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https://api.github.com/repos/pandas-dev/pandas/issues/6401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6401/events | https://github.com/pandas-dev/pandas/pull/6401 | 27,834,612 | MDExOlB1bGxSZXF1ZXN0MTI2NzQ5MTU= | 6,401 | BUG: Float64Index with nans not comparing correctly | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6402 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6402/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6402/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6402/events | https://github.com/pandas-dev/pandas/issues/6402 | 27,838,616 | MDU6SXNzdWUyNzgzODYxNg== | 6,402 | using DataFrame.interpolate with inplace=True causes error | {
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```
/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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} | 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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} | 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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} | 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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} | 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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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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} | 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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} | 8 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6418 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6418/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6418/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6418/events | https://github.com/pandas-dev/pandas/issues/6418 | 27,962,228 | MDU6SXNzdWUyNzk2MjIyOA== | 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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} | 0 | 2014-02-20T13:50:35Z | 2014-06-18T04:07:44Z | 2014-02-20T14:22:57Z | CONTRIBUTOR | null | related #6032
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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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} | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6425 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6425/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6425/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6425/events | https://github.com/pandas-dev/pandas/issues/6425 | 28,005,771 | MDU6SXNzdWUyODAwNTc3MQ== | 6,425 | groupby w/ levels on MultiIndex and then interpolate | {
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```
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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} | [] | closed | false | null | [] | null | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6429/events | https://github.com/pandas-dev/pandas/pull/6429 | 28,018,515 | MDExOlB1bGxSZXF1ZXN0MTI3Nzk1MzI= | 6,429 | BUG: punt to user when passing overlapping replacement values in a nested dict | {
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} | [] | closed | false | null | [] | null | 2 | 2014-02-21T04:36:52Z | 2014-06-15T09:28:23Z | 2014-02-21T12:00:51Z | MEMBER | null | closes #5338
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https://api.github.com/repos/pandas-dev/pandas/issues/6430 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6430/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6430/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6430/events | https://github.com/pandas-dev/pandas/pull/6430 | 28,019,059 | MDExOlB1bGxSZXF1ZXN0MTI3Nzk4MTc= | 6,430 | BUG: correctly tokenize local variable references | {
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} | [] | closed | false | null | [] | null | 0 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6431 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6431/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6431/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6431/events | https://github.com/pandas-dev/pandas/issues/6431 | 28,038,275 | MDU6SXNzdWUyODAzODI3NQ== | 6,431 | apply function to groupby-ed dataframe, can't take args | {
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} | [] | closed | false | null | [] | null | 4 | 2014-02-21T12:22:11Z | 2017-06-01T14:09:42Z | 2014-02-21T12:57:52Z | NONE | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6435 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6435/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6435/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6435/events | https://github.com/pandas-dev/pandas/pull/6435 | 28,061,655 | MDExOlB1bGxSZXF1ZXN0MTI4MDM0NTI= | 6,435 | DOC: link to pandas-cookbook instructions | {
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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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https://travis-ci.org/pydata/pandas/jobs/19331373
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] | closed | false | null | [] | null | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6439 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6439/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6439/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6439/events | https://github.com/pandas-dev/pandas/issues/6439 | 28,085,211 | MDU6SXNzdWUyODA4NTIxMQ== | 6,439 | 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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} | 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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} | [] | closed | false | null | [] | null | 2 | 2014-02-22T12:52:31Z | 2014-06-27T12:10:32Z | 2014-02-24T12:56:13Z | MEMBER | null | Closes #5221
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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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} | 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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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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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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} | 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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} | 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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} | [] | closed | false | null | [] | null | 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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https://travis-ci.org/pydata/pandas/jobs/19460011
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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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6458 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6458/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6458/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6458/events | https://github.com/pandas-dev/pandas/pull/6458 | 28,132,531 | MDExOlB1bGxSZXF1ZXN0MTI4Mzc4NTU= | 6,458 | Add references to isnull in notnull docs and vice versa | {
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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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} | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6464 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6464/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6464/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6464/events | https://github.com/pandas-dev/pandas/issues/6464 | 28,193,619 | MDU6SXNzdWUyODE5MzYxOQ== | 6,464 | xs on Series with MultiIndex gives KeyError | {
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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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} | 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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} | 0 | 2014-02-24T22:01:52Z | 2014-07-07T14:22:54Z | 2014-02-24T23:35:20Z | MEMBER | null | closes #6466
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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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} | 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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} | 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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} | 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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} | 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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} | 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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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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} | 0 | 2014-02-25T21:26:09Z | 2014-06-16T06:11:09Z | 2014-02-25T21:29:11Z | CONTRIBUTOR | null | closes #6397
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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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} | 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):
And the comparison for v0.13.1:
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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} | 7 | 2014-02-26T00:16:30Z | 2014-07-05T22:45:54Z | 2014-02-27T01:19:04Z | CONTRIBUTOR | null | closes #6479
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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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} | [] | closed | false | null | [] | null | 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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} | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/6487 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6487/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6487/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6487/events | https://github.com/pandas-dev/pandas/issues/6487 | 28,357,266 | MDU6SXNzdWUyODM1NzI2Ng== | 6,487 | HDFStore.append() throws ValueError for DataFrame created with combine_first() | {
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} | [] | closed | false | null | [] | null | 2 | 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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} | 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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} | [] | closed | false | null | [] | null | 1 | 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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} | 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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} | 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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```
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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} | 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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} | 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, 1663-1665, 1693-1704, 1760-1763, 1776, 1784-1785, 1805-1810, 1826-1827, 1848-1850, 1922-1944, 1972, 1975, 1980, 1984, 1988-1989, 2003, 2017, 2025-2026, 2045, 2101, 2107-2118, 2137-2143, 2175-2182, 2215-2217, 2234, 2237, 2246-2254, 2260-2266, 2293, 2300-2301, 2304, 2309, 2313, 2340-2342, 2361-2367, 2384, 2393-2398, 2410-2412, 2424-2427, 2431-2435, 2451-2455, 2483, 2491-2499
pandas.core.sparse 3 0 0 0 100%
pandas.core.strings 350 91 140 68 68% 14-17, 39, 42-68, 75, 80-85, 99, 104-105, 107-109, 112-113, 117, 178, 183, 277, 284, 303-309, 314, 359, 366, 377-382, 443, 488-499, 502-504, 570, 572, 620, 624, 776-777, 794-795, 844, 847-852, 922-923, 927-928, 947-948
pandas.info 0 0 0 0 100%
pandas.io 0 0 0 0 100%
pandas.io.api 11 0 0 0 100%
pandas.io.clipboard 2 0 0 0 100%
pandas.io.common 67 20 8 4 68% 15, 19, 98, 118-119, 122-134, 151, 157-160
pandas.io.date_converters 38 29 6 6 20% 8-10, 14-17, 22-28, 33-40, 44-46, 50-62
pandas.io.excel 354 265 165 158 18% 33, 40-42, 46-49, 100-107, 125-141, 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, 1433-1436, 1439, 1444-1447, 1451, 1453-1459, 1461-1470, 1475, 1479, 1483-1484, 1488, 1492, 1497, 1502, 1505, 1511-1517, 1521-1524, 1530-1535, 1541, 1547-1549, 1557-1566, 1571-1581, 1588, 1593, 1597, 1606, 1611-1612, 1615, 1618, 1642-1648, 1650-1652, 1654-1656, 1679-1681, 1683-1684, 1688-1689, 1693-1694, 1696-1698, 1700-1706, 1713, 1719-1722, 1730, 1732, 1735-1738, 1745-1746, 1748-1754, 1756-1757, 1760-1767, 1773-1782, 1785, 1791-1815, 1818-1829, 1832, 1836-1841, 1844, 1848-1849, 1855-1857, 1859, 1865-1866, 1868-1869, 1873, 1878, 1882-1892, 1899-1903, 1907-1932, 1936-1943, 1947-1951, 1955-1957, 1961-1963, 1972, 1975, 1978, 1981, 1989, 2011-2013, 2022, 2025, 2027-2029, 2038, 2042-2043, 2045-2048, 2052-2053, 2057-2058, 2066, 2070, 2074, 2078, 2090, 2094, 2098, 2102, 2107, 2111, 2119-2121, 2125, 2131, 2133-2135, 2164, 2170, 2173-2178, 2181, 2185-2186, 2192, 2196, 2201-2202, 2205, 2209-2217, 2226-2230, 2232-2239, 2242-2246, 2257-2263, 2265-2267, 2270-2284, 2288-2290, 2293-2296, 2299-2300, 2303-2305, 2308-2310, 2315-2318, 2320-2327, 2330-2332, 2334-2336, 2338-2339, 2342-2343, 2349-2352, 2356-2357, 2360-2367, 2370-2373, 2378-2383, 2389-2391, 2396, 2399-2403, 2406-2410, 2412-2467, 2473-2476, 2482-2485, 2491-2495, 2504-2507, 2510-2511, 2513, 2516-2519, 2527-2530, 2538-2543, 2551-2558, 2565-2576, 2584-2593, 2597-2609, 2618-2645, 2648-2663, 2666-2681, 2695-2696, 2730-2737, 2741, 2745, 2749-2757, 2764-2767, 2771, 2774-2792, 2799, 2805-2808, 2810, 2816, 2821, 2825, 2830, 2834, 2838, 2842, 2847, 2851, 2856, 2873, 2877, 2881, 2885-2893, 2897-2912, 2918-2923, 2929-2941, 2948-2950, 2959, 2962-2963, 2993-3033, 3041-3056, 3060, 3067-3080, 3084-3093, 3117-3133, 3142-3150, 3153-3183, 3195-3203, 3205-3213, 3218-3227, 3239-3242, 3245-3253, 3258-3261, 3270-3275, 3281, 3287, 3294-3344, 3351-3368, 3376-3391, 3399-3411, 3414-3419, 3421-3424, 3472-3543, 3548, 3559, 3579-3612, 3618-3647, 3655-3656, 3660-3665, 3668-3670, 3674-3679, 3681, 3691-3696, 3699-3720, 3725-3739, 3743-3779, 3792, 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, 630-633, 637-639, 644-646, 649-655, 658-661, 668-674, 678-681, 684, 690, 735-747, 757, 762-791, 793-796, 804-814
pandas.tools 0 0 0 0 100%
pandas.tools.describe 3 1 0 0 67% 17
pandas.tools.merge 802 250 395 207 62% 111-149, 222-242, 269, 271, 317-320, 351-364, 380, 382-383, 386, 411, 414-415, 417-418, 422, 475, 486-503, 560, 575-576, 614, 617-618, 712, 714, 736-737, 775, 777-778, 829-844, 863, 872, 955, 968, 971, 974, 1023-1024, 1039, 1044-1046, 1064, 1066-1067, 1126, 1134-1135, 1179, 1184, 1194, 1200-1276, 1320-1322, 1329, 1334, 1338, 1341-1343, 1350, 1359-1361, 1370-1467
pandas.tools.pivot 198 145 85 70 24% 70-78, 84-88, 93-101, 113-123, 129, 132, 135, 140, 143, 153-188, 193-205, 209-222, 229, 232, 236-252, 256-288, 293, 356-377, 381-394
pandas.tools.plotting 1342 1064 600 567 16% 25-26, 99-136, 164-165, 192-195, 241-282, 285-305, 307-310, 312-314, 319-325, 327-333, 340-341, 345-346, 349-355, 375-408, 410-438, 461-473, 475-504, 523-534, 537-565, 606-660, 662-665, 682-695, 712-734, 736-737, 835, 841, 843, 856-857, 867, 872-873, 879-888, 895-896, 900-903, 912-914, 920, 922-923, 926, 928-940, 944, 948-949, 964-975, 978, 985-986, 990, 999, 1002-1010, 1013, 1017-1034, 1038-1049, 1059-1075, 1077-1084, 1087-1088, 1093-1102, 1105-1116, 1120-1132, 1136-1141, 1144-1145, 1148-1159, 1162-1168, 1173, 1175, 1178-1181, 1187-1188, 1191-1207, 1211-1222, 1224-1231, 1234-1235, 1237-1245, 1249-1258, 1262-1264, 1267-1270, 1275-1282, 1285-1289, 1292-1310, 1313-1316, 1326, 1329-1338, 1341-1346, 1350, 1353-1360, 1363-1373, 1377, 1379-1405, 1408-1410, 1413-1421, 1424-1462, 1465-1479, 1482-1491, 1496-1518, 1534-1546, 1554-1563, 1566-1567, 1571-1580, 1583-1598, 1602-1608, 1611-1613, 1616-1628, 1637-1638, 1642-1643, 1648-1666, 1767-1776, 1779, 1788-1789, 1799-1801, 1804-1810, 1828-1832, 1879-1925, 1953-1991, 1998-2026, 2031-2036, 2060-2063, 2067-2085, 2120-2133, 2137-2178, 2182-2186, 2220-2236, 2240-2261, 2306-2330, 2336-2378, 2384-2402, 2406-2411, 2414-2417, 2421-2435, 2518-2594, 2598-2603, 2607-2612
pandas.tools.tile 118 103 60 58 10% 68-101, 136-141, 147-196, 201-220, 224-250, 256-260
pandas.tools.util 19 9 4 2 52% 6-8, 24-32
pandas.tseries 0 0 0 0 100%
pandas.tseries.api 7 0 0 0 100%
pandas.tseries.converter 646 541 231 230 12% 35-37, 41-48, 55-62, 67-71, 76, 86-98, 108-118, 120, 124-130, 147, 156-183, 186, 196-201, 210-213, 224-230, 233, 236, 238, 245-257, 260, 263, 271-272, 275, 279, 281-282, 286-341, 344, 350-373, 377-394, 407-420, 422, 436, 438, 449-454, 458-673, 675-681, 685-743, 745-751, 755-789, 791-797, 801-817, 821-827, 829-832, 858, 860-867, 872-879, 884-889, 891-898, 906-909, 911-913, 937-938, 940-947, 952-953, 956-962, 970-976, 979-983
pandas.tseries.frequencies 521 116 261 117 70% 49, 53-55, 57, 61-62, 64, 68-69, 71, 90, 96, 98-99, 116, 249-250, 273-275, 319, 358-359, 365, 388-390, 393-395, 402-403, 604, 622-623, 625-628, 652, 661, 723, 738, 745-749, 752-764, 766, 768-769, 772, 780, 795, 801, 811-812, 817, 823-825, 831-832, 843-845, 852-853, 862-874, 881, 903, 911, 914, 918, 920, 922, 924, 928-932, 960-967, 969, 971, 973, 975, 979, 989-993, 1002-1004
pandas.tseries.index 1085 364 506 272 60% 45, 64, 163-164, 177, 186, 188, 192, 201-208, 213, 218, 220, 224-226, 246, 253, 266, 269, 273, 297, 311, 325, 333-334, 337, 341, 349, 365-366, 387, 397, 400, 428, 436-439, 445, 447, 467, 475, 481, 485, 503-509, 511-517, 543-544, 547-559, 568-573, 586-587, 592, 604-607, 612-619, 631-637, 644-645, 648, 673-674, 676-679, 682, 689-696, 699-702, 705-706, 712, 720, 723, 763-764, 771, 786, 794, 796-798, 801, 810, 826-831, 833-836, 839-842, 858-860, 862-868, 871, 925, 948-949, 952-953, 993, 1010, 1021-1022, 1025, 1029-1030, 1034-1042, 1058, 1064, 1072-1076, 1080, 1083, 1089, 1097-1100, 1131-1139, 1152, 1159-1169, 1173, 1176-1182, 1184, 1187, 1191-1203, 1210, 1214-1236, 1240, 1253, 1260-1273, 1280, 1303-1305, 1308-1309, 1311, 1318-1320, 1343-1355, 1358-1364, 1371, 1374, 1377, 1381, 1393, 1405, 1416-1418, 1432-1433, 1463, 1470, 1497, 1501-1502, 1525, 1532-1533, 1537, 1548-1553, 1556, 1562, 1582-1594, 1604-1605, 1608-1609, 1611-1612, 1622-1630, 1649, 1673-1687, 1708-1737, 1746-1750, 1756-1760, 1776-1779, 1929-1933, 1944, 1950-1957, 1971, 1984-1985, 2000, 2009-2010, 2015, 2018, 2022, 2031-2035
pandas.tseries.offsets 1028 310 394 248 61% 100, 107, 112-115, 121, 175, 184-186, 189, 206, 214, 243, 274-275, 278, 290, 351-357, 359, 361-362, 364-379, 389, 401, 413, 417-421, 500, 508, 520, 548, 582, 633, 635, 672, 687-689, 691, 693-703, 712-715, 719-723, 774, 777-778, 794-795, 803, 806, 809, 814, 825, 829, 866, 870, 873, 881, 884-886, 905, 931, 934, 936, 969, 1024, 1099, 1103, 1122-1125, 1192, 1211, 1217, 1224-1226, 1231-1234, 1242-1243, 1248, 1271, 1274, 1279-1280, 1289-1290, 1292-1295, 1348, 1352-1357, 1361-1362, 1365, 1368, 1373-1380, 1383-1431, 1434-1437, 1440-1442, 1445-1455, 1458-1459, 1463-1464, 1467, 1470-1473, 1476, 1482-1493, 1501, 1555-1560, 1566, 1569-1605, 1608-1615, 1618-1627, 1630-1642, 1646-1647, 1652, 1664, 1680-1688, 1692-1694, 1699, 1715, 1727, 1731-1734, 1743-1759, 1768, 1861-1863, 1872-1876, 1882, 1893, 1933
pandas.tseries.period 669 378 252 211 36% 30-31, 38-39, 83, 85, 89-90, 101, 110-119, 135-138, 142, 145, 148, 151-152, 157-163, 169-172, 204, 213, 218, 238-245, 266, 428-429, 437-451, 453, 457, 464-467, 471-475, 484-495, 497-499, 579, 585, 599, 602-603, 608, 615-666, 668-670, 673-677, 681, 693-700, 703, 712, 715-720, 723-724, 735-740, 746-749, 753, 764, 775, 794-798, 800, 803-804, 806, 810, 816-819, 825, 842-849, 852, 868-871, 874, 877, 883, 890-922, 932-942, 944-945, 951-973, 976, 979-999, 1002-1003, 1009-1015, 1017, 1020-1023, 1028-1029, 1031, 1034, 1036-1037, 1046-1050, 1057, 1062, 1066-1067, 1070-1071, 1096, 1098-1101, 1103, 1106-1107, 1110-1112, 1118-1123, 1137-1150, 1153-1162, 1166-1169, 1174-1175, 1180, 1198, 1203-1204, 1224-1249, 1252-1260, 1264, 1266-1275, 1280-1287, 1291-1298, 1309, 1314
pandas.tseries.resample 231 73 101 69 57% 49-52, 86, 91-94, 112, 123, 135, 151-152, 161, 163, 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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} | 0 | 2014-02-27T13:58:22Z | 2014-06-19T18:33:42Z | 2014-02-27T14:45:14Z | CONTRIBUTOR | null | closes #6493
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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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} | [] | closed | false | null | [] | null | 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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```
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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