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} | 2 | 2016-08-16T02:42:01Z | 2021-05-01T23:16:39Z | null | CONTRIBUTOR | null | xref #13961
```
In [3]: df = pd.DataFrame({'date': pd.period_range('2015-01-01', freq='D', periods=5),
...: 'a': np.arange(5)},
...: index=pd.MultiIndex.from_arrays([
...: [1,2,3,4,5],
...: pd.period_range('2015-01-01', freq='D', periods=5)],
...: names=['v','d']))
In [4]: df
Out[4]:
a date
v d
1 2015-01-01 0 2015-01-01
2 2015-01-02 1 2015-01-02
3 2015-01-03 2 2015-01-03
4 2015-01-04 3 2015-01-04
5 2015-01-05 4 2015-01-05
In [5]: df.groupby(pd.Grouper(key='date', freq='2D')).sum()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-7d905f24cc6a> in <module>()
----> 1 df.groupby(pd.Grouper(key='date', freq='2D')).sum()
C:\Users\Chris\Documents\python-dev\pandas\pandas\core\generic.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, **kwargs)
3962 return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
3963 sort=sort, group_keys=group_keys, squeeze=squeeze,
-> 3964 **kwargs)
3965
3966 def asfreq(self, freq, method=None, how=None, normalize=False):
C:\Users\Chris\Documents\python-dev\pandas\pandas\core\groupby.py in groupby(obj, by, **kwds)
1495 raise TypeError('invalid type: %s' % type(obj))
1496
-> 1497 return klass(obj, by, **kwds)
1498
1499
C:\Users\Chris\Documents\python-dev\pandas\pandas\core\groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, **kwargs)
365 level=level,
366 sort=sort,
--> 367 mutated=self.mutated)
368
369 self.obj = obj
C:\Users\Chris\Documents\python-dev\pandas\pandas\core\groupby.py in _get_grouper(obj, key, axis, level, sort, mutated)
2381 # a passed-in Grouper, directly convert
2382 if isinstance(key, Grouper):
-> 2383 binner, grouper, obj = key._get_grouper(obj)
2384 if key.key is None:
2385 return grouper, [], obj
C:\Users\Chris\Documents\python-dev\pandas\pandas\tseries\resample.py in _get_grouper(self, obj)
1027 def _get_grouper(self, obj):
1028 # create the resampler and return our binner
-> 1029 r = self._get_resampler(obj)
1030 r._set_binner()
1031 return r.binner, r.grouper, r.obj
C:\Users\Chris\Documents\python-dev\pandas\pandas\tseries\resample.py in _get_resampler(self, obj, kind)
1015 groupby=self,
1016 kind=kind,
-> 1017 axis=self.axis)
1018 elif isinstance(ax, TimedeltaIndex):
1019 return TimedeltaIndexResampler(obj,
C:\Users\Chris\Documents\python-dev\pandas\pandas\tseries\resample.py in __init__(self, obj, groupby, axis, kind, **kwargs)
88
89 if self.groupby is not None:
---> 90 self.groupby._set_grouper(self._convert_obj(obj), sort=True)
91
92 def __unicode__(self):
C:\Users\Chris\Documents\python-dev\pandas\pandas\tseries\resample.py in _convert_obj(self, obj)
764 # convert to timestamp
765 if not (self.kind is None or self.kind == 'period'):
--> 766 obj = obj.to_timestamp(how=self.convention)
767 return obj
768
C:\Users\Chris\Documents\python-dev\pandas\pandas\core\frame.py in to_timestamp(self, freq, how, axis, copy)
5174 axis = self._get_axis_number(axis)
5175 if axis == 0:
-> 5176 new_data.set_axis(1, self.index.to_timestamp(freq=freq, how=how))
5177 elif axis == 1:
5178 new_data.set_axis(0, self.columns.to_timestamp(freq=freq, how=how))
AttributeError: 'MultiIndex' object has no attribute 'to_timestamp'
```
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} | 1 | 2016-08-16T11:31:25Z | 2016-08-16T13:19:04Z | 2016-08-16T13:19:00Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({'items':[1,2,3], 'test': [2,3,4]})
df.items
```
#### Expected Output
```
0 1
1 2
2 3
Name: items, dtype: int64
```
#### Given Output
`AttributeError: 'DataFrame' object has no attribute 'items'`
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.0
nose: 1.3.7
pip: 8.1.2
setuptools: 7.0
Cython: 0.24.1
numpy: 1.9.3
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 5.0.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.1
bs4: 4.3.2
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.6.6.None
psycopg2: None
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} | 2 | 2016-08-16T13:57:56Z | 2016-08-17T10:33:41Z | 2016-08-17T10:33:24Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
1st attempt to make dtype related tests more comprehensive.
Validate whether tests classes have test methods covering dtype and Series/Index. Some of them are empty yet, going to add it gradually.
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} | 2 | 2016-08-16T16:41:16Z | 2019-11-10T19:11:28Z | 2019-11-10T19:11:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
In [2]: pd.DataFrame({'x': pd.Categorical('a b c d e'.split())}).iloc[0]
Out[2]: 'a'
```
#### Expected Output
I think this should be a Series with one element:
``` python
0 a
dtype: category
Categories (5, object): [a, b, c, d, e]
```
by analogy with the non-Categorical case:
``` python
In [3]: pd.DataFrame({'x': 'a b c d e'.split()}).iloc[0]
Out[3]:
x a
Name: 0, dtype: object
```
This bug also exhibits with `.loc[0]` and `.ix[0]`. It doesn't happen if the original dataframe has more than one column, even if they're all categorical.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14012 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14012/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14012/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14012/events | https://github.com/pandas-dev/pandas/issues/14012 | 171,473,817 | MDU6SXNzdWUxNzE0NzM4MTc= | 14,012 | read_csv treats \x00 as EOL instead of null value | {
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} | 4 | 2016-08-16T17:35:10Z | 2016-08-17T10:26:54Z | 2016-08-17T10:26:53Z | NONE | null | Not sure if this is a bug, but it took me a long time to figure out what was going on in a much bigger datafile than the sample one below.
#### Code Sample, a copy-pastable example if possible
import pandas
import StringIO
data='''var1,var2,var3
1,2,0
2,\x00,0
3,4,0
4,5,0
'''
print pandas.read_csv(StringIO.StringIO(data))
#### Expected Output
A table with 4 rows instead of 5, or an error.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14013 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14013/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14013/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14013/events | https://github.com/pandas-dev/pandas/issues/14013 | 171,489,750 | MDU6SXNzdWUxNzE0ODk3NTA= | 14,013 | Rolling groupby should not maintain the by column in the resulting DataFrame | {
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} | 10 | 2016-08-16T18:50:15Z | 2020-02-20T14:14:13Z | 2018-05-09T10:24:38Z | CONTRIBUTOR | null | I found another oddity while digging through #13966.
Begin with the initial DataFrame in that issue:
```
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.arange(40)})
```
Save the grouping:
```
In [215]: g = df.groupby('A')
```
Compute the rolling sum:
```
In [216]: r = g.rolling(4)
In [217]: r.sum()
Out[217]:
A B
A
1 0 NaN NaN
1 NaN NaN
2 NaN NaN
3 4.0 6.0
4 4.0 10.0
5 4.0 14.0
6 4.0 18.0
7 4.0 22.0
8 4.0 26.0
9 4.0 30.0
... ... ...
2 30 8.0 114.0
31 8.0 118.0
3 32 NaN NaN
33 NaN NaN
34 NaN NaN
35 12.0 134.0
36 12.0 138.0
37 12.0 142.0
38 12.0 146.0
39 12.0 150.0
[40 rows x 2 columns]
```
It maintains the `by` column (`A`)! That column should not be in the resulting DataFrame.
It gets weirder if I compute the sum over the entire grouping and then re-do the rolling calculation. Now `by` column is gone as expected:
```
In [218]: g.sum()
Out[218]:
B
A
1 190
2 306
3 284
In [219]: r.sum()
Out[219]:
B
A
1 0 NaN
1 NaN
2 NaN
3 6.0
4 10.0
5 14.0
6 18.0
7 22.0
8 26.0
9 30.0
... ...
2 30 114.0
31 118.0
3 32 NaN
33 NaN
34 NaN
35 134.0
36 138.0
37 142.0
38 146.0
39 150.0
[40 rows x 1 columns]
```
So the grouping summation has some sort of side effect.
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https://api.github.com/repos/pandas-dev/pandas/issues/14014 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14014/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14014/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14014/events | https://github.com/pandas-dev/pandas/issues/14014 | 171,501,126 | MDU6SXNzdWUxNzE1MDExMjY= | 14,014 | .groupby() .value_counts() incompatible with .reset_index() in 0.18.1 | {
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] | closed | false | null | [] | null | 2 | 2016-08-16T19:46:37Z | 2016-08-16T20:43:04Z | 2016-08-16T20:43:03Z | NONE | null | #### Code Sample
``` python
df = pd.DataFrame([[0,1],[0,1],[0,2],[1,1]], columns=['a','b'])
df
a b
0 0 1
1 0 1
2 0 2
3 1 1
df.groupby('a').b.value_counts().reset_index()
ValueError: cannot insert b, already exists
```
#### Expected Output
In version 0.18.0, the output was:
``` python
a b 0
0 0 1 2
1 0 2 1
2 1 1 1
dtype: int64
```
The difference is that now the groupby() value_counts() operation returns a Series named equivalently to the column on which value_counts() was computed.
``` python
df.groupby('a').b.value_counts()
```
0.18.0
``` python
a b
0 1 2
2 1
1 1 1
dtype: int64
```
0.18.1 (including 0.18.1+367.g6b7857b)
``` python
a b
0 1 2
2 1
1 1 1
Name: b, dtype: int64
```
This change in behavior is not completely unexpected given that outside of groupby(), value_counts() has historically returned a Series named equivalently to the column the operation was performed on:
``` python
df.a.value_counts()
0 3
1 1
Name: a, dtype: int64
```
A manual workaround would be to rename the Series before reset_index() as follows:
``` python
g = df.groupby('a').b.value_counts()
g.name = 0
g.reset_index()
a b 0
0 0 1 2
1 0 2 1
2 1 1 1
```
However, the one-line functionality was much appreciated. Being able to pass a new name to value_counts() could solve this issue?
#### output of `pd.show_versions()`
<pre>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-21-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1 (also verified with 0.18.1+367.g6b7857b)
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</pre>
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https://api.github.com/repos/pandas-dev/pandas/issues/14015 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14015/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14015/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14015/events | https://github.com/pandas-dev/pandas/issues/14015 | 171,526,646 | MDU6SXNzdWUxNzE1MjY2NDY= | 14,015 | BUG: multi-indexing sorting on axis=1 on >0 levels | {
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} | 12 | 2016-08-16T21:50:27Z | 2017-04-07T15:18:25Z | 2017-04-07T15:18:24Z | NONE | null | In a DataFrame with MultiIndex, sorting on the level with date values does not do anything (order remains unchanged). This happens for both row and column indexes. In my case I am starting with a string index, converting that to datetime index, have not tried with datetime values at the start.
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame([[1, 2], [6, 7]])
df.columns = pd.MultiIndex.from_tuples([(0, '8/11/2016 12:00:00 AM'), (0, '8/9/2016 12:00:00 AM')], names=['l1', 'Date'])
df.columns.set_levels(df.columns.levels[1].to_datetime(), level=1, inplace=True)
df.sort_index(axis=1, level=1)
```
#### Expected Output
Columns ordered by date is the expected output.
```
Date 2016-08-09 00:00:00 2016-08-11 00:00:00
0 2 1
1 7 6
```
Actual output are columns in the original sort order (not even lexicographically sorted).
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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} | 1 | 2016-08-16T23:00:03Z | 2019-10-21T19:52:16Z | null | CONTRIBUTOR | null | This is a follow-up issue to #13854.
#### Code Sample, a copy-pastable example if possible
``` python
cidx = pd.CategoricalIndex(['y', 'x'], categories=list("xyz"), ordered=True)
df = pd.DataFrame([[10, 11, 12]])
pd.concat([df, df], keys=cidx).index.levels[0]
Out[32]: Index(['y', 'x'], dtype='object')
```
#### Expected Output
```
CategoricalIndex(['y', 'x'], categories=['x', 'y', 'z'], ordered=True, dtype='category')
```
I decided to put it separately because a fix requires more changes than just a factorization introduced in #13854.
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} | 3 | 2016-08-16T23:37:04Z | 2019-10-14T00:39:14Z | 2019-10-14T00:39:13Z | CONTRIBUTOR | null | `make_axis_dummies` has some problems with an axis containing a `CategoricalIndex` with extra categories.
#### Code Sample, a copy-pastable example if possible
``` python
# category `z` is not used
cidx = pd.CategoricalIndex(list("xy"), categories=list("xyz"))
df = pd.DataFrame([[10, 11]], columns=cidx)
ldf = pd.Panel({'A': df, 'B': df}).to_frame()
from pandas.core.reshape import make_axis_dummies
make_axis_dummies(ldf)
Out[9]:
minor x y
major minor
0 x 1.0 0.0
y 0.0 1.0
make_axis_dummies(ldf, transform=lambda x: x)
Out[10]:
x y z
major minor
0 x 1.0 0.0 0.0
y 0.0 1.0 0.0
```
#### Expected Output
I believe `make_axis_dummies(ldf)` and `make_axis_dummies(ldf, transform=lambda x: x)` should be equal.
#### output of `pd.show_versions()`
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 5d791cc7d955c0b074ad602eb03fa32bd3e17503
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.20-1
machine: x86_64
processor: Intel(R)_Core(TM)_i5-2520M_CPU_@_2.50GHz
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.18.1+368.g5d791cc
nose: 1.3.7
pip: 8.1.2
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.0
...
```
In fact, this may be an issue with `Panel.to_frame()` rather than `make_axis_dummies`:
``` python
ldf.index.levels[1]
Out[13]: CategoricalIndex(['x', 'y'], categories=['x', 'y', 'z'], ordered=False, name='minor', dtype='category')
```
I'd expect this level should contain all categories: `CategoricalIndex(['x', 'y', 'z'], categories=['x', 'y', 'z'], ...)` - even if `'z'` is not used. If it had then the both outputs of `make_axis_dummies` would be as in `Out[10]`.
(Somewhat related to #13854.)
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} | 10 | 2016-08-16T23:57:54Z | 2021-05-17T15:17:30Z | 2021-05-17T15:17:30Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
idx = pd.MultiIndex.from_product([['A'], [0, 1]])
df = pd.DataFrame({'cat': pd.Categorical(['a', 'b'])}, index=idx)
df
Out[19]:
cat
A 0 a
1 b
df.unstack()
Out[21]:
cat
0 1
A a b
```
Categorical dtype is lost:
``` python
df.unstack().dtypes
Out[22]:
cat 0 object
1 object
dtype: object
```
But it works ok for a Series:
``` python
df['cat'].unstack()
Out[24]:
0 1
A a b
df['cat'].unstack().dtypes
Out[25]:
0 category
1 category
dtype: object
```
#### Expected Output
I believe `df.unstack()` should preserve categorical dtypes as `df['cat'].unstack()` does.
#### output of `pd.show_versions()`
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 5d791cc7d955c0b074ad602eb03fa32bd3e17503
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.20-1
machine: x86_64
processor: Intel(R)_Core(TM)_i5-2520M_CPU_@_2.50GHz
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.18.1+368.g5d791cc
nose: 1.3.7
pip: 8.1.2
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.0
...
```
This is related to #13743 (and PR #13854), though a bit more difficult to fix.
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https://api.github.com/repos/pandas-dev/pandas/issues/14019 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14019/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14019/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14019/events | https://github.com/pandas-dev/pandas/pull/14019 | 171,570,854 | MDExOlB1bGxSZXF1ZXN0ODE1ODE4NTE= | 14,019 | BUG: Parse NULL char as null value | {
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} | 3 | 2016-08-17T04:15:09Z | 2016-08-17T10:27:04Z | 2016-08-17T10:26:54Z | MEMBER | null | Fixes bug in C parser in which the `NULL` character (`'\x00'`) was being interpreted as a true line terminator, escape character, or comment character because it was used to indicate that a user had not specified these values. As a result, if the data contains this value, it was being incorrectly parsed. It should be parsed as `NULL`.
Closes #14012.
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https://api.github.com/repos/pandas-dev/pandas/issues/14020 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14020/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14020/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14020/events | https://github.com/pandas-dev/pandas/issues/14020 | 171,654,083 | MDU6SXNzdWUxNzE2NTQwODM= | 14,020 | gbq.py: silently downcasting INTEGER columns to FLOAT is problematic | {
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} | 4 | 2016-08-17T13:14:35Z | 2017-02-09T17:11:31Z | 2017-02-09T17:11:31Z | NONE | null | In many cases INTEGER columns in bigquery are used to store IDs. Such IDs are then likely to be silently corrupted on conversion to dataframe. There seems no good way around this, even if it's clear a priori that the query result will only return non-null INTEGER columns.
I understand the motivation (the ability to represent NULL values easily and efficiently), but this both seems an undesirable situation (at the very least pandas should give a warning for integers that are not exactly convertible to floats) and inconsistent to how sql queries work in pandas.
I tried to see if it would be possible to consider the 'mode' (NULLABLE or not) of the field when doing the conversion, but that part of the schema information turns out to be of very limited value in bigquery since even trivial projections turn REQUIRED fields into NULLABLE.
I'm not sure what the best solution is, but I think bpq and sql should behave identically with respect to NULLs and that at the very least there should be a warning on information losing integer->float conversions.
So my preference would be to check the returned json query data for nulls and use np.dtype(int64) if there are none in the INTEGER column, and otherwise 'object'. Where that's deemed to inefficient, I think it's better to add a `read_csv` style dtypes argument to allow users to explicitly request float representation for a particular column (or just cast directly in the pseudo-SQL query itself). I'd rather have my code be slower (and possibly see a runtime warning to that effect) than unwittingly corrupt data (without any warning).
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https://api.github.com/repos/pandas-dev/pandas/issues/14021 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14021/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14021/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14021/events | https://github.com/pandas-dev/pandas/issues/14021 | 171,664,159 | MDU6SXNzdWUxNzE2NjQxNTk= | 14,021 | Better error message when fillna is used with NaN with category dtype | {
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} | 2 | 2016-08-17T13:56:40Z | 2016-08-26T20:50:24Z | 2016-08-26T20:50:24Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
import numpy as np
tst = pd.DataFrame({'a':[1,2,1,np.nan],'b':[np.nan, np.nan, np.nan, np.nan]}, dtype='category')
tst.fillna(value=tst.median()) # here second column median is evaluated to np.nan
```
and the result is:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)
1875 try:
-> 1876 return self._engine.get_loc(key)
1877 except KeyError:
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4027)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3891)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)()
KeyError: <class 'object'>
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-127-6817ed5716b0> in <module>()
2 tst = pd.DataFrame({'a':[1,2,1,np.nan],
3 'b':[np.nan, np.nan, np.nan, np.nan]}, dtype='category')
----> 4 tst.fillna(value=tst.median())
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/frame.py in fillna(self, value, method, axis, inplace, limit, downcast, **kwargs)
2754 self).fillna(value=value, method=method, axis=axis,
2755 inplace=inplace, limit=limit,
-> 2756 downcast=downcast, **kwargs)
2757
2758 @Appender(_shared_docs['shift'] % _shared_doc_kwargs)
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
3164 continue
3165 obj = result[k]
-> 3166 obj.fillna(v, limit=limit, inplace=True)
3167 return result
3168 elif not com.is_list_like(value):
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/series.py in fillna(self, value, method, axis, inplace, limit, downcast, **kwargs)
2350 axis=axis, inplace=inplace,
2351 limit=limit, downcast=downcast,
-> 2352 **kwargs)
2353
2354 @Appender(generic._shared_docs['shift'] % _shared_doc_kwargs)
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
3151 new_data = self._data.fillna(value=value, limit=limit,
3152 inplace=inplace,
-> 3153 downcast=downcast)
3154
3155 elif isinstance(value, (dict, com.ABCSeries)):
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/internals.py in fillna(self, **kwargs)
2865
2866 def fillna(self, **kwargs):
-> 2867 return self.apply('fillna', **kwargs)
2868
2869 def downcast(self, **kwargs):
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
2830
2831 kwargs['mgr'] = self
-> 2832 applied = getattr(b, f)(**kwargs)
2833 result_blocks = _extend_blocks(applied, result_blocks)
2834
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/internals.py in fillna(self, value, limit, inplace, downcast, mgr)
1884 values = self.values if inplace else self.values.copy()
1885 values = self._try_coerce_result(values.fillna(value=value,
-> 1886 limit=limit))
1887 return [self.make_block(values=values)]
1888
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/util/decorators.py in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/core/categorical.py in fillna(self, value, method, limit)
1415 if mask.any():
1416 values = values.copy()
-> 1417 values[mask] = self.categories.get_loc(value)
1418
1419 return Categorical(values, categories=self.categories,
/home/dan/.local/opt/miniconda3/envs/mathbs/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)
1876 return self._engine.get_loc(key)
1877 except KeyError:
-> 1878 return self._engine.get_loc(self._maybe_cast_indexer(key))
1879
1880 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4027)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3891)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)()
KeyError: <class 'object'>
```
When trying the above with float, it works as expected (nans are "replaced" by nans).
#### Expected Output
```
a b
0 1.0 NaN
1 2.0 NaN
2 1.0 NaN
3 1.0 NaN
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.24.1
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14022 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14022/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14022/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14022/events | https://github.com/pandas-dev/pandas/issues/14022 | 171,675,171 | MDU6SXNzdWUxNzE2NzUxNzE= | 14,022 | wrong addition between Series of type datetime64 and timedelta when tz is set and different from UTC | {
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} | 2 | 2016-08-17T14:39:35Z | 2016-08-18T06:55:11Z | 2016-08-17T18:55:23Z | CONTRIBUTOR | null | It seems there is an issue when adding a timedelta to a Series of type datetime64[ns, some tz]. There seems to be an issue with the offset/DST.
#### Code Sample, a copy-pastable example if possible
```
import pandas
sr = pandas.Series(pandas.DatetimeIndex(["2016-11-03 16:00:00+01:00", "2016-10-25 17:00:00+02:00"]).tz_localize("UTC").tz_convert(tz="Europe/Brussels"))
print(sr)
print(sr + pandas.to_timedelta(0, unit='h'))
```
#### Actual Output
```
0 2016-11-03 16:00:00+01:00
1 2016-10-25 17:00:00+02:00
dtype: datetime64[ns, Europe/Brussels]
0 2016-11-03 16:00:00+01:00
1 2016-10-25 16:00:00+02:00
dtype: datetime64[ns, Europe/Brussels]
```
#### Expected Output
```
0 2016-11-03 16:00:00+01:00
1 2016-10-25 17:00:00+02:00
dtype: datetime64[ns, Europe/Brussels]
0 2016-11-03 16:00:00+01:00
1 2016-10-25 17:00:00+02:00
dtype: datetime64[ns, Europe/Brussels]
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
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xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 5 | 2016-08-17T18:18:22Z | 2019-05-20T00:27:59Z | 2016-08-17T21:26:57Z | NONE | null | I've been screenshotting the dataframes from Jupyter in my notes but it would be really cool if you could save the actual renderings. I looked it up and I think this guy figured out how to do it http://stackoverflow.com/questions/19726663/how-to-save-the-pandas-dataframe-series-data-as-a-figure
Feature request:
DF = pd.DataFrame(somedata)
DF.to_render(path/to/file, format = "pdf")
or something along those lines to get something like:
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} | 10 | 2016-08-17T20:11:46Z | 2017-05-12T16:25:19Z | 2017-05-12T16:25:18Z | CONTRIBUTOR | null | When converting a fractional year to a `Timedelta` object the float is truncated:
```
print pd.to_timedelta(1., unit='Y')
365 days 05:49:12
print pd.to_timedelta(1.5, unit='Y')
365 days 05:49:12
print pd.to_timedelta(0.5, unit='Y')
0 days 00:00:00
print pd.to_timedelta(0.9, unit='Y')
0 days 00:00:00
```
Using a float with the hour unit is OK:
```
print pd.to_timedelta(0.9, unit='H')
0 days 00:54:00
print pd.to_timedelta(1., unit='H')
0 days 01:00:00
```
Pandas version:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 21.2.1
Cython: 0.20.1
numpy: 1.11.0
scipy: 0.16.0
statsmodels: 0.7.0.dev-51faa1a
xarray: None
IPython: 4.2.0
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.28.0
pandas_datareader: None
```
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} | 2 | 2016-08-18T00:46:19Z | 2016-08-18T10:00:32Z | 2016-08-18T10:00:32Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
fix test to meet period dtype #13941.
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} | 22 | 2016-08-18T01:44:22Z | 2017-06-04T17:02:55Z | 2017-06-01T22:12:15Z | CONTRIBUTOR | null | This basically adds a method to give access to the coding returned by `grouper.group_info[0]`, i.e. the number of the group that each row is in. This is a natural parallel to cumcount(), and while it's not the world's most important feature it's come in handy for me from time to time and deserves a public method, IMHO.
- [X] closes #11642
- [X] 9 tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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"color": "ffa... | closed | false | null | [] | null | 0 | 2016-08-18T02:55:24Z | 2016-08-19T12:41:42Z | 2016-08-18T03:27:20Z | CONTRIBUTOR | null | - [ ] closes #13919
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
**Why this change is needed:** Categorical variables are by definition single types, so to allow them to take on various different kinds of values is misleading. Object dtypes should only be allowed when ALL strings or ALL periods are passed (due to the way there are handled internally).
**The result of this PR** will raise a `TypeError` when a categorical is created that has an object dtype but doesn't contain all `string` or all `period` values.
I have a couple questions:
When using `MultiIndex.from_arrays`, it creates Categories [here](https://github.com/pydata/pandas/blob/master/pandas/indexes/multi.py#L867), which can have mixed dtypes, and unfortunately my code disallows this. Any tips on how to circumvent this?
Also, [this test in `test_constructor`](https://github.com/pydata/pandas/blob/master/pandas/tests/test_categorical.py#L209) is problematic bc it converts the catergories `dtype` to `object` if `NaN` is in the categories (although this is deprecated). Should I change my code to allow this, or can I assert that this produces a TypeError
Any feedback is appreciated, thanks
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} | 6 | 2016-08-18T03:47:55Z | 2017-01-10T13:19:30Z | 2017-01-10T13:18:21Z | CONTRIBUTOR | null | Fix the fact that we don't skip the rows when inferring colspecs by passing skiprows down the chain until it's needed.
- [X] closes #11256
- [X] 3 tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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} | 3 | 2016-08-18T06:38:00Z | 2016-08-19T02:07:49Z | 2016-08-18T10:04:45Z | NONE | null | when I want to sort one of the columns with a specific order, there is the only way to do this right now. example, we have this dataframe
``` python
import pandas as pd
df = pd.DataFrame({'good': [10], 'excellent': [20], 'bad': [10]})
df.head()
bad excellent good
0 10 20 10
```
and If I want to sort the columns with the `good excellent bad` order, there is the only way
``` py
pd.DataFrame(df, columns=['good', 'excellent', 'bad'])
good excellent bad
0 10 20 10
```
but this is ugly.
can you consider this way of solution?
``` py
import pandas as pd
df = pd.DataFrame({'good': [10], 'excellent': [20], 'bad': [10]})
df.sort_columns(specific_order=['good', excellent', 'bad'])
good excellent bad
0 10 20 10
```
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} | 9 | 2016-08-18T08:05:13Z | 2016-08-22T05:56:42Z | 2016-08-20T13:25:01Z | CONTRIBUTOR | null | - [x] closes #13967
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 12 | 2016-08-18T08:05:49Z | 2016-11-25T14:40:49Z | 2016-11-25T14:40:49Z | CONTRIBUTOR | null | A lot of time I can't find out why a change in a commit was made because it either has no proper commit message or the commit message just says what was changed, but not why.
Add text to the PR template to require such a problem description, so that one can comprehend why a change was made even years later.
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} | 6 | 2016-08-18T08:19:38Z | 2016-12-14T16:45:20Z | 2016-11-18T22:54:23Z | CONTRIBUTOR | null | Currently some issues are just code examples without a description **why** a change should be done. This leads to problems when (years later) the current behaviour is questioned and no one can remember why it was changed.
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} | 5 | 2016-08-18T09:31:23Z | 2016-08-18T11:38:20Z | 2016-08-18T10:33:13Z | NONE | null | I have tried to reduce this problem as small as possible, two data frames attached each with 291 rows.
[pickles.zip](https://github.com/pydata/pandas/files/424538/pickles.zip)
```
df1 = pd.read_pickle('df1.pkl')
df2 = pd.read_pickle('df2.pkl')
merged = df1.merge(df2, how='outer', on=['DateTime', 'valid_datetime'])
#this results in nan values on the join keys
merged.shape[0] == merged[['DateTime', 'valid_datetime']].dropna().shape[0]
```
Note that if the DateTime and valid_datetime columns are converted to strings then this works fine.
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} | 1 | 2016-08-18T14:01:10Z | 2016-08-18T14:39:13Z | 2016-08-18T14:38:51Z | CONTRIBUTOR | null | Using the .where operation on a localized DataFrame Column may raise an exception due to a failure to tz_localize the result of the operation in DatetimeTZBlock
#### Code Sample, a copy-pastable example if possible
```
import pandas
import pytz
TZ = pytz.timezone("Europe/Brussels")
# TZ = None # <== with TZ = None, there is no exception
d = datetime(2012, 3, 24, 16, tzinfo=TZ)
# d = pytz.timezone(TZ).localize(d)
idx = pandas.date_range("2012/03/24", "2012/03/26", tz=TZ, freq='H')
df = pandas.Series(None, index=idx, name="d").to_frame()
df["d"] = idx
print("following line is OK")
df["d"].where(df["d"] > d, d)
df["d"] = idx[::-1] # tz_localize will fail
print("following line raises exception")
df["d"].where(df["d"] > d, d)
```
#### Actual Output
```
following line is OK
following line raises exception
==> raises exception "pytz.exceptions.NonExistentTimeError: 2012-03-25 02:00:00" / "TypeError: Could not operate [1332607320000000000] with block values [2012-03-25 02:00:00]"
```
#### Expected Output
```
following line is OK
following line raises exception
[no exception raised]
```
#### output of `pd.show_versions()`
INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
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- [x] tests added / passed
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- [x] tests added / passed pandas/tests/test_multilevel.py
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corrected how iloc handles tuple-keys for multiindex
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} | 4 | 2016-08-19T02:26:41Z | 2016-08-19T21:37:45Z | 2016-08-19T21:36:29Z | MEMBER | null | `to_wide` was deprecated since 2011 (https://github.com/pydata/pandas/commit/1134c9f6c40398d8eba37c312c88ce294e31c6c5)
xref https://github.com/pydata/pandas/issues/13777
No tests for this deprecation beforehand to remove :disappointed:
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} | 1 | 2016-08-19T02:50:53Z | 2016-08-19T10:08:54Z | 2016-08-19T10:08:21Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
df1 = pd.DataFrame(np.arange(9).reshape(3, 3), columns=['a', 'b', 'c'])
df3 = pd.DataFrame(np.arange(20, 26).reshape(3, 2), columns=['a', 'd'], index=[2, 3, 4])
pd.concat([df1, df3], axis=1, join='inner')
a b c a d
0 0 1 2 NaN NaN
1 3 4 5 NaN NaN
```
#### Expected Output
```
a b c a d
2 6.0 7.0 8.0 20.0 21.0
```
#### output of `pd.show_versions()`
```
### INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14041 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14041/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14041/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14041/events | https://github.com/pandas-dev/pandas/pull/14041 | 172,060,545 | MDExOlB1bGxSZXF1ZXN0ODE5MzAzOTM= | 14,041 | CLN: Drop lags parameter from Panel.shift | {
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} | 2 | 2016-08-19T06:00:38Z | 2016-08-19T09:02:54Z | 2016-08-19T09:02:46Z | MEMBER | null | Title is self-explanatory.
xref #6928
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https://api.github.com/repos/pandas-dev/pandas/issues/14042 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14042/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14042/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14042/events | https://github.com/pandas-dev/pandas/issues/14042 | 172,067,750 | MDU6SXNzdWUxNzIwNjc3NTA= | 14,042 | numpy.split on non-UTC, tz-aware data undergoes UTC roundtrip | {
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} | 4 | 2016-08-19T07:04:29Z | 2020-03-21T20:19:05Z | 2020-03-21T20:19:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
indices = pd.date_range('2016-01-01 00:00:00+0200', freq='S', periods=10)
np.split(indices, indices_or_sections=[])
Out:
[DatetimeIndex(['2015-12-31 20:00:00+02:00', '2015-12-31 20:00:01+02:00',
'2015-12-31 20:00:02+02:00', '2015-12-31 20:00:03+02:00',
'2015-12-31 20:00:04+02:00', '2015-12-31 20:00:05+02:00',
'2015-12-31 20:00:06+02:00', '2015-12-31 20:00:07+02:00',
'2015-12-31 20:00:08+02:00', '2015-12-31 20:00:09+02:00'],
dtype='datetime64[ns, pytz.FixedOffset(120)]', freq='S')]
```
#### Expected Output
```
[DatetimeIndex(['2016-01-01 00:00:00+02:00', '2016-01-01 00:00:01+02:00',
'2016-01-01 00:00:02+02:00', '2016-01-01 00:00:03+02:00',
'2016-01-01 00:00:04+02:00', '2016-01-01 00:00:05+02:00',
'2016-01-01 00:00:06+02:00', '2016-01-01 00:00:07+02:00',
'2016-01-01 00:00:08+02:00', '2016-01-01 00:00:09+02:00'],
dtype='datetime64[ns, pytz.FixedOffset(120)]', freq='S')]
```
#### output of `pd.show_versions()`
```
In [16]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 1.2.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.1
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: 2.42.0
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14043 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14043/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14043/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14043/events | https://github.com/pandas-dev/pandas/issues/14043 | 172,075,026 | MDU6SXNzdWUxNzIwNzUwMjY= | 14,043 | TypeError: 'NoneType' object is not callable | {
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] | closed | false | null | [] | null | 3 | 2016-08-19T07:55:36Z | 2019-10-07T01:34:07Z | 2019-10-07T01:34:07Z | NONE | null | When running the 0.18.1 self tests with python-3.5.2 on NetBSD, I see a couple of test failures. One is tracked in #12337, here's the log for the others:
```
======================================================================
ERROR: test_secondary_kde (pandas.tseries.tests.test_plotting.TestTSPlot)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tseries/tests/test_plotting.py", line 611, in test_secondary_kde
ax = ser.plot(secondary_y=True, kind='density')
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_df_legend_labels (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 2675, in test_df_legend_labels
ax = df.plot(kind=kind, legend=True)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kde_colors (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3020, in test_kde_colors
ax = df.plot.kde(color=custom_colors)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3855, in kde
return self(kind='kde', **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kde_colors_and_styles_subplots (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3043, in test_kde_colors_and_styles_subplots
axes = df.plot(kind='kde', subplots=True)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kde_df (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 2491, in test_kde_df
ax = _check_plot_works(df.plot, kind='kde')
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kde_missing_vals (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 2512, in test_kde_missing_vals
_check_plot_works(df.plot, kind='kde')
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kind_both_ways (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3186, in test_kind_both_ways
df.plot(kind=kind)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_memory_leak (pandas.tests.test_graphics.TestDataFramePlots)
Check that every plot type gets properly collected.
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3647, in test_memory_leak
results[kind] = weakref.proxy(df.plot(kind=kind, **args))
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_no_legend (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 2765, in test_no_legend
ax = df.plot(kind=kind, legend=False)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_plot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1322, in test_plot
df.plot.line(blarg=True)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3760, in line
return self(kind='line', x=x, y=y, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1707, in _make_plot
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1722, in _plot
lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1342, in _plot
return ax.plot(*args, **kwds)
File "/usr/pkg/lib/python3.5/site-packages/matplotlib/__init__.py", line 1819, in inner
return func(ax, *args, **kwargs)
File "/usr/pkg/lib/python3.5/site-packages/matplotlib/axes/_axes.py", line 1382, in plot
for line in self._get_lines(*args, **kwargs):
File "/usr/pkg/lib/python3.5/site-packages/matplotlib/axes/_base.py", line 381, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "/usr/pkg/lib/python3.5/site-packages/matplotlib/axes/_base.py", line 369, in _plot_args
seg = func(x[:, j % ncx], y[:, j % ncy], kw, kwargs)
File "/usr/pkg/lib/python3.5/site-packages/matplotlib/axes/_base.py", line 276, in _makeline
seg = mlines.Line2D(x, y, **kw)
File "/usr/pkg/lib/python3.5/site-packages/matplotlib/lines.py", line 380, in __init__
self.update(kwargs)
File "/usr/pkg/lib/python3.5/site-packages/matplotlib/artist.py", line 859, in update
raise AttributeError('Unknown property %s' % k)
AttributeError: Unknown property blarg
======================================================================
ERROR: test_hist_kde (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1023, in test_hist_kde
_check_plot_works(self.ts.plot.kde)
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3671, in kde
return self(kind='kde', **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_hist_kde_color (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1078, in test_hist_kde_color
ax = self.ts.plot.kde(logy=True, color='r')
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3671, in kde
return self(kind='kde', **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kde_kwargs (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1038, in test_kde_kwargs
ind=linspace(-100, 100, 20))
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3671, in kde
return self(kind='kde', **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kde_missing_vals (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1052, in test_kde_missing_vals
_check_plot_works(s.plot.kde)
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3671, in kde
return self(kind='kde', **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_kind_both_ways (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1099, in test_kind_both_ways
s.plot(kind=kind)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_plot (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 557, in test_plot
_check_plot_works(self.series[:5].plot, kind=kind)
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_series_grid_settings (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1193, in test_series_grid_settings
plotting._common_kinds)
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 480, in _check_grid_settings
obj.plot(kind=kind, **kws)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_valid_object_plot (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 1117, in test_valid_object_plot
_check_plot_works(s.plot, kind=kind)
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 3566, in __call__
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2645, in plot_series
**kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2073, in _make_plot
stacking_id=stacking_id, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 2127, in _plot
gkde = gaussian_kde(y, bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
======================================================================
ERROR: test_scatter_plot_legacy (pandas.tests.test_graphics_others.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File ".../pandas-0.18.1/pandas/tests/test_graphics_others.py", line 408, in test_scatter_plot_legacy
_check_plot_works(scat, diagonal='kde')
File ".../pandas-0.18.1/pandas/tests/test_graphics.py", line 3982, in _check_plot_works
ret = f(**kwargs)
File ".../pandas-0.18.1/pandas/tests/test_graphics_others.py", line 402, in scat
return plotting.scatter_matrix(df, **kwds)
File ".../pandas-0.18.1/pandas/tools/plotting.py", line 370, in scatter_matrix
gkde = gaussian_kde(y)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 171, in __init__
self.set_bandwidth(bw_method=bw_method)
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 498, in set_bandwidth
self._compute_covariance()
File "/usr/pkg/lib/python3.5/site-packages/scipy/stats/kde.py", line 513, in _compute_covariance
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
File "/usr/pkg/lib/python3.5/site-packages/scipy/linalg/basic.py", line 747, in det
a_det, info = fdet(a1, overwrite_a=overwrite_a)
TypeError: 'NoneType' object is not callable
----------------------------------------------------------------------
Ran 10272 tests in 1498.940s
FAILED (errors=20, skipped=572)
```
Any ideas what could cause this? Missing dependency, missing support in libc, something else?
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} | 4 | 2016-08-19T10:27:52Z | 2016-08-21T14:15:14Z | 2016-08-19T10:29:32Z | NONE | null | closes #13565
whatsnew
extended doc string for unique addressing the issue with timezone aware datetimes ( see #13565 )
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} | 4 | 2016-08-19T11:52:41Z | 2016-08-19T21:00:26Z | 2016-08-19T20:41:37Z | MEMBER | null | Follow-up of #13951, rebase of PR #13065
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"color": "AD7... | closed | false | null | [] | null | 13 | 2016-08-19T13:04:32Z | 2016-09-13T22:55:03Z | 2016-09-11T20:26:01Z | CONTRIBUTOR | null | - [ ] closes #13919
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
**Why this change is needed:** Categorical variables are by definition single types, so to allow them to take on different types of values is misleading. Object dtypes should only be allowed when ALL strings or ALL periods are passed (due to the way there are handled internally).
**The result of this PR** will raise a `TypeError` when a categorical is created that has an object dtype but doesn't contain all `string` or all `period` values.
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} | 4 | 2016-08-19T13:42:54Z | 2018-10-25T13:33:20Z | 2018-10-25T13:33:15Z | MEMBER | null | When we pass a list of numbers, it parsed as date as it is. The same values is regarded as `ordinal` if it is passed as an array.
#### Code Sample, a copy-pastable example if possible
```
raw = [2005, 2007, 2009]
pd.PeriodIndex(raw, freq='A')
# PeriodIndex(['2005', '2007', '2009'], dtype='period[A-DEC]', freq='A-DEC')
pd.PeriodIndex(np.array(raw), freq='A')
# PeriodIndex(['3975', '3977', '3979'], dtype='period[A-DEC]', freq='A-DEC')
```
#### Expected Output
Because `PeriodIndex` has a `data` and `ordinal` arg, it should be regarded as:
- date if it is passed via `data` kwd
- `ordinal` if it is passed via `ordinal` kwd
#### output of `pd.show_versions()`
on current master.
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} | 5 | 2016-08-19T15:09:14Z | 2016-08-20T13:09:47Z | 2016-08-20T13:09:35Z | NONE | null | I have DataFrame:
```
import pandas as pd
import numpy as np
rng = pd.date_range('01-01-1988',periods=130000,freq='H')
long_df = pd.DataFrame(np.random.randn(130000,4),index = rng, columns=['bar','baz','foo','zoo'])
dfmi = long_df.stack().sort_index()
```
---
```
print (dfmi)
1988-01-01 00:00:00 bar -1.129213
baz 1.405388
foo 0.482324
zoo -2.315226
1988-01-01 01:00:00 bar -0.533171
baz 1.906526
foo -0.745095
zoo 0.351300
1988-01-01 02:00:00 bar -0.162411
baz -0.218296
foo 0.327074
zoo -0.508463
1988-01-01 03:00:00 bar -0.676780
baz -1.137270
foo 0.389457
zoo -0.517306
1988-01-01 04:00:00 bar 1.368110
baz 1.106048
foo 1.342662
zoo -0.215235
1988-01-01 05:00:00 bar -1.284003
baz 0.216028
foo 0.889421
zoo -0.965881
1988-01-01 06:00:00 bar 1.522085
baz 0.436332
foo 0.114714
zoo 0.342456
1988-01-01 07:00:00 bar -0.176803
baz -0.365471
2002-10-30 08:00:00 foo -1.196314
zoo 1.245414
2002-10-30 09:00:00 bar 1.271792
baz -0.688911
foo -0.852729
zoo 0.869926
2002-10-30 10:00:00 bar -0.487386
baz -0.161470
foo 0.593564
zoo -0.440397
2002-10-30 11:00:00 bar 0.856882
baz -0.297341
foo 2.106393
zoo 0.186903
2002-10-30 12:00:00 bar 1.551650
baz 1.152780
foo 0.726544
zoo -0.334287
2002-10-30 13:00:00 bar 0.483035
baz 1.573808
foo -0.734948
zoo 0.005149
2002-10-30 14:00:00 bar 0.077451
baz 0.783963
foo -0.111543
zoo -0.637871
2002-10-30 15:00:00 bar 0.383890
baz -0.368546
foo 0.503162
zoo 0.324679
dtype: float64
```
---
```
print (dfmi.index.get_level_values(0))
DatetimeIndex(['1988-01-01 00:00:00', '1988-01-01 00:00:00',
'1988-01-01 00:00:00', '1988-01-01 00:00:00',
'1988-01-01 01:00:00', '1988-01-01 01:00:00',
'1988-01-01 01:00:00', '1988-01-01 01:00:00',
'1988-01-01 02:00:00', '1988-01-01 02:00:00',
...
'2002-10-30 13:00:00', '2002-10-30 13:00:00',
'2002-10-30 14:00:00', '2002-10-30 14:00:00',
'2002-10-30 14:00:00', '2002-10-30 14:00:00',
'2002-10-30 15:00:00', '2002-10-30 15:00:00',
'2002-10-30 15:00:00', '2002-10-30 15:00:00'],
dtype='datetime64[ns]', length=520000, freq='H')
```
If select by **year**, **month** and **day**, it works nice:
```
print (dfmi.loc['2001-01-01'])
2001-01-01 00:00:00 bar -0.349633
baz -1.945182
foo 0.422883
zoo -1.183061
2001-01-01 01:00:00 bar -0.014788
baz -0.646235
foo -1.449877
zoo 0.422516
2001-01-01 02:00:00 bar 0.197731
baz -0.307682
foo -1.293725
zoo 0.283953
2001-01-01 03:00:00 bar 0.725005
baz 1.182862
foo -0.349092
zoo 1.354510
2001-01-01 04:00:00 bar -0.516522
baz 0.582963
foo -0.577522
zoo 0.710188
2001-01-01 05:00:00 bar -0.453547
baz -1.054144
foo 0.911428
zoo -0.417231
2001-01-01 06:00:00 bar -1.117797
baz -0.315343
foo 1.441117
zoo 1.135960
2001-01-01 07:00:00 bar -0.607946
baz 1.210047
...
2001-01-01 16:00:00 foo -1.435443
zoo 0.045783
2001-01-01 17:00:00 bar -0.590686
baz 1.115333
foo -0.558769
zoo -0.522037
2001-01-01 18:00:00 bar -0.862442
baz 0.917343
foo 0.759948
zoo -0.004601
2001-01-01 19:00:00 bar -1.901064
baz 0.281404
foo -0.704072
zoo -0.104176
2001-01-01 20:00:00 bar -0.703224
baz -0.170848
foo 0.547919
zoo 1.199431
2001-01-01 21:00:00 bar 1.198095
baz -0.416019
foo 1.551871
zoo -0.472543
2001-01-01 22:00:00 bar -0.239550
baz -0.401000
foo 0.959729
zoo 0.783011
2001-01-01 23:00:00 bar 0.415624
baz 0.093273
foo 0.712913
zoo -1.026016
dtype: float64
```
But if select by **year** or **year** and **month**, it return all DataFrame, there is not slicing.
```
print (dfmi.loc['2001-01'])
print (dfmi.loc['2001'])
```
Is it bug or not implemented yet? Thank you.
```
print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None
```
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} | 1 | 2016-08-19T19:39:00Z | 2016-08-19T20:44:48Z | 2016-08-19T20:44:27Z | NONE | null | total_rows is only set in to_gbq() if verbose = True. If verbose = False, it is reference before assignment.
#### Code Sample, a copy-pastable example if possible
data.to_gbq('ANYTABLE',project_id ='ANYPROJECT',verbose = False,if_exists = 'append')
'UnboundLocalError: local variable 'total_rows' referenced before assignment'
#### Expected Output
Expected output is a successful upload to GBQ.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 19.4
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.2
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 2 | 2016-08-19T22:13:18Z | 2016-11-15T22:20:35Z | 2016-08-26T20:50:24Z | CONTRIBUTOR | null | - [x] closes #14021
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 34 | 2016-08-20T14:05:04Z | 2019-07-05T00:46:58Z | 2019-07-05T00:46:58Z | CONTRIBUTOR | null | When getting the values of a dataframe with multiple columns with a single type datetime64[ns,UTC], it returns a numpy array with dtype=object instead of dtype=datetime64[ns,UTC].
When returning a single column with dtype=datetime64[ns,UTC] or returning a multiple columns of dtype=datetime64[ns] (ie without tz), it works.
Looking at the code, it appears that SingleBlockManager.is_mixed_type returns False in the first case (datetime64[ns,UTC]) and True in the two other cases (single datetime64[ns,UTC] or multiple datetime64[ns]).
#### Code Sample, a copy-pastable example if possible
```
import pandas
idx = pandas.date_range("2010", "2011", tz="UTC")
df = pandas.DataFrame(index=idx)
df["A"] = idx
df["B"] = idx
print(df[["B"]].values.dtype)
print(df[["A", "B"]].values.dtype)
```
#### Actual Output
```
datetime64[ns, UTC]
object
```
#### Expected Output
```
datetime64[ns, UTC]
datetime64[ns, UTC]
```
#### output of `pd.show_versions()`
INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 7 | 2016-08-20T14:45:54Z | 2016-12-10T15:38:17Z | 2016-12-10T15:38:14Z | CONTRIBUTOR | null | - [x] closes #11847
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Changed the way in which the original data frame is copied (dropped use of .values, since it does not preserve dtypes).
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} | 2 | 2016-08-20T17:09:15Z | 2016-08-24T23:28:45Z | 2016-08-24T23:28:45Z | NONE | null | When I call to_csv of dataframe object, I am not required to explicitly set path parameter. The default one is None and will return a string. I thought the same thing for series object. This time the path parameter becomes explicitly required. I wonder if it is better to keep consistency between them.
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} | 3 | 2016-08-20T18:06:24Z | 2016-08-21T14:18:05Z | 2016-08-21T14:17:47Z | MEMBER | null | Converts `usecols` to a `set` just like we do on the C engine side.
Closes #12546.
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} | 4 | 2016-08-21T02:56:25Z | 2016-08-21T19:34:47Z | 2016-08-21T19:34:41Z | MEMBER | null | 1) Update documentation to state that scalars are accepted for `na_values`
2) Accept scalars for the values when a dictionary is passed in for `na_values`
Closes #12224.
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} | 8 | 2016-08-21T13:40:55Z | 2021-05-01T23:20:10Z | 2021-05-01T23:20:10Z | CONTRIBUTOR | null | after #13145 some RunTimeWarnings remain in the codebase. We should look at these and silence (using `np.errstate`), or fix if we are testing something odd. (running the full test suite will show these; I have extracted them, some are showing a level too low in the stack).
- [ ] `pandas/pandas/tseries/index.py:526: RuntimeWarning: overflow encountered in long_scalars return lambda x: Timestamp(x, freq=self.offset, tz=self.tz)`
- [ ] `pandas/pandas/tseries/tests/test_tslib.py:453: RuntimeWarning: overflow encountered in long_scalar self.assertIs(ts.tz_localize(tz, errors='coerce'),`
- [ ] `pandas/pandas/core/internals.py:2341: RuntimeWarning: overflow encountered in long_scalar f = lambda x: lib.Timestamp(x, tz=self.values.tz)`
- [ ] `pandas/pandas/core/internals.py:4753: ComplexWarning: Casting complex values to real discards the imaginary part nn_at = nn.astype(v.dtype)`
- [ ] `pandas/pandas/tests/test_algos.py:735: RuntimeWarning: overflow encountered in long_scalars np.array([pd.Timestamp(d, tz='US/Eastern') for d in dt]),`
- [ ] `pandas/pandas/core/ops.py:650: RuntimeWarning: invalid value encountered in double_scalars lambda x: op(x, rvalues))`
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} | 2 | 2016-08-21T14:11:36Z | 2016-08-21T19:53:49Z | 2016-08-21T19:53:49Z | CONTRIBUTOR | null | I ran into this when I accidentally passed my codes to the `ordered` argument
#### Code Sample, a copy-pastable example if possible
``` python
In [2]: pd.Categorical.from_codes([0, 0, 1], categories=['a', 'b', 'c'], ordered=np.array([0, 1, 2]))
Out[2]: ---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/IPython/core/formatters.py in __call__(self, obj)
668 type_pprinters=self.type_printers,
669 deferred_pprinters=self.deferred_printers)
--> 670 printer.pretty(obj)
671 printer.flush()
672 return stream.getvalue()
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/IPython/lib/pretty.py in pretty(self, obj)
381 if callable(meth):
382 return meth(obj, self, cycle)
--> 383 return _default_pprint(obj, self, cycle)
384 finally:
385 self.end_group()
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/IPython/lib/pretty.py in _default_pprint(obj, p, cycle)
501 if _safe_getattr(klass, '__repr__', None) not in _baseclass_reprs:
502 # A user-provided repr. Find newlines and replace them with p.break_()
--> 503 _repr_pprint(obj, p, cycle)
504 return
505 p.begin_group(1, '<')
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
692 """A pprint that just redirects to the normal repr function."""
693 # Find newlines and replace them with p.break_()
--> 694 output = repr(obj)
695 for idx,output_line in enumerate(output.splitlines()):
696 if idx:
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/core/base.py in __repr__(self)
70 Yields Bytestring in Py2, Unicode String in py3.
71 """
---> 72 return str(self)
73
74
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/core/base.py in __str__(self)
49
50 if compat.PY3:
---> 51 return self.__unicode__()
52 return self.__bytes__()
53
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/core/categorical.py in __unicode__(self)
1571 result = self._tidy_repr(_maxlen)
1572 elif len(self._codes) > 0:
-> 1573 result = self._get_repr(length=len(self) > _maxlen)
1574 else:
1575 result = ('[], %s' %
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/core/categorical.py in _get_repr(self, length, na_rep, footer)
1562 formatter = fmt.CategoricalFormatter(self, length=length,
1563 na_rep=na_rep, footer=footer)
-> 1564 result = formatter.to_string()
1565 return compat.text_type(result)
1566
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/formats/format.py in to_string(self)
134 result = [u('[') + result + u(']')]
135 if self.footer:
--> 136 footer = self._get_footer()
137 if footer:
138 result.append(footer)
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/formats/format.py in _get_footer(self)
105 footer += "Length: %d" % len(self.categorical)
106
--> 107 level_info = self.categorical._repr_categories_info()
108
109 # Levels are added in a newline
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/core/categorical.py in _repr_categories_info(self)
1540 start = True
1541 cur_col_len = len(levheader) # header
-> 1542 sep_len, sep = (3, " < ") if self.ordered else (2, ", ")
1543 linesep = sep.rstrip() + "\n" # remove whitespace
1544 for val in category_strs:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
If you look through the traceback, the error happens in the `repr`, not at construction time.
The regular `Categorical` constructor does catch this (error message could be improved though).
#### Expected Output
`TypeError("`ordered`must be a boolean, got {!r} instead")`
#### output of `pd.show_versions()`
master
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https://api.github.com/repos/pandas-dev/pandas/issues/14059 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14059/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14059/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14059/events | https://github.com/pandas-dev/pandas/pull/14059 | 172,331,653 | MDExOlB1bGxSZXF1ZXN0ODIxMTE1NTI= | 14,059 | BUG: Validate the ordered parameter for Categorical | {
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} | 3 | 2016-08-21T17:39:12Z | 2016-08-21T19:59:15Z | 2016-08-21T19:53:49Z | MEMBER | null | Title is self-explanatory. Closes #14058.
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} | 4 | 2016-08-21T20:45:32Z | 2016-08-22T20:51:44Z | 2016-08-22T20:47:18Z | CONTRIBUTOR | null | - [x] closes #13549
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
-read_csv with engine=c throws error when encoding=UTF_16 (anything other than utf-16)
-improved nosetests and moved to in pandas/io/tests/common.py
-passes `pep8radius upstream/master --diff` and `git diff upstream/master | flake8 --diff`
-put what's new entry in 0.19.0 in accordance with milestone posted on issue
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https://api.github.com/repos/pandas-dev/pandas/issues/14061 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14061/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14061/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14061/events | https://github.com/pandas-dev/pandas/pull/14061 | 172,352,006 | MDExOlB1bGxSZXF1ZXN0ODIxMjI5ODU= | 14,061 | ENH: add parameter to to_html for HTML border | {
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} | 2 | 2016-08-22T00:53:20Z | 2017-04-05T02:08:04Z | 2016-08-31T08:07:31Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/11563
Previously this was hardcoded to `<table border="1" ...>`. Now we allow an option to `to_html`.
The only controversial bit here may be the option. I really don't like having options control output, but given that this is essentially a repr I think it's OK. It's also the only way to expose it to the user since `_repr_html_` can't take any arguments. I also selfishly need this [for my own library](https://github.com/tomaugspurger/stitch), where the user wouldn't be able to pass an option 😄
Interestingly, the `border="0"` is seemingly redundant in the notebook:
Looking at the CSS, I see jupyter applying a border-width of 0.909091px in their style, though I can't remember if I've adjusted the default CSS on this computer; will check later.
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https://api.github.com/repos/pandas-dev/pandas/issues/14062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14062/events | https://github.com/pandas-dev/pandas/pull/14062 | 172,356,026 | MDExOlB1bGxSZXF1ZXN0ODIxMjU0MTI= | 14,062 | WIP: add sort_levels to MultiIndex.from_product | {
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] | closed | false | null | [] | null | 2 | 2016-08-22T01:55:01Z | 2016-11-16T21:29:30Z | 2016-11-16T21:29:30Z | MEMBER | null | Currently, `from_product` always sorts levels in the resulting MultiIndex. This means that the result does not necessarily have lexsorted labels/codes.
This PR adds an option to not sort levels when calling `from_product`. Using this option yields a few benefits:
1. It's simpler -- resulting levels on the MultiIndex are exactly those you passed in.
2. It's marginally faster -- you don't need to sort the levels.
3. The resulting MultiIndex is always lex-sorted. This is handy if you want to be able to index it effeciently.
The downside is that the result can be a little less intuitive, because levels and labels do not have the same sort order (#14015).
I'm suggesting this option because it would be useful for xarray (to fix https://github.com/pydata/xarray/issues/980) -- otherwise we would simply put it in our own utility function.
I'm not certain that this is the best argument name, or that I've described it in a way that is clear if you don't understand MultiIndex internals.
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2016-08-22T03:56:09Z | 2016-08-24T23:28:49Z | 2016-08-24T23:28:45Z | MEMBER | null | Default `path=None` in `Series.to_csv()` for consistency with `DataFrame.to_csv()`.
Closes #14054.
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} | 30 | 2016-08-22T14:39:57Z | 2017-02-09T17:12:17Z | 2017-02-09T17:11:31Z | NONE | null | fixes:
- lost precision for longs above 2^53
- and floats above 10^4
Also contains `test_gbq.py` clean up
- [x] closes #14020, #14305
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] updated docs
- [x] whatsnew entry
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} | 7 | 2016-08-22T19:27:03Z | 2016-08-31T16:10:31Z | 2016-08-31T16:10:31Z | NONE | null | I have the following code:
`dfEL = pd.read_csv(IN_PATH, delimiter='\\u00A7', encoding='utf-8')`
which I've also tried with other ways of writing the delimiter, e.g.:
`dfEL = pd.read_csv(IN_PATH, delimiter='§', encoding='utf-8')`
These other methods don't work, and generate a `UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc2 in position 10: unexpected end of data`.
The first method, though, won't use the C regex engine:
```
dfEL = pd.read_csv(IN_PATH, delimiter='\\u00A7', encoding='utf-8')
C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
if __name__ == '__main__':
```
Shouldn't this only be considered one character and still use (the I presume faster) C engine?
Sample data - there's a lot of messiness in the rightmost column, which is why an unusual separator was used:
```
foo§1457431587429§$request_details.bar
foo§1457431587429§$request_details.foo.bar
foo§1457431587429§$request_details.foo.foo.bar
foo§1457431587429§null
foo§1457431587429§null
foo§1457431587429§null
foo§1457431587429§$request_type
foo§1457431587429§$additional_details
foo§1457431587429§$Generic_Params.success_msg_folder+$request_type.action+'_'+$request_type.object
§1459972605829§$Path
§1459972605829§$Name
§1441995198746§$original.original
§1441995198746§null
```
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 10 | 2016-08-22T21:42:16Z | 2016-09-13T19:45:06Z | 2016-08-27T13:31:30Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Using the following reader leads to omission of the dates resulting in no index:
```
file_, index_col=0,
header=4, sep=';', quoting=1, parse_dates=True,
encoding='latin-1', dayfirst=True,
# decimal=',', thousands='.'
```
#### Expected Output
dataframe with pyhon numeric data and datecol as index
#### output of `pd.show_versions()`
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14067 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14067/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14067/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14067/events | https://github.com/pandas-dev/pandas/issues/14067 | 172,571,818 | MDU6SXNzdWUxNzI1NzE4MTg= | 14,067 | to_sql: Does not produce a hierarchical index when using SQLAlchemy as the dbAPI | {
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"default": false,
"description": "to_sql, read_sql, read_s... | open | false | null | [] | null | 0 | 2016-08-22T22:38:26Z | 2020-05-16T03:46:56Z | null | NONE | null | Based on this post here:
http://stackoverflow.com/questions/39089513/pandas-sqlalchemy-engine-does-not-produce-hierarchical-index-but-legacy-mode-do
#### data ('test.csv'):
```
AlignmentId,Classifier,Value
aln1,classifier1,True
aln1,classifier2,False
aln2,classifier1,True
```
#### Sample code:
```
import pandas as pd
import sqlalchemy
import sqlite3
sqla_con = sqlalchemy.create_engine('sqlite:///test.db')
legacy_con = sqlite3.connect('test.db')
df = pd.read_csv('test.csv', index_col=[0, 1])
df.to_sql('legacy', legacy_con)
df.to_sql('sqlalchemy', sqla_con)
```
#### Output (inspecting the resulting sqlite database):
```
$sqlite3
SQLite version 3.13.0 2016-05-18 10:57:30
Enter ".help" for usage hints.
Connected to a transient in-memory database.
Use ".open FILENAME" to reopen on a persistent database.
sqlite> .open test.db
sqlite> .schema
CREATE TABLE "legacy" (
"AlignmentId" TEXT,
"Classifier" TEXT,
"Value" INTEGER
);
CREATE INDEX "ix_legacy_AlignmentId_Classifier"ON "legacy" ("AlignmentId","Classifier");
CREATE TABLE sqlalchemy (
"AlignmentId" TEXT,
"Classifier" TEXT,
"Value" BOOLEAN,
CHECK ("Value" IN (0, 1))
);
CREATE INDEX "ix_sqlalchemy_Classifier" ON sqlalchemy ("Classifier");
CREATE INDEX "ix_sqlalchemy_AlignmentId" ON sqlalchemy ("AlignmentId");
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-504.3.3.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14068 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14068/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14068/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14068/events | https://github.com/pandas-dev/pandas/issues/14068 | 172,576,542 | MDU6SXNzdWUxNzI1NzY1NDI= | 14,068 | TimedeltaIndex + Timestamp -> no overflow error | {
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... | closed | false | null | [] | {
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} | 3 | 2016-08-22T23:11:42Z | 2016-09-27T10:43:45Z | 2016-09-27T10:43:45Z | CONTRIBUTOR | null | When adding a TimedeltaIndex and a Timestamp this overflows instead of raising (pydata/xarray#975).
```
import pandas as pd
# overflow error (correct)
pd.to_timedelta(106580, 'D') + pd.Timestamp('2000')
# no overflow error (?)
pd.to_timedelta([106580], 'D') + pd.Timestamp('2000')
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.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: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.6
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: 0.7.2-73-g584e703
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
edit: 'D' as unit
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https://api.github.com/repos/pandas-dev/pandas/issues/14069 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14069/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14069/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14069/events | https://github.com/pandas-dev/pandas/pull/14069 | 172,597,107 | MDExOlB1bGxSZXF1ZXN0ODIyOTMyNjk= | 14,069 | TST: Add tests for internal EOF in read_csv | {
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} | 2 | 2016-08-23T02:13:48Z | 2016-08-24T23:27:49Z | 2016-08-24T23:27:45Z | MEMBER | null | Title is self-explanatory. Closes #5500.
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https://api.github.com/repos/pandas-dev/pandas/issues/14070 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14070/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14070/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14070/events | https://github.com/pandas-dev/pandas/issues/14070 | 172,611,747 | MDU6SXNzdWUxNzI2MTE3NDc= | 14,070 | DatetimeIndex.to_period does not support multiple of frequencies | {
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... | open | false | null | [] | null | 4 | 2016-08-23T04:50:53Z | 2021-05-01T23:21:41Z | null | CONTRIBUTOR | null | The code below is the simplest way I found to "snap" a DateTimeIndex to some "frequency" (is there any better way?).
I would have expected the to_period method to work as flexibly as the resample method that accepts any rule (like 15T, 2H, ...) but the to_period method does not appear to handle properly multiples of frequencies (and neither raises an exception).
#### Code Sample, a copy-pastable example if possible
```
import pandas
import datetime
dt=pandas.date_range('2016-01-01 00:00','2016-01-01 05:00',freq='15T',closed="left")
print(dt.to_period('H').start_time) # snap to the hour : OK
print(dt.to_period('2H').start_time) # snap to even hours : not OK
```
#### Actual Output
```
DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 01:00:00', '2016-01-01 01:00:00',
'2016-01-01 01:00:00', '2016-01-01 01:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 03:00:00', '2016-01-01 03:00:00',
'2016-01-01 03:00:00', '2016-01-01 03:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00'],
dtype='datetime64[ns]', freq=None)
DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 01:00:00', '2016-01-01 01:00:00',
'2016-01-01 01:00:00', '2016-01-01 01:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 03:00:00', '2016-01-01 03:00:00',
'2016-01-01 03:00:00', '2016-01-01 03:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00'],
dtype='datetime64[ns]', freq=None)
```
#### Expected Output
```
DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 01:00:00', '2016-01-01 01:00:00',
'2016-01-01 01:00:00', '2016-01-01 01:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 03:00:00', '2016-01-01 03:00:00',
'2016-01-01 03:00:00', '2016-01-01 03:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00'],
dtype='datetime64[ns]', freq=None)
DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 00:00:00', '2016-01-01 00:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 02:00:00', '2016-01-01 02:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00',
'2016-01-01 04:00:00', '2016-01-01 04:00:00'],
dtype='datetime64[ns]', freq=None)
```
#### output of `pd.show_versions()`
INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14071 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14071/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14071/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14071/events | https://github.com/pandas-dev/pandas/issues/14071 | 172,681,976 | MDU6SXNzdWUxNzI2ODE5NzY= | 14,071 | DatetimeIndex periods: Change in behavior between 0.17.1 and 0.18.0 | {
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} | 1 | 2016-08-23T12:05:58Z | 2016-08-24T23:26:32Z | 2016-08-24T23:26:10Z | NONE | null | This is not necessarily a bug, but a change in behavior that could not find reported elsewhere.
``` python
import pandas as pd,datetime as dt
pd.DatetimeIndex(freq=pd.tseries.offsets.DateOffset(months=6),end=dt.date(2016,1,1),
closed='right',periods=3)
```
In version 0.17.1 you get
``` python
DatetimeIndex(['2015-07-01', '2016-01-01'], dtype='datetime64[ns]',
freq='<DateOffset: kwds={'months': 6}>')
```
however, in version 0.18.1 you get
``` python
DatetimeIndex(['2015-01-01', '2015-07-01', '2016-01-01'], dtype='datetime64[ns]',
freq='<DateOffset: kwds={'months': 6}>')
```
The new behavior seems more intuitive. Is this change documented anywhere? Thanks!
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https://api.github.com/repos/pandas-dev/pandas/issues/14072 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14072/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14072/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14072/events | https://github.com/pandas-dev/pandas/issues/14072 | 172,724,859 | MDU6SXNzdWUxNzI3MjQ4NTk= | 14,072 | pivot_table margins bottom-left total does not correspond to other content when dropna=False | {
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} | 3 | 2016-08-23T15:07:00Z | 2016-09-13T22:10:38Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
In:
M = pd.DataFrame([
[1, 'a', 'A'],
[1, 'b', 'B'],
[1, 'c', None]],
columns=['x', 'y', 'z'])
P = M.pivot_table(values='x', index='y', columns='z', aggfunc='sum', fill_value=0, margins=True, dropna=False)
P
Out:
| z | A | B | All |
| --- | --- | --- | --- |
| y | | | |
| a | 1.0 | 0.0 | 1.0 |
| b | 0.0 | 1.0 | 1.0 |
| All | 1.0 | 1.0 | 3.0 |
#### Expected Output
Either
| z | A | B | All |
| --- | --- | --- | --- |
| y | | | |
| a | 1.0 | 0.0 | 1.0 |
| b | 0.0 | 1.0 | 1.0 |
| All | 1.0 | 1.0 | 2.0 |
or
| z | A | B | None | All |
| --- | --- | --- | --- | --- |
| y | | | | |
| a | 1.0 | 0.0 | 0.0 | 1.0 |
| b | 0.0 | 1.0 | 0.0 | 1.0 |
| c | 0.0 | 0.0 | 1.0 | 1.0 |
| All | 1.0 | 1.0 | 1.0 | 3.0 |
depending on dropna's meaning for this case
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.1
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.20.1post0
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14073 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14073/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14073/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14073/events | https://github.com/pandas-dev/pandas/pull/14073 | 172,773,910 | MDExOlB1bGxSZXF1ZXN0ODI0MTc3OTU= | 14,073 | DOC: split docstring into multiple lines in excel.py | {
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} | 10 | 2016-08-23T18:36:41Z | 2016-09-15T14:59:32Z | 2016-09-15T14:59:32Z | CONTRIBUTOR | null | - [x] closes #13967
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
A follow-up on #14030.
Not sure whether this is the good way to do it - this seems too verbose.
I will add it to `parsers.py` or move it to `common.py` once it's done.
I also thought about moving the function to `Appender` to fix the length problem for good if necessary.
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} | 3 | 2016-08-23T22:55:13Z | 2021-05-01T23:24:00Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
x axis truncated by second call
```
ser1 = pd.Series({0:1, 1:2, 2:3})
ser2 = pd.Series({0:.5, 1:.1})
_, axes = plt.subplots(1, 2, sharex=True, sharey=True)
ser1.plot(ax=axes[0])
ser2.plot(ax=axes[1])
```
#### Expected Output
Using only matplotlib:
```
ser1 = pd.Series({0:1, 1:2, 2:3})
ser2 = pd.Series({0:.5, 1:.1})
_, axes = plt.subplots(1, 2, sharex=True, sharey=True)
axes[0].plot(ser1.index, ser1.values)
axes[1].plot(ser2.index, ser2.values)
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.1
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.2
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14075 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14075/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14075/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14075/events | https://github.com/pandas-dev/pandas/pull/14075 | 172,851,109 | MDExOlB1bGxSZXF1ZXN0ODI0NzI0Mjg= | 14,075 | add utility methods get_groups() and iter_groups() to class _GroupBy | {
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} | 3 | 2016-08-24T02:32:16Z | 2016-08-25T01:45:10Z | 2016-08-25T00:44:25Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
added utility methods get_groups() and iter_groups() to groupby objects
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 1 | 2016-08-24T04:01:57Z | 2016-11-30T20:20:13Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
t = [1, 2, 3, 4, 5]
tt = [2, 3, 4, 5, 7]
d1 = DataFrame(t, columns=['a'], index=t)
d2 = DataFrame(tt, columns=['b'])
r = pd.merge(d1, d2, how='left', left_index=True, right_on='b', copy=False)
print(r)
pd.show_versions()
#### Expected Output
/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/bin/python3.5 /var/root/finalworkspace/python/daas/core/process_data.py
a b
4 1 1.0
0 2 2.0
1 3 3.0
2 4 4.0
3 5 5.0
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.4
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: 0.6.7.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
Process finished with exit code 0
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https://api.github.com/repos/pandas-dev/pandas/issues/14077 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14077/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14077/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14077/events | https://github.com/pandas-dev/pandas/pull/14077 | 172,888,121 | MDExOlB1bGxSZXF1ZXN0ODI0OTczMjE= | 14,077 | BUG: Don't parse index column as numeric when parse_dates=True | {
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} | 4 | 2016-08-24T07:57:28Z | 2016-08-27T13:31:37Z | 2016-08-27T13:31:30Z | MEMBER | null | When a thousands parameter is specified, if the index column data contains that thousands value for date purposes (e.g. '.'), do not interpret those characters as the thousands parameter.
Closes #14066.
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} | 2 | 2016-08-24T13:03:49Z | 2016-08-25T10:14:42Z | 2016-08-24T22:42:24Z | NONE | null | Feature request: it would be nice if `aggregate` allowed classes as an argument, using its constructor as the function to aggregate by. In the code sample it recognises `frozenset` as an iterable because it happens to have such an instance method.
I'd suggest a check like:
``` python
>>> inspect.isclass(frozenset)
True
>>> inspect.isclass(frozenset())
False
```
(I don't think a sane person would make a class with a `@staticmethod __iter__` returning the functions to aggregate by)
#### Code Sample, a copy-pastable example if possible
``` python
>>> pd.DataFrame(np.ones((2,2)), columns=[0,1]).groupby(1)[[0]].agg(frozenset)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/limyreth/.local/lib/python3.5/site-packages/pandas/core/groupby.py", line 3597, in aggregate
return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
File "/home/limyreth/.local/lib/python3.5/site-packages/pandas/core/groupby.py", line 3114, in aggregate
result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
File "/home/limyreth/.local/lib/python3.5/site-packages/pandas/core/base.py", line 564, in _aggregate
return self._aggregate_multiple_funcs(arg, _level=_level), None
File "/home/limyreth/.local/lib/python3.5/site-packages/pandas/core/base.py", line 616, in _aggregate_multiple_funcs
return concat(results, keys=keys, axis=1)
File "/home/limyreth/.local/lib/python3.5/site-packages/pandas/tools/merge.py", line 845, in concat
copy=copy)
File "/home/limyreth/.local/lib/python3.5/site-packages/pandas/tools/merge.py", line 878, in __init__
raise ValueError('No objects to concatenate')
ValueError: No objects to concatenate
```
It can be worked around by using a lambda:
``` python
pd.DataFrame(np.ones((2,2)), columns=[0,1]).groupby(1)[[0]].agg(lambda x: frozenset(x))
```
#### Expected Output
``` python
0
1
1.0 (1.0)
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.4-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.6.7
Cython: None
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: None
sphinx: 1.4.5
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.0b2
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14079 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14079/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14079/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14079/events | https://github.com/pandas-dev/pandas/issues/14079 | 172,989,572 | MDU6SXNzdWUxNzI5ODk1NzI= | 14,079 | Improve poor performance of groupby resampling | {
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} | 5 | 2016-08-24T15:48:46Z | 2021-05-25T01:56:58Z | 2016-08-24T22:56:36Z | NONE | null | When trying to resample transactions data where there are infrequent transactions for a large number of people, I get horrible performance. Compare the function annualize with the clunkier but faster annualize2 below. Ideally resample should be able to handle multiindex data and resample on 1 of the dimensions without the need to resort to groupby.
#### Code Sample, a copy-pastable example if possible
```
import pandas
import numpy as np
def annualize(df):
def resample(df):
df = df.set_index('DATE')
df = df.resample('A').ffill()
df = df.reset_index()
return df
df = df.groupby('ID', as_index = False).apply(resample)
df['YEAR'] = df['DATE'].dt.year
df = df.reset_index(drop = True)
return df
def annualize2(df):
#DF must be sorted by ID then DATE
df = df.sort_values(['ID','DATE']).reset_index(drop = True)
#Extract the year from the date and keep last observation from each year
df['YEAR'] = df['DATE'].dt.year
df = df.drop_duplicates(subset = ['ID','YEAR'], keep = 'last')
#Get the YEAR and ID of the next record down
ndf = df[['ID', 'YEAR']].shift(-1)
#Want to duplicate records where ID's between record pairs match and spacing is more than year
match = (ndf['ID'] == df['ID'])
reps = np.array(np.maximum(match*(ndf['YEAR'] - df['YEAR']),1), dtype = int)
reps[-1] = 1 # the last value will by -inf
#Do the duplication
df = df.loc[np.repeat(df.index, reps)]
#Now we need to fix the year field
cs = np.ones(len(df))
cs[np.cumsum(reps[:-1])] -= reps[:-1]
df['YEAR'] = df['YEAR'] + np.cumsum(cs)-1
df = df.reset_index(drop = True)
return df
if __name__ == '__main__':
import StringIO
import timeit
#Generate some transaction record data
data='''ID,DATE,STATUS
1,3/1/2005,A
1,4/30/2005,B
1,6/30/2010,C
1,8/20/2010,D
'''
df = pandas.read_csv(StringIO.StringIO(data))
df['DATE'] = pandas.to_datetime(df['DATE'])
print(annualize(df))
print(annualize2(df))
#then repeat the rows of the table to get a more realistic sample size for measuring performance
df = df.loc[np.tile(df.index.values, 10000)]
df['ID'] = np.repeat(np.arange(10000), 4)
print('annualize')
print(timeit.timeit('annualize(df)', setup = 'from __main__ import annualize, df', number = 1))
print('annualize2')
print(timeit.timeit('annualize2(df)', setup = 'from __main__ import annualize2, df', number = 1))
```
#### Expected Output
```
DATE ID STATUS YEAR
0 2005-12-31 1 B 2005
1 2006-12-31 1 B 2006
2 2007-12-31 1 B 2007
3 2008-12-31 1 B 2008
4 2009-12-31 1 B 2009
5 2010-12-31 1 D 2010
ID DATE STATUS YEAR
0 1 2005-04-30 B 2005.0
1 1 2005-04-30 B 2006.0
2 1 2005-04-30 B 2007.0
3 1 2005-04-30 B 2008.0
4 1 2005-04-30 B 2009.0
5 1 2010-08-20 D 2010.0
annualize
26.4788940619
annualize2
0.0899452309903
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14080 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14080/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14080/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14080/events | https://github.com/pandas-dev/pandas/issues/14080 | 172,992,293 | MDU6SXNzdWUxNzI5OTIyOTM= | 14,080 | Adding big offset to timedelta generates a python crash | {
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} | 10 | 2016-08-24T15:59:39Z | 2018-11-18T21:56:56Z | 2018-11-18T21:56:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
##### In:
```
import pandas as pd
from pandas.tseries.frequencies import to_offset
d = pd.Timestamp("2000/1/1")
d + to_offset("D")*100**25
```
##### Out:
**=> python crash**
Fatal Python error: Cannot recover from stack overflow.
Current thread 0x00002b00 (most recent call first):
File "C:\Users\geoffroy.destaintot\Miniconda3\envs\pd-0.18\lib\site-packages\pandas\tseries\offsets.py", line 2526 in delta
File "C:\Users\geoffroy.destaintot\Miniconda3\envs\pd-0.18\lib\site-packages\pandas\tseries\offsets.py", line 2535 in apply
File "C:\Users\geoffroy.destaintot\Miniconda3\envs\pd-0.18\lib\site-packages\pandas\tseries\offsets.py", line 2493 in **add**
File "C:\Users\geoffroy.destaintot\Miniconda3\envs\pd-0.18\lib\site-packages\pandas\tseries\offsets.py", line 390 in **radd**
File "C:\Users\geoffroy.destaintot\Miniconda3\envs\pd-0.18\lib\site-packages\pandas\tseries\offsets.py", line 2535 in apply
File "C:\Users\geoffroy.destaintot\Miniconda3\envs\pd-0.18\lib\site-packages\pandas\tseries\offsets.py", line 2493 in **add**
File "C:\Users\geoffroy.destaintot\Miniconda3\envs\pd-0.18\lib\site-packages\pandas\tseries\offsets.py", line 390 in **radd**
...
#### Expected Output
Satisfactory behaviour when using python timedeltas:
##### In:
```
import datetime as dt
import pandas as pd
from pandas.tseries.frequencies import to_offset
d = pd.Timestamp("2000/1/1")
d + dt.timedelta(days=1)*100**25
```
##### Out:
**=> python error**
Traceback (most recent call last):
File "C:/Users/geoffroy.destaintot/Documents/Local/Informatique/Projets/2016-08-django-debug/to_offset_bug.py", line 11, in <module>
d + dt.timedelta(days=1)_100_*25
OverflowError: Python int too large to convert to C long
#### output of `pd.show_versions()`
(same behaviour with pandas 0.17.1, 0.16.2, 0.15.2)
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.6
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14081 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14081/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14081/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14081/events | https://github.com/pandas-dev/pandas/issues/14081 | 172,995,419 | MDU6SXNzdWUxNzI5OTU0MTk= | 14,081 | ValueError plotting bar plot from DataFrame with existing Axes | {
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] | closed | false | null | [] | null | 3 | 2016-08-24T16:11:21Z | 2016-08-24T16:37:11Z | 2016-08-24T16:34:25Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
The following code works, and when pasted in a Jupyter Notebook or QT console yields the expected bar plot:
``` python
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
# Create some data to plot
df = pd.DataFrame(
dict(Feature1=[10, 20, 30, 20, 10], Feature2=[3, 2, 3, 2, 3]),
index=['a', 'b', 'c', 'd', 'e']
)
_ = df.plot(kind='bar', legend=False, y='Feature1', yerr='Feature2', rot=0)
```
However, as soon as I try to supply a custom `Axes` instance (so the plot gets put where I want it), it throws an error. Running the following code after the above example throws an error:
``` python
fig = plt.figure(0)
ax = fig.add_subplot(111)
_ = df.plot(kind='bar', legend=False, y='Feature1', yerr='Feature2', rot=0, axes=ax)
```
Here is the traceback:
```
Traceback (most recent call last):
File "<stdin>", line 12, in <module>
File "/usr/local/miniconda3/lib/python3.5/site-packages/pandas/tools/plotting.py", line 3740, in __call__
sort_columns=sort_columns, **kwds)
File "/usr/local/miniconda3/lib/python3.5/site-packages/pandas/tools/plotting.py", line 2614, in plot_frame
**kwds)
File "/usr/local/miniconda3/lib/python3.5/site-packages/pandas/tools/plotting.py", line 2441, in _plot
plot_obj.generate()
File "/usr/local/miniconda3/lib/python3.5/site-packages/pandas/tools/plotting.py", line 1028, in generate
self._make_plot()
File "/usr/local/miniconda3/lib/python3.5/site-packages/pandas/tools/plotting.py", line 1971, in _make_plot
log=self.log, **kwds)
File "/usr/local/miniconda3/lib/python3.5/site-packages/pandas/tools/plotting.py", line 1918, in _plot
return ax.bar(x, y, w, bottom=start, log=log, **kwds)
File "/usr/local/miniconda3/lib/python3.5/site-packages/matplotlib/__init__.py", line 1812, in inner
return func(ax, *args, **kwargs)
File "/usr/local/miniconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py", line 2134, in bar
self.add_patch(r)
File "/usr/local/miniconda3/lib/python3.5/site-packages/matplotlib/axes/_base.py", line 1780, in add_patch
self._set_artist_props(p)
File "/usr/local/miniconda3/lib/python3.5/site-packages/matplotlib/axes/_base.py", line 865, in _set_artist_props
a.axes = self
File "/usr/local/miniconda3/lib/python3.5/site-packages/matplotlib/artist.py", line 237, in axes
raise ValueError("Can not reset the axes. You are "
ValueError: Can not reset the axes. You are probably trying to re-use an artist in more than one Axes which is not supported
```
#### Expected Output
I would expect the second plot to work identically to the first plot in this example.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.6-gentoo
machine: x86_64
processor: Intel(R) Core(TM) i3-3240 CPU @ 3.40GHz
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: None
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
```
(FWIW, I just built and tried the latest version of pandas and got the same behavior:
```
pandas: 0.18.1+391.g6645b2b
```
)
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https://api.github.com/repos/pandas-dev/pandas/issues/14082 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14082/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14082/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14082/events | https://github.com/pandas-dev/pandas/issues/14082 | 173,014,700 | MDU6SXNzdWUxNzMwMTQ3MDA= | 14,082 | DataFrame construction breaks python on numpy empty_like | {
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} | 1 | 2016-08-24T17:41:43Z | 2016-08-24T17:53:38Z | 2016-08-24T17:53:11Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
http://stackoverflow.com/questions/39129419/why-does-dataframe-construction-break-python-on-numpy-empty-like
```
import numpy as np
import pandas as pd
a = np.array([None, None], dtype=object)
print(a) # [None None]
dfa = pd.DataFrame(a)
print(dfa)
# 0
#0 None
#1 None
b = np.empty_like(a)
print(b)
#[None None]
(a == b).all()
#True
dfb = pd.DataFrame(b) # Fine so far
print(dfb.values)
#[[None]
#[None]]
print(dfb) #Crash
```
#### Expected Output
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 18.2
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14083 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14083/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14083/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14083/events | https://github.com/pandas-dev/pandas/issues/14083 | 173,063,525 | MDU6SXNzdWUxNzMwNjM1MjU= | 14,083 | Enabling auto-collapsing of Excel dumped from df | {
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"description": "read_exc... | open | false | null | [] | null | 0 | 2016-08-24T21:33:58Z | 2016-08-25T21:46:21Z | null | NONE | null | Dear all,
In my job, we use a lot of Pandas (though not always very professionally). One very useful case is to build a dataframe conatining data from several instruments for several items. I tend to use a lot of multi-indexing columns.
In order to view (and modify) the columns, we like to save the dataframe in an Excel file, look at the data and possibly manually modify some values.
But with a lot of columns (espcially mult-indexed) we tend to have very big Excel files, that are not as readable as they should be. That is why I like to group some columns and enable the possibility to collapse them (and un-collapse them if I want to look at them).
I did not find a way to do it in Pandas ; but there it is possible in xlsxwriter. So I developed a short solution to automatically group all columns from the same multiindex column.
If you guys think it is interesting to add the option to Pandas I'd be happy to try to let my work be useful to others -I could try to add it to Pandas though I'm new to GitHub. What do you guys think? Feel free to say no :)
Below is a toy example, and attached are the non-grouped and the grouped columns Excel sheets.
Please don't be mad at the code, it's really quick&dirty way.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
from openpyxl.cell import get_column_letter
import copy
def save_df(df,ofn='output.xlsx'):
writer = pd.ExcelWriter(ofn, engine='xlsxwriter')
df.to_excel(writer, 'Sheet1')
writer.save()
def save_df_collapsing(df, ofn='output.xlsx'):
writer = pd.ExcelWriter(ofn, engine='xlsxwriter')
df.to_excel(writer, 'Sheet1')
worksheet = writer.sheets['Sheet1']
col_mins = []
col_maxs = []
for ind0 in df.columns.levels[0]:
slice_ind = df.columns.get_loc(ind0)
try:
slice_ind = slice_ind.indices(100000)
col_min = slice_ind[0] + 2
col_max = slice_ind[1]
except:
col_min = np.min(np.nonzero(slice_ind)) + 2
col_max = np.max(np.nonzero(slice_ind))
col_mins.append(col_min)
col_maxs.append(col_max)
span = '%s:%s' % (get_column_letter(col_min), get_column_letter(col_max))
worksheet.set_column(span, None, None, {'level': 1})
temp = copy.copy(df[ind0])
ind1s = np.unique([ind1[0] for ind1 in temp.columns])
for ind1 in ind1s:
slice_ind1 = df.columns.get_loc((ind0, ind1)).indices(100000)
col_min1 = slice_ind1[0] + 2
if col_min1 in col_mins:
col_min1 += 1
col_max1 = slice_ind1[1]
if col_max1 in col_maxs:
col_max1 -= 1
# worksheet.set_column(col_min,col_max, None, None, {'level': 2})
span1 = '%s:%s' % (get_column_letter(col_min1), get_column_letter(col_max1))
worksheet.set_column(span1, None, None, {'level': 2, 'hidden': True})
writer.save()
temperatures = np.arange(100)
columns = pd.MultiIndex.from_product([['Absolute','Relative'],['ValueA', 'ValueB', 'ValueC'], temperatures])
df = pd.DataFrame(columns=columns)
df.loc['Item1', ('Relative','ValueA')] = np.arange(100)
df.loc['Item1', ('Relative','ValueB')] = 2 * np.arange(100)
df.loc['Item1', ('Relative','ValueC')] = 3 * np.arange(100)
df.loc['Item1', ('Absolute','ValueA')] = np.arange(100) + 25
df.loc['Item1', ('Absolute','ValueB')] = 2 * np.arange(100) + 25
df.loc['Item1', ('Absolute','ValueC')] = 3 * np.arange(100) + 25
df.loc['Item2', ('Relative','ValueA')] = 10 * np.arange(100)
df.loc['Item2', ('Relative','ValueB')] = 20 * np.arange(100)
df.loc['Item2', ('Relative','ValueC')] = 30 * np.arange(100)
df.loc['Item2', ('Absolute','ValueA')] = 10 * np.arange(100) + 25
df.loc['Item2', ('Absolute','ValueB')] = 20 * np.arange(100) + 25
df.loc['Item2', ('Absolute','ValueC')] = 30 * np.arange(100) + 25
print df
save_df(df,'output_no_collapse.xlsx')
save_df_collapsing(df,'output_collapsed.xlsx')
```
[output_collapsed.xlsx](https://github.com/pydata/pandas/files/435715/output_collapsed.xlsx)
[output_no_collapse.xlsx](https://github.com/pydata/pandas/files/435714/output_no_collapse.xlsx)
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https://api.github.com/repos/pandas-dev/pandas/issues/14084 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14084/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14084/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14084/events | https://github.com/pandas-dev/pandas/issues/14084 | 173,072,677 | MDU6SXNzdWUxNzMwNzI2Nzc= | 14,084 | resample drops dtype('O') columns from DataFrame (but not Series) | {
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} | 3 | 2016-08-24T22:25:55Z | 2016-08-31T23:34:57Z | 2016-08-24T22:36:50Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
import numpy
import pandas as pd
idx = pd.date_range('1/1/2016', periods=100, freq='d')
z = pd.DataFrame({"z": numpy.zeros(len(idx))}, index=idx)
obj = pd.DataFrame({"obj": numpy.zeros(len(idx), dtype=numpy.dtype('O'))}, index=idx)
nan = pd.DataFrame({"nan": numpy.zeros(len(idx)) + float('nan')}, index=idx)
objnan = pd.DataFrame([], columns=['objnan'], index=idx[0:0]) # dtype('O') nan after join
df_list = [z, obj, nan, objnan]
def r1w(x):
return x.resample('1w').sum()
resample_join_result = r1w(pd.DataFrame().join(df_list, how='outer'))
print(resample_join_result.shape) # (15, 2) --- I thought this should be (15, 4)
join_resample_result = pd.DataFrame().join([r1w(x) for x in df_list], how='outer')
print(join_resample_result.shape) # (15, 4)
for columnname in join_resample_result.columns:
if columnname not in resample_join_result.columns:
print("DataFrame.resample missing column: " + str(columnname) + " (" + str(join_result[columnname].dtype) + ")")
```
#### Expected Output
I would have expected `resample_join_result` to have all four columns and be the same as `join_resample_result`, but they are not, because it seems `pandas.DataFrame.resample` drops dtype('O') (object) columns; while `pandas.Series.resample` converts those columns into numeric dtypes.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.22.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.0
```
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} | 3 | 2016-08-25T00:24:10Z | 2016-08-25T22:20:22Z | 2016-08-25T22:20:12Z | CONTRIBUTOR | null | - [x] closes #11757
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2016-08-25T08:57:01Z | 2016-09-01T08:19:15Z | 2016-09-01T08:19:15Z | NONE | null | Simple to reproduce:
``` python
import pandas as pd
try:
pd.read_csv('this_file_does_not_exist.csv')
except FileNotFoundError:
print('This should execute.')
```
Instead, I get an OSError, which tells me in text that `OSError: File b'this_file_does_not_exist.csv' does not exist`.
https://docs.python.org/3.5/library/exceptions.html seems to suggest that OSError now has various subclasses, e.g. FileNotFoundError, for specific errors such as this one.
Output of `pd.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 2 | 2016-08-25T19:42:39Z | 2016-08-25T20:40:09Z | 2016-08-25T19:55:50Z | NONE | null | Adding a datetime.timedelta to a series of datetime64[ns, UTC] breaks when using the Series .add(offset) method, but works when doing series + offset. This behaviour seems unexpected to me.
Furthermore, adding a pd.Timedelta drops timezone.
``` python
import pandas as pd
import datetime
import pytz
datetime_series = pd.Series([datetime.datetime(2016, 5, 1, tzinfo = pytz.utc)])
offset = datetime.timedelta(days=1)
print datetime_series.dtype
>>> datetime64[ns, UTC]
# Fails
datetime_series.add(offset)
----------------------------------------------------
---------
TypeError Traceback (most recent call last)
<ipython-input-12-89cc70a4801f> in <module>()
8
9 # Fails
---> 10 datetime_series.add(offset)
11
12 # Works as expected
/home/adrian/.virtualenvs/pandas/local/lib/python2.7/site-packages/pandas/core/ops.pyc in flex_wrapper(self, other, level, fill_value, axis)
937 self = self.fillna(fill_value)
938
--> 939 return self._constructor(op(self.values, other),
940 self.index).__finalize__(self)
941
TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and `dtype('O')
```
#### Expected Output
``` python
# Works as expected
datetime_series + offset`
>>> 0 2016-05-02 00:00:00+00:00
>>> dtype: datetime64[ns, UTC]
```
Moreover, if the usages of `offset` in all of the above code are replaced with `pd.Timedelta(offset)` then the breaking line works but drops timezone. The line described as working does not drop timezone when replacing with `pd.Timedelta(offset)`
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.0.0
Cython: None
numpy: 1.9.0
scipy: 0.14.0
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.4
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: 1.1
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.26.0
pandas_datareader: None
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} | 2 | 2016-08-25T19:46:12Z | 2016-09-08T10:35:49Z | 2016-09-08T10:35:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
import pytz
import datetime
import pandas as pd
foo = pd.Series(datetime.datetime(2016, 8, 23, 12, tzinfo=pytz.utc))
foo - datetime.datetime(2016, 8, 22, 12, tzinfo=pytz.utc)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-52-0639980e7d31> in <module>()
----> 1 foo - datetime.datetime(2016, 8, 1, 12, tzinfo=pytz.utc)
/Users/charon/.virtualenvs/iwoca-django/lib/python2.7/site-packages/pandas/core/ops.pyc in wrapper(left, right, name, na_op)
607
608 time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name,
--> 609 na_op)
610
611 if time_converted is None:
/Users/charon/.virtualenvs/iwoca-django/lib/python2.7/site-packages/pandas/core/ops.pyc in maybe_convert_for_time_op(cls, left, right, name, na_op)
567 return None
568
--> 569 return cls(left, right, name, na_op)
570
571
/Users/charon/.virtualenvs/iwoca-django/lib/python2.7/site-packages/pandas/core/ops.pyc in __init__(self, left, right, name, na_op)
281
282 lvalues = self._convert_to_array(left, name=name)
--> 283 rvalues = self._convert_to_array(right, name=name, other=lvalues)
284
285 self.name = name
/Users/charon/.virtualenvs/iwoca-django/lib/python2.7/site-packages/pandas/core/ops.pyc in _convert_to_array(self, values, name, other)
419 elif not (isinstance(values, (np.ndarray, ABCSeries)) and
420 is_datetime64_dtype(values)):
--> 421 values = tslib.array_to_datetime(values)
422 elif inferred_type in ('timedelta', 'timedelta64'):
423 # have a timedelta, convert to to ns here
/Users/charon/.virtualenvs/iwoca-django/lib/python2.7/site-packages/pandas/tslib.so in pandas.tslib.array_to_datetime (pandas/tslib.c:41972)()
/Users/charon/.virtualenvs/iwoca-django/lib/python2.7/site-packages/pandas/tslib.so in pandas.tslib.array_to_datetime (pandas/tslib.c:38943)()
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
#### Expected Output
``` python
0 1 days
dtype: timedelta64[ns]
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: 8.1.2
setuptools: 3.6
Cython: 0.23.2
numpy: 1.11.1
scipy: 0.14.0
statsmodels: 0.6.1
xarray: None
IPython: 2.3.1
sphinx: None
patsy: 0.4.1
dateutil: 2.3
pytz: 2014.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.1
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: 1.1
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: 2.26.0
pandas_datareader: None
```
Note, doing
```
foo.astype(datetime.datetime) - datetime.datetime(2016, 8, 22, tzinfo=pytz.utc)
```
or
```
foo.astype(datetime.datetime) - pd.Timestamp(datetime.datetime(2016, 8, 22, tzinfo=pytz.utc)
```
works.
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https://api.github.com/repos/pandas-dev/pandas/issues/14089 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14089/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14089/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14089/events | https://github.com/pandas-dev/pandas/issues/14089 | 173,329,137 | MDU6SXNzdWUxNzMzMjkxMzc= | 14,089 | df.ix causing exception when index is not from 0 | {
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} | 2 | 2016-08-25T22:46:31Z | 2016-08-26T10:20:20Z | 2016-08-26T10:20:20Z | NONE | null | #### Code Sample, a copy-pastable example if possible
When the index is set from 0, df.ix[0,0] works fine.
```
>>> df = pd.DataFrame({'A':[1,2,3], 'B':[10,20,30]},index=[0,1,2])
>>> df
A B
0 1 10
1 2 20
2 3 30
>>> df.ix[0,1]
10
```
When the index is set from other 0, df.ix[0,0] cases exception.
```
>>> df2 = pd.DataFrame({'A':[1,2,3], 'B':[10,20,30]},index=[2,3,4])
>>> df2
A B
2 1 10
3 2 20
4 3 30
>>> df2.ix[1,0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\python27\lib\site-packages\pandas\core\indexing.py", line 75, in __ge
titem__
return self._getitem_tuple(key)
File "c:\python27\lib\site-packages\pandas\core\indexing.py", line 784, in _ge
titem_tuple
return self._getitem_lowerdim(tup)
File "c:\python27\lib\site-packages\pandas\core\indexing.py", line 908, in _ge
titem_lowerdim
section = self._getitem_axis(key, axis=i)
File "c:\python27\lib\site-packages\pandas\core\indexing.py", line 1018, in _g
etitem_axis
return self._get_label(key, axis=axis)
File "c:\python27\lib\site-packages\pandas\core\indexing.py", line 93, in _get
_label
return self.obj._xs(label, axis=axis)
File "c:\python27\lib\site-packages\pandas\core\generic.py", line 1749, in xs
loc = self.index.get_loc(key)
File "c:\python27\lib\site-packages\pandas\indexes\base.py", line 1947, in get
_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas\index.pyx", line 137, in pandas.index.IndexEngine.get_loc (pandas
\index.c:4066)
File "pandas\index.pyx", line 159, in pandas.index.IndexEngine.get_loc (pandas
\index.c:3930)
File "pandas\hashtable.pyx", line 303, in pandas.hashtable.Int64HashTable.get_
item (pandas\hashtable.c:6527)
File "pandas\hashtable.pyx", line 309, in pandas.hashtable.Int64HashTable.get_
item (pandas\hashtable.c:6465)
KeyError: 1L
```
#### output of `pd.show_versions()`
> > > pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 20.10.1
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 2 | 2016-08-26T10:41:20Z | 2016-09-08T10:35:56Z | 2016-09-08T10:35:41Z | CONTRIBUTOR | null | - [x] closes #14088
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2016-08-26T11:35:57Z | 2016-08-27T15:59:25Z | 2016-08-27T15:21:00Z | NONE | null | Try to install pandas via pip, but it is failed:
[error.txt](https://github.com/pydata/pandas/files/439154/error.txt)
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} | 2 | 2016-08-26T18:19:55Z | 2016-08-26T20:21:45Z | 2016-08-26T20:21:02Z | CONTRIBUTOR | null | - [x] closes #13509
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Currently, trying to retrieve one tuple-valued entry in a `Series` with
a `Float64Index` will fail (GH 13509). This fixes that bug.
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} | 4 | 2016-08-26T19:28:18Z | 2020-12-22T23:45:29Z | 2020-12-22T23:45:29Z | CONTRIBUTOR | null | from [comment](https://github.com/pydata/pandas/pull/13981#issuecomment-239846027) on #13981
```
In [1]: pd.Index([datetime.datetime(2012,1,1), datetime.datetime(2012,1, 2)], tz="Europe/Brussels")
Out[1]: DatetimeIndex(['2012-01-01 00:00:00+01:00', '2012-01-02 00:00:00+01:00'], dtype='datetime64[ns, Europe/Brussels]', freq=None)
In [2]: pd.Index([1, 2], tz="Europe/Brussels")
Out[2]: DatetimeIndex(['1970-01-01 00:00:00+01:00', '1970-01-01 00:00:00+01:00'], dtype='datetime64[ns, Europe/Brussels]', freq=None)
In [3]: pd.Index(['20120102', '20120103'], tz="Europe/Brussels")
Out[3]: DatetimeIndex(['2012-01-02 00:00:00+01:00', '2012-01-03 00:00:00+01:00'], dtype='datetime64[ns, Europe/Brussels]', freq=None)
```
[1] is fine/expected
[2] should be disallowed (e.g. integer construction)
[3] is ok, maybe should disallow to be consistent.
I am not sure what this would break. But we don't necessarily want to force datetime construction on a generic `Index` (as opposed to using `.to_datetime` or a constructor, e.g. `pd.date_range`). This is a bit too much inference.
So the rule would be that it has to be actual datetimes/Timestamps, if tz is provided (which is a form of dtype).
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} | 21 | 2016-08-27T03:58:40Z | 2016-09-07T13:16:02Z | 2016-09-07T13:16:02Z | MEMBER | null | The doc-string at the top of <a href="https://github.com/pydata/pandas/blob/0e61847e111a3ba181f16c8b9b974c74d360ad2e/pandas/core/datetools.py">datetools.py</a> seems to indicate this is just a hodge-podge of datetime-related objects. Seems like that could be cleaned up unless there is good reason to have it.
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} | 4 | 2016-08-27T05:33:15Z | 2016-08-31T16:05:06Z | 2016-08-31T16:05:06Z | CONTRIBUTOR | null | I think the resolvers argument of pandas.eval requires a tuple and not a list
```
resolvers : list of dict-like or None, optional
A list of objects implementing the __getitem__ special method that you can use to inject an additional collection of namespaces to use for variable lookup. For example, this is used in the query() method to inject the index and columns variables that refer to their respective DataFrame instance attributes.
```
as with a list I get a
```
kwargs['resolvers'] = kwargs.get('resolvers', ()) + resolvers
TypeError: can only concatenate tuple (not "list") to tuple
```
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Partially addresses #8254.
Closes #5612 because `pd.to_datetime` does have a `format` argument.
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} | 4 | 2016-08-27T12:39:10Z | 2016-12-14T14:57:41Z | 2016-12-14T14:54:21Z | CONTRIBUTOR | null | - [x] closes #12651
- [x] passes `git diff upstream/master | flake8 --diff`
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} | 9 | 2016-08-27T15:53:54Z | 2016-12-10T14:33:32Z | 2016-12-10T14:30:31Z | MEMBER | null | Related to https://github.com/pydata/pandas/issues/10849 (similar to https://github.com/pydata/pandas/pull/10998)
Just putting this up, was doing some time ago, but not yet fully ready, and thought to already merge this (if the content is OK) to not get too much conflicts.
Gist: removing redundancy by gathering tests in classes and sharing setup functions (you can see the diff change that it removed quite some lines of code). At the same time, I am cleaning up the test names a bit, to make them shorter/more pythonic (eg `DatetimeIndex.time_add_timedelta` instead of `time_datetimeindex_add_timedelta.time_datetimeindex_add_timedelta`).
The only disadvantage of this cleanup is that for people who already did run the benchmarks for older versions of pandas, this will have to be redone.
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} | 5 | 2016-08-27T16:01:08Z | 2016-08-28T13:14:59Z | 2016-08-28T13:14:55Z | CONTRIBUTOR | null | Fixes failing windows build
https://ci.appveyor.com/project/jreback/pandas-465/build/job/p8hju341jyhnkogc
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} | 3 | 2016-08-27T17:51:19Z | 2016-08-29T12:42:52Z | 2016-08-29T12:42:43Z | MEMBER | null | 1) Deprecate `NaT.to_datetime`
2) Deprecate `Timestamp.to_datetime`
3) Issue `UserWarning` in `Timestamp.to_datetime` when nanoseconds is non-zero
`NaT` is also a `_Timestamp`, so the second deprecation covered the first one as well.
Closes #8254.
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... | open | false | null | [] | null | 1 | 2016-08-27T20:51:53Z | 2016-08-29T12:27:11Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
#!/usr/bin/env python3
import matplotlib.pyplot as plt
import pandas as pd
# some data
s1 = pd.Series(data=[5,7,6,8,7], index=[1,2,3,4,5])
s2 = pd.Series(data=[6,4,5,3,4], index=[1,2,3,4,5])
ax = plt.subplot(2, 1, 1)
s1.plot(ax=ax)
s2.plot(ax=ax, secondary_y=True)
assert len(ax.xaxis.get_minor_ticks()) == 0
ax2 = plt.subplot(2, 1, 2)
#s1.plot(ax=ax2) # uncomment me
assert len(ax.xaxis.get_minor_ticks()) == 0
plt.show()
```
#### Expected Output
Labels on x-axis (no minor ticks).
Plotting on ax2 not affects ax.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: ru_RU.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.1.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 2 | 2016-08-28T00:08:49Z | 2016-08-28T13:06:06Z | 2016-08-28T13:05:43Z | NONE | null | #### Code Sample, a copy-pastable example if possible
import pandas as pd
import numpy as np
from time import time
import sys
df_data = pd.DataFrame(np.random.randint(0,int(1e6),int(20e6)), columns=['pop_id'])
df_data['PL_dB'] = 50 + np.random.random(df_data.shape[0]) \* 100
df_data['Rx_dBm'] = 23 - df_data.PL_dB
df_data['noise_mW'] = (10.**(df_data.Rx_dBm / 10.)).astype('float32')
start = time()
df_data.sort_values(by=['pop_id', 'Rx_dBm'], ascending=[True, False], inplace=True)
df_data.reset_index(drop=True, inplace=True)
print("Sort took {:0.2f} seconds".format(time() - start))
print('Python version ' + sys.version)
print('pandas version ' + pd.**version**)
#### output of `pd.show_versions()`
## For Python 2.7
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
## For Python 3.5
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
## Results with Python 2.7
Sort took 40.91 seconds
Python version 2.7.12 |Anaconda custom (64-bit)| (default, Jun 29 2016, 11:07:13) [MSC v.1500 64 bit (AMD64)]
pandas version 0.18.1
## Results with Python 3.5
Sort took 81.30 seconds
Python version 3.5.2 |Continuum Analytics, Inc.| (default, Jul 5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]
pandas version 0.18.1
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} | 1 | 2016-08-28T02:18:43Z | 2016-08-28T13:11:55Z | 2016-08-28T13:11:39Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame({'a': [1, 1, 2, 2], 'b': pd.to_datetime(['a', 'a', '12-23-2015', '12-24-2015'],
errors='coerce')})
df.groupby('a').b.first().dtype
```
#### Expected Output
last line is supposed to show datetime64[ns] but it actually is float64
this only happens when the entire group has NaT datetime
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.0.0
Cython: 0.24.1
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.3
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.14
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14105 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14105/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14105/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14105/events | https://github.com/pandas-dev/pandas/pull/14105 | 173,634,787 | MDExOlB1bGxSZXF1ZXN0ODMwMDkxMzY= | 14,105 | DEPR: Deprecate pandas.core.datetools | {
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} | 27 | 2016-08-28T07:54:54Z | 2016-09-07T13:24:30Z | 2016-09-07T13:16:02Z | MEMBER | null | Title is self-explanatory. Closes #14094.
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https://api.github.com/repos/pandas-dev/pandas/issues/14106 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14106/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14106/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14106/events | https://github.com/pandas-dev/pandas/issues/14106 | 173,637,022 | MDU6SXNzdWUxNzM2MzcwMjI= | 14,106 | MultiIndex with dateutil tzlocal data corruption | {
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} | 2 | 2016-08-28T09:05:12Z | 2016-08-28T13:20:14Z | 2016-08-28T13:14:10Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
In [1]: %paste
import datetime
from dateutil.tz import tzlocal, tzutc
import pandas
d = {
datetime.datetime(2016, 8, 26, 20, 23, tzinfo=tzlocal()): {
'A': 10481.0,
'B': 12,
'C': 'text'
},
datetime.datetime(2016, 8, 26, 20, 24, tzinfo=tzlocal()): {
'A': 10480.5,
'B': 13,
'C': 'text'
}
}
df = pandas.DataFrame(d).T
df2 = df.set_index(['C'], append=True)
## -- End pasted text --
In [2]: df
Out[2]:
A B C
2016-08-26 20:23:00+02:00 10481 12 text
2016-08-26 20:24:00+02:00 10480.5 13 text
In [3]: df2
Out[3]:
A B
C
2016-08-26 20:23:00+02:00 text 10481 12
1970-01-01 00:00:00+01:00 text 10480.5 13
```
#### Expected Output
```
In [3]: df2
Out[3]:
A B
C
2016-08-26 20:23:00+02:00 text 10481 12
2016-08-26 20:24:00+02:00 text 10480.5 13
```
Note, issue goes away when removing the `tzinfo=tzlocal()`. Or just using `tzinfo=tzutc()`. It can also be reproduced using `MultiIndex()` with tuples instead of `set_index()`.
#### Using pytz works
```
import datetime
import pytz
import tzlocal
import pandas
local_tz = tzlocal.get_localzone()
d = {
datetime.datetime(2016, 8, 26, 20, 23, tzinfo=pytz.utc).astimezone(local_tz): {
'A': 10481.0,
'B': 12,
'C': 'text'
},
datetime.datetime(2016, 8, 26, 20, 24, tzinfo=pytz.utc).astimezone(local_tz): {
'A': 10480.5,
'B': 13,
'C': 'text'
}
}
df = pandas.DataFrame(d).T
df2 = df.set_index(['C'], append=True)
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.6-300.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.18.0
nose: None
pip: 8.0.2
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.6.1
blosc: None
bottleneck: 0.6.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.2rc2
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
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https://api.github.com/repos/pandas-dev/pandas/issues/14107 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14107/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14107/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14107/events | https://github.com/pandas-dev/pandas/issues/14107 | 173,641,221 | MDU6SXNzdWUxNzM2NDEyMjE= | 14,107 | pandas.core.groupby.SeriesGroupBy.hist doesn't propagate **kwargs | {
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"description": nul... | closed | false | null | [] | null | 3 | 2016-08-28T11:11:42Z | 2021-05-01T23:26:12Z | 2021-05-01T23:26:11Z | NONE | null | When calling `my_groupby.hist(bins=8, histtype='stepfilled')` I would expect the same behavior as when calling `my_series.hist(bins=8, histtype='stepfilled')` separately for each of the groups.
However, while the 'bins' parameter is taken into account, the 'histtype' parameter is ignored and not passed along to matplotlib's implementation.
Looking into the code, it appears that `pandas.core.groupby._whitelist_method_generator` only passes along named arguments of the wrapped method (hist in our example). While the signature was fixed to include any `*args` or `**kwargs` arguments (done to fix #8733), they are not passed on.
Note: the problem applies to all 'wrappers' generated by _whitelist_method_generator (in addition to 'hist'), and also to any use of `*args` (in addition to `**kwargs`).
We are working on a PR and will send it soon.
#### output of `pd.show_versions()`
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 24.0.0
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
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