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closes #7192
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"de... | closed | false | null | [] | null | 3 | 2014-05-22T00:33:25Z | 2014-05-22T01:32:21Z | 2014-05-22T01:32:21Z | NONE | null | Hi,
I would find it very helpful to be able to merge dataframes without common columns, (which is allowed in R), e.g.
```
x = DataFrame({'farm' : ['A','B','C']})
y = DataFrame({'crop':['wheat','millet']})
```
so that `x.merge(y, how='outer')` returns:
```
farm crop
0 A wheat
1 A millet
2 B wheat
3 B millet
4 C wheat
5 C millet
```
Currently pandas returns: `MergeError: No common columns to perform merge on`.
One work-around is to set the indices of `x` and `y` to zero, perform a `join` and the reset the index, as per this [StackOverflow post](http://stackoverflow.com/questions/13269890/cartesian-product-in-pandas). Another use case is [here](http://stackoverflow.com/questions/23331753/how-do-i-copy-rows-in-a-pandas-dataframe-and-add-an-id-column).
Alternatively, if there is a better solution, please let me know. Thanks!
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} | 0 | 2014-05-22T02:01:05Z | 2014-07-16T09:07:10Z | 2014-05-22T11:59:37Z | CONTRIBUTOR | null | parital on #7131
CLN: make chunksize an integer in to_csv to avoid float slicing semantics
CLN: make nxes integers in _get_layout
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For example:
import pandas
x = [0, 0, 0,0 ,1]
pandas.qcut(x, 2)
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} | 59 | 2014-05-22T15:15:41Z | 2014-08-10T22:25:36Z | 2014-08-10T22:25:36Z | CONTRIBUTOR | null | related is #7146, #7206
maybe provide a `to_index()` function and/or a class-like accessor
`.values_to_index` (too long though)
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"color": "a1... | closed | false | null | [] | null | 3 | 2014-05-22T15:25:54Z | 2014-06-18T10:40:22Z | 2014-05-27T18:00:03Z | NONE | null | I see a performance degradation with the following code between 0.12 and 0.13.1:
For 0.12:
```
df = DataFrame(columns=['A','B'], index=date_range('1/1/2000', periods=20, freq='D'))
d_start = df.index[10]
d_end = df.index[11]
%timeit df.loc[d_start:d_end, 'A'] = 1
10000 loops, best of 3: 87.8 µs per loop
```
However, for 0.13.1:
```
10000 loops, best of 3: 203 µs per loop
```
I know that I shouldn't be looping on df.loc. However I find it concerning that there is a performance degradation. Is this a known thing? Thank you for your help.
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} | [] | closed | false | null | [] | null | 10 | 2014-05-22T17:28:50Z | 2014-05-22T20:11:27Z | 2014-05-22T19:49:55Z | CONTRIBUTOR | null | found that my numpy-vbench setup was not working for a while due to need for fresh cython... upgraded cython. Now I am trying to render pages where I got code hitting https://github.com/pydata/pandas/issues/4547 but even rebuilding pandas current master ( v0.14.0rc1-51-gccd593f) using cython 0.19.2+git5-g0c6fdf0-1~nd70+1 resulted in the same failure, so I decided to change my code but got stuck with inability to reference entries in the DataFrame using its own index items:
This session is with prev release:
```
(Pdb) print pandas.__version__
0.13.1
(Pdb) print means[means.index[0]] # works for this one
0.000877753633415
(Pdb) print results[results.index[0]] # but doesn't for another one with the same index
*** KeyError: u'no item named 2011-03-13 11:39:17'
(Pdb) print np.all(results.index == means.index)
True
(Pdb) print results.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-03-13 11:39:17, ..., 2014-05-19 13:35:52]
Length: 1735, Freq: None, Timezone: None
(Pdb) print results
revision ncalls timing traceback
timestamp
2011-03-13 11:39:17 c3f4e89 43690 0.000868 None
2011-03-14 00:44:47 c5c3cb9 43690 0.000855 None
(Pdb) print (results.index == results.index[0])[:2] # clearly item is there
[ True False]
```
in current master it was similar:
```
(Pdb) print pandas.__version__
0.14.0rc1-51-gccd593f
(Pdb) print (results.index == results.index[0])[:2] # clearly item is there
[ True False]
(Pdb) print results[results.index[0]]
*** KeyError: Timestamp('2011-03-13 11:39:17')
```
am I doing something stupid or there is a bug somewhere? didn't look into how those timestamps stored in the sqlite DB and brought back -- may be loading/unpickling? them back somehow causes this behavior... just awkward
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} | 27 | 2014-05-22T19:49:17Z | 2014-05-27T19:57:40Z | 2014-05-27T19:50:10Z | CONTRIBUTOR | null | seemed to work just fine (with 0.13.1) and I do not see why it should not if you are decorating numpy arrays here:
```
File "/home/neurodebian/proj/numpy-vbench/vbench/vbench/analysis.py", line 83, in __call__
Tts, Tps = ss.ttest_1samp(test_samples.timing, results.timing)
File "/usr/lib/python2.7/dist-packages/scipy/stats/stats.py", line 2887, in ttest_1samp
t = np.where((d==0)*(v==0), 1.0, t) #define t=0/0 = 1, identical mean, var
File "/home/neurodebian/deb/gits/pkg-exppsy/pandas/pandas/core/ops.py", line 505, in wrapper
return left._constructor(wrap_results(na_op(lvalues, rvalues)),
File "/home/neurodebian/deb/gits/pkg-exppsy/pandas/pandas/core/ops.py", line 445, in na_op
raise_on_error=True, **eval_kwargs)
File "/home/neurodebian/deb/gits/pkg-exppsy/pandas/pandas/computation/expressions.py", line 196, in evaluate
_bool_arith_check(op_str, a, b)
File "/home/neurodebian/deb/gits/pkg-exppsy/pandas/pandas/computation/expressions.py", line 177, in _bool_arith_check
"dtypes" % op_str)
NotImplementedError: operator '*' not implemented for bool dtypes
```
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} | 7 | 2014-05-22T20:11:27Z | 2021-04-11T04:36:37Z | 2021-04-11T04:36:37Z | CONTRIBUTOR | null | related https://github.com/pydata/pandas/issues/7209
Might be a nicer error message (gist of this)
Hey you are trying to access a single element on AXIS 1, you can do it with a slice if you want, or use .loc to access the frame by row
```
In [3]: df = DataFrame(1,index=pd.date_range('20130101',periods=5),columns=['value'])
In [4]: df['20130102']
KeyError: '20130102'
In [5]: df['20130102':]
Out[5]:
value
2013-01-02 1
2013-01-03 1
2013-01-04 1
2013-01-05 1
```
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``` python
import pandas as pd
import numpy as np
## generate data
df = pd.DataFrame(np.random.rand(4,5), index=list('abcd'), columns=list('ABCDE'))
df.index.name = 'letters'
df = df.set_index(keys='E' , append=True)
## save to hdf5
h5name = 'tst.h5'
key = 'tst_key'
df.to_hdf(h5name, key,
mode='a', append=True,
data_columns = df.index.names+df.columns.tolist(),
index=False,
complevel=5, complib='blosc',
#expectedrows = expectedrows ,
)
## load part of df
cols2load = list('BCD')
print 'before loading: \n\t cols2load = {}'.format(cols2load)
df_ = pd.read_hdf(h5name, key, columns= cols2load)
print 'after loading: \n\t cols2load = {}'.format(cols2load)
```
The printed output:
> before loading:
> cols2load = ['B', 'C', 'D']
> after loading:
> cols2load = ['E', 'letters', 'B', 'C', 'D']
pd.**version** = '0.13.1'
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} | 1 | 2014-05-22T22:57:11Z | 2014-05-23T12:48:21Z | 2014-05-23T12:48:21Z | CONTRIBUTOR | null | http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#custom-business-days-experimental
http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#experimental-features
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closes #7169 .
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\+ added some entries in api.rst
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CLN: cleanup related to (GH7206) don't create the disabled properties at all for Series
related #7206
related #7213
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} | 156 | 2014-05-23T13:13:36Z | 2014-09-19T14:42:05Z | 2014-07-14T21:42:11Z | CONTRIBUTOR | null | This PR creates a `CategoricalBlock` as a first class internal object, on par with blocks like `Datetime,Timedelta,Object,Numeric` etc.
closes #5313
closes #5314
closes #3943
TODOS
- [x] test with `select_dtypes` #7434, https://github.com/jreback/pandas/commit/a43c6c041c7870c63992bc15b427adc35f7ab5d8
**Code Changes**
- [x] `factor_agg/group_agg` in `core/frame.py` -> look at unittests // a google search didn't turn up any questions/usage -> remove them?
- [x] Add a API change note about the group_agg and factor_agg
- [x] Add a API change note about Categorical.labels -> Categorical.codes
- [x] Printing level information when a Series of type categorical is printed?
- [x] Fix the remaining "FIXME" in tests
- [x] Groupby -> include each level, even if group is empty
- [x] Pivotable -> include each level, even if group is empty
- [x] `Series(categorical).describe()` / `Categorical.unique()` -> should this return all levels or only used levels?
- [x] Series(cat).describe() -> show information about the levels?
- [x] sort by index?
- [x] df.to_csv: fails due to a slicer error?
- [x] concat/append -> should retain categoricals
- [x] concat/append -> should raise on different levels
- [x] sorting a dataframe by a categorical variable does not use the level ordering
- [x] sorting by a unsortable categorical should not be possible and should raise
- [x] reorder_levels: raise if set(old_levels) != set(new_levels)?
- [x] min/max and numeric_only=True
- [x] df.to_hdf: fails due to `categorical.T` not implemented
- [x] `Category.describe()` with empty levels (will be fixed with groupby)
- [x] TST: apply -> look into it if/how it works -> should probably convert first and don't try to preserve the categorical
- [x] TST: def test_sort_dataframe(self): -> sort df, cats must also be sorted!
- [x] TST: Add tests about some more numpy function that should fail
- [x] labels vs. pointers: should the (internal) name of integer array be renamed? -> I would like to see a rename to something with a underscore and it would be nice to rename it to another name (`_pointers`, `_level_idx`, `_level_pointer`)
**Documentation**
- [x] Add release notes
- [x] Add a API change note about the old constructor mode
- [x] Document the Categorical methods
- [x] Link classes/methods in categorical.rst -> not done, just a link to API docs and in special cases.
- [x] Add Categorical API to API docs
**Future**
- [ ] HDF support? -> see above... #7621
- [ ] meta infos / "Contrasts": R's factor has a "contrast" property which indicates how this factor should be used in statistical models -> don't do it in the PR
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} | 0 | 2014-05-23T13:26:55Z | 2014-05-23T14:18:45Z | 2014-05-23T14:18:45Z | CONTRIBUTOR | null | This works in the non-multi-index case, so is a bug
http://stackoverflow.com/questions/23830248/potential-bug-pandas-multiindex-and-0-d-sized-values
```
np.random.seed(1234)
T = 100 # time
N = 80 # firms
TIndex = np.arange(0, T)
FIndex = np.arange(0, N)
index = pd.MultiIndex.from_product([TIndex, FIndex], names=['time', 'firm'])
df = pd.DataFrame(-999, columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
t, n = 0, 2
df.loc[(t,n), 'X'] = np.array(0)
```
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} | 6 | 2014-05-23T16:04:19Z | 2016-10-12T23:04:52Z | 2014-06-04T14:03:24Z | CONTRIBUTOR | null | Hi,
I have lxml, and when trying to parse html with tables through pandas I found some problems:
1. HTMLParser is set to `recover=False` contrary to lxml's default value. This is unfortunate because parser will fail on any problem in html page, like missing closing tag or similar, which IMHO happens too often to be justifiable. If such strict rules should be considered, I'd suggest parsing html document with default parser values, and then apply this restriction only on table fragments.
2. HTMLParser will almost always deliver wrong content on tables encoded in anything other then ascii. There is no encoding argument in `pandas.io.html.read_html()` and lxml doesn't do magic unless http-equiv attribute is correctly declared in html document, or encoding argument is passed to HTMLParser.
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} | 8 | 2014-05-23T16:40:37Z | 2016-02-12T14:30:24Z | 2016-02-12T14:30:24Z | CONTRIBUTOR | null | I posted this on https://groups.google.com/forum/#!topic/pydata/FnHBkkdBIFY.
In IPython, trying to .plot() a Series with a CustomBusinessDay frquency produces "ValueError: Unknown freqstr: C". See below. I imagine DataFrame.plot() would have a similar issue.
Am running http://pandas.pydata.org/pandas-build/dev/pandas-0.14.0rc1-51-gccd593f.win-amd64-py3.4.exe and matplotlib 1.2.0.
```
import pandas as pd
from pandas.tseries.offsets import CustomBusinessDay
s = pd.Series(range(100, 121),
index=pd.bdate_range(start='2014-05-01', end='2014-06-01',
freq=CustomBusinessDay(holidays=['2014-05-26'])))
s.plot()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-8a6b3fabc67e> in <module>()
3
4 s = pd.Series(range(100,121), index=pd.bdate_range(start='2014-05-01', end='2014-06-01', freq=CustomBusinessDay(holidays=['2014-05-26'])))
----> 5 s.plot()
C:\Python34\lib\site-packages\pandas\tools\plotting.py in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
2254 secondary_y=secondary_y, **kwds)
2255
-> 2256 plot_obj.generate()
2257 plot_obj.draw()
2258
C:\Python34\lib\site-packages\pandas\tools\plotting.py in generate(self)
900 self._compute_plot_data()
901 self._setup_subplots()
--> 902 self._make_plot()
903 self._add_table()
904 self._make_legend()
C:\Python34\lib\site-packages\pandas\tools\plotting.py in _make_plot(self)
1547 self._neg_prior = np.zeros(len(self.data))
1548
-> 1549 if self._is_ts_plot():
1550 data = self._maybe_convert_index(self.data)
1551 self._make_ts_plot(data)
C:\Python34\lib\site-packages\pandas\tools\plotting.py in _is_ts_plot(self)
1541 def _is_ts_plot(self):
1542 # this is slightly deceptive
-> 1543 return not self.x_compat and self.use_index and self._use_dynamic_x()
1544
1545 def _make_plot(self):
C:\Python34\lib\site-packages\pandas\tools\plotting.py in _use_dynamic_x(self)
1537 return False
1538
-> 1539 return (freq is not None) and self._is_dynamic_freq(freq)
1540
1541 def _is_ts_plot(self):
C:\Python34\lib\site-packages\pandas\tools\plotting.py in _is_dynamic_freq(self, freq)
1510 freq = get_base_alias(freq)
1511 freq = get_period_alias(freq)
-> 1512 return freq is not None and self._no_base(freq)
1513
1514 def _no_base(self, freq):
C:\Python34\lib\site-packages\pandas\tools\plotting.py in _no_base(self, freq)
1518 and isinstance(self.data.index, DatetimeIndex)):
1519 import pandas.tseries.frequencies as freqmod
-> 1520 base = freqmod.get_freq(freq)
1521 x = self.data.index
1522 if (base <= freqmod.FreqGroup.FR_DAY):
C:\Python34\lib\site-packages\pandas\tseries\frequencies.py in get_freq(freq)
67 def get_freq(freq):
68 if isinstance(freq, compat.string_types):
---> 69 base, mult = get_freq_code(freq)
70 freq = base
71 return freq
C:\Python34\lib\site-packages\pandas\tseries\frequencies.py in get_freq_code(freqstr)
105
106 base, stride = _base_and_stride(freqstr)
--> 107 code = _period_str_to_code(base)
108
109 return code, stride
C:\Python34\lib\site-packages\pandas\tseries\frequencies.py in _period_str_to_code(freqstr)
612 alias = _period_alias_dict[freqstr]
613 except KeyError:
--> 614 raise ValueError("Unknown freqstr: %s" % freqstr)
615
616 return _period_code_map[alias]
ValueError: Unknown freqstr: C
```
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... | closed | false | null | [] | null | 2 | 2014-05-23T22:29:51Z | 2014-07-30T01:45:36Z | 2014-05-23T22:44:09Z | NONE | null | For an ordered index, there is a pandas.Index.asof() method that returns the _most recent_ label to the label that's passed to it. However, in some cases it's useful to have a method that returns the _nearest_ label to the label that is passed to it. For example:
``` python
>>> holidays = pandas.to_datetime(['2014-01-01','2014-01-20','2014-02-17'])
>>> print holidays.nearest('2014-01-15')
2014-01-20 00:00:00
```
Thoughts?
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https://api.github.com/repos/pandas-dev/pandas/issues/7224 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7224/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7224/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7224/events | https://github.com/pandas-dev/pandas/issues/7224 | 34,223,779 | MDU6SXNzdWUzNDIyMzc3OQ== | 7,224 | PERF: Slow down in DF sort() functions from Pandas 0.12 to 0.13? | {
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} | [] | closed | false | null | [] | null | 2 | 2014-05-24T02:46:30Z | 2014-05-25T04:45:11Z | 2014-05-24T14:21:19Z | CONTRIBUTOR | null | I've noticed a major loss of performance in the use of DataFrame sort() functions when switching from Pandas 0.12.0 to Pandas 0.13.1. Does anyone know what it is due to?
For example let's execute the following code in both environments (see below for show_version() outputs):
``` python
from itertools import product
idx_values = list(product([0,1,2],range(100),range(700),range(5)))
idx = pd.MultiIndex.from_tuples(idx_values)
df = pd.DataFrame(np.array(np.random.random([len(idx),10])), idx)
print df
print df.index.is_lexsorted()
```
```
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 1050000 entries, (0, 0, 0, 0) to (2, 99, 699, 4)
Data columns (total 10 columns):
...
True
```
So far so good and there is no significant difference in speed. But now sorting the (already sorted) data yields:
``` python
timeit df.sortlevel()
```
```
10 loops, best of 3: 97 ms per loop # Pd0.12
10 loops, best of 3: 487 ms per loop # Pd0.13.1
```
The difference can be worse and reaches factors of 30+ with the following index for example:
``` python
idx2 = pd.MultiIndex.from_tuples(list(product(
[0,1,2],
pd.date_range('20140101','20140331'),
pd.date_range('20120101','20131231'),
range(5))))
df2 = pd.DataFrame(np.array(np.random.random([len(idx2),10])), idx2)
timeit df2.sortlevel()
```
```
10 loops, best of 3: 99 ms per loop # Pd0.12
1 loops, best of 3: 3.81 s per loop # Pd0.13.1
```
Notice that both indexes having roughly the same length, the execution time for the last 2 examples is similar under Pd0.12. However that is not the case for Pd0.13, which becomes yet much slower when using dates.
This also applies to sort(), inplace sorting, non-presorted data, and non-multiindex data. For example
``` python
idx3 = pd.date_range('1999-12-31', '1900-01-01', freq='-1D')
df3 = pd.DataFrame(np.array(np.random.random([len(idx3),10])), idx3)
timeit df3.sort()
```
```
100 loops, best of 3: 2.15 ms per loop # Pd0.12
100 loops, best of 3: 3.85 ms per loop # Pd0.13.1
```
Here is the output of show_versions(). Everything is identical in both environments except the 'pandas' line:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: ja_JP.UTF-8
pandas: 0.12.0 -> 0.13.1
Cython: 0.20
numpy: 1.8.0
scipy: 0.13.2
statsmodels: None
IPython: 1.2.1
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: 0.7.0
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: 0.9.1
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7225 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7225/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7225/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7225/events | https://github.com/pandas-dev/pandas/pull/7225 | 34,234,050 | MDExOlB1bGxSZXF1ZXN0MTYzMDU4MTE= | 7,225 | BUG: boxplot returns incorrect dict | {
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} | 4 | 2014-05-24T13:45:55Z | 2014-06-13T14:48:24Z | 2014-05-27T17:50:52Z | MEMBER | null | #7096 returns incorrect `dict` when number of columns and subplots are different.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas.util.testing as tm
n=100
gender = tm.choice(['Male', 'Female'], size=n)
classroom = tm.choice(['A', 'B', 'C'], size=n)
df = pd.DataFrame({'gender': gender,
'classroom': classroom,
'height': np.random.normal(66, 4, size=n),
'weight': np.random.normal(161, 32, size=n),
'category': np.random.randint(4, size=n)})
df.boxplot(by='classroom', return_type='axes')
# {'category': array([<matplotlib.axes.AxesSubplot object at 0x104700e90>,
# <matplotlib.axes.AxesSubplot object at 0x10671ca90>], dtype=object),
# 'height': array([<matplotlib.axes.AxesSubplot object at 0x106744dd0>,
# <matplotlib.axes.AxesSubplot object at 0x106766b50>], dtype=object)}
# This must be a dict with 3 keys ('category', 'height' and 'weight') which value is AxesSubplot (not numpy.array).
```
Also, `boxplot` sometimes return `OrderedDict` and sometimes `dict` inconsistently.
```
df.boxplot(by='classroom', return_type='dict')
# OrderedDict([('category', {'medians': [<matplotlib.lines.Line2D object at 0x106fe8c10>,
# ...
```
The fix makes `boxplot` to always return `OrderedDict` which has correct mapping of keys and values
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} | 3 | 2014-05-24T14:40:20Z | 2014-06-13T03:13:25Z | 2014-05-30T18:12:20Z | MEMBER | null | Actually not a bug, but allows `bar` and `barh` plot to accept `bottom` and `left` kw respectively to specify the starting point.
Currently, passing `bottom` or `left` keyword results in `TypeError`
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5, 5))
df.plot(kind='bar', bottom=1, ax=axes[0])
# TypeError: bar() got multiple values for keyword argument 'bottom'
```
### After fix
```
import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 2)
df = pd.DataFrame(np.random.randn(5, 5))
df.plot(kind='bar', bottom=1, ax=axes[0])
df2 = pd.DataFrame(np.random.rand(5, 5))
df2.plot(kind='barh', left=[1, 2, 3, 4, 5], stacked=True, ax=axes[1])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7227 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7227/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7227/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7227/events | https://github.com/pandas-dev/pandas/issues/7227 | 34,235,745 | MDU6SXNzdWUzNDIzNTc0NQ== | 7,227 | BUG: resample/groupby with NaT in the grouper | {
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} | 2 | 2014-05-24T15:07:11Z | 2014-06-14T14:00:51Z | 2014-06-14T14:00:51Z | CONTRIBUTOR | null | related to this: https://github.com/pydata/pandas/issues/6992
These are broken
```
In [70]: df.set_index('A').resample('M',how='count')
ValueError: month must be in 1..12
In [73]: df.groupby(pd.Grouper(freq='MS',key='A')).count()
ValueError: month must be in 1..12
```
Separately, this should not also return the A column
```
In [74]: df.dropna(how='any',subset=['A']).groupby(pd.Grouper(key='A',freq='M')).count()
Out[74]:
A B
A
2014-10-31 3 3
2014-11-30 2 2
2014-12-31 1 1
```
```
In [57]: data
Out[57]:
0 2014-10-01
1 2014-10-01
2 2014-10-31
3 2014-11-15
4 2014-11-30
5 NaT
6 2014-12-01
dtype: datetime64[ns]
In [58]: df = DataFrame(dict(A = data, B = np.arange(len(data))))
```
This is a work-around
```
In [59]: df.dropna(how='any',subset=['A']).set_index('A').resample('M',how='count')
Out[59]:
B
A
2014-10-31 3
2014-11-30 2
2014-12-31 1
```
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} | 0 | 2014-05-24T15:09:20Z | 2014-06-19T19:27:10Z | 2014-06-19T19:27:10Z | CONTRIBUTOR | null | ```
In [76]: data
Out[76]:
0 2014-10-01
1 2014-10-01
2 2014-10-31
3 2014-11-15
4 2014-11-30
5 NaT
6 2014-12-01
dtype: datetime64[ns]
```
```
In [78]: DatetimeIndex(data).to_period(freq='M')
Out[78]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: M
[2014-10, ..., 2014-12]
length: 7
In [79]: DatetimeIndex(data).to_period(freq='M').values
Out[79]: array([ 537, 537, 537, 538, 538, 3507, 539])
In [80]: DatetimeIndex(data).to_period(freq='M').to_timestamp()
Out[80]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-10-01, ..., 2014-12-01]
Length: 7, Freq: None, Timezone: None
In [81]: DatetimeIndex(data).to_period(freq='M').to_timestamp().values
Out[81]:
array(['2014-09-30T20:00:00.000000000-0400',
'2014-09-30T20:00:00.000000000-0400',
'2014-09-30T20:00:00.000000000-0400',
'2014-10-31T20:00:00.000000000-0400',
'2014-10-31T20:00:00.000000000-0400',
'2262-03-31T20:00:00.000000000-0400',
'2014-11-30T19:00:00.000000000-0500'], dtype='datetime64[ns]')
```
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} | 5 | 2014-05-24T15:14:25Z | 2016-10-12T23:04:52Z | 2014-06-21T20:15:09Z | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_thousands_macau_index_col (pandas.io.tests.test_html.TestReadHtml)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\tests\test_html.py", line 410, in test_thousands_macau_index_col
dfs = self.read_html(macau_data, index_col=0, header=0)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\tests\test_html.py", line 96, in read_html
return read_html(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\html.py", line 840, in read_html
parse_dates, tupleize_cols, thousands, attrs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\html.py", line 709, in _parse
raise_with_traceback(retained)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\compat\__init__.py", line 705, in raise_with_traceback
raise exc.with_traceback(traceback)
UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 6275: character maps to <undefined>
======================================================================
ERROR: test_thousands_macau_stats (pandas.io.tests.test_html.TestReadHtml)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\tests\test_html.py", line 401, in test_thousands_macau_stats
attrs={'class': 'style1'})
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\tests\test_html.py", line 96, in read_html
return read_html(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\html.py", line 840, in read_html
parse_dates, tupleize_cols, thousands, attrs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\io\html.py", line 709, in _parse
raise_with_traceback(retained)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\compat\__init__.py", line 705, in raise_with_traceback
raise exc.with_traceback(traceback)
UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 6275: character maps to <undefined>
----------------------------------------------------------------------
Ran 7205 tests in 459.919s
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.0.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: None
nose: 1.3.0
Cython: 0.20.1
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: None
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3
matplotlib: 1.3.1
openpyxl: 2.0.3
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.2
pymysql: None
psycopg2: None
```
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} | 1 | 2014-05-24T15:27:13Z | 2014-06-17T12:35:42Z | 2014-06-17T12:35:42Z | CONTRIBUTOR | null | wheels aleady posted, update to current version in requirements-3.4.txt
compiled
```
lxml==3.3.5
numexpr==2.4
```
python only
```
html5lib
beautifulsoup4
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7231 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7231/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7231/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7231/events | https://github.com/pandas-dev/pandas/issues/7231 | 34,241,777 | MDU6SXNzdWUzNDI0MTc3Nw== | 7,231 | Docs do not compile | {
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] | closed | false | null | [] | null | 3 | 2014-05-24T20:14:24Z | 2014-05-25T12:19:39Z | 2014-05-25T01:51:09Z | CONTRIBUTOR | null | Using pandas v 0.13.0-354-g63ce94d, and sphinx v 1.1.3, Docs fail to compile. Output is as follows.
```
(pandas)[dev_box@localhost doc]$ python make.py html
Running Sphinx v1.1.3
loading pickled environment... failed: [Errno 2] No such file or directory: '/home/dev_box/python_virt/pandas/pandas/src/pandas/doc/build/doctrees/environment.pickle'
[autosummary] generating autosummary for: 10min.rst, api.rst, basics.rst, comparison_with_r.rst, comparison_with_sql.rst, computation.rst, contributing.rst, cookbook.rst, dsintro.rst, ecosystem.rst, ..., r_interface.rst, release.rst, remote_data.rst, reshaping.rst, rplot.rst, sparse.rst, timeseries.rst, tutorials.rst, visualization.rst, whatsnew.rst
loading intersphinx inventory from http://docs.python.org/objects.inv...
loading intersphinx inventory from http://statsmodels.sourceforge.net/devel/objects.inv...
building [html]: targets for 1177 source files that are out of date
updating environment: 1177 added, 0 changed, 0 removed
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "/home/dev_box/python_virt/pandas/pandas/src/pandas/doc/sphinxext/ipython_directive.py", line 322, in process_input_line
source_raw = splitter.source_raw_reset()[1]
AttributeError: 'IPythonInputSplitter' object has no attribute 'source_raw_reset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/sphinx/cmdline.py", line 189, in main
app.build(force_all, filenames)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/sphinx/application.py", line 204, in build
self.builder.build_update()
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/sphinx/builders/__init__.py", line 196, in build_update
'out of date' % len(to_build))
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/sphinx/builders/__init__.py", line 216, in build
purple, length):
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/sphinx/builders/__init__.py", line 120, in status_iterator
for item in iterable:
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/sphinx/environment.py", line 613, in update_generator
self.read_doc(docname, app=app)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/sphinx/environment.py", line 761, in read_doc
pub.publish()
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/core.py", line 217, in publish
self.settings)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/readers/__init__.py", line 72, in read
self.parse()
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/readers/__init__.py", line 78, in parse
self.parser.parse(self.input, document)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/__init__.py", line 172, in parse
self.statemachine.run(inputlines, document, inliner=self.inliner)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 170, in run
input_source=document['source'])
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/statemachine.py", line 239, in run
context, state, transitions)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/statemachine.py", line 460, in check_line
return method(match, context, next_state)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 2301, in explicit_markup
self.explicit_list(blank_finish)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 2331, in explicit_list
match_titles=self.state_machine.match_titles)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 319, in nested_list_parse
node=node, match_titles=match_titles)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 195, in run
results = StateMachineWS.run(self, input_lines, input_offset)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/statemachine.py", line 239, in run
context, state, transitions)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/statemachine.py", line 460, in check_line
return method(match, context, next_state)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 2604, in explicit_markup
nodelist, blank_finish = self.explicit_construct(match)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 2311, in explicit_construct
return method(self, expmatch)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 2054, in directive
directive_class, match, type_name, option_presets)
File "/home/dev_box/python_virt/pandas/pandas/lib/python3.3/site-packages/docutils/parsers/rst/states.py", line 2103, in run_directive
result = directive_instance.run()
File "/home/dev_box/python_virt/pandas/pandas/src/pandas/doc/sphinxext/ipython_directive.py", line 811, in run
rgxin, rgxout, promptin, promptout = self.setup()
File "/home/dev_box/python_virt/pandas/pandas/src/pandas/doc/sphinxext/ipython_directive.py", line 770, in setup
self.shell = EmbeddedSphinxShell(exec_lines, self.state)
File "/home/dev_box/python_virt/pandas/pandas/src/pandas/doc/sphinxext/ipython_directive.py", line 306, in __init__
self.process_input_line(line, store_history=False)
File "/home/dev_box/python_virt/pandas/pandas/src/pandas/doc/sphinxext/ipython_directive.py", line 326, in process_input_line
buflist = self.cout.buflist
AttributeError: '_io.StringIO' object has no attribute 'buflist'
> /home/dev_box/python_virt/pandas/pandas/src/pandas/doc/sphinxext/ipython_directive.py(326)process_input_line()
-> buflist = self.cout.buflist
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7232 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7232/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7232/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7232/events | https://github.com/pandas-dev/pandas/pull/7232 | 34,241,846 | MDExOlB1bGxSZXF1ZXN0MTYzMDg2MzU= | 7,232 | BUG: multi-index output formatting is buggy (GH7174) | {
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} | 16 | 2014-05-24T20:17:51Z | 2014-06-27T11:31:39Z | 2014-05-25T16:06:45Z | CONTRIBUTOR | null | Closes #7174
Adjusts the truncation in the notebook for multiindex dfs. Both rows and columns are affected.
http://nbviewer.ipython.org/gist/bjonen/492fea9559fd73edf579
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} | 9 | 2014-05-24T20:19:20Z | 2014-10-01T23:41:39Z | 2014-08-19T12:50:59Z | NONE | null | With a recent checkout from master (bfd5348d824a721dd0d896bb06e63e4ad801ba51), running this
```
python3.4 `which nosetests` pandas
```
gives me this:
```
======================================================================
ERROR: test_dateindex_conversion (pandas.tseries.tests.test_converter.TestDateTimeConverter)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python3.4/dist-packages/pandas/tseries/tests/test_converter.py", line 77, in test_dateindex_conversion
xp = converter.dates.date2num(dateindex)
File "/usr/local/lib/python3.4/dist-packages/matplotlib/dates.py", line 310, in date2num
return _to_ordinalf_np_vectorized(d)
File "/usr/local/lib/python3.4/dist-packages/numpy/lib/function_base.py", line 1573, in __call__
return self._vectorize_call(func=func, args=vargs)
File "/usr/local/lib/python3.4/dist-packages/numpy/lib/function_base.py", line 1633, in _vectorize_call
ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
File "/usr/local/lib/python3.4/dist-packages/numpy/lib/function_base.py", line 1597, in _get_ufunc_and_otypes
outputs = func(*inputs)
File "/usr/local/lib/python3.4/dist-packages/matplotlib/dates.py", line 204, in _to_ordinalf
base = float(dt.toordinal())
AttributeError: 'numpy.datetime64' object has no attribute 'toordinal'
----------------------------------------------------------------------
Ran 7152 tests in 4107.297s
FAILED (SKIP=381, errors=1)
```
Looks like it's actually matplotlib's fault -- I notice there's a relevant ticket here:
https://github.com/matplotlib/matplotlib/issues/2259
But is this test expected to fail, as a result of that? And if so, should it be marked as skipped?
I'm using a very recent checkout of matplotlib (https://github.com/matplotlib/matplotlib/commit/4b1bd6301d69f856deca9c614af563f5fb4d1e90).
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} | 5 | 2014-05-24T22:12:03Z | 2014-07-09T12:37:50Z | 2014-07-07T19:27:56Z | MEMBER | null | Includes 3 minor fixes for `hist`.
- `DataFrame.hist` raises `AttributeError` when the target column specifiedwith `by` kw only contains a single value.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas.util.testing as tm
n=100
gender = tm.choice(['Male', 'Female'], size=n)
classroom = tm.choice(['A', 'B', 'C'], size=n)
single = tm.choice(['S'], size=n)
df = pd.DataFrame({'gender': gender,
'classroom': classroom,
'single': single,
'height': np.random.normal(66, 4, size=n),
'weight': np.random.normal(161, 32, size=n),
'category': np.random.randint(4, size=n)})
df.hist(by='single')
# AttributeError: 'AxesSubplot' object has no attribute 'ravel'
```
- `hist` can accept `rot` kw only when `by` is specified, but `rot` actually does nothing. I understand `rot` value should be used for `xticklabels` rotation if `xrot` is not specified?
```
df.hist(rot=45)
# AttributeError: Unknown property rot
# -> This is OK because we can use xrot kw
df.hist(by='classroom', rot=45)
# It raises no error, but ticks are NOT rotated.
```
- `hist` always displays `xticklabels` on all axes even if specifying `sharex=True`. Other plots only draws `xticklabels` on bottom axes if `sharex` is True.
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"following_url": "https://api.github.com/users/TomAugspurger/following{/other_u... | null | 3 | 2014-05-25T15:00:55Z | 2015-03-08T16:49:48Z | 2015-03-08T16:49:48Z | CONTRIBUTOR | null | 
It is used correctly in `str(df)` and the terminal repr; I think just `_html` is the problem. I'll see if I can track it down quick.
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https://api.github.com/repos/pandas-dev/pandas/issues/7236 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7236/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7236/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7236/events | https://github.com/pandas-dev/pandas/pull/7236 | 34,311,632 | MDExOlB1bGxSZXF1ZXN0MTYzMzg3MjA= | 7,236 | DOC: clean-up docstrings of option functions | {
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} | [] | closed | false | null | [] | null | 0 | 2014-05-26T14:33:11Z | 2014-06-12T21:41:35Z | 2014-05-26T20:22:27Z | MEMBER | null | Some clean-up after my last additions to api.rst to get rid off all warnings:
- some clean up of api.rst itself
- some clean-up of the docstrings of the option functions so they render nicely with sphinx
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https://api.github.com/repos/pandas-dev/pandas/issues/7237 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7237/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7237/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7237/events | https://github.com/pandas-dev/pandas/issues/7237 | 34,316,818 | MDU6SXNzdWUzNDMxNjgxOA== | 7,237 | Bug: Selection of Multi-Index Columns | {
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} | [] | closed | false | null | [] | null | 2 | 2014-05-26T16:00:50Z | 2014-05-26T21:06:39Z | 2014-05-26T19:16:12Z | NONE | null | Hi everybody,
lets take the following DataFrame:
```
df = pd.DataFrame({
'Region' : ['A', 'A', 'C', 'C', 'C', 'B'],
'Store' : ['A', ' B ', 'A ', 'B ', 'A ', 'A'],
'Quantity': [1, 1, 2, 3, 4, 5],
'Date' : [
'2009-04-05', '2009-04-05', '2010-05-05', '2009-08-05',
'2011-02-05', '2009-04-05']})
df['Date'] = df['Date'].apply(lambda x: pd.to_datetime(x))
df.set_index(['Date','Region','Store'], inplace=True)
df_1 = df.unstack(level=[1,2])
```
Now when I try to query:
```
df_1['Quantity']['A']['A']
```
or
```
df_1['Quantity', 'A', 'A']
```
I get the correct subset, but if I query
```
df_1['Quantity']['A']['B']
```
pandas raises an exception.
What do you think about this?
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} | 4 | 2014-05-26T20:15:08Z | 2014-05-26T20:36:18Z | 2014-05-26T20:28:19Z | MEMBER | null | ```
In [1]: sio = StringIO()
In [2]: pd.to_msgpack(sio, 'a', 'b')
In [3]: pd.read_msgpack(sio)
Out[3]: []
```
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Updates 10min.rst to demonstrate isin
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```
>>> pd.Series([], dtype=str).str.extract('()')
Series([], dtype: float64)
>>> pd.Series([], dtype=str).str.extract('()()')
ValueError: Shape of passed values is (0, 0), indices imply (2, 0)
```
I expect it to return an empty DataFrame with the correct columns.
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"color": "AD... | closed | false | null | [] | null | 2 | 2014-05-27T08:45:54Z | 2014-05-27T12:39:18Z | 2014-05-27T12:26:42Z | MEMBER | null | Pandas specifies an Index API in the docs: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-internal-details
However, it's not currently possible (as far as I can tell) to create an object that can be used as a pandas Index unless it's an actual `pandas.Index` subclass.
From the perspective of someone who would like to be able to create custom Index-like objects, I would prefer not to need to subclass `pandas.Index` so the implementation details of my index can be decoupled from pandas. I might still use a pandas.Index internally for speed, but in my experience composition is generally easier to reason about than inheritance.
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} | 8 | 2014-05-27T12:16:05Z | 2019-06-17T19:56:37Z | null | CONTRIBUTOR | null | hdf5 read still fails to read back some utf8 encoded strings back correctly.
I written a [test script](https://gist.github.com/wabu/79ffa83dd368b3a15438) that:
- puts random valid utf8 strings into a hdf5 file
- reads them back with pytables and decodes them
- uses HDFStore.select to read them
The hdfstore often fails, but the data is stored correctly inside the hdf5 file.
Here's an example:
```
orig h5ed
0 '7PFKۑw\x1d\x1bxyv\x03;S' '\x002\x003\x004\x005\x006\x007\x008\x009'
1 '\x12漣\x03_L' ':\x00;\x00<\x00=\x00>\x00?\x00@\x00A\x00B'
2 'ӉwxIy⣂&QC\x14' '\x00C\x00D\x00E\x00F\x00G\x00H\x00I\x00J'
3 'u\x01\x10aۋZ9<s' '\x00T\x00U\x00V\x00W\x00X\x00Y\x00Z\x00['
4 '!\x0b\x06зt\x13QMR' '\x00c'
```
see #6505 for a previous issue on the topic
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} | 16 | 2014-05-27T14:08:54Z | 2014-06-17T04:57:02Z | 2014-05-27T19:50:10Z | MEMBER | null | closes #7210
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} | 0 | 2014-05-27T15:53:30Z | 2014-06-02T16:33:13Z | 2014-06-02T16:33:13Z | MEMBER | null | right now `boto` tries to authenticate and therefore public buckets aren't readable, but you can read them with `urllib2.urlopen(url)`
example:
this fails if you don't have your credentials set up
```
In [12]: df = pd.read_csv('s3://nypug/tips.csv')
```
but this works:
```
In [15]: url = 'https://s3.amazonaws.com/nypug/tips.csv'
In [16]: pd.read_csv(url)
```
kind of trivial but would be nice to be consistent
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} | 3 | 2014-05-27T16:05:46Z | 2014-05-27T19:58:40Z | 2014-05-27T17:49:27Z | CONTRIBUTOR | null | Was trying to build a fresh pkg of current master 0.14.0~rc1+git73-g8793356 and got hiccup with
```
======================================================================
ERROR: test_to_string_truncate_indices (pandas.tests.test_format.TestDataFrameFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0~rc1+git73-g8793356/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_format.py", line 414, in test_to_string_truncate_indices
print(df)
UnicodeEncodeError: 'ascii' codec can't encode characters in position 177-178: ordinal not in range(128)
----------------------------------------------------------------------
Ran 6694 tests in 557.813s
FAILED (SKIP=482, errors=1)
```
please do not "print" in the tests... makes it harder to get through the output to the actual report on failures (and `assert("blah" in str(df))` and `assert("bleh" in repr(df))` would be more functional)
```
> python3 --version
Python 3.3.5
> locale
LANG=C
LANGUAGE=
LC_CTYPE="C"
LC_NUMERIC="C"
LC_TIME="C"
LC_COLLATE="C"
LC_MONETARY="C"
LC_MESSAGES="C"
LC_PAPER="C"
LC_NAME="C"
LC_ADDRESS="C"
LC_TELEPHONE="C"
LC_MEASUREMENT="C"
LC_IDENTIFICATION="C"
LC_ALL=C
```
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} | 0 | 2014-05-27T16:26:28Z | 2014-06-26T12:58:15Z | 2014-05-27T17:49:27Z | CONTRIBUTOR | null | closes #7247
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} | 6 | 2014-05-27T17:59:08Z | 2018-03-07T09:53:48Z | 2018-03-07T09:53:42Z | CONTRIBUTOR | null | no particular order, prob need a bit of description; these are suggestions, if people think they don't belong ok then
https://bitbucket.org/zzzeek/calchipan
https://github.com/keeganmccallum/sql4pandas
https://github.com/yhat/pandasql/
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} | 9 | 2014-05-27T19:55:54Z | 2014-05-28T13:54:34Z | 2014-05-28T13:25:36Z | MEMBER | null | I just ran `./test_perf.sh -b v0.13.0 -t HEAD -r '.*query.*'` and this is what I got:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
query_datetime_index | 7.5057 | 375.5414 | 0.0200 |
query_datetime_series | 7.4463 | 12.2663 | 0.6071 |
query_with_boolean_selection | 14.4973 | 17.2227 | 0.8418 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
want to make sure this is expected
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`temp.boxplot(sym="k.", by='column_1', return_type='both')`
I get:
```
> boxplot(data, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)
2369 elif return_type == 'both':
2370 ret = dict((k, BP(ax=ax, lines=line)) for
-> 2371 (k, line), ax in zip(d.items(), axes))
2372 else:
2373 if layout is not None:
TypeError: izip argument #2 must support iteration
```
This is with `master`
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} | 1 | 2014-05-28T01:34:14Z | 2014-05-28T16:58:42Z | 2014-05-28T16:58:23Z | CONTRIBUTOR | null | while building 0.14.0~rc1+git79-g1fa5dd4 , fails on wheezy:
```
======================================================================
FAIL: test_read_dta2 (pandas.io.tests.test_stata.TestStata)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0~rc1+git79-g1fa5dd4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_stata.py", line 152, in test_read_dta2
tm.assert_frame_equal(parsed_114, expected)
File "/tmp/buildd/pandas-0.14.0~rc1+git79-g1fa5dd4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 585, in assert_frame_equal
check_exact=check_exact)
File "/tmp/buildd/pandas-0.14.0~rc1+git79-g1fa5dd4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 532, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2550)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1873)
File "testing.pyx", line 107, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2092)
AssertionError: First object is not null, second is null: 1970-01-17 88:00:00 != 1970-04-16 64:12:43.145224
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.14.0.dev
nose: 1.1.2
Cython: 0.15.1
numpy: 1.6.2
scipy: 0.10.1
statsmodels: 0.5.0
IPython: None
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.3.1
numexpr: 2.0.1
matplotlib: 1.1.1rc2
openpyxl: 1.7.0
xlrd: 0.6.1
xlwt: 0.7.4
xlsxwriter: None
lxml: None
bs4: 4.1.0
html5lib: 0.95-dev
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 2 | 2014-05-28T04:31:01Z | 2014-05-28T14:57:34Z | 2014-05-28T14:57:34Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_grouped_box_return_type (pandas.tests.test_graphics.TestDataFrameGroupByPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0~rc1+git79-g1fa5dd4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 2286, in test_grouped_box_return_type
self._check_box_dict(returned, t, klass, categories2)
File "/tmp/buildd/pandas-0.14.0~rc1+git79-g1fa5dd4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 2252, in _check_box_dict
self.assertEqual(sorted(returned.keys()), sorted(expected_keys))
AssertionError: Lists differ: ['A', 'C', 'D', 'E', 'F', 'G',... != ['A', 'B', 'C', 'D', 'E', 'F',...
First differing element 1:
C
B
Second list contains 1 additional elements.
First extra element 9:
J
- ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
+ ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
? +++++
```
## INSTALLED VERSIONS
commit: None
python: 2.7.4.final.0
python-bits: 32
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.14.0.dev
nose: 1.1.2
Cython: 0.17.4
numpy: 1.7.1
scipy: 0.11.0
statsmodels: 0.5.0
IPython: None
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.4.0
numexpr: 2.0.1
matplotlib: 1.2.1
openpyxl: 1.7.0
xlrd: 0.6.1
xlwt: 0.7.4
xlsxwriter: None
lxml: None
bs4: 4.1.2
html5lib: 0.95-dev
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
```
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} | [] | closed | false | null | [] | null | 1 | 2014-05-28T05:30:28Z | 2014-07-19T06:21:48Z | 2014-05-28T14:52:10Z | CONTRIBUTOR | null | Just a stub for 0.14.1 (?) release notes. Not in index yet.
This look ok @jreback ? cc #6318
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} | 7 | 2014-05-28T07:17:22Z | 2014-05-30T23:32:33Z | 2014-05-30T23:32:33Z | CONTRIBUTOR | null | Consider allowing negative values for `loc` parameter of `DatetimeIndex.insert(loc, item)`. Currently, negative parameters result in an "off by 1" return value. For example:
```
In [ 1]: index = pandas.DatetimeIndex(start = '01/01/2000', freq = 'B', periods = 5)
In [ 2]: dt = pandas.Timestamp('01/01/2013')
In [ 3]: new_ind = index.insert(-1, dt)
In [ 4]: new_ind[-1]
Out[ 4]: Timestamp('2000-01-07 00:00:00', tz=None)
```
But:
```
In [ 5]: new_ind[-2]
Out[ 5]: Timestamp('2013-01-01 00:00:00', tz=None)
```
I can put together a quick fork if it's a change you'd like. If not, I think it makes sense to change the docstring to say that "negative values are not allowed."
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} | [] | closed | false | null | [] | null | 2 | 2014-05-28T09:05:31Z | 2014-06-22T15:28:06Z | 2014-06-22T15:28:06Z | CONTRIBUTOR | null | When you do apply a reduction operation on a Panel with axis > 0, it causes the returned panel to have its axes transposed compared to the original. This is how it behaves in an example in the documentation, although I cannot find an explicit mention of it (if it isn't mentioned, perhaps it should be?).
However, this is not the case with Panel4D. For all axis values, the input axis order and the output axis order are the same.
This means code that works with reduction operations has to make a special case depending on the axis and the number of dimensions of the object.
How to reproduce the issue:
Setup:
``` python
>>> import pandas as pd
>>> import numpy as np
>>>
>>> arr4 = np.random.rand(5, 7, 11, 13)
>>> arr3 = arr4[:,:,:,0]
>>>
>>> pan4 = pd.Panel4D(arr4)
>>> pan3 = pd.Panel(arr3)
```
This is all good:
``` python
>>> arr3.sum(0).shape == pan3.sum(0).shape
True
>>> arr4.sum(0).shape == pan4.sum(0).shape
True
>>> arr3.sum(0).shape
(7,11)
>>> pan3.sum(0).shape
(7,11)
>>> arr4.sum(0).shape
(7,11,13)
>>> pan4.sum(0).shape
(7,11,13)
```
Now try the same thing with axis=1:
``` python
>>> arr3.sum(1).shape == pan3.sum(1).shape
False
>>> arr4.sum(1).shape == pan4.sum(1).shape
True
>>> arr3.sum(1).shape
(5,11)
>>> pan3.sum(1).shape
(11,5)
>>> arr4.sum(1).shape
(5,11,13)
>>> pan4.sum(1).shape
(5,11,13)
```
And axis=2:
``` python
>>> arr3.sum(2).shape == pan3.sum(2).shape
False
>>> arr4.sum(2).shape == pan4.sum(2).shape
True
>>> arr3.sum(2).shape
(5,7)
>>> pan3.sum(2).shape
(7,5)
>>> arr4.sum(2).shape
(5,7,13)
>>> pan4.sum(2).shape
(5,7,13)
```
And axis=3 for Panel4D:
``` python
>>> arr4.sum(3).shape == pan4.sum(3).shape
True
>>> arr4.sum(3).shape
(5,7,11)
>>> pan4.sum(3).shape
(5,7,11)
```
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} | 8 | 2014-05-28T09:07:25Z | 2021-04-11T04:37:33Z | null | MEMBER | null | Seeing this SO question: http://stackoverflow.com/questions/23896088/dropping-rows-in-dataframe-if-found-in-another-one/, I was wondering if this is something that could be provided as functionality to the `.isin()` method.
The problem at the moment to use `isin` to check for the occurence of a full row in a DataFrame is that a) `isin` checks for the values in each column seperately, indepenently of the values in other columns (so you cannot check if the values occur together in the same row) and b) `isin` also checks if the index label matches.
Or are there better ways to check for the occurence of a full row in a DataFrame?
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} | [] | closed | false | null | [] | null | 1 | 2014-05-28T12:03:13Z | 2014-05-28T12:22:13Z | 2014-05-28T12:22:13Z | NONE | null | The example below creates a dataframe df, adds a new boolean column D with False values, then uses groupby and idxmax to select some rows in which to set column D to True. After doing so, the dataframe is in a strange state where direct access to the column via `df.D` or `df['D']` shows the updated values, but printing or saving the whole dataframe shows the original false values.
``` python
In [1]: import pandas as pd
In [2]: pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.1post0
numpy: 1.8.1
scipy: 0.13.3
statsmodels: None
IPython: 1.2.1
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
bq: None
apiclient: None
In [3]: df = pd.DataFrame({
...: 'A':[1, 2, 2, 2, 2],
...: 'B':[10, 30, 20, 60, 40],
...: 'C':[False, False, False, False, False]},
...: columns=['A', 'B', 'C'])
In [4]: df['D'] = False
In [5]: df['D'][list(df.groupby('A')['B'].idxmax().values)] = True
In [6]: df.D
Out[6]:
0 True
1 False
2 False
3 True
4 False
Name: D, dtype: bool
In [7]: df
Out[7]:
A B C D
0 1 10 False False
1 2 30 False False
2 2 20 False False
3 2 60 False False
4 2 40 False False
[5 rows x 4 columns]
In [8]: df.values
Out[8]:
array([[1, 10, False, False],
[2, 30, False, False],
[2, 20, False, False],
[2, 60, False, False],
[2, 40, False, False]], dtype=object)
```
If I remove column C (which isn't used), the effect disappears and the dataframe appears to be updated like I expected.
If I split out the indexing expression on line 5, as below, the effect disappears:
``` python
indexes = list(df.groupby('A')['B'].idxmax().values)
df['D'][indexes] = True
```
The effect also disappears if I only split out the groupby invocation:
``` python
group = df.groupby('A')
df['D'][list(group['B'].idxmax().values)] = True
```
In all cases, I expect the dataframe to have the same True values when printed as are observed by directly accessing the column. Failing that, an exception that explains what I'm doing wrong.
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https://api.github.com/repos/pandas-dev/pandas/issues/7260 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7260/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7260/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7260/events | https://github.com/pandas-dev/pandas/pull/7260 | 34,466,100 | MDExOlB1bGxSZXF1ZXN0MTY0MjY4NTA= | 7,260 | TST/BUG: Fix grouped_box_return_type | {
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} | 2 | 2014-05-28T13:30:58Z | 2014-07-16T09:07:45Z | 2014-05-28T14:57:34Z | MEMBER | null | Closes #7254.
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https://api.github.com/repos/pandas-dev/pandas/issues/7261 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7261/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7261/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7261/events | https://github.com/pandas-dev/pandas/issues/7261 | 34,467,040 | MDU6SXNzdWUzNDQ2NzA0MA== | 7,261 | API: Should Index.min and max use nanmin and nanmax? | {
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} | 1 | 2014-05-28T13:42:55Z | 2014-05-30T14:24:07Z | 2014-05-30T14:24:07Z | MEMBER | null | Index and Series `min` and `max` handles `nan` and `NaT` differently. Even though `min` and `max` are defined in `IndexOpsMixin`, `Series` doesn't use them and use `NDFrame` definitions.
```
pd.Index([np.nan, 1.0]).min()
# nan
pd.Index([np.nan, 1.0]).max()
# nan
pd.DatetimeIndex([pd.NaT, '2011-01-01']).min()
# NaT
pd.DatetimeIndex([pd.NaT, '2011-01-01']).max()
#2011-01-01 00:00:00
# Series excludes nan and NaT
pd.Series([np.nan, 1.0]).min()
#1.0
pd.Series([np.nan, 1.0]).max()
#1.0
pd.Series([pd.NaT, pd.Timestamp('2011-01-01')]).min()
#2011-01-01 00:00:00
pd.Series([pd.NaT, pd.Timestamp('2011-01-01')]).max()
#2011-01-01 00:00:00
```
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] | closed | false | null | [] | null | 6 | 2014-05-28T13:58:06Z | 2014-05-28T16:06:09Z | 2014-05-28T14:23:08Z | CONTRIBUTOR | null | The `align` kwarg of `Axes.bar{h}` will take care of all of that logic for you. Making it so that the tick labels do not match the data coordinates cause problems when trying to plot overlays (see http://stackoverflow.com/q/23909249/380231).
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} | 16 | 2014-05-28T14:09:09Z | 2014-06-20T02:22:23Z | 2014-06-19T14:24:51Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_class_ops (pandas.tseries.tests.test_timeseries.TestTimestamp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0~rc1+git79-g1fa5dd4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_timeseries.py", line 2711, in test_class_ops
compare(Timestamp.now('UTC'),datetime.now(pytz.timezone('UTC')))
File "/tmp/buildd/pandas-0.14.0~rc1+git79-g1fa5dd4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_timeseries.py", line 2708, in compare
self.assertEqual(int(Timestamp(x).value/1e9), int(Timestamp(y).value/1e9))
AssertionError: 1401254445 != 1401254446
----------------------------------------------------------------------
Ran 7152 tests in 664.150s
```
```
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.14.0.dev
nose: 1.3.1
Cython: 0.20.1post0
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: None
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 3.1.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7264/events | https://github.com/pandas-dev/pandas/pull/7264 | 34,479,612 | MDExOlB1bGxSZXF1ZXN0MTY0MzUxNjk= | 7,264 | DOC/TST: small change to 10min.rst / fixes for (GH7252, GH7263) | {
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} | 0 | 2014-05-28T15:50:21Z | 2014-06-19T12:45:44Z | 2014-05-28T16:58:23Z | CONTRIBUTOR | null | TST: fix stata issue with NaT (GH7252)
closes #7252
closes #7263
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} | 0 | 2014-05-28T17:55:36Z | 2014-05-28T18:56:10Z | 2014-05-28T18:56:10Z | CONTRIBUTOR | null | As here
http://nipy.bic.berkeley.edu/builders/pandas-py2.x-sid-sparc/builds/768/steps/shell_7/logs/stdio
on a sparc box (which I think some of you have access to) or on debian build boxes on powerpc and s390x:
https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=powerpc&ver=0.14.0~rc1%2Bgit79-g1fa5dd4-1&stamp=1401244093
```
======================================================================
FAIL: test_value_counts_unique_nunique (pandas.tests.test_base.TestIndexOps)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py2_x-sid-sparc/build/venv/lib/python2.7/site-packages/pandas-0.14.0rc1_79_g1fa5dd4-py2.7-linux-sparc64.egg/pandas/tests/test_base.py", line 271, in test_value_counts_unique_nunique
self.assertTrue(result[0] is pd.NaT)
AssertionError: False is not true
```
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} | 0 | 2014-05-28T18:18:23Z | 2014-06-13T03:05:58Z | 2014-05-28T18:56:10Z | CONTRIBUTOR | null | related #5781
closes #7265
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} | 3 | 2014-05-28T18:24:28Z | 2021-04-11T04:38:44Z | null | NONE | null | Here is the idea. I have predefined index and I'm grouping by timestamps in it, aggregating preceding values of the interval:
``` python
index = DatetimeIndex([datetime(2014, 1, 11, 11, 30), datetime(2014, 1, 12, 15), datetime(2014, 1, 15, 22)])
s = Series({
datetime(2014, 1, 10, 0): 1,
datetime(2014, 1, 11, 10): 2,
datetime(2014, 1, 11, 11): 3,
datetime(2014, 1, 12, 12): 3,
datetime(2014, 1, 15, 16): 4,
datetime(2014, 1, 15, 22): 5
})
print s.groupby(lambda x: index[(index >= x).argmax()]).sum()
```
---
```
2014-01-11 11:30:00 6
2014-01-12 15:00:00 3
2014-01-15 22:00:00 9
```
I don't know how effecient this code, but I think it might be a very useful feature as long as "resample" accepts only offsets or deltas. And "reindex" method doesn't have 'how' argument.
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} | 15 | 2014-05-28T18:42:04Z | 2014-07-03T03:41:24Z | 2014-05-30T23:32:19Z | CONTRIBUTOR | null | closes #7256
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Here is some test code:
``` python
import pandas as pd
from datetime import datetime
from pandas.util.print_versions import show_versions
show_versions()
test = pd.Series(['here is some sample text' for x in range(100000)])
def test_regex(pattern):
now = datetime.now()
match_result = test.str.match(pattern)
print "Using match:", datetime.now() - now
now = datetime.now()
extract_result = test.str.extract(pattern)
print "Using extract:", datetime.now() - now
print "SINGLE GROUP"
test_regex('.*some (.).*')
print "MULTIPLE GROUPS"
test_regex('.*some (.)(.).*')
```
On my machine (running pandas: 0.13.0rc1-64-gceec8bf), this reports:
```
SINGLE GROUP
Using match: 0:00:00.090317
Using extract: 0:00:00.116123
MULTIPLE GROUPS
Using match: 0:00:00.094432
Using extract: 0:00:15.857041
```
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} | 6 | 2014-05-29T00:00:06Z | 2014-09-22T14:42:25Z | 2014-05-29T18:03:43Z | MEMBER | null | This allows custom ndarray-like objects which aren't actual ndarrays
to be smoothly cast to a pandas.Index:
https://github.com/pydata/pandas/issues/5460#issuecomment-44474502
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} | 2 | 2014-05-29T01:41:48Z | 2016-07-20T22:04:50Z | 2016-07-20T22:04:49Z | CONTRIBUTOR | null | This would be consistent with other pandas methods.
The reason I'm running into it is having some NaNs scattered across some `int` and `bool` columns, which converts to float / objects. If I discard those NaNs, it would be nice to do
`df = df.astype({'my_bool', 'bool', 'my_int': 'int'})`
instead of
``` python
df['my_bool'] = df.my_bool.astype('bool')
df['my_int'] = df.my_int.astype('int')
```
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} | 11 | 2014-05-29T03:20:08Z | 2014-07-01T00:07:43Z | 2014-05-29T11:10:05Z | CONTRIBUTOR | null | Otherwise comparison would always result in "low endian" . Seems to resolve all the gory stata files reading tests on sparc at least
closes #5781
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} | 2 | 2014-05-29T07:52:03Z | 2015-01-21T02:22:04Z | 2015-01-18T21:36:35Z | CONTRIBUTOR | null | fixes #2419
Hmmm `np.random.choice` not available on numpy < 1.7.
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} | 4 | 2014-05-29T12:44:26Z | 2014-06-26T09:08:58Z | 2014-05-30T10:38:22Z | MEMBER | null | Related to discussion on mailing list https://mail.python.org/pipermail/pandas-dev/2014-May/000272.html
So I gave it a try, moved all content of release.rst to the whatsnew file (main chunk were the bug fixes, and for the rest some api changes and enhancements that were not listed in whatsnew). All content that was in release is not in whatsnew.
Can you see what you think of this?
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} | 1 | 2014-05-29T13:10:18Z | 2014-06-13T19:38:11Z | 2014-05-30T15:35:32Z | MEMBER | null | This solves latter exclude column issue described in #7227.
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} | 7 | 2014-05-29T13:54:32Z | 2014-06-13T16:34:47Z | 2014-05-30T19:02:13Z | MEMBER | null | `DataFrame.hist` without `by` keyword raises `TypeError` when it contains non-numeric column.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas.util.testing as tm
n=100
gender = tm.choice(['Male', 'Female'], size=n)
classroom = tm.choice(['A', 'B', 'C'], size=n)
single = tm.choice(['S'], size=n)
df = pd.DataFrame({'gender': gender,
'classroom': classroom,
'height': np.random.normal(66, 4, size=n),
'weight': np.random.normal(161, 32, size=n),
'category': np.random.randint(4, size=n)})
df.hist(by='gender')
# This works, even though 'classroom' (str) column in still contained
df.hist()
# TypeError: cannot concatenate 'str' and 'float' objects
```
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} | 1 | 2014-05-29T14:14:57Z | 2018-07-06T21:37:01Z | 2018-07-06T21:37:01Z | CONTRIBUTOR | null | `Series.eq` should have same signature as `DataFrame.eq`
including `axis,level` args
add docs for these as well
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https://api.github.com/repos/pandas-dev/pandas/issues/7279 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7279/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7279/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7279/events | https://github.com/pandas-dev/pandas/pull/7279 | 34,587,897 | MDExOlB1bGxSZXF1ZXN0MTY1MDA3OTI= | 7,279 | BUG: Index.min and max doesnt handle nan and NaT properly | {
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} | 11 | 2014-05-29T21:40:05Z | 2014-09-09T23:30:37Z | 2014-09-09T23:25:57Z | CONTRIBUTOR | null | fixes #4533
_need to fix test in numpy 1.6..._
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https://api.github.com/repos/pandas-dev/pandas/issues/7282 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7282/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7282/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7282/events | https://github.com/pandas-dev/pandas/issues/7282 | 34,611,362 | MDU6SXNzdWUzNDYxMTM2Mg== | 7,282 | PERF: pandas import is too slow | {
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] | closed | false | null | [] | null | 48 | 2014-05-30T02:29:43Z | 2020-09-22T13:39:04Z | 2014-07-07T15:45:05Z | NONE | null | The following test demonstrates the problem... the contents of `testme.py` is literally `import pandas`; however, it takes almost 6 seconds to import pandas on my Lenovo T60.
```
[mpenning@Mudslide panex]$ time python testme.py
real 0m5.759s
user 0m5.612s
sys 0m0.120s
[mpenning@Mudslide panex]$
[mpenning@Mudslide panex]$ uname -a
Linux Mudslide 3.2.0-4-686-pae #1 SMP Debian 3.2.57-3+deb7u1 i686 GNU/Linux
[mpenning@Mudslide panex]$ python -V
Python 2.7.3
[mpenning@Mudslide panex]$
[mpenning@Mudslide panex]$ pip freeze
Babel==1.3
Cython==0.20.1
Flask==0.10.1
Flask-Babel==0.8
Flask-Login==0.2.7
Flask-Mail==0.7.6
Flask-OpenID==1.1.1
Flask-SQLAlchemy==0.16
Flask-WTF==0.8.4
Flask-WhooshAlchemy==0.54a
Jinja2==2.7.1
MarkupSafe==0.18
Pygments==1.6
SQLAlchemy==0.7.9
Sphinx==1.2.2
Tempita==0.5.1
WTForms==1.0.5
Werkzeug==0.9.4
Whoosh==2.5.4
argparse==1.2.1
backports.ssl-match-hostname==3.4.0.2
blinker==1.3
ciscoconfparse==1.1.1
decorator==3.4.0
docutils==0.11
dulwich==0.9.6
## FIXME: could not find svn URL in dependency_links for this package:
flup==1.0.3.dev-20110405
hg-git==0.5.0
ipaddr==2.1.11
itsdangerous==0.23
matplotlib==1.3.1
mercurial==3.0
mock==1.0.1
nose==1.3.3
numexpr==2.4
numpy==1.8.1
numpydoc==0.4
pandas==0.13.1
pyparsing==2.0.2
python-dateutil==2.2
python-openid==2.2.5
pytz==2013b
six==1.6.1
speaklater==1.3
sqlalchemy-migrate==0.7.2
tables==3.1.1
tornado==3.2.1
wsgiref==0.1.2
```
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x = pd.Series([1, 'X'])
test = x.astype(str)
print type(test[0]) # should be str, instead is int
test2 = np.array(x).astype(str)
print type(test[0]) # is str!
```
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} | 25 | 2014-05-30T15:35:31Z | 2014-07-02T00:35:36Z | 2014-06-02T11:29:30Z | CONTRIBUTOR | null | Since today I've the following issue when importing Pandas on Python 2.6
```
2014-05-30 15:14:29.999 | File "gnocchi/carbonara.py", line 22, in <module>
2014-05-30 15:14:29.999 | import pandas
2014-05-30 15:14:29.999 | File "/home/jenkins/workspace/gate-gnocchi-python26/.tox/py26/lib/python2.6/site-packages/pandas/__init__.py", line 45, in <module>
2014-05-30 15:14:30.000 | from pandas.io.api import *
2014-05-30 15:14:30.000 | File "/home/jenkins/workspace/gate-gnocchi-python26/.tox/py26/lib/python2.6/site-packages/pandas/io/api.py", line 7, in <module>
2014-05-30 15:14:30.000 | from pandas.io.excel import ExcelFile, ExcelWriter, read_excel
2014-05-30 15:14:30.000 | File "/home/jenkins/workspace/gate-gnocchi-python26/.tox/py26/lib/python2.6/site-packages/pandas/io/excel.py", line 626, in <module>
2014-05-30 15:14:30.000 | .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))
2014-05-30 15:14:30.000 | ValueError: zero length field name in format
```
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} | 22 | 2014-05-30T15:54:36Z | 2014-06-12T12:04:55Z | 2014-06-02T11:29:30Z | MEMBER | null | Also test against an unsupported version of openpyxl (2.0.3)
closes #7284
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} | 4 | 2014-05-30T20:12:56Z | 2014-06-16T12:52:02Z | 2014-06-16T12:52:02Z | CONTRIBUTOR | null | When trying to write a Stata dataset with strings containing upper latin-1 characters (which are allowed by the Stata format), I get an encoding error.
``` python
import pandas.io.stata as sta
sr = sta.StataReader('pandas/pandas/io/tests/data/stata1_encoding.dta')
df = sr.data()
sw = sta.StataWriter('stata1_encoding_dup.dta', df)
sw.write_file()
```
I get the following output:
``` python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\io\stata.py", line 1242, in write_file
self._write_data_nodates()
File "C:\Python27\lib\site-packages\pandas\io\stata.py", line 1326, in _write_data_nodates
self._write(var)
File "C:\Python27\lib\site-packages\pandas\io\stata.py", line 1104, in _write
self._file.write(to_write)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xfc' in position 1:ordinal not in range(128)
```
Machine info:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: AMD64 Family 16 Model 6 Stepping 3, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: None
numexpr: None
matplotlib: None
openpyxl: 1.8.6
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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} | 8 | 2014-05-30T20:14:26Z | 2021-04-11T04:40:27Z | null | CONTRIBUTOR | null | This might not necessarily a new thing since 0.14, but I find the output of group.head() not appropriate for a grouping:
``` python
df = pd.DataFrame(np.random.randn(6,2))
df['A'] = [1,2,2,1,1,2]
df
```
```
0 1 A
0 -0.047101 0.828542 1
1 1.617815 0.362700 2
2 1.366453 -1.116897 2
3 0.086743 -0.611371 1
4 1.918440 -1.230909 1
5 -1.003828 -0.592541 2
```
``` python
g = df.groupby('A')
g.head(2)
```
```
0 1 A
0 -0.047101 0.828542 1
1 1.617815 0.362700 2
2 1.366453 -1.116897 2
3 0.086743 -0.611371 1
```
My expectation of the previous output would be:
```
0 1 A
0 -0.047101 0.828542 1
3 0.086743 -0.611371 1
1 1.617815 0.362700 2
2 1.366453 -1.116897 2
```
because, after all, this is the result of a grouping, so things should be displayed grouped, shouldn't they?
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https://api.github.com/repos/pandas-dev/pandas/issues/7288 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7288/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7288/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7288/events | https://github.com/pandas-dev/pandas/issues/7288 | 34,687,350 | MDU6SXNzdWUzNDY4NzM1MA== | 7,288 | `nan != 'KORD'` error in test_multiple_date_cols_chunked | {
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} | [] | closed | false | null | [] | null | 4 | 2014-05-30T22:47:30Z | 2014-05-31T01:19:20Z | 2014-05-31T01:19:20Z | CONTRIBUTOR | null | Running pandas tests as part of scipy-stack in multiple OSX / Python install variations:
https://travis-ci.org/matthew-brett/scipy-stack-osx-testing/builds/26419147
For at least Python.org 3.3.5, no virtualenv, I see this error:
```
======================================================================
FAIL: test_multiple_date_cols_chunked (pandas.io.tests.test_parsers.TestCParserLowMemory)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/io/tests/test_parsers.py", line 1740, in test_multiple_date_cols_chunked
tm.assert_frame_equal(chunks[2], df[4:])
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/util/testing.py", line 585, in assert_frame_equal
check_exact=check_exact)
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/util/testing.py", line 532, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2536)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1859)
File "testing.pyx", line 69, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1556)
AssertionError: nan != 'KORD'
```
See: https://travis-ci.org/matthew-brett/scipy-stack-osx-testing/jobs/26419150
For the exact same set of packages / Python version but in a virtualenv, I don't see the error:
https://travis-ci.org/matthew-brett/scipy-stack-osx-testing/jobs/26419153
Is there something random in the test or the code that could explain this?
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https://api.github.com/repos/pandas-dev/pandas/issues/7289 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7289/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7289/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7289/events | https://github.com/pandas-dev/pandas/issues/7289 | 34,692,965 | MDU6SXNzdWUzNDY5Mjk2NQ== | 7,289 | BUG: Unable to parse monthly-string in datetime.pxd | {
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} | [] | closed | false | null | [] | null | 5 | 2014-05-31T01:51:06Z | 2014-05-31T17:37:12Z | 2014-05-31T08:36:26Z | MEMBER | null | Travis build looks fail with following error.
```
File "/home/travis/virtualenv/python2.6.9/lib/python2.6/site-packages/pandas/tseries/tests/test_period.py", line 1976, in test_range_slice_outofbounds
...
File "datetime.pxd", line 137, in datetime._string_to_dts (pandas/tslib.c:52202)
ValueError: Unable to parse 2013/06
```
The failure is caused by test in `DatetimeIndex`, strangely `PeriodIndex` has no problem.
I don't know where `datetime.pxd` is derived from, but one assumption is the `_string_to_dts` uses todays date (31) in parsing process.
```
import pandas as pd
didx = pd.DatetimeIndex(start='2013/10/01', freq='D', periods=10)
pidx = pd.PeriodIndex(start='2013/10/01', freq='D', periods=10)
for idx in [didx, pidx]:
df = pd.DataFrame(dict(units=[100 + i for i in range(10)]), index=idx)
for key in ['2013/{0}'.format(i) for i in range(1, 13)]:
try:
df[key:'2013/03']
except ValueError, e:
print(type(idx), key, e)
# will prints
(<class 'pandas.tseries.index.DatetimeIndex'>, '2013/2', ValueError('Unable to parse 2013/2',))
(<class 'pandas.tseries.index.DatetimeIndex'>, '2013/4', ValueError('Unable to parse 2013/4',))
(<class 'pandas.tseries.index.DatetimeIndex'>, '2013/6', ValueError('Unable to parse 2013/6',))
(<class 'pandas.tseries.index.DatetimeIndex'>, '2013/9', ValueError('Unable to parse 2013/9',))
(<class 'pandas.tseries.index.DatetimeIndex'>, '2013/11', ValueError('Unable to parse 2013/11',))
```
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} | [] | closed | false | null | [] | null | 0 | 2014-05-31T04:16:41Z | 2014-06-28T10:58:37Z | 2014-05-31T08:36:26Z | MEMBER | null | Closes #7289.
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https://api.github.com/repos/pandas-dev/pandas/issues/7291 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7291/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7291/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7291/events | https://github.com/pandas-dev/pandas/pull/7291 | 34,695,590 | MDExOlB1bGxSZXF1ZXN0MTY1NjM4MjY= | 7,291 | fix string format for Python 2.6 | {
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} | [] | closed | false | null | [] | null | 2 | 2014-05-31T05:07:04Z | 2014-06-18T15:50:56Z | 2014-05-31T08:47:29Z | NONE | null | This PR fixes a minor string formatting problem when running under Python 2.6
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} | 8 | 2014-05-31T05:28:50Z | 2018-01-31T11:34:13Z | 2018-01-31T11:34:12Z | NONE | null | In the first case, is it a bug (those NaNs at the end) or a feature? I just don't get the reason behind this behavior:
```
In[5]: s = Series(range(10), date_range('2014', periods=10, freq='H'))
In[6]: s
Out[6]:
2014-05-31 00:00:00 0
2014-05-31 01:00:00 1
2014-05-31 02:00:00 2
2014-05-31 03:00:00 3
2014-05-31 04:00:00 4
2014-05-31 05:00:00 5
2014-05-31 06:00:00 6
2014-05-31 07:00:00 7
2014-05-31 08:00:00 8
2014-05-31 09:00:00 9
Freq: H, dtype: int64
In[7]: s.pct_change(periods=1, freq='5H')
Out[7]:
2014-05-31 00:00:00 NaN
2014-05-31 01:00:00 NaN
2014-05-31 02:00:00 NaN
2014-05-31 03:00:00 NaN
2014-05-31 04:00:00 NaN
2014-05-31 05:00:00 inf
2014-05-31 06:00:00 5.000000
2014-05-31 07:00:00 2.500000
2014-05-31 08:00:00 1.666667
2014-05-31 09:00:00 1.250000
2014-05-31 10:00:00 NaN
2014-05-31 11:00:00 NaN
2014-05-31 12:00:00 NaN
2014-05-31 13:00:00 NaN
2014-05-31 14:00:00 NaN
dtype: float64
```
but this seems ok:
```
In[8]: s.pct_change(periods=5)
Out[8]:
2014-05-31 00:00:00 NaN
2014-05-31 01:00:00 NaN
2014-05-31 02:00:00 NaN
2014-05-31 03:00:00 NaN
2014-05-31 04:00:00 NaN
2014-05-31 05:00:00 inf
2014-05-31 06:00:00 5.000000
2014-05-31 07:00:00 2.500000
2014-05-31 08:00:00 1.666667
2014-05-31 09:00:00 1.250000
Freq: H, dtype: float64
```
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} | [] | closed | false | null | [] | null | 4 | 2014-05-31T08:08:39Z | 2014-05-31T21:38:13Z | 2014-05-31T21:38:13Z | CONTRIBUTOR | null | Another annoying one, this time with more than one failure, but again, fail then not fail on a repeat run of the same tests.
Fails here:
A) https://travis-ci.org/matthew-brett/scipy-stack-osx-testing/jobs/26434163
B) http://nipy.bic.berkeley.edu/builders/scipy-stack-3.4.0-wheel-requires/builds/20/steps/shell_8/logs/stdio
Message:
```
======================================================================
FAIL: test_dict_complex (pandas.io.tests.test_packers.TestNumpy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/travis/venv/lib/python3.4/site-packages/pandas/io/tests/test_packers.py", line 153, in test_dict_complex
all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values())))
AssertionError: False is not true
======================================================================
FAIL: test_dict_numpy_complex (pandas.io.tests.test_packers.TestNumpy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/travis/venv/lib/python3.4/site-packages/pandas/io/tests/test_packers.py", line 165, in test_dict_numpy_complex
all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values())))
AssertionError: False is not true
```
Fail A is on travis OSX 10.9 virtual machine running macports Python 3.4.1 in a virtualenv. Previous apparently identical run does not have this failure: https://travis-ci.org/matthew-brett/scipy-stack-osx-testing/jobs/26429175
Fail B is on an old clean 10.6 machine running Python.org 3.4.0. Subsequent identical run does not fail: http://nipy.bic.berkeley.edu/builders/scipy-stack-3.4.0-wheel-requires/builds/21
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} | [] | closed | false | null | [] | null | 2 | 2014-05-31T09:00:22Z | 2014-05-31T09:13:37Z | 2014-05-31T09:07:40Z | CONTRIBUTOR | null | Sorry to be the error guy - but all variations now giving:
```
======================================================================
ERROR: test_range_slice_outofbounds (pandas.tseries.tests.test_period.TestPeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "index.pyx", line 521, in pandas.index.DatetimeEngine.get_loc (pandas/index.c:9337)
File "hashtable.pyx", line 381, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7008)
TypeError: an integer is required
```
With continuing stream of KeyError errors. ?Data changed upstream somewhere?
https://travis-ci.org/matthew-brett/scipy-stack-osx-testing/jobs/26441421
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} | 5 | 2014-05-31T15:50:18Z | 2014-06-17T11:22:59Z | 2014-06-17T11:22:59Z | CONTRIBUTOR | null | ```
test_interp_regression (__main__.TestSeries) ... > /home/dsm/sys/pandas/pandas/tests/stringsource(327)View.MemoryView.memoryview.__cinit__ (scipy/interpolate/_ppoly.c:19922)()
(Pdb) cont
ERROR
======================================================================
ERROR: test_interp_regression (__main__.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_generic.py", line 501, in test_interp_regression
interp_s = ser.reindex(new_index).interpolate(method='pchip')
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/generic.py", line 2582, in interpolate
**kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/internals.py", line 2197, in interpolate
return self.apply('interpolate', **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/internals.py", line 2164, in apply
applied = getattr(b, f)(**kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/internals.py", line 667, in interpolate
**kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/internals.py", line 733, in _interpolate
interp_values = np.apply_along_axis(func, axis, data)
File "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py", line 81, in apply_along_axis
res = func1d(arr[tuple(i.tolist())],*args)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/internals.py", line 730, in func
bounds_error=False, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/common.py", line 1489, in interpolate_1d
bounds_error=bounds_error, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_12_g150cb3b-py2.7-linux-i686.egg/pandas/core/common.py", line 1541, in _interpolate_scipy_wrapper
new_y = method(x, y, new_x)
File "/usr/local/lib/python2.7/dist-packages/scipy/interpolate/_monotone.py", line 221, in pchip_interpolate
return P(x)
File "/usr/local/lib/python2.7/dist-packages/scipy/interpolate/_monotone.py", line 98, in __call__
out = self._bpoly(x, der, extrapolate)
File "/usr/local/lib/python2.7/dist-packages/scipy/interpolate/interpolate.py", line 689, in __call__
self._evaluate(x, nu, extrapolate, out)
File "/usr/local/lib/python2.7/dist-packages/scipy/interpolate/interpolate.py", line 1087, in _evaluate
self.x, x, nu, bool(extrapolate), out, self.c.dtype)
File "_ppoly.pyx", line 846, in scipy.interpolate._ppoly.evaluate_bernstein (scipy/interpolate/_ppoly.c:15014)
File "stringsource", line 622, in View.MemoryView.memoryview_cwrapper (scipy/interpolate/_ppoly.c:23370)
File "stringsource", line 327, in View.MemoryView.memoryview.__cinit__ (scipy/interpolate/_ppoly.c:19922)
ValueError: buffer source array is read-only
----------------------------------------------------------------------
Ran 63 tests in 3.981s
FAILED (SKIP=1, errors=1)
```
Version info:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 32
OS: Linux
OS-release: 3.11.0-20-generic
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.14.0-12-g150cb3b
nose: 1.3.1
Cython: 0.20.1
numpy: 1.9.0.dev-ef7901d
scipy: 0.15.0.dev-93656a8
statsmodels: 0.6.0.dev-985037f
IPython: 2.0.0-dev
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.1
bottleneck: 0.8.0
tables: None
numexpr: 2.3.1
matplotlib: 1.4.x
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.3
bs4: 4.3.2
html5lib: 1.0b3
bq: None
apiclient: 1.2
rpy2: None
sqlalchemy: 0.9.3
pymysql: None
psycopg2: None
```
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} | [] | closed | false | null | [] | null | 11 | 2014-05-31T17:50:48Z | 2014-07-02T11:30:39Z | 2014-06-01T02:51:45Z | CONTRIBUTOR | null | re-branch of https://github.com/pydata/pandas/pull/7253#issue-34432164
-Move `percentileofscore` to `stats.misc`; Update `percentileRank` to reflect the move.
-Change testing references to `compat.scipy.rankdata()` , to use `scipy.stats.rankdata` directly
-Delete compat.scipy
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"updated_at": "2014-09-05T10:24:18Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/27"
} | 17 | 2014-05-31T17:55:39Z | 2014-07-02T11:46:29Z | 2014-07-02T11:46:29Z | CONTRIBUTOR | null | In 0.13 I could pass a window length greater than the length of the `Series` passed to `rolling_var` (or, of course, `rolling_std`). In 0.14 that raises an error. Behavior is unchanged from 0.13 for other rolling functions:
``` python
data = """
x
0.1
0.5
0.3
0.2
0.7
"""
df = pd.read_csv(StringIO(data),header=True)
>>> pd.rolling_mean(df['x'],window=6,min_periods=2)
0 NaN
1 0.300
2 0.300
3 0.275
4 0.360
dtype: float64
>>> pd.rolling_skew(df['x'],window=6,min_periods=2)
0 NaN
1 NaN
2 3.903128e-15
3 7.528372e-01
4 6.013638e-01
dtype: float64
>>> pd.rolling_skew(df['x'],window=6,min_periods=6)
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
```
Those work, but not `rolling_var`:
``` python
>>> pd.rolling_var(df['x'],window=6,min_periods=2)
Traceback (most recent call last):
File "./foo.py", line 187, in <module>
print pd.rolling_var(df['x'],window=6,min_periods=2)
File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 594, in f
center=center, how=how, **kwargs)
File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 346, in _rolling_moment
result = calc(values)
File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 340, in <lambda>
**kwds)
File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 592, in call_cython
return func(arg, window, minp, **kwds)
File "algos.pyx", line 1177, in pandas.algos.roll_var (pandas/algos.c:28449)
IndexError: Out of bounds on buffer access (axis 0)
```
If this is the new desired default behavior for the rolling functions, I can work around it. I do like the behavior of `rolling_skew` and `rolling_mean` better. It was nice default behavior for me when I was doing rolling standard deviations for reasonably large financial data panels.
It looks to me like the issue is caused by the fact that the 0.14 algo for rolling variance is implemented such that the initial loop (`roll_var` (algos.pyx)) is the following:
``` python
for i from 0 <= i < win:
```
So it loops to `win` even when `win > N`.
It looks like to me that the other rolling functions try to implement their algos in such a way that the first loop counts over the following:
``` python
for i from 0 <= i < minp - 1:
```
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.10-200.fc20.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.0
nose: 1.3.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.6.0.dev-b52bc09
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.3
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
Non
```
Karl D.
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https://api.github.com/repos/pandas-dev/pandas/issues/7298 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7298/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7298/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7298/events | https://github.com/pandas-dev/pandas/pull/7298 | 34,714,649 | MDExOlB1bGxSZXF1ZXN0MTY1NzAxNTU= | 7,298 | TST: fix intermittent dict_complex test failures | {
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} | [] | closed | false | null | [] | null | 3 | 2014-05-31T20:38:43Z | 2014-07-01T00:16:02Z | 2014-05-31T21:38:13Z | CONTRIBUTOR | null | `test_dict_complex` and `test_dict_numpy_complex` would give occasional test
failures on Python 3.4. The test was written to depend on the order of values
from `dict.values()`; remove that dependency.
Closes gh-7293.
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https://api.github.com/repos/pandas-dev/pandas/issues/7299 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7299/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7299/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7299/events | https://github.com/pandas-dev/pandas/pull/7299 | 34,716,194 | MDExOlB1bGxSZXF1ZXN0MTY1NzA3OTU= | 7,299 | BUG: DatetimeIndex.insert doesnt preserve name and tz | {
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} | 21 | 2014-05-31T22:06:03Z | 2014-06-12T06:09:49Z | 2014-06-06T12:58:23Z | MEMBER | null | `DatetimeIndex.insert` doesn't preserve `name` and `tz` attributes. Also modified `DatetimeIndex.asobject` to return an object `Index` which has the same name as original to cover the case when the result is being object `Index`.
```
# normal Index preserves its name after insertion
idx = pd.Index([1, 2, 3], name='normal')
inserted = idx.insert(0, 1)
inserted.name
# normal
# But DatetimeIndex doesn't
dtidx = pd.DatetimeIndex([datetime.datetime(2011, 1, 3), datetime.datetime(2011, 1, 4)], freq='M', name='dtidx')
inserted = dtidx.insert(0, datetime.datetime(2011, 1, 5))
inserted.name
# None
```
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} | 1 | 2014-05-31T22:07:57Z | 2016-10-12T23:04:53Z | 2014-06-01T21:14:22Z | MEMBER | null | From SO question (http://stackoverflow.com/questions/23974664/unable-to-query-a-local-variable-in-pandas-0-14-0), referencing a local variable gives an error when this is a string (workaround seems to be to wrap it in a list):
```
In [39]: fills = pd.DataFrame({'Symbol':['BUD US', 'BUD US', 'IBM US', 'IBM US'], 'Price':[109.70, 109.72, 183.30, 183.35]})
In [40]: fills
Out[40]:
Price Symbol
0 109.70 BUD US
1 109.72 BUD US
2 183.30 IBM US
3 183.35 IBM US
In [41]: my_symbol = ['BUD US']
In [42]: fills.query('Symbol==@my_symbol')
Out[42]:
Price Symbol
0 109.70 BUD US
1 109.72 BUD US
In [43]: my_symbol = 'BUD US'
In [44]: fills.query('Symbol==@my_symbol')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-44-c16846018a13> in <module>()
----> 1 fills.query('Symbol==@my_symbol')
...
/home/joris/scipy/pandas/pandas/computation/expr.pyc in _rewrite_membership_op(self, node, left, right)
341 # of one string, kind of a hack
342 if right_str:
--> 343 self.env.remove_tmp(right.name)
344 name = self.env.add_tmp([right.value])
345 right = self.term_type(name, self.env)
/home/joris/scipy/pandas/pandas/computation/scope.pyc in remove_tmp(self, name)
287 The name of a temporary to be removed
288 """
--> 289 del self.temps[name]
290
291 @property
KeyError: '__pd_eval_local_my_symbol'
> /home/joris/scipy/pandas/pandas/computation/scope.py(289)remove_tmp()
288 """
--> 289 del self.temps[name]
290
```
@cpcloud
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