 PyEval_EvalFrameEx + 22496
17 org.python.python 0x00000001000c4746 PyEval_EvalCodeEx + 2118
18 org.python.python 0x00000001000c2b80 PyEval_EvalFrameEx + 22496
19 org.python.python 0x00000001000c4746 PyEval_EvalCodeEx + 2118
20 org.python.python 0x00000001000c4866 PyEval_EvalCode + 54
21 org.python.python 0x00000001000e8c1c PyRun_InteractiveOneFlags + 380
22 org.python.python 0x00000001000e8e7e PyRun_InteractiveLoopFlags + 78
23 org.python.python 0x00000001000e9691 PyRun_AnyFileExFlags + 161
24 org.python.python 0x000000010010040d Py_Main + 3101
25 org.python.python 0x0000000100000f14 0x100000000 + 3860
Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000103167d88 rbx: 0x0000000000000000 rcx: 0x00000001012b4326 rdx: 0x0000000000000000
rdi: 0x0000000000000000 rsi: 0x00007fff5fbfba70 rbp: 0x00007fff5fbfbbc0 rsp: 0x00007fff5fbfbb90
r8: 0x0000000000000000 r9: 0x0000000102f00000 r10: 0x0000000000000000 r11: 0x0000000001ec1f48
r12: 0x00007fff5fbfbdd8 r13: 0x00007fff5fbfbddc r14: 0x00007fff5fbfbdd8 r15: 0x0000000000000000
rip: 0x0000000101293567 rfl: 0x0000000000010202 cr2: 0x0000000000000000
Logical CPU: 0
Error Code: 0x00000006
Trap Number: 14
Binary Images:
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Calls made by all processes on this machine:
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VM Region Summary:
ReadOnly portion of Libraries: Total=95.6M resident=25.9M(27%) swapped_out_or_unallocated=69.7M(73%)
Writable regions: Total=98.5M written=59.2M(60%) resident=66.6M(68%) swapped_out=0K(0%) unallocated=31.9M(32%)
REGION TYPE VIRTUAL
=========== =======
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MALLOC (admin) 16K
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shared memory 4K
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} | 0 | 2013-10-31T12:04:13Z | 2014-07-16T08:38:18Z | 2013-10-31T12:30:10Z | CONTRIBUTOR | null | related #5394
This now works
```
In [1]: arr = np.array([1.0,2.0],dtype=object)
In [3]: pandas.core.common._possibly_downcast_to_dtype(arr,'infer')
Out[3]: array([1, 2])
```
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} | 1 | 2013-10-31T15:40:32Z | 2013-12-07T14:22:13Z | 2013-12-07T14:22:13Z | CONTRIBUTOR | null | In working with Google Analytics I've found new segments being named with underscores included. This relates to the pandas/io/ga module.
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} | 2 | 2013-10-31T15:41:15Z | 2014-06-22T05:58:40Z | 2013-11-21T13:44:48Z | CONTRIBUTOR | null | This pr fixes a typo in the type checking of binary moments. It makes the error messages below consistent:
> > > import pandas
> > > a = pandas.DataFrame([1,2,3,4,5,6])
> > > pandas.rolling_cov(list(), a, 3)
> > > ValueError: arguments to moment function must be of type ndarray/DataFrame
> > >
> > > pandas.rolling_cov(a, list(), 3)
> > > TypeError: unsupported type: <type 'list'>
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} | 13 | 2013-10-31T17:24:27Z | 2014-06-27T14:53:37Z | 2013-12-07T14:22:02Z | CONTRIBUTOR | null | Adds support for underscores to Google's new segment format. The issue referenced is here:
closes https://github.com/pydata/pandas/issues/5398
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