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https://api.github.com/repos/pandas-dev/pandas/issues/6401
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27,834,612
MDExOlB1bGxSZXF1ZXN0MTI2NzQ5MTU=
6,401
BUG: Float64Index with nans not comparing correctly
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0
2014-02-18T22:35:45Z
2014-06-15T18:24:42Z
2014-02-19T00:13:47Z
CONTRIBUTOR
null
relalted to #5231
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27,838,616
MDU6SXNzdWUyNzgzODYxNg==
6,402
using DataFrame.interpolate with inplace=True causes error
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3
2014-02-18T23:37:16Z
2014-02-19T00:47:25Z
2014-02-18T23:42:07Z
NONE
null
I got the error below. Looking at the code, it looks like there's a return statement missing above - the code should never get to this point, or should return the DataFrame given as argument, whenever inplace=True. ``` /Library/Python/2.7/site-packages/pandas/core/generic.pyc in interpolate(self, method, axis, limit, inplace, downcast, **kwargs) 2304 if axis == 1: 2305 res = res.T -> 2306 return res 2307 2308 #---------------------------------------------------------------------- UnboundLocalError: local variable 'res' referenced before assignment ```
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27,843,453
MDU6SXNzdWUyNzg0MzQ1Mw==
6,403
ExcelFile parse of empty sheet fails with "IndexError: list index out of range"
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2
2014-02-19T01:12:46Z
2015-06-20T16:20:11Z
2015-06-20T16:20:11Z
NONE
null
Using pandas 0.13.1 on OS X Mavericks to parse a blank Excel spreadsheet causes "IndexError: list index out of range". Apparently the default header=0 in `_parse_excel` causes the execution of `_trim_excel_header(data[header])`. Perhaps when nrows==0 this should not be executed. ``` python import pandas as pd xl_file = pd.ExcelFile('blank.xlsx') xl_file.parse('Sheet1') #Sheet1 has no data ``` STDERR: ``` Traceback (most recent call last): File "/Users/myourshaw/lab/pypeline/python2/excel_example.py", line 10, in <module> xl_file.parse('Sheet1') File "/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/excel.py", line 208, in parse **kwds) File "/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/excel.py", line 291, in _parse_excel data[header] = _trim_excel_header(data[header]) IndexError: list index out of range ```
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6,404
Quick Frame Shift Implementation
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20
2014-02-19T04:52:04Z
2014-06-19T03:19:48Z
2014-03-20T04:08:33Z
CONTRIBUTOR
null
Fix for issue #5609
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6,405
Remove the special case for empty dataframes.
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9
2014-02-19T08:20:05Z
2014-06-16T20:50:30Z
2014-03-14T17:39:00Z
CONTRIBUTOR
null
Fixes issue #6062.
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6,406
Make to_csv return a string if no path or buffer is provided.
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6
2014-02-19T08:32:22Z
2014-06-27T23:38:40Z
2014-03-09T19:56:17Z
CONTRIBUTOR
null
Fixes #6061.
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infer_freq broken in 0.13.1
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3
2014-02-19T11:26:23Z
2014-02-20T14:00:33Z
2014-02-19T13:49:54Z
CONTRIBUTOR
null
``` python In [3]: dates = pd.date_range('01-Jan-2015', '01-Jan-2016', freq='MS') ...: s = pd.TimeSeries(1, dates) ...: pd.infer_freq(s) ...: Traceback (most recent call last): File "<ipython-input-3-a7de7a7e9245>", line 3, in <module> pd.infer_freq(s) File "C:\dev\bin\Anaconda\lib\site-packages\pandas\tseries\frequencies.py", line 656, in infer_freq inferer = _FrequencyInferer(index, warn=warn) File "C:\dev\bin\Anaconda\lib\site-packages\pandas\tseries\frequencies.py", line 680, in __init__ self.is_monotonic = self.index.is_monotonic File "C:\dev\bin\Anaconda\lib\site-packages\pandas\core\generic.py", line 1815, in __getattr__ (type(self).__name__, name)) AttributeError: 'Series' object has no attribute 'is_monotonic' In [4]: pd.__version__ Out[4]: '0.13.1' ```
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API: pd.infer_freq interacts correctly with a Series
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0
2014-02-19T13:01:14Z
2014-07-16T08:55:10Z
2014-02-19T13:49:54Z
CONTRIBUTOR
null
ENH: pd.infer_freq() will accept a Series as input API: pd.infer_freq() will now raise a TypeError if given an invalid Series/Index type (GH6407) closes #6407
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ENH: allow datelike to be aggregated in resample
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0
2014-02-19T13:40:36Z
2020-03-31T04:20:56Z
null
CONTRIBUTOR
null
http://stackoverflow.com/questions/21878599/pandas-drops-timestamp-columns-in-resample ``` init_time=pd.date_range(start='2010-01-01 00:00', end='2010-01-05 00:00', freq='12H') valid_time=pd.date_range(start='2010-01-01 00:00', end='2010-01-05 00:00', freq='12H') data = np.random.random(len(valid_time)) frame = pd.DataFrame(index=valid_time, data=data) frame['init_time'] = init_time ``` Works, but a little hacky ``` In [48]: numeric = frame.resample('D',how='mean') In [49]: datelike = frame.loc[:,frame.dtypes.isin([np.dtype('datetime64[ns]')])] In [50]: datelike = datelike.resample('D', how=lambda x: Timestamp(x.view('i8').mean())) In [51]: concat([numeric,datelike],axis=1) Out[51]: 0 init_time 2010-01-01 0.798880 2010-01-01 06:00:00 2010-01-02 0.859781 2010-01-02 06:00:00 2010-01-03 0.515503 2010-01-03 06:00:00 2010-01-04 0.505557 2010-01-04 06:00:00 2010-01-05 0.979835 2010-01-05 00:00:00 [5 rows x 2 columns] ```
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BUG: 32/64-bit issue with indexing
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10
2014-02-19T14:29:29Z
2014-02-19T20:37:41Z
2014-02-19T17:48:54Z
CONTRIBUTOR
null
https://groups.google.com/forum/#!topic/pydata/90QNz8EY74Q fails on 32-bit ``` In [1]: df = pd.DataFrame({"A": range(2), "B": [pd.Timestamp('2000-01-1')]*2}) In [2]: df Out[2]: A B 0 0 2000-01-01 1 1 2000-01-01 [2 rows x 2 columns] In [3]: df.groupby("A")["B"].transform(min) Out[3]: 0 2000-01-01 1 2000-01-01 Name: B, dtype: datetime64[ns] ```
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doc building taking a very long time
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18
2014-02-19T14:39:46Z
2014-02-20T07:48:15Z
2014-02-20T02:16:03Z
MEMBER
null
I remember these building in less than 5 minutes in the past.
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DOC: use ipython in bool replace doc warning
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2014-02-19T14:45:12Z
2014-07-03T12:54:24Z
2014-02-19T15:10:29Z
MEMBER
null
quick doc fix
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TST: GH6410 / numpy 4328
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2014-07-13T12:21:16Z
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BLD: add optional numpy_dev build
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10
2014-02-20T09:06:33Z
2017-09-01T12:19:31Z
2017-09-01T12:19:31Z
CONTRIBUTOR
null
Is this a bug or a feature that I don't understand: ``` python data = ['20100102 121314', '20100102 121315'] print (pd.to_datetime(data, format='%Y%m%d %H%M%S', utc=True)[0]).__repr__() print (pd.to_datetime(pd.Series(data), format='%Y%m%d %H%M%S', utc=True)[0]).__repr__() print (pd.to_datetime(pd.TimeSeries(data), format='%Y%m%d %H%M%S', utc=True)[0]).__repr__() ``` results in ``` bash Timestamp('2010-01-02 12:13:14+0000', tz='UTC') Timestamp('2010-01-02 12:13:14', tz=None) Timestamp('2010-01-02 12:13:14', tz=None) ``` Using version '0.13.1' --- Other example in https://github.com/pandas-dev/pandas/issues/15760
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ENH: SQL through SQLAlchemy - performance
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2014-02-20T13:40:31Z
2018-07-06T21:57:26Z
2018-07-06T21:57:26Z
CONTRIBUTOR
null
This issue is to track performance measurement and improvements to the SQLalchemy-based SQL io support in pandas. TODO - [ ] Get perfromance measurements working - [ ] Determine bottlenecks - [ ] Address bottleneck
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ENH #6416 improve performance on SQL insert
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2014-02-20T13:43:24Z
2014-06-16T22:21:39Z
2014-02-20T14:29:55Z
CONTRIBUTOR
null
This empirically significantly improves write performance by allowing SQLAlchemy to use the `executemany` method of the driver if available.
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6,418
BUG: reindex() and reindex_like() fill behavior is different in pandas 12.0 and 13.1?
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5
2014-02-20T13:44:56Z
2014-02-20T16:54:29Z
2014-02-20T16:48:06Z
NONE
null
I just came across an issue which caused me serious troubles since upgrading from pandas 12.0 to 13.1. It happens when using a fill method with a reindex() or reindex_like() method. Moreover, those method are not giving consistent results anymore! I have not tested how this issue or originates from changed .ffill() and similar method, but I see it propagates to resample(). Could not find any recent mentioning of the strange behavior and no hints in the docs or What's New section. This is the problem I encounter using pandas 12.0 (with numpy 1.7.1, in both, 32bit Python 2.7.5 Python x,y and 64bit, WinPython-64bit-2.7.4.1; windows 7) and pandas 13.1 (D:\PortableApps\WinPython-64bit-2.7.6.2, numpy 1.8.0). Pandas 12.0 behavior is the same for the 32 bit and 64 bit versions, so this cannot explain the problem. Code: ``` import pandas as pd # Make low frequency timeseries: i30 = index=pd.date_range('2002-02-02', periods=4, freq='30T') s=pd.Series(np.arange(4.), index=i30) s[2] = np.NaN # Upsample by factor 3 with reindex() and resample() methods: i10 = pd.date_range(i30[0], i30[-1], freq='10T') s10 = s.reindex(index=i10, method='bfill') s10_2 = s.reindex(index=i10, method='bfill', limit=2) r10 = s.resample('10Min', fill_method='bfill') r10_2 = s.resample('10Min', fill_method='bfill', limit=2) ``` In pandas 12.0: s10 equals s10_2 equals r10 equals r10_2 ``` s10 Out[60]: 2002-02-02 00:00:00 0 2002-02-02 00:10:00 1 2002-02-02 00:20:00 1 2002-02-02 00:30:00 1 2002-02-02 00:40:00 NaN 2002-02-02 00:50:00 NaN 2002-02-02 01:00:00 NaN 2002-02-02 01:10:00 3 2002-02-02 01:20:00 3 2002-02-02 01:30:00 3 Freq: 10T, dtype: float64 ``` In pandas 13.1: s10 does not equal s10_2; s10 has all NaN's filled ``` s10 Out[120]: 2002-02-02 00:00:00 0 2002-02-02 00:10:00 1 2002-02-02 00:20:00 1 2002-02-02 00:30:00 1 2002-02-02 00:40:00 3 2002-02-02 00:50:00 3 2002-02-02 01:00:00 3 2002-02-02 01:10:00 3 2002-02-02 01:20:00 3 2002-02-02 01:30:00 3 Freq: 10T, dtype: float64 ``` Same holds for resampled series r10 Conclusion: in pandas 13.1, all is filled if limit=None which breaks with the pandas 12.0 behavior. I think the 12.0 behavior is mre sensible; only fill the gaps created from upsampling. This even more import for the reindex_like method because there the "limit" key cannot limit which gaps are filled in pandas 13.1: ``` s.reindex_like(s10, method='bfill', limit=2) Out[121]: 2002-02-02 00:00:00 0 2002-02-02 00:10:00 1 2002-02-02 00:20:00 1 2002-02-02 00:30:00 1 2002-02-02 00:40:00 3 2002-02-02 00:50:00 3 2002-02-02 01:00:00 3 2002-02-02 01:10:00 3 2002-02-02 01:20:00 3 2002-02-02 01:30:00 3 Freq: 10T, dtype: float64 ``` Hope this is clear and I can be reproduced? I hope this can be fixed soon. But of course, if you can reproduce this behavior and it has indeed change from 12.0 to 13.1, this should be in the docs
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6,419
BUG: inconcistency in contained values of a Series created from a DatetimeIndex with a tz
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0
2014-02-20T13:50:35Z
2014-06-18T04:07:44Z
2014-02-20T14:22:57Z
CONTRIBUTOR
null
related #6032
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6,420
ENH #6416: performance improvements on write
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11
2014-02-20T14:24:40Z
2014-06-26T17:26:04Z
2014-02-20T18:14:10Z
CONTRIBUTOR
null
- tradoff higher memory use for faster writes. This replaces the earlier PR where the history was a mess!
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6,421
REGR: Bug in Series.reindex when specifying a method with some nan values was inconsistent (GH6418)
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0
2014-02-20T16:32:36Z
2014-06-13T10:02:08Z
2014-02-20T16:48:06Z
CONTRIBUTOR
null
closes #6418
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ENH: Add functions for creating dataframes with NaNs
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3
2014-02-20T22:07:49Z
2014-07-16T08:55:27Z
2014-02-25T12:40:14Z
CONTRIBUTOR
null
Some convenience functions for making missing data.
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Passing invalid unit to to_timedelta is silently ignored
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0
2014-02-20T22:45:28Z
2014-06-30T14:35:18Z
2014-06-30T14:35:18Z
CONTRIBUTOR
null
For example: ``` In [25]: pd.to_timedelta([2,10], unit="days") Out[25]: 0 00:00:00.000000 1 00:00:00.000000 dtype: timedelta64[ns] ```
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28,005,450
MDU6SXNzdWUyODAwNTQ1MA==
6,424
interpolate w/ method=time does not work with timedeltas
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14
2014-02-20T23:17:25Z
2016-12-10T11:20:10Z
2016-12-10T11:19:49Z
CONTRIBUTOR
null
``` In [98]: pd.DataFrame({'v':[1,2,3]},index=pd.to_timedelta([1,2,3], unit="d")).interpolate(method="time") --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-98-9c399944f329> in <module>() ----> 1 pd.DataFrame({'v':[1,2,3]},index=pd.to_timedelta([1,2,3], unit="d")).interpolate(method="time") /usr/lib64/python2.7/site-packages/pandas/core/generic.py in interpolate(self, method, axis, limit, inplace, downcast, **kwargs) 2530 inplace=inplace, 2531 downcast=downcast, -> 2532 **kwargs) 2533 2534 if inplace: /usr/lib64/python2.7/site-packages/pandas/core/internals.py in interpolate(self, *args, **kwargs) 2402 2403 def interpolate(self, *args, **kwargs): -> 2404 return self.apply('interpolate', *args, **kwargs) 2405 2406 def shift(self, *args, **kwargs): /usr/lib64/python2.7/site-packages/pandas/core/internals.py in apply(self, f, *args, **kwargs) 2373 2374 else: -> 2375 applied = getattr(blk, f)(*args, **kwargs) 2376 2377 if isinstance(applied, list): /usr/lib64/python2.7/site-packages/pandas/core/internals.py in interpolate(self, method, axis, index, values, inplace, limit, fill_value, coerce, downcast, **kwargs) 819 inplace=inplace, 820 downcast=downcast, --> 821 **kwargs) 822 823 raise ValueError("invalid method '{0}' to interpolate.".format(method)) /usr/lib64/python2.7/site-packages/pandas/core/internals.py in _interpolate(self, method, index, values, fill_value, axis, limit, inplace, downcast, **kwargs) 880 881 # interp each column independently --> 882 interp_values = np.apply_along_axis(func, axis, data) 883 884 blocks = [make_block(interp_values, self.items, self.ref_items, /usr/lib64/python2.7/site-packages/numpy/lib/shape_base.pyc in apply_along_axis(func1d, axis, arr, *args) 77 outshape = asarray(arr.shape).take(indlist) 78 i.put(indlist, ind) ---> 79 res = func1d(arr[tuple(i.tolist())],*args) 80 # if res is a number, then we have a smaller output array 81 if isscalar(res): /usr/lib64/python2.7/site-packages/pandas/core/internals.py in func(x) 877 return com.interpolate_1d(index, x, method=method, limit=limit, 878 fill_value=fill_value, --> 879 bounds_error=False, **kwargs) 880 881 # interp each column independently /usr/lib64/python2.7/site-packages/pandas/core/common.py in interpolate_1d(xvalues, yvalues, method, limit, fill_value, bounds_error, **kwargs) 1362 if not getattr(xvalues, 'is_all_dates', None): 1363 # if not issubclass(xvalues.dtype.type, np.datetime64): -> 1364 raise ValueError('time-weighted interpolation only works ' 1365 'on Series or DataFrames with a ' 1366 'DatetimeIndex') ValueError: time-weighted interpolation only works on Series or DataFrames with a DatetimeIndex ```
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6,425
groupby w/ levels on MultiIndex and then interpolate
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1
2014-02-20T23:22:48Z
2014-02-20T23:24:12Z
2014-02-20T23:24:12Z
CONTRIBUTOR
null
I would expect something like this to work: ``` In [111]: df_1 = pd.DataFrame({'v':[1,np.nan,3]},index=pd.to_datetime([1,2,3], unit="d")) pd.concat({'a':df_1, 'b':df_1}).groupby(level=0).apply(lambda x:x.interpolate(method="time")) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-111-a2f8b8d05920> in <module>() 1 df_1 = pd.DataFrame({'v':[1,np.nan,3]},index=pd.to_datetime([1,2,3], unit="d")) ----> 2 pd.concat({'a':df_1, 'b':df_1}).groupby(level=0).apply(lambda x:x.interpolate(method="time")) /usr/lib64/python2.7/site-packages/pandas/core/groupby.py in apply(self, func, *args, **kwargs) 422 return func(g, *args, **kwargs) 423 --> 424 return self._python_apply_general(f) 425 426 def _python_apply_general(self, f): /usr/lib64/python2.7/site-packages/pandas/core/groupby.py in _python_apply_general(self, f) 425 426 def _python_apply_general(self, f): --> 427 keys, values, mutated = self.grouper.apply(f, self.obj, self.axis) 428 429 return self._wrap_applied_output(keys, values, /usr/lib64/python2.7/site-packages/pandas/core/groupby.py in apply(self, f, data, axis) 881 # group might be modified 882 group_axes = _get_axes(group) --> 883 res = f(group) 884 if not _is_indexed_like(res, group_axes): 885 mutated = True /usr/lib64/python2.7/site-packages/pandas/core/groupby.py in f(g) 420 @wraps(func) 421 def f(g): --> 422 return func(g, *args, **kwargs) 423 424 return self._python_apply_general(f) <ipython-input-111-a2f8b8d05920> in <lambda>(x) 1 df_1 = pd.DataFrame({'v':[1,np.nan,3]},index=pd.to_datetime([1,2,3], unit="d")) ----> 2 pd.concat({'a':df_1, 'b':df_1}).groupby(level=0).apply(lambda x:x.interpolate(method="time")) /usr/lib64/python2.7/site-packages/pandas/core/generic.py in interpolate(self, method, axis, limit, inplace, downcast, **kwargs) 2507 2508 if isinstance(self.index, MultiIndex) and method != 'linear': -> 2509 raise ValueError("Only `method=linear` interpolation is supported " 2510 "on MultiIndexes.") 2511 ValueError: Only `method=linear` interpolation is supported on MultiIndexes. ``` This operation does work on the underlying data: ``` In [114]: print df_1.interpolate(method="time") v 1970-01-02 1 1970-01-03 2 1970-01-04 3 [3 rows x 1 columns] ```
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6,426
indexing using iterator (Frame.ix[#]) changed - .ix does not work, .iloc does
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5
2014-02-21T00:03:46Z
2014-02-21T03:36:19Z
2014-02-21T02:11:48Z
NONE
null
Hi all, Not sure if this is known or not - couldn't find anything like it in the list of problems. I use data frames that basically have a column of serial numbers, and then two columns after that that contain multidimensional arrays representing data for each serial number (say md1 and md2). On both pandas 0.13.0 and 0.13.1: Frame[md1].ix[0] fails with Exception: Data must be 1-dimensional but Frame[md1].iloc[0] returns the array as expected. It seems to work before v0.13.0. I used it in some loop constructs like: [ do something with Frame[md1].ix[idx] for idx in Frame.index ] the workaround is: [ do something with Frame[md1].iloc[idx] for idx in range(len(Frame.index)) ] Same happens with md2 column, or anything multidimensional ( I have 3x50 arrays in each md column) So, I am re-writing a bunch of stuff as I find this error - but I was wondering - is this now how things work and I am "doing it wrong" or have I found a legit bug? Thanks!
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SparseDataFrame.shift does not respond to axis
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4
2014-02-21T02:33:14Z
2019-09-18T14:02:17Z
2019-09-18T14:02:17Z
CONTRIBUTOR
null
The shift method in SparseDataFrame does not respond to axis correctly. ``` >>> import pandas as pd >>> import numpy as np >>> df = pd.SparseDataFrame(np.random.rand(10,5)) >>> df.shift(1) 0 1 2 3 4 0 NaN NaN NaN NaN NaN 1 0.015673 0.456047 0.451249 0.297851 0.178720 2 0.393355 0.594275 0.496614 0.936675 0.311961 3 0.255445 0.141111 0.750324 0.994727 0.965763 4 0.473577 0.644481 0.584495 0.796462 0.689525 5 0.744432 0.918232 0.715706 0.672892 0.598817 6 0.732707 0.110259 0.756422 0.534970 0.449421 7 0.340564 0.907769 0.543032 0.214241 0.564122 8 0.291026 0.616647 0.102853 0.940241 0.693239 9 0.445545 0.760656 0.035569 0.398808 0.050392 [10 rows x 5 columns] >>> df.shift(1,axis=1) 0 1 2 3 4 0 NaN NaN NaN NaN NaN 1 0.015673 0.456047 0.451249 0.297851 0.178720 2 0.393355 0.594275 0.496614 0.936675 0.311961 3 0.255445 0.141111 0.750324 0.994727 0.965763 4 0.473577 0.644481 0.584495 0.796462 0.689525 5 0.744432 0.918232 0.715706 0.672892 0.598817 6 0.732707 0.110259 0.756422 0.534970 0.449421 7 0.340564 0.907769 0.543032 0.214241 0.564122 8 0.291026 0.616647 0.102853 0.940241 0.693239 9 0.445545 0.760656 0.035569 0.398808 0.050392 [10 rows x 5 columns] >>> ``` Using Pandas version 0.13.1
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MDU6SXNzdWUyODAxNzA5OQ==
6,428
spaces converted to todays date with to_datetime and read_csv date parse
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0
2014-02-21T03:46:02Z
2016-12-14T11:07:26Z
2016-12-14T11:07:26Z
CONTRIBUTOR
null
``` In [265]: s = pd.Series(['10/18/2006', '10/18/2008', ' ']) In [266]: pd.to_datetime(s) Out[266]: 0 2006-10-18 1 2008-10-18 2 2014-02-20 dtype: datetime64[ns] In [268]: pd.to_datetime(s, errors='raise') Out[268]: 0 2006-10-18 1 2008-10-18 2 2014-02-20 dtype: datetime64[ns] ``` _Note: Can use `pd.to_datetime(s, format='%m/%d/%Y', coerce=True)`._ This also seems to be the case when reading from csv. ``` csv = """case,opdate 7,10/18/2006 7,10/18/2008 621, """ In [282]: pd.read_csv(StringIO(csv), parse_dates=['opdate']) Out[282]: case opdate 0 7 2006-10-18 1 7 2008-10-18 2 621 2014-02-20 [3 rows x 2 columns] ``` See this SO question http://stackoverflow.com/questions/21922978/safest-pandas-read-csv-with-missing-dates/21924845#21924845 Maybe stems from: ``` In [284]: pd.datetools.parse(' ') Out[284]: datetime.datetime(2014, 2, 20, 0, 0) ```
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6,429
BUG: punt to user when passing overlapping replacement values in a nested dict
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2
2014-02-21T04:36:52Z
2014-06-15T09:28:23Z
2014-02-21T12:00:51Z
MEMBER
null
closes #5338
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6,430
BUG: correctly tokenize local variable references
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2014-02-21T04:56:23Z
2014-06-27T15:16:18Z
2014-02-21T05:09:15Z
MEMBER
null
Forgot to actually do this in #6366 even though I put a release notes entry in
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6,431
apply function to groupby-ed dataframe, can't take args
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2014-02-21T12:22:11Z
2017-06-01T14:09:42Z
2014-02-21T12:57:52Z
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null
TypeError: getTrans12() got an unexpected keyword argument 'args' File "d:\Scripts\new model parameters\genNewParametersFromDb.py", line 99, in <module> df.groupby(['pooltype1','CollType','ltv_buc', 'ltv']).apply(getTrans12,args=(6,)), File "C:\Anaconda\Lib\site-packages\pandas\core\groupby.py", line 331, in apply return self._python_apply_general(f) File "C:\Anaconda\Lib\site-packages\pandas\core\groupby.py", line 378, in _python_apply_general keys, values, mutated = self.grouper.apply(f, self.obj, self.axis) File "C:\Anaconda\Lib\site-packages\pandas\core\groupby.py", line 674, in apply res = f(group) File "C:\Anaconda\Lib\site-packages\pandas\core\groupby.py", line 330, in <lambda> f = lambda g: func(g, _args, *_kwargs) modified a little bit and it works... ``` def xapply(self, func, args=(), **kwargs): func = _intercept_function(func) if args: f = lambda g: func(g, *args, **kwargs) else: f = func return self._python_apply_general(f) ```
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6,432
CLN: minimize tokenizer passes
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2014-02-21T13:04:32Z
2014-07-16T08:55:31Z
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DOC: Add section on using in operator with Series, DataFrame
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5
2014-02-21T16:23:14Z
2014-06-27T12:10:20Z
2014-03-19T23:16:05Z
CONTRIBUTOR
null
closes #5571
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melting of time-series
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1
2014-02-21T17:24:20Z
2014-03-22T21:13:10Z
2014-03-22T21:12:26Z
CONTRIBUTOR
null
I have a standard DataFrame indexed by ``` python <class 'pandas.tseries.index.DatetimeIndex'> [2009-07-08 07:00:00.134000, ..., 2009-07-08 10:00:01.297000] Length: 84384, Freq: None, Timezone: None ``` and 189 columns named like this: ``` python Index([u'a3_01', u'a3_02', u'a3_03', u'a3_04', u'a3_05', u'a3_06', u'a3_07', u'a3_08', u'a3_09', u'a3_10', u'a3_11', u'a3_12', u'a3_13', u'a3_14', u'a3_15', u'a3_16', u'a3_17', u'a3_18', u'a3_19', u'a3_20', u'a3_21', u'a4_01', u'a4_02',...], dtype='object') ``` Why is it that the following melt operation creates a KeyError: ``` python pd.melt(rad, id_vars=[rad.index]) KeyError: "['2009-07-08T00:00:00.134000000-0700' '2009-07-08T00:00:00.262000000-0700'\n '2009-07-08T00:00:00.390000000-0700' ...,\n '2009-07-08T03:00:01.041000000-0700' '2009-07-08T03:00:01.169000000-0700'\n '2009-07-08T03:00:01.297000000-0700'] not in index" ``` but if I reset_index() first it works: ``` python pd.melt(rad.reset_index(), id_vars=['index']) ``` I am aware that the time-index will become non-unique after `melting`, but shouldn't this actually work? Or maybe it is a feature request that the `id_vars` accepts a DatetimeIndex as input?
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28,061,655
MDExOlB1bGxSZXF1ZXN0MTI4MDM0NTI=
6,435
DOC: link to pandas-cookbook instructions
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1
2014-02-21T18:22:31Z
2014-06-26T17:26:04Z
2014-02-21T19:13:42Z
CONTRIBUTOR
null
Some people were confused about how to use this cookbook, so adding a link to the setup instructions. see: https://github.com/jvns/pandas-cookbook/issues/7 this SO question: https://stackoverflow.com/questions/21868105/problems-with-pandas-tutorial-missing-file-bikes-csv-and-special-characters-in
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6,436
Possibly spurious GroupBy failure on Travis
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3
2014-02-21T20:11:55Z
2014-02-22T15:13:36Z
2014-02-22T15:13:36Z
MEMBER
null
Can't really investigate right now https://travis-ci.org/pydata/pandas/jobs/19331373
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Losing index and sort on merge
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7
2014-02-21T20:45:03Z
2015-10-20T22:24:43Z
2015-10-20T22:24:43Z
CONTRIBUTOR
null
I'm almost positive I've filed an issue about this before, but I don't find it. This is a pretty common operation when working with panel data. You want to merge in some constant values into a DataFrame that has repeated keys but unique index. ``` df = pd.util.testing.makeDataFrame() # yes! df["group"] = 0 df.ix[range(0, 20, 2), "group"] = 1. df["time"] = np.random.randint(5, size=len(df)) df_x = pd.DataFrame(np.c_[np.r_[np.arange(5), np.arange(5)], np.repeat([0,1], 5)], columns=["time", "group"]) df_x["constant_info"] = np.random.randn(len(df_x)) df.merge(df_x, on=["time", "group"]) ``` Where'd my index go? Where'd my sort go, so I could even recover the index? ``` [~/] [65]: pd.version.version [65]: '0.13.1-254-g150f323' ```
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PERF/API: concat improvements
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0
2014-02-21T22:20:49Z
2014-07-13T20:55:14Z
2014-02-21T22:44:05Z
CONTRIBUTOR
null
PERF: Perf issue in concatting with empty objects, closes #3259 API: concat will now concatenate mixed Series and DataFrames, closes #2385
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BUG: pandas.tools.util.cartesian_product doesn't work on DatetimeIndex objects
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5
2014-02-22T01:09:16Z
2014-07-01T07:16:44Z
2014-02-23T21:59:25Z
MEMBER
null
This breaks the new `pandas.MultiIndex.from_product` function when one of the product arrays is DatetimeIndex. Observe: ``` >>> import pandas as pd >>> from pandas.tools.util import cartesian_product >>> x, y = cartesian_product([[1, 2], pd.date_range('2000-01-01', periods=2)]) >>> print x [1 1 2 2] >>> print y.values ['1999-12-31T16:00:00.000000000-0800' '1999-12-31T16:00:00.000000000-0800' '2000-01-01T16:00:00.000000000-0800' '2000-01-01T16:00:00.000000000-0800'] ``` If `cartesian_product` was working properly, the dates should actually be [1999, 2000, 1999, 2000]. The simple fix would be to convert everything into a numpy array before calculating the cartesian product.
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PERF: optimize index.__getitem__ for slice & boolean mask indexers
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18
2014-02-22T12:13:35Z
2014-06-25T21:47:23Z
2014-02-28T15:21:09Z
CONTRIBUTOR
null
This patch fixes performance issues discussed in and closes #6370. There's an API change though: no type inference will now happen upon resulting index, e.g. here's what it would look like on current master: ``` python In [1]: pd.Index([1,2,3, 'a', 'b', 'c']) Out[1]: Index([1, 2, 3, u'a', u'b', u'c'], dtype='object') In [2]: _1[[0,1,2]] Out[2]: Int64Index([1, 2, 3], dtype='int64') ``` And this is how it looks with the patch: ``` python In [1]: pd.Index([1,2,3, 'a', 'b', 'c']) Out[1]: Index([1, 2, 3, u'a', u'b', u'c'], dtype='object') In [2]: _1[[0,1,2]] Out[2]: Index([1, 2, 3], dtype='object') ``` On the bright side, I'd say using mixed-type indices seems a quite rare scenario and the performance improvement is worth it. ## Performance Before: ``` python In [1]: import pandas.util.testing as tm In [2]: idx = tm.makeStringIndex(1000000) In [3]: mask = np.arange(1000000) % 3 == 0 In [4]: series_mask = pd.Series(mask); mask Out[4]: array([ True, False, False, ..., False, False, True], dtype=bool) In [5]: timeit idx[:-1] 100000 loops, best of 3: 1.57 µs per loop In [6]: timeit idx[::2] 100 loops, best of 3: 14.6 ms per loop In [7]: timeit idx[mask] 100 loops, best of 3: 15.4 ms per loop In [8]: timeit idx[series_mask] 100 loops, best of 3: 15.4 ms per loop In [9]: pd.__version__ Out[9]: '0.13.1-278-gaf63b99' ``` ## Performance After: ``` python In [1]: import pandas.util.testing as tm In [2]: idx = tm.makeStringIndex(1000000) In [3]: mask = np.arange(1000000) % 3 == 0 In [4]: series_mask = pd.Series(mask) In [5]: timeit idx[:-1] 1000000 loops, best of 3: 1.56 µs per loop In [6]: timeit idx[::2] 100000 loops, best of 3: 4.89 µs per loop In [7]: timeit idx[mask] 100 loops, best of 3: 11.4 ms per loop In [8]: timeit idx[series_mask] 100 loops, best of 3: 11.4 ms per loop In [9]: pd.__version__ Out[9]: '0.13.1-279-gbc810f0' ```
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6,441
DOC: add explanation to doc/sphinxext
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2
2014-02-22T12:52:31Z
2014-06-27T12:10:32Z
2014-02-24T12:56:13Z
MEMBER
null
Closes #5221
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DOC: small doc fixes
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2014-02-22T13:40:35Z
2014-07-16T08:55:46Z
2014-02-22T13:40:47Z
MEMBER
null
To fix two sphinx warnings.
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BUG/TST: iloc will now raise IndexError on out-of-bounds list indexers (GH6296 / GH6299)
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0
2014-02-22T14:39:38Z
2014-06-25T15:14:08Z
2014-02-22T15:13:36Z
CONTRIBUTOR
null
TST: fix spurious tests for test_groupby, closes #6436 BUG/TST: iloc will now raise IndexError on out-of-bounds list indexers promotoing consistency with python/numpy syntax. The out-of-bounds for slice indexers will continue to work (again for consistency) (#6296 / #6299) DOC: makes tz attribute default to `None` to remove build-doc warnings
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6,444
sym_diff failure on 3.4?
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15
2014-02-22T17:42:08Z
2014-02-23T21:51:05Z
2014-02-23T21:51:05Z
CONTRIBUTOR
null
`test_symmetric_diff` is failing after trying out pandas on a fresh 3.4 pull. It passes on 3.3 with the same numpy trunk version. It seems to have to do with the nan section: ``` >>> from pandas import Index >>> import numpy as np >>> idx1 = Index([1, 2, np.nan]) >>> idx2 = Index([0, 1, np.nan]) >>> result = idx1.sym_diff(idx2) >>> expected = Index([0.0, np.nan, 2.0, np.nan]) # oddness with nans >>> nans = pd.isnull(expected) >>> result Float64Index([0.0, nan, nan, 2.0], dtype='object') >>> expected Float64Index([0.0, nan, 2.0, nan], dtype='object') >>> nans array([False, True, False, True], dtype=bool) >>> result[nans] Float64Index([nan, 2.0], dtype='object') ``` Version info (basically a fresh 3.4 build with only numpy dev installed): ``` >>> pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.4.0.candidate.1 python-bits: 32 OS: Linux OS-release: 3.8.0-35-generic machine: i686 processor: i686 byteorder: little LC_ALL: None LANG: en_CA.UTF-8 pandas: 0.13.1-282-g9564ead Cython: None numpy: 1.9.0.dev-2d6ea6e scipy: None statsmodels: None IPython: None sphinx: None patsy: None scikits.timeseries: None dateutil: 2.2 pytz: 2013.9 bottleneck: None tables: None numexpr: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None bq: None apiclient: None rpy2: None sqlalchemy: None pymysql: None psycopg2: None ```
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read_html shouldn't fail when ISPs reroute nonexistent URLs
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2014-02-22T18:36:52Z
2014-02-27T19:02:44Z
2014-02-27T19:02:44Z
MEMBER
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TWC reroutes URLs like `http://www.a23950sdfa908sd.com` to their search page, which has HTML and thus `test_html.test_invalid_url` won't correctly fail with a `URLError`.
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INT/CLN: clean up block slicing semantics
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2014-02-22T18:54:24Z
2014-07-15T12:58:49Z
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CONTRIBUTOR
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MDExOlB1bGxSZXF1ZXN0MTI4MjY0Nzc=
6,447
BUG/TST: read_html should follow pandas conventions when creating empty data
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null
7
2014-02-22T21:33:01Z
2014-06-26T13:14:42Z
2014-02-27T19:02:45Z
MEMBER
null
closes #5129 closes #6445 This a very specific bug fix. Only using `lxml` exposes this bug, whereas using `bs4` raises an exception. `lxml` drops data which allows it to parse the multiindex header differently and succeed.
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28,110,714
MDExOlB1bGxSZXF1ZXN0MTI4Mjg3NTY=
6,448
CLN: remove dead code in pandas.computation
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null
6
2014-02-23T02:39:16Z
2014-07-16T08:55:56Z
2014-02-23T21:30:03Z
MEMBER
null
closes #5041
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MDU6SXNzdWUyODExNDYzNw==
6,449
min() and max() on df.index cause maximum recursion depth exceeded Error
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1
2014-02-23T08:12:17Z
2014-02-23T12:25:55Z
2014-02-23T12:25:55Z
NONE
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``` Python import pandas as pd import numpy as np df = pd.DataFrame(np.random.random((4, 3)), columns=['A', 'B', 'C']) df.index.max() # This will cause RuntimeError: maximum recursion depth exceeded ``` Happens on pandas v0.13.1
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6,450
BLD: add benchmarks for all single-axis indexers
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31
2014-02-23T10:51:23Z
2015-01-18T21:41:12Z
2015-01-18T21:41:12Z
CONTRIBUTOR
null
This PR will add benchmarks for all available single-axis container indexing patterns discussed in #6328. It is currently in WIP state, but the ideas and comments are welcome. Multi-axis indexers are left for future to avoid overcomplicating things from the beginning. Part of functionality implemented in this PR is caching `tm.makeCustomIndex` results to avoid spending several seconds in setup of each benchmark. It may deserve a separate PR to implement this caching within `pandas.util.testing` module. TODO: - [x] axis != 0 benchmarks - [x] container shape specification - [ ] benchmarking `obj.__getitem__` - [ ] benchmarking `obj.xs` - [ ] multiindex axes - [ ] non-unique axes - [ ] non-string index benchmarks
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BUG FIX: cartesian_product now converts all arguments to ndarrays
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1
2014-02-23T18:30:44Z
2014-06-17T01:05:26Z
2014-02-23T21:59:25Z
MEMBER
null
Fixes #6439.
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2
2014-02-23T20:47:42Z
2014-03-04T11:13:16Z
2014-03-04T11:13:16Z
CONTRIBUTOR
null
Index ``` df = pd.util.testing.makeDataFrame() df.index.name = 'name' df.set_index(df.index) df.set_index(df.index).index.names ``` MultiIndex ``` from statsmodels.datasets import grunfeld data = sm.datasets.grunfeld.load_pandas().data data = data.set_index(['firm', 'year']) data.set_index(data.index).index.names ``` Version ``` [~/] [33]: pd.version.version [33]: '0.13.1-254-g150f323' ```
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28,127,356
MDExOlB1bGxSZXF1ZXN0MTI4MzU1OTM=
6,453
BUG/TST: sorting of NaNs on sym_diff
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7
2014-02-23T21:09:43Z
2017-05-15T21:15:51Z
2014-02-23T21:51:05Z
CONTRIBUTOR
null
Closes https://github.com/pydata/pandas/issues/6444, which was failing on Python 3.4 I don't actually have python 3.4 running yet, virtualenv is complaining about my 3.4 executable. These test _should_ pass on 2.6+. I put the release note under the bug section, but I can move it to the API section if you think that's more appropriate.
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MDU6SXNzdWUyODEyODQxNg==
6,454
travis failure
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4
2014-02-23T21:53:59Z
2014-02-23T22:01:13Z
2014-02-23T22:01:13Z
MEMBER
null
gosh i broke something https://travis-ci.org/pydata/pandas/jobs/19460011
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6,455
str_extract should work for timeseries, bug 6348
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2014-02-23T22:02:34Z
2014-06-20T16:15:38Z
2014-02-23T22:04:15Z
CONTRIBUTOR
null
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6,456
read_html and read_json should provide a cache
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7
2014-02-23T22:24:04Z
2014-12-11T18:26:15Z
2014-02-24T20:24:02Z
NONE
null
Hello, `pandas.io.html.read_html` should provide a cache such as what requests_cache provides https://requests-cache.readthedocs.org/en/latest/ persistence into sqlite, mongodb, redis... is very convenient. Maybe some other pandas functions could also use this cache mechanism. `pandas.io.json.read_json` for example This is what I do ``` import requests import requests_cache expire_after = 15*60 # expire_after=delay_second or None requests_cache.install_cache('req_cache', backend='sqlite', expire_after=expire_after) import json import pandas as pd from StringIO import StringIO req = requests.get(url) io = StringIO(req.content) df = pd.read_html(io) ``` Kind regards
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isnull() and notnull() - documentation
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3
2014-02-23T22:34:42Z
2014-02-24T04:40:53Z
2014-02-24T04:40:53Z
NONE
null
Hi, It will be nice if someone can add that `isnull()` function have a contrary which is called `notnull()` and `notnull()` function have a contrary which is called `isnull()` into documentation http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.notnull.html http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.isnull.html That's probably stupid for you but I was searching a long time to find that (I was looking for `isnotnull()`) Maybe this documentation could also tell that `df[df['col'].notnull()]` is same as ``df[~df['col'].isnull()]` Kind regards
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6,458
Add references to isnull in notnull docs and vice versa
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2014-02-24T00:57:13Z
2014-06-15T16:48:49Z
2014-02-24T04:40:53Z
CONTRIBUTOR
null
closes #6457
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6,459
ENH: Preserve .names in df.set_index(df.index)
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10
2014-02-24T04:28:11Z
2014-06-14T22:09:22Z
2014-03-04T11:13:16Z
CONTRIBUTOR
null
closes #6452. This causes a slight change in behavior in @jseabold's second example. Previously, `df.set_index(df.index)` would convert a `MultiIndex` into an `Index` of tuples: ``` In [7]: from statsmodels.datasets import grunfeld In [8]: data = sm.datasets.grunfeld.load_pandas().data In [9]: data = data.set_index(['firm', 'year']) In [10]: data.set_index(data.index).index.names Out[10]: [None] In [11]: data Out[11]: <class 'pandas.core.frame.DataFrame'> MultiIndex: 220 entries, (General Motors, 1935.0) to (American Steel, 1954.0) Data columns (total 3 columns): invest 220 non-null values value 220 non-null values capital 220 non-null values dtypes: float64(3) In [13]: data.set_index(data.index) Out[13]: <class 'pandas.core.frame.DataFrame'> Index: 220 entries, (General Motors, 1935.0) to (American Steel, 1954.0) Data columns (total 3 columns): invest 220 non-null values value 220 non-null values capital 220 non-null values dtypes: float64(3) ``` This change makes it so the index remains a `MultiIndex`.
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famafrench, pandas.io.data.DataReader error for 'F-F_Momentum_Factor' data
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5
2014-02-24T06:48:21Z
2014-04-02T14:16:49Z
2014-02-25T18:56:13Z
CONTRIBUTOR
null
I think there is a very minor issue in `pandas.io.data.DataReader` for pandas 0.13.1 caused by an inconsistent extension in Ken French's data library. The DataReader can't process the 'F-F_Momentum_Factor' data from Ken French's website: ``` python import pandas as pd import pandas.io.data as web mom = web.DataReader("F-F_Momentum_Factor", "famafrench") ``` Error: ``` Traceback (most recent call last): File "./example.py", line 6, in <module> mom = web.DataReader("F-F_Momentum_Factor", "famafrench") File "/usr/lib64/python2.7/site-packages/pandas/io/data.py", line 85, in DataReader return get_data_famafrench(name) File "/usr/lib64/python2.7/site-packages/pandas/io/data.py", line 497, in get_data_famafrench data = zf.open(name + '.txt').readlines() File "/usr/lib64/python2.7/zipfile.py", line 957, in open zinfo = self.getinfo(name) File "/usr/lib64/python2.7/zipfile.py", line 905, in getinfo 'There is no item named %r in the archive' % name) KeyError: "There is no item named 'F-F_Momentum_Factor.txt' in the archive" ``` The issue appears to be caused by the fact that when 'F-F_Momentum_Factor.zip' is unzipped the underlying file is 'F-F_Momentum_Factor.TXT' and `get_data_famafrench(name)` in `data.py` assumes the extension will be lower case (I believe this is true for all the other data files on Ken's website but for whatever reason has never been true for the momentum factor file). Here is the relevant code in `get_data_famafrench(name)`: ``` python with ZipFile(tmpf, 'r') as zf: data = zf.open(name + '.txt').readlines() ``` There is probably a better solution to the issue but I changed the preceding to the following and it seems to work: ``` python with ZipFile(tmpf, 'r') as zf: data = zf.open(zf.namelist()[0]).readlines() ``` Example: ``` python mom = web.DataReader("F-F_Momentum_Factor", "famafrench")[1] print mom.head(10) ``` Output: | | 1 Mom | | --: | --: | | 192701 | 0.49 | | 192702 | -0.69 | | 192703 | 5.41 | | 192704 | 3.83 | | 192705 | 3.73 | | 192706 | -0.65 | | 192707 | 5.03 | | 192708 | 1.15 | | 192709 | 1.55 | | 192710 | -0.07 | Also here is the output from `pd.show_versions()`: ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.5.final.0 python-bits: 64 OS: Linux OS-release: 3.13.3-201.fc20.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.13.1 Cython: 0.20 numpy: 1.8.0 scipy: 0.12.1 statsmodels: 0.5.0 IPython: 0.13.2 sphinx: 1.1.3 patsy: 0.2.1 scikits.timeseries: None dateutil: 2.2 pytz: 2013.9 bottleneck: 0.8.0 tables: 3.0.0 numexpr: 2.3 matplotlib: 1.3.1 openpyxl: 1.8.3 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: 0.5.2 sqlalchemy: None lxml: 3.2.4 bs4: None html5lib: None bq: None apiclient: None ```
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6,461
DOC: Added entries into ecosystem.rst
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5
2014-02-24T10:37:21Z
2014-06-25T09:05:20Z
2014-04-22T15:32:59Z
CONTRIBUTOR
null
Added some entries to ecosystem.rst. Basically just searched Nullege for pandas. There was a lot more, but thought this made a good start.
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BUG: inconsistency in some timedelta64[ns] reduction operations
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0
2014-02-24T12:12:04Z
2014-02-25T20:43:00Z
2014-02-25T20:43:00Z
CONTRIBUTOR
null
``` In [1]: s = pd.to_timedelta(range(4),unit='d') In [2]: s Out[2]: 0 0 days 1 1 days 2 2 days 3 3 days dtype: timedelta64[ns] In [3]: s.mean() Out[3]: 0 1 days, 12:00:00 dtype: timedelta64[ns] In [4]: s.sum() Out[4]: 518400000000000 ```
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infer_freq fails on Index
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3
2014-02-24T16:54:02Z
2014-02-24T21:34:44Z
2014-02-24T21:32:47Z
CONTRIBUTOR
null
Continuing with the my projects are the pandas test suite theme of issue reports. This broke for me on a recent upgrade. Ok. ``` pd.infer_freq(['2004-01', '2004-02', '2004-03', '2004-04']) ``` No longer works. ``` pd.infer_freq(pd.Index(['2004-01', '2004-02', '2004-03', '2004-04'])) ``` Seems like this should still work. ``` [12]: pd.version.version [12]: '0.13.1-254-g150f323' ```
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28,193,619
MDU6SXNzdWUyODE5MzYxOQ==
6,464
xs on Series with MultiIndex gives KeyError
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closed
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null
1
2014-02-24T20:31:09Z
2014-02-24T20:44:33Z
2014-02-24T20:44:33Z
NONE
null
``` In [3]: df Out[3]: A B C D first second third bar one 1 4 1 8 9 two 1 7 5 5 0 baz one 1 6 6 8 0 three 2 5 3 5 3 [4 rows x 4 columns] In [4]: df.xs('one', level=1) Out[4]: A B C D first third bar 1 4 1 8 9 baz 1 6 6 8 0 [2 rows x 4 columns] In [5]: df[['A']].xs('one', level=1) Out[5]: A first third bar 1 4 baz 1 6 [2 rows x 1 columns] In [6]: df['A'].xs('one', level=1) --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-11-62c0cf8731b1> in <module>() ----> 1 df['A'].xs('one', level=1) C:\Anaconda\lib\site-packages\pandas\core\series.pyc in _xs(self, key, axis, lev el, copy) 437 438 def _xs(self, key, axis=0, level=None, copy=True): --> 439 return self.__getitem__(key) 440 441 xs = _xs C:\Anaconda\lib\site-packages\pandas\core\series.pyc in __getitem__(self, key) 482 def __getitem__(self, key): 483 try: --> 484 result = self.index.get_value(self, key) 485 if isinstance(result, np.ndarray): 486 return self._constructor(result,index=[key]*len(result)) .__finalize__(self) C:\Anaconda\lib\site-packages\pandas\core\index.pyc in get_value(self, series, k ey) 2294 raise InvalidIndexError(key) 2295 else: -> 2296 raise e1 2297 except Exception: # pragma: no cover 2298 raise e1 KeyError: 'one' ```
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28,194,408
MDExOlB1bGxSZXF1ZXN0MTI4NzI4OTQ=
6,465
COMPAT: infer_freq compat on passing an Index of strings (GH6463)
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0
2014-02-24T20:39:32Z
2014-07-09T18:05:14Z
2014-02-24T21:32:47Z
CONTRIBUTOR
null
closes #6463
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28,197,745
MDU6SXNzdWUyODE5Nzc0NQ==
6,466
str.split ignores n if pat is None
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1
2014-02-24T21:17:53Z
2016-10-12T23:04:40Z
2014-02-24T23:35:20Z
CONTRIBUTOR
null
as observed in: http://stackoverflow.com/questions/21998590/ `str.split` ignores `n` if `pat` is `None` relevant source code: ``` # pandas/core/strings.py def str_split(arr, pat=None, n=None): if pat is None: if n is None or n == 0: n = -1 f = lambda x: x.split() .... ```
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6,467
BUG: split should respect maxsplit when no pat is given
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0
2014-02-24T22:01:52Z
2014-07-07T14:22:54Z
2014-02-24T23:35:20Z
MEMBER
null
closes #6466
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6,468
pd.Timestamp("now") returns oddly
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9
2014-02-24T22:56:54Z
2015-03-03T01:32:19Z
2015-03-03T01:32:19Z
CONTRIBUTOR
null
I would expect these two return the same: ``` In [10]: pd.Timestamp.now() Out[10]: Timestamp('2014-02-24 17:54:18.370128', tz=None) In [11]: pd.Timestamp("now") Out[11]: Timestamp('2014-02-24 22:54:18', tz=None) ```
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MDU6SXNzdWUyODIxMTk5Mw==
6,469
INT: mixin for PeriodIndex / Datetimeindex for common methods
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9
2014-02-25T00:20:48Z
2019-07-23T15:27:41Z
2019-07-23T15:27:40Z
CONTRIBUTOR
null
related #6779 - [x] `asobject` (#7544) - [x] `__unicode__` (#7602) - [x] `min/max` (#7609) - [x] `summary` (#9116) - [x] `equals` (#13986) - [x] `isin` - [x] `unique` (#7540, #7843) - [x] `value_counts` (#7735) - [x] `resolution` (#7708) - [x] `__iter__` (#7702, #7709, #7720) - [x] `__add__`, `__iadd__`, `__sub__`, `__isub__` (#7741) - [x] `__getitem__` (#10292, for `DatetimeIndex` and `TimedeltaIndex`) (#13987, for `PeriodIndex`) - [x] `repeat` (#13995) - [x] `NaT` related methods / property. `hasnans`, `_isnan`, `_na_value` (#13997) next priorities: - [x] `get_duplicates` - [x] `repeat` - [ ] `get_value` / `get_value_maybe_box` - [ ] `searchsorted` - [x] `argmin` / `argmax`
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28,229,869
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6,470
BUG: Fixes error in DataReader when getting "F-F_Momentum_Factor" data
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5
2014-02-25T08:25:23Z
2014-06-18T12:15:40Z
2014-02-25T18:55:55Z
CONTRIBUTOR
null
Hopefully I didn't screw-up using git and github. This PR fixes the minor issue outlined in `GH6460`. I didn't write any new tests but I added the "F-F_Momentum_Factor" data request to the existing tests of the famafrench data reader. closes #6460
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6,471
PERF: Float64Index to use Float hashtable backend
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11
2014-02-25T11:05:57Z
2016-10-12T23:04:40Z
2014-04-14T10:55:43Z
CONTRIBUTOR
null
https://groups.google.com/forum/m/#!topic/pydata/zUxl7rOHVNY Currently `Float64Index` uses the `Object` back-end for ease of implementation. See Here: https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L1881 - change the backend to use `Float64Engine` - the cythob code needs fixin in order to make it work. - needs vbench for perf indications - needs pickle validation that it is backward compat (it should be w/o changes)
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MDU6SXNzdWUyODI2Mzc3Mw==
6,472
ENH: option to turn off escaping special characters in to_latex
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1
2014-02-25T17:01:53Z
2014-04-21T17:43:04Z
2014-04-21T17:43:04Z
CONTRIBUTOR
null
https://github.com/pydata/pandas/pull/5374
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6,473
ENH: Allow custom frequencies
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5
2014-02-25T17:18:36Z
2014-06-27T12:10:32Z
2014-02-25T17:56:39Z
CONTRIBUTOR
null
Allows custom frequencies other than business day in testing utilities. Keeps the old default. closes #4541
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MDU6SXNzdWUyODI3MzUwMA==
6,474
DataFrame column name conflict yields empty DataFrame in version 0.13.1
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1
2014-02-25T19:03:56Z
2014-02-25T19:05:59Z
2014-02-25T19:05:59Z
NONE
null
import pandas as pd s = pd.Series([1,2,3],name='seriesName') df = pd.DataFrame(s,columns=['columnName']) In v 0.12, df is the expected DataFrame w/ values [1,2,3] and df.columns = ['columnName'] In v 0.13.1, df is an empty DataFrame. This seems to be an error, unless there's a reason for the change.
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28,279,989
MDExOlB1bGxSZXF1ZXN0MTI5MjE4MzI=
6,475
BUG: Bug in sum of a timedelta64[ns] series (GH6462)
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0
2014-02-25T20:25:11Z
2014-07-02T17:34:49Z
2014-02-25T20:43:00Z
CONTRIBUTOR
null
closes #6462
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MDU6SXNzdWUyODI4NDg5MQ==
6,476
xsel not working?
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1
2014-02-25T21:12:43Z
2015-10-20T22:24:57Z
2015-10-20T22:24:57Z
CONTRIBUTOR
null
I was doing maintenance on an old box, and I just noticed that `clipboard_set` didn't seem to work for me when I only had `xsel` installed and not `xclip`. Installing `xclip` things work fine. Possible that this was a local issue, but just wanted to park this here in case anyone comes across it.
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BUG: Bug in resample with a timezone and certain offsets (GH6397)
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0
2014-02-25T21:26:09Z
2014-06-16T06:11:09Z
2014-02-25T21:29:11Z
CONTRIBUTOR
null
closes #6397
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6,478
in read_csv, warn_bad_lines and error_bad_lines have misleading names
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2
2014-02-25T21:40:21Z
2017-01-12T22:23:32Z
2017-01-12T22:22:38Z
NONE
null
read_csv will still fail if *_bad_lines=False. *_bad_lines only applies when the number of delimiters is wrong. This is misleading, a line with a poorly formatted date is what I would think of as a "bad_line" that should be ignored (cf https://stackoverflow.com/questions/22026181 )
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Serious performance regression in DataFrame construction with monthly DatetimeIndex
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15
2014-02-25T21:42:54Z
2014-03-05T21:55:05Z
2014-02-27T01:19:04Z
CONTRIBUTOR
null
Hi, After upgrading from v0.12.0 to v0.13.1, I noticed about a 100% slowdown on a pandas-heavy project. I've just started looking, but I've come up with a test case that shows a time-complexity change from O(1) to O(n) (~240x slowdown for my inputs). Here's the comparison for v0.12.0 (y-axis is milliseconds): ![perf_12](https://f.cloud.github.com/assets/440095/2263404/746ae6ce-9e65-11e3-92c2-5d7d3d2c6cf9.png) And the comparison for v0.13.1: ![perf_131](https://f.cloud.github.com/assets/440095/2263407/791db6a6-9e65-11e3-940e-cb97ffc53809.png) The test code (I'll convert this to vbench later): ``` rows = 1000 columns = 10 data = DataFrame(np.random.random((rows, columns)), index=DatetimeIndex(start='1/1/1900', periods=rows, freq='M')) d = {} for col in data: d[col] = data[col] %timeit DataFrame(d) ``` Daily indices don't appear to be affected, though I suspect other frequencies are impacted. I'm seeing similar regressions in v0.13.0.
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28,293,247
MDExOlB1bGxSZXF1ZXN0MTI5MjkxNjY=
6,480
CLN: remove vestigial count code
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1
2014-02-25T23:00:14Z
2014-06-18T00:00:32Z
2014-02-25T23:26:49Z
CONTRIBUTOR
null
This should close #6092. There was some buggy code but it was never being called, and this removes it.
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28,297,836
MDExOlB1bGxSZXF1ZXN0MTI5MzIwMDc=
6,481
PERF: perf improvements in DataFrame construction with a non-daily datelike index (GH6479)
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7
2014-02-26T00:16:30Z
2014-07-05T22:45:54Z
2014-02-27T01:19:04Z
CONTRIBUTOR
null
closes #6479
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28,315,622
MDU6SXNzdWUyODMxNTYyMg==
6,482
index repr should show name(s) attribute
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0
2014-02-26T08:13:38Z
2015-05-09T21:51:59Z
2015-05-09T21:51:59Z
CONTRIBUTOR
null
Think this would be useful. maybe related #5734
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6,483
error in row indexing using loc
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1
2014-02-26T11:01:50Z
2014-02-26T12:07:05Z
2014-02-26T12:07:05Z
NONE
null
I am trying to get the value of a dataframe column that contains numpy arrays. The index is a timestamp.The dataframe column has the dtype 'O', which is essential for reproducing the bug. ``` python import pandas as pd import numpy as np from pandas import Timestamp timestamps = pd.Series(data=[Timestamp('2014-07-01 11:19:07.888000', tz=None),Timestamp('2014-07-01 11:19:08.094000', tz=None),Timestamp('2014-07-01 11:19:08.299000', tz=None)],name='timestamp') data = pd.Series(dtype='O',data=[np.array([1,2,3]),np.array([4,5,6]),np.array([7,8,9])],index=timestamps) df = pd.DataFrame(data=data,columns=['a']) i = df.index[0] #this works as intended, produces a single row of the dataframe df.loc[i] #this gives an error (which it shouldn't do). It should have produced the array [1,2,3] df.a.loc[i0] #this works as a workaround, but it's a bit ugly df.a.get_value(i) ``` I really hopes that this can get fixed, thanks!
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PERF: non-info axes slicing on Panels is slow
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11
2014-02-26T14:14:36Z
2014-02-27T23:57:36Z
2014-02-27T23:32:25Z
CONTRIBUTOR
null
Assume we have two panels: ``` >>> pn1.shape (13, 5412, 162) >>> pn2.shape (12, 5412, 162) ``` 12 fields are float64 while one field in `pn1` is `datetime64[ns]`. This slows pretty much all operations (slicing, querying, anything) down by a huge factor: ``` >>> %timeit pn2.values 100000 loops, best of 3: 4.06 µs per loop >>> %timeit pn1.values 1 loops, best of 3: 5.31 s per loop ``` Is there an unofficial rule of not using datetime64 in the first place, is it some weird coercion bug (it seems to try and coerce everything down to floats), does it have anything to do with panels?
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BUG/ENH: dtype change on enlargement
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5
2014-02-26T14:54:47Z
2019-12-24T22:17:32Z
null
CONTRIBUTOR
null
http://stackoverflow.com/questions/22044766/adding-row-to-pandas-dataframe-changes-dtype/22045011#22045011 A bit non-trival (maybe coerce back after) if possible? maybe just change this to an actual append? ``` In [1]: df = DataFrame({'a' : range(10)}, dtype='i4') In [2]: df Out[2]: a 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 [10 rows x 1 columns] In [3]: df.dtypes Out[3]: a int32 dtype: object In [4]: df.loc[10] = 99 In [5]: df Out[5]: a 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 99 [11 rows x 1 columns] In [6]: df.dtypes Out[6]: a float64 dtype: object ```
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MDExOlB1bGxSZXF1ZXN0MTI5NjI2MDM=
6,486
PERF: perf improvements in single-dtyped indexing (GH6484)
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0
2014-02-26T17:28:26Z
2014-06-27T15:34:17Z
2014-02-27T23:32:25Z
CONTRIBUTOR
null
closes #6484 seems on a full vbench to somewhat speedup some reindexing operations.. but this particular is not vbenched ``` In [13]: pn = pd.Panel(np.random.random((12, 5412, 162))) In [14]: ix = np.ones(pn.shape[1], dtype=bool) In [15]: ix[np.random.random(ix.size) > 0.5] = 0 In [16]: %timeit pn.loc[0, ix] # one field, as fast as dataframe 1000 loops, best of 3: 981 µs per loop In [17]: %timeit pn.loc[[0, 1], ix] # two fields, 233x slower >>> %timeit pn.loc[:, ix] 100 loops, best of 3: 18.6 ms per loop In [18]: >>> %timeit pn.loc[:, ix] 10 loops, best of 3: 47.5 ms per loop In [19]: pn_t = pn.swapaxes(0, 1).copy() # try .loc on the first axis In [20]: %timeit pn_t.loc[ix] 10 loops, best of 3: 42.8 ms per loop ```
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MDU6SXNzdWUyODM1NzI2Ng==
6,487
HDFStore.append() throws ValueError for DataFrame created with combine_first()
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2014-02-26T18:33:42Z
2014-02-26T23:34:24Z
2014-02-26T18:41:15Z
NONE
null
Expected: The df is appended w/o throwing an exception The df created using combine_first() has same axis as the df originally appended. This appears to be IFF the index is updated. Steps: 1. Create df. 2. Append it to hdf store (newly created). 3. df2 = df.copy() + alter index 4. Append df2 to hdf store. The following IPython output demonstrates the issue: ``` Python In [2]: import os, sys ...: from datetime import datetime ...: import numpy as np # will use a lot in examples ...: randn = np.random.randn ...: from pandas import DataFrame, Series,date_range ...: from pandas.util.print_versions import show_versions ...: import pandas as pd ...: ...: ...: print('versions:') ...: show_versions() ...: ...: def append_test(fname, key, df1, df2): ...: try: os.unlink(fname) ...: except: pass ...: ...: store = pd.HDFStore(fname) ...: store.append(key, df1, format='table') ...: store.close() ...: ...: df3 = df1.combine_first(df2) ...: print('index type:\ndf1: %r\ndf2: %r\ndf3: %r' % (type(df1.index[0]), type(df2.index[0]), type(df3.index[0]))) ...: print('df1: %r\ndf2: %r\ndf3: %r' % (df1, df2, df3)) ...: ...: store = pd.HDFStore(fname) ...: store.append(key, df3, format='table') ...: store.close() ...: ...: fname = 'test_table.h5' ...: key = 'df' ...: sz=5 versions: INSTALLED VERSIONS ------------------ Python: 2.7.4.final.0 OS: Linux Release: 3.8.0-35-generic Processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.13.0 Cython: 0.19.2 Numpy: 1.8.0 Scipy: 0.11.0 statsmodels: 0.5.0 patsy: 0.2.1 scikits.timeseries: Not installed dateutil: 2.2 pytz: 2013.9 bottleneck: 0.8.0 PyTables: 3.0.0 numexpr: 2.2.2 matplotlib: 1.3.1 openpyxl: 1.7.0 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: Not installed sqlalchemy: 0.8.4 lxml: 3.1.0 bs4: 4.3.2 html5lib: Not installed bigquery: Not installed apiclient: Not installed In [3]: # works (index not touched) ...: df1 = DataFrame(dict(A=list(range(sz)), B=list(range(sz)))) ...: df2 = df1 + 1 ...: append_test(fname, key, df1, df2) index type: df1: <type 'numpy.int64'> df2: <type 'numpy.int64'> df3: <type 'numpy.int64'> df1: A B 0 0 0 1 1 1 2 2 2 3 3 3 4 4 4 [5 rows x 2 columns] df2: A B 0 1 1 1 2 2 2 3 3 3 4 4 4 5 5 [5 rows x 2 columns] df3: A B 0 0 0 1 1 1 2 2 2 3 3 3 4 4 4 [5 rows x 2 columns] In [4]: # errors (increment df2's index) ...: df1 = DataFrame(dict(A=list(range(sz)), B=list(range(sz)))) ...: df2 = df1.copy() ...: df2.index = df2.index + 1 ...: append_test(fname, key, df1, df2) index type: df1: <type 'numpy.int64'> df2: <type 'numpy.int64'> df3: <type 'numpy.int64'> df1: A B 0 0 0 1 1 1 2 2 2 3 3 3 4 4 4 [5 rows x 2 columns] df2: A B 1 0 0 2 1 1 3 2 2 4 3 3 5 4 4 [5 rows x 2 columns] df3: A B 0 0 0 1 1 1 2 2 2 3 3 3 4 4 4 5 4 4 [6 rows x 2 columns] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-4-07d7d3b66f85> in <module>() 3 df2 = df1.copy() 4 df2.index = df2.index + 1 ----> 5 append_test(fname, key, df1, df2) <ipython-input-2-35a49d394732> in append_test(fname, key, df1, df2) 24 25 store = pd.HDFStore(fname) ---> 26 store.append(key, df3, format='table') 27 store.close() 28 /usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in append(self, key, value, format, append, columns, dropna, **kwargs) 885 kwargs = self._validate_format(format, kwargs) 886 self._write_to_group(key, value, append=append, dropna=dropna, --> 887 **kwargs) 888 889 def append_to_multiple(self, d, value, selector, data_columns=None, /usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs) 1244 1245 # write the object -> 1246 s.write(obj=value, append=append, complib=complib, **kwargs) 1247 1248 if s.is_table and index: /usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs) 3537 self.create_axes(axes=axes, obj=obj, validate=append, 3538 min_itemsize=min_itemsize, -> 3539 **kwargs) 3540 3541 if not self.is_exists: /usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs) 3258 # validate the axes if we have an existing table 3259 if validate: -> 3260 self.validate(existing_table) 3261 3262 def process_axes(self, obj, columns=None): /usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in validate(self, other) 2759 raise ValueError( 2760 "invalid combinate of [%s] on appending data [%s] " -> 2761 "vs current table [%s]" % (c, sax, oax)) 2762 2763 # should never get here ValueError: invalid combinate of [values_axes] on appending data [name->values_block_0,cname->values_block_0,dtype->float64,shape->(2, 6)] vs current table [name->values_block_0,cname->values_block_0,dtype->int64,shape->None] In [5]: ```
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28,361,934
MDU6SXNzdWUyODM2MTkzNA==
6,488
ENH: Html table export: Add of other attributes
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7
2014-02-26T19:31:53Z
2021-02-20T20:46:58Z
2021-02-20T20:46:58Z
NONE
null
The to_html method allows to specify a set of classes for table rendering However, I would like to specify other attribute to the table markup Typically I want to use the DataTables (table plug-in for jQuery) library, that requires another attribute for rendering table Here is an example https://datatables.net/release-datatables/examples/basic_init/multi_col_sort.html In method write_result of class HTMLFormatter in pandas/core/format.py there is this command that I would like to update with other attributes ``` self.write('<table border="1" class="%s">' % ' '.join(_classes), indent) ``` I tweak it for my personal needs, and it looks like this. ``` self.write('<table border="1" class="%s" id="example">' % ' '.join(_classes), indent) ``` But I would like to be more general. I would like to give a dictionary to the to_html method that will expand and produce the line below. Is it possible?
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6,489
DOC: Expand on freq in shift usage.
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2014-02-26T21:38:21Z
2014-06-27T12:10:39Z
2014-02-26T21:59:18Z
CONTRIBUTOR
null
I was filing an ENH issue before I noticed that the docstring was telling me how to do what I wanted to do. Added a few lines to clarify this for people who are dense like me.
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28,384,375
MDU6SXNzdWUyODM4NDM3NQ==
6,490
pivot_table on non existent column
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1
2014-02-27T00:42:22Z
2014-05-16T17:47:22Z
2014-05-16T17:47:10Z
CONTRIBUTOR
null
If you try to access a column which doesn't exist you get an error ``` In [11]: df = pd.DataFrame([[1, 2, 3]], columns=list('ABC')) In [12]: df.pivot_table('no', 'A', 'B') DataError: No numeric types to aggregate ``` Should probably say that the column isn't there. vaguely related #4733 ?
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MDU6SXNzdWUyODM4OTE1Mg==
6,491
to_latex truncates string names
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2
2014-02-27T02:29:59Z
2021-05-24T13:44:07Z
2021-05-24T13:44:06Z
CONTRIBUTOR
null
Tried to use `to_latex` to make a table containing long string names. It truncated them and inserted `...`. Drive-by bug issue report. Will update with MWE later.
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6,492
DataFrame.get_value() doesn't work with no unique monotonic MultiIndex
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4
2014-02-27T03:32:36Z
2014-02-27T23:29:44Z
2014-02-27T23:29:44Z
NONE
null
Here is the test code: ``` import pandas as pd from numpy.random import randint midx = pd.MultiIndex.from_arrays([randint(0, 3, 20), randint(10, 13, 20)]).order() df = pd.DataFrame({"x":randint(0, 100, 20)}, index=midx) df.get_value((1, 10), "x") ``` It seems that the `values.searchsorted()` failed in `_get_loc_duplicates()` in index.pyx. Currently, I use following code to get the value: ``` class Loc(object): def __init__(self, loc): self.loc = loc def __cmp__(self, other): return cmp(self.loc, other) df.get_value(Loc((1, 10)), "x") ```
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6,493
iat, iloc don't work with no unique index
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1
2014-02-27T05:07:07Z
2014-02-27T14:45:25Z
2014-02-27T14:45:14Z
NONE
null
``` import pandas as pd s = pd.Series(range(5), index=[1,1,2,2,3]) s.iat[2] ``` which returns `array([2, 3], dtype=int64)`, I think the result should be `2`. `s.iloc[2]` works, but `s.iloc[[2, 3]]` raise error.
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6,494
Benchmark coverage reports
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1
2014-02-27T08:53:26Z
2018-07-06T21:45:36Z
2018-07-06T21:45:36Z
CONTRIBUTOR
null
This is very much a work in progress, but I've hacked together a copy of vbench that can produce benchmark coverage reports using nosetests and coverage. The following results are _very_ preliminary, as many benchmarks are failing in the current setup (especially PyTables) and I have to manually intervene to get this report at the moment. In any case, the results are: ``` Name Stmts Miss Branch BrMiss Cover Missing ------------------------------------------------------------------------------- pandas 29 0 0 0 100% pandas.compat 380 252 130 121 27% 50-55, 61-90, 97, 100, 103, 129, 134-137, 143, 166-169, 178-188, 202-203, 212-214, 224, 240-241, 245-246, 269-277, 284-288, 295-298, 302-306, 310-314, 318-326, 334-350, 356, 360, 364, 368, 372-373, 377-378, 388-408, 419-425, 430-433, 450-456, 460, 467-470, 477-480, 483, 489, 493, 498, 506-507, 532, 535, 545-547, 560-562, 584-598, 603, 609-610, 634-641, 650-657, 666-674, 683-693, 696-697, 702-705, 725-728, 736-744, 747-750, 753-754 pandas.compat.chainmap 21 8 10 6 55% 11-12, 16-20, 25 pandas.compat.chainmap_impl 75 34 8 6 52% 5-6, 16-24, 67, 73, 76, 79, 89, 93, 102-104, 109, 112, 115-118, 122-125, 129-132, 136 pandas.compat.pickle_compat 71 47 19 18 28% 15-48, 51-52, 76-97 pandas.compat.scipy 59 52 26 26 8% 62-81, 88, 115-130, 149-151, 219-241 pandas.computation 0 0 0 0 100% pandas.computation.align 96 27 45 20 67% 17-27, 48, 54, 80, 100, 107, 124-129, 133, 171-183 pandas.computation.api 2 0 0 0 100% pandas.computation.common 14 5 2 2 56% 8-10, 18-20 pandas.computation.engines 63 10 4 2 82% 32-33, 94, 120-126, 144 pandas.computation.eval 62 12 28 9 77% 30, 39-40, 46, 63, 71-72, 90, 126, 136, 239-240 pandas.computation.expr 330 103 110 56 64% 54-55, 77, 103, 318, 333-350, 371, 386, 390, 396, 405-406, 409-411, 417, 423-424, 427-428, 434, 437-450, 454-464, 477-495, 498-514, 519-544, 606, 636, 645, 655 pandas.computation.expressions 101 58 48 39 35% 56, 63, 69-89, 94-118, 127-150, 177, 197, 207-208, 213-214, 220-222 pandas.computation.ops 231 74 48 30 63% 34, 46, 68, 119, 146, 150, 216-224, 231-239, 276-282, 311-314, 331, 355-374, 380-385, 390-396, 399-405, 429-432, 458-464, 468-469, 472 pandas.computation.pytables 350 272 153 153 16% 26-28, 34-38, 41, 45-54, 58, 64, 68, 76-80, 83, 87-128, 132-136, 141, 147, 152, 156-157, 163-206, 209, 215, 220-224, 228, 232-266, 269-272, 281, 287, 299, 303-327, 333-337, 344-358, 369-371, 376-380, 384, 387-389, 394-404, 408-430, 433, 436, 472-504, 509-556, 559-561, 566-577, 585-587, 592-596, 601-606 pandas.computation.scope 103 37 24 9 64% 32-38, 44-45, 72-74, 109-112, 130, 135-137, 172, 226-235, 244-254, 269-278, 288, 293 pandas.core 0 0 0 0 100% pandas.core.algorithms 187 61 67 33 63% 46-47, 65-67, 75, 158, 194, 196, 203-205, 219-220, 228, 236-265, 324-327, 333, 339, 345-356, 363, 376-378, 396-404 pandas.core.api 22 0 0 0 100% pandas.core.array 17 0 10 0 100% pandas.core.base 143 48 32 24 59% 33, 74, 78, 85, 93-96, 110-112, 118, 123, 127-129, 132-134, 139, 144, 147, 151, 155-156, 171-174, 178, 189-190, 199-201, 218-219, 224, 228-229, 238-239, 242-243, 248, 258, 264-265, 269-270 pandas.core.categorical 109 49 28 23 47% 19-24, 26-28, 81-82, 86, 88-89, 140, 143, 146-148, 154-156, 159-161, 163-168, 172-176, 179, 182-193, 196-203, 217-220, 228-232 pandas.core.common 1400 518 744 428 56% 143, 162-171, 195-199, 212, 233-238, 243-244, 247-258, 288, 314-318, 367, 372-373, 401-402, 421, 423, 429-430, 439, 443-446, 456, 460-461, 589-592, 596, 599, 695-698, 702, 712, 720, 723, 725, 727, 729-734, 744-746, 749, 777-780, 789, 808-811, 817, 826-850, 861, 865, 884, 896-897, 907-908, 915-922, 934-936, 941, 947-964, 968, 982-984, 989-990, 992-993, 1003, 1011-1015, 1029-1032, 1041-1042, 1063, 1070-1074, 1085, 1092, 1094, 1096, 1098, 1107, 1120, 1132, 1140, 1145, 1149-1152, 1163-1166, 1168-1169, 1172-1173, 1176, 1178-1179, 1183-1184, 1201-1208, 1212-1215, 1224-1225, 1235, 1249, 1251-1252, 1261, 1265, 1267-1268, 1274-1277, 1279-1283, 1286-1290, 1302-1306, 1308, 1311, 1335-1336, 1340, 1360, 1363, 1387-1392, 1396-1399, 1405, 1416-1417, 1422-1428, 1438, 1444-1453, 1463-1467, 1478-1480, 1483, 1485-1492, 1494, 1498-1499, 1503, 1524, 1540-1541, 1551, 1576, 1589, 1596-1601, 1613, 1631, 1663, 1676, 1685-1686, 1691, 1694, 1698, 1711-1718, 1732, 1736-1738, 1760, 1763, 1776-1779, 1809-1813, 1816-1818, 1824, 1829-1830, 1832, 1839-1840, 1852-1854, 1883-1885, 1906-1909, 1924, 1932-1933, 1940-1941, 1977, 1981-1986, 1990, 1994-1995, 1998-1999, 2009, 2018, 2020, 2031-2036, 2039, 2054, 2085, 2101, 2119, 2121, 2146, 2159-2163, 2167, 2171-2176, 2207, 2210-2217, 2219-2238, 2242-2243, 2246, 2248, 2252, 2260, 2262, 2271-2274, 2284, 2287, 2290, 2293, 2296, 2309-2330, 2333-2336, 2362-2363, 2366-2367, 2384-2388, 2391-2407, 2410-2427, 2448, 2454-2459, 2463-2473, 2505-2510, 2513, 2522-2527, 2530, 2537-2543, 2581-2599, 2607-2621, 2657-2659, 2667, 2679, 2682, 2685, 2688-2692, 2700-2701, 2712, 2733-2736, 2749-2752, 2760 pandas.core.config 279 101 107 73 55% 80, 105-116, 118-125, 129-140, 145-157, 175-176, 182, 187, 189-195, 198, 221-222, 343, 350-356, 359, 362-363, 395, 408, 475, 497, 513-514, 572-580, 588-607, 613-637, 715, 738, 752 pandas.core.config_init 101 11 14 8 83% 198-207, 291-292, 324, 341 pandas.core.daterange 21 8 4 2 60% 29, 37-48 pandas.core.datetools 43 14 6 6 59% 14-15, 41-57 pandas.core.format 1185 389 596 368 57% 73-78, 81-83, 86-106, 109, 114-123, 126-131, 138-148, 151-155, 158, 165-167, 170, 172, 175-177, 181-184, 187-189, 197, 205, 208, 210, 218, 233, 242, 301, 342, 348, 374-378, 430-432, 442-444, 448-452, 454, 464, 468-470, 481, 484-485, 488-493, 515, 532, 534, 539, 554, 598, 604, 608, 635-636, 645, 655, 687-689, 703, 719, 725, 728-733, 740, 749-786, 823-824, 837, 889-892, 901-908, 973, 994, 999-1000, 1015, 1032, 1042, 1049-1059, 1061, 1063-1065, 1067, 1077-1079, 1084, 1091, 1095-1100, 1106, 1109-1113, 1123, 1125-1143, 1148, 1162-1163, 1195, 1202-1204, 1206, 1212-1215, 1219, 1222-1223, 1235-1236, 1240-1262, 1319-1324, 1365, 1367-1368, 1370, 1380, 1384, 1391-1393, 1396, 1399-1400, 1407, 1422, 1424, 1430, 1433, 1439-1446, 1451, 1454, 1461-1463, 1468, 1474-1475, 1478, 1487-1492, 1500-1506, 1508-1512, 1518-1521, 1524-1531, 1535, 1555, 1561, 1574-1579, 1584-1593, 1598-1599, 1612, 1614, 1658, 1665-1667, 1672, 1682, 1684, 1710-1711, 1716, 1732, 1744-1745, 1748, 1782, 1785, 1791-1800, 1806, 1810, 1816, 1835-1839, 1843-1845, 1848-1862, 1879, 1891, 1915, 1922-1931, 1973-1974, 1978, 2008-2016, 2056-2057, 2080-2116, 2133-2134 pandas.core.frame 1849 830 911 589 49% 203-212, 214, 222-226, 228, 235, 238, 252, 257-260, 273, 300-310, 330, 334, 346-349, 378, 392-393, 400-405, 410, 419, 429-431, 458, 479, 482-484, 504-505, 529, 539, 541, 566, 568-573, 575-584, 588, 590-592, 595, 620-632, 653, 657-658, 705, 738-741, 745-747, 750-777, 779-788, 793-794, 800, 802-804, 812, 825, 832-833, 835, 862-863, 868, 875, 882, 885-889, 914-930, 933-941, 947-948, 987-988, 1006-1007, 1023-1066, 1197-1215, 1248-1251, 1268, 1418-1470, 1474-1475, 1569, 1590, 1618-1619, 1623, 1631, 1642, 1664, 1667, 1681, 1705-1706, 1709-1711, 1715-1717, 1721, 1724-1731, 1735, 1807-1808, 1868, 1882, 1893-1894, 1898-1902, 1908-1915, 1920-1924, 1929, 1939-1942, 1945, 1949-1955, 2010, 2012, 2017, 2020, 2033, 2046-2048, 2054, 2084, 2092, 2216, 2224-2225, 2228, 2233-2234, 2236-2237, 2248, 2294, 2301, 2305-2308, 2315-2330, 2337-2345, 2352-2358, 2403-2405, 2413-2414, 2419, 2426-2428, 2494-2497, 2540, 2580, 2583, 2585, 2593, 2599, 2606-2609, 2618, 2659, 2664-2667, 2673, 2678-2679, 2696-2698, 2700, 2727-2729, 2744-2752, 2759-2766, 2774-2782, 2792-2798, 2802, 2806, 2816-2818, 2857-2864, 2868, 2873-2875, 2896-2901, 2904-2935, 2943-2946, 2956, 2960, 2990-2992, 3028-3041, 3043-3054, 3070, 3161, 3163-3164, 3239, 3299, 3304, 3307-3308, 3316, 3322, 3326-3333, 3338-3348, 3355, 3394-3404, 3410-3417, 3434, 3448-3449, 3455-3458, 3484, 3517-3529, 3531-3533, 3537, 3595-3597, 3604-3629, 3636-3637, 3669, 3675-3692, 3715-3730, 3749-3759, 3762-3780, 3798-3810, 3813-3815, 3821, 3827, 3858, 3864, 3875-3883, 3888-3897, 3903, 3928-3929, 3931-3933, 3959-3964, 3975, 3981-4021, 4025-4034, 4063-4067, 4094-4098, 4107, 4127-4129, 4147-4160, 4197-4206, 4228-4240, 4259-4267, 4270-4275, 4326-4341, 4344-4348, 4371, 4411-4412, 4414-4415, 4418-4430, 4458, 4461, 4510, 4519, 4522, 4526-4529, 4538-4551, 4559, 4564, 4574-4581, 4584-4585, 4587-4588, 4596, 4600, 4604-4605, 4618-4648, 4659, 4674-4695, 4704, 4743-4757, 4769, 4782, 4794-4795, 4797-4799, 4803, 4839-4844 pandas.core.generic 1349 621 614 405 48% 45, 49-54, 57-64, 93, 95, 98, 127, 130-131, 144-145, 149, 216-217, 234-237, 258-260, 266-268, 280-283, 293-295, 306-308, 331, 346, 365, 370, 385-394, 437, 443, 454, 457-470, 482-486, 501-505, 539-574, 596-599, 622, 652, 691-694, 698, 701, 714, 738-749, 755-768, 925-928, 932-935, 959-960, 998-1001, 1004, 1035, 1048-1052, 1055-1058, 1068, 1078, 1083, 1092-1116, 1126-1129, 1134-1139, 1148-1149, 1169-1170, 1264-1283, 1301-1305, 1308, 1320, 1349-1359, 1407-1409, 1415-1416, 1420-1432, 1435, 1460-1461, 1475-1476, 1493-1502, 1569-1570, 1584, 1608, 1700, 1705-1711, 1731-1750, 1756-1759, 1765-1768, 1798, 1813-1822, 1855, 1865-1866, 1870-1871, 1885, 1915, 1925, 1938-1939, 1954-1955, 1978-1985, 1990, 2096, 2105, 2107-2114, 2126-2128, 2140, 2143, 2147-2148, 2154-2165, 2177, 2182, 2291-2292, 2300-2341, 2348, 2352-2371, 2375-2387, 2392-2400, 2406-2410, 2420, 2429, 2495-2497, 2503, 2506, 2510, 2516, 2531-2535, 2539, 2553, 2563, 2582-2591, 2605-2608, 2622-2625, 2699-2700, 2715-2719, 2737-2743, 2802-2817, 2836-2847, 2884, 2914, 2928-2929, 2941, 2954-2981, 2986, 3021-3027, 3030, 3039-3053, 3059-3073, 3077-3122, 3127-3129, 3174, 3181, 3208-3243, 3264-3292, 3308-3323, 3340-3355, 3371-3383, 3406-3414, 3420-3423, 3522-3535, 3544, 3546, 3548, 3565, 3569, 3578-3580, 3590-3608 pandas.core.groupby 1507 454 616 356 62% 101-103, 105-107, 116-126, 130-140, 225, 253, 266, 271, 283-285, 288, 305-310, 314, 338, 364-368, 379, 467, 488, 499-500, 530-532, 579-584, 613, 638, 640-641, 645-647, 649, 651-653, 655, 670-672, 695, 719, 722, 742, 755, 759, 764, 768, 773, 802, 828, 932-935, 940-946, 959-965, 999, 1101-1105, 1120, 1160-1161, 1186-1196, 1199-1202, 1224, 1232-1234, 1239-1240, 1249-1254, 1265, 1285, 1296-1298, 1303-1305, 1320, 1324, 1327, 1330, 1385-1386, 1393-1395, 1432, 1456-1457, 1467, 1470, 1483, 1491-1492, 1496-1499, 1508-1509, 1511-1512, 1518, 1529, 1540, 1556-1558, 1566-1574, 1577-1578, 1604, 1629, 1636, 1654, 1721, 1731-1733, 1736-1741, 1744, 1746, 1748-1772, 1786, 1790, 1800, 1805-1808, 1811-1814, 1816-1817, 1820, 1840-1854, 1859-1861, 1865, 1888-1907, 1913-1927, 1934-1948, 1977, 1982-1983, 1986, 1990-1991, 2017, 2028-2035, 2044-2045, 2055-2058, 2061-2073, 2078-2099, 2102-2108, 2110, 2116-2120, 2122, 2136-2142, 2147, 2149, 2151-2163, 2166-2175, 2179, 2186, 2212-2213, 2225-2274, 2278-2288, 2297, 2337-2338, 2348, 2352, 2389, 2395-2397, 2399-2407, 2409-2411, 2435, 2446-2448, 2456, 2459, 2475-2476, 2486, 2503-2505, 2513, 2526, 2533, 2536, 2542, 2559, 2561-2564, 2568-2572, 2575, 2583, 2588, 2620-2621, 2628, 2632, 2649-2652, 2657-2658, 2660-2670, 2675-2689, 2728, 2745, 2770-2772, 2783, 2789-2791, 2795-2801, 2804, 2813, 2870-2871, 2884, 2898-2899, 2912-2913, 3031, 3035-3042 pandas.core.index 1882 691 852 468 58% 53, 141, 159, 214, 219, 256, 258, 262, 302, 304-308, 317, 356, 389, 392-393, 400, 404, 415, 425, 444, 459, 472, 474, 476, 527-533, 543-545, 560, 565-569, 586, 594, 612-613, 631, 645, 648-650, 660, 681, 685-686, 702, 720, 738-742, 744, 746, 774, 794-795, 799, 802, 829-832, 837, 840, 851-856, 865-868, 882, 888, 903, 914-916, 921-923, 927, 938-939, 968, 978-980, 986-987, 1013, 1016, 1019-1020, 1023, 1067, 1070-1071, 1105, 1111-1113, 1119-1120, 1136, 1188, 1196, 1201, 1205, 1210, 1222, 1225, 1237, 1243, 1247, 1250, 1295, 1307, 1313, 1321-1324, 1353, 1365-1368, 1374, 1376-1378, 1381, 1384-1386, 1390-1391, 1401-1402, 1411-1412, 1415, 1417, 1419, 1425, 1429, 1438-1448, 1457-1461, 1465, 1468, 1474-1486, 1489-1491, 1501, 1518, 1526-1532, 1537, 1575-1577, 1579, 1585-1587, 1589-1593, 1598, 1601-1602, 1632, 1670-1671, 1675-1681, 1692-1693, 1697, 1701-1705, 1755, 1802, 1813, 1815, 1851-1853, 1894, 1902, 1915, 1918-1921, 1925-1928, 1935-1943, 1948-1956, 1959-1961, 1967-1973, 1977, 2011, 2013, 2015, 2017-2022, 2051, 2056, 2087, 2107-2113, 2117, 2132, 2139, 2160-2164, 2166-2167, 2200-2214, 2236, 2240, 2268-2271, 2273-2275, 2284, 2308, 2312, 2321, 2323-2324, 2326, 2332, 2337, 2342, 2351, 2361, 2370-2371, 2389, 2391-2393, 2411-2415, 2420-2426, 2428-2434, 2447-2455, 2472-2478, 2495-2500, 2510, 2518, 2533, 2568-2573, 2589, 2594-2597, 2604, 2682, 2686, 2692, 2747-2748, 2751, 2755-2756, 2760, 2765-2772, 2779, 2827, 2835-2836, 2839, 2855-2878, 2881-2888, 2927, 2943-2954, 2964-2973, 3009, 3057, 3060, 3066-3069, 3072-3075, 3096, 3099-3102, 3106, 3112-3118, 3122-3128, 3166-3180, 3183-3209, 3224-3234, 3252, 3259-3262, 3266-3279, 3285, 3289-3364, 3381-3396, 3402, 3425-3471, 3488-3500, 3519, 3539-3545, 3564-3567, 3582-3597, 3608-3634, 3641-3644, 3662-3683, 3694-3695, 3708-3712, 3715-3716, 3733-3735, 3738, 3752, 3755, 3761, 3766, 3773-3774, 3783, 3789, 3796, 3800-3803, 3816-3819, 3858-3861, 3872-3888, 3895-3900, 3904-3909, 3916 pandas.core.indexing 941 388 539 329 52% 54-56, 89, 106, 110, 113, 131, 133-136, 140, 155, 157, 168, 171, 197-218, 257, 279, 298, 304-305, 311, 316, 321-323, 329, 339, 345, 348, 359-360, 364-365, 367-375, 385, 387, 397, 408, 419-424, 428-430, 442-443, 445-447, 451-452, 456, 460, 488, 496, 499-514, 530, 532-536, 541, 543-544, 547-554, 558, 563-566, 570-594, 597-612, 616, 618-620, 623-663, 666-667, 685-686, 688, 691-693, 702, 705, 710, 734-735, 739, 745-746, 753-768, 775, 784, 786, 800, 805, 814-815, 833-834, 847, 863, 868-870, 876-878, 882-884, 896, 899-901, 905, 916, 920, 928, 979-986, 1023, 1053-1054, 1057, 1060, 1067-1070, 1073-1075, 1087-1089, 1094-1095, 1106, 1109-1111, 1116-1123, 1134, 1137, 1162-1168, 1172-1183, 1195-1263, 1269-1270, 1272, 1277, 1280, 1282-1289, 1301-1309, 1316, 1323-1345, 1351, 1357, 1366-1367, 1379, 1388, 1392, 1398-1401, 1410, 1413-1422, 1425-1432, 1446, 1450-1456, 1471, 1473, 1475, 1477, 1481, 1484, 1514-1523, 1534-1539, 1559, 1578-1582, 1595, 1598, 1613, 1633, 1664, 1668, 1673-1687 pandas.core.internals 2291 735 1058 548 62% 61, 110-111, 129, 137, 141, 167-168, 190, 193-195, 209, 228-234, 329-332, 345-354, 368, 384, 397, 405, 408-409, 431, 455, 466-467, 492-495, 497-508, 523, 528, 532-534, 541, 627, 638, 647, 653, 670, 673, 697-698, 702, 706-707, 711, 715, 722, 736-737, 742, 745-771, 776-799, 804, 816, 829, 840-841, 857, 868-871, 895, 962, 997-999, 1019, 1025-1026, 1035-1036, 1042-1050, 1058, 1085-1086, 1099, 1103, 1115, 1133-1134, 1139-1141, 1149, 1202-1203, 1224-1225, 1240, 1242-1243, 1248, 1253, 1262-1263, 1267-1268, 1287, 1289-1290, 1293, 1295, 1305, 1308-1310, 1313-1321, 1325, 1329-1336, 1339, 1344-1349, 1351-1353, 1357, 1365, 1368, 1371-1372, 1377, 1381-1383, 1443, 1461, 1465, 1475, 1481-1486, 1489, 1495, 1504, 1507, 1514, 1518-1519, 1522, 1524, 1548-1554, 1556, 1561-1598, 1602, 1613, 1620-1623, 1628-1631, 1643-1644, 1647-1649, 1657-1661, 1665, 1669-1671, 1675-1679, 1693, 1708, 1716, 1730-1731, 1761-1762, 1765, 1776-1777, 1794, 1796, 1800, 1818, 1820, 1824, 1828, 1834, 1837, 1845, 1852, 1878-1880, 1884-1887, 1892-1895, 1897-1900, 1905, 1915-1921, 1942, 1953-1957, 1961, 1972-1973, 2025, 2084, 2090, 2093, 2096, 2118, 2152-2153, 2164-2171, 2215, 2252-2253, 2315, 2332-2334, 2337, 2341-2345, 2352-2353, 2359, 2367, 2407-2410, 2440, 2452, 2467-2470, 2472-2477, 2485, 2488, 2495-2497, 2501, 2505, 2525, 2528, 2558-2559, 2564-2565, 2590-2593, 2597-2599, 2606, 2618-2627, 2631-2632, 2648, 2675-2688, 2713, 2726, 2729, 2733, 2757, 2764, 2769, 2798-2801, 2809-2854, 2869-2884, 2907-2909, 2919-2920, 2926-2950, 2959-2963, 2968-2977, 2983-2990, 3008, 3018, 3038-3069, 3080, 3089-3097, 3125-3128, 3157, 3175-3192, 3198-3201, 3220-3231, 3241, 3272-3274, 3289, 3292, 3318-3334, 3363-3370, 3382-3383, 3411-3418, 3423-3427, 3440-3448, 3456, 3464, 3480-3490, 3495-3510, 3520-3526, 3531-3533, 3537-3550, 3553-3567, 3570-3571, 3574-3575, 3580-3591, 3595-3603, 3608, 3629, 3640, 3642, 3650-3667, 3675-3676, 3679, 3724-3731, 3735, 3745-3747, 3764, 3772, 3776, 3782, 3791-3792, 3796-3800, 3816-3819, 3828-3829, 3847-3848, 3868, 3871, 3874, 3880-3882, 3896-3898, 3924-3933, 4022-4030, 4036-4043, 4048, 4058-4059, 4077, 4082-4093, 4096, 4098, 4100, 4128, 4140-4142, 4145, 4156-4159, 4178-4179, 4187, 4189, 4194-4207 pandas.core.nanops 421 174 175 116 51% 20, 66, 69-70, 74-76, 79-83, 88, 97-98, 102, 104, 116-122, 173, 187, 196-198, 206, 213, 219, 249, 262, 264, 266, 269, 277-283, 285-290, 302, 314-318, 328-349, 352, 357-382, 400-404, 409-417, 420-425, 430-438, 443-446, 450, 454-455, 460, 465-466, 469, 471-472, 479-482, 490-499, 517-518, 522, 528-531, 539-542, 546, 548-551, 554, 558-559, 562-571, 583-591, 597, 603, 605-611, 621-632, 648-649, 656-657 pandas.core.ops 450 217 237 154 46% 116, 119-123, 125-126, 226, 268-269, 276-305, 315, 323-324, 329, 331-365, 378, 385-399, 430, 444-455, 463, 471, 516-525, 531-533, 541, 547-549, 564, 575, 589-609, 623-629, 634-647, 674-683, 731-750, 761, 763-770, 772-790, 805-825, 829-863, 876, 905-917, 921-926, 934-955, 959-966 pandas.core.panel 647 375 239 188 36% 46-53, 95, 97-100, 142, 144, 150, 175, 177-178, 195-196, 202, 237-240, 259-261, 266-271, 277, 297, 303, 321, 325-336, 344, 347, 350-356, 364, 381-382, 427-436, 442, 466, 472, 493, 496-500, 502, 504-516, 519-520, 524-528, 532-535, 538-549, 551-557, 588-589, 620-640, 643-645, 647-648, 651-653, 656-670, 674-679, 682-684, 702, 720, 738-740, 743-748, 758-766, 771, 789-791, 811-813, 818, 821, 824-864, 894-901, 906-910, 914, 918-945, 950-974, 979-988, 991, 994, 997-1000, 1004-1014, 1018-1027, 1034-1051, 1055-1056, 1058-1060, 1073-1077, 1083, 1090, 1104-1110, 1126-1153, 1157, 1183-1205, 1229-1237, 1241-1251, 1270-1272, 1276, 1278, 1303, 1308, 1323, 1333, 1345-1347, 1353-1361, 1364, 1389-1400, 1405 pandas.core.panel4d 6 0 0 0 100% pandas.core.panelnd 55 23 18 13 51% 31-35, 53-54, 62-67, 69-71, 75-77, 83, 85, 89, 91-95 pandas.core.reshape 473 182 166 101 56% 152-154, 158-168, 210-211, 254-263, 266, 271, 301-304, 307-308, 311, 334, 346-347, 377-387, 406-408, 434-450, 455-456, 470-478, 482, 485, 508-511, 540, 542-543, 546, 571, 575, 593, 675, 684, 694, 703, 755-778, 781-787, 847-872, 893-898, 948-982, 1006-1023, 1028-1053, 1060-1061 pandas.core.series 1001 438 384 253 50% 71-73, 129, 133, 145, 172, 177, 181, 190, 194-195, 201, 203-205, 207, 215, 219, 225, 227, 244-245, 256, 260, 275-279, 294, 298, 302, 310, 319, 323, 328, 340, 343, 347, 356, 373, 394, 403, 406-422, 429, 433, 454, 458-459, 462-463, 466, 496-498, 500, 507-508, 512, 531, 533-536, 548-551, 555-567, 572-574, 579, 586-587, 592, 594, 596-605, 607-609, 613, 615, 624, 631, 633-636, 644, 647, 656, 658-668, 671-674, 676-678, 682-690, 694, 704-706, 713-714, 718-722, 761-768, 791-811, 822-831, 836, 843-846, 851-853, 862, 866-871, 905, 918, 920, 942, 946, 965-966, 979, 998, 1024-1030, 1045-1046, 1068-1088, 1135-1136, 1147, 1157, 1177, 1221, 1260-1261, 1281-1283, 1286-1289, 1292, 1310-1315, 1318, 1321-1327, 1329-1337, 1339-1353, 1375-1378, 1397-1400, 1427, 1442-1444, 1447-1457, 1462-1467, 1488-1491, 1519-1527, 1530, 1532, 1536-1537, 1542, 1563-1567, 1572, 1587-1592, 1603-1607, 1634, 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143-147, 199-203, 216, 228, 232-241, 243, 252-308, 312, 316-317, 320, 323, 329, 331, 336-338, 341-345, 391-401, 411, 416, 432, 439, 444-458, 461-466, 472-479, 483, 486, 490, 499-507, 513, 517-569, 581-591, 602-608, 614, 619-655, 676-691, 702-712, 724-730, 736, 741-775, 789-817 pandas.io.gbq 169 137 66 66 14% 22-30, 89, 111-125, 153-167, 211-344, 388-466, 509-530 pandas.io.html 273 169 80 79 30% 45, 91-97, 112-124, 169-171, 197-199, 327-329, 333-337, 340-344, 347-351, 353, 370-373, 376, 379, 385, 398, 401, 404, 407, 410-415, 418-421, 424-425, 443-447, 472, 475, 478, 482, 485-489, 495-501, 519-555, 558, 561, 564, 567, 573, 579-582, 588-609, 637-658, 662, 666-684, 693-710, 828-838 pandas.io.json 362 230 206 186 27% 41, 44, 84, 185-186, 189-192, 200, 225, 234-235, 238, 252-255, 263, 268-273, 277-282, 292-308, 311-343, 345-346, 353-359, 368-379, 382-386, 391-395, 407-409, 414, 427-435, 439-441, 444, 453-472, 482, 489-495, 501-519, 522-527, 531-553, 587-617, 669-756 pandas.io.packers 264 169 144 119 29% 68, 98, 109-114, 138, 143, 151-152, 155-156, 168, 179-181, 197-198, 206, 216-221, 226-231, 239-258, 269, 280-281, 290, 302-317, 353, 367-434, 442-540, 604-605, 609-641 pandas.io.parsers 1140 693 599 481 32% 438-439, 444, 446, 448-450, 452, 477-478, 480, 518-521, 525, 539, 542, 556, 576-577, 587, 600-607, 613, 615, 617, 625, 630, 690-699, 704, 717-726, 732-747, 752, 755, 762-777, 786, 793-804, 809, 819-839, 842-869, 872-892, 896-909, 916, 918, 920-935, 940, 966, 968, 1008-1010, 1024, 1029-1030, 1033, 1044, 1055-1060, 1063, 1068, 1073-1074, 1085-1087, 1089-1093, 1097-1106, 1116, 1133-1136, 1139-1146, 1149-1151, 1198-1199, 1203, 1212-1237, 1249-1262, 1264-1336, 1341-1364, 1367, 1370-1371, 1375-1428, 1431-1457, 1461-1464, 1468-1480, 1484-1486, 1490-1497, 1502-1522, 1524-1570, 1574-1600, 1606-1610, 1618-1635, 1641-1644, 1647-1664, 1672-1685, 1689-1692, 1702, 1707, 1724, 1729-1742, 1746, 1751, 1758, 1762-1764, 1779-1793, 1798-1812, 1815-1824, 1830-1839, 1843-1853, 1855-1876, 1880-1886, 1905, 1908-1909, 1916, 1946, 1948, 1954, 1960-1962, 1970-1971, 1992-1995, 2010, 2012-2027, 2033-2062, 2066-2076, 2081-2089, 2094-2114, 2118-2126, 2130-2137, 2143, 2161-2179, 2183-2189, 2193-2206, 2209-2218, 2232-2234, 2237 pandas.io.pickle 18 2 2 1 85% 46, 52 pandas.io.pytables 2209 1551 894 863 22% 54-55, 60, 63, 80-87, 89, 226-243, 278, 285-287, 320-343, 396-400, 402-404, 409-410, 414, 417, 420, 423, 427-432, 439-444, 447, 450-459, 461-471, 475, 483, 489-490, 503-563, 569-571, 578-580, 598-604, 618-621, 647-669, 684-685, 689-692, 713, 739-742, 747-757, 761-765, 771-798, 821-824, 847-874, 907-916, 942-943, 953-964, 966-987, 989-999, 1022, 1044, 1047, 1049, 1053, 1055-1057, 1083, 1085-1109, 1113, 1118-1121, 1124-1126, 1129, 1133-1138, 1144-1151, 1155-1156, 1159-1183, 1189-1219, 1222-1224, 1228, 1232-1237, 1240-1247, 1249-1250, 1252-1253, 1255-1259, 1270-1279, 1282-1284, 1307-1312, 1314-1320, 1323-1334, 1337-1338, 1341-1343, 1399, 1405, 1409-1410, 1413-1420, 1424, 1428, 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3796-3798, 3802-3846, 3859, 3862, 3866-3870, 3875-3889, 3899-3904, 3916, 3920, 3924-3932, 3937-3950, 3966, 3969-3977, 3982-3990, 4001-4003, 4007, 4019-4036, 4041-4045, 4049-4114, 4123-4144, 4148-4157, 4163-4172, 4177-4194, 4198-4202, 4206-4212, 4218-4221, 4238-4281, 4285-4294, 4309-4314, 4320-4338, 4343-4350 pandas.io.sql 403 319 136 136 16% 32-35, 40, 44-54, 62-69, 71-76, 101-103, 132, 137-138, 165-170, 224, 255-256, 279-280, 323-332, 340-361, 375-402, 405, 408, 411, 414, 417-420, 423-437, 441-467, 470-478, 481-495, 511-547, 550-569, 572-586, 595, 599, 610-616, 620, 623-624, 627-628, 632-646, 649-651, 655, 658-661, 664, 669-670, 674-677, 680-681, 735, 738, 742-756, 759-766, 772-791, 794-808, 814-818, 821-837, 840-849, 852-853, 857-870, 873-876, 892-894, 897-903, 906, 909-910, 927-930, 936-938, 944-945 pandas.io.stata 570 479 294 285 12% 47-49, 100-127, 129-136, 150-166, 168-175, 198, 200-203, 211, 222-239, 261, 275, 292-295, 327-343, 346-378, 382, 385-389, 394-402, 405-418, 422-432, 436-461, 463-491, 497-545, 548, 552-555, 559-561, 563-565, 569-573, 575-580, 582, 585-589, 594, 631-632, 635-662, 666-683, 686-697, 718-731, 738-747, 751-773, 777, 783, 789-792, 796-799, 803-808, 815, 822, 831-833, 837-849, 868-881, 883-885, 903-906, 908-918, 961-973, 979-983, 988-1017, 1020-1030, 1033-1059, 1065-1131, 1134-1137, 1140-1159, 1163-1185, 1188-1194 pandas.sparse 0 0 0 0 100% pandas.sparse.api 5 0 0 0 100% pandas.sparse.array 267 124 102 78 45% 29-40, 55-68, 71, 76, 82, 86, 94, 129, 131, 140-143, 169-171, 181, 188-189, 209, 218, 254, 264-270, 275-277, 283-287, 290-295, 299-309, 320-329, 332-345, 356-357, 359-368, 375-378, 385-388, 403-408, 412, 416-418, 429-433, 446-449, 461-469, 474-476, 484, 503, 506, 514, 521-522 pandas.sparse.frame 407 255 222 187 30% 58, 60-61, 63-65, 67-75, 92, 98-99, 107, 121, 127, 134-142, 148, 169-174, 188, 190-203, 220-239, 249-250, 259-262, 270, 278-281, 285-288, 293-297, 303-307, 313-319, 321-327, 334, 340, 343-344, 372-373, 377-383, 385-388, 403-409, 415-417, 422-447, 449-451, 458-476, 481, 493-504, 507, 514-518, 526-552, 556, 561-572, 578-584, 587-601, 605-608, 611-615, 620-626, 629-631, 635-648, 656, 664, 679, 698-699, 702-703, 705-707, 713-716, 733, 748-759, 761-767, 775, 796-813, 816-820 pandas.sparse.list 64 35 16 16 36% 22-25, 29, 36-45, 74-82, 86-88, 100-101, 112, 123-128, 132-142 pandas.sparse.panel 246 171 84 82 23% 29, 32-38, 71-99, 103-104, 107-108, 115, 126, 136, 140, 145, 153, 157, 163, 166, 185-188, 194-197, 200, 203-204, 207-210, 214-215, 237-238, 242, 244-245, 249, 255-262, 273-282, 292, 299-333, 355-376, 385-386, 388-392, 395-411, 413-421, 424, 430-447, 468-469, 485-486, 495, 499, 508-531, 535-555 pandas.sparse.series 284 150 104 77 41% 47-57, 77-82, 114-116, 122, 130-133, 136-137, 144, 147, 160, 162, 168, 176, 180, 183-186, 196, 218, 230, 238, 249, 258-259, 266-267, 276, 280-282, 288, 299-300, 304, 312-333, 337, 343, 347, 353-370, 374, 385, 406-410, 424-425, 449-460, 468-473, 481-486, 490-491, 498-500, 520-522, 538-543, 555-557, 567-570, 577, 579-586, 592-597, 600-622, 640-644 pandas.stats 0 0 0 0 100% pandas.stats.api 3 0 0 0 100% pandas.stats.common 24 18 8 6 25% 13-21, 25-30, 36-41 pandas.stats.fama_macbeth 110 79 16 16 25% 21-27, 35-45, 49, 53, 57, 61, 65, 68, 72, 76, 80, 84, 92-116, 119, 123-139, 147-158, 162, 165-188, 193, 197-199, 203, 211-226 pandas.stats.interface 39 34 28 28 7% 86-135 pandas.stats.math 48 38 8 8 18% 16-23, 28-31, 36-39, 43-44, 73-92, 119-130 pandas.stats.moments 328 162 100 84 43% 182-194, 201-211, 218-219, 225-226, 230-253, 256-258, 281-283, 286, 289, 292, 329, 335, 341-353, 358, 363-365, 367-368, 374, 387-391, 397-399, 408-419, 431-437, 440, 447-449, 458-465, 468-470, 477, 480, 485, 489-490, 494-497, 510, 518, 534-537, 545, 616-619, 662-665, 728-747, 750-756, 761-768, 776-778, 787-791, 842, 875, 883-884, 893-894, 916-918, 954-955 pandas.stats.ols 666 514 149 149 19% 50-74, 93-101, 108, 112, 116, 121, 126, 131, 136, 141, 149, 154, 159, 164, 169, 174-199, 207, 227-262, 267, 275, 280-283, 288, 293, 298, 303, 308, 313, 318, 323, 328, 333, 338, 343, 350-361, 365, 376-380, 383, 388-393, 396, 401, 433-457, 467-471, 475-508, 513-514, 519, 526-586, 589, 594, 630-635, 638-651, 665, 670, 675, 680, 685-689, 696, 700, 705, 712, 723-725, 730, 747-754, 765, 773, 778-780, 784, 788, 792, 795-824, 827-845, 848-872, 875-910, 915, 920, 925, 930, 935-976, 981-987, 991-1030, 1041, 1045-1050, 1055, 1057, 1062, 1067-1071, 1076, 1081-1121, 1126-1144, 1149-1170, 1175, 1180, 1184-1195, 1199, 1203, 1207-1216, 1219-1229, 1232, 1236-1237, 1241, 1245, 1258-1262, 1283-1306, 1309-1311, 1320-1340, 1351, 1355-1363 pandas.stats.plm 388 291 108 108 20% 37-55, 75-114, 117, 123-161, 165, 172-199, 209-212, 222-248, 258-290, 293-296, 305, 310, 313, 319, 324, 329, 335-338, 342-349, 352-354, 359, 364-366, 370-371, 376, 384, 387-389, 393-403, 407, 418, 421-423, 427, 436, 440, 445-455, 460, 464, 466-468, 485-487, 510-526, 536, 541, 556-557, 562, 567-572, 577-615, 621-623, 627, 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165, 168, 180, 182-187, 192-196, 198, 205-206, 228-231, 234, 240-243, 249, 252-255, 263-264, 282-289, 296, 299, 309, 318-319, 322, 325, 329, 348-356, 362, 381-399 pandas.tseries.timedeltas 118 102 62 62 9% 32-62, 75-82, 88-142, 153-202 pandas.tseries.tools 321 140 152 93 51% 37, 40-41, 47, 54-55, 64-65, 93-99, 115, 128, 130-135, 142-161, 167, 169-172, 176, 182, 242-248, 264-265, 275-276, 280-292, 303, 306-307, 335-336, 338-339, 344-361, 397, 416, 421-441, 447-452, 457, 472, 504, 507, 511, 514-527, 529, 531, 533, 542, 548-563, 571, 578-585 pandas.util 0 0 0 0 100% pandas.util.decorators 63 26 12 9 53% 11-13, 60, 70-72, 111, 117-124, 162-183 pandas.util.print_versions 71 62 45 45 8% 12-53, 57-130, 135-147 pandas.util.terminal 70 63 16 16 8% 28-42, 46-67, 74-88, 92-117 pandas.version 2 0 0 0 100% ``` The basic idea is: - use vbench to gather Benchmarks - make a mapping between a Benchmark and a nosetests test case - output all the new test files - run `nosetests --with-coverage --cover-branches` on them I'll be submitting PRs to vbench and pandas when this is more ready, but hopefully this can be a useful starting point for improving vbench coverage.
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MDExOlB1bGxSZXF1ZXN0MTMwMDMyNzk=
6,495
BUG: Bug in iat/iloc with duplicate indices on a Series (6493)
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0
2014-02-27T13:58:22Z
2014-06-19T18:33:42Z
2014-02-27T14:45:14Z
CONTRIBUTOR
null
closes #6493
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PERF: transform speedup
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4
2014-02-27T15:50:39Z
2014-08-10T01:24:50Z
2014-06-11T14:06:07Z
CONTRIBUTOR
null
http://stackoverflow.com/questions/22072943/faster-way-to-transform-group-with-mean-value-in-pandas/22073449#22073449 I think this actually will work in a general case. but prob only really makes sense when you have a cythonized work function (or a ufunc) that is much faster than iteratively calling the groups. ``` np.random.seed(0) N = 120000 N_TRANSITIONS = 1400 # generate groups transition_points = np.random.permutation(np.arange(N))[:N_TRANSITIONS] transition_points.sort() transitions = np.zeros((N,), dtype=np.bool) transitions[transition_points] = True g = transitions.cumsum() df = pd.DataFrame({ "signal" : np.random.rand(N)}) ``` ``` In [44]: grp = df["signal"].groupby(g) In [45]: result2 = df["signal"].groupby(g).transform(np.mean) In [47]: %timeit df["signal"].groupby(g).transform(np.mean) 1 loops, best of 3: 535 ms per loop ``` Using broadcasting ``` In [43]: result = pd.concat([ Series([r]*len(grp.groups[i])) for i, r in enumerate(grp.mean().values) ],ignore_index=True) In [42]: %timeit pd.concat([ Series([r]*len(grp.groups[i])) for i, r in enumerate(grp.mean().values) ],ignore_index=True) 10 loops, best of 3: 119 ms per loop In [46]: result.equals(result2) Out[46]: True ``` I think you might need to set the index of the returned on the broadcast result (it happens to work here because its a default index ``` result = pd.concat([ Series([r]*len(grp.groups[i])) for i, r in enumerate(grp.mean().values) ],ignore_index=True) result.index = df.index ``` Final Result is best ``` pd.Series(np.repeat(grp.mean().values, grp.count().values)) ```
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Possible regression in df.copy() in 0.13
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4
2014-02-27T16:39:46Z
2014-02-27T17:27:36Z
2014-02-27T16:53:01Z
NONE
null
``` python pd.DataFrame(index=[0])["a"] = 1 # this works pd.DataFrame(index=[0]).copy()["a"] = 1 # this doesn't ``` The latter command raises: ``` pandas/core/internals.pyc in _add_new_block(self, item, value, loc) 3040 new_block = make_block(value, self.items[loc:loc + 1].copy(), 3041 self.items, fastpath=True) -> 3042 self.blocks.append(new_block) 3043 3044 # set ref_locs based on the this new block AttributeError: 'numpy.ndarray' object has no attribute 'append' ``` I'm using pandas 0.13. This used to work fine in 0.12
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6,498
str.extract fails when non-default index is present
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3
2014-02-27T18:15:01Z
2014-02-27T18:41:02Z
2014-02-27T18:24:47Z
NONE
null
Input: ``` import pandas as pd values = ['11', '23', '45', '98'] noindex = pd.Series(data=values, index=None) yesindex = pd.Series(data=values, index=list('abcd')) extractno = noindex.str.extract('(\d)') print extractno extractyes = yesindex.str.extract('(\d)') print extractyes ``` Output: ``` 0 1 1 2 2 4 3 9 dtype: object a NaN b NaN c NaN d NaN dtype: object ```
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28,442,202
MDExOlB1bGxSZXF1ZXN0MTMwMTYwOTY=
6,499
DOC: show users how to emulate R c function with iloc slicing and r_
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2014-02-27T18:29:50Z
2014-06-27T23:40:56Z
2014-02-27T22:30:27Z
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28,443,954
MDExOlB1bGxSZXF1ZXN0MTMwMTcxMjI=
6,500
DOC: Clarify that methods taking a MultiIndex level index also accept the name
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3
2014-02-27T18:51:36Z
2014-06-30T15:30:00Z
2014-02-27T21:19:23Z
MEMBER
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(trivial doc fix - I have checked it applies to each case fixed)
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