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} | 7 | "2012-05-23T21:30:03Z" | "2012-09-09T00:39:01Z" | "2012-05-29T15:48:44Z" | CONTRIBUTOR | null | Would it be feasible to have the docs at http://pandas.pydata.org/pandas-docs/dev updated after each change or nightly?
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```
In [5]: DatetimeIndex(ujson.decode(ujson.encode(rng)))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-5-e33bac335db8> in <module>()
----> 1 DatetimeIndex(ujson.decode(ujson.encode(rng)))
/home/wesm/code/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
227 data = _str_to_dt_array(data, offset)
228 else:
--> 229 data = tools.to_datetime(data)
230 data = np.asarray(data, dtype='M8[ns]')
231
/home/wesm/code/pandas/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst)
93 return lib.string_to_datetime(com._ensure_object(arg),
94 raise_=errors == 'raise',
---> 95 dayfirst=dayfirst)
96
97 try:
/home/wesm/code/pandas/pandas/_tseries.so in pandas._tseries.string_to_datetime (pandas/src/tseries.c:34314)()
TypeError: object of type 'long' has no len()
```
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```
In [2]: ujson.encode(datetime.now())
Out[2]: '1337804262'
In [3]: np.array(datetime.now(), dtype='M8[ns]').view('i8')
Out[3]: array(1337804288505948000)
```
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 3 | "2012-05-24T00:22:21Z" | "2013-07-17T14:11:01Z" | "2013-07-17T14:11:01Z" | MEMBER | null | May want to be able to globally export millisecond timestamps for some JS applications. cc @Komnomnomnom
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https://api.github.com/repos/pandas-dev/pandas/issues/1306 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1306/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1306/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1306/events | https://github.com/pandas-dev/pandas/issues/1306 | 4,724,923 | MDU6SXNzdWU0NzI0OTIz | 1,306 | Incorrect join of DataFrames with non-unique datetime indices | {
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} | 3 | "2012-05-24T02:52:45Z" | "2012-05-25T02:47:03Z" | "2012-05-25T02:45:12Z" | NONE | null | I'm not sure whether joining of DFs with non-unique indices is now supported, but it's not giving an error and this simple example don't make sense:
```
In [11]: df1 = pandas.DataFrame({'x': ['a']}, index=[np.datetime64('2012')])
In [12]: df2 = pandas.DataFrame({'y': ['b', 'c']}, index=[np.datetime64('2012')] * 2)
In [13]: df1
Out[13]:
x
1970-01-16 08:09:36 a
In [14]: df2
Out[14]:
y
1970-01-16 08:09:36 b
1970-01-16 08:09:36 c
In [15]: df1.join(df2, how='inner')
Out[15]:
x y
1970-01-16 08:09:36 a b
```
Shouldn't the 1st row of df1 join to both rows of df2?
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} | 0 | "2012-05-24T03:28:21Z" | "2012-05-24T22:10:03Z" | "2012-05-24T22:10:03Z" | MEMBER | null | ```
In [6]: df1
Out[6]:
x
1970-01-16 08:09:36 a
In [7]: df2
Out[7]:
y
1970-01-16 08:09:36 b
1970-01-16 08:09:36 c
In [8]: merge(df1, df2)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-8-90d5dd612b4d> in <module>()
----> 1 merge(df1, df2)
/home/wesm/code/pandas/pandas/tools/merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
30 right_index=right_index, sort=sort, suffixes=suffixes,
31 copy=copy)
---> 32 return op.get_result()
33 if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame'
34
/home/wesm/code/pandas/pandas/tools/merge.pyc in get_result(self)
174
175 def get_result(self):
--> 176 join_index, left_indexer, right_indexer = self._get_join_info()
177
178 # this is a bit kludgy
/home/wesm/code/pandas/pandas/tools/merge.pyc in _get_join_info(self)
247 else:
248 # max groups = largest possible number of distinct groups
--> 249 left_key, right_key, max_groups = self._get_group_keys()
250
251 join_func = _join_functions[self.how]
/home/wesm/code/pandas/pandas/tools/merge.pyc in _get_group_keys(self)
411 group_sizes.append(count)
412
--> 413 left_group_key = get_group_index(left_labels, group_sizes)
414 right_group_key = get_group_index(right_labels, group_sizes)
415
/home/wesm/code/pandas/pandas/core/groupby.pyc in get_group_index(label_list, shape)
1921 return label_list[0]
1922
-> 1923 n = len(label_list[0])
1924 group_index = np.zeros(n, dtype=np.int64)
1925 mask = np.zeros(n, dtype=bool)
IndexError: list index out of range
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1308 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1308/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1308/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1308/events | https://github.com/pandas-dev/pandas/issues/1308 | 4,732,631 | MDU6SXNzdWU0NzMyNjMx | 1,308 | Undescriptive exception when calling fillna() with invalid method | {
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} | 1 | "2012-05-24T13:11:58Z" | "2012-05-29T12:31:08Z" | "2012-05-29T12:31:08Z" | CONTRIBUTOR | null | ## In [235]: data.geq.fillna(method='ffil')
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 2051, in fillna
new_values = self.values.take(indexer)
UnboundLocalError: local variable 'indexer' referenced before assignment
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} | 8 | "2012-05-24T13:14:18Z" | "2013-11-27T19:56:58Z" | "2012-05-24T17:33:37Z" | CONTRIBUTOR | null | The ujson source code was tab-intended, this commit converts the ugly tabs to (four) spaces.
Also fixed some memory leaks and some compiler warnings.
All tests pass, tested on 64 bit OSX and 32 bit Ubuntu with Python 2.7.
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https://api.github.com/repos/pandas-dev/pandas/issues/1311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1311/events | https://github.com/pandas-dev/pandas/pull/1311 | 4,735,479 | MDExOlB1bGxSZXF1ZXN0MTQyMTM2NQ== | 1,311 | BUG: raise exception when merging with insufficient info | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1312/events | https://github.com/pandas-dev/pandas/issues/1312 | 4,736,404 | MDU6SXNzdWU0NzM2NDA0 | 1,312 | Docs about dropping duplicate index values using groupby | {
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} | 0 | "2012-05-24T15:23:57Z" | "2012-05-29T00:57:23Z" | "2012-05-29T00:57:23Z" | MEMBER | null | ```
a = pandas.Series([1,2,3,10,4,5,20,6], [1,2,3,1,4,5,2,6])
a.groupby(level=0).first() / last()
```
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```
a = pandas.Series([1,2,3,10,4,5,20,6], Index([1,2,3,1,4,5,2,6], name='foo'))
a.groupby(level=0).sum()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1314/events | https://github.com/pandas-dev/pandas/issues/1314 | 4,744,538 | MDU6SXNzdWU0NzQ0NTM4 | 1,314 | Handle other unit types in datetime64 scalar objects | {
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} | 8 | "2012-05-24T22:12:09Z" | "2012-05-25T20:42:13Z" | "2012-05-25T20:42:13Z" | MEMBER | null | ```
In [4]: val
Out[4]: 2012-02-05 00:00:00
In [5]: type(val)
Out[5]: numpy.datetime64
In [6]: val.dtype
Out[6]: dtype('datetime64[D]')
In [7]: DatetimeIndex([val])
Out[7]:
<class 'pandas.tseries.index.DatetimeIndex'>
[1970-01-01 00:00:00.000015375, ..., 1970-01-01 00:00:00.000015375]
Length: 1, Freq: None, Timezone: None
```
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} | 5 | "2012-05-25T15:46:01Z" | "2012-05-27T16:33:52Z" | "2012-05-27T16:33:52Z" | CONTRIBUTOR | null | Default behavior for Series/DataFrame constructor is object dtype:
In [8]: Series([datetime.today()]).dtype
Out[8]: dtype('object')
In [2]: df = DataFrame({'a':[datetime.today()]})
In [3]: df.dtypes
Out[3]: a object
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} | 16 | "2012-05-25T18:32:36Z" | "2012-05-28T15:51:39Z" | "2012-05-28T15:51:39Z" | MEMBER | null | @Komnomnomnom this is a good one. UltraJSON does not work on Python 3. This is going to be fun
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https://api.github.com/repos/pandas-dev/pandas/issues/1321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1321/events | https://github.com/pandas-dev/pandas/issues/1321 | 4,761,598 | MDU6SXNzdWU0NzYxNTk4 | 1,321 | DataFrame.ix with boolean arrays | {
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} | 0 | "2012-05-25T22:02:55Z" | "2012-05-26T16:28:15Z" | "2012-05-26T16:28:15Z" | CONTRIBUTOR | null | Works right in one dimension but not both:
In [19]: df = DataFrame(np.random.randn(3, 2))
In [20]: df
Out[20]:
0 1
0 0.666325 0.812192
1 0.023781 1.719397
2 0.318738 0.227240
In [21]: df.ix[df.index==0, :]
Out[21]:
0 1
0 0.666325 0.812192
In [24]: df.ix[:, df.columns==1]
Out[24]:
1
0 0.812192
1 1.719397
2 0.227240
But not both:
In [23]: df.ix[df.index==0, df.columns==1]
Out[23]:
0 1
True 0.023781 1.719397
False 0.666325 0.812192
False 0.666325 0.812192
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} | 1 | "2012-05-25T23:12:41Z" | "2012-05-26T18:04:41Z" | "2012-05-26T18:04:36Z" | CONTRIBUTOR | null | test coverage for non-timeseries modules in 0.8 and related bug fixes
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} | 0 | "2012-05-26T21:05:25Z" | "2013-12-04T00:58:08Z" | "2012-05-28T16:33:23Z" | MEMBER | null | ```
In [7]: s
Out[7]:
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
In [8]: s.astype('O')
Out[8]:
0 1970-01-11 184:00:00
1 1970-01-11 208:00:00
2 1970-01-11 232:00:00
3 1970-01-11 00:00:00
4 1970-01-11 24:00:00
5 1970-01-11 48:00:00
6 1970-01-11 72:00:00
7 1970-01-11 96:00:00
8 1970-01-11 120:00:00
9 1970-01-11 144:00:00
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1325 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1325/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1325/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1325/events | https://github.com/pandas-dev/pandas/pull/1325 | 4,774,020 | MDExOlB1bGxSZXF1ZXN0MTQzNzMzNA== | 1,325 | Andrews curves | {
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} | 1 | "2012-05-27T23:35:33Z" | "2014-06-22T12:54:54Z" | "2012-06-02T19:04:46Z" | CONTRIBUTOR | null | Andrews curves, with a test and documentation.
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https://api.github.com/repos/pandas-dev/pandas/issues/1326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1326/events | https://github.com/pandas-dev/pandas/pull/1326 | 4,780,050 | MDExOlB1bGxSZXF1ZXN0MTQzOTYxNg== | 1,326 | Get JSON extension to work with Python 3 #1319 | {
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} | 5 | "2012-05-28T13:09:26Z" | "2014-07-23T23:10:31Z" | "2012-05-28T15:51:13Z" | CONTRIBUTOR | null | Python 3 fixes for ujson lib. All relevant tests pass (see below).
Tested on 64 bit OSX on Python 2.5, 2.6, 2.7, 3.1 & 3.2
Tested on 32 bit Ubuntu on Python 3.2
Apart from the define macros the handling for attribute / key names and the module initialisation code needed to be updated.
Some other notes:
1. I did not install scipy so the scipy tests failed but this should have no relevance for ujson
2. The `test_encodeBigEscape` in `test_ujson` seems to consume a lot of memory (or is more memory limited) in Python 3. It only passed in my Ubuntu VM when I upped its RAM to 2GB.
3. Several unicode tests now skipped for Python 2.6 (mostly the same as those skipped for 2.5). I didn't investigate this too much apart from checking that they failed with 2.6 for the original ujson library as well (they did).
4. `test_encodeListLongConversion` needs to pass `dtype=np.int64` to succeed for Python 3 on 32 bit. Under Python 3 numpy converts `int` to `np.int32` on 32 bit machines (whereas under Python 2 it's `long` -> `np.int64`).
5. I did not test on Windows :-/
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} | 1 | "2012-05-28T14:11:19Z" | "2012-05-28T17:59:39Z" | "2012-05-28T17:59:39Z" | CONTRIBUTOR | null | ```
In [16]: ts = tm.makeTimeSeries()
In [17]: ts.resample('W')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/pandas/<ipython-input-17-d49462deed14> in <module>()
----> 1 ts.resample('W')
...
~/pandas/pandas/lib.so in pandas.lib.generate_bins_dt64 (pandas/src/tseries.c:60325)()
ValueError: Values falls after last bin
```
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} | 0 | "2012-05-28T16:12:31Z" | "2012-05-28T22:58:47Z" | "2012-05-28T22:58:47Z" | CONTRIBUTOR | null | ```
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1329 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1329/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1329/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1329/events | https://github.com/pandas-dev/pandas/issues/1329 | 4,782,562 | MDU6SXNzdWU0NzgyNTYy | 1,329 | Scalars don't propagate in DataFrame constructor | {
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} | 0 | "2012-05-28T16:20:36Z" | "2012-05-28T16:46:33Z" | "2012-05-28T16:46:33Z" | MEMBER | null | This only works if an index is specified; fails in the index inference process
```
In [1]: DataFrame({'a' : randn(10), 'b' : 6})
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-1-16dbfdf3cea7> in <module>()
----> 1 DataFrame({'a' : randn(10), 'b' : 6})
/home/wesm/code/pandas/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
367 mgr = self._init_mgr(data, index, columns, dtype=dtype, copy=copy)
368 elif isinstance(data, dict):
--> 369 mgr = self._init_dict(data, index, columns, dtype=dtype)
370 elif isinstance(data, ma.MaskedArray):
371 mask = ma.getmaskarray(data)
/home/wesm/code/pandas/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
446 # figure out the index, if necessary
447 if index is None:
--> 448 index = extract_index(data)
449 else:
450 index = _ensure_index(index)
/home/wesm/code/pandas/pandas/core/frame.pyc in extract_index(data)
4540 else:
4541 have_raw_arrays = True
-> 4542 raw_lengths.append(len(v))
4543
4544 if have_series or have_dicts:
TypeError: object of type 'int' has no len()
```
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} | 0 | "2012-05-29T00:15:16Z" | "2012-05-29T00:18:46Z" | "2012-05-29T00:18:46Z" | MEMBER | null | ```
In [33]: rng
Out[33]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-04-13 00:00:00, ..., 2012-03-06 00:00:00]
Length: 100, Freq: W-TUE, Timezone: None
In [34]: rng.tz_localize('US/Eastern')
Out[34]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-04-13 04:00:00, ..., 2012-03-06 05:00:00]
Length: 100, Freq: W-TUE, Timezone: US/Eastern
```
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} | 1 | "2012-05-29T13:24:34Z" | "2016-07-24T03:41:53Z" | "2016-07-24T03:41:34Z" | CONTRIBUTOR | null | A dataframe or time series with annual averages when used by science and engineering applications is often normalised.
This means that the 29th. Februray is removed from the data series.
A util function for this purpose would be helpful.
Tags:
Enhancement, timeseries, low-priority
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} | 0 | "2012-05-29T17:38:08Z" | "2012-06-01T20:23:52Z" | "2012-06-01T20:23:52Z" | MEMBER | null | Ideally would like to be able to do:
`plt.set_xlim(['1/1/2005', '1/1/2012'])`
or even pass datetime.datetime objects. Not sure how feasible this is. I think the trick is setting the `converter` field on the subplot's X axis according to:
```
In [22]: ax.xaxis.convert_units??
Type: instancemethod
Base Class: <type 'instancemethod'>
String Form:<bound method XAxis.convert_units of <matplotlib.axis.XAxis object at 0x3809310>>
Namespace: Interactive
File: /home/wesm/code/repos/matplotlib/lib/matplotlib/axis.py
Definition: ax.xaxis.convert_units(self, x)
Source:
def convert_units(self, x):
if self.converter is None:
self.converter = munits.registry.get_converter(x)
if self.converter is None:
#print 'convert_units returning identity: units=%s, converter=%s'%(self.units, self.converter)
return x
ret = self.converter.convert(x, self.units, self)
#print 'convert_units converting: axis=%s, units=%s, converter=%s, in=%s, out=%s'%(self, self.units, self.converter, x, ret)
return ret
```
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improve multiple date parsing docs (parse_dates, keep_date_col, date_parser)
community supported date parser examples
thousands separator
comments
option to squeeze single column output into Series
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} | 3 | "2012-05-30T15:28:01Z" | "2012-07-13T03:14:35Z" | "2012-07-13T03:14:35Z" | NONE | null | Simple example:
I have a dataframe with a few columns I wish to remove, I do this:
``` python
index = MultiIndex.from_tuples([('Test',n) for n in range(10)])
a = DataFrame(np.ones((10,10)), index)
a = a.drop(index[0:4])
a.index
Out[149]:
MultiIndex([('Test', 4), ('Test', 5), ('Test', 6), ('Test', 7), ('Test', 8),
('Test', 9)], dtype=object)
# Correct, expected_output
a.index.levels[1]
Out[151]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)
# In the level 1 index the index of the dropped columns are still present, they haven't been dropped.
```
I have a very very hackish fix that works - obviously inelegant:
``` python
a.index = MultiIndex.from_tuples(a.index.tolist())
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1351 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1351/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1351/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1351/events | https://github.com/pandas-dev/pandas/issues/1351 | 4,818,566 | MDU6SXNzdWU0ODE4NTY2 | 1,351 | Segfault in test_conv_annual (test_period.py , when running nosetests) | {
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} | 4 | "2012-05-30T15:55:36Z" | "2012-06-02T23:17:57Z" | "2012-06-02T23:17:57Z" | NONE | null | This happened with the current Pandas on an EC2 machine (Amazon Linux/ Debian squeeze).
I attached the gdb trace. Unfortunately I don't understand what's going on but maybe somebody else does.
```
(gdb) run test_period.py
Starting program: /usr/local/bin/python test_period.py
[Thread debugging using libthread_db enabled]
nose.config: INFO: Ignoring files matching ['^\\.', '^_', '^setup\\.py$']
test_conv_annual (__main__.TestFreqConversion) ...
Program received signal SIGSEGV, Segmentation fault.
0x00007ffff6dde819 in free () from /lib/libc.so.6
(gdb) where
#0 0x00007ffff6dde819 in free () from /lib/libc.so.6
#1 0x00007ffff28659ee in skts_strftime (ordinal=148, freq=2000,
args=0x1f97300) at pandas/src/period.c:1234
#2 0x00007ffff2865fc1 in period_to_string (value=148, freq=2000)
at pandas/src/period.c:1333
#3 0x00007ffff26e2395 in __pyx_pf_6pandas_3lib_120period_ordinal_to_string (
__pyx_self=0x0, __pyx_v_value=148, __pyx_v_freq=2000)
at pandas/src/tseries.c:48155
#4 0x00007ffff26e2339 in __pyx_pw_6pandas_3lib_121period_ordinal_to_string (
__pyx_self=0x0, __pyx_args=0x1f599c0, __pyx_kwds=0x0)
at pandas/src/tseries.c:48123
#5 0x00007ffff7a232d9 in PyCFunction_Call (func=0x15c7d08, arg=0x1f599c0,
kw=0x0) at Objects/methodobject.c:85
#6 0x00007ffff7aca504 in call_function (pp_stack=0x7fffffff6420, oparg=2)
at Python/ceval.c:4021
#7 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x2078090, throwflag=0)
at Python/ceval.c:2666
#8 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1648ca0, globals=0x16e6ba0,
locals=0x0, args=0x1f97248, argcount=1, kws=0x0, kwcount=0, defs=0x0,
defcount=0, closure=0x0) at Python/ceval.c:3253
#9 0x00007ffff79ff7a1 in function_call (func=0x137e990, arg=0x1f97220, kw=0x0)
at Objects/funcobject.c:526
#10 0x00007ffff79c0453 in PyObject_Call (func=0x137e990, arg=0x1f97220, kw=0x0)
---Type <return> to continue, or q <return> to quit---
at Objects/abstract.c:2529
#11 0x00007ffff79dc87c in instancemethod_call (func=0x137e990, arg=0x1f97220,
kw=0x0) at Objects/classobject.c:2578
#12 0x00007ffff79c0453 in PyObject_Call (func=0x1f95860, arg=0x7ffff7fa7060,
kw=0x0) at Objects/abstract.c:2529
#13 0x00007ffff7ac9bf5 in PyEval_CallObjectWithKeywords (func=0x1f95860,
arg=0x7ffff7fa7060, kw=0x0) at Python/ceval.c:3890
#14 0x00007ffff7a605ad in slot_tp_repr (self=0x2038300)
at Objects/typeobject.c:5326
#15 0x00007ffff7a24f82 in PyObject_Repr (v=0x2038300) at Objects/object.c:381
#16 0x00007ffff7ab90d8 in builtin_repr (self=0x0, v=0x2038300)
at Python/bltinmodule.c:2139
#17 0x00007ffff7aca2c2 in call_function (pp_stack=0x7fffffff6d20, oparg=1)
at Python/ceval.c:4009
#18 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x2077e40, throwflag=0)
at Python/ceval.c:2666
#19 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0xa991a0, globals=0xadced0,
locals=0x0, args=0x2077d90, argcount=2, kws=0x2077da0, kwcount=0,
defs=0xaa0d08, defcount=3, closure=0x0) at Python/ceval.c:3253
#20 0x00007ffff7acab78 in fast_function (func=0xaaaed0,
pp_stack=0x7fffffff7240, n=2, na=2, nk=0) at Python/ceval.c:4117
#21 0x00007ffff7aca704 in call_function (pp_stack=0x7fffffff7240, oparg=2)
at Python/ceval.c:4042
---Type <return> to continue, or q <return> to quit---
#22 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x2077bc0, throwflag=0)
at Python/ceval.c:2666
#23 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0xf0b7d0, globals=0xd26c20,
locals=0x0, args=0x2036308, argcount=2, kws=0x2036318, kwcount=0,
defs=0xf182b8, defcount=1, closure=0x0) at Python/ceval.c:3253
#24 0x00007ffff7acab78 in fast_function (func=0xf163a8,
pp_stack=0x7fffffff7760, n=2, na=2, nk=0) at Python/ceval.c:4117
#25 0x00007ffff7aca704 in call_function (pp_stack=0x7fffffff7760, oparg=2)
at Python/ceval.c:4042
#26 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x20360a0, throwflag=0)
at Python/ceval.c:2666
#27 0x00007ffff7acaa47 in fast_function (func=0x19c54f8,
pp_stack=0x7fffffff7b00, n=1, na=1, nk=0) at Python/ceval.c:4107
#28 0x00007ffff7aca704 in call_function (pp_stack=0x7fffffff7b00, oparg=0)
at Python/ceval.c:4042
#29 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x20340e0, throwflag=0)
at Python/ceval.c:2666
#30 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0xfd2670, globals=0xfc2110,
locals=0x0, args=0x1f553d0, argcount=2, kws=0x7ffff7fa7088, kwcount=0,
defs=0xfff638, defcount=1, closure=0x0) at Python/ceval.c:3253
#31 0x00007ffff79ff7a1 in function_call (func=0x1006798, arg=0x1f553a8,
kw=0x2033830) at Objects/funcobject.c:526
#32 0x00007ffff79c0453 in PyObject_Call (func=0x1006798, arg=0x1f553a8,
---Type <return> to continue, or q <return> to quit---
kw=0x2033830) at Objects/abstract.c:2529
#33 0x00007ffff7acbaf6 in ext_do_call (func=0x1006798,
pp_stack=0x7fffffff8038, flags=3, na=1, nk=0) at Python/ceval.c:4334
#34 0x00007ffff7ac5101 in PyEval_EvalFrameEx (f=0x2033ed0, throwflag=0)
at Python/ceval.c:2705
#35 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0xfd2880, globals=0xfc2110,
locals=0x0, args=0x1f50d30, argcount=2, kws=0x0, kwcount=0, defs=0x0,
defcount=0, closure=0x0) at Python/ceval.c:3253
#36 0x00007ffff79ff7a1 in function_call (func=0x10068e8, arg=0x1f50d08, kw=0x0)
at Objects/funcobject.c:526
#37 0x00007ffff79c0453 in PyObject_Call (func=0x10068e8, arg=0x1f50d08, kw=0x0)
at Objects/abstract.c:2529
#38 0x00007ffff79dc87c in instancemethod_call (func=0x10068e8, arg=0x1f50d08,
kw=0x0) at Objects/classobject.c:2578
#39 0x00007ffff79c0453 in PyObject_Call (func=0x1d50560, arg=0x1f974c0, kw=0x0)
at Objects/abstract.c:2529
#40 0x00007ffff7a60a91 in slot_tp_call (self=0x1f97c30, args=0x1f974c0,
kwds=0x0) at Objects/typeobject.c:5403
#41 0x00007ffff79c0453 in PyObject_Call (func=0x1f97c30, arg=0x1f974c0, kw=0x0)
at Objects/abstract.c:2529
#42 0x00007ffff7acb42b in do_call (func=0x1f97c30, pp_stack=0x7fffffff8970,
na=1, nk=0) at Python/ceval.c:4239
#43 0x00007ffff7aca726 in call_function (pp_stack=0x7fffffff8970, oparg=1)
---Type <return> to continue, or q <return> to quit---
at Python/ceval.c:4044
#44 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x2033b70, throwflag=0)
at Python/ceval.c:2666
#45 0x00007ffff7acaa47 in fast_function (func=0x1ce8450,
pp_stack=0x7fffffff8d10, n=2, na=2, nk=0) at Python/ceval.c:4107
#46 0x00007ffff7aca704 in call_function (pp_stack=0x7fffffff8d10, oparg=1)
at Python/ceval.c:4042
#47 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x2032d10, throwflag=0)
at Python/ceval.c:2666
#48 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1ce00f0, globals=0x1d15470,
locals=0x0, args=0x1d3c6a0, argcount=2, kws=0x7ffff7fa7088, kwcount=0,
defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:3253
#49 0x00007ffff79ff7a1 in function_call (func=0x1ce83a8, arg=0x1d3c678,
kw=0x2032230) at Objects/funcobject.c:526
#50 0x00007ffff79c0453 in PyObject_Call (func=0x1ce83a8, arg=0x1d3c678,
kw=0x2032230) at Objects/abstract.c:2529
#51 0x00007ffff7acbaf6 in ext_do_call (func=0x1ce83a8,
pp_stack=0x7fffffff9248, flags=3, na=1, nk=0) at Python/ceval.c:4334
#52 0x00007ffff7ac5101 in PyEval_EvalFrameEx (f=0x2032b00, throwflag=0)
at Python/ceval.c:2705
#53 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1ccfa90, globals=0x1d15470,
locals=0x0, args=0x1f762e0, argcount=2, kws=0x0, kwcount=0, defs=0x0,
defcount=0, closure=0x0) at Python/ceval.c:3253
---Type <return> to continue, or q <return> to quit---
#54 0x00007ffff79ff7a1 in function_call (func=0x1cd6d80, arg=0x1f762b8, kw=0x0)
at Objects/funcobject.c:526
#55 0x00007ffff79c0453 in PyObject_Call (func=0x1cd6d80, arg=0x1f762b8, kw=0x0)
at Objects/abstract.c:2529
#56 0x00007ffff79dc87c in instancemethod_call (func=0x1cd6d80, arg=0x1f762b8,
kw=0x0) at Objects/classobject.c:2578
#57 0x00007ffff79c0453 in PyObject_Call (func=0x1f86960, arg=0x1f975a0, kw=0x0)
at Objects/abstract.c:2529
#58 0x00007ffff7a60a91 in slot_tp_call (self=0x1f97e60, args=0x1f975a0,
kwds=0x0) at Objects/typeobject.c:5403
#59 0x00007ffff79c0453 in PyObject_Call (func=0x1f97e60, arg=0x1f975a0, kw=0x0)
at Objects/abstract.c:2529
#60 0x00007ffff7acb42b in do_call (func=0x1f97e60, pp_stack=0x7fffffff9b80,
na=1, nk=0) at Python/ceval.c:4239
#61 0x00007ffff7aca726 in call_function (pp_stack=0x7fffffff9b80, oparg=1)
at Python/ceval.c:4044
#62 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x2031c70, throwflag=0)
at Python/ceval.c:2666
#63 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1cefbf0, globals=0x1d16290,
locals=0x0, args=0x1f76100, argcount=2, kws=0x7ffff7fa7088, kwcount=0,
defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:3253
#64 0x00007ffff79ff7a1 in function_call (func=0x1d27ed0, arg=0x1f760d8,
kw=0x20308e0) at Objects/funcobject.c:526
---Type <return> to continue, or q <return> to quit---
#65 0x00007ffff79c0453 in PyObject_Call (func=0x1d27ed0, arg=0x1f760d8,
kw=0x20308e0) at Objects/abstract.c:2529
#66 0x00007ffff7acbaf6 in ext_do_call (func=0x1d27ed0,
pp_stack=0x7fffffffa0b8, flags=3, na=1, nk=0) at Python/ceval.c:4334
#67 0x00007ffff7ac5101 in PyEval_EvalFrameEx (f=0x2031a60, throwflag=0)
at Python/ceval.c:2705
#68 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1cef930, globals=0x1d16290,
locals=0x0, args=0x1f76358, argcount=2, kws=0x0, kwcount=0, defs=0x0,
defcount=0, closure=0x0) at Python/ceval.c:3253
#69 0x00007ffff79ff7a1 in function_call (func=0x1d27cd8, arg=0x1f76330, kw=0x0)
at Objects/funcobject.c:526
#70 0x00007ffff79c0453 in PyObject_Call (func=0x1d27cd8, arg=0x1f76330, kw=0x0)
at Objects/abstract.c:2529
#71 0x00007ffff79dc87c in instancemethod_call (func=0x1d27cd8, arg=0x1f76330,
kw=0x0) at Objects/classobject.c:2578
#72 0x00007ffff79c0453 in PyObject_Call (func=0x1f86560, arg=0x1f976f0, kw=0x0)
at Objects/abstract.c:2529
#73 0x00007ffff7a60a91 in slot_tp_call (self=0x1f97ca0, args=0x1f976f0,
kwds=0x0) at Objects/typeobject.c:5403
#74 0x00007ffff79c0453 in PyObject_Call (func=0x1f97ca0, arg=0x1f976f0, kw=0x0)
at Objects/abstract.c:2529
#75 0x00007ffff7acb42b in do_call (func=0x1f97ca0, pp_stack=0x7fffffffa9f0,
na=1, nk=0) at Python/ceval.c:4239
---Type <return> to continue, or q <return> to quit---
#76 0x00007ffff7aca726 in call_function (pp_stack=0x7fffffffa9f0, oparg=1)
at Python/ceval.c:4044
#77 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x20312a0, throwflag=0)
at Python/ceval.c:2666
#78 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1cefbf0, globals=0x1d16290,
locals=0x0, args=0x1f76268, argcount=2, kws=0x7ffff7fa7088, kwcount=0,
defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:3253
#79 0x00007ffff79ff7a1 in function_call (func=0x1d27ed0, arg=0x1f76240,
kw=0x2030790) at Objects/funcobject.c:526
#80 0x00007ffff79c0453 in PyObject_Call (func=0x1d27ed0, arg=0x1f76240,
kw=0x2030790) at Objects/abstract.c:2529
#81 0x00007ffff7acbaf6 in ext_do_call (func=0x1d27ed0,
pp_stack=0x7fffffffaf28, flags=3, na=1, nk=0) at Python/ceval.c:4334
#82 0x00007ffff7ac5101 in PyEval_EvalFrameEx (f=0x2031090, throwflag=0)
at Python/ceval.c:2705
#83 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1cef930, globals=0x1d16290,
locals=0x0, args=0x1f43cb8, argcount=2, kws=0x0, kwcount=0, defs=0x0,
defcount=0, closure=0x0) at Python/ceval.c:3253
#84 0x00007ffff79ff7a1 in function_call (func=0x1d27cd8, arg=0x1f43c90, kw=0x0)
at Objects/funcobject.c:526
#85 0x00007ffff79c0453 in PyObject_Call (func=0x1d27cd8, arg=0x1f43c90, kw=0x0)
at Objects/abstract.c:2529
#86 0x00007ffff79dc87c in instancemethod_call (func=0x1d27cd8, arg=0x1f43c90,
---Type <return> to continue, or q <return> to quit---
kw=0x0) at Objects/classobject.c:2578
#87 0x00007ffff79c0453 in PyObject_Call (func=0x1f57f60, arg=0x1f97760, kw=0x0)
at Objects/abstract.c:2529
#88 0x00007ffff7a60a91 in slot_tp_call (self=0x1fa1ed0, args=0x1f97760,
kwds=0x0) at Objects/typeobject.c:5403
#89 0x00007ffff79c0453 in PyObject_Call (func=0x1fa1ed0, arg=0x1f97760, kw=0x0)
at Objects/abstract.c:2529
#90 0x00007ffff7acb42b in do_call (func=0x1fa1ed0, pp_stack=0x7fffffffb860,
na=1, nk=0) at Python/ceval.c:4239
#91 0x00007ffff7aca726 in call_function (pp_stack=0x7fffffffb860, oparg=1)
at Python/ceval.c:4044
#92 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x202f280, throwflag=0)
at Python/ceval.c:2666
#93 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1cefbf0, globals=0x1d16290,
locals=0x0, args=0x1f761f0, argcount=2, kws=0x7ffff7fa7088, kwcount=0,
defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:3253
#94 0x00007ffff79ff7a1 in function_call (func=0x1d27ed0, arg=0x1f761c8,
kw=0x202f130) at Objects/funcobject.c:526
#95 0x00007ffff79c0453 in PyObject_Call (func=0x1d27ed0, arg=0x1f761c8,
kw=0x202f130) at Objects/abstract.c:2529
#96 0x00007ffff7acbaf6 in ext_do_call (func=0x1d27ed0,
pp_stack=0x7fffffffbd98, flags=3, na=1, nk=0) at Python/ceval.c:4334
#97 0x00007ffff7ac5101 in PyEval_EvalFrameEx (f=0x202ef20, throwflag=0)
---Type <return> to continue, or q <return> to quit---
at Python/ceval.c:2705
#98 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1cef930, globals=0x1d16290,
locals=0x0, args=0x1f59f10, argcount=2, kws=0x0, kwcount=0, defs=0x0,
defcount=0, closure=0x0) at Python/ceval.c:3253
#99 0x00007ffff79ff7a1 in function_call (func=0x1d27cd8, arg=0x1f59ee8, kw=0x0)
at Objects/funcobject.c:526
#100 0x00007ffff79c0453 in PyObject_Call (func=0x1d27cd8, arg=0x1f59ee8,
kw=0x0) at Objects/abstract.c:2529
#101 0x00007ffff79dc87c in instancemethod_call (func=0x1d27cd8, arg=0x1f59ee8,
kw=0x0) at Objects/classobject.c:2578
#102 0x00007ffff79c0453 in PyObject_Call (func=0x1e450e0, arg=0x1d32ca0,
kw=0x0) at Objects/abstract.c:2529
#103 0x00007ffff7a60a91 in slot_tp_call (self=0x1f8f990, args=0x1d32ca0,
kwds=0x0) at Objects/typeobject.c:5403
#104 0x00007ffff79c0453 in PyObject_Call (func=0x1f8f990, arg=0x1d32ca0,
kw=0x0) at Objects/abstract.c:2529
#105 0x00007ffff7acb42b in do_call (func=0x1f8f990, pp_stack=0x7fffffffc6d0,
na=1, nk=0) at Python/ceval.c:4239
#106 0x00007ffff7aca726 in call_function (pp_stack=0x7fffffffc6d0, oparg=1)
at Python/ceval.c:4044
#107 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x202dec0, throwflag=0)
at Python/ceval.c:2666
#108 0x00007ffff7acaa47 in fast_function (func=0x1d2c300,
---Type <return> to continue, or q <return> to quit---
pp_stack=0x7fffffffca70, n=2, na=2, nk=0) at Python/ceval.c:4107
#109 0x00007ffff7aca704 in call_function (pp_stack=0x7fffffffca70, oparg=1)
at Python/ceval.c:4042
#110 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x202d1c0, throwflag=0)
at Python/ceval.c:2666
#111 0x00007ffff7acaa47 in fast_function (func=0x1d2c648,
pp_stack=0x7fffffffce10, n=1, na=1, nk=0) at Python/ceval.c:4107
#112 0x00007ffff7aca704 in call_function (pp_stack=0x7fffffffce10, oparg=0)
at Python/ceval.c:4042
#113 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x1d74860, throwflag=0)
at Python/ceval.c:2666
#114 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1033460, globals=0xfc4cc0,
locals=0x0, args=0x1d32d38, argcount=1, kws=0x1c5be98, kwcount=6,
defs=0x1026928, defcount=10, closure=0x0) at Python/ceval.c:3253
#115 0x00007ffff79ff7a1 in function_call (func=0x1055798, arg=0x1d32d10,
kw=0x1d6ee60) at Objects/funcobject.c:526
#116 0x00007ffff79c0453 in PyObject_Call (func=0x1055798, arg=0x1d32d10,
kw=0x1d6ee60) at Objects/abstract.c:2529
#117 0x00007ffff79dc87c in instancemethod_call (func=0x1055798, arg=0x1d32d10,
kw=0x1d6ee60) at Objects/classobject.c:2578
#118 0x00007ffff79c0453 in PyObject_Call (func=0x1d3e760, arg=0x1d32d10,
kw=0x1d6ee60) at Objects/abstract.c:2529
#119 0x00007ffff7acbaf6 in ext_do_call (func=0x1d3e760,
---Type <return> to continue, or q <return> to quit---
pp_stack=0x7fffffffd628, flags=2, na=1, nk=5) at Python/ceval.c:4334
#120 0x00007ffff7ac5101 in PyEval_EvalFrameEx (f=0x1b06250, throwflag=0)
at Python/ceval.c:2705
#121 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x19bcca0, globals=0x19f5580,
locals=0x0, args=0xf18638, argcount=1, kws=0x1d2f088, kwcount=3,
defs=0x1cdbb08, defcount=11, closure=0x0) at Python/ceval.c:3253
#122 0x00007ffff79ff7a1 in function_call (func=0x1d2c3a8, arg=0xf18610,
kw=0x1d6dab0) at Objects/funcobject.c:526
#123 0x00007ffff79c0453 in PyObject_Call (func=0x1d2c3a8, arg=0xf18610,
kw=0x1d6dab0) at Objects/abstract.c:2529
#124 0x00007ffff79dc87c in instancemethod_call (func=0x1d2c3a8, arg=0xf18610,
kw=0x1d6dab0) at Objects/classobject.c:2578
#125 0x00007ffff79c0453 in PyObject_Call (func=0x1d3e6e0, arg=0x7ffff7fa7060,
kw=0x1d6dab0) at Objects/abstract.c:2529
#126 0x00007ffff7a61898 in slot_tp_init (self=0x1d32c30, args=0x7ffff7fa7060,
kwds=0x1d6dab0) at Objects/typeobject.c:5663
#127 0x00007ffff7a4e3e1 in type_call (type=0x1d67230, args=0x7ffff7fa7060,
kwds=0x1d6dab0) at Objects/typeobject.c:737
#128 0x00007ffff79c0453 in PyObject_Call (func=0x1d67230, arg=0x7ffff7fa7060,
kw=0x1d6dab0) at Objects/abstract.c:2529
#129 0x00007ffff7acbaf6 in ext_do_call (func=0x1d67230,
pp_stack=0x7fffffffdf28, flags=2, na=0, nk=1) at Python/ceval.c:4334
#130 0x00007ffff7ac5101 in PyEval_EvalFrameEx (f=0x1d13e10, throwflag=0)
---Type <return> to continue, or q <return> to quit---
at Python/ceval.c:2705
#131 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x19cc670, globals=0x19f5580,
locals=0x0, args=0x683b00, argcount=0, kws=0x683b00, kwcount=2,
defs=0x1d2a1d8, defcount=1, closure=0x0) at Python/ceval.c:3253
#132 0x00007ffff7acab78 in fast_function (func=0x1d2c840,
pp_stack=0x7fffffffe450, n=4, na=0, nk=2) at Python/ceval.c:4117
#133 0x00007ffff7aca704 in call_function (pp_stack=0x7fffffffe450, oparg=512)
at Python/ceval.c:4042
#134 0x00007ffff7ac4e9e in PyEval_EvalFrameEx (f=0x683970, throwflag=0)
at Python/ceval.c:2666
#135 0x00007ffff7ac79ba in PyEval_EvalCodeEx (co=0x1185eb0, globals=0x6836d0,
locals=0x6836d0, args=0x0, argcount=0, kws=0x0, kwcount=0, defs=0x0,
defcount=0, closure=0x0) at Python/ceval.c:3253
#136 0x00007ffff7abcff8 in PyEval_EvalCode (co=0x1185eb0, globals=0x6836d0,
locals=0x6836d0) at Python/ceval.c:667
#137 0x00007ffff7afb1d6 in run_mod (mod=0x1237138,
filename=0x7fffffffed79 "test_period.py", globals=0x6836d0,
locals=0x6836d0, flags=0x7fffffffe9c0, arena=0x686f90)
at Python/pythonrun.c:1353
#138 0x00007ffff7afb153 in PyRun_FileExFlags (fp=0x683940,
filename=0x7fffffffed79 "test_period.py", start=257, globals=0x6836d0,
locals=0x6836d0, closeit=1, flags=0x7fffffffe9c0)
at Python/pythonrun.c:1339
---Type <return> to continue, or q <return> to quit---
#139 0x00007ffff7af98bb in PyRun_SimpleFileExFlags (fp=0x683940,
filename=0x7fffffffed79 "test_period.py", closeit=1, flags=0x7fffffffe9c0)
at Python/pythonrun.c:943
#140 0x00007ffff7af8ec7 in PyRun_AnyFileExFlags (fp=0x683940,
filename=0x7fffffffed79 "test_period.py", closeit=1, flags=0x7fffffffe9c0)
at Python/pythonrun.c:747
#141 0x00007ffff7b15234 in Py_Main (argc=2, argv=0x7fffffffeb48)
at Modules/main.c:639
#142 0x0000000000400784 in main (argc=2, argv=0x7fffffffeb48)
at ./Modules/python.c:23
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1352 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1352/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1352/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1352/events | https://github.com/pandas-dev/pandas/issues/1352 | 4,822,253 | MDU6SXNzdWU0ODIyMjUz | 1,352 | MultiIndex partial slicing error | {
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} | 1 | "2012-05-30T19:05:27Z" | "2012-06-03T15:57:36Z" | "2012-06-03T15:56:58Z" | CONTRIBUTOR | null | ```
In [10]: idx = MultiIndex(labels=[[0,0,0],[0,1,1],[1,0,1]],levels=[['a','b'],['x','y'],['p','q']])
In [11]: df = DataFrame(np.random.rand(3,2),index=idx)
In [12]: df.ix[('a','y')]
Out[12]:
0 1
p 0.788191 0.781931
q 0.566332 0.486645
In [13]: df[('a','y')]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/home/adam/code/qplib/<ipython-input-13-d3c63a882cb1> in <module>()
----> 1 df[('a','y')]
/home/adam/code/pandas/pandas/core/frame.pyc in __getitem__(self, key)
1655 return self._getitem_multilevel(key)
1656 else:
-> 1657 return self._get_item_cache(key)
1658
1659 def _getitem_array(self, key):
/home/adam/code/pandas/pandas/core/generic.pyc in _get_item_cache(self, item)
472 except Exception:
473 try:
--> 474 values = self._data.get(item)
475 res = self._box_item_values(item, values)
476 cache[item] = res
/home/adam/code/pandas/pandas/core/internals.pyc in get(self, item)
756
757 def get(self, item):
--> 758 _, block = self._find_block(item)
759 return block.get(item)
760
/home/adam/code/pandas/pandas/core/internals.pyc in _find_block(self, item)
846
847 def _find_block(self, item):
--> 848 self._check_have(item)
849 for i, block in enumerate(self.blocks):
850 if item in block:
/home/adam/code/pandas/pandas/core/internals.pyc in _check_have(self, item)
853 def _check_have(self, item):
854 if item not in self.items:
--> 855 raise KeyError('no item named %s' % str(item))
856
857 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: "no item named ('a', 'y')"
In [14]: df.ix[('a','y'),:]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/home/adam/code/qplib/<ipython-input-14-19a9d60d6a3b> in <module>()
----> 1 df.ix[('a','y'),:]
/home/adam/code/pandas/pandas/core/indexing.pyc in __getitem__(self, key)
31 pass
/home/adam/code/pandas/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
96 def _getitem_tuple(self, tup):
97 try:
---> 98 return self._getitem_lowerdim(tup)
99 except IndexingError:
100 pass
/home/adam/code/pandas/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
165 if isinstance(ax0, MultiIndex):
166 try:
--> 167 return self._get_label(tup, axis=0)
168 except TypeError:
169 # slices are unhashable
/home/adam/code/pandas/pandas/core/indexing.pyc in _get_label(self, label, axis)
39 return self.obj.xs(label, axis=axis, copy=False)
40 except Exception:
---> 41 return self.obj.xs(label, axis=axis, copy=True)
42
43 def _slice(self, obj, axis=0):
/home/adam/code/pandas/pandas/core/frame.pyc in xs(self, key, axis, level, copy)
1894 index = self.index
1895 if isinstance(index, MultiIndex):
-> 1896 loc, new_index = self.index.get_loc_level(key)
1897 else:
1898 loc = self.index.get_loc(key)
/home/adam/code/pandas/pandas/core/index.pyc in get_loc_level(self, key, level)
2000 for i, k in enumerate(key):
2001 if not isinstance(k, slice):
-> 2002 k = self._get_level_indexer(k, level=i)
2003 if isinstance(k, slice):
2004 # everything
/home/adam/code/pandas/pandas/core/index.pyc in _get_level_indexer(self, key, level)
2030 def _get_level_indexer(self, key, level=0):
2031 level_index = self.levels[level]
-> 2032 loc = level_index.get_loc(key)
2033 labels = self.labels[level]
2034
/home/adam/code/pandas/pandas/core/index.pyc in get_loc(self, key)
623 loc : int
624 """
--> 625 return self._engine.get_loc(key)
626
627 def get_value(self, series, key):
/home/adam/code/pandas/pandas/lib.so in pandas.lib.IndexEngine.get_loc (pandas/src/tseries.c:112968)()
/home/adam/code/pandas/pandas/lib.so in pandas.lib.IndexEngine.get_loc (pandas/src/tseries.c:112835)()
/home/adam/code/pandas/pandas/lib.so in pandas.lib.PyObjectHashTable.get_item (pandas/src/tseries.c:22698)()
/home/adam/code/pandas/pandas/lib.so in pandas.lib.PyObjectHashTable.get_item (pandas/src/tseries.c:22652)()
KeyError: ('a', 'y')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1353 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1353/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1353/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1353/events | https://github.com/pandas-dev/pandas/issues/1353 | 4,824,224 | MDU6SXNzdWU0ODI0MjI0 | 1,353 | pandas.unique should return ndarray | {
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} | 1 | "2012-05-30T20:48:58Z" | "2014-06-13T02:43:53Z" | "2012-06-02T18:59:01Z" | CONTRIBUTOR | null | want NA's in my corr matrix?
Right now it throws division by zero and falls back to extremely slow code path -
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http://pandas.pydata.org/pandas-docs/dev/timeseries.html#up-and-downsampling
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} | 0 | "2012-05-31T00:22:16Z" | "2012-06-01T20:53:24Z" | "2012-06-01T20:53:24Z" | MEMBER | null | Also reported on mailing list
```
In [4]: ts.plot()
Out[4]: <matplotlib.axes.AxesSubplot at 0x41cc310>
In [5]: ts.index.fr
ts.index.freq ts.index.freqstr
In [5]: ts.index.freq
Out[5]: <10 Days>
In [6]: ts.index.freq = None
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/<ipython-input-6-d89557d7f8e5> in <module>()
----> 1 ts.index.freq = None
AttributeError: can't set attribute
In [7]: ts.index.offset = None
In [8]: ts
Out[8]:
2000-01-01 2.963378
2000-01-11 -0.685998
2000-01-21 0.392812
2000-01-31 -0.143893
2000-02-10 -1.162283
2000-02-20 -0.460511
2000-03-01 -0.751741
2000-03-11 -0.567428
2000-03-21 -0.439105
2000-03-31 0.040322
2000-04-10 -0.929786
2000-04-20 -0.620005
2000-04-30 0.482502
2000-05-10 0.109013
2000-05-20 0.657817
2000-05-30 -0.668386
2000-06-09 -1.797074
2000-06-19 -0.197738
2000-06-29 -0.312481
2000-07-09 1.240477
2000-07-19 0.447583
2000-07-29 0.188429
2000-08-08 -0.117540
2000-08-18 -1.145495
2000-08-28 0.479124
2000-09-07 0.309968
2000-09-17 0.769928
2000-09-27 -0.501629
2000-10-07 0.164322
2000-10-17 -0.554113
2000-10-27 0.468588
2000-11-06 0.857458
2000-11-16 0.508256
2000-11-26 -0.452384
2000-12-06 1.732538
2000-12-16 0.695495
2000-12-26 0.441233
2001-01-05 -0.271561
In [9]: ts.index.offset = None
In [10]: ts.plot()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/<ipython-input-10-132f3667ee95> in <module>()
----> 1 ts.plot()
/home/wesm/code/pandas/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, logy, **kwds)
758 legend=False, grid=grid, label=label, **kwds)
759
--> 760 plot_obj.generate()
761 plot_obj.draw()
762
/home/wesm/code/pandas/pandas/tools/plotting.pyc in generate(self)
239 self._compute_plot_data()
240 self._setup_subplots()
--> 241 self._make_plot()
242 self._post_plot_logic()
243 self._adorn_subplots()
/home/wesm/code/pandas/pandas/tools/plotting.pyc in _make_plot(self)
426 if self.use_index and self.has_ts_index:
427 data = self._maybe_convert_index(self.data)
--> 428 self._make_ts_plot(data)
429 else:
430 x = self._get_xticks()
/home/wesm/code/pandas/pandas/tools/plotting.pyc in _make_ts_plot(self, data, **kwargs)
478
479 label = com._stringify(self.label)
--> 480 tsplot(data, plotf, ax=ax, label=label, **kwargs)
481 ax.grid(self.grid)
482 else:
/home/wesm/code/pandas/pandas/tseries/plotting.pyc in tsplot(series, plotf, *args, **kwargs)
114 # format args and lot
115 args = _check_plot_params(series, series.index, freq, *args)
--> 116 plotted = plotf(ax, *args, **kwargs)
117
118 format_dateaxis(ax, ax.freq)
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in plot(self, *args, **kwargs)
3892 lines = []
3893
-> 3894 for line in self._get_lines(*args, **kwargs):
3895 self.add_line(line)
3896 lines.append(line)
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in _grab_next_args(self, *args, **kwargs)
321 return
322 if len(remaining) <= 3:
--> 323 for seg in self._plot_args(remaining, kwargs):
324 yield seg
325 return
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in _plot_args(self, tup, kwargs)
299 x = np.arange(y.shape[0], dtype=float)
300
--> 301 x, y = self._xy_from_xy(x, y)
302
303 if self.command == 'plot':
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in _xy_from_xy(self, x, y)
215 def _xy_from_xy(self, x, y):
216 if self.axes.xaxis is not None and self.axes.yaxis is not None:
--> 217 bx = self.axes.xaxis.update_units(x)
218 by = self.axes.yaxis.update_units(y)
219
/home/wesm/code/repos/matplotlib/lib/matplotlib/axis.pyc in update_units(self, data)
1276 """
1277
-> 1278 converter = munits.registry.get_converter(data)
1279 if converter is None:
1280 return False
/home/wesm/code/repos/matplotlib/lib/matplotlib/units.pyc in get_converter(self, x)
131
132 if converter is None and iterable(x):
--> 133 for thisx in x:
134 # Make sure that recursing might actually lead to a solution, if
135 # we are just going to re-examine another item of the same kind,
/home/wesm/code/pandas/pandas/tseries/period.pyc in __iter__(self)
685 def __iter__(self):
686 for val in self.values:
--> 687 yield Period(ordinal=val, freq=self.freq)
688
689 @property
/home/wesm/code/pandas/pandas/tseries/period.pyc in __init__(self, value, freq, ordinal, year, month, quarter, day, hour, minute, second)
136 base, mult = _gfc(freq)
137 if mult != 1:
--> 138 raise ValueError('Only mult == 1 supported')
139
140 if self.ordinal is None:
ValueError: Only mult == 1 supported
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1358 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1358/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1358/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1358/events | https://github.com/pandas-dev/pandas/issues/1358 | 4,828,178 | MDU6SXNzdWU0ODI4MTc4 | 1,358 | Add accelerated Cython median groupby method | {
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} | 0 | "2012-05-31T01:02:42Z" | "2013-12-04T00:58:15Z" | "2012-07-12T17:45:45Z" | MEMBER | null | Use linear-time median finder
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} | 14 | "2012-05-31T12:23:11Z" | "2012-06-04T18:39:03Z" | "2012-06-04T18:39:03Z" | NONE | null | platform: Windows 7
python: Python: Enthought Python Distribution, Version: 7.1-1 (32-bit)
> > > numpy.**version**
> > > '1.6.2'
> > > scipy.**version**
> > > '0.10.1'
> > > $:git rev-parse master
> > > 5b98fdfeae2ef42d78242c4cca1e0959b9c710db
$ python setup.py install
```
...
g++ -shared build\temp.win32-2.7\Release\pandas\src\ujson\python\ujson.o build\temp.win32-2.7\Release\pandas\src\ujson\python\objtojson.o build\temp.w
in32-2.7\Release\pandas\src\ujson\python\jsontoobj.o build\temp.win32-2.7\Release\pandas\src\ujson\lib\ultrajsonenc.o build\temp.win32-2.7\Release\pan
das\src\ujson\lib\ultrajsondec.o build\temp.win32-2.7\Release\pandas\src\datetime\np_datetime.o -LC:\Python27\lib\site-packages\numpy\core\lib -LC:\Py
thon27\libs -LC:\Python27\PCbuild -lnpymath -lpython27 -lmsvcr90 -o build\lib.win32-2.7\pandas\_ujson.pyd
Found executable C:\Python27\Scripts\g++.exe
Warning: .drectve `/manifestdependency:"type='win32' name='Microsoft.VC90.CRT' version='9.0.21022.8' processorArchitecture='x86' publicKeyToken='1fc8b
3b9a1e18e3b'" /DEFAULTLIB:"python27.lib" /DEFAULTLIB:"MSVCRT" /DEFAULTLIB:"OLDNAMES" ' unrecognized
Warning: .drectve `/manifestdependency:"type='win32' name='Microsoft.VC90.CRT' version='9.0.21022.8' processorArchitecture='x86' publicKeyToken='1fc8b
3b9a1e18e3b'" /DEFAULTLIB:"python27.lib" /DEFAULTLIB:"MSVCRT" /DEFAULTLIB:"OLDNAMES" ' unrecognized
C:\Python27\lib\site-packages\numpy\core\lib/npymath.lib(build/temp.win32-2.7/numpy/core/src/npymath/halffloat.obj):(.text+0x636): undefined reference
to `_allshl'
C:\Python27\lib\site-packages\numpy\core\lib/npymath.lib(build/temp.win32-2.7/numpy/core/src/npymath/halffloat.obj):(.text+0x659): undefined reference
to `_aullshr'
C:\Python27\lib\site-packages\numpy\core\lib/npymath.lib(build/temp.win32-2.7/numpy/core/src/npymath/halffloat.obj):(.text+0x7c5): undefined reference
to `_allmul'
C:\Python27\lib\site-packages\numpy\core\lib/npymath.lib(build/temp.win32-2.7/numpy/core/src/npymath/halffloat.obj):(.text+0x7e1): undefined reference
to `_allmul'
C:\Python27\lib\site-packages\numpy\core\lib/npymath.lib(build/temp.win32-2.7/numpy/core/src/npymath/halffloat.obj):(.text+0x831): undefined reference
to `_allmul'
C:\Python27\lib\site-packages\numpy\core\lib/npymath.lib(build/temp.win32-2.7/numpy/core/src/npymath/halffloat.obj):(.text+0x84f): undefined reference
to `_allmul'
collect2: ld returned 1 exit status
error: Command "g++ -shared build\temp.win32-2.7\Release\pandas\src\ujson\python\ujson.o build\temp.win32-2.7\Release\pandas\src\ujson\python\objtojso
n.o build\temp.win32-2.7\Release\pandas\src\ujson\python\jsontoobj.o build\temp.win32-2.7\Release\pandas\src\ujson\lib\ultrajsonenc.o build\temp.win32
-2.7\Release\pandas\src\ujson\lib\ultrajsondec.o build\temp.win32-2.7\Release\pandas\src\datetime\np_datetime.o -LC:\Python27\lib\site-packages\numpy\
core\lib -LC:\Python27\libs -LC:\Python27\PCbuild -lnpymath -lpython27 -lmsvcr90 -o build\lib.win32-2.7\pandas\_ujson.pyd" failed with exit status 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1361 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1361/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1361/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1361/events | https://github.com/pandas-dev/pandas/issues/1361 | 4,835,411 | MDU6SXNzdWU0ODM1NDEx | 1,361 | Error and failure in nosetests (Ubuntu 12.04) | {
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} | 9 | "2012-05-31T12:24:07Z" | "2014-12-28T20:08:39Z" | "2012-06-03T14:05:21Z" | NONE | null | ```
======================================================================
ERROR: test_map (pandas.tseries.tests.test_period.TestPeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/arthur/python-packages/pandas/pandas/tseries/tests/test_period.py", line 1523, in test_map
assert_array_equal(result, expected)
File "/usr/local/lib/python2.7/dist-packages/numpy/testing/utils.py", line 753, in assert_array_equal
verbose=verbose, header='Arrays are not equal')
File "/usr/local/lib/python2.7/dist-packages/numpy/testing/utils.py", line 612, in assert_array_compare
x_isnan[x_isna] = False
File "/home/arthur/python-packages/pandas/pandas/core/index.py", line 282, in __setitem__
raise Exception(str(self.__class__) + ' object is immutable')
Exception: <class 'pandas.tseries.period.PeriodIndex'> object is immutable
======================================================================
FAIL: test_unicode_index (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/arthur/python-packages/pandas/pandas/io/tests/test_pytables.py", line 661, in test_unicode_index
self._check_roundtrip(s, tm.assert_series_equal)
File "/home/arthur/python-packages/pandas/pandas/io/tests/test_pytables.py", line 566, in _check_roundtrip
comparator(retrieved, obj, **kwargs)
File "/home/arthur/python-packages/pandas/pandas/util/testing.py", line 129, in assert_series_equal
assert(left.index.equals(right.index))
AssertionError
```
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} | 0 | "2012-05-31T15:22:50Z" | "2012-06-02T20:00:29Z" | "2012-06-02T20:00:29Z" | MEMBER | null | ```
df.groupby('key1')['data1'].transform('mean')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/book_scripts/notebooks/<ipython-input-42-46fdb55e8ac6> in <module>()
----> 1 df.groupby('key1')['data1'].transform('mean')
/home/wesm/code/pandas/pandas/core/groupby.pyc in transform(self, func, *args, **kwargs)
1298 for name, group in self:
1299 object.__setattr__(group, 'name', name)
-> 1300 res = func(group, *args, **kwargs)
1301 indexer = self.obj.index.get_indexer(group.index)
1302 np.put(result, indexer, res)
TypeError: 'str' object is not callable
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1363 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1363/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1363/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1363/events | https://github.com/pandas-dev/pandas/issues/1363 | 4,839,336 | MDU6SXNzdWU0ODM5MzM2 | 1,363 | Issue with read_csv and string variables starting with (unclosed) quotes | {
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} | 0 | "2012-05-31T15:34:09Z" | "2012-05-31T17:25:44Z" | "2012-05-31T17:25:44Z" | NONE | null | I seem to have trouble importing strings from a csv file.
```
from pandas import *
from StringIO import StringIO
```
# ---
This works:
``` python
file = StringIO("""\
label1,label2,label3
index1,a,c,e
index2,b,d,f
""")
a0=read_csv(file)
```
```
label1 label2 label3
index1 a c e
index2 b d f
```
# ---
But this does not work (with a double quote in front of one of the values):
``` python
file = StringIO("""\
label1,label2,label3
index1,"a,c,e
index2,b,d,f
""")
a1=read_csv(file)
```
```
Error: newline inside string
```
However the following two cases do work (double quote not in front, or closing double quote):
# ---
``` python
file = StringIO("""\
label1,label2,label3
index1,a",c,e
index2,b,d,f
""")
a2=read_csv(file)
```
```
label1 label2 label3
index1 a" c e
index2 b d f
```
# ---
``` python
file = StringIO("""\
label1,label2,label3
index1,"a",c,e
index2,b,d,f
""")
a3=read_csv(file)
```
```
label1 label2 label3
index1 a c e
index2 b d f
```
This is the case both with 0.8.0 and 0.7.3.
A quick look at the code hints that the issue stems from the fact that the parser, `csv.reader`, is given the 'excel' dialect. Maybe it would be worth considering exposing the dialect specification to the user?
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} | 0 | "2012-05-31T15:52:00Z" | "2012-06-02T19:55:10Z" | "2012-06-02T19:55:10Z" | MEMBER | null | ```
In [46]: print df; df.groupby('key1').transform(lambda x: x.mean())
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/book_scripts/notebooks/<ipython-input-46-656cf72f4323> in <module>()
1 print df
----> 2 df.groupby('key1').transform(lambda x: x.mean())
/home/wesm/code/pandas/pandas/core/groupby.pyc in transform(self, func, *args, **kwargs)
1639 group.T.values[:] = res
1640 else:
-> 1641 group.values[:] = res
1642
1643 applied.append(group)
ValueError: operands could not be broadcast together with shapes (3,4) (2)
data1 data2 key1 key2
0 0.984916 0.904550 a one
1 -1.879638 0.585887 a two
2 0.457879 -0.721776 b one
3 1.585887 0.050336 b two
4 -0.701547 0.525808 a one
```
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|
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} | 2 | "2012-05-31T18:33:28Z" | "2012-06-02T21:31:21Z" | "2012-06-02T21:31:02Z" | NONE | null | Sorry if there is a specific reason why this is not supported, but with boolean dataframe indexing, I would expect the following to work:
http://pandas.pydata.org/pandas-docs/dev/indexing.html#indexing-a-dataframe-with-a-boolean-dataframe
```
In [36]: df_rand = pd.DataFrame(np.random.randn(10,10))
In [37]: df_rand < 0
Out[37]:
0 1 2 3 4 5 6 7 8 9
0 False False True False True False True False True True
1 False True True False True False False True False True
2 True True True False True False False True False False
3 False False True False True True True False False True
4 True False False True False False True False False False
5 False False False False False False False False False False
6 False True False False True True True False False True
7 False False False True False False True False True False
8 True True True False True True True False False False
9 False False True True True False True True True False
In [38]: df_rand[df_rand < 0] = -0.1
In [39]: df_rand[df_rand < 0] += 0.1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
C:\Python27\Scripts\<ipython-input-39-6dc58d9235e9> in <module>()
----> 1 df_rand[df_rand < 0] += 0.1
C:\Python27\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
1655 return self._getitem_multilevel(key)
1656 else:
-> 1657 return self._get_item_cache(key)
1658
1659 def _getitem_array(self, key):
C:\Python27\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
479 from pandas.core.frame import DataFrame
480 if isinstance(item, DataFrame):
--> 481 raise ValueError('Cannot index using (boolean) dataframe')
482 raise
483
ValueError: Cannot index using (boolean) dataframe
In [40]: print pd.__version__
0.8.0b1
```
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} | 5 | "2012-06-01T07:41:28Z" | "2015-10-25T19:47:06Z" | "2015-10-25T17:51:01Z" | CONTRIBUTOR | null | > Enhanced time series plotting; adaptation of scikits.timeseries matplotlib-based plotting code
Let's add sample plots to this.
Continuing discussion on doc, I suggest to take a similar approach like statsmodels:
http://statsmodels.sourceforge.net/devel/generated/statsmodels.graphics.functional.rainbowplot.html#statsmodels.graphics.functional.rainbowplot
The plot in the docs is generated from the docstring
the Sphinx source:
http://statsmodels.sourceforge.net/devel/_sources/generated/statsmodels.graphics.functional.rainbowplot.txt
the docstring:
https://github.com/statsmodels/statsmodels/blob/master/statsmodels/graphics/functional.py#L208
So
```
.. ipython:: python
ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
@savefig series_plot_basic.png width=4.5in
ts.plot()
```
could be moved to a docstrin in cases where it makes sense.
Can we not reuse the test code that may already be in the code base?
https://github.com/pydata/pandas/blob/master/pandas/tests/test_graphics.py#L39
The major problem is that the test writing set-up in pandas is highly modular while docstrings à la statsmodels are more verbose.
But, taking a second look at the docstring above, the tests could actually be used to produce doc plots:
instead
```
.. plot:: plots/graphics_functional_rainbowplot.py
```
use
```
.. plot:: plots/file_with_plot_examples.py
```
In file `file_with_plot_examples.py` you may for the mean time import the test functions.
On the other hand, such plot files:
https://github.com/statsmodels/statsmodels/blob/master/docs/source/plots/graphics_functional_rainbowplot.py
coudl be easily contributed by pandas users for more cases that core devs are probably thinging of currently.
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} | 1 | "2012-06-01T10:11:56Z" | "2014-07-20T11:43:00Z" | "2012-06-02T18:52:02Z" | CONTRIBUTOR | null | npymath causing build woes on win32 (#1309 and #1360)
Removed half float support. `npy_isnan` and `npy_isinf` are macros defined in the npymath header so linking isn't necessary.
Also, build fix for mingw32.
Tested on Python 2.7 on 64-bit OSX, 32-bit Ubuntu and 32-bit WinXP (VS 2008 and mingw32).
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} | 1 | "2012-06-01T13:11:34Z" | "2012-06-07T10:24:52Z" | "2012-06-07T10:24:52Z" | CONTRIBUTOR | null | ``` python
In [22]: df
Out[22]:
0 1 2
A 1 2 0.659855 0.340490 0.351028
2 0.695581 0.264714 0.389929
B 2 0.105016 0.070774 0.114629
In [23]: df.to_csv('foo.csv')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
TypeError: only integer arrays with one element can be converted to an index
In [24]: df1
Out[24]:
0 1 2
a 0.313015 0.631747 0.091818
a 0.505238 0.260553 0.793378
b 0.441791 0.029851 0.139490
In [25]: df1.to_csv('foo.csv')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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} | 1 | "2012-06-01T13:17:57Z" | "2012-06-03T17:11:58Z" | "2012-06-03T17:11:15Z" | CONTRIBUTOR | null | Since irow translates the integer index to a label one and then retrieves the row, in case of duplicate index multiple rows are returned.
``` python
In [34]: df = pandas.DataFrame(np.random.rand(3,3), columns=list('ABC'), index=list('aab'))
In [35]: df
Out[35]:
A B C
a 0.031057 0.528825 0.694592
a 0.705572 0.122005 0.754107
b 0.475423 0.048636 0.224495
In [36]: df.irow(0)
Out[36]:
A B C
a 0.031057 0.528825 0.694592
a 0.705572 0.122005 0.754107
In [37]: df.irow(1)
Out[37]:
A B C
a 0.031057 0.528825 0.694592
a 0.705572 0.122005 0.754107
In [38]: df.irow(2)
Out[38]:
A 0.475423
B 0.048636
C 0.224495
Name: b
```
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} | 2 | "2012-06-01T14:10:13Z" | "2012-06-01T16:03:49Z" | "2012-06-01T16:01:43Z" | CONTRIBUTOR | null | originally raised on pydata mailing list:
In [50]: DataFrame({'x': [1., 1.]}).to_sparse(fill_value=0).x.mean()
Out[50]: 0.5
In [51]: DataFrame({'x': [1., 1.]}).to_sparse().x.mean()
Out[51]: 1.0
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} | 9 | "2012-06-01T15:56:55Z" | "2012-06-02T00:52:09Z" | "2012-06-02T00:52:09Z" | NONE | null | I installed from source 0.8.0b1 and it broke several of my programs (date related issues). I tried to simply re-install 0.7.3 which removed the future warning but I am still having problems with code that was working but now does not. I also updated Cython.
What is the best way to start over with a clean install of pandas 0.7.3?
Thanks
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} | 1 | "2012-06-01T19:16:14Z" | "2021-04-11T01:51:22Z" | null | MEMBER | null | http://stackoverflow.com/questions/10839701/time-weighted-average-with-pandas/10856106#10856106
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} | 3 | "2012-06-01T21:44:04Z" | "2012-06-02T18:48:48Z" | "2012-06-02T18:47:55Z" | NONE | null | Using pandas 0.8 and numpy 1.7, I get errors when I add columns that are of type datetime64 for everything but very short columns:
```
In [105]: t = np.arange(start='2012-01-01 00:00', stop='2012-01-02 00:00', dtype='datetime64[h]')
In [106]: df = pandas.DataFrame(np.arange(len(t)))
In [107]: df['t'] = t
In [108]: df
Out[108]:
0 t
0 0 2012-01-01 05:00:00
1 1 2012-01-01 06:00:00
2 2 2012-01-01 07:00:00
3 3 2012-01-01 08:00:00
4 4 2012-01-01 09:00:00
5 5 2012-01-01 10:00:00
6 6 2012-01-01 11:00:00
7 7 2012-01-01 12:00:00
8 8 2012-01-01 13:00:00
9 9 2012-01-01 14:00:00
10 10 2012-01-01 15:00:00
11 11 2012-01-01 16:00:00
12 12 2012-01-01 17:00:00
13 13 2012-01-01 18:00:00
14 14 2012-01-01 19:00:00
15 15 2012-01-01 20:00:00
16 16 2012-01-01 21:00:00
17 17 2012-01-01 22:00:00
18 18 2012-01-01 23:00:00
19 19 2012-01-02 00:00:00
20 20 2012-01-02 01:00:00
21 21 2012-01-02 02:00:00
22 22 2012-01-02 03:00:00
23 23 2012-01-02 04:00:00
In [109]: t = np.arange(start='2012-01-01 00:00', stop='2012-01-02 00:00', dtype='datetime64[m]')
In [110]: df = pandas.DataFrame(np.arange(len(t)))
In [111]: df['t'] = t
In [112]: df
Out[112]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/work/<ipython-input-112-7ed0097d7e9e> in <module>()
----> 1 df
/usr/local/lib/python3.2/site-packages/ipython-0.12-py3.2.egg/IPython/core/displayhook.py in __call__(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
/usr/local/lib/python3.2/site-packages/ipython-0.12-py3.2.egg/IPython/core/displayhook.py in compute_format_data(self, result)
148 MIME type representation of the object.
149 """
--> 150 return self.shell.display_formatter.format(result)
151
152 def write_format_data(self, format_dict):
/usr/local/lib/python3.2/site-packages/ipython-0.12-py3.2.egg/IPython/core/formatters.py in format(self, obj, include, exclude)
124 continue
125 try:
--> 126 data = formatter(obj)
127 except:
128 # FIXME: log the exception
/usr/local/lib/python3.2/site-packages/ipython-0.12-py3.2.egg/IPython/core/formatters.py in __call__(self, obj)
444 type_pprinters=self.type_printers,
445 deferred_pprinters=self.deferred_printers)
--> 446 printer.pretty(obj)
447 printer.flush()
448 return stream.getvalue()
/usr/local/lib/python3.2/site-packages/ipython-0.12-py3.2.egg/IPython/lib/pretty.py in pretty(self, obj)
349 if hasattr(obj_class, '_repr_pretty_'):
350 return obj_class._repr_pretty_(obj, self, cycle)
--> 351 return _default_pprint(obj, self, cycle)
352 finally:
353 self.end_group()
/usr/local/lib/python3.2/site-packages/ipython-0.12-py3.2.egg/IPython/lib/pretty.py in _default_pprint(obj, p, cycle)
469 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
470 # A user-provided repr.
--> 471 p.text(repr(obj))
472 return
473 p.begin_group(1, '<')
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in __repr__(self)
570 buf = StringIO()
571 if self._need_info_repr_():
--> 572 self.info(buf=buf, verbose=self._verbose_info)
573 else:
574 self.to_string(buf=buf)
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in info(self, verbose, buf)
1343 lines.append('Data columns:')
1344 space = max([len(_stringify(k)) for k in self.columns]) + 4
-> 1345 counts = self.count()
1346 assert(len(cols) == len(counts))
1347 for col, count in counts.items():
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in count(self, axis, level, numeric_only)
3997 result = Series(counts, index=frame._get_agg_axis(axis))
3998 else:
-> 3999 result = DataFrame.apply(frame, Series.count, axis=axis)
4000
4001 return result
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, args, **kwds)
3537 return self._apply_raw(f, axis)
3538 else:
-> 3539 return self._apply_standard(f, axis)
3540 else:
3541 return self._apply_broadcast(f, axis)
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in _apply_standard(self, func, axis, ignore_failures)
3592 try:
3593 for k, v in series_gen:
-> 3594 results[k] = func(v)
3595 except Exception as e:
3596 try:
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/series.py in count(self, level)
1072 return Series(counts, index=level_index)
1073
-> 1074 return notnull(self.values).sum()
1075
1076 def value_counts(self):
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/common.py in notnull(obj)
93 boolean ndarray or boolean
94 '''
---> 95 res = isnull(obj)
96 if np.isscalar(res):
97 return not res
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/core/common.py in isnull(obj)
72 result = np.array(obj).view('i8') == lib.iNaT
73 else:
---> 74 result = -np.isfinite(obj)
75 return result
76 elif isinstance(obj, PandasObject):
TypeError: ("ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''", 'occurred at index t')
```
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} | 3 | "2012-06-02T06:19:39Z" | "2014-06-14T20:48:19Z" | "2012-06-02T18:55:02Z" | CONTRIBUTOR | null | I had the compile error below when I built 0.8.0 beta1. The build was on Mac OS X 10.7.3, using python.org's python version 2.7.3, numpy version 1.6.2, Apple clang version 3.1.
<pre>
clang: pandas/src/ujson/python/JSONtoObj.c
pandas/src/ujson/python/JSONtoObj.c:159:36: error: expression is not assignable
((char*)PyArray_DATA(ret)) = new_data;
~~~~~~~~~~~~~~~~~~~~~~~~~~ ^
pandas/src/ujson/python/JSONtoObj.c:292:44: error: expression is not assignable
((char*)PyArray_DATA(npyarr->ret)) = new_data;
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^
</pre>
This patch changes those lines to use PyArray_BYTES instead, so that the assignment is to an lvalue. The build then succeeds, and nose tests pass. I also successfully tested on a linux box (Scientific Linux 6.0), running python 2.6.6, numpy 1.6.2, and gcc 4.4.4. (I don't have a windows box to test on)
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 1 | "2012-06-02T20:42:11Z" | "2013-09-21T12:34:58Z" | "2013-09-21T12:34:58Z" | MEMBER | null | Reduce scipy dependency
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} | 1 | "2012-06-03T06:07:51Z" | "2014-06-14T10:27:19Z" | "2012-06-04T18:43:56Z" | CONTRIBUTOR | null | These are testing tools.
Tox is run locally and can be used on code that is not even checked in. It runs the test suite on various Python versions.
Travis CI is a hosted CI system that can run tests whenever new commits are pushed to the GitHub repo.
You can see the Travis CI status for my fork at http://travis-ci.org/#!/msabramo/pandas -- it's failing at the moment -- it probably needs some tweaks as I am completely new to pandas and I'm probably not building it correctly.
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dtypes also seem to be ignored in Index constructor
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python make.py html
Running Sphinx v1.1.3
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/sphinx/cmdline.py", line 188, in main
warningiserror, tags)
File "/usr/lib/python2.7/dist-packages/sphinx/application.py", line 102, in __init__
confoverrides or {}, self.tags)
File "/usr/lib/python2.7/dist-packages/sphinx/config.py", line 216, in __init__
exec code in config
File "pandas_github/doc/source/conf.py", line 73, in <module>
import pandas
File "pandas_github/pandas/__init__.py", line 15, in <module>
raise ImportError('C extensions not built: if you installed already '
ImportError: C extensions not built: if you installed already verify that you are not importing from the source directory
> pandas_github/pandas/__init__.py(15)<module>()
-> raise ImportError('C extensions not built: if you installed already '
(Pdb)
(Pdb)
```
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Added useful Sphinx builtin extensions and extlinks aliases for github
Overview on available installers and minor update in the migration guide
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} | 0 | "2012-06-04T20:00:26Z" | "2012-06-05T00:55:51Z" | "2012-06-05T00:55:51Z" | MEMBER | null | Almost 100% sure this won't do the right thing
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https://api.github.com/repos/pandas-dev/pandas/issues/1390 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1390/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1390/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1390/events | https://github.com/pandas-dev/pandas/issues/1390 | 4,896,600 | MDU6SXNzdWU0ODk2NjAw | 1,390 | Pandas install fails on Python 3.3 | {
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```
File "/home/vagrant/builds/msabramo/pandas/pandas/__init__.py", line 24, in <module>
from pandas.core.api import *
File "/home/vagrant/builds/msabramo/pandas/pandas/core/api.py", line 10, in <module>
from pandas.core.format import (set_printoptions, reset_printoptions,
File "<string>", line None
SyntaxError: invalid syntax (format.py, line 186)
```
I think it's because that line uses a Unicode literal which doesn't exist in 3.2:
``` python
info_line = (u'Empty %s\nColumns: %s\nIndex: %s'
```
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} | 0 | "2012-06-04T21:18:58Z" | "2012-06-05T01:22:34Z" | "2012-06-05T01:22:34Z" | MEMBER | null | This is a bit much:
```
Timestamp(2012, 3, 10, 22, 0, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)
```
Probably ISO8601-ish would be preferred
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} | 2 | "2012-06-05T03:29:02Z" | "2012-06-06T17:17:56Z" | "2012-06-06T17:17:56Z" | NONE | null | Hi, just read the new article about timezone handing of pandas 0.8: http://wesmckinney.com/blog/?p=506
I see some issue of the design choice, and then I installed the pandas dev version to verify my guessing.
With the new timezone handling of Timestamp:
``` Python
In [18]: ts_orig = Timestamp('3/11/2012 04:00')
In [19]: ts_new = ts_orig.tz_convert("Asia/Shanghai")
In [20]: ts_orig
Out[20]: <Timestamp: 2012-03-11 04:00:00>
In [21]: ts_new
Out[21]: <Timestamp: 2012-03-11 04:00:00+0800 CST, tz=Asia/Shanghai>
In [22]: ts_orig.value
Out[22]: 1331438400000000000
In [23]: ts_new.value
Out[23]: 1331409600000000000
```
The problem was, a timestamp really should be a Unix time which is **"defined as the number of seconds that have elapsed since midnight Coordinated Universal Time (UTC), January 1, 1970"**. This means on every machine, unixtime or timestamp should be the same. **They do not changes.**
For different timezone, this is a representation problem, eg: if user changed machine to Asia/Shanghai, then we should display datetime to user with +8hours.
So, I think the correct and expected behavior maybe(iff timestamp really mean a timestamp):
``` Python
In [18]: ts_orig = Timestamp('3/11/2012 04:00')
In [19]: ts_new = ts_orig.tz_convert("Asia/Shanghai")
In [20]: ts_orig
Out[20]: <Timestamp: 2012-03-11 04:00:00>
In [21]: ts_new
Out[21]: <Timestamp: 2012-03-11 12:00:00+0800 CST, tz=Asia/Shanghai>
In [22]: ts_orig.value
Out[22]: 1331438400000000000
In [23]: ts_new.value
Out[23]: 1331438400000000000
```
This seems nature to me. But I don't understand the full design choice of pandas new timezone handing, so I open this issue for discussion.
- http://en.wikipedia.org/wiki/Unixtime
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https://api.github.com/repos/pandas-dev/pandas/issues/1394 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1394/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1394/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1394/events | https://github.com/pandas-dev/pandas/issues/1394 | 4,903,043 | MDU6SXNzdWU0OTAzMDQz | 1,394 | Series.append() excepts with some funky overlapping dates | {
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} | 1 | "2012-06-05T06:47:28Z" | "2012-06-05T15:42:46Z" | "2012-06-05T15:41:50Z" | CONTRIBUTOR | null | With pandas v0.8.0b1
In [90]: u
Out[90]:
2012-05-08 01:45:00 86
2012-05-08 01:50:00 170
2012-05-08 01:55:00 130
2012-05-08 02:00:00 206
2012-05-08 02:05:00 52
2012-05-08 02:10:00 4
Freq: 5T
In [91]: u.append(u)
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 1566, in append
return concat(to_concat, ignore_index=False, verify_integrity=True)
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 836, in concat
verify_integrity=verify_integrity)
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 895, in **init**
self.new_axes = self._get_new_axes()
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 1066, in _get_new_axes
concat_axis = self._get_concat_axis()
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 1097, in _get_concat_axis
self._maybe_check_integrity(concat_axis)
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 1106, in _maybe_check_integrity
% str(overlap))
Exception: Indexes have overlapping values: [1970-01-16 225:45:00, 1970-01-16 225:50:00, 1970-01-16 225:55:00, 1970-01-16 226:00:00, 1970-01-16 226:05:00, 1970-01-16 226:10:00]
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https://api.github.com/repos/pandas-dev/pandas/issues/1395 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1395/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1395/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1395/events | https://github.com/pandas-dev/pandas/issues/1395 | 4,906,924 | MDU6SXNzdWU0OTA2OTI0 | 1,395 | datetime.date support in 0.8 new timeseries framework | {
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} | 1 | "2012-06-05T11:52:39Z" | "2012-09-18T15:04:48Z" | "2012-09-18T15:04:48Z" | NONE | null | Is is the intention that datetime.date would be supported as a valid input type for some methods? I have observed that it is only partially supported. Example, when using 0.8.0b1:
```
# Works
pandas.BDay().rollforward(datetime.date(2011, 11, 30))
# Doesn't work (returns None)
pandas.Day().rollforward(datetime.date(2011, 11, 30))
# Works
pandas.QuarterEnd().rollback(datetime.date(2011, 11, 30))
# Doesn't work (raises Exception, code expected datetime.datetime object)
pandas.YearEnd().rollback(datetime.date(2011, 11, 30))
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1396 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1396/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1396/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1396/events | https://github.com/pandas-dev/pandas/issues/1396 | 4,907,398 | MDU6SXNzdWU0OTA3Mzk4 | 1,396 | New errors in nosetests | {
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} | 1 | "2012-06-05T12:29:01Z" | "2012-06-07T15:27:05Z" | "2012-06-07T15:26:45Z" | NONE | null | ```
ERROR: test_index_astype_datetime64 (pandas.tseries.tests.test_timeseries.TestTimeSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/hadoop/pandas/pandas/tseries/tests/test_timeseries.py", line 402, in test_index_astype_datetime64
casted = idx.astype(np.dtype('M8[D]'))
File "/home/hadoop/pandas/pandas/core/index.py", line 126, in astype
return Index(self.values.astype(dtype), name=self.name,
TypeError: Cannot cast datetime.datetime object from metadata [us] to [D] according to the rule 'same_kind'
======================================================================
ERROR: test_operators_date (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/hadoop/pandas/pandas/tests/test_series.py", line 1485, in test_operators_date
result = self.objSeries + timedelta(1)
File "/home/hadoop/pandas/pandas/core/series.py", line 85, in wrapper
return Series(na_op(self.values, other),
File "/home/hadoop/pandas/pandas/core/series.py", line 61, in na_op
result[mask] = op(x[mask], y)
TypeError: unsupported operand type(s) for +: 'long' and 'datetime.timedelta'
======================================================================
FAIL: test_from_json_to_json (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/hadoop/pandas/pandas/tests/test_series.py", line 377, in test_from_json_to_json
_check_all_orients(objSeries)
File "/home/hadoop/pandas/pandas/tests/test_series.py", line 357, in _check_all_orients
_check_orient(series, "columns", dtype=dtype)
File "/home/hadoop/pandas/pandas/tests/test_series.py", line 350, in _check_orient
assert_series_equal(series, unser)
File "/home/hadoop/pandas/pandas/util/testing.py", line 126, in assert_series_equal
assert_almost_equal(left.values, right.values)
File "/home/hadoop/pandas/pandas/util/testing.py", line 87, in assert_almost_equal
assert_almost_equal(a[i], b[i])
File "/home/hadoop/pandas/pandas/util/testing.py", line 77, in assert_almost_equal
assert a == b, (a, b)
AssertionError: ('958435200000000000', 9.584352e+17)
----------------------------------------------------------------------
Ran 2047 tests in 52.125s
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1397 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1397/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1397/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1397/events | https://github.com/pandas-dev/pandas/issues/1397 | 4,907,783 | MDU6SXNzdWU0OTA3Nzgz | 1,397 | UTC localization / conversion broken in Timestamp | {
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} | 0 | "2012-06-05T12:56:30Z" | "2012-06-05T16:38:04Z" | "2012-06-05T16:38:04Z" | MEMBER | null | ```
In [5]: ts_orig = Timestamp('3/11/2012 04:00', tz='utc')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-5-709530384ffe> in <module>()
----> 1 ts_orig = Timestamp('3/11/2012 04:00', tz='utc')
/home/wesm/code/pandas/pandas/lib.so in pandas.lib.Timestamp.__new__ (pandas/src/tseries.c:29685)()
/home/wesm/code/pandas/pandas/lib.so in pandas.lib.convert_to_tsobject (pandas/src/tseries.c:34455)()
AttributeError: 'NoneType' object has no attribute '_utcoffset'
```
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} | 1 | "2012-06-05T14:10:46Z" | "2012-06-05T15:48:07Z" | "2012-06-05T15:47:53Z" | CONTRIBUTOR | null | Function declaration in `src/period.h`
```
int get_yq(int64_t ordinal, int freq, int *quarter, int *year);
```
doesn't match definition in `src/period.c`
```
int get_yq(npy_int64 ordinal, int freq, int *quarter, int *year) {
```
Changing `int64_t` to `npy_int64` in `period.h` fixes the build and all tests pass on OSX.
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https://api.github.com/repos/pandas-dev/pandas/issues/1399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1399/events | https://github.com/pandas-dev/pandas/pull/1399 | 4,909,225 | MDExOlB1bGxSZXF1ZXN0MTQ5NDQ3Nw== | 1,399 | mingw32 build / test failures in ujson | {
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} | 9 | "2012-06-05T14:12:19Z" | "2014-07-22T15:20:25Z" | "2012-06-10T17:57:28Z" | CONTRIBUTOR | null | Fixes for ujson #1387
First commit fixes mingw build issues. Builds ok and tests pass on my mingw and VS 2008, also tested on OSX. Needs testing on enthought mingw though.
More commits to come.
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https://api.github.com/repos/pandas-dev/pandas/issues/1400 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1400/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1400/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1400/events | https://github.com/pandas-dev/pandas/issues/1400 | 4,910,160 | MDU6SXNzdWU0OTEwMTYw | 1,400 | read_table with malformed lines | {
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} | [] | closed | false | null | [] | null | 1 | "2012-06-05T14:56:11Z" | "2012-06-05T21:35:13Z" | "2012-06-05T21:35:13Z" | CONTRIBUTOR | null | On the smaller file, a useful error message is raised (bad line number could improve this)
``` python
[2]: cv = pandas.read_table('./bad_mojo2.csv', delimiter="\t")
<snip>
Error: newline inside string
```
On the bigger file, it "works" but an error should be raised here too. Can run this code to reproduce
http://www.pastie.org/4032080
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